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29,854 次观看 • 8 个月前 •via X (Twitter)

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BREAKING: Introducing All Access from Every 📧, our new membership tier for the best builders in AI All Access subs get the Builder Pack which includes $7,000 in credits and free usage to the models + tool stack we use Every 📧. All Access subscribers get: - $1,000 in Codex / @ChatGPTapp for Work credits - 12 months free of Cursor Pro+ - $4,000 in PostHog credits including self-driving to automatically fix bugs and identify issues in your production app - 1 year free of Framer - 6 months free of Notion And much more! (Did I mention $1,000 in Codex credits? It's time to build!) Get all access: Why All Access and the Builder Pack This is the best time in history to build something. For a long time, it’s been possible to one-shot impressive demos, but they’d fall flat the minute they hit production. But the release of GPT-5.6-Sol and Fable 5 heralds a new era: Everyone can build, launch, and maintain the software that they’ve always dreamed of. Everyone is a builder now. There’s just one catch: Building with AI is very expensive. (Ask me how I know.) (Alright, I’ll tell you. I accidentally used 2 billion tokens overnight this week on a big GPT-5.6-Sol run. Worth it.) This is unique in the history of technology. For most of the personal computing era, a billionaire and a solo builder could buy essentially the same top-of-the-line Mac. AI changes that: The more tokens you can afford, the more you can make. And we want to make that accessible to more people. That’s why the main feature of our new All Access plan is the Builder Pack: more than $7,000 in credits and discounts on the full stack we use to run Every, from idea to production—Codex, Claude, PostHog, Render, Gemini, FLORA, and more. Early-bird membership is only $500/year for the next 24 hours—and the Codex credits alone are worth $1,000. (I could’ve used it for my overnight run this week.) Now we’re handing it to you. Get all access: Meet the Builder Pack It's got more than $7,000 in offers from 10 of the AI products we use to write, design, build, and run Every 📧: BUILD - $1,000 in Codex credits plus one month of ChatGPT for business - Twelve months free of Cursor Pro+ - One month free of Claude Max - Three months free of Google AI Pro DESIGN - One year free of Framer Pro - One month free of FLORA © Max HOST - $300 in Render credits IMPROVE - $4,000 in PostHog credits - Six months free of Notion Business - Six months free of AgentMail We rely on these every day, and we tried to put together a package that helps you comprehensively for each part of the process of building and running software in AI. What comes with All Access - Everything in an existing paid Every membership: our daily writing, guides, camps, and software like Monologue, Cora, Sparkle, and Spiral - The Builder Pack, with more than $7,000 in partner offers - Unlimited email accounts use of Cora and unlimited Spiral usage - Members-only programming with me and the Every team and me Get All Access:
2:14

BREAKING: Introducing All Access from Every 📧, our new membership tier for the best builders in AI All Access subs get the Builder Pack which includes $7,000 in credits and free usage to the models + tool stack we use Every 📧. All Access subscribers get: - $1,000 in Codex / @ChatGPTapp for Work credits - 12 months free of Cursor Pro+ - $4,000 in PostHog credits including self-driving to automatically fix bugs and identify issues in your production app - 1 year free of Framer - 6 months free of Notion And much more! (Did I mention $1,000 in Codex credits? It's time to build!) Get all access: Why All Access and the Builder Pack This is the best time in history to build something. For a long time, it’s been possible to one-shot impressive demos, but they’d fall flat the minute they hit production. But the release of GPT-5.6-Sol and Fable 5 heralds a new era: Everyone can build, launch, and maintain the software that they’ve always dreamed of. Everyone is a builder now. There’s just one catch: Building with AI is very expensive. (Ask me how I know.) (Alright, I’ll tell you. I accidentally used 2 billion tokens overnight this week on a big GPT-5.6-Sol run. Worth it.) This is unique in the history of technology. For most of the personal computing era, a billionaire and a solo builder could buy essentially the same top-of-the-line Mac. AI changes that: The more tokens you can afford, the more you can make. And we want to make that accessible to more people. That’s why the main feature of our new All Access plan is the Builder Pack: more than $7,000 in credits and discounts on the full stack we use to run Every, from idea to production—Codex, Claude, PostHog, Render, Gemini, FLORA, and more. Early-bird membership is only $500/year for the next 24 hours—and the Codex credits alone are worth $1,000. (I could’ve used it for my overnight run this week.) Now we’re handing it to you. Get all access: Meet the Builder Pack It's got more than $7,000 in offers from 10 of the AI products we use to write, design, build, and run Every 📧: BUILD - $1,000 in Codex credits plus one month of ChatGPT for business - Twelve months free of Cursor Pro+ - One month free of Claude Max - Three months free of Google AI Pro DESIGN - One year free of Framer Pro - One month free of FLORA © Max HOST - $300 in Render credits IMPROVE - $4,000 in PostHog credits - Six months free of Notion Business - Six months free of AgentMail We rely on these every day, and we tried to put together a package that helps you comprehensively for each part of the process of building and running software in AI. What comes with All Access - Everything in an existing paid Every membership: our daily writing, guides, camps, and software like Monologue, Cora, Sparkle, and Spiral - The Builder Pack, with more than $7,000 in partner offers - Unlimited email accounts use of Cora and unlimited Spiral usage - Members-only programming with me and the Every team and me Get All Access:

Dan Shipper

183,498 次观看 • 2 个月前

Our goal has always been to offer you car care solutions that are both effective and affordable. For KSh 10,000 per year, you can join our membership package, which includes the following services: 1. AFTER GARAGE INSPECTIONS This involves inspecting your car after it has been worked on by a garage to ensure quality work was done and that the reported issues were properly resolved. We also carry out a detailed follow-up to confirm that the parts claimed to have been replaced were indeed changed. Data is collected before and after repairs to ensure accurate and factual findings. 2. DETAILED INSPECTION EVERY FOUR MONTHS Every four months, we conduct a comprehensive inspection of your vehicle, focusing on the engine, transmission, auto-electrical systems, suspension, tyre wear patterns, and more. Early diagnosis helps you take preventive action by addressing issues before they fully develop and become costly repairs. 3. AFTER SERVICE COMPUTERISED DIAGNOSIS Every time you take your car for an oil service, we carry out a computerized diagnostic scan focusing on the engine and gearbox. Some dishonest garages exaggerate fault reports to justify unnecessary repairs and sell parts. At 4Real, our reports are completely independent, as we are not in the business of repairing cars, but rather providing unbiased car care through professional inspections. 4. FREE PRE-PURCHASE INSPECTIONS If you are a member and intend to buy a car, you are entitled to 1 FREE pre-purchase inspection. We also offer additional services such as Pre-Purchase Inspections where we inspect the car you intend to buy and give you a very detailed yet easy to understand report on the car. It helps you decide on whether to buy the car or not. We are very independent on our reports. Other than that we also offer car consultancy. If you are unsure which car to buy, we provide a detailed comparison between your shortlisted options, helping you choose the car that will serve you best. Our comparison report is based on: 1. Initial buying price 2. Cost of maintenance 3. Fuel consumption 4. Safety, performance & comfort 5. Practicality 6. Reliability 7. Resale value over time Car consultancy services costs KSh 1,000, but it is FREE for those under our membership packages. To access our services, contact us on 0729 686 646.
1:54

Our goal has always been to offer you car care solutions that are both effective and affordable. For KSh 10,000 per year, you can join our membership package, which includes the following services: 1. AFTER GARAGE INSPECTIONS This involves inspecting your car after it has been worked on by a garage to ensure quality work was done and that the reported issues were properly resolved. We also carry out a detailed follow-up to confirm that the parts claimed to have been replaced were indeed changed. Data is collected before and after repairs to ensure accurate and factual findings. 2. DETAILED INSPECTION EVERY FOUR MONTHS Every four months, we conduct a comprehensive inspection of your vehicle, focusing on the engine, transmission, auto-electrical systems, suspension, tyre wear patterns, and more. Early diagnosis helps you take preventive action by addressing issues before they fully develop and become costly repairs. 3. AFTER SERVICE COMPUTERISED DIAGNOSIS Every time you take your car for an oil service, we carry out a computerized diagnostic scan focusing on the engine and gearbox. Some dishonest garages exaggerate fault reports to justify unnecessary repairs and sell parts. At 4Real, our reports are completely independent, as we are not in the business of repairing cars, but rather providing unbiased car care through professional inspections. 4. FREE PRE-PURCHASE INSPECTIONS If you are a member and intend to buy a car, you are entitled to 1 FREE pre-purchase inspection. We also offer additional services such as Pre-Purchase Inspections where we inspect the car you intend to buy and give you a very detailed yet easy to understand report on the car. It helps you decide on whether to buy the car or not. We are very independent on our reports. Other than that we also offer car consultancy. If you are unsure which car to buy, we provide a detailed comparison between your shortlisted options, helping you choose the car that will serve you best. Our comparison report is based on: 1. Initial buying price 2. Cost of maintenance 3. Fuel consumption 4. Safety, performance & comfort 5. Practicality 6. Reliability 7. Resale value over time Car consultancy services costs KSh 1,000, but it is FREE for those under our membership packages. To access our services, contact us on 0729 686 646.

4Real

14,965 次观看 • 8 个月前

🚨 We are repeating 1995 dotcom bubble pattern literally 1:1 Over a decade in markets and I've never seen this many crash indicators flashing at once Start with the biggest one: the IPO wave. Everyone thinks the SpaceX trade is over, but it hasnt even started. Aug 6 will change everything. $SPCX did the largest IPO in history on June 12. $135, $1.77T valuation, $75B raised. Then it ripped to $225 in 4 days and dumped 35% back to $147. The crowd that bought the hype is already underwater. And here's the thing: an IPO is not the start of the run. It's the exit. The day insiders + early backers turn paper into cash. This time they built the buyer out of millions of retail accounts. Fidelity cut its IPO minimum from $500k to $2k, a 99.6% cut. Robinhood, SoFi: no minimum at all. Retail got 30% of the offering when the standard is 5-10%. They dropped every barrier to get u IN. Then bolted the exit: sell in the first 15 days and ur flagged a flipper, with bans escalating up to a lifetime one tied to ur SSN. You dont build a one-way door unless u already know which way the crowd has to run. History shows the ending every time. Truist studied 30 major tech IPOs since 2012: Median year-1 drawdown: -54% Only 43% green 6 months in 11 of 30 fell 64-90% $FB, $SNAP, $UBER, $HOOD: same script. Huge debut, endless hype, lockups expire, early money hands u the bag. Now price SpaceX into that: over 110x sales, $4.9B loss last year, $41.3B accumulated deficit. CFRA rated it a SELL under an hour after open. Morningstar fair value: $63. It trades at $162. And the real distribution event is still ahead: Aug 6. First earnings report = insiders finally unlock 20% of their stock, +10% more if price holds high. At the same time $SPCX enters the Nasdaq-100, so index funds are FORCED to buy it near the top. Forced buyers meeting real sellers. That's the window. Moreover, this is just IPO #1. OpenAI (~$850-900B) and Anthropic (~$965B) already filed. No dates yet, but the window is open and they'll fly through it. 3 of the biggest listings in history, all AI-linked, all landing into the same indexes. Which brings us to what actually holds all of this up: the AI money-loop. $800B+ in circular financing rn: 1/ $NVDA invests in OpenAI 2/ OpenAI signs $300B in cloud deals w/ $ORCL 3/ Oracle buys billions in Nvidia chips to fill them 4/ Nvidia books the revenue, stock pumps, funds more deals Same dollars circling between a handful of names, dressed up as organic demand. The cracks are already showing. OpenAI is on track to lose ~$14B this year. Nvidia's $100B OpenAI deal was reported "on ice". $ORCL dumped 30% in a quarter on fears OpenAI cant pay its bills. Michael Burry, the guy who called 2008, is shorting $NVDA and comparing this to Enron-style vendor financing. And here's how 1 crack becomes a cascade: Microsoft AI revenue disappoints -> cuts Azure spend -> hits Nvidia revenue -> tanks CoreWeave valuation -> chokes OpenAI funding -> Oracle left holding $300B in commitments nobody pays for. Pull 1 card and the whole circle tightens. This is exactly the dotcom script. Back then Nortel and Lucent financed their own customers to fake demand. The loop looked unbreakable, until one link missed a payment. Then the entire thing unwound in months. Insiders rich, retail wrecked. Same in 2021 SPAC mania. Same now, just 10x the size. How to max profit on it? I have an idea... I already made 6 figs shorting SpaceX publicly, posted it all in real time. And I'm not stopping there. Soon I'm sharing my biggest trade ever, confident it makes me millions. Dropping it in my free private group: (btw I'll stop accepting new requests soon, so hurry up) A lot of people are going to wish they joined before the doors close.
0:29

🚨 We are repeating 1995 dotcom bubble pattern literally 1:1 Over a decade in markets and I've never seen this many crash indicators flashing at once Start with the biggest one: the IPO wave. Everyone thinks the SpaceX trade is over, but it hasnt even started. Aug 6 will change everything. $SPCX did the largest IPO in history on June 12. $135, $1.77T valuation, $75B raised. Then it ripped to $225 in 4 days and dumped 35% back to $147. The crowd that bought the hype is already underwater. And here's the thing: an IPO is not the start of the run. It's the exit. The day insiders + early backers turn paper into cash. This time they built the buyer out of millions of retail accounts. Fidelity cut its IPO minimum from $500k to $2k, a 99.6% cut. Robinhood, SoFi: no minimum at all. Retail got 30% of the offering when the standard is 5-10%. They dropped every barrier to get u IN. Then bolted the exit: sell in the first 15 days and ur flagged a flipper, with bans escalating up to a lifetime one tied to ur SSN. You dont build a one-way door unless u already know which way the crowd has to run. History shows the ending every time. Truist studied 30 major tech IPOs since 2012: Median year-1 drawdown: -54% Only 43% green 6 months in 11 of 30 fell 64-90% $FB, $SNAP, $UBER, $HOOD: same script. Huge debut, endless hype, lockups expire, early money hands u the bag. Now price SpaceX into that: over 110x sales, $4.9B loss last year, $41.3B accumulated deficit. CFRA rated it a SELL under an hour after open. Morningstar fair value: $63. It trades at $162. And the real distribution event is still ahead: Aug 6. First earnings report = insiders finally unlock 20% of their stock, +10% more if price holds high. At the same time $SPCX enters the Nasdaq-100, so index funds are FORCED to buy it near the top. Forced buyers meeting real sellers. That's the window. Moreover, this is just IPO #1. OpenAI (~$850-900B) and Anthropic (~$965B) already filed. No dates yet, but the window is open and they'll fly through it. 3 of the biggest listings in history, all AI-linked, all landing into the same indexes. Which brings us to what actually holds all of this up: the AI money-loop. $800B+ in circular financing rn: 1/ $NVDA invests in OpenAI 2/ OpenAI signs $300B in cloud deals w/ $ORCL 3/ Oracle buys billions in Nvidia chips to fill them 4/ Nvidia books the revenue, stock pumps, funds more deals Same dollars circling between a handful of names, dressed up as organic demand. The cracks are already showing. OpenAI is on track to lose ~$14B this year. Nvidia's $100B OpenAI deal was reported "on ice". $ORCL dumped 30% in a quarter on fears OpenAI cant pay its bills. Michael Burry, the guy who called 2008, is shorting $NVDA and comparing this to Enron-style vendor financing. And here's how 1 crack becomes a cascade: Microsoft AI revenue disappoints -> cuts Azure spend -> hits Nvidia revenue -> tanks CoreWeave valuation -> chokes OpenAI funding -> Oracle left holding $300B in commitments nobody pays for. Pull 1 card and the whole circle tightens. This is exactly the dotcom script. Back then Nortel and Lucent financed their own customers to fake demand. The loop looked unbreakable, until one link missed a payment. Then the entire thing unwound in months. Insiders rich, retail wrecked. Same in 2021 SPAC mania. Same now, just 10x the size. How to max profit on it? I have an idea... I already made 6 figs shorting SpaceX publicly, posted it all in real time. And I'm not stopping there. Soon I'm sharing my biggest trade ever, confident it makes me millions. Dropping it in my free private group: (btw I'll stop accepting new requests soon, so hurry up) A lot of people are going to wish they joined before the doors close.

𝗰𝘆𝗰𝗹𝗼𝗽

86,494 次观看 • 2 个月前

I paid Alex & Leila Hormozi $5,000 for their 2-day scaling workshop. Why? To grow my business from $6 million to $12 million in 2025. These 12 lessons from the event will help me get there: 1. The fastest-moving entrepreneurs are obsessive resource allocators. Similar to investors, they seek the best risk-adjusted returns with the resources they have. The main resources of the business are: • Time (of the team) • Attention (of the team) • And capital (of the business) So resource allocation is: • Aligning attention on the most important thing • Properly allocating everyone’s time to achieve that thing the fastest • Strategically investing capital to accelerate the outcome or increase its likelihood of achievement 2. $3m to $10m in EBITDA is where the majority of the value in a business is created. $3m in EBITDA likely gets a 1x multiple, so $3m of enterprise value. The process of going to $10m (when done well), not only 3.3x’s the EBITDA, but can take the multiple from 1 to 4 -> which is a 13.2x return. The EV goes from $3m to $40m, and that is the stage we are in right now as a business. 3. LTV:CAC are two metrics you must have staring at you and constantly audited. LTV = lifetime value of the customer CAC = customer acquisition cost The scope of calculating those is beyond this write-up, but basically you want this metric to be ~8:1 or higher when aggressively scaling a service-based business. On top of that, these are the only two metrics that you can “improve” in your business → either making customers worth more or reducing the cost to acquire them. You should be able to tie every project on your list directly to the improvement of one of these metrics. 4. We need a single dashboard with the most important metrics in the business. The quality of the dashboard is: • How many people use it on a daily basis • And how clearly they can connect their performance to the performance of the main numbers on the dashboard. We have data thrown about across Airtable, Google Sheets, and various Slack channels. Now, it’s time to unite them such that we can make even better decisions as a team. 5. Leveling up in business is transitioning from selling to people to selling to employees. In the beginning, you are the one creating all of the value. Over time, you will replace yourself out of certain functions that are customer-facing (if you are approaching business correctly). However, your job then becomes selling to your employees to spark their highest performance and retain them. 6. Brand is the best way to improve LTV and reduce CAC at the same time. It makes it cheaper to acquire customers since you have fixed media expenses (just labor) but unlimited upside in the number of eyeballs you can reach. It increases LTV because the continued content you create makes customers likely to keep purchasing because they associate the good content with the purchase they made, whether it’s free content or not. 7. Every single thing in your business is trainable, you just lack the skill of training. Seeing their presentations, their handshakes, the way they repeat the question back to the audience, it was so clear that Alex & Leila did this first, then obsessively role-played and drilled each person on their performance until it was indistinguishable from theirs. 8. The people doing it at the highest level of an obsessive, intentional standard. It was so evident the way these employees conducted themselves that they: • Loved working there • Loved the culture of high performance • And had been trained with extreme repetition and attention to detail 9. Past $3-5m in revenue, anything “new” starts with “who” not “how.” I made the mistake last year of trying to “bootstrap” our cold ads initiative (while continuing to run the rest of the business & sales team). I spent roughly ~200 hours on this throughout the year, which took time away from both my content and the management of the sales team. But for whatever reason, I thought I “had” to be the one who got it off the ground, then handed it off to a new hire or media buyer. But I had the sequence flipped. I should have spent the first 50 hours finding a world-class director of paid marketing, someone with far more experience than me building out a cold traffic acquisition system. Heck, I could have even spent 200 hours on it and ended up with a far greater return than I ended up with. 10. Excellence is a remarkably high number of extremely small details done well. Throughout the workshop, I paid close attention to the event operations, taking notes on how to run a great in-person event in case we wanted to do so in the future. Several things stood out that were clearly “iterations” from prior events, all based around eliminating the small, annoying parts of attending any kind of seminar. • High-quality food • Greeters at the door • Clear bathroom signs • A barista for fresh coffee • WiFi signs posted everywhere • Constant 15-minute breaks every 90 minutes The list goes on and on. 11. Any change you make in a business you should expect a 20% “decrease” in performance to start. That makes the hurdle rate to doing “new” at least 20% for it to be worth it, and arguably 40%. This happens because the switching cost leads to an immediate drop just from having to retrain the team. Change a meeting cadence, change a sales script, change an onboarding flow, all of these are going to come with a switching cost the team must overcome. Therefore, the highest risk-adjusted return is always to just do more or better or whatever you’re already doing, rather than add something new. 12. The ultimate size of the business is the sum of the intelligence of its people. Alex laid out this golden nugget during one of his talks and I found it interesting for a few reasons. First, because of his definition of intelligence = speed of learning, that means the ultimate size of the company is how quickly everyone can learn things. And so said another way, the ultimate size of the company is correlated to the speed of its iterations. The second reason I found this interesting is because you can create a culture of iteration through constant, rapid feedback on every behavior. And when I say constant, I mean constant. You could tell they’ve built this culture by the way their presenters all presented the exact same way as Alex and Leila. Aaand that’s it! I go deeper into all these lessons in this video, check it out: Timestamps 00:37 The Fastest Moving Entrepreneurs Are Obsessive Resource Allocators 04:09 $3m To $10m EBITDA Is Where The Majority Of The Value In A Business Is Created 07:00 LTV:CAC Are Two Metrics You Must Have Staring At You 10:04 You Need A Single Dashboard With The Most Important Metrics In The Business 12:03 Leveling Up In Business Is Transitioning To Selling To People To Selling To Employees 14:10 Brand Is The Best Way To Improve LTV And Reduce CAC At The Same Time 16:02 Every Single Thing In Your Business Is Trainable, You Just Lack The Skill Of Training 18:54 The People Doing It At The Highest Level Have An Obsessive, Intentional Standard 20:04 Past $3-5m In Revenue, Anything "New" Starts With "Who" Not "How" 23:33 Excellence Is A Remarkably High Number Of Extremely Small Details Done Well 26:23 Any Change You Make In A Business You Should Expect A 20% "Decrease" In Performance To Start 28:07 The Ultimate Size Of The Business Is The Sum Of The Intelligence Of It's People
31:43

I paid Alex & Leila Hormozi $5,000 for their 2-day scaling workshop. Why? To grow my business from $6 million to $12 million in 2025. These 12 lessons from the event will help me get there: 1. The fastest-moving entrepreneurs are obsessive resource allocators. Similar to investors, they seek the best risk-adjusted returns with the resources they have. The main resources of the business are: • Time (of the team) • Attention (of the team) • And capital (of the business) So resource allocation is: • Aligning attention on the most important thing • Properly allocating everyone’s time to achieve that thing the fastest • Strategically investing capital to accelerate the outcome or increase its likelihood of achievement 2. $3m to $10m in EBITDA is where the majority of the value in a business is created. $3m in EBITDA likely gets a 1x multiple, so $3m of enterprise value. The process of going to $10m (when done well), not only 3.3x’s the EBITDA, but can take the multiple from 1 to 4 -> which is a 13.2x return. The EV goes from $3m to $40m, and that is the stage we are in right now as a business. 3. LTV:CAC are two metrics you must have staring at you and constantly audited. LTV = lifetime value of the customer CAC = customer acquisition cost The scope of calculating those is beyond this write-up, but basically you want this metric to be ~8:1 or higher when aggressively scaling a service-based business. On top of that, these are the only two metrics that you can “improve” in your business → either making customers worth more or reducing the cost to acquire them. You should be able to tie every project on your list directly to the improvement of one of these metrics. 4. We need a single dashboard with the most important metrics in the business. The quality of the dashboard is: • How many people use it on a daily basis • And how clearly they can connect their performance to the performance of the main numbers on the dashboard. We have data thrown about across Airtable, Google Sheets, and various Slack channels. Now, it’s time to unite them such that we can make even better decisions as a team. 5. Leveling up in business is transitioning from selling to people to selling to employees. In the beginning, you are the one creating all of the value. Over time, you will replace yourself out of certain functions that are customer-facing (if you are approaching business correctly). However, your job then becomes selling to your employees to spark their highest performance and retain them. 6. Brand is the best way to improve LTV and reduce CAC at the same time. It makes it cheaper to acquire customers since you have fixed media expenses (just labor) but unlimited upside in the number of eyeballs you can reach. It increases LTV because the continued content you create makes customers likely to keep purchasing because they associate the good content with the purchase they made, whether it’s free content or not. 7. Every single thing in your business is trainable, you just lack the skill of training. Seeing their presentations, their handshakes, the way they repeat the question back to the audience, it was so clear that Alex & Leila did this first, then obsessively role-played and drilled each person on their performance until it was indistinguishable from theirs. 8. The people doing it at the highest level of an obsessive, intentional standard. It was so evident the way these employees conducted themselves that they: • Loved working there • Loved the culture of high performance • And had been trained with extreme repetition and attention to detail 9. Past $3-5m in revenue, anything “new” starts with “who” not “how.” I made the mistake last year of trying to “bootstrap” our cold ads initiative (while continuing to run the rest of the business & sales team). I spent roughly ~200 hours on this throughout the year, which took time away from both my content and the management of the sales team. But for whatever reason, I thought I “had” to be the one who got it off the ground, then handed it off to a new hire or media buyer. But I had the sequence flipped. I should have spent the first 50 hours finding a world-class director of paid marketing, someone with far more experience than me building out a cold traffic acquisition system. Heck, I could have even spent 200 hours on it and ended up with a far greater return than I ended up with. 10. Excellence is a remarkably high number of extremely small details done well. Throughout the workshop, I paid close attention to the event operations, taking notes on how to run a great in-person event in case we wanted to do so in the future. Several things stood out that were clearly “iterations” from prior events, all based around eliminating the small, annoying parts of attending any kind of seminar. • High-quality food • Greeters at the door • Clear bathroom signs • A barista for fresh coffee • WiFi signs posted everywhere • Constant 15-minute breaks every 90 minutes The list goes on and on. 11. Any change you make in a business you should expect a 20% “decrease” in performance to start. That makes the hurdle rate to doing “new” at least 20% for it to be worth it, and arguably 40%. This happens because the switching cost leads to an immediate drop just from having to retrain the team. Change a meeting cadence, change a sales script, change an onboarding flow, all of these are going to come with a switching cost the team must overcome. Therefore, the highest risk-adjusted return is always to just do more or better or whatever you’re already doing, rather than add something new. 12. The ultimate size of the business is the sum of the intelligence of its people. Alex laid out this golden nugget during one of his talks and I found it interesting for a few reasons. First, because of his definition of intelligence = speed of learning, that means the ultimate size of the company is how quickly everyone can learn things. And so said another way, the ultimate size of the company is correlated to the speed of its iterations. The second reason I found this interesting is because you can create a culture of iteration through constant, rapid feedback on every behavior. And when I say constant, I mean constant. You could tell they’ve built this culture by the way their presenters all presented the exact same way as Alex and Leila. Aaand that’s it! I go deeper into all these lessons in this video, check it out: Timestamps 00:37 The Fastest Moving Entrepreneurs Are Obsessive Resource Allocators 04:09 $3m To $10m EBITDA Is Where The Majority Of The Value In A Business Is Created 07:00 LTV:CAC Are Two Metrics You Must Have Staring At You 10:04 You Need A Single Dashboard With The Most Important Metrics In The Business 12:03 Leveling Up In Business Is Transitioning To Selling To People To Selling To Employees 14:10 Brand Is The Best Way To Improve LTV And Reduce CAC At The Same Time 16:02 Every Single Thing In Your Business Is Trainable, You Just Lack The Skill Of Training 18:54 The People Doing It At The Highest Level Have An Obsessive, Intentional Standard 20:04 Past $3-5m In Revenue, Anything "New" Starts With "Who" Not "How" 23:33 Excellence Is A Remarkably High Number Of Extremely Small Details Done Well 26:23 Any Change You Make In A Business You Should Expect A 20% "Decrease" In Performance To Start 28:07 The Ultimate Size Of The Business Is The Sum Of The Intelligence Of It's People

Dickie Bush

62,195 次观看 • 1 年前

BEARISH ON OPENAI The investment case for OpenAI has never been more precarious than it is right now in late 2025. What was once a company that seemed destined to dominate the artificial intelligence revolution has revealed itself to be a structurally disadvantaged challenger fighting a defensive war on multiple fronts. The company anticipates burning through roughly $9 billion this year on $13 billion in sales, a cash burn rate of approximately 70% of revenue. This is not the profile of a company poised to capture monopolistic profits from a transformative technology; it is the profile of a utility company spending astronomical sums to deliver a commodity product that competitors are increasingly giving away for free. The financial trajectory only becomes more alarming when examined over a longer time horizon. The documents show OpenAI projects that by 2028, its operating losses will balloon to roughly three-quarters of that year’s revenue, driven primarily by ballooning spending on computing costs. The company has painted a rosy picture of eventual profitability by 2029 or 2030, but this projection requires believing that OpenAI can grow revenue from roughly $13 billion today to $125 billion or more while simultaneously maintaining pricing power in a market where every major technology company and numerous startups are racing to commoditize the very product OpenAI sells. The cash burn is expected to reach $115 billion cumulatively through 2029, according to The Information. These numbers represent a staggering bet that requires near-perfect execution across multiple dimensions over half a decade. The most damning evidence against OpenAI’s long-term viability is the evaporation of its technological moat. In 2023, GPT-4 felt like genuine magic, a capability that no other company could replicate. Today, that lead has effectively vanished. The sudden availability of frontier-level open-source models is expected to dramatically accelerate AI development globally, potentially reshaping entire industries and altering the balance of power in the tech world. Meta’s Llama series, Mistral’s increasingly capable models, and even Chinese competitors like DeepSeek have demonstrated that the core technology powering ChatGPT is replicable and, in many cases, distributable for free. When your product becomes commoditized, the economics become brutal, and OpenAI finds itself in the position of trying to sell bottled water in a world where tap water has become indistinguishable in quality. The competitive pressure from open-source alternatives is compounding rapidly. The open source movement in AI has grown exponentially over the past few years. Instead of relying solely on expensive, closed models from major tech companies, developers and researchers worldwide can now access, modify, and improve upon state-of-the-art LLMs. This democratization is existential for OpenAI’s business model. Enterprises that once paid premium prices for API access now have the option to run comparable models on their own infrastructure at a fraction of the cost, with the added benefits of data privacy and customization. The value proposition that justified OpenAI’s premium pricing has eroded faster than anyone anticipated, and there is no indication that this trend will reverse. Perhaps nothing illustrates OpenAI’s structural weakness more clearly than the behavior of its most important partner. Microsoft is dancing to its own tune in the artificial intelligence revolution, and Wall Street cannot stop watching. Despite pouring approximately $13 billion into OpenAI over several years, DA Davidson analyst Gil Luria estimates that just 17 percent of Microsoft’s total Azure revenue comes from artificial intelligence workloads. More critically, only 6 percent of that total ties directly to reselling OpenAI’s models, while approximately 75 percent is generated from Azure AI. Microsoft is building its own models, hedging with Anthropic, and quietly reducing its dependency on the very company it funded. When your largest investor is simultaneously your biggest competitor and is actively developing alternatives to your core product, the strategic implications are dire. Leaders at Microsoft believe Anthropic’s latest models — Claude Sonnet 4, specifically — perform better than OpenAI’s in certain functions, like creating aesthetically pleasing PowerPoint presentations. This is not a minor technical preference; it represents a fundamental shift in how Microsoft views its partnership with OpenAI. Microsoft is dramatically escalating its AI independence strategy. At an internal town hall Thursday, Microsoft AI chief Mustafa Suleyman revealed the company is making “significant investments” in compute capacity to build frontier models that can compete directly with OpenAI, Google, and Meta. The company that was supposed to be OpenAI’s path to distribution and scale is instead preparing for a future where OpenAI is just one vendor among many, if not an outright competitor. The leadership exodus at OpenAI over the past year has been nothing short of catastrophic. In September 2024, Murati announced that she was stepping down as CTO. This move came amid a wider executive exodus as OpenAI chief research officer Bob McGrew and a vice president of research, Barret Zoph, also announced their departures soon after. Mira Murati was not a minor figure; she was instrumental in the development of ChatGPT, Dall-E, and Sora. Her departure, along with co-founder Ilya Sutskever, safety leader Jan Leike, and co-founder John Schulman who joined rival Anthropic, has left CEO Sam Altman without much of the leadership team that helped him build OpenAI into an AI juggernaut. Hannah Wong, the executive who steered OpenAI through its most chaotic period, has announced she’s leaving the company just this month, continuing the pattern of senior departures that suggests something fundamentally broken in the organization’s culture or direction. The distribution problem facing OpenAI may be its most insurmountable challenge. Apple and Google control the smartphones that billions of people use every day. Microsoft controls the productivity software that enterprises depend upon. OpenAI, by contrast, must convince users to deliberately open a separate application and type their queries into a text box. In a world of agentic AI where assistants need access to your email, calendar, and files to be useful, an AI embedded directly into your operating system has an overwhelming structural advantage over a standalone chatbot. OpenAI is trying to be a consumer product company without owning any of the surfaces where consumers actually spend their time, competing against incumbents who can simply bundle AI capabilities directly into products that already have hundreds of millions of daily active users. The nuclear-to-solar analogy captures the fundamental economic transformation that is devastating OpenAI’s business model. Just as nuclear power required enormous upfront capital expenditure for centralized power plants, AI in its current form requires massive data center investments to train and serve models. But the direction of travel is unmistakably toward distributed intelligence that runs locally on devices. A major part of the pitch is practicality. Lample emphasizes that Ministral 3 can run on a single GPU, making it deployable on affordable hardware — from on-premise servers to laptops, robots, and other edge devices that may have limited connectivity. When powerful AI models can run on a smartphone or a laptop without any cloud connection, the entire economic rationale for paying premium prices to access centralized AI infrastructure disappears. OpenAI is building nuclear reactors in a world that is rapidly installing solar panels on every rooftop. The proposed $1 trillion IPO valuation is perhaps the clearest signal that something is deeply wrong with the OpenAI story. In the first half of the year, OpenAI lost $13.5 billion, on revenue of $4.3 billion. It is on track to lose $27 billion for the year. One estimate shows OpenAI will burn $115 billion by 2029. Asking public market investors to pay $1 trillion for a company that loses more than twice as much as it earns is not a growth story; it is an exit strategy. The sophisticated investors who funded OpenAI’s private rounds are looking for a way to transfer their risk to retail investors and pension funds who may not fully understand the unit economics of the business. A recent report by HSBC estimated that the company will remain in the unprofitable category until 2029 and that the company will need an additional $207 billion to fund its ambitions. Sam Altman’s leadership represents another structural liability for the company. His background is as a startup investor and evangelist, not as an operational executive who has scaled a capital-intensive industrial operation. The pivot from nonprofit research lab to for-profit corporation to public benefit corporation to anticipated public company has been accompanied by legal and governance structures designed primarily to protect Altman’s control rather than to create shareholder value. Going public means answering a lot more of those kinds of questions, every single quarter, forever. When asked about financial concerns in a friendly podcast interview, Altman’s dismissive response revealed a leader uncomfortable with the scrutiny that public markets will inevitably bring. The adults in the room have largely departed, leaving a company that desperately needs disciplined execution led by someone whose strengths lie elsewhere. The comparison to Netscape is instructive. Netscape proved that the internet was real and created genuine value, but it had no sustainable moat against an incumbent who could bundle the browser directly into the operating system. OpenAI has proven that large language models are real and valuable, but it faces the same structural disadvantage against incumbents who can bundle AI directly into operating systems, productivity suites, and cloud platforms. The value will accrue to the companies that own the distribution channels and the hardware, not to the company that demonstrated the technology was possible. OpenAI is destined to become a historical footnote, remembered as the company that ignited the AI revolution but failed to capture the economic value it created. The only bull case for OpenAI is the AGI lottery ticket: the possibility that the company achieves artificial general intelligence before anyone else and thereby transcends all normal economic analysis. But there is no evidence that OpenAI is any closer to AGI than Google, Anthropic, or DeepMind. The company’s advantage was never secret research breakthroughs; it was first-mover advantage in commercialization. That advantage has now been erased by competitors who can match or exceed OpenAI’s capabilities while benefiting from existing ecosystems, distribution channels, and the willingness to operate AI as a loss leader to drive engagement with more profitable products. The secret sauce was never secret, and there was never any sauce. The endgame for OpenAI is unlikely to be the triumphant dominance that early investors imagined. The most probable outcomes range from gradual irrelevance as a backend provider, to financial restructuring under pressure from creditors, to absorption by Microsoft or another well-capitalized technology company looking to acquire the remaining talent and intellectual property at a discount. Despite its current losses, OpenAI’s long-term prospects are bolstered by the explosive growth of the AI market. But growth in the overall AI market does not guarantee success for any individual company, particularly one with no moat, no ecosystem, and a cost structure that requires selling a commodity at premium prices. The AI revolution is real, but OpenAI’s role in capturing its economic value is far from assured. For anyone considering an investment in OpenAI at anything close to current valuations, the prudent course is to stay far away and watch from the sidelines as economic reality catches up with hype.
19:01

BEARISH ON OPENAI The investment case for OpenAI has never been more precarious than it is right now in late 2025. What was once a company that seemed destined to dominate the artificial intelligence revolution has revealed itself to be a structurally disadvantaged challenger fighting a defensive war on multiple fronts. The company anticipates burning through roughly $9 billion this year on $13 billion in sales, a cash burn rate of approximately 70% of revenue. This is not the profile of a company poised to capture monopolistic profits from a transformative technology; it is the profile of a utility company spending astronomical sums to deliver a commodity product that competitors are increasingly giving away for free. The financial trajectory only becomes more alarming when examined over a longer time horizon. The documents show OpenAI projects that by 2028, its operating losses will balloon to roughly three-quarters of that year’s revenue, driven primarily by ballooning spending on computing costs. The company has painted a rosy picture of eventual profitability by 2029 or 2030, but this projection requires believing that OpenAI can grow revenue from roughly $13 billion today to $125 billion or more while simultaneously maintaining pricing power in a market where every major technology company and numerous startups are racing to commoditize the very product OpenAI sells. The cash burn is expected to reach $115 billion cumulatively through 2029, according to The Information. These numbers represent a staggering bet that requires near-perfect execution across multiple dimensions over half a decade. The most damning evidence against OpenAI’s long-term viability is the evaporation of its technological moat. In 2023, GPT-4 felt like genuine magic, a capability that no other company could replicate. Today, that lead has effectively vanished. The sudden availability of frontier-level open-source models is expected to dramatically accelerate AI development globally, potentially reshaping entire industries and altering the balance of power in the tech world. Meta’s Llama series, Mistral’s increasingly capable models, and even Chinese competitors like DeepSeek have demonstrated that the core technology powering ChatGPT is replicable and, in many cases, distributable for free. When your product becomes commoditized, the economics become brutal, and OpenAI finds itself in the position of trying to sell bottled water in a world where tap water has become indistinguishable in quality. The competitive pressure from open-source alternatives is compounding rapidly. The open source movement in AI has grown exponentially over the past few years. Instead of relying solely on expensive, closed models from major tech companies, developers and researchers worldwide can now access, modify, and improve upon state-of-the-art LLMs. This democratization is existential for OpenAI’s business model. Enterprises that once paid premium prices for API access now have the option to run comparable models on their own infrastructure at a fraction of the cost, with the added benefits of data privacy and customization. The value proposition that justified OpenAI’s premium pricing has eroded faster than anyone anticipated, and there is no indication that this trend will reverse. Perhaps nothing illustrates OpenAI’s structural weakness more clearly than the behavior of its most important partner. Microsoft is dancing to its own tune in the artificial intelligence revolution, and Wall Street cannot stop watching. Despite pouring approximately $13 billion into OpenAI over several years, DA Davidson analyst Gil Luria estimates that just 17 percent of Microsoft’s total Azure revenue comes from artificial intelligence workloads. More critically, only 6 percent of that total ties directly to reselling OpenAI’s models, while approximately 75 percent is generated from Azure AI. Microsoft is building its own models, hedging with Anthropic, and quietly reducing its dependency on the very company it funded. When your largest investor is simultaneously your biggest competitor and is actively developing alternatives to your core product, the strategic implications are dire. Leaders at Microsoft believe Anthropic’s latest models — Claude Sonnet 4, specifically — perform better than OpenAI’s in certain functions, like creating aesthetically pleasing PowerPoint presentations. This is not a minor technical preference; it represents a fundamental shift in how Microsoft views its partnership with OpenAI. Microsoft is dramatically escalating its AI independence strategy. At an internal town hall Thursday, Microsoft AI chief Mustafa Suleyman revealed the company is making “significant investments” in compute capacity to build frontier models that can compete directly with OpenAI, Google, and Meta. The company that was supposed to be OpenAI’s path to distribution and scale is instead preparing for a future where OpenAI is just one vendor among many, if not an outright competitor. The leadership exodus at OpenAI over the past year has been nothing short of catastrophic. In September 2024, Murati announced that she was stepping down as CTO. This move came amid a wider executive exodus as OpenAI chief research officer Bob McGrew and a vice president of research, Barret Zoph, also announced their departures soon after. Mira Murati was not a minor figure; she was instrumental in the development of ChatGPT, Dall-E, and Sora. Her departure, along with co-founder Ilya Sutskever, safety leader Jan Leike, and co-founder John Schulman who joined rival Anthropic, has left CEO Sam Altman without much of the leadership team that helped him build OpenAI into an AI juggernaut. Hannah Wong, the executive who steered OpenAI through its most chaotic period, has announced she’s leaving the company just this month, continuing the pattern of senior departures that suggests something fundamentally broken in the organization’s culture or direction. The distribution problem facing OpenAI may be its most insurmountable challenge. Apple and Google control the smartphones that billions of people use every day. Microsoft controls the productivity software that enterprises depend upon. OpenAI, by contrast, must convince users to deliberately open a separate application and type their queries into a text box. In a world of agentic AI where assistants need access to your email, calendar, and files to be useful, an AI embedded directly into your operating system has an overwhelming structural advantage over a standalone chatbot. OpenAI is trying to be a consumer product company without owning any of the surfaces where consumers actually spend their time, competing against incumbents who can simply bundle AI capabilities directly into products that already have hundreds of millions of daily active users. The nuclear-to-solar analogy captures the fundamental economic transformation that is devastating OpenAI’s business model. Just as nuclear power required enormous upfront capital expenditure for centralized power plants, AI in its current form requires massive data center investments to train and serve models. But the direction of travel is unmistakably toward distributed intelligence that runs locally on devices. A major part of the pitch is practicality. Lample emphasizes that Ministral 3 can run on a single GPU, making it deployable on affordable hardware — from on-premise servers to laptops, robots, and other edge devices that may have limited connectivity. When powerful AI models can run on a smartphone or a laptop without any cloud connection, the entire economic rationale for paying premium prices to access centralized AI infrastructure disappears. OpenAI is building nuclear reactors in a world that is rapidly installing solar panels on every rooftop. The proposed $1 trillion IPO valuation is perhaps the clearest signal that something is deeply wrong with the OpenAI story. In the first half of the year, OpenAI lost $13.5 billion, on revenue of $4.3 billion. It is on track to lose $27 billion for the year. One estimate shows OpenAI will burn $115 billion by 2029. Asking public market investors to pay $1 trillion for a company that loses more than twice as much as it earns is not a growth story; it is an exit strategy. The sophisticated investors who funded OpenAI’s private rounds are looking for a way to transfer their risk to retail investors and pension funds who may not fully understand the unit economics of the business. A recent report by HSBC estimated that the company will remain in the unprofitable category until 2029 and that the company will need an additional $207 billion to fund its ambitions. Sam Altman’s leadership represents another structural liability for the company. His background is as a startup investor and evangelist, not as an operational executive who has scaled a capital-intensive industrial operation. The pivot from nonprofit research lab to for-profit corporation to public benefit corporation to anticipated public company has been accompanied by legal and governance structures designed primarily to protect Altman’s control rather than to create shareholder value. Going public means answering a lot more of those kinds of questions, every single quarter, forever. When asked about financial concerns in a friendly podcast interview, Altman’s dismissive response revealed a leader uncomfortable with the scrutiny that public markets will inevitably bring. The adults in the room have largely departed, leaving a company that desperately needs disciplined execution led by someone whose strengths lie elsewhere. The comparison to Netscape is instructive. Netscape proved that the internet was real and created genuine value, but it had no sustainable moat against an incumbent who could bundle the browser directly into the operating system. OpenAI has proven that large language models are real and valuable, but it faces the same structural disadvantage against incumbents who can bundle AI directly into operating systems, productivity suites, and cloud platforms. The value will accrue to the companies that own the distribution channels and the hardware, not to the company that demonstrated the technology was possible. OpenAI is destined to become a historical footnote, remembered as the company that ignited the AI revolution but failed to capture the economic value it created. The only bull case for OpenAI is the AGI lottery ticket: the possibility that the company achieves artificial general intelligence before anyone else and thereby transcends all normal economic analysis. But there is no evidence that OpenAI is any closer to AGI than Google, Anthropic, or DeepMind. The company’s advantage was never secret research breakthroughs; it was first-mover advantage in commercialization. That advantage has now been erased by competitors who can match or exceed OpenAI’s capabilities while benefiting from existing ecosystems, distribution channels, and the willingness to operate AI as a loss leader to drive engagement with more profitable products. The secret sauce was never secret, and there was never any sauce. The endgame for OpenAI is unlikely to be the triumphant dominance that early investors imagined. The most probable outcomes range from gradual irrelevance as a backend provider, to financial restructuring under pressure from creditors, to absorption by Microsoft or another well-capitalized technology company looking to acquire the remaining talent and intellectual property at a discount. Despite its current losses, OpenAI’s long-term prospects are bolstered by the explosive growth of the AI market. But growth in the overall AI market does not guarantee success for any individual company, particularly one with no moat, no ecosystem, and a cost structure that requires selling a commodity at premium prices. The AI revolution is real, but OpenAI’s role in capturing its economic value is far from assured. For anyone considering an investment in OpenAI at anything close to current valuations, the prudent course is to stay far away and watch from the sidelines as economic reality catches up with hype.

David Shapiro (L/0)

69,180 次观看 • 9 个月前

Made $313 → $2,382,780 in 4 Days Using a Claude AI Bot on Polymarket. 26,738 trades. 98% win rate. Full blockchain proof. Every single trade verifiable on-chain. I've made the exact step-by-step guide to build this Claude Polymarket bot from scratch. You've been trading for 3 years. Still red. He gave Claude $313. Woke up rich. Free for 24 hours. To get this Setup guide: 1. Comment "Money" 2. Like and Retweet 3. Follow me Himanshu Kumar (so i can DM you) Full 2-hour video tutorial attached. Every single click and command explained. Beginner to running bot. Now let me break down exactly how this works. Save this post. This is the most important trading breakdown you'll ever read. ↓ Let's start with the number that should make you sick. $313. That's what this wallet started with. Not $50,000. Not $10,000. Not even $1,000. $313. Less than your monthly Netflix + Uber Eats + Spotify combined. 4 months later: $2,382,780.80. That's a 7,942x return. While you spent those same 4 months staring at charts, drawing trendlines, panic selling, revenge trading, and ending the month exactly where you started. Minus the $200 you lost on that "sure thing." Same 4 months. Same market. Same opportunities. He had a bot. You had feelings. Guess who won. Save this post right now. What I'm about to explain is the exact mechanism behind every dollar of that $2.38M. Follow Himanshu Kumar so you don't miss the rest. ↓ How Polymarket actually works and why bots print money on it. Polymarket is a prediction market. Will BTC be higher in 15 minutes? Yes or No. Will the Fed raise rates? Yes or No. You buy shares between $0 and $1. If you're right, your share settles at $1. If you're wrong, it settles at $0. Simple. Now here's where it gets interesting. Polymarket updates its prices SLOWER than the real market moves. When BTC drops 0.6% on Binance, Polymarket still shows old odds for about 2.7 seconds. 2.7 seconds. In those 2.7 seconds, the bot already knows the outcome. It's not predicting. It's not guessing. It's reading information that already exists and trading before Polymarket catches up. That's not trading. That's collecting free money with a 2.7 second head start. And you're over there using a 15-indicator TradingView setup trying to "predict" where BTC goes next. The bot doesn't predict anything. It just reads faster than you. That's the entire edge. Save this post because if you understand this one concept you understand how millionaires are being made on Polymarket right now. Follow Himanshu Kumar for more breakdowns like this. ↓ Let me walk you through one single trade. A new 15-minute BTC contract opens on Polymarket. Odds are 50/50. Fair price. 10 minutes in, BTC drops 0.6% on Binance. Hard, fast move. The real probability of BTC being lower at expiry is now about 78%. Polymarket still shows 54/46. The bot sees this instantly. Binance WebSocket feed. Under 50ms latency. The edge is 24 percentage points. On a binary contract, that's basically free money. Bot calculates position size using Kelly Criterion. Executes via Polymarket's API. Done. Within 2-3 seconds, other participants update the odds. 54/46 moves toward 78/22. Bot either exits for immediate profit or holds to resolution. Either way, the trade was entered with near-certainty of a positive outcome. Now repeat this 200-500 times per day. $313 → $2,382,780 in 4 months. Not magic. Not prediction. Not luck. Industrial-scale exploitation of a market inefficiency that still exists today. And you're still placing one manual trade per day and calling yourself a "trader." This is the mechanism behind every single dollar. Bookmark this post so you can study it again. Follow Himanshu Kumar because I'm breaking down each strategy separately. ↓ There are 4 strategies. Not all Claude bots do the same thing. Strategy 1: Latency Arbitrage. Win rate: 85-98%. What 0x8dxd used. Monitor Binance price feeds. When Polymarket odds lag behind reality by 3-5%, buy the correct side before the market corrects. No forecasting. No model. No sentiment analysis. Pure speed. You're not guessing. You're reading an outcome that has already happened. Strategy 2: Oracle Arbitrage. Win rate: 78-85%. Chainlink oracle price feeds occasionally diverge from Polymarket's implied prices. When they do, the settlement direction is known. Fewer opportunities. Higher certainty when they appear. Strategy 3: News-Driven Trading. Win rate: 60-75%. Claude ingests real-time news. Government filings. Central bank statements. On-chain data. Assesses probability impact before retail traders even finish reading the headline. Lower win rate because interpretation introduces uncertainty. But works on ANY market category, not just crypto. Strategy 4: Market Making. Return: 2-5% per month. Place buy and sell orders on both sides. Capture the spread. No prediction required. Most consistent. Hardest to blow up. Compounds aggressively over time. You didn't even know there were 4 strategies. You thought "trading bot" meant one thing. That's how far behind you are. 4 strategies. 4 different risk profiles. 4 ways to make money while you sleep. Save this post. Follow Himanshu Kumar for the deep dive into each one. ↓ The timeline that should haunt you. December 2025: Bot launches with $313. Nobody notices. January 6, 2026: Wallet hits ~$438,000. 140x in 30 days. 6,615 predictions. 98% win rate. Finbold reports it. Crypto Twitter explodes. March 10, 2026: Head-to-head test. Claude bot: $1,000 → $14,216 in 48 hours. +1,322%. OpenClaw bot: fully liquidated. Same market. Same timeframe. Claude won because of better risk management. OpenClaw died because it overleveraged. March 16, 2026: Someone trains a swarm model on 3 years of NBA data. Result: +$1.49M on Polymarket. April 2026: 0x8dxd final verified balance: $2,382,780.80. 26,738 trades. 4 months. This all happened while you were "waiting for the right time to start." The right time was December 2025. The second best time is right now. But you'll probably wait until it's too late. That's what you always do. Every date on this timeline is a day you could have started but didn't. Save this post. Follow Himanshu Kumar so you at least start today. ↓ Why Claude and not ChatGPT? This isn't opinion. It's data. March 2026 head-to-head: Claude bot: +1,322%. OpenClaw (GPT-based): liquidated. Same prompt. Same market. Same conditions. Researchers found Claude's code included: > More defensive edge cases > More conservative default parameters > Better error handling > More legible code for debugging > Proper Kelly Criterion position sizing > Hard drawdown kill switches ChatGPT's code overleveraged into a losing sequence and couldn't recover. Claude's code sized positions conservatively, stopped trading when drawdown thresholds hit, and survived to compound another day. The difference between +1,322% and liquidation wasn't the strategy. It was the risk management. And Claude writes better risk management than ChatGPT. That's not a debate. That's a $15,216 difference in 48 hours. But sure, keep using ChatGPT because "everyone uses it." Everyone's broke too. Coincidence? Stop using the popular tool. Start using the profitable one. Save this post. Follow Himanshu Kumar for more Claude vs ChatGPT comparisons with real data. ↓ Why humans lose to bots. Every single time. Same strategy. Same market. Same period. Bots: ~$206,000 profit. Humans: ~$100,000 profit. 2x gap. Same strategy. Here's why: 1. Late entries. By the time you identify the lag, verify your reasoning, and click buy, the 2.7 second window is gone. The bot executes in under 100ms. You execute in 30 seconds. The opportunity doesn't exist for 30 seconds. 2. Emotional sizing. You oversize when "confident." Undersize when scared. Exact opposite of Kelly math. The bot sizes based on edge. Every time. No feelings. 3. Fatigue. You make worse decisions at hour 6 than at hour 1. The bot makes the same decision at hour 72 that it made at hour 1. 4. Drawdown psychology. After 3 losses you either panic quit or double down trying to recover. Both destroy capital. The bot has a kill switch. It stops. It doesn't feel anything. You're not competing with other humans anymore. You're competing with machines that don't sleep, don't feel, don't flinch. And you're losing. The data doesn't lie. Humans lose to bots 2x on the same strategy. Save this post. Follow Himanshu Kumar for the complete bot setup that removes you from the equation. ↓ What can go wrong. Because I'm not going to lie to you. Most people who build this bot will NOT 7,942x their money. Some will lose their initial capital. Here's what can kill you: Edge compression. The arbitrage window was 12 seconds in 2024. It's 2.7 seconds now. It's shrinking. At some point it hits zero for retail operators. This is a time-limited opportunity. Not a permanent income stream. Rule changes. Polymarket can change contract mechanics, settlement rules, or API terms overnight. What worked yesterday can lose money tomorrow. Risk management bugs. A 98% win rate strategy with broken position sizing will blow up your account on the one losing trade. The March 2026 experiment proved this. Claude survived. OpenClaw got liquidated. Same strategy. Different risk management. That's why the 2-hour video tutorial walks through every single risk parameter. Because the strategy doesn't kill you. Bad risk management kills you. This is the section most "gurus" delete. I'm keeping it because I'd rather you make money safely than blow up and blame me. Save this post. Follow Himanshu Kumar for honest breakdowns, not hype. ↓ The step-by-step to build your own. Step 1: Set up a Polymarket wallet. Fund with USDC via Polygon network. Start with $100-$300 for testing. Step 2: Generate API credentials. CLOB API key from docs.polymarket .com. Store private key in environment variable. Never hardcode it. Never share it. Step 3: Prompt Claude to build the bot. Use Claude Code for best results. It reads your filesystem, executes code, and iterates on errors autonomously. Step 4: Paper trade for at least one week. Minimum 200 completed trades. Win rate must be above 70% before going live. This step is NOT optional. Step 5: Configure risk management. Max single position: 8% of portfolio. Daily loss limit: -20% with auto halt. Kill switch at -40% drawdown. Telegram alerts on every threshold. Step 6: Go live small. $1-5 per trade. Watch every trade for first week. Compare to paper results. Scale only on evidence. Skip steps 4 and 5 and you will lose your money. That's not a warning. That's a guarantee. This is your complete build guide. Save this post. Follow Himanshu Kumar because I'll be posting the exact Claude prompts for each strategy. ↓ The edge exists right now. Not next month. Not "when you're ready." Right now. The arbitrage window is 2.7 seconds. It was 12 seconds in 2024. It's shrinking every week. Every day you wait, more bots enter the space. The window gets smaller. Your potential returns get smaller. The bots already running have a compounding advantage. They're making money today that they'll use to make more money tomorrow. You're reading about it and telling yourself "I'll look into this next weekend." That's what you said last weekend. And the weekend before that. The best time to start was 6 months ago. The second best time is today. But you already know you're going to bookmark this and never open it again. Prove me wrong. ↓ Full 2-hour video tutorial attached. Every single click. Every command. Every parameter. From zero to running bot. Beginner friendly. Nothing skipped. A similar bot has already earned $2,382,780. Full blockchain proof in the article below. The video is free. The tools are free. The edge still exists. The only thing that costs money is another month of doing nothing while bots eat every opportunity you're too slow to catch. Follow Himanshu Kumar for the complete series covering every automated income stream using Claude. Prediction markets are just the beginning. Save this post. Bookmark it. Screenshot it. Whatever you need to do so you actually watch the video and build the bot instead of just reading about people who did. You Must Follow me Himanshu Kumar, so i can send you DM.
2:22:35

Made $313 → $2,382,780 in 4 Days Using a Claude AI Bot on Polymarket. 26,738 trades. 98% win rate. Full blockchain proof. Every single trade verifiable on-chain. I've made the exact step-by-step guide to build this Claude Polymarket bot from scratch. You've been trading for 3 years. Still red. He gave Claude $313. Woke up rich. Free for 24 hours. To get this Setup guide: 1. Comment "Money" 2. Like and Retweet 3. Follow me Himanshu Kumar (so i can DM you) Full 2-hour video tutorial attached. Every single click and command explained. Beginner to running bot. Now let me break down exactly how this works. Save this post. This is the most important trading breakdown you'll ever read. ↓ Let's start with the number that should make you sick. $313. That's what this wallet started with. Not $50,000. Not $10,000. Not even $1,000. $313. Less than your monthly Netflix + Uber Eats + Spotify combined. 4 months later: $2,382,780.80. That's a 7,942x return. While you spent those same 4 months staring at charts, drawing trendlines, panic selling, revenge trading, and ending the month exactly where you started. Minus the $200 you lost on that "sure thing." Same 4 months. Same market. Same opportunities. He had a bot. You had feelings. Guess who won. Save this post right now. What I'm about to explain is the exact mechanism behind every dollar of that $2.38M. Follow Himanshu Kumar so you don't miss the rest. ↓ How Polymarket actually works and why bots print money on it. Polymarket is a prediction market. Will BTC be higher in 15 minutes? Yes or No. Will the Fed raise rates? Yes or No. You buy shares between $0 and $1. If you're right, your share settles at $1. If you're wrong, it settles at $0. Simple. Now here's where it gets interesting. Polymarket updates its prices SLOWER than the real market moves. When BTC drops 0.6% on Binance, Polymarket still shows old odds for about 2.7 seconds. 2.7 seconds. In those 2.7 seconds, the bot already knows the outcome. It's not predicting. It's not guessing. It's reading information that already exists and trading before Polymarket catches up. That's not trading. That's collecting free money with a 2.7 second head start. And you're over there using a 15-indicator TradingView setup trying to "predict" where BTC goes next. The bot doesn't predict anything. It just reads faster than you. That's the entire edge. Save this post because if you understand this one concept you understand how millionaires are being made on Polymarket right now. Follow Himanshu Kumar for more breakdowns like this. ↓ Let me walk you through one single trade. A new 15-minute BTC contract opens on Polymarket. Odds are 50/50. Fair price. 10 minutes in, BTC drops 0.6% on Binance. Hard, fast move. The real probability of BTC being lower at expiry is now about 78%. Polymarket still shows 54/46. The bot sees this instantly. Binance WebSocket feed. Under 50ms latency. The edge is 24 percentage points. On a binary contract, that's basically free money. Bot calculates position size using Kelly Criterion. Executes via Polymarket's API. Done. Within 2-3 seconds, other participants update the odds. 54/46 moves toward 78/22. Bot either exits for immediate profit or holds to resolution. Either way, the trade was entered with near-certainty of a positive outcome. Now repeat this 200-500 times per day. $313 → $2,382,780 in 4 months. Not magic. Not prediction. Not luck. Industrial-scale exploitation of a market inefficiency that still exists today. And you're still placing one manual trade per day and calling yourself a "trader." This is the mechanism behind every single dollar. Bookmark this post so you can study it again. Follow Himanshu Kumar because I'm breaking down each strategy separately. ↓ There are 4 strategies. Not all Claude bots do the same thing. Strategy 1: Latency Arbitrage. Win rate: 85-98%. What 0x8dxd used. Monitor Binance price feeds. When Polymarket odds lag behind reality by 3-5%, buy the correct side before the market corrects. No forecasting. No model. No sentiment analysis. Pure speed. You're not guessing. You're reading an outcome that has already happened. Strategy 2: Oracle Arbitrage. Win rate: 78-85%. Chainlink oracle price feeds occasionally diverge from Polymarket's implied prices. When they do, the settlement direction is known. Fewer opportunities. Higher certainty when they appear. Strategy 3: News-Driven Trading. Win rate: 60-75%. Claude ingests real-time news. Government filings. Central bank statements. On-chain data. Assesses probability impact before retail traders even finish reading the headline. Lower win rate because interpretation introduces uncertainty. But works on ANY market category, not just crypto. Strategy 4: Market Making. Return: 2-5% per month. Place buy and sell orders on both sides. Capture the spread. No prediction required. Most consistent. Hardest to blow up. Compounds aggressively over time. You didn't even know there were 4 strategies. You thought "trading bot" meant one thing. That's how far behind you are. 4 strategies. 4 different risk profiles. 4 ways to make money while you sleep. Save this post. Follow Himanshu Kumar for the deep dive into each one. ↓ The timeline that should haunt you. December 2025: Bot launches with $313. Nobody notices. January 6, 2026: Wallet hits ~$438,000. 140x in 30 days. 6,615 predictions. 98% win rate. Finbold reports it. Crypto Twitter explodes. March 10, 2026: Head-to-head test. Claude bot: $1,000 → $14,216 in 48 hours. +1,322%. OpenClaw bot: fully liquidated. Same market. Same timeframe. Claude won because of better risk management. OpenClaw died because it overleveraged. March 16, 2026: Someone trains a swarm model on 3 years of NBA data. Result: +$1.49M on Polymarket. April 2026: 0x8dxd final verified balance: $2,382,780.80. 26,738 trades. 4 months. This all happened while you were "waiting for the right time to start." The right time was December 2025. The second best time is right now. But you'll probably wait until it's too late. That's what you always do. Every date on this timeline is a day you could have started but didn't. Save this post. Follow Himanshu Kumar so you at least start today. ↓ Why Claude and not ChatGPT? This isn't opinion. It's data. March 2026 head-to-head: Claude bot: +1,322%. OpenClaw (GPT-based): liquidated. Same prompt. Same market. Same conditions. Researchers found Claude's code included: > More defensive edge cases > More conservative default parameters > Better error handling > More legible code for debugging > Proper Kelly Criterion position sizing > Hard drawdown kill switches ChatGPT's code overleveraged into a losing sequence and couldn't recover. Claude's code sized positions conservatively, stopped trading when drawdown thresholds hit, and survived to compound another day. The difference between +1,322% and liquidation wasn't the strategy. It was the risk management. And Claude writes better risk management than ChatGPT. That's not a debate. That's a $15,216 difference in 48 hours. But sure, keep using ChatGPT because "everyone uses it." Everyone's broke too. Coincidence? Stop using the popular tool. Start using the profitable one. Save this post. Follow Himanshu Kumar for more Claude vs ChatGPT comparisons with real data. ↓ Why humans lose to bots. Every single time. Same strategy. Same market. Same period. Bots: ~$206,000 profit. Humans: ~$100,000 profit. 2x gap. Same strategy. Here's why: 1. Late entries. By the time you identify the lag, verify your reasoning, and click buy, the 2.7 second window is gone. The bot executes in under 100ms. You execute in 30 seconds. The opportunity doesn't exist for 30 seconds. 2. Emotional sizing. You oversize when "confident." Undersize when scared. Exact opposite of Kelly math. The bot sizes based on edge. Every time. No feelings. 3. Fatigue. You make worse decisions at hour 6 than at hour 1. The bot makes the same decision at hour 72 that it made at hour 1. 4. Drawdown psychology. After 3 losses you either panic quit or double down trying to recover. Both destroy capital. The bot has a kill switch. It stops. It doesn't feel anything. You're not competing with other humans anymore. You're competing with machines that don't sleep, don't feel, don't flinch. And you're losing. The data doesn't lie. Humans lose to bots 2x on the same strategy. Save this post. Follow Himanshu Kumar for the complete bot setup that removes you from the equation. ↓ What can go wrong. Because I'm not going to lie to you. Most people who build this bot will NOT 7,942x their money. Some will lose their initial capital. Here's what can kill you: Edge compression. The arbitrage window was 12 seconds in 2024. It's 2.7 seconds now. It's shrinking. At some point it hits zero for retail operators. This is a time-limited opportunity. Not a permanent income stream. Rule changes. Polymarket can change contract mechanics, settlement rules, or API terms overnight. What worked yesterday can lose money tomorrow. Risk management bugs. A 98% win rate strategy with broken position sizing will blow up your account on the one losing trade. The March 2026 experiment proved this. Claude survived. OpenClaw got liquidated. Same strategy. Different risk management. That's why the 2-hour video tutorial walks through every single risk parameter. Because the strategy doesn't kill you. Bad risk management kills you. This is the section most "gurus" delete. I'm keeping it because I'd rather you make money safely than blow up and blame me. Save this post. Follow Himanshu Kumar for honest breakdowns, not hype. ↓ The step-by-step to build your own. Step 1: Set up a Polymarket wallet. Fund with USDC via Polygon network. Start with $100-$300 for testing. Step 2: Generate API credentials. CLOB API key from docs.polymarket .com. Store private key in environment variable. Never hardcode it. Never share it. Step 3: Prompt Claude to build the bot. Use Claude Code for best results. It reads your filesystem, executes code, and iterates on errors autonomously. Step 4: Paper trade for at least one week. Minimum 200 completed trades. Win rate must be above 70% before going live. This step is NOT optional. Step 5: Configure risk management. Max single position: 8% of portfolio. Daily loss limit: -20% with auto halt. Kill switch at -40% drawdown. Telegram alerts on every threshold. Step 6: Go live small. $1-5 per trade. Watch every trade for first week. Compare to paper results. Scale only on evidence. Skip steps 4 and 5 and you will lose your money. That's not a warning. That's a guarantee. This is your complete build guide. Save this post. Follow Himanshu Kumar because I'll be posting the exact Claude prompts for each strategy. ↓ The edge exists right now. Not next month. Not "when you're ready." Right now. The arbitrage window is 2.7 seconds. It was 12 seconds in 2024. It's shrinking every week. Every day you wait, more bots enter the space. The window gets smaller. Your potential returns get smaller. The bots already running have a compounding advantage. They're making money today that they'll use to make more money tomorrow. You're reading about it and telling yourself "I'll look into this next weekend." That's what you said last weekend. And the weekend before that. The best time to start was 6 months ago. The second best time is today. But you already know you're going to bookmark this and never open it again. Prove me wrong. ↓ Full 2-hour video tutorial attached. Every single click. Every command. Every parameter. From zero to running bot. Beginner friendly. Nothing skipped. A similar bot has already earned $2,382,780. Full blockchain proof in the article below. The video is free. The tools are free. The edge still exists. The only thing that costs money is another month of doing nothing while bots eat every opportunity you're too slow to catch. Follow Himanshu Kumar for the complete series covering every automated income stream using Claude. Prediction markets are just the beginning. Save this post. Bookmark it. Screenshot it. Whatever you need to do so you actually watch the video and build the bot instead of just reading about people who did. You Must Follow me Himanshu Kumar, so i can send you DM.

Himanshu Kumar

53,994 次观看 • 6 个月前

CANCEL Your Weekend Plans, and Learn Claude Code Today. $5,000/month. $10,000/month. $20,000/month. People are building entire apps and charging clients thousands using Claude Code. You're still Googling 'how to center a div.' While you're binge-watching a show you won't remember next week, a 19 year old with zero coding experience just built a $5,000 SaaS product in one afternoon using the tool I'm about to break down. Same laptop. Same internet. Same 24 hours. He has Claude Code. You have Netflix. That's the only difference. This YouTube video is a goldmine. Full Claude Code tutorial. Beginner to pro. Every feature. Every setup step. Every best practice. Zero prior knowledge needed. Save it. Watch it tonight. Not tomorrow. Tonight. Save this post. This is your complete Claude Code roadmap. Lose it and you lose the next 12 months of income. Follow Himanshu Kumar so you don't miss the breakdowns for each feature. ↓ 1. Understand What Claude Code Actually Is. You think Claude Code is just another chatbot. It's not. And that misunderstanding is why you're broke. ChatGPT gives you text. Claude Code gives you software. It runs in your terminal. It reads your entire codebase. It writes files directly to your project. It runs commands on your machine. It debugs errors autonomously. It builds features end to end. You're not chatting. You're deploying a developer. One that works 24/7. Never asks for a raise. Never calls in sick. Never pushes broken code at 5 PM on a Friday. People are charging clients $5,000-$10,000 for apps they built with Claude Code in 3 hours. And you didn't even know this tool existed because you're still asking ChatGPT to write you a to-do list. The gap between you and people making money with AI isn't intelligence. It's awareness. Now you're aware. Save this post. Follow Himanshu Kumar for the complete breakdown of every Claude Code feature. ↓ 2. Set Up Claude Code Properly. Most people quit here. "It's too complicated." "I don't know terminal." "I'll set it up later." Later never comes. And "complicated" means "I watched for 30 seconds and gave up." The setup takes 10 minutes. Install Node.js. Install Claude Code via npm. Authenticate your account. Open your terminal. Done. 10 minutes. You spent longer this morning deciding what to have for breakfast. The video walks through every single click. Every command. Every screen. Assuming you know absolutely nothing. If you can download an app on your phone, you can set up Claude Code. It's the same level of difficulty. But you'll still tell yourself it's "too technical" because that excuse is more comfortable than admitting you're just scared to try something new. This is the setup that everything else builds on. Skip it and nothing works. ↓ 3. Use the Desktop App. You don't even need to live in the terminal if you don't want to. Claude Code has a desktop app. Clean interface. Visual feedback. Everything you need without touching command line. But here's the thing most people don't know: The desktop app isn't just a pretty wrapper. It lets you manage projects visually. See file changes in real time. Switch between projects instantly. The people making money with Claude Code use the desktop app for client projects because it's faster to manage multiple builds simultaneously. You're still opening 14 browser tabs to organize one project. They open one app and everything's there. Efficiency isn't a personality trait. It's a tool choice. Save this post. Follow Himanshu Kumar for the desktop app workflow that handles 5 client projects at once. ↓ 4. Install the Right Dependencies. This is where beginners silently fail and blame the tool. Claude Code needs certain dependencies installed to work properly. Miss one and everything breaks. Then you go on Twitter and say "Claude Code doesn't work." It works fine. You just didn't read the setup guide. The video covers every dependency you need. What to install. How to install it. How to verify it's working. No guessing. No Stack Overflow rabbit holes at midnight. No "why isn't this working" for 3 hours. Watch the dependency section once. Follow every step. Never deal with setup issues again. You spent more time last week troubleshooting a printer than this takes. ↓ 5. Work Inside Your Code Editor. Claude Code integrates directly with your code editor. VS Code. Cursor. Whatever you use. It's not a separate window you alt-tab between. It's right there. In your workflow. You type a request. Claude writes the code. The code appears in your editor. You review it. Accept it. Done. No copy pasting between windows. No reformatting code that got mangled in transit. No "which version was the right one." It's like pair programming with someone who never gets distracted, never argues about naming conventions, and actually writes code that works on the first try. Your current coding process is: Google the problem, read 5 answers on Stack Overflow, copy the wrong one, debug for an hour, find the right one, paste it in, break something else, repeat. Claude Code's process is: describe what you want, get working code, move on with your life. Same hour. One method produces working software. The other produces frustration and a browser history full of Stack Overflow tabs. Stop coding the hard way. Save this post. Follow Himanshu Kumar for code editor setup guides and integration tips. ↓ 6. Master Basic Usage. Most people learn 5% of a tool and say they "know" it. You "know" Photoshop because you can crop an image. You "know" Excel because you can sum a column. You "know" Claude Code because you asked it one question. Basic usage means: How to give Claude Code context about your project. How to ask for changes to existing code. How to generate new files and features. How to review what Claude produces. How to iterate when the output isn't perfect. These basics are the foundation of everything. Skip them and every advanced feature feels confusing. Master them and every advanced feature feels obvious. The video breaks down each one with real examples. Not theory. Actual usage on actual projects. You've been using AI tools at 5% capacity and wondering why your results are 5% of what others get. Save this post. Follow Himanshu Kumar for daily Claude Code usage tips. ↓ 7. Learn Every Command. Claude Code has commands that most users never discover. Because most users type one message and expect magic. That's not how professionals use it. Professionals use specific commands that tell Claude Code exactly what to do, how to do it, and what constraints to follow. The difference between a beginner and someone making $10K/month with Claude Code is knowing which command to use and when. The video walks through every single one. Not just what they do. But when to use each one. And why one command is better than another for specific situations. You've been using Claude Code like a hammer. These commands turn it into a full toolbox. Stop treating a power tool like a blunt instrument. Save this post. Follow Himanshu Kumar for the command cheat sheet I use daily. ↓ 8. Understand Modes and Shortcuts. Speed matters. The person who builds an app in 2 hours charges $5,000. The person who builds the same app in 2 days charges $2,000. Same app. Same quality. Different speed. Different income. Claude Code has modes that change how it operates. And shortcuts that cut your workflow time in half. Most people don't know either exists. They use Claude Code in default mode for everything. Like driving a car in first gear on the highway. Technically it works. But everyone is passing you. The video shows you every mode. Every shortcut. Every time-saving trick that separates the people charging $2,000 per project from the people charging $10,000. Speed is money. Literally. Save this post. Follow Himanshu Kumar for the shortcuts that cut my build time by 60%. ↓ 9. Write a Proper Planning Prompt. This is the section that separates amateurs from professionals. And it's the section most people skip. A planning prompt tells Claude Code what you're building before you start building it. Architecture. File structure. Technologies. Features. Constraints. Edge cases. Without a planning prompt, Claude Code guesses. And guessing produces garbage. With a planning prompt, Claude Code executes a clear plan. And clear plans produce working software. The video shows you exactly how to write a planning prompt that makes Claude Code produce professional-grade output on the first try. "But I just want to start coding." That's why your code breaks every time. That's why you restart projects 4 times. That's why nothing you build ever gets finished. Because you refuse to plan. A 5-minute planning prompt saves you 5 hours of debugging. But you'd rather skip the 5 minutes and suffer through the 5 hours because patience isn't your thing. And that's exactly why you're not making money. Planning is the most underpaid skill in coding. And the most overpaid when you master it. Save this post. Follow Himanshu Kumar for the planning prompt templates I use for every client project. ↓ 10. Choose the Right Model. Claude Code lets you select different AI models. Not all models are the same. Not all tasks need the same model. Using the most powerful model for a simple task wastes credits. Using a basic model for a complex task wastes time. The video explains: Which model to use for quick fixes. Which model to use for complex architecture. Which model to use for debugging. Which model to use for code generation. Most people pick one model and use it for everything. That's like using a sledgehammer to hang a picture frame. Model selection is strategy. And strategy is money. The people making $10K/month with Claude Code are strategic about every credit they spend. You're burning through credits because you use the most expensive model to write a hello world. ↓ 11. Use Git and Version Control. If you're not using version control, you're one mistake away from losing everything. Claude Code integrates with Git. Every change tracked. Every version saved. Every mistake reversible. Without Git: Claude makes a change. It breaks something. You can't undo it. You start over. 3 hours wasted. With Git: Claude makes a change. It breaks something. You roll back in 5 seconds. Keep working. Version control isn't optional. It's insurance. And the people not using it are the same people who say "I lost my entire project" like it's something that just happens. It doesn't just happen. It happens because you didn't set up Git. The video walks through the entire Git integration. Save this post. Follow Himanshu Kumar for the Git workflow that's saved every project I've ever built. ↓ 12. Set Up Claude.MD and Memory. This is the feature that makes Claude Code feel like a real team member instead of a stranger you explain everything to every time. ClaudeMD is a memory file. You tell Claude Code about your project once. It remembers forever. Coding style preferences. Project architecture decisions. Technology stack. File naming conventions. Business logic rules. Without ClaudeMD: Every new conversation starts from zero. You explain the same things repeatedly. Output is inconsistent. With ClaudeMD: Claude knows your project. Claude follows your rules. Claude produces consistent, professional code. The difference between a sloppy freelancer and a reliable agency is consistency. Claude. MD gives you consistency without the agency overhead. Most people don't set this up and wonder why Claude Code gives different answers every time. ↓ 13. Automate with Tasks. This is where Claude Code stops being a tool and starts being an employee. Tasks let you define repeating workflows. "Every time I push code, run tests." "Every time I create a new file, add boilerplate." "Every time I start a session, check for errors." Automated. Hands-free. Consistent. You're doing these things manually every single day. The same checks. The same steps. The same routine. Tasks do them automatically. So you can focus on the work that actually makes money. Every manual task you automate is time you get back. And time is the only thing you can never make more of. Save this post. Follow Himanshu Kumar for the task automation templates that run my entire workflow. ↓ 14. Explore Features Most People Never Touch. The video covers features that 95% of Claude Code users don't know exist. Because they watched a 3-minute TikTok about Claude Code and think they're experts now. They're not. They're using 5% of a tool that can do everything. The full tutorial goes deep into features that most tutorials skip because they're "too advanced." They're not too advanced. They're too valuable for lazy creators to bother explaining. This video explains all of them. Clearly. For beginners. The 5% of features you don't know about are the 5% that make people rich. ↓ Let's zoom out. I just broke down 14 sections of Claude Code. Setup and installation. Desktop app. Dependencies. Code editor integration. Basic usage. Commands. Modes and shortcuts. Planning prompts. Model selection. Git and version control. Memory and Claude. MD. Tasks and automation. Advanced features. All in one video. All free. All beginner friendly. The person who masters even half of these in the next 2 weeks will be in the top 1% of Claude Code users. The top 1% of Claude Code users are the ones charging $5,000-$10,000 per project and building them in a single afternoon. Everyone else is asking ChatGPT to fix their resume. Same tools. Same access. Completely different outcomes. Because one person treats AI like a toy. And the other treats it like a business. ↓ Here's the hard truth nobody wants to hear. You don't have a talent problem. You don't have an intelligence problem. You don't have a resources problem. You have an action problem. Everything I just listed has a free tutorial right here in the attached video. 33 minutes. That's it. 33 minutes to learn the tool that people are using to build $5,000-$20,000/month businesses. You spent more time today scrolling Twitter than it takes to watch this video. You spent more time this week watching Netflix than it takes to master Claude Code basics. You spent more time this month doing nothing than it would take to completely change your income. The information is free. The tool is accessible. The opportunity is here. The only thing missing is you caring enough to start. ↓ CANCEL your plans this week. This isn't optional anymore. The people learning Claude Code right now will be building apps for the people who didn't learn it. That's not a prediction. That's already happening. Companies are replacing $150/hour developers with one person and Claude Code. If you code: learn Claude Code or become half as valuable by next year. If you don't code: learn Claude Code or miss the biggest opportunity to start earning from tech without a CS degree. There's no path forward that doesn't include AI coding tools. None. You have one window. Right now. This week. ↓ Here's your action plan for the next 7 days: Day 1: Watch the full video. Install Claude Code. Set up dependencies. Day 2: Learn basic usage. Try 5 different commands. Day 3: Write your first planning prompt. Build a small project. Day 4: Set up Claude. MD. Configure your memory file. Day 5: Master modes and shortcuts. Build a second project faster. Day 6: Set up Git integration. Automate with tasks. Day 7: Build something real. A tool, an app, a website. Ship it. 7 days. One tool. One completely different skill set. One completely different income potential. Or 7 more days of scrolling Twitter watching other people build things while you "plan to start." Your call. ↓ This is the most important video you'll watch this year. 33 minutes. Complete Claude Code mastery. From zero to building real projects. Save this post. Come back to it every single day this week. Check off each section as you complete it. Follow Himanshu Kumar for daily Claude Code breakdowns, advanced tutorials, and the exact workflows that are turning beginners into $10K/month builders. The only thing between you and $10K/month with Claude Code is this video and 7 days. Don't waste them. You Must Follow me Himanshu Kumar, so i can send you DM.
35:48

CANCEL Your Weekend Plans, and Learn Claude Code Today. $5,000/month. $10,000/month. $20,000/month. People are building entire apps and charging clients thousands using Claude Code. You're still Googling 'how to center a div.' While you're binge-watching a show you won't remember next week, a 19 year old with zero coding experience just built a $5,000 SaaS product in one afternoon using the tool I'm about to break down. Same laptop. Same internet. Same 24 hours. He has Claude Code. You have Netflix. That's the only difference. This YouTube video is a goldmine. Full Claude Code tutorial. Beginner to pro. Every feature. Every setup step. Every best practice. Zero prior knowledge needed. Save it. Watch it tonight. Not tomorrow. Tonight. Save this post. This is your complete Claude Code roadmap. Lose it and you lose the next 12 months of income. Follow Himanshu Kumar so you don't miss the breakdowns for each feature. ↓ 1. Understand What Claude Code Actually Is. You think Claude Code is just another chatbot. It's not. And that misunderstanding is why you're broke. ChatGPT gives you text. Claude Code gives you software. It runs in your terminal. It reads your entire codebase. It writes files directly to your project. It runs commands on your machine. It debugs errors autonomously. It builds features end to end. You're not chatting. You're deploying a developer. One that works 24/7. Never asks for a raise. Never calls in sick. Never pushes broken code at 5 PM on a Friday. People are charging clients $5,000-$10,000 for apps they built with Claude Code in 3 hours. And you didn't even know this tool existed because you're still asking ChatGPT to write you a to-do list. The gap between you and people making money with AI isn't intelligence. It's awareness. Now you're aware. Save this post. Follow Himanshu Kumar for the complete breakdown of every Claude Code feature. ↓ 2. Set Up Claude Code Properly. Most people quit here. "It's too complicated." "I don't know terminal." "I'll set it up later." Later never comes. And "complicated" means "I watched for 30 seconds and gave up." The setup takes 10 minutes. Install Node.js. Install Claude Code via npm. Authenticate your account. Open your terminal. Done. 10 minutes. You spent longer this morning deciding what to have for breakfast. The video walks through every single click. Every command. Every screen. Assuming you know absolutely nothing. If you can download an app on your phone, you can set up Claude Code. It's the same level of difficulty. But you'll still tell yourself it's "too technical" because that excuse is more comfortable than admitting you're just scared to try something new. This is the setup that everything else builds on. Skip it and nothing works. ↓ 3. Use the Desktop App. You don't even need to live in the terminal if you don't want to. Claude Code has a desktop app. Clean interface. Visual feedback. Everything you need without touching command line. But here's the thing most people don't know: The desktop app isn't just a pretty wrapper. It lets you manage projects visually. See file changes in real time. Switch between projects instantly. The people making money with Claude Code use the desktop app for client projects because it's faster to manage multiple builds simultaneously. You're still opening 14 browser tabs to organize one project. They open one app and everything's there. Efficiency isn't a personality trait. It's a tool choice. Save this post. Follow Himanshu Kumar for the desktop app workflow that handles 5 client projects at once. ↓ 4. Install the Right Dependencies. This is where beginners silently fail and blame the tool. Claude Code needs certain dependencies installed to work properly. Miss one and everything breaks. Then you go on Twitter and say "Claude Code doesn't work." It works fine. You just didn't read the setup guide. The video covers every dependency you need. What to install. How to install it. How to verify it's working. No guessing. No Stack Overflow rabbit holes at midnight. No "why isn't this working" for 3 hours. Watch the dependency section once. Follow every step. Never deal with setup issues again. You spent more time last week troubleshooting a printer than this takes. ↓ 5. Work Inside Your Code Editor. Claude Code integrates directly with your code editor. VS Code. Cursor. Whatever you use. It's not a separate window you alt-tab between. It's right there. In your workflow. You type a request. Claude writes the code. The code appears in your editor. You review it. Accept it. Done. No copy pasting between windows. No reformatting code that got mangled in transit. No "which version was the right one." It's like pair programming with someone who never gets distracted, never argues about naming conventions, and actually writes code that works on the first try. Your current coding process is: Google the problem, read 5 answers on Stack Overflow, copy the wrong one, debug for an hour, find the right one, paste it in, break something else, repeat. Claude Code's process is: describe what you want, get working code, move on with your life. Same hour. One method produces working software. The other produces frustration and a browser history full of Stack Overflow tabs. Stop coding the hard way. Save this post. Follow Himanshu Kumar for code editor setup guides and integration tips. ↓ 6. Master Basic Usage. Most people learn 5% of a tool and say they "know" it. You "know" Photoshop because you can crop an image. You "know" Excel because you can sum a column. You "know" Claude Code because you asked it one question. Basic usage means: How to give Claude Code context about your project. How to ask for changes to existing code. How to generate new files and features. How to review what Claude produces. How to iterate when the output isn't perfect. These basics are the foundation of everything. Skip them and every advanced feature feels confusing. Master them and every advanced feature feels obvious. The video breaks down each one with real examples. Not theory. Actual usage on actual projects. You've been using AI tools at 5% capacity and wondering why your results are 5% of what others get. Save this post. Follow Himanshu Kumar for daily Claude Code usage tips. ↓ 7. Learn Every Command. Claude Code has commands that most users never discover. Because most users type one message and expect magic. That's not how professionals use it. Professionals use specific commands that tell Claude Code exactly what to do, how to do it, and what constraints to follow. The difference between a beginner and someone making $10K/month with Claude Code is knowing which command to use and when. The video walks through every single one. Not just what they do. But when to use each one. And why one command is better than another for specific situations. You've been using Claude Code like a hammer. These commands turn it into a full toolbox. Stop treating a power tool like a blunt instrument. Save this post. Follow Himanshu Kumar for the command cheat sheet I use daily. ↓ 8. Understand Modes and Shortcuts. Speed matters. The person who builds an app in 2 hours charges $5,000. The person who builds the same app in 2 days charges $2,000. Same app. Same quality. Different speed. Different income. Claude Code has modes that change how it operates. And shortcuts that cut your workflow time in half. Most people don't know either exists. They use Claude Code in default mode for everything. Like driving a car in first gear on the highway. Technically it works. But everyone is passing you. The video shows you every mode. Every shortcut. Every time-saving trick that separates the people charging $2,000 per project from the people charging $10,000. Speed is money. Literally. Save this post. Follow Himanshu Kumar for the shortcuts that cut my build time by 60%. ↓ 9. Write a Proper Planning Prompt. This is the section that separates amateurs from professionals. And it's the section most people skip. A planning prompt tells Claude Code what you're building before you start building it. Architecture. File structure. Technologies. Features. Constraints. Edge cases. Without a planning prompt, Claude Code guesses. And guessing produces garbage. With a planning prompt, Claude Code executes a clear plan. And clear plans produce working software. The video shows you exactly how to write a planning prompt that makes Claude Code produce professional-grade output on the first try. "But I just want to start coding." That's why your code breaks every time. That's why you restart projects 4 times. That's why nothing you build ever gets finished. Because you refuse to plan. A 5-minute planning prompt saves you 5 hours of debugging. But you'd rather skip the 5 minutes and suffer through the 5 hours because patience isn't your thing. And that's exactly why you're not making money. Planning is the most underpaid skill in coding. And the most overpaid when you master it. Save this post. Follow Himanshu Kumar for the planning prompt templates I use for every client project. ↓ 10. Choose the Right Model. Claude Code lets you select different AI models. Not all models are the same. Not all tasks need the same model. Using the most powerful model for a simple task wastes credits. Using a basic model for a complex task wastes time. The video explains: Which model to use for quick fixes. Which model to use for complex architecture. Which model to use for debugging. Which model to use for code generation. Most people pick one model and use it for everything. That's like using a sledgehammer to hang a picture frame. Model selection is strategy. And strategy is money. The people making $10K/month with Claude Code are strategic about every credit they spend. You're burning through credits because you use the most expensive model to write a hello world. ↓ 11. Use Git and Version Control. If you're not using version control, you're one mistake away from losing everything. Claude Code integrates with Git. Every change tracked. Every version saved. Every mistake reversible. Without Git: Claude makes a change. It breaks something. You can't undo it. You start over. 3 hours wasted. With Git: Claude makes a change. It breaks something. You roll back in 5 seconds. Keep working. Version control isn't optional. It's insurance. And the people not using it are the same people who say "I lost my entire project" like it's something that just happens. It doesn't just happen. It happens because you didn't set up Git. The video walks through the entire Git integration. Save this post. Follow Himanshu Kumar for the Git workflow that's saved every project I've ever built. ↓ 12. Set Up Claude.MD and Memory. This is the feature that makes Claude Code feel like a real team member instead of a stranger you explain everything to every time. ClaudeMD is a memory file. You tell Claude Code about your project once. It remembers forever. Coding style preferences. Project architecture decisions. Technology stack. File naming conventions. Business logic rules. Without ClaudeMD: Every new conversation starts from zero. You explain the same things repeatedly. Output is inconsistent. With ClaudeMD: Claude knows your project. Claude follows your rules. Claude produces consistent, professional code. The difference between a sloppy freelancer and a reliable agency is consistency. Claude. MD gives you consistency without the agency overhead. Most people don't set this up and wonder why Claude Code gives different answers every time. ↓ 13. Automate with Tasks. This is where Claude Code stops being a tool and starts being an employee. Tasks let you define repeating workflows. "Every time I push code, run tests." "Every time I create a new file, add boilerplate." "Every time I start a session, check for errors." Automated. Hands-free. Consistent. You're doing these things manually every single day. The same checks. The same steps. The same routine. Tasks do them automatically. So you can focus on the work that actually makes money. Every manual task you automate is time you get back. And time is the only thing you can never make more of. Save this post. Follow Himanshu Kumar for the task automation templates that run my entire workflow. ↓ 14. Explore Features Most People Never Touch. The video covers features that 95% of Claude Code users don't know exist. Because they watched a 3-minute TikTok about Claude Code and think they're experts now. They're not. They're using 5% of a tool that can do everything. The full tutorial goes deep into features that most tutorials skip because they're "too advanced." They're not too advanced. They're too valuable for lazy creators to bother explaining. This video explains all of them. Clearly. For beginners. The 5% of features you don't know about are the 5% that make people rich. ↓ Let's zoom out. I just broke down 14 sections of Claude Code. Setup and installation. Desktop app. Dependencies. Code editor integration. Basic usage. Commands. Modes and shortcuts. Planning prompts. Model selection. Git and version control. Memory and Claude. MD. Tasks and automation. Advanced features. All in one video. All free. All beginner friendly. The person who masters even half of these in the next 2 weeks will be in the top 1% of Claude Code users. The top 1% of Claude Code users are the ones charging $5,000-$10,000 per project and building them in a single afternoon. Everyone else is asking ChatGPT to fix their resume. Same tools. Same access. Completely different outcomes. Because one person treats AI like a toy. And the other treats it like a business. ↓ Here's the hard truth nobody wants to hear. You don't have a talent problem. You don't have an intelligence problem. You don't have a resources problem. You have an action problem. Everything I just listed has a free tutorial right here in the attached video. 33 minutes. That's it. 33 minutes to learn the tool that people are using to build $5,000-$20,000/month businesses. You spent more time today scrolling Twitter than it takes to watch this video. You spent more time this week watching Netflix than it takes to master Claude Code basics. You spent more time this month doing nothing than it would take to completely change your income. The information is free. The tool is accessible. The opportunity is here. The only thing missing is you caring enough to start. ↓ CANCEL your plans this week. This isn't optional anymore. The people learning Claude Code right now will be building apps for the people who didn't learn it. That's not a prediction. That's already happening. Companies are replacing $150/hour developers with one person and Claude Code. If you code: learn Claude Code or become half as valuable by next year. If you don't code: learn Claude Code or miss the biggest opportunity to start earning from tech without a CS degree. There's no path forward that doesn't include AI coding tools. None. You have one window. Right now. This week. ↓ Here's your action plan for the next 7 days: Day 1: Watch the full video. Install Claude Code. Set up dependencies. Day 2: Learn basic usage. Try 5 different commands. Day 3: Write your first planning prompt. Build a small project. Day 4: Set up Claude. MD. Configure your memory file. Day 5: Master modes and shortcuts. Build a second project faster. Day 6: Set up Git integration. Automate with tasks. Day 7: Build something real. A tool, an app, a website. Ship it. 7 days. One tool. One completely different skill set. One completely different income potential. Or 7 more days of scrolling Twitter watching other people build things while you "plan to start." Your call. ↓ This is the most important video you'll watch this year. 33 minutes. Complete Claude Code mastery. From zero to building real projects. Save this post. Come back to it every single day this week. Check off each section as you complete it. Follow Himanshu Kumar for daily Claude Code breakdowns, advanced tutorials, and the exact workflows that are turning beginners into $10K/month builders. The only thing between you and $10K/month with Claude Code is this video and 7 days. Don't waste them. You Must Follow me Himanshu Kumar, so i can send you DM.

Himanshu Kumar

101,793 次观看 • 5 个月前

$AMD $5 Trillion is Inevitable LT| Agentic AI🧵 Agentic AI is the new $5 Trillion TAM 🚨🚨🚨 This thead will do Comp with $INTC and how to quantify this massive Agentic AI demand spike, and forcing Jensen to rush a CPU design. Global Agentic AI Market size is estimated to be $3-$5Trillion TAM by 2030(McKinsey) Quantifying the demand from agentic AI for AMD involves assessing the broader market growth for agentic systems, their unique computational requirements (particularly for CPUs in orchestration and reasoning tasks), and AMD's positioning very well through products like EPYC processors and partnerships. AMD EPYC Venice is the most superior choice in 2026-2027 for most Agentic AI workloads Agentic AI refers to autonomous AI agents that perform multi-step tasks, involving sequential logic, tool integration, and decision-making workloads that heavily rely on CPUs for handling orchestration, memory management, and context switching, rather than just GPU-parallelized training or batch inference. Agentic AI is often cited as 40-100x more "hungry" than traditional AI due to its continuous, 24/7 operation and complex workflows. This stems from factors like chain-of-thought reasoning (multiple LLM calls per query), API/tool interactions, memory management, and orchestration loops, which can generate 10-100x more tokens and require real-time responsiveness. For example, a single agentic query might trigger 5-20 model inferences, making it 10-20x more compute-intensive than simple chatbots, and the always-on nature compounds this to 40-100x overall. Nvidia's CEO has highlighted this as driving "easily 100x more computation" for inference in agentic/reasoning setups. AMD's EPYC Venice (6th Gen EPYC, codenamed "Venice") and Intel's Xeon 7 Diamond Rapids represent the pinnacle of server CPU technology in 2026, both targeting high-performance data center workloads like AI inference, agentic AI orchestration, cloud computing, and HPC. Venice builds on AMD's Zen 6 architecture, emphasizing core density and efficiency, while Diamond Rapids leverages Intel's Panther Cove P-cores for balanced performance. Both chips adopt similar advancements like 16-channel DDR5 memory and PCIe Gen 6, but differ in core counts, process nodes, and overall design philosophy. Intel has faced acute supply constraints across its Xeon lineup, including legacy nodes (Intel 7/3) and the ramping 18A process for next-gen parts. Intel shortage is expected with lead times up to 6 months or longer. 1. AMD EPYC Venice vs Intel Xeon 7 Diamond Rapids Architecture AMD: Zen 6 chiplet design with 8 CCDs and dual IODs Intel: Panther Cove P-cores; multi-die architecture with 4 compute tiles Core/Thread Count AMD: Up to 256 cores / 512 threads (Zen 6c variant) Intel: Up to 192 cores / 192 threads Process Node AMD: TSMC N2 (2nm) Intel: Intel 18A (1.8nm-class); in-house fab Memory Support AMD: 16-channel DDR5; up to 1.6 TB/s bandwidth. Intel: 16-channel DDR5 ; up to 1.6 TB/s bandwidth I/O and Connectivity AMD: PCIe Gen 6 (up to 128 lanes); twice the CPU-to-GPU bandwidth Intel: PCIe Gen 6 (up to 128 lanes); LGA 9324 socket Power (TDP) AMD: Starting 400-500W, potentially lower due to efficiency gains from TSMC 2nm Intel: Starting 400-500W, as it targets competitive efficiency Performance Projections AMD: Up to 70% uplift vs. 5th Gen Turin (1.7x in multi-threaded/AI tasks) Intel: ~40% faster than Granite Rapids (Xeon 6, 128-core). Lags AMD in per-core perf and 40-50% behind Venice core-for-core comp Target Workloads AMD: AI inference/orchestration, HPC, cloud virtualization. Partnerships Intel: Hyperscale AI, general enterprise. Custom silicon Pricing: AMD: estimated $10k-$20k for top SKUs Intel: estimated $8-$18k Availability: AMD: Significant Ramp H2 2026 due to higher allocation from TSMC Intel: H1-H2 2026 delayed, but trying to catch up Overall: ~Venice's 256 cores provide a 33% edge over Diamond Rapids' 192, making it superior for massively parallel tasks like AI training/inference or virtualization ~TSMC's N2 vs. Intel 18A debates rage on which is "better," but AMD's mature chiplet approach yields better density ( 32 cores/CCD vs. Intel's 48/tile). Venice's redesign reduces latency, aiding agentic AI where CPUs handle orchestration ~ Early projections show Venice widening AMD's lead matching or exceeding Diamond Rapids' perf with fewer watts in multi-threaded benchmarks. Intel's no-SMT design (to prioritize AI) handicaps it vs. AMD's 512 threads, though Clearwater Forest (E-core) could compete in density-focused niches. ~Power & Cooling: Both push above 400-500W, demanding liquid cooling. ~AMD been taking market share now above 40%. AMD EPYC Venice emerges as the superior choice in 2026 for most server workloads. Its higher core/thread count (256/512 vs. 192/192), stronger per-core performance, and architecture optimized for AI-driven tasks (agentic orchestration with GPU integration) provide decisive advantages in throughput, scalability, and efficiency. Projections indicate Venice delivering 1.7x the performance of prior gens while widening the gap over Intel ( 40-70% leads in multi-threaded benchmarks). AMD's fabless model with TSMC ensures reliable scaling, and its ecosystem ( open ROCm) appeals to AI adopters. Intel's Diamond Rapids is competitive in single-threaded enterprise apps and custom hyperscale ( NVLink), with potential fab advantages for supply/security. However, without SMT and lower density, it falls short in core-for-core battles—exposing Intel to another generation of AMD dominance unless 18A yields surprise efficiency gains. For data centers prioritizing raw compute ( AI, HPC), Venice wins; for Intel-centric ecosystems or specialized I/O, Diamond Rapids holds ground. Real benchmarks post-launch will confirm, but logic points to AMD pulling ahead. 2. Market size , Potential Revenue and Supply Global Agentic AI market size is projected to be $3-$5 Trillion by 2030 according to McKinsey, where consensus points to 40-50% CAGR driven by small to large enterprise demand. I also wrote a full thread on how and why Agentic AI is so explosive that AMD will blow all anlaysts estimate for subscribers. Link below if you are interested. AMD's data center segment hit a record $5.4B in Q4 2025 (up 39% YoY), with EPYC shipments ramping due to agentic demand. With 2GW of deployment in H2 2026, AMD AI data center revenue has $40-$50B+ at the lowest or most conservative projection; or Total Revenue in the $77-$94B For FY2026. However, Agentic AI massive demand spike could send EPYC revenue 3x to 4x in the next few years, potentially surpassing MI series GPU demand as enterprises prioritize CPU-dense Rack setups. This is pushing $NVDA Jensen to rush a CPU design and acquired Groq, a new CPU player due to this massive TAM. Noted that this is just popping just in weeks, highlighting we are just so early in this AI Supercycle and the pace of adoption is insane, and clearly productivity will skyrocket. Why? Because Agentic AI is 24/7 Smart AI agent working for you or your businesses is a mad compelling, and it is estimated to be 40-100x more Inference Hugnry! Many experts already said it is impossible to project this kind of Inference Demand. AI CapEx is expected to ramp up even more in 2027-2028-2029 and 2030 as Global Agentic AI is going to scale to $3-$5 Trillion TAM by 2030. The nature of Agentic is driving higher CPU/GPU ratio, with CPUs handling 50-90% of Agentic workflows. For example, The current Helios Rack: 18 compute trays per rack with 72 GPUs + 18 CPUs. The beauty of this $META and $AMD long term partnership is, that it is absolutely flexible to adjust racks to higher CPU rato or equal to service different needs. Helios rack can be easily swap to 2 GPUs 2CPUs or even CPUs only trays for dedicated orchestration/head nodes. You see, the beauty of this open rack-scale is flexibility and evolvability. If Agentic AI demand pushes much higher, AMD should be able to adjust variant trays without abandoning Heilos Rack. We can't talk just about massive Agentic AI demand without talking about the Supply side or TSMC. TSMC, AMD's primary foundry for advanced nodes ( Zen 6/Venice on N2/2nm), is addressing AI-driven shortages through massive expansions. TSMC accelerates fab construction with up to 10 facilities targeted for 2026. TSMC is accelerating its domestic manufacturing expansion, with industry sources indicating that as many as ten fabs could be under construction or preparing to begin operations across Taiwan’s major science parks. TSMC Capex: $52-56B in 2026 (up 37% YoY), with $45B already approved for new/upgraded capacities. 70-80% for advanced processes (2nm/A16), 10-20% for packaging (CoWoS quadrupling to 120-140K wafers/month by late 2026). In addition, Taiwanese companies (led by TSMC) commit to at least $250B in direct investments in US-based advanced semiconductor, AI, and energy production/innovation capacity.Taiwan provides $250B in government credit guarantees to facilitate additional investments and build a full US semiconductor ecosystem (including industrial parks). TSMC completed a second land purchase in Arizona (January 2026) for gigafab scaling, with an additional $100B+ (potentially four more modules) to further expand and qualify for tariff exemptions. AMD with secured 12GW from OpenAI and $META and massive Agentic AI will mean higher priority acess to 20-30% more wafers on TSMC advanced nodes, as TSMC has multi-year agreements with AMD for AI chips. Dr. C. C. Wei, CEO of TSMC quote: "I spend a lot of time in the last three or four months talking to my customer and then customers. Customer. I want to make sure that my customers demand are real. I talk to those cloud service providers, all of them. Their answer is. I'm quite satisfied with their answer. Actually they show me the evidence that the AI really help their business. So they grow their business successfully and he or she in their financial return. So I also double check their financial status. They are very rich." Amid shortages, the US buildout ensures AMD can ramp production of Instinct GPUs and EPYC CPUs without the constraints hitting competitors like Intel. By diversifying away from Taiwan (85% of advanced nodes today), the agreement mitigates supply disruptions, ensuring stable flows for AMD's chips. Scaling production and securing supply will matter for AMD the most in the next 5-10 years growth. The growth could be 80-100% YoY or higher; or it could be in the 60%. The aggressive TSMC supply ramp is reassuring the higher growth point. Conclusion: AMD stands at a pivotal inflection point in 2026, where the explosive rise of agentic AI demanding 40-100x more inference compute through its 24/7, multi-step orchestration positions the company to potentially triple its EPYC CPU revenue to $45-60B+ by 2028 while scaling Instinct GPUs to tens of billions annually by 2027. Agentic AI demand could push AI CapEx closer to $1 Trillion in 2027, far higher than most estimates. Dr. Lisa Su, AMD's visionary CEO, is masterfully securing supply to harness this massive demand by prioritizing operational execution and deep TSMC collaboration, ensuring readiness for the second-half 2026 AI ramp. Dr. Su has explicitly called out surging EPYC demand for agentic tasks where CPUs power head nodes and traditional workloads alongside GPUs while guiding for data center dominance through proactive capacity planning and partnerships like Nutanix ($150M investment for open agentic platforms) or providing tens of millions CPUs for OpenAI, $META, $ORCL, $AMZN, $MSFT, $GOOGL and others. Her strategy includes multi-year TSMC agreements for advanced nodes (N2 for Venice CPUs and future Instincts), diversifying beyond Taiwan to mitigate risks, and unveiling innovations like the MI455X GPU at CES 2026, which she touted as enabling "the next trillion-dollar market opportunity" in physical AI. Dr. Su's forward-looking vision predicting AI reaching 5 billion users emphasizes "AI everywhere," backed by hardware like Ryzen AI chips, all while declaring demand "going through the roof" and committing to scale without bottlenecks. TSMC's aggressive ramp-up, fueled by $52-56B in 2026 capex (up 37% YoY) and 10+ new fabs across Taiwan, the US (Arizona cluster expanding to 6+ modules with $165B+ investment), Japan, and Europe, provides profound reassurance for AMD's supply stability. The January 2026 US-Taiwan agreement committing $250B in investments and credit guarantees for US reshoring accelerates this, granting tariff relief (15% rates with 1.5-2.5x exemptions) tied to capacity buildouts, enabling TSMC to potentially double output over the decade to meet AI wafer hunger. This translates to 20-30% higher wafer allocations on key nodes, sidestepping Intel-like shortages and empowering Dr. Su's team to deliver on hyperscaler demands without disruption. Ultimately, this synergy cements AMD's leadership in the agentic era, promising sustained growth, $5T+ valuations at scale, and a resilient path forward as AI reshapes the world. This is NOT Financial Advice! Video source: AMD CES 2026
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$AMD $5 Trillion is Inevitable LT| Agentic AI🧵 Agentic AI is the new $5 Trillion TAM 🚨🚨🚨 This thead will do Comp with $INTC and how to quantify this massive Agentic AI demand spike, and forcing Jensen to rush a CPU design. Global Agentic AI Market size is estimated to be $3-$5Trillion TAM by 2030(McKinsey) Quantifying the demand from agentic AI for AMD involves assessing the broader market growth for agentic systems, their unique computational requirements (particularly for CPUs in orchestration and reasoning tasks), and AMD's positioning very well through products like EPYC processors and partnerships. AMD EPYC Venice is the most superior choice in 2026-2027 for most Agentic AI workloads Agentic AI refers to autonomous AI agents that perform multi-step tasks, involving sequential logic, tool integration, and decision-making workloads that heavily rely on CPUs for handling orchestration, memory management, and context switching, rather than just GPU-parallelized training or batch inference. Agentic AI is often cited as 40-100x more "hungry" than traditional AI due to its continuous, 24/7 operation and complex workflows. This stems from factors like chain-of-thought reasoning (multiple LLM calls per query), API/tool interactions, memory management, and orchestration loops, which can generate 10-100x more tokens and require real-time responsiveness. For example, a single agentic query might trigger 5-20 model inferences, making it 10-20x more compute-intensive than simple chatbots, and the always-on nature compounds this to 40-100x overall. Nvidia's CEO has highlighted this as driving "easily 100x more computation" for inference in agentic/reasoning setups. AMD's EPYC Venice (6th Gen EPYC, codenamed "Venice") and Intel's Xeon 7 Diamond Rapids represent the pinnacle of server CPU technology in 2026, both targeting high-performance data center workloads like AI inference, agentic AI orchestration, cloud computing, and HPC. Venice builds on AMD's Zen 6 architecture, emphasizing core density and efficiency, while Diamond Rapids leverages Intel's Panther Cove P-cores for balanced performance. Both chips adopt similar advancements like 16-channel DDR5 memory and PCIe Gen 6, but differ in core counts, process nodes, and overall design philosophy. Intel has faced acute supply constraints across its Xeon lineup, including legacy nodes (Intel 7/3) and the ramping 18A process for next-gen parts. Intel shortage is expected with lead times up to 6 months or longer. 1. AMD EPYC Venice vs Intel Xeon 7 Diamond Rapids Architecture AMD: Zen 6 chiplet design with 8 CCDs and dual IODs Intel: Panther Cove P-cores; multi-die architecture with 4 compute tiles Core/Thread Count AMD: Up to 256 cores / 512 threads (Zen 6c variant) Intel: Up to 192 cores / 192 threads Process Node AMD: TSMC N2 (2nm) Intel: Intel 18A (1.8nm-class); in-house fab Memory Support AMD: 16-channel DDR5; up to 1.6 TB/s bandwidth. Intel: 16-channel DDR5 ; up to 1.6 TB/s bandwidth I/O and Connectivity AMD: PCIe Gen 6 (up to 128 lanes); twice the CPU-to-GPU bandwidth Intel: PCIe Gen 6 (up to 128 lanes); LGA 9324 socket Power (TDP) AMD: Starting 400-500W, potentially lower due to efficiency gains from TSMC 2nm Intel: Starting 400-500W, as it targets competitive efficiency Performance Projections AMD: Up to 70% uplift vs. 5th Gen Turin (1.7x in multi-threaded/AI tasks) Intel: ~40% faster than Granite Rapids (Xeon 6, 128-core). Lags AMD in per-core perf and 40-50% behind Venice core-for-core comp Target Workloads AMD: AI inference/orchestration, HPC, cloud virtualization. Partnerships Intel: Hyperscale AI, general enterprise. Custom silicon Pricing: AMD: estimated $10k-$20k for top SKUs Intel: estimated $8-$18k Availability: AMD: Significant Ramp H2 2026 due to higher allocation from TSMC Intel: H1-H2 2026 delayed, but trying to catch up Overall: ~Venice's 256 cores provide a 33% edge over Diamond Rapids' 192, making it superior for massively parallel tasks like AI training/inference or virtualization ~TSMC's N2 vs. Intel 18A debates rage on which is "better," but AMD's mature chiplet approach yields better density ( 32 cores/CCD vs. Intel's 48/tile). Venice's redesign reduces latency, aiding agentic AI where CPUs handle orchestration ~ Early projections show Venice widening AMD's lead matching or exceeding Diamond Rapids' perf with fewer watts in multi-threaded benchmarks. Intel's no-SMT design (to prioritize AI) handicaps it vs. AMD's 512 threads, though Clearwater Forest (E-core) could compete in density-focused niches. ~Power & Cooling: Both push above 400-500W, demanding liquid cooling. ~AMD been taking market share now above 40%. AMD EPYC Venice emerges as the superior choice in 2026 for most server workloads. Its higher core/thread count (256/512 vs. 192/192), stronger per-core performance, and architecture optimized for AI-driven tasks (agentic orchestration with GPU integration) provide decisive advantages in throughput, scalability, and efficiency. Projections indicate Venice delivering 1.7x the performance of prior gens while widening the gap over Intel ( 40-70% leads in multi-threaded benchmarks). AMD's fabless model with TSMC ensures reliable scaling, and its ecosystem ( open ROCm) appeals to AI adopters. Intel's Diamond Rapids is competitive in single-threaded enterprise apps and custom hyperscale ( NVLink), with potential fab advantages for supply/security. However, without SMT and lower density, it falls short in core-for-core battles—exposing Intel to another generation of AMD dominance unless 18A yields surprise efficiency gains. For data centers prioritizing raw compute ( AI, HPC), Venice wins; for Intel-centric ecosystems or specialized I/O, Diamond Rapids holds ground. Real benchmarks post-launch will confirm, but logic points to AMD pulling ahead. 2. Market size , Potential Revenue and Supply Global Agentic AI market size is projected to be $3-$5 Trillion by 2030 according to McKinsey, where consensus points to 40-50% CAGR driven by small to large enterprise demand. I also wrote a full thread on how and why Agentic AI is so explosive that AMD will blow all anlaysts estimate for subscribers. Link below if you are interested. AMD's data center segment hit a record $5.4B in Q4 2025 (up 39% YoY), with EPYC shipments ramping due to agentic demand. With 2GW of deployment in H2 2026, AMD AI data center revenue has $40-$50B+ at the lowest or most conservative projection; or Total Revenue in the $77-$94B For FY2026. However, Agentic AI massive demand spike could send EPYC revenue 3x to 4x in the next few years, potentially surpassing MI series GPU demand as enterprises prioritize CPU-dense Rack setups. This is pushing $NVDA Jensen to rush a CPU design and acquired Groq, a new CPU player due to this massive TAM. Noted that this is just popping just in weeks, highlighting we are just so early in this AI Supercycle and the pace of adoption is insane, and clearly productivity will skyrocket. Why? Because Agentic AI is 24/7 Smart AI agent working for you or your businesses is a mad compelling, and it is estimated to be 40-100x more Inference Hugnry! Many experts already said it is impossible to project this kind of Inference Demand. AI CapEx is expected to ramp up even more in 2027-2028-2029 and 2030 as Global Agentic AI is going to scale to $3-$5 Trillion TAM by 2030. The nature of Agentic is driving higher CPU/GPU ratio, with CPUs handling 50-90% of Agentic workflows. For example, The current Helios Rack: 18 compute trays per rack with 72 GPUs + 18 CPUs. The beauty of this $META and $AMD long term partnership is, that it is absolutely flexible to adjust racks to higher CPU rato or equal to service different needs. Helios rack can be easily swap to 2 GPUs 2CPUs or even CPUs only trays for dedicated orchestration/head nodes. You see, the beauty of this open rack-scale is flexibility and evolvability. If Agentic AI demand pushes much higher, AMD should be able to adjust variant trays without abandoning Heilos Rack. We can't talk just about massive Agentic AI demand without talking about the Supply side or TSMC. TSMC, AMD's primary foundry for advanced nodes ( Zen 6/Venice on N2/2nm), is addressing AI-driven shortages through massive expansions. TSMC accelerates fab construction with up to 10 facilities targeted for 2026. TSMC is accelerating its domestic manufacturing expansion, with industry sources indicating that as many as ten fabs could be under construction or preparing to begin operations across Taiwan’s major science parks. TSMC Capex: $52-56B in 2026 (up 37% YoY), with $45B already approved for new/upgraded capacities. 70-80% for advanced processes (2nm/A16), 10-20% for packaging (CoWoS quadrupling to 120-140K wafers/month by late 2026). In addition, Taiwanese companies (led by TSMC) commit to at least $250B in direct investments in US-based advanced semiconductor, AI, and energy production/innovation capacity.Taiwan provides $250B in government credit guarantees to facilitate additional investments and build a full US semiconductor ecosystem (including industrial parks). TSMC completed a second land purchase in Arizona (January 2026) for gigafab scaling, with an additional $100B+ (potentially four more modules) to further expand and qualify for tariff exemptions. AMD with secured 12GW from OpenAI and $META and massive Agentic AI will mean higher priority acess to 20-30% more wafers on TSMC advanced nodes, as TSMC has multi-year agreements with AMD for AI chips. Dr. C. C. Wei, CEO of TSMC quote: "I spend a lot of time in the last three or four months talking to my customer and then customers. Customer. I want to make sure that my customers demand are real. I talk to those cloud service providers, all of them. Their answer is. I'm quite satisfied with their answer. Actually they show me the evidence that the AI really help their business. So they grow their business successfully and he or she in their financial return. So I also double check their financial status. They are very rich." Amid shortages, the US buildout ensures AMD can ramp production of Instinct GPUs and EPYC CPUs without the constraints hitting competitors like Intel. By diversifying away from Taiwan (85% of advanced nodes today), the agreement mitigates supply disruptions, ensuring stable flows for AMD's chips. Scaling production and securing supply will matter for AMD the most in the next 5-10 years growth. The growth could be 80-100% YoY or higher; or it could be in the 60%. The aggressive TSMC supply ramp is reassuring the higher growth point. Conclusion: AMD stands at a pivotal inflection point in 2026, where the explosive rise of agentic AI demanding 40-100x more inference compute through its 24/7, multi-step orchestration positions the company to potentially triple its EPYC CPU revenue to $45-60B+ by 2028 while scaling Instinct GPUs to tens of billions annually by 2027. Agentic AI demand could push AI CapEx closer to $1 Trillion in 2027, far higher than most estimates. Dr. Lisa Su, AMD's visionary CEO, is masterfully securing supply to harness this massive demand by prioritizing operational execution and deep TSMC collaboration, ensuring readiness for the second-half 2026 AI ramp. Dr. Su has explicitly called out surging EPYC demand for agentic tasks where CPUs power head nodes and traditional workloads alongside GPUs while guiding for data center dominance through proactive capacity planning and partnerships like Nutanix ($150M investment for open agentic platforms) or providing tens of millions CPUs for OpenAI, $META, $ORCL, $AMZN, $MSFT, $GOOGL and others. Her strategy includes multi-year TSMC agreements for advanced nodes (N2 for Venice CPUs and future Instincts), diversifying beyond Taiwan to mitigate risks, and unveiling innovations like the MI455X GPU at CES 2026, which she touted as enabling "the next trillion-dollar market opportunity" in physical AI. Dr. Su's forward-looking vision predicting AI reaching 5 billion users emphasizes "AI everywhere," backed by hardware like Ryzen AI chips, all while declaring demand "going through the roof" and committing to scale without bottlenecks. TSMC's aggressive ramp-up, fueled by $52-56B in 2026 capex (up 37% YoY) and 10+ new fabs across Taiwan, the US (Arizona cluster expanding to 6+ modules with $165B+ investment), Japan, and Europe, provides profound reassurance for AMD's supply stability. The January 2026 US-Taiwan agreement committing $250B in investments and credit guarantees for US reshoring accelerates this, granting tariff relief (15% rates with 1.5-2.5x exemptions) tied to capacity buildouts, enabling TSMC to potentially double output over the decade to meet AI wafer hunger. This translates to 20-30% higher wafer allocations on key nodes, sidestepping Intel-like shortages and empowering Dr. Su's team to deliver on hyperscaler demands without disruption. Ultimately, this synergy cements AMD's leadership in the agentic era, promising sustained growth, $5T+ valuations at scale, and a resilient path forward as AI reshapes the world. This is NOT Financial Advice! Video source: AMD CES 2026

Mike

44,460 次观看 • 7 个月前

CANCEL Your Weekend Plans, & Learn Claude Code Today. This Claude Code teaches more about vibe-coding in 30 mins than most tutorials do in hours. Save this, it'll change how you build forever People are building entire apps and charging clients $5,000 to $20,000 using Claude Code. This Claude Code video is a goldmine. Full Claude Code tutorial. Beginner to pro. Every feature. Every setup step. Every best practice. Zero prior knowledge needed. Save it. Watch it tonight. Not tomorrow. Tonight. Follow Himanshu Kumar so you don't miss the breakdowns for each feature. This is your complete Claude Code roadmap. Lose it and you lose the next 12 months of income. ↓ 1. Understand What Claude Code Actually Is. You think Claude Code is just another chatbot. It's not. And that misunderstanding is why you're broke. ChatGPT gives you text. Claude Code gives you software. It runs in your terminal. It reads your entire codebase. It writes files directly to your project. It runs commands on your machine. It debugs errors autonomously. It builds features end to end. You're not chatting. You're deploying a developer. One that works 24/7. Never asks for a raise. Never calls in sick. Never pushes broken code at 5 PM on a Friday. People are charging clients $5,000-$10,000 for apps they built with Claude Code in 3 hours. And you didn't even know this tool existed because you're still asking ChatGPT to write you a to-do list. The gap between you and people making money with AI isn't intelligence. It's awareness. Now you're aware. Save this post. Follow Himanshu Kumar for the complete breakdown of every Claude Code feature. ↓ 2. Set Up Claude Code Properly. Most people quit here. "It's too complicated." "I don't know terminal." "I'll set it up later." Later never comes. And "complicated" means "I watched for 30 seconds and gave up." The setup takes 10 minutes. Install Node.js. Install Claude Code via npm. Authenticate your account. Open your terminal. Done. 10 minutes. You spent longer this morning deciding what to have for breakfast. The video walks through every single click. Every command. Every screen. Assuming you know absolutely nothing. If you can download an app on your phone, you can set up Claude Code. It's the same level of difficulty. But you'll still tell yourself it's "too technical" because that excuse is more comfortable than admitting you're just scared to try something new. This is the setup that everything else builds on. Skip it and nothing works. ↓ 3. Use the Desktop App. You don't even need to live in the terminal if you don't want to. Claude Code has a desktop app. Clean interface. Visual feedback. Everything you need without touching command line. But here's the thing most people don't know: The desktop app isn't just a pretty wrapper. It lets you manage projects visually. See file changes in real time. Switch between projects instantly. The people making money with Claude Code use the desktop app for client projects because it's faster to manage multiple builds simultaneously. You're still opening 14 browser tabs to organize one project. They open one app and everything's there. Efficiency isn't a personality trait. It's a tool choice. Save this post. Follow Himanshu Kumar for the desktop app workflow that handles 5 client projects at once. ↓ 4. Install the Right Dependencies. This is where beginners silently fail and blame the tool. Claude Code needs certain dependencies installed to work properly. Miss one and everything breaks. Then you go on Twitter and say "Claude Code doesn't work." It works fine. You just didn't read the setup guide. The video covers every dependency you need. What to install. How to install it. How to verify it's working. No guessing. No Stack Overflow rabbit holes at midnight. No "why isn't this working" for 3 hours. Watch the dependency section once. Follow every step. Never deal with setup issues again. You spent more time last week troubleshooting a printer than this takes. ↓ 5. Work Inside Your Code Editor. Claude Code integrates directly with your code editor. VS Code. Cursor. Whatever you use. It's not a separate window you alt-tab between. It's right there. In your workflow. You type a request. Claude writes the code. The code appears in your editor. You review it. Accept it. Done. No copy pasting between windows. No reformatting code that got mangled in transit. No "which version was the right one." It's like pair programming with someone who never gets distracted, never argues about naming conventions, and actually writes code that works on the first try. Your current coding process is: Google the problem, read 5 answers on Stack Overflow, copy the wrong one, debug for an hour, find the right one, paste it in, break something else, repeat. Claude Code's process is: describe what you want, get working code, move on with your life. Same hour. One method produces working software. The other produces frustration and a browser history full of Stack Overflow tabs. Stop coding the hard way. Save this post. Follow Himanshu Kumar for code editor setup guides and integration tips. ↓ 6. Master Basic Usage. Most people learn 5% of a tool and say they "know" it. You "know" Photoshop because you can crop an image. You "know" Excel because you can sum a column. You "know" Claude Code because you asked it one question. Basic usage means: How to give Claude Code context about your project. How to ask for changes to existing code. How to generate new files and features. How to review what Claude produces. How to iterate when the output isn't perfect. These basics are the foundation of everything. Skip them and every advanced feature feels confusing. Master them and every advanced feature feels obvious. The video breaks down each one with real examples. Not theory. Actual usage on actual projects. You've been using AI tools at 5% capacity and wondering why your results are 5% of what others get. Save this post. Follow Himanshu Kumar for daily Claude Code usage tips. ↓ 7. Learn Every Command. Claude Code has commands that most users never discover. Because most users type one message and expect magic. That's not how professionals use it. Professionals use specific commands that tell Claude Code exactly what to do, how to do it, and what constraints to follow. The difference between a beginner and someone making $10K/month with Claude Code is knowing which command to use and when. The video walks through every single one. Not just what they do. But when to use each one. And why one command is better than another for specific situations. You've been using Claude Code like a hammer. These commands turn it into a full toolbox. Stop treating a power tool like a blunt instrument. Save this post. Follow Himanshu Kumar for the command cheat sheet I use daily. ↓ 8. Understand Modes and Shortcuts. Speed matters. The person who builds an app in 2 hours charges $5,000. The person who builds the same app in 2 days charges $2,000. Same app. Same quality. Different speed. Different income. Claude Code has modes that change how it operates. And shortcuts that cut your workflow time in half. Most people don't know either exists. They use Claude Code in default mode for everything. Like driving a car in first gear on the highway. Technically it works. But everyone is passing you. The video shows you every mode. Every shortcut. Every time-saving trick that separates the people charging $2,000 per project from the people charging $10,000. Speed is money. Literally. Save this post. Follow Himanshu Kumar for the shortcuts that cut my build time by 60%. ↓ 9. Write a Proper Planning Prompt. This is the section that separates amateurs from professionals. And it's the section most people skip. A planning prompt tells Claude Code what you're building before you start building it. Architecture. File structure. Technologies. Features. Constraints. Edge cases. Without a planning prompt, Claude Code guesses. And guessing produces garbage. With a planning prompt, Claude Code executes a clear plan. And clear plans produce working software. The video shows you exactly how to write a planning prompt that makes Claude Code produce professional-grade output on the first try. "But I just want to start coding." That's why your code breaks every time. That's why you restart projects 4 times. That's why nothing you build ever gets finished. Because you refuse to plan. A 5-minute planning prompt saves you 5 hours of debugging. But you'd rather skip the 5 minutes and suffer through the 5 hours because patience isn't your thing. And that's exactly why you're not making money. Planning is the most underpaid skill in coding. And the most overpaid when you master it. Save this post. Follow Himanshu Kumar for the planning prompt templates I use for every client project. ↓ 10. Choose the Right Model. Claude Code lets you select different AI models. Not all models are the same. Not all tasks need the same model. Using the most powerful model for a simple task wastes credits. Using a basic model for a complex task wastes time. The video explains: Which model to use for quick fixes. Which model to use for complex architecture. Which model to use for debugging. Which model to use for code generation. Most people pick one model and use it for everything. That's like using a sledgehammer to hang a picture frame. Model selection is strategy. And strategy is money. The people making $10K/month with Claude Code are strategic about every credit they spend. You're burning through credits because you use the most expensive model to write a hello world. ↓ 11. Use Git and Version Control. If you're not using version control, you're one mistake away from losing everything. Claude Code integrates with Git. Every change tracked. Every version saved. Every mistake reversible. Without Git: Claude makes a change. It breaks something. You can't undo it. You start over. 3 hours wasted. With Git: Claude makes a change. It breaks something. You roll back in 5 seconds. Keep working. Version control isn't optional. It's insurance. And the people not using it are the same people who say "I lost my entire project" like it's something that just happens. It doesn't just happen. It happens because you didn't set up Git. The video walks through the entire Git integration. Save this post. Follow Himanshu Kumar for the Git workflow that's saved every project I've ever built. ↓ 12. Set Up Claude MD and Memory. This is the feature that makes Claude Code feel like a real team member instead of a stranger you explain everything to every time. ClaudeMD is a memory file. You tell Claude Code about your project once. It remembers forever. Coding style preferences. Project architecture decisions. Technology stack. File naming conventions. Business logic rules. Without ClaudeMD: Every new conversation starts from zero. You explain the same things repeatedly. Output is inconsistent. With ClaudeMD: Claude knows your project. Claude follows your rules. Claude produces consistent, professional code. The difference between a sloppy freelancer and a reliable agency is consistency. Claude. MD gives you consistency without the agency overhead. Most people don't set this up and wonder why Claude Code gives different answers every time. ↓ 13. Automate with Tasks. This is where Claude Code stops being a tool and starts being an employee. Tasks let you define repeating workflows. "Every time I push code, run tests." "Every time I create a new file, add boilerplate." "Every time I start a session, check for errors." Automated. Hands-free. Consistent. You're doing these things manually every single day. The same checks. The same steps. The same routine. Tasks do them automatically. So you can focus on the work that actually makes money. Every manual task you automate is time you get back. And time is the only thing you can never make more of. Save this post. Follow Himanshu Kumar for the task automation templates that run my entire workflow. ↓ 14. Explore Features Most People Never Touch. The video covers features that 95% of Claude Code users don't know exist. Because they watched a 3-minute TikTok about Claude Code and think they're experts now. They're not. They're using 5% of a tool that can do everything. The full tutorial goes deep into features that most tutorials skip because they're "too advanced." They're not too advanced. They're too valuable for lazy creators to bother explaining. This video explains all of them. Clearly. For beginners. The 5% of features you don't know about are the 5% that make people rich. ↓ Let's zoom out. I just broke down 14 sections of Claude Code. Setup and installation. Desktop app. Dependencies. Code editor integration. Basic usage. Commands. Modes and shortcuts. Planning prompts. Model selection. Git and version control. Memory and Claude. MD. Tasks and automation. Advanced features. All in one video. All free. All beginner friendly. The person who masters even half of these in the next 2 weeks will be in the top 1% of Claude Code users. The top 1% of Claude Code users are the ones charging $5,000-$10,000 per project and building them in a single afternoon. Everyone else is asking ChatGPT to fix their resume. Same tools. Same access. Completely different outcomes. Because one person treats AI like a toy. And the other treats it like a business. ↓ Here's the hard truth nobody wants to hear. You don't have a talent problem. You don't have an intelligence problem. You don't have a resources problem. You have an action problem. Everything I just listed has a free tutorial right here in the attached video. 33 minutes. That's it. 33 minutes to learn the tool that people are using to build $5,000-$20,000/month businesses. You spent more time today scrolling Twitter than it takes to watch this video. You spent more time this week watching Netflix than it takes to master Claude Code basics. You spent more time this month doing nothing than it would take to completely change your income. The information is free. The tool is accessible. The opportunity is here. The only thing missing is you caring enough to start. ↓ CANCEL your plans this week. This isn't optional anymore. The people learning Claude Code right now will be building apps for the people who didn't learn it. That's not a prediction. That's already happening. Companies are replacing $150/hour developers with one person and Claude Code. If you code: learn Claude Code or become half as valuable by next year. If you don't code: learn Claude Code or miss the biggest opportunity to start earning from tech without a CS degree. There's no path forward that doesn't include AI coding tools. None. You have one window. Right now. This week. ↓ Here's your action plan for the next 7 days: Day 1: Watch the full video. Install Claude Code. Set up dependencies. Day 2: Learn basic usage. Try 5 different commands. Day 3: Write your first planning prompt. Build a small project. Day 4: Set up Claude. MD. Configure your memory file. Day 5: Master modes and shortcuts. Build a second project faster. Day 6: Set up Git integration. Automate with tasks. Day 7: Build something real. A tool, an app, a website. Ship it. 7 days. One tool. One completely different skill set. One completely different income potential. Or 7 more days of scrolling Twitter watching other people build things while you "plan to start." Your call. ↓ This is the most important video you'll watch this year. 33 minutes. Complete Claude Code mastery. From zero to building real projects. Save this post. Come back to it every single day this week. Check off each section as you complete it. Follow Himanshu Kumarfor daily Claude Code breakdowns, advanced tutorials, and the exact workflows that are turning beginners into $10K/month builders. The only thing between you and $10K/month with Claude Code is this video and 7 days. Don't waste them. You Must Follow me Himanshu Kumar, so i can send you DM.
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CANCEL Your Weekend Plans, & Learn Claude Code Today. This Claude Code teaches more about vibe-coding in 30 mins than most tutorials do in hours. Save this, it'll change how you build forever People are building entire apps and charging clients $5,000 to $20,000 using Claude Code. This Claude Code video is a goldmine. Full Claude Code tutorial. Beginner to pro. Every feature. Every setup step. Every best practice. Zero prior knowledge needed. Save it. Watch it tonight. Not tomorrow. Tonight. Follow Himanshu Kumar so you don't miss the breakdowns for each feature. This is your complete Claude Code roadmap. Lose it and you lose the next 12 months of income. ↓ 1. Understand What Claude Code Actually Is. You think Claude Code is just another chatbot. It's not. And that misunderstanding is why you're broke. ChatGPT gives you text. Claude Code gives you software. It runs in your terminal. It reads your entire codebase. It writes files directly to your project. It runs commands on your machine. It debugs errors autonomously. It builds features end to end. You're not chatting. You're deploying a developer. One that works 24/7. Never asks for a raise. Never calls in sick. Never pushes broken code at 5 PM on a Friday. People are charging clients $5,000-$10,000 for apps they built with Claude Code in 3 hours. And you didn't even know this tool existed because you're still asking ChatGPT to write you a to-do list. The gap between you and people making money with AI isn't intelligence. It's awareness. Now you're aware. Save this post. Follow Himanshu Kumar for the complete breakdown of every Claude Code feature. ↓ 2. Set Up Claude Code Properly. Most people quit here. "It's too complicated." "I don't know terminal." "I'll set it up later." Later never comes. And "complicated" means "I watched for 30 seconds and gave up." The setup takes 10 minutes. Install Node.js. Install Claude Code via npm. Authenticate your account. Open your terminal. Done. 10 minutes. You spent longer this morning deciding what to have for breakfast. The video walks through every single click. Every command. Every screen. Assuming you know absolutely nothing. If you can download an app on your phone, you can set up Claude Code. It's the same level of difficulty. But you'll still tell yourself it's "too technical" because that excuse is more comfortable than admitting you're just scared to try something new. This is the setup that everything else builds on. Skip it and nothing works. ↓ 3. Use the Desktop App. You don't even need to live in the terminal if you don't want to. Claude Code has a desktop app. Clean interface. Visual feedback. Everything you need without touching command line. But here's the thing most people don't know: The desktop app isn't just a pretty wrapper. It lets you manage projects visually. See file changes in real time. Switch between projects instantly. The people making money with Claude Code use the desktop app for client projects because it's faster to manage multiple builds simultaneously. You're still opening 14 browser tabs to organize one project. They open one app and everything's there. Efficiency isn't a personality trait. It's a tool choice. Save this post. Follow Himanshu Kumar for the desktop app workflow that handles 5 client projects at once. ↓ 4. Install the Right Dependencies. This is where beginners silently fail and blame the tool. Claude Code needs certain dependencies installed to work properly. Miss one and everything breaks. Then you go on Twitter and say "Claude Code doesn't work." It works fine. You just didn't read the setup guide. The video covers every dependency you need. What to install. How to install it. How to verify it's working. No guessing. No Stack Overflow rabbit holes at midnight. No "why isn't this working" for 3 hours. Watch the dependency section once. Follow every step. Never deal with setup issues again. You spent more time last week troubleshooting a printer than this takes. ↓ 5. Work Inside Your Code Editor. Claude Code integrates directly with your code editor. VS Code. Cursor. Whatever you use. It's not a separate window you alt-tab between. It's right there. In your workflow. You type a request. Claude writes the code. The code appears in your editor. You review it. Accept it. Done. No copy pasting between windows. No reformatting code that got mangled in transit. No "which version was the right one." It's like pair programming with someone who never gets distracted, never argues about naming conventions, and actually writes code that works on the first try. Your current coding process is: Google the problem, read 5 answers on Stack Overflow, copy the wrong one, debug for an hour, find the right one, paste it in, break something else, repeat. Claude Code's process is: describe what you want, get working code, move on with your life. Same hour. One method produces working software. The other produces frustration and a browser history full of Stack Overflow tabs. Stop coding the hard way. Save this post. Follow Himanshu Kumar for code editor setup guides and integration tips. ↓ 6. Master Basic Usage. Most people learn 5% of a tool and say they "know" it. You "know" Photoshop because you can crop an image. You "know" Excel because you can sum a column. You "know" Claude Code because you asked it one question. Basic usage means: How to give Claude Code context about your project. How to ask for changes to existing code. How to generate new files and features. How to review what Claude produces. How to iterate when the output isn't perfect. These basics are the foundation of everything. Skip them and every advanced feature feels confusing. Master them and every advanced feature feels obvious. The video breaks down each one with real examples. Not theory. Actual usage on actual projects. You've been using AI tools at 5% capacity and wondering why your results are 5% of what others get. Save this post. Follow Himanshu Kumar for daily Claude Code usage tips. ↓ 7. Learn Every Command. Claude Code has commands that most users never discover. Because most users type one message and expect magic. That's not how professionals use it. Professionals use specific commands that tell Claude Code exactly what to do, how to do it, and what constraints to follow. The difference between a beginner and someone making $10K/month with Claude Code is knowing which command to use and when. The video walks through every single one. Not just what they do. But when to use each one. And why one command is better than another for specific situations. You've been using Claude Code like a hammer. These commands turn it into a full toolbox. Stop treating a power tool like a blunt instrument. Save this post. Follow Himanshu Kumar for the command cheat sheet I use daily. ↓ 8. Understand Modes and Shortcuts. Speed matters. The person who builds an app in 2 hours charges $5,000. The person who builds the same app in 2 days charges $2,000. Same app. Same quality. Different speed. Different income. Claude Code has modes that change how it operates. And shortcuts that cut your workflow time in half. Most people don't know either exists. They use Claude Code in default mode for everything. Like driving a car in first gear on the highway. Technically it works. But everyone is passing you. The video shows you every mode. Every shortcut. Every time-saving trick that separates the people charging $2,000 per project from the people charging $10,000. Speed is money. Literally. Save this post. Follow Himanshu Kumar for the shortcuts that cut my build time by 60%. ↓ 9. Write a Proper Planning Prompt. This is the section that separates amateurs from professionals. And it's the section most people skip. A planning prompt tells Claude Code what you're building before you start building it. Architecture. File structure. Technologies. Features. Constraints. Edge cases. Without a planning prompt, Claude Code guesses. And guessing produces garbage. With a planning prompt, Claude Code executes a clear plan. And clear plans produce working software. The video shows you exactly how to write a planning prompt that makes Claude Code produce professional-grade output on the first try. "But I just want to start coding." That's why your code breaks every time. That's why you restart projects 4 times. That's why nothing you build ever gets finished. Because you refuse to plan. A 5-minute planning prompt saves you 5 hours of debugging. But you'd rather skip the 5 minutes and suffer through the 5 hours because patience isn't your thing. And that's exactly why you're not making money. Planning is the most underpaid skill in coding. And the most overpaid when you master it. Save this post. Follow Himanshu Kumar for the planning prompt templates I use for every client project. ↓ 10. Choose the Right Model. Claude Code lets you select different AI models. Not all models are the same. Not all tasks need the same model. Using the most powerful model for a simple task wastes credits. Using a basic model for a complex task wastes time. The video explains: Which model to use for quick fixes. Which model to use for complex architecture. Which model to use for debugging. Which model to use for code generation. Most people pick one model and use it for everything. That's like using a sledgehammer to hang a picture frame. Model selection is strategy. And strategy is money. The people making $10K/month with Claude Code are strategic about every credit they spend. You're burning through credits because you use the most expensive model to write a hello world. ↓ 11. Use Git and Version Control. If you're not using version control, you're one mistake away from losing everything. Claude Code integrates with Git. Every change tracked. Every version saved. Every mistake reversible. Without Git: Claude makes a change. It breaks something. You can't undo it. You start over. 3 hours wasted. With Git: Claude makes a change. It breaks something. You roll back in 5 seconds. Keep working. Version control isn't optional. It's insurance. And the people not using it are the same people who say "I lost my entire project" like it's something that just happens. It doesn't just happen. It happens because you didn't set up Git. The video walks through the entire Git integration. Save this post. Follow Himanshu Kumar for the Git workflow that's saved every project I've ever built. ↓ 12. Set Up Claude MD and Memory. This is the feature that makes Claude Code feel like a real team member instead of a stranger you explain everything to every time. ClaudeMD is a memory file. You tell Claude Code about your project once. It remembers forever. Coding style preferences. Project architecture decisions. Technology stack. File naming conventions. Business logic rules. Without ClaudeMD: Every new conversation starts from zero. You explain the same things repeatedly. Output is inconsistent. With ClaudeMD: Claude knows your project. Claude follows your rules. Claude produces consistent, professional code. The difference between a sloppy freelancer and a reliable agency is consistency. Claude. MD gives you consistency without the agency overhead. Most people don't set this up and wonder why Claude Code gives different answers every time. ↓ 13. Automate with Tasks. This is where Claude Code stops being a tool and starts being an employee. Tasks let you define repeating workflows. "Every time I push code, run tests." "Every time I create a new file, add boilerplate." "Every time I start a session, check for errors." Automated. Hands-free. Consistent. You're doing these things manually every single day. The same checks. The same steps. The same routine. Tasks do them automatically. So you can focus on the work that actually makes money. Every manual task you automate is time you get back. And time is the only thing you can never make more of. Save this post. Follow Himanshu Kumar for the task automation templates that run my entire workflow. ↓ 14. Explore Features Most People Never Touch. The video covers features that 95% of Claude Code users don't know exist. Because they watched a 3-minute TikTok about Claude Code and think they're experts now. They're not. They're using 5% of a tool that can do everything. The full tutorial goes deep into features that most tutorials skip because they're "too advanced." They're not too advanced. They're too valuable for lazy creators to bother explaining. This video explains all of them. Clearly. For beginners. The 5% of features you don't know about are the 5% that make people rich. ↓ Let's zoom out. I just broke down 14 sections of Claude Code. Setup and installation. Desktop app. Dependencies. Code editor integration. Basic usage. Commands. Modes and shortcuts. Planning prompts. Model selection. Git and version control. Memory and Claude. MD. Tasks and automation. Advanced features. All in one video. All free. All beginner friendly. The person who masters even half of these in the next 2 weeks will be in the top 1% of Claude Code users. The top 1% of Claude Code users are the ones charging $5,000-$10,000 per project and building them in a single afternoon. Everyone else is asking ChatGPT to fix their resume. Same tools. Same access. Completely different outcomes. Because one person treats AI like a toy. And the other treats it like a business. ↓ Here's the hard truth nobody wants to hear. You don't have a talent problem. You don't have an intelligence problem. You don't have a resources problem. You have an action problem. Everything I just listed has a free tutorial right here in the attached video. 33 minutes. That's it. 33 minutes to learn the tool that people are using to build $5,000-$20,000/month businesses. You spent more time today scrolling Twitter than it takes to watch this video. You spent more time this week watching Netflix than it takes to master Claude Code basics. You spent more time this month doing nothing than it would take to completely change your income. The information is free. The tool is accessible. The opportunity is here. The only thing missing is you caring enough to start. ↓ CANCEL your plans this week. This isn't optional anymore. The people learning Claude Code right now will be building apps for the people who didn't learn it. That's not a prediction. That's already happening. Companies are replacing $150/hour developers with one person and Claude Code. If you code: learn Claude Code or become half as valuable by next year. If you don't code: learn Claude Code or miss the biggest opportunity to start earning from tech without a CS degree. There's no path forward that doesn't include AI coding tools. None. You have one window. Right now. This week. ↓ Here's your action plan for the next 7 days: Day 1: Watch the full video. Install Claude Code. Set up dependencies. Day 2: Learn basic usage. Try 5 different commands. Day 3: Write your first planning prompt. Build a small project. Day 4: Set up Claude. MD. Configure your memory file. Day 5: Master modes and shortcuts. Build a second project faster. Day 6: Set up Git integration. Automate with tasks. Day 7: Build something real. A tool, an app, a website. Ship it. 7 days. One tool. One completely different skill set. One completely different income potential. Or 7 more days of scrolling Twitter watching other people build things while you "plan to start." Your call. ↓ This is the most important video you'll watch this year. 33 minutes. Complete Claude Code mastery. From zero to building real projects. Save this post. Come back to it every single day this week. Check off each section as you complete it. Follow Himanshu Kumarfor daily Claude Code breakdowns, advanced tutorials, and the exact workflows that are turning beginners into $10K/month builders. The only thing between you and $10K/month with Claude Code is this video and 7 days. Don't waste them. You Must Follow me Himanshu Kumar, so i can send you DM.

Himanshu Kumar

85,668 次观看 • 4 个月前

TOPIC #107: PI NETWORK IS A STABLE COIN? -WHO DECIDES PI FULLY OM FIXED VALUE? Dear GCV army, I hope you are all doing great! First of all, I would like to express my sincere gratitude for all your hard work. Many of you have achieved significant milestones, and it’s evident that you are making a great difference. Our influence has grown significantly, with an increasing number of social media posts and YouTubers publicly supporting us. I can see that more and more people are beginning to understand why we advocate for GCV. Today's meeting aims to alleviate any doubts you may have, allowing you to relax and feel confident as we embark on our historic journey together. I will answer the questions I’ve received and address some important issues we need to focus on to maintain our community's efficiency, particularly regarding our Generals, which will be the topic next weekend. I put the questions I received here. "A question addressed to Ms. Doris Yin in the emergency meeting 1– In light of the rapidly changing global circumstances and the increasing discussion about stablecoins backed by U.S. Treasury bonds, how do you see the future role of the Pi Network in this context? And what practical steps should the GCV army take now to accelerate this path? 2_ There are those who promote the idea that the price of Pi is what appears in the market (currently around $0.49) and compare it to the price of GCV within the ecosystem (314,159 Pi = 1 good or service). They say if Pi’s price rises to $2, it means that the value within The ecosystem is approximately 2 million dollars. With sincere appreciation and discipline." This is from the Arab head of GCV Ambassador Mr. Mohammed. Another question: "Hello, my Global Ambassador, I am Ateba Joseph, Ecological Ambassador in Cameroon And a member of the GCV army, I am delighted to exchange with you. Regarding the meeting with the GCV army on Sunday, July 27, 2025.. Here is my concern: A few days ago, a correspondence indicated that Pi is not or is not yet a stable coin. Upon reading this information, we have provided many explanations to help the pioneers understand this. I hope you will focus more on this statement to further strengthen our understanding of the subject. Thank you for taking my concerns into consideration" Thank you for the above questions; my answers are below. The first question concerns stablecoins. Many pioneers are hoping that Pi can be recognized by the U.S. government as a stablecoin. I wrote an article on this in May. On July 18, 2025, President Trump signed the Guiding and Establishing National Innovation for US Stablecoins Act (the GENIUS Act) into law. This legislation establishes a regulatory framework for payment stablecoins and marks the first federal legislation on digital assets enacted since President Trump issued an executive order aimed at making the U.S. the “crypto capital of the world.” U.S.-issued stablecoins are expected to become the primary means of dollar transactions globally, especially in emerging markets with unstable local currencies. The sponsors of the GENIUS Act estimate that by 2030, stablecoin issuers may collectively become the largest holders of U.S. Treasuries, surpassing foreign central banks. From this, we can see that U.S. stablecoins must maintain reserves backing outstanding payment stablecoins on a one-to-one basis, consisting only of specified assets, including U.S. dollars and short-term Treasury securities. It is clear that the Pi Network will not take this path, as it is not part of our plan. A stablecoin is essentially a digital representation of the U.S. dollar. All stablecoin issuers do not create a new currency; rather, it’s akin to purchasing chips at a casino – you must use U.S. dollars to buy those chips. However, Pi is a completely new currency. It does not need to be backed up by U.S. dollars or U.S. Treasuries to be used. If that were the case, we wouldn’t need to establish an ecosystem or have a three-year enclosed mainnet. I previously mentioned the possibility of Pi being an algorithmic stablecoin since only algorithmic stablecoins do not need to be backed by U.S. dollars. However, algorithmic stablecoins have faced significant failures in the past. The collapse of the Terra (LUNA) cryptocurrency resulted in a loss of at least $40 billion in market capitalization, with estimates reaching as high as $60 billion. TerraUSD (UST), an algorithmic stablecoin, lost its peg to the U.S. dollar, contributing to its overall collapse. The new stablecoin legislation recently passed through the Senate effectively ties the U.S. Treasury to crypto, as it essentially bets the government’s cash flow on digital tokens and market speculation. This legislation requires stablecoins to be backed by short-term Treasury bills, generating an estimated $2–$3 trillion in new demand for government debt, which is nearly half the current size of the T-bill market. On paper, this looks beneficial, but in reality, it creates a circular feedback loop: crypto demand fuels stablecoins, stablecoins buy T-bills, and T-bills fund government deficits. The government becomes reliant on speculative capital flows. Thus, we should understand why the U.S. government will not support the Pi Network as a stablecoin, as they require stablecoin issuers to buy T-bills and can no longer trust algorithmic stablecoins. So, what is the future of the Pi Network as a currency? From my perspective, Pi is already listed on exchange markets. It cannot be classified as a security because it is mined freely and is not an ICO. Instead, it should be categorized as a commodity, similar to Bitcoin and ETH. When a currency is listed for trading on an exchange, its price is determined by the balance of supply and demand. However, Pi is a currency in its own right; it has inherent value from Pi holders -Pioneers. Historically, currency has served as a medium of exchange. A medium of exchange is a widely accepted item for buying goods and services in an economy. It facilitates transactions by eliminating the need for a barter system, where goods are directly exchanged for other goods. In modern economies, money (such as currency) serves as the primary medium of exchange. **Functions of Money:** One of the core functions of money is to serve as a medium of exchange, enabling the smooth transfer of value between buyers and sellers, thereby simplifying trade and economic activity. **Examples:** In modern economies, this typically includes currency (paper money, coins) or digital money. In specific historical contexts, other items, such as cigarettes in prisoner-of-war camps, have also served as mediums of exchange. **Importance of Acceptance:** For a medium of exchange to function effectively, it must be widely accepted and trusted within the relevant community. **Not the Same as a Payment Method:** While credit cards and checks are used for payments, they do not serve as mediums of exchange themselves. Therefore, stablecoin is not a new currency. It is more likely to have a credit card or check character. It is a USD digital status. From the analysis presented, we can draw the following conclusions: The current price of Pi on the exchange market primarily serves as a temporary measure to facilitate broad expansion. While this is not our primary objective, it constitutes a strategic approach towards achieving our mission. To gain a clearer perspective, we must adopt a higher-level view of the overall vision for the Pi Network. The mission and vision of Pi Network clearly articulate that it is not intended to function as a commodity for sale, nor is it meant to be an investment vehicle or a speculative security. Instead, it is crucial to recognize that Pi is designed to be a medium of exchange—a new form of currency. As pioneers in this venture, we have the unique opportunity to acquire Pi through free mining. However, it is important to note that the current mining rate is relatively slow. To overcome this limitation and to further our goal of mass adoption, it is essential for more individuals to join the Pi Network and participate in holding Pi. One efficient way to accelerate this process is by allowing Pi to be traded on the exchange market, which can result in rapid and widespread adoption. Since Pi can be mined for free, a lower price could make it more accessible to a larger number of people. It's important to focus on our primary goal during this pre-full Open Mainnet (OM) phase: mass adoption, rather than aiming for high prices, which many pioneers expected. Some pioneers want to sell when the price increases, but if too many sell, it could undermine our goal of achieving mass adoption. This scenario is reminiscent of historical instances when shells served as currency—readily accessible from the sea or buy from the village market. For shells to function effectively as currency, a collective effort was needed to hold and circulate them within the village. If only a select few individuals possess the shells, the currency lacks the necessary circulation to sustain an economy. Hence, our goal should not be centered on achieving a high price; instead, we should strive to make Pi more affordable so that a greater number of individuals can acquire and hold it, thereby fostering a thriving economic ecosystem. Of course, the rising price will build up merchants' confidence to accept it as payment. This is why we refer to it as a buyback campaign, which aims to achieve mass adoption and foster ecosystem confidence. As Pi evolves into a currency, the question of its value becomes pertinent. Given that it is a new currency, its value is not immediately clear. This presents an opportunity for us, the pioneers, to play a crucial role in defining it. The determination of Pi's value is not the responsibility of a central authority such as CT, the government, or the exchange. Instead, it will emerge from a decentralized consensus within the community, which collectively owns Pi. This concept is akin to ancient times when the value of shells was not determined by the sellers. Rather, the value was derived from the collective agreement of the village that utilized them as currency. I hope this elaboration clarifies the distinction between value and price, enabling a deeper understanding of the foundational principles that drive our mission with Pi Network. Pi represents a groundbreaking innovation—a revolution that is poised for long-term economic development on a global scale, rather than perpetuating cycles of plunder and exploitation. By harnessing the power of blockchain technology, Pi empowers ordinary individuals, which creates an inherent conflict of interest with the U.S. government in the short term. Should the U.S. government endorse the Pi Network, it raises questions about the viability of U.S. treasuries and who would ultimately purchase them. Consequently, the government may prioritize support for stablecoins backed by the U.S. dollar and U.S. Treasury securities, as this can help alleviate the U.S. government's issues with limited demand. However, I previously mentioned the potential for Pi to emerge as an algorithmic stablecoin. At that time, the Genius Bill had not yet been enacted. If the Pi Network gains acceptance from the U.S. government, its growth could become rapid and expansive, leading to widespread adoption in other nations. This path would position Pi as a legitimate currency in nearly every country, contingent upon certain conditions. For instance, if the price of Pi in the exchange market can align with the GCV, this could be achieved through a buyback mechanism involving 10 million pioneers. Such a scenario would indicate that Pi differs significantly from past algorithmic stablecoin failures, presenting a compelling case for the U.S. government to view Pi as a low-risk asset. However, it presents a significant challenge to be collectively reached by pioneers, and there are other conditions that we cannot achieve in a short time. While it might appear that Pi Network conflicts with the U.S. dollar or stablecoins in the short term, it has the potential to address the broader issue of overprinting currency, which has plagued the U.S. and many other nations. This would benefit international trade by alleviating concerns about currency appreciation or depreciation in international transactions. The global economy indeed requires a super sovereign currency—one that ensures stability for future generations and fosters lasting peace and prosperity. To comprehend Pi as a currency, it is crucial to recognize that we must cultivate long-term value by generating GCV data. In the short term, our focus needs to be on establishing a robust exchange market and decentralized applications (DApps) to drive mass adoption. If this is understood, there should be no need to feel discouraged by the current low price of Pi. The true value of Pi as a currency derives not from the exchange market, trading platforms, or governmental endorsement, but rather from our community's collective efforts and engagement. You might wonder how a government could adopt Pi, given that it does not take the form of a stablecoin. I would counter with the example of Bitcoin, which has thrived even in environments where many countries have imposed bans. Currently, Pi is transitioning from its traditional commodity status to being recognized as a currency, meaning governmental awareness of Pi Network is still in development. As such, existing regulations generally pertain to older forms of cryptocurrency rather than our innovative approach. Our branding as a digital currency, rather than a cryptocurrency, is intentional. Dr. Nicolas has expressed concerns that many aspects of conventional cryptocurrencies pose challenges to government frameworks and public trust, often leading to economic harm rather than benefit. Our commitment to Know Your Customer (KYC) and Know Your Business (KYB) protocols distinguishes us by mitigating money laundering risks and protecting Pi holders from speculative practices. Many businesses face bankruptcy or closure because consumers lack the disposable income to engage in spending. Imagine how Pi could enable those businesses to survive and thrive—people could utilize Pi to make purchases and easily convert it into fiat currency to sustain operations, thereby preserving many jobs. The function in our wallet that allows users to "buy" Pi is not merely a feature; it represents a vision for the future where conversion to fiat currency can happen immediately, without dependency on third-party exchanges. Moving forward, we can establish a fixed rate (the GCV) for conversions. Once larger institutions and prominent companies recognize the low-risk profile of joining Pi Network due to its GCV stability, we can expect a considerable influx of participants seeking to gain a competitive advantage. You may ask how companies would finance the purchase of Pi at GCV rates. This is an insightful question. My perspective is that the demand for Pi’s stable value will inherently incentivize investments. Much like why individuals purchase stablecoins for their convenience in facilitating cross-border transactions, Pi will appeal to consumers and businesses alike, particularly because we are leveraging Web 3.0 blockchain technology, AI-driven platforms, and a rich ecosystem of decentralized applications (DApps). We are cultivating a loyal customer base that recognizes the value of this innovation. We understand that high-net-worth individuals seek safe investment opportunities. While U.S. treasury bonds currently represent a secure asset class, they are not without risk. Therefore, if Pi Network can maintain a limited supply coupled with blockchain technology and a consistent GCV, it is plausible that affluent investors would allocate a portion of their capital to acquire Pi. This would lead to fiat inflows whenever there is increased demand for Pi, establishing an equilibrium between Pi and fiat currencies. This interplay is why I believe DApps are critically significant. We need broader usage of Pi in real-world applications. I hope my analysis has helped clarify why the price of Pi should not overly concern us. Buying Pi to hold onto it allows pioneers to accumulate more, while building merchant confidence is essential to kickstart the ecosystem. Merchants will be motivated to see Pi’s price appreciation since this removes the risks for DApps and service providers who depend on exchange market prices. A rise in demand for Pi will subsequently reduce its supply, which is beneficial for price increases. I look forward to discussing Pi GCV army management in another session. Thank you for your time. Let’s continue striving for greatness together. Doris Yin 🪷🪷🪷 Founder, Global GCV Movement Disclaimer: This speech is intended solely for educational purposes within the GCV community. The views and content shared here represent my personal perspective and are part of the GCV movement, but do not reflect the official position of the Pi Core Team (PCT). Pi Network represents a new revolution, meaning there is no existing example for us to follow and no guiding manual. As Dr. Fan mentioned, we cannot predict what will happen around the next corner. Therefore, we must practice and forge our own path. As more people traverse this journey, the road will become clearer.
0:55

TOPIC #107: PI NETWORK IS A STABLE COIN? -WHO DECIDES PI FULLY OM FIXED VALUE? Dear GCV army, I hope you are all doing great! First of all, I would like to express my sincere gratitude for all your hard work. Many of you have achieved significant milestones, and it’s evident that you are making a great difference. Our influence has grown significantly, with an increasing number of social media posts and YouTubers publicly supporting us. I can see that more and more people are beginning to understand why we advocate for GCV. Today's meeting aims to alleviate any doubts you may have, allowing you to relax and feel confident as we embark on our historic journey together. I will answer the questions I’ve received and address some important issues we need to focus on to maintain our community's efficiency, particularly regarding our Generals, which will be the topic next weekend. I put the questions I received here. "A question addressed to Ms. Doris Yin in the emergency meeting 1– In light of the rapidly changing global circumstances and the increasing discussion about stablecoins backed by U.S. Treasury bonds, how do you see the future role of the Pi Network in this context? And what practical steps should the GCV army take now to accelerate this path? 2_ There are those who promote the idea that the price of Pi is what appears in the market (currently around $0.49) and compare it to the price of GCV within the ecosystem (314,159 Pi = 1 good or service). They say if Pi’s price rises to $2, it means that the value within The ecosystem is approximately 2 million dollars. With sincere appreciation and discipline." This is from the Arab head of GCV Ambassador Mr. Mohammed. Another question: "Hello, my Global Ambassador, I am Ateba Joseph, Ecological Ambassador in Cameroon And a member of the GCV army, I am delighted to exchange with you. Regarding the meeting with the GCV army on Sunday, July 27, 2025.. Here is my concern: A few days ago, a correspondence indicated that Pi is not or is not yet a stable coin. Upon reading this information, we have provided many explanations to help the pioneers understand this. I hope you will focus more on this statement to further strengthen our understanding of the subject. Thank you for taking my concerns into consideration" Thank you for the above questions; my answers are below. The first question concerns stablecoins. Many pioneers are hoping that Pi can be recognized by the U.S. government as a stablecoin. I wrote an article on this in May. On July 18, 2025, President Trump signed the Guiding and Establishing National Innovation for US Stablecoins Act (the GENIUS Act) into law. This legislation establishes a regulatory framework for payment stablecoins and marks the first federal legislation on digital assets enacted since President Trump issued an executive order aimed at making the U.S. the “crypto capital of the world.” U.S.-issued stablecoins are expected to become the primary means of dollar transactions globally, especially in emerging markets with unstable local currencies. The sponsors of the GENIUS Act estimate that by 2030, stablecoin issuers may collectively become the largest holders of U.S. Treasuries, surpassing foreign central banks. From this, we can see that U.S. stablecoins must maintain reserves backing outstanding payment stablecoins on a one-to-one basis, consisting only of specified assets, including U.S. dollars and short-term Treasury securities. It is clear that the Pi Network will not take this path, as it is not part of our plan. A stablecoin is essentially a digital representation of the U.S. dollar. All stablecoin issuers do not create a new currency; rather, it’s akin to purchasing chips at a casino – you must use U.S. dollars to buy those chips. However, Pi is a completely new currency. It does not need to be backed up by U.S. dollars or U.S. Treasuries to be used. If that were the case, we wouldn’t need to establish an ecosystem or have a three-year enclosed mainnet. I previously mentioned the possibility of Pi being an algorithmic stablecoin since only algorithmic stablecoins do not need to be backed by U.S. dollars. However, algorithmic stablecoins have faced significant failures in the past. The collapse of the Terra (LUNA) cryptocurrency resulted in a loss of at least $40 billion in market capitalization, with estimates reaching as high as $60 billion. TerraUSD (UST), an algorithmic stablecoin, lost its peg to the U.S. dollar, contributing to its overall collapse. The new stablecoin legislation recently passed through the Senate effectively ties the U.S. Treasury to crypto, as it essentially bets the government’s cash flow on digital tokens and market speculation. This legislation requires stablecoins to be backed by short-term Treasury bills, generating an estimated $2–$3 trillion in new demand for government debt, which is nearly half the current size of the T-bill market. On paper, this looks beneficial, but in reality, it creates a circular feedback loop: crypto demand fuels stablecoins, stablecoins buy T-bills, and T-bills fund government deficits. The government becomes reliant on speculative capital flows. Thus, we should understand why the U.S. government will not support the Pi Network as a stablecoin, as they require stablecoin issuers to buy T-bills and can no longer trust algorithmic stablecoins. So, what is the future of the Pi Network as a currency? From my perspective, Pi is already listed on exchange markets. It cannot be classified as a security because it is mined freely and is not an ICO. Instead, it should be categorized as a commodity, similar to Bitcoin and ETH. When a currency is listed for trading on an exchange, its price is determined by the balance of supply and demand. However, Pi is a currency in its own right; it has inherent value from Pi holders -Pioneers. Historically, currency has served as a medium of exchange. A medium of exchange is a widely accepted item for buying goods and services in an economy. It facilitates transactions by eliminating the need for a barter system, where goods are directly exchanged for other goods. In modern economies, money (such as currency) serves as the primary medium of exchange. **Functions of Money:** One of the core functions of money is to serve as a medium of exchange, enabling the smooth transfer of value between buyers and sellers, thereby simplifying trade and economic activity. **Examples:** In modern economies, this typically includes currency (paper money, coins) or digital money. In specific historical contexts, other items, such as cigarettes in prisoner-of-war camps, have also served as mediums of exchange. **Importance of Acceptance:** For a medium of exchange to function effectively, it must be widely accepted and trusted within the relevant community. **Not the Same as a Payment Method:** While credit cards and checks are used for payments, they do not serve as mediums of exchange themselves. Therefore, stablecoin is not a new currency. It is more likely to have a credit card or check character. It is a USD digital status. From the analysis presented, we can draw the following conclusions: The current price of Pi on the exchange market primarily serves as a temporary measure to facilitate broad expansion. While this is not our primary objective, it constitutes a strategic approach towards achieving our mission. To gain a clearer perspective, we must adopt a higher-level view of the overall vision for the Pi Network. The mission and vision of Pi Network clearly articulate that it is not intended to function as a commodity for sale, nor is it meant to be an investment vehicle or a speculative security. Instead, it is crucial to recognize that Pi is designed to be a medium of exchange—a new form of currency. As pioneers in this venture, we have the unique opportunity to acquire Pi through free mining. However, it is important to note that the current mining rate is relatively slow. To overcome this limitation and to further our goal of mass adoption, it is essential for more individuals to join the Pi Network and participate in holding Pi. One efficient way to accelerate this process is by allowing Pi to be traded on the exchange market, which can result in rapid and widespread adoption. Since Pi can be mined for free, a lower price could make it more accessible to a larger number of people. It's important to focus on our primary goal during this pre-full Open Mainnet (OM) phase: mass adoption, rather than aiming for high prices, which many pioneers expected. Some pioneers want to sell when the price increases, but if too many sell, it could undermine our goal of achieving mass adoption. This scenario is reminiscent of historical instances when shells served as currency—readily accessible from the sea or buy from the village market. For shells to function effectively as currency, a collective effort was needed to hold and circulate them within the village. If only a select few individuals possess the shells, the currency lacks the necessary circulation to sustain an economy. Hence, our goal should not be centered on achieving a high price; instead, we should strive to make Pi more affordable so that a greater number of individuals can acquire and hold it, thereby fostering a thriving economic ecosystem. Of course, the rising price will build up merchants' confidence to accept it as payment. This is why we refer to it as a buyback campaign, which aims to achieve mass adoption and foster ecosystem confidence. As Pi evolves into a currency, the question of its value becomes pertinent. Given that it is a new currency, its value is not immediately clear. This presents an opportunity for us, the pioneers, to play a crucial role in defining it. The determination of Pi's value is not the responsibility of a central authority such as CT, the government, or the exchange. Instead, it will emerge from a decentralized consensus within the community, which collectively owns Pi. This concept is akin to ancient times when the value of shells was not determined by the sellers. Rather, the value was derived from the collective agreement of the village that utilized them as currency. I hope this elaboration clarifies the distinction between value and price, enabling a deeper understanding of the foundational principles that drive our mission with Pi Network. Pi represents a groundbreaking innovation—a revolution that is poised for long-term economic development on a global scale, rather than perpetuating cycles of plunder and exploitation. By harnessing the power of blockchain technology, Pi empowers ordinary individuals, which creates an inherent conflict of interest with the U.S. government in the short term. Should the U.S. government endorse the Pi Network, it raises questions about the viability of U.S. treasuries and who would ultimately purchase them. Consequently, the government may prioritize support for stablecoins backed by the U.S. dollar and U.S. Treasury securities, as this can help alleviate the U.S. government's issues with limited demand. However, I previously mentioned the potential for Pi to emerge as an algorithmic stablecoin. At that time, the Genius Bill had not yet been enacted. If the Pi Network gains acceptance from the U.S. government, its growth could become rapid and expansive, leading to widespread adoption in other nations. This path would position Pi as a legitimate currency in nearly every country, contingent upon certain conditions. For instance, if the price of Pi in the exchange market can align with the GCV, this could be achieved through a buyback mechanism involving 10 million pioneers. Such a scenario would indicate that Pi differs significantly from past algorithmic stablecoin failures, presenting a compelling case for the U.S. government to view Pi as a low-risk asset. However, it presents a significant challenge to be collectively reached by pioneers, and there are other conditions that we cannot achieve in a short time. While it might appear that Pi Network conflicts with the U.S. dollar or stablecoins in the short term, it has the potential to address the broader issue of overprinting currency, which has plagued the U.S. and many other nations. This would benefit international trade by alleviating concerns about currency appreciation or depreciation in international transactions. The global economy indeed requires a super sovereign currency—one that ensures stability for future generations and fosters lasting peace and prosperity. To comprehend Pi as a currency, it is crucial to recognize that we must cultivate long-term value by generating GCV data. In the short term, our focus needs to be on establishing a robust exchange market and decentralized applications (DApps) to drive mass adoption. If this is understood, there should be no need to feel discouraged by the current low price of Pi. The true value of Pi as a currency derives not from the exchange market, trading platforms, or governmental endorsement, but rather from our community's collective efforts and engagement. You might wonder how a government could adopt Pi, given that it does not take the form of a stablecoin. I would counter with the example of Bitcoin, which has thrived even in environments where many countries have imposed bans. Currently, Pi is transitioning from its traditional commodity status to being recognized as a currency, meaning governmental awareness of Pi Network is still in development. As such, existing regulations generally pertain to older forms of cryptocurrency rather than our innovative approach. Our branding as a digital currency, rather than a cryptocurrency, is intentional. Dr. Nicolas has expressed concerns that many aspects of conventional cryptocurrencies pose challenges to government frameworks and public trust, often leading to economic harm rather than benefit. Our commitment to Know Your Customer (KYC) and Know Your Business (KYB) protocols distinguishes us by mitigating money laundering risks and protecting Pi holders from speculative practices. Many businesses face bankruptcy or closure because consumers lack the disposable income to engage in spending. Imagine how Pi could enable those businesses to survive and thrive—people could utilize Pi to make purchases and easily convert it into fiat currency to sustain operations, thereby preserving many jobs. The function in our wallet that allows users to "buy" Pi is not merely a feature; it represents a vision for the future where conversion to fiat currency can happen immediately, without dependency on third-party exchanges. Moving forward, we can establish a fixed rate (the GCV) for conversions. Once larger institutions and prominent companies recognize the low-risk profile of joining Pi Network due to its GCV stability, we can expect a considerable influx of participants seeking to gain a competitive advantage. You may ask how companies would finance the purchase of Pi at GCV rates. This is an insightful question. My perspective is that the demand for Pi’s stable value will inherently incentivize investments. Much like why individuals purchase stablecoins for their convenience in facilitating cross-border transactions, Pi will appeal to consumers and businesses alike, particularly because we are leveraging Web 3.0 blockchain technology, AI-driven platforms, and a rich ecosystem of decentralized applications (DApps). We are cultivating a loyal customer base that recognizes the value of this innovation. We understand that high-net-worth individuals seek safe investment opportunities. While U.S. treasury bonds currently represent a secure asset class, they are not without risk. Therefore, if Pi Network can maintain a limited supply coupled with blockchain technology and a consistent GCV, it is plausible that affluent investors would allocate a portion of their capital to acquire Pi. This would lead to fiat inflows whenever there is increased demand for Pi, establishing an equilibrium between Pi and fiat currencies. This interplay is why I believe DApps are critically significant. We need broader usage of Pi in real-world applications. I hope my analysis has helped clarify why the price of Pi should not overly concern us. Buying Pi to hold onto it allows pioneers to accumulate more, while building merchant confidence is essential to kickstart the ecosystem. Merchants will be motivated to see Pi’s price appreciation since this removes the risks for DApps and service providers who depend on exchange market prices. A rise in demand for Pi will subsequently reduce its supply, which is beneficial for price increases. I look forward to discussing Pi GCV army management in another session. Thank you for your time. Let’s continue striving for greatness together. Doris Yin 🪷🪷🪷 Founder, Global GCV Movement Disclaimer: This speech is intended solely for educational purposes within the GCV community. The views and content shared here represent my personal perspective and are part of the GCV movement, but do not reflect the official position of the Pi Core Team (PCT). Pi Network represents a new revolution, meaning there is no existing example for us to follow and no guiding manual. As Dr. Fan mentioned, we cannot predict what will happen around the next corner. Therefore, we must practice and forge our own path. As more people traverse this journey, the road will become clearer.

Doris Yin 东方紫莲🪷

17,742 次观看 • 1 年前

Moneytaur study blueprint 🗺️ The process I used to go from not knowing what an order block is to pulling cash from the crypto markets in under 6 months using 🎯 Master concepts. Proof of performance, past 120 days👇 Start date: 09/03/2025 Requirements: - A PC/laptop - Wifi - A basic understanding of trading. ( What candlesticks are, how to actually place trades , etc ) - A free mind - Time or the ability to free up time. Starting: - Structure and routine - Stick to that routine + Pre mortem plan. - Notion / Obsidian setup. The first thing you need to create is a clear routine moulded around how you intend to approach this very large and complex task. This will not be linear and you will naturally adapt it as you progress but especially in the beginning some resemblance of structure each day is vital. This is an individual process but it is important to understand from the beginning that this will require a majority of your free time assuming you work a full time Job or study as a student. For me in the beginning this looked like: - Wake up at 6:30. - Shower - Study/work for 1h 45m before leaving for work. - 09:00 -> 17:00 work - 17:30 Exercise / Train - Eat - 19:00 resume study/work - 22:30 Start to wind down and get ready to sleep. It changed several times over the months and especially now I am full time but this is irrelevant, the only thing that matters is sticking with what you choose. Whatever your own routine may look like, it is important to understand it will inevitably require sacrifice. --- The next thing once you have established a draft framework of your routine is ensuring you will actually stick to that routine. Something I implemented which I found particularly beneficial was the concept of a Pre-Mortem plan. This involves creating several scenarios of a future in which you have failed and working backwards from each of these to find where it went wrong. Here is a video which explains it fully: When I did this I came up with 3 scenarios as well as prevention and cure for each. In the 6 months that followed each scenario presented at some point but I was able to catch them early due to having done this. The last thing is to not over complicate this, don't hyper focus on systems and loose momentum optimizing each detail. Just ensure you do the fucking work. I was a little guilty of the above at times, trying to craft the perfect routine. In reality the person who just gets up, drinks too much coffee and works his ass off out performs the workflow perfectionist who visualizes and repeats affirmations, any day of the week. --- Next you need somewhere to store your notes, journal your trades and build your knowledge. For me this was Obsidian but I have also used Notion before and it is an equally viable option. Whichever one of these you choose be warned you will inevitably want to bang your head against a wall trying to use them for the first few days, but they will both click pretty quick and are 100% better options the word document or paper alternative. Here is my full obsidian setup tutorial: Here is a link to MisterPA 's notion Journal: Here is how I create "Meta-Notes" using obsidian: The process: - How I did it. - How I would do it if doing it again. Now I did things the "hard way" and manually worked my way back through each of MT's tweets starting in 2021, reading every one and logging those that I felt where relevant. You can see in my first post: the very first system I used to do this. I quickly adapted though after about a week and focused less on just logging each relevant tweet but trying to find and focusing on those which contained the most information. There where a lot of charts I looked at then skipped over because especially at the start of his timeline they contained little useful information and my time was better spent finding those where there was something to decode. Now this does not mean skip out on "work" just use your time efficiently. -- If however if I was to start from the beginning again with the goal of levelling up technical understanding as quickly as possible I would take a different approach. To start with I would familiarise myself with all relevant SMC concepts, I have linked the best free recourses for this below 👇 CryptoChase beginner friendly index: Barncore's "The Moneytaur Way" series: Gian's Trading bootcamp playlist: Following this I would then work through all of Taur's subscription posts working backwards, recreating his charts and taking notes on his logic. The subscription feed has the highest value density and least noise. Video example of my notes from his subscription posts 👇: --- Okay so now once you have a basic understanding of concepts and can re-recreate them on charts of your own it is time to put this in to practice. The next step is vigorous backtesting, you can use the trading view tool but I think trade Zella offers a more use friendly option if you pay for the subscription. Especially as it allows you to change timeframes without skipping ahead to candle close time of the timeframe you change too ( like Trading view does ) *my only note would be that their LTF/Micro TF data feed with be different to brokerage charts you will use on Trading view, to start with though you should not be going low enough that this is an issue. When you backtest in this context, treat it like real trading. That means journal and logging like you would if real cash was on the line. Take time, do not rush and focus on quality. Stick to BTC, ETH, Major FX pairs or indices as these assets are less reliant on confluence, backtesting a shitcoin is near useless as whether levels work or not will be highly dependent on Majors PA. Go on HTF, scroll back a couple years and try not too look at chart while doing so and then begin. Start with HTF analysis and work down to 2H or wherever you feel comfortable, chart it fully and then identify setups. Make rough notes / plans and then press play, execute the setups as they hit, log and journal trade management as well as observations and key notes. It is very important to not cheat when you do this, do not skip back and adjust your stoploss because it hit by 0.1%, do not skip back and adjust plan because you missed a block and your TP got frontrun. Instead these are the things you journal, embrace these mistakes because they are the cheapest mistakes you are going to make. Grind this, do it for hours, put some music on and enjoy. To start with focus on HTF's, as you get better and start netting $ on paper you can drop the timeframes and increase the difficulty. HTF = Normal, MTF = Medium, LTF = Hard. Even if you do not intend to day trade, learning how to read the lower TF's that force you to think faster, harder and prepare you for lower win rates / loss streaks can greatly improve your ability on higher TF's. While you are doing this as you start to have concepts click you now want to build up your real trading experience, take a sum of money that you care about but will be okay loosing and dedicate this to live trading. Start taking real trades and expect net losses in the beginning. This is where you will make you 2nd cheapest mistakes. This is also where you can begin to learn about your psychology. You may encounter some elements already in backtesting but the real market is where true colours really start to show. Mental issues are inevitable and part of the game, get used to them and start working to identify and fix them. Reading and applying books like Trading in the Zone and Mental Game of Trading are important and will help a lot but there is no easy fix, for some stuff you I believe you just have to get used to it and it goes away with experience. Losses suck at the beginning but after you loose 100 times you starting getting pretty numb to it, same goes for the winners. To accelerate the learning process, build connections and get advice there is also always the option of private groups, while I never personally chose this route and committed to learning everything through my own endeavours there is no denying that having nearly all the information you need structured and compiled in one place is valuable and can save time. Beyond this having access to real time thoughts and opinions of profitable traders can accelerate performance, however it carries the risk of being a double edged sword if not used properly, if relying on it like a crutch and using it as a substitute for real work you will not succeed. With that said if you take it for what it is, a learning opportunity then I believe it can be very beneficial. I am not a member of, nor affiliated with any paid group. There are now many options available within the community, all run by different people with different styles, tailored to different needs. If I was to make a recommendation though, as a non-member, it would be Albert & Co's 618'ers simply due to the diversity in styles of the traders running it and results I have seen from members I know personally. It is important that as you start to trade with real capital you reduce noise in your social feeds or eliminate it all together. You do not need 5 different opinions, you also do not need 2 people telling you the same thing in their own way so you feel re-assured. What you do need is to develop your independent thinking as a trader and be comfortable making different decisions to others, even traders ahead of yourself if it fits with your system or understanding of market. Taur here is perhaps an exception as this is who you are learning from but down the line a real test of your own ability and independence will be being able to stick with your own plan even when it differs from his. Don't get me wrong, counter trading him is retarded but you must learn to adapt his gift to your own style. This will make sense at some point. The next stage is taking your understanding of specific concepts to higher level as you simultaneously snowball experience. Look back through your journal and review where you lost money and made money, do not over extrapolate from a small sample but start to take notes and observe if trends in performance emerge. This is the beginning of the transition to self reliance, you now understand the strategy but must learn for yourself when and where it works. Here you can also learn more nuanced secondary concepts such as VSA, orderflow etc and add these to your game where appropriate. Do NOT get lost in the sauce though and remember mastery of basics is key. IMO a big focus should be understanding correlation thoroughly but especially on HTF's this is the most important thing and what triggers the majority of large swings where most of your cash will be made and losses recovered. Some people will disagree with me here but IMO you should also not be *focusing* on Odd TF's. These are secondary at best and most people overweight their significance leading to avoidable losses while wondering why price did not care about their 327minute Breaker Block which they think is the key to the market. Study Taurs feed and take note of how he mostly uses: 3M, 1M, 3W, 2W, 1W, 5D, 4D, 3D, 2D, 1D, 12H, 8H, 6H, 4H, 2H, 1H, 30m, 15m + micro time frames. The only thing left is time and repetition, you must show up each day and really do this, for months. Maybe you start to see result's, you catch your first key swing and where able to trade where others froze. Congratulations. Learn from these winners and repeat the actions. Find what assets work best for you, find your style, refine and grow. --- The last thing I will include is a short list of tools or links that can be helpful. - Trading view tutorial: - Dictionary: - Market news Calendar: --- Thank you too all those who have read this, I hope this has been helpful for the beginners who want to start but are just not sure how. 🫶 Don't just bookmark this and move on, start 🙃
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Moneytaur study blueprint 🗺️ The process I used to go from not knowing what an order block is to pulling cash from the crypto markets in under 6 months using 🎯 Master concepts. Proof of performance, past 120 days👇 Start date: 09/03/2025 Requirements: - A PC/laptop - Wifi - A basic understanding of trading. ( What candlesticks are, how to actually place trades , etc ) - A free mind - Time or the ability to free up time. Starting: - Structure and routine - Stick to that routine + Pre mortem plan. - Notion / Obsidian setup. The first thing you need to create is a clear routine moulded around how you intend to approach this very large and complex task. This will not be linear and you will naturally adapt it as you progress but especially in the beginning some resemblance of structure each day is vital. This is an individual process but it is important to understand from the beginning that this will require a majority of your free time assuming you work a full time Job or study as a student. For me in the beginning this looked like: - Wake up at 6:30. - Shower - Study/work for 1h 45m before leaving for work. - 09:00 -> 17:00 work - 17:30 Exercise / Train - Eat - 19:00 resume study/work - 22:30 Start to wind down and get ready to sleep. It changed several times over the months and especially now I am full time but this is irrelevant, the only thing that matters is sticking with what you choose. Whatever your own routine may look like, it is important to understand it will inevitably require sacrifice. --- The next thing once you have established a draft framework of your routine is ensuring you will actually stick to that routine. Something I implemented which I found particularly beneficial was the concept of a Pre-Mortem plan. This involves creating several scenarios of a future in which you have failed and working backwards from each of these to find where it went wrong. Here is a video which explains it fully: When I did this I came up with 3 scenarios as well as prevention and cure for each. In the 6 months that followed each scenario presented at some point but I was able to catch them early due to having done this. The last thing is to not over complicate this, don't hyper focus on systems and loose momentum optimizing each detail. Just ensure you do the fucking work. I was a little guilty of the above at times, trying to craft the perfect routine. In reality the person who just gets up, drinks too much coffee and works his ass off out performs the workflow perfectionist who visualizes and repeats affirmations, any day of the week. --- Next you need somewhere to store your notes, journal your trades and build your knowledge. For me this was Obsidian but I have also used Notion before and it is an equally viable option. Whichever one of these you choose be warned you will inevitably want to bang your head against a wall trying to use them for the first few days, but they will both click pretty quick and are 100% better options the word document or paper alternative. Here is my full obsidian setup tutorial: Here is a link to MisterPA 's notion Journal: Here is how I create "Meta-Notes" using obsidian: The process: - How I did it. - How I would do it if doing it again. Now I did things the "hard way" and manually worked my way back through each of MT's tweets starting in 2021, reading every one and logging those that I felt where relevant. You can see in my first post: the very first system I used to do this. I quickly adapted though after about a week and focused less on just logging each relevant tweet but trying to find and focusing on those which contained the most information. There where a lot of charts I looked at then skipped over because especially at the start of his timeline they contained little useful information and my time was better spent finding those where there was something to decode. Now this does not mean skip out on "work" just use your time efficiently. -- If however if I was to start from the beginning again with the goal of levelling up technical understanding as quickly as possible I would take a different approach. To start with I would familiarise myself with all relevant SMC concepts, I have linked the best free recourses for this below 👇 CryptoChase beginner friendly index: Barncore's "The Moneytaur Way" series: Gian's Trading bootcamp playlist: Following this I would then work through all of Taur's subscription posts working backwards, recreating his charts and taking notes on his logic. The subscription feed has the highest value density and least noise. Video example of my notes from his subscription posts 👇: --- Okay so now once you have a basic understanding of concepts and can re-recreate them on charts of your own it is time to put this in to practice. The next step is vigorous backtesting, you can use the trading view tool but I think trade Zella offers a more use friendly option if you pay for the subscription. Especially as it allows you to change timeframes without skipping ahead to candle close time of the timeframe you change too ( like Trading view does ) *my only note would be that their LTF/Micro TF data feed with be different to brokerage charts you will use on Trading view, to start with though you should not be going low enough that this is an issue. When you backtest in this context, treat it like real trading. That means journal and logging like you would if real cash was on the line. Take time, do not rush and focus on quality. Stick to BTC, ETH, Major FX pairs or indices as these assets are less reliant on confluence, backtesting a shitcoin is near useless as whether levels work or not will be highly dependent on Majors PA. Go on HTF, scroll back a couple years and try not too look at chart while doing so and then begin. Start with HTF analysis and work down to 2H or wherever you feel comfortable, chart it fully and then identify setups. Make rough notes / plans and then press play, execute the setups as they hit, log and journal trade management as well as observations and key notes. It is very important to not cheat when you do this, do not skip back and adjust your stoploss because it hit by 0.1%, do not skip back and adjust plan because you missed a block and your TP got frontrun. Instead these are the things you journal, embrace these mistakes because they are the cheapest mistakes you are going to make. Grind this, do it for hours, put some music on and enjoy. To start with focus on HTF's, as you get better and start netting $ on paper you can drop the timeframes and increase the difficulty. HTF = Normal, MTF = Medium, LTF = Hard. Even if you do not intend to day trade, learning how to read the lower TF's that force you to think faster, harder and prepare you for lower win rates / loss streaks can greatly improve your ability on higher TF's. While you are doing this as you start to have concepts click you now want to build up your real trading experience, take a sum of money that you care about but will be okay loosing and dedicate this to live trading. Start taking real trades and expect net losses in the beginning. This is where you will make you 2nd cheapest mistakes. This is also where you can begin to learn about your psychology. You may encounter some elements already in backtesting but the real market is where true colours really start to show. Mental issues are inevitable and part of the game, get used to them and start working to identify and fix them. Reading and applying books like Trading in the Zone and Mental Game of Trading are important and will help a lot but there is no easy fix, for some stuff you I believe you just have to get used to it and it goes away with experience. Losses suck at the beginning but after you loose 100 times you starting getting pretty numb to it, same goes for the winners. To accelerate the learning process, build connections and get advice there is also always the option of private groups, while I never personally chose this route and committed to learning everything through my own endeavours there is no denying that having nearly all the information you need structured and compiled in one place is valuable and can save time. Beyond this having access to real time thoughts and opinions of profitable traders can accelerate performance, however it carries the risk of being a double edged sword if not used properly, if relying on it like a crutch and using it as a substitute for real work you will not succeed. With that said if you take it for what it is, a learning opportunity then I believe it can be very beneficial. I am not a member of, nor affiliated with any paid group. There are now many options available within the community, all run by different people with different styles, tailored to different needs. If I was to make a recommendation though, as a non-member, it would be Albert & Co's 618'ers simply due to the diversity in styles of the traders running it and results I have seen from members I know personally. It is important that as you start to trade with real capital you reduce noise in your social feeds or eliminate it all together. You do not need 5 different opinions, you also do not need 2 people telling you the same thing in their own way so you feel re-assured. What you do need is to develop your independent thinking as a trader and be comfortable making different decisions to others, even traders ahead of yourself if it fits with your system or understanding of market. Taur here is perhaps an exception as this is who you are learning from but down the line a real test of your own ability and independence will be being able to stick with your own plan even when it differs from his. Don't get me wrong, counter trading him is retarded but you must learn to adapt his gift to your own style. This will make sense at some point. The next stage is taking your understanding of specific concepts to higher level as you simultaneously snowball experience. Look back through your journal and review where you lost money and made money, do not over extrapolate from a small sample but start to take notes and observe if trends in performance emerge. This is the beginning of the transition to self reliance, you now understand the strategy but must learn for yourself when and where it works. Here you can also learn more nuanced secondary concepts such as VSA, orderflow etc and add these to your game where appropriate. Do NOT get lost in the sauce though and remember mastery of basics is key. IMO a big focus should be understanding correlation thoroughly but especially on HTF's this is the most important thing and what triggers the majority of large swings where most of your cash will be made and losses recovered. Some people will disagree with me here but IMO you should also not be *focusing* on Odd TF's. These are secondary at best and most people overweight their significance leading to avoidable losses while wondering why price did not care about their 327minute Breaker Block which they think is the key to the market. Study Taurs feed and take note of how he mostly uses: 3M, 1M, 3W, 2W, 1W, 5D, 4D, 3D, 2D, 1D, 12H, 8H, 6H, 4H, 2H, 1H, 30m, 15m + micro time frames. The only thing left is time and repetition, you must show up each day and really do this, for months. Maybe you start to see result's, you catch your first key swing and where able to trade where others froze. Congratulations. Learn from these winners and repeat the actions. Find what assets work best for you, find your style, refine and grow. --- The last thing I will include is a short list of tools or links that can be helpful. - Trading view tutorial: - Dictionary: - Market news Calendar: --- Thank you too all those who have read this, I hope this has been helpful for the beginners who want to start but are just not sure how. 🫶 Don't just bookmark this and move on, start 🙃

Ace

45,752 次观看 • 10 个月前

Warren Buffett turns 93 today! To celebrate, I'm sharing the greatest lecture he ever gave together with his 94 (!) best investment quotes. 1. Rule No. 1 is never lose money. Rule No. 2 is never forget Rule No. 1. 2. Diversification is a protection against ignorance. It makes very little sense for those who know what they're doing. 3. Do not take yearly results too seriously. Instead, focus on four or five-year averages. 4. All there is to investing is picking good stocks at good times and staying with them as long as they remain good companies. 5. American business - and consequently a basket of stocks - is virtually certain to be worth far more in the years ahead. 6. An investor should act as though he had a lifetime decision card with just twenty punches on it. 7. And so the important thing we do with managers, generally, is to find the .400 hitters and then not tell them how to swing. 8. The most important quality for an investor is temperament, not intellect. You need a temperament that neither derives great pleasure from being with the crowd or against the crowd. 9. Bitcoin has no unique value at all. 10. Buy a stock the way you would buy a house. Understand and like it such that you'd be content to own it in the absence of any market. 11. The years ahead will occasionally deliver major market declines - even panics - that will affect virtually all stocks. No one can tell you when these traumas will occur. 12. I insist on a lot of time being spent, almost every day, to just sit and think. That is very uncommon in American business. 13. Buy companies with strong histories of profitability and with a dominant business franchise. 14. For the investor, a too-high purchase price for the stock of an excellent company can undo the effects of a subsequent decade of favorable business developments. 15. I believe in giving my kids enough so they can do anything, but not so much that they can do nothing. 16. The world went mad. What we learn from history is that people don’t learn from history. 17. The key to investing is not assessing how much an industry is going to affect society, or how much it will grow, but rather determining the competitive advantage of any given company and, above all, the durability of that advantage. 18. Among the various propositions offered to you, if you invested in a very low cost index fund - where you don't put the money in at one time, but average in over 10 years - you'll do better than 90% of people who start investing at the same time. 19. Because if you're wrong and rates go to 2 percent, which I don't think they will, you pay it off. It's a one-way renegotiation. It is an incredibly attractive instrument for the homeowner and you've got a one-way bet. 20. Cash is to a business as oxygen is to an individual: never thought about when it is present, the only thing in mind when it is absent. 21. Don't get caught up with what other people are doing. Being a contrarian isn't the key but being a crowd follower isn't either. You need to detach yourself emotionally. 22. For 240 years it's been a terrible mistake to bet against America, and now is no time to start. 23. I never attempt to make money on the stock market. I buy on the assumption that they could close the market the next day and not reopen it for five years. 24. I have no views as to where it (gold) will be, but the one thing I can tell you is it won't do anything between now and then except look at you. Whereas, you know, Coca-Cola will be making money, and I think Wells Fargo will be making a lot of money, and there will be a lot -- and it's a lot -- it's a lot better to have a goose that keeps laying eggs than a goose that just sits there and eats insurance and storage and a few things like that. 25. I just sit in my office and read all day. 26. I won't say if my candidate doesn't win, and probably half the time they haven't, I'm going to take my ball and go home 27. If returns are going to be 7 or 8 percent and you're paying 1 percent for fees, that makes an enormous difference in how much money you're going to have in retirement. 28. We want products where people feel like kissing you instead of slapping you. 29. If you aren't willing to own a stock for ten years, don't even think about owning it for ten minutes. 30. The most important investment you can make is one in yourself. 31. If you buy things you do not need, soon you will have to sell things you need. 32. If you don't feel comfortable making a rough estimate of the asset's future earnings, just forget it and move on. 33. If you like spending six to eight hours per week working on investments, do it. If you don't, then dollar-cost average into index funds. 34. If you're in the luckiest 1% of humanity, you owe it to the rest of humanity to think about the other 99%. 35. If you're smart, you're going to make a lot of money without borrowing. 36. In the 20th century, the United States endured two world wars and other traumatic and expensive military conflicts; the Depression; a dozen or so recessions and financial panics; oil shocks; a flu epidemic; and the resignation of a disgraced president. Yet the Dow rose from 66 to 11,497. 37. In the 54 years (Charlie Munger and I) have worked together, we have never forgone an attractive purchase because of the macro or political environment, or the views of other people. In fact, these subjects never come up when we make decisions 38. In the business world, the rearview mirror is always clearer than the windshield. 39. Investors should remember that excitement and expenses are their enemies. 40. It is a terrible mistake for investors with long-term horizons to measure their investment 'risk' by their portfolio's ratio of bonds to stocks. 41. It is not necessary to do extraordinary things to get extraordinary results. 42. It takes 20 years to build a reputation and five minutes to ruin it. If you think about that, you'll do things differently. 43. The one thing I will tell you is the worst investment you can have is cash. Everybody is talking about cash being king and all that sort of thing. Cash is going to become worth less over time. But good businesses are going to become worth more over time. 44. It's been an ideal period for investors: A climate of fear is their best friend. Those who invest only when commentators are upbeat end up paying a heavy price for meaningless reassurance. 45. It's better to hang out with people better than you. Pick out associates whose behavior is better than yours and you'll drift in that direction. 46. It's better to have a partial interest in the Hope diamond than to own all of a rhinestone. 47. It's far better to buy a wonderful company at a fair price than a fair company at a wonderful price. 48. Just pick a broad index like the S&P 500. Don't put your money in all at once; do it over a period of time. 49. Keep things simple and don't swing for the fences. When promised quick profits, respond with a quick "no”. 50. Lose money for the firm, and I will be understanding. Lose a shred of reputation for the firm, and I will be ruthless. 51. Many management teams are just deciding they're gonna buy X billions over X months. That's no way to buy things. You buy when selling for less than they are worth. ... It's not a complicated equation to figure out whether it is beneficial or not to repurchase shares. 52. The difference between successful people and really successful people is that really successful people say no to almost everything. 53. Most people get interested in stocks when everyone else is. The time to get interested is when no one else is. You can't buy what is popular and do well. 54. Never invest in a business you cannot understand. 55. Your premium brand had better be delivering something special, or it’s not going to get the business. 56. One can best prepare themselves for the economic future by investing in your own education. If you study hard and learn at a young age, you will be in the best circumstances to secure your future. 57. The most important thing to do if you find yourself in a hole is to stop digging. 58. One thing that could help would be to write down the reason you are buying a stock before your purchase. Write down "I am buying Microsoft at $300 billion because..." Force yourself to write this down. It clarifies your mind and discipline. 59. Only when the tide goes out do you discover who's been swimming naked. 60. Opportunities come infrequently. When it rains gold, put out the bucket, not the thimble. 61. Price is what you pay. Value is what you get. 62. Read 500 pages like this every day. That's how knowledge works. It builds up, like compound interest. All of you can do it, but I guarantee not many of you will do it. 63. Risk comes from not knowing what you're doing. 64. If a business does well, the stock eventually follows. 65. Since I know of no way to reliably predict market movements, I recommend that you purchase Berkshire shares only if you expect to hold them for at least five years. Those who seek short-term profits should look elsewhere. 66. Someone's sitting in the shade today because someone planted a tree a long time ago 67. The best thing that happens to us is when a great company gets into temporary trouble... We want to buy them when they're on the operating table. 68. Speculation is most dangerous when it looks easiest. 69. Stay away from it. It's a mirage, basically...The idea that it has some huge intrinsic value is a joke in my view. 70. The best chance to deploy capital is when things are going down. 71. The stock market is a no-called-strike game. You don't have to swing at everything -- you can wait for your pitch. 72. There is nothing wrong with a 'know nothing' investor who realizes it. The problem is when you are a 'know nothing' investor but you think you know something. 73. This does not bother Charlie and me. Indeed, we enjoy such price declines if we have funds available to increase our positions. 74. Too-big-to-fail is not a fallback position at Berkshire. Instead, we will always arrange our affairs so that any requirements for cash we may conceivably have will be dwarfed by our own liquidity. 75. There are all kinds of businesses that Charlie and I don’t understand, but that doesn’t cause us to stay up at night. It just means we go on to the next one, and that’s what the individual investor should do. 76. You can’t buy what is popular and do well. 77. We never want to count on the kindness of strangers in order to meet tomorrow's obligations. When forced to choose, I will not trade even a night's sleep for the chance of extra profits. 78. We will reject interesting opportunities rather than over-leverage our balance sheet. 79. We've long felt that the only value of stock forecasters is to make fortune tellers look good. Even now, Charlie and I continue to believe that short-term market forecasts are poison and should be kept locked up in a safe place, away from children and also from grown-ups who behave in the market like children. 80. What is smart at one price is stupid at another. 81. What we learn from history is that people don't learn from history. 82. When stock can be bought below a business's value it is probably the best use of cash. 83. When trillions of dollars are managed by Wall Streeters charging high fees, it will usually be the managers who reap outsized profits, not the clients. 84. When we own portions of outstanding businesses with outstanding managements, our favorite holding period is forever. 85. When you have able managers of high character running businesses about which they are passionate, you can have a dozen or more reporting to you and still have time for an afternoon nap. Conversely, if you have even one person reporting to you who is deceitful, inept or uninterested, you will find yourself with more than you can handle. 86. Whether we're talking about socks or stocks, I like buying quality merchandise when it is marked down. 87. Widespread fear is your friend as an investor because it serves up bargain purchases. 88. You are neither right nor wrong because the crowd disagrees with you. You are right because your data and reasoning are right. 89. You can't borrow money at 18 or 20 percent and come out ahead. 90. You can't produce a baby in one month by getting nine women pregnant. 91. The most important quality for an investor is temperament, not intellect… You need a temperament that neither derives great pleasure from being with the crowd or against the crowd. 92. You don't need to be a rocket scientist. Investing is not a game where the guy with the 160 IQ beats the guy with 130 IQ. You only have to be able to evaluate companies within your circle of competence. 93. The size of your circle of competence is not very important; knowing its boundaries, however, is vital.
1:12:41

Warren Buffett turns 93 today! To celebrate, I'm sharing the greatest lecture he ever gave together with his 94 (!) best investment quotes. 1. Rule No. 1 is never lose money. Rule No. 2 is never forget Rule No. 1. 2. Diversification is a protection against ignorance. It makes very little sense for those who know what they're doing. 3. Do not take yearly results too seriously. Instead, focus on four or five-year averages. 4. All there is to investing is picking good stocks at good times and staying with them as long as they remain good companies. 5. American business - and consequently a basket of stocks - is virtually certain to be worth far more in the years ahead. 6. An investor should act as though he had a lifetime decision card with just twenty punches on it. 7. And so the important thing we do with managers, generally, is to find the .400 hitters and then not tell them how to swing. 8. The most important quality for an investor is temperament, not intellect. You need a temperament that neither derives great pleasure from being with the crowd or against the crowd. 9. Bitcoin has no unique value at all. 10. Buy a stock the way you would buy a house. Understand and like it such that you'd be content to own it in the absence of any market. 11. The years ahead will occasionally deliver major market declines - even panics - that will affect virtually all stocks. No one can tell you when these traumas will occur. 12. I insist on a lot of time being spent, almost every day, to just sit and think. That is very uncommon in American business. 13. Buy companies with strong histories of profitability and with a dominant business franchise. 14. For the investor, a too-high purchase price for the stock of an excellent company can undo the effects of a subsequent decade of favorable business developments. 15. I believe in giving my kids enough so they can do anything, but not so much that they can do nothing. 16. The world went mad. What we learn from history is that people don’t learn from history. 17. The key to investing is not assessing how much an industry is going to affect society, or how much it will grow, but rather determining the competitive advantage of any given company and, above all, the durability of that advantage. 18. Among the various propositions offered to you, if you invested in a very low cost index fund - where you don't put the money in at one time, but average in over 10 years - you'll do better than 90% of people who start investing at the same time. 19. Because if you're wrong and rates go to 2 percent, which I don't think they will, you pay it off. It's a one-way renegotiation. It is an incredibly attractive instrument for the homeowner and you've got a one-way bet. 20. Cash is to a business as oxygen is to an individual: never thought about when it is present, the only thing in mind when it is absent. 21. Don't get caught up with what other people are doing. Being a contrarian isn't the key but being a crowd follower isn't either. You need to detach yourself emotionally. 22. For 240 years it's been a terrible mistake to bet against America, and now is no time to start. 23. I never attempt to make money on the stock market. I buy on the assumption that they could close the market the next day and not reopen it for five years. 24. I have no views as to where it (gold) will be, but the one thing I can tell you is it won't do anything between now and then except look at you. Whereas, you know, Coca-Cola will be making money, and I think Wells Fargo will be making a lot of money, and there will be a lot -- and it's a lot -- it's a lot better to have a goose that keeps laying eggs than a goose that just sits there and eats insurance and storage and a few things like that. 25. I just sit in my office and read all day. 26. I won't say if my candidate doesn't win, and probably half the time they haven't, I'm going to take my ball and go home 27. If returns are going to be 7 or 8 percent and you're paying 1 percent for fees, that makes an enormous difference in how much money you're going to have in retirement. 28. We want products where people feel like kissing you instead of slapping you. 29. If you aren't willing to own a stock for ten years, don't even think about owning it for ten minutes. 30. The most important investment you can make is one in yourself. 31. If you buy things you do not need, soon you will have to sell things you need. 32. If you don't feel comfortable making a rough estimate of the asset's future earnings, just forget it and move on. 33. If you like spending six to eight hours per week working on investments, do it. If you don't, then dollar-cost average into index funds. 34. If you're in the luckiest 1% of humanity, you owe it to the rest of humanity to think about the other 99%. 35. If you're smart, you're going to make a lot of money without borrowing. 36. In the 20th century, the United States endured two world wars and other traumatic and expensive military conflicts; the Depression; a dozen or so recessions and financial panics; oil shocks; a flu epidemic; and the resignation of a disgraced president. Yet the Dow rose from 66 to 11,497. 37. In the 54 years (Charlie Munger and I) have worked together, we have never forgone an attractive purchase because of the macro or political environment, or the views of other people. In fact, these subjects never come up when we make decisions 38. In the business world, the rearview mirror is always clearer than the windshield. 39. Investors should remember that excitement and expenses are their enemies. 40. It is a terrible mistake for investors with long-term horizons to measure their investment 'risk' by their portfolio's ratio of bonds to stocks. 41. It is not necessary to do extraordinary things to get extraordinary results. 42. It takes 20 years to build a reputation and five minutes to ruin it. If you think about that, you'll do things differently. 43. The one thing I will tell you is the worst investment you can have is cash. Everybody is talking about cash being king and all that sort of thing. Cash is going to become worth less over time. But good businesses are going to become worth more over time. 44. It's been an ideal period for investors: A climate of fear is their best friend. Those who invest only when commentators are upbeat end up paying a heavy price for meaningless reassurance. 45. It's better to hang out with people better than you. Pick out associates whose behavior is better than yours and you'll drift in that direction. 46. It's better to have a partial interest in the Hope diamond than to own all of a rhinestone. 47. It's far better to buy a wonderful company at a fair price than a fair company at a wonderful price. 48. Just pick a broad index like the S&P 500. Don't put your money in all at once; do it over a period of time. 49. Keep things simple and don't swing for the fences. When promised quick profits, respond with a quick "no”. 50. Lose money for the firm, and I will be understanding. Lose a shred of reputation for the firm, and I will be ruthless. 51. Many management teams are just deciding they're gonna buy X billions over X months. That's no way to buy things. You buy when selling for less than they are worth. ... It's not a complicated equation to figure out whether it is beneficial or not to repurchase shares. 52. The difference between successful people and really successful people is that really successful people say no to almost everything. 53. Most people get interested in stocks when everyone else is. The time to get interested is when no one else is. You can't buy what is popular and do well. 54. Never invest in a business you cannot understand. 55. Your premium brand had better be delivering something special, or it’s not going to get the business. 56. One can best prepare themselves for the economic future by investing in your own education. If you study hard and learn at a young age, you will be in the best circumstances to secure your future. 57. The most important thing to do if you find yourself in a hole is to stop digging. 58. One thing that could help would be to write down the reason you are buying a stock before your purchase. Write down "I am buying Microsoft at $300 billion because..." Force yourself to write this down. It clarifies your mind and discipline. 59. Only when the tide goes out do you discover who's been swimming naked. 60. Opportunities come infrequently. When it rains gold, put out the bucket, not the thimble. 61. Price is what you pay. Value is what you get. 62. Read 500 pages like this every day. That's how knowledge works. It builds up, like compound interest. All of you can do it, but I guarantee not many of you will do it. 63. Risk comes from not knowing what you're doing. 64. If a business does well, the stock eventually follows. 65. Since I know of no way to reliably predict market movements, I recommend that you purchase Berkshire shares only if you expect to hold them for at least five years. Those who seek short-term profits should look elsewhere. 66. Someone's sitting in the shade today because someone planted a tree a long time ago 67. The best thing that happens to us is when a great company gets into temporary trouble... We want to buy them when they're on the operating table. 68. Speculation is most dangerous when it looks easiest. 69. Stay away from it. It's a mirage, basically...The idea that it has some huge intrinsic value is a joke in my view. 70. The best chance to deploy capital is when things are going down. 71. The stock market is a no-called-strike game. You don't have to swing at everything -- you can wait for your pitch. 72. There is nothing wrong with a 'know nothing' investor who realizes it. The problem is when you are a 'know nothing' investor but you think you know something. 73. This does not bother Charlie and me. Indeed, we enjoy such price declines if we have funds available to increase our positions. 74. Too-big-to-fail is not a fallback position at Berkshire. Instead, we will always arrange our affairs so that any requirements for cash we may conceivably have will be dwarfed by our own liquidity. 75. There are all kinds of businesses that Charlie and I don’t understand, but that doesn’t cause us to stay up at night. It just means we go on to the next one, and that’s what the individual investor should do. 76. You can’t buy what is popular and do well. 77. We never want to count on the kindness of strangers in order to meet tomorrow's obligations. When forced to choose, I will not trade even a night's sleep for the chance of extra profits. 78. We will reject interesting opportunities rather than over-leverage our balance sheet. 79. We've long felt that the only value of stock forecasters is to make fortune tellers look good. Even now, Charlie and I continue to believe that short-term market forecasts are poison and should be kept locked up in a safe place, away from children and also from grown-ups who behave in the market like children. 80. What is smart at one price is stupid at another. 81. What we learn from history is that people don't learn from history. 82. When stock can be bought below a business's value it is probably the best use of cash. 83. When trillions of dollars are managed by Wall Streeters charging high fees, it will usually be the managers who reap outsized profits, not the clients. 84. When we own portions of outstanding businesses with outstanding managements, our favorite holding period is forever. 85. When you have able managers of high character running businesses about which they are passionate, you can have a dozen or more reporting to you and still have time for an afternoon nap. Conversely, if you have even one person reporting to you who is deceitful, inept or uninterested, you will find yourself with more than you can handle. 86. Whether we're talking about socks or stocks, I like buying quality merchandise when it is marked down. 87. Widespread fear is your friend as an investor because it serves up bargain purchases. 88. You are neither right nor wrong because the crowd disagrees with you. You are right because your data and reasoning are right. 89. You can't borrow money at 18 or 20 percent and come out ahead. 90. You can't produce a baby in one month by getting nine women pregnant. 91. The most important quality for an investor is temperament, not intellect… You need a temperament that neither derives great pleasure from being with the crowd or against the crowd. 92. You don't need to be a rocket scientist. Investing is not a game where the guy with the 160 IQ beats the guy with 130 IQ. You only have to be able to evaluate companies within your circle of competence. 93. The size of your circle of competence is not very important; knowing its boundaries, however, is vital.

Compounding Quality

621,113 次观看 • 3 年前

77 Reasons Why I’ve Invested Over $8,000,000+ in MultiversX (EGLD) and Why EGLD Will Crush It in 2025 (My Investment Thesis). I publicly shared my portfolio on X. EGLD is A) Better than BTC B) Everything that ETH wants to be C) The GameStop of Crypto 1. EGLD is verifiably the most scalable (theoretically unlimited) L1 chain in the world, theoretically capable of over 10 million TPS (thanks to adaptive state sharding). 2. e-Gold is digital gold. It has the best tokenomics among all L1s, similarly scarce to BTC, with a maximum supply of 31.4 million coins. Currently, 27.68 million coins are in circulation. 3. EGLD will be the most decentralized cryptocurrency in the world thanks to sharding and minimal hardware requirements for running nodes. It’s already second only to Ethereum with 3,618 validator nodes. 4. EGLD has extremely low fees, around ~$0.002 per transaction. 5. EGLD is extremely secure. No wallet drains like on ETH/SOL; assets are owned natively (not via a smart contract). There is no MEV risk (front-running bots). 6. EGLD is the only chain in the world with an on-chain Guardian (two-phase verification), making it impossible for a hacker to steal your funds—even if they have your private keys (seed phrase). 7. EGLD is carbon-neutral and eco-friendly, not wasting energy like BTC and other PoW chains. It’s exceptionally efficient, scalable, global, and sustainable. 8. EGLD has the best UX in crypto. Download the xPortal wallet—it’s like discovering Apple in Web3. The interface is simple, flawless, and you barely realize you’re using crypto. Instead of addresses, you use HeroTags. The app features all dApps, everything runs smoothly, and the visuals are beautifully designed. The explorer, web wallet, etc. follow the same high-quality user experience. 9. EGLD supports native assets, unlike Ethereum, for example. 10. EGLD is the first chain to fully implement horizontal (theoretically unlimited) sharding without compromising on decentralization—unlike Solana and others that attempt vertical scaling, leading to multiple network downtimes (11+ times) and huge hardware demands for validators, ultimately harming decentralization. 11. EGLD makes setting up a validator agency extremely easy. Even complete IT beginners can do it. The UX and documentation are superb. I personally set up the “EGLDSqueeze” agency in about 30 minutes. Managing it is straightforward via the web wallet, which feels like managing a Facebook page. This simplifies decentralization enormously. 12. EGLD allows literally anyone (even your grandma) to participate in decentralization, since nodes can run on a Raspberry Pi or a relatively affordable phone. Imagine millions of people worldwide securing the network, validating transactions without even knowing it. This can’t be done with BTC, where setting up profitable mining operations is prohibitively expensive. 13. WASM-Based Virtual Machine: You can write smart contracts in your favorite language, compile them, and run them via the fastest VM in the world. 14. EGLD has been tested at an incredible 263,000 TPS using its sharding mechanism and low hardware requirements. Allegedly, by mid-next year (April), they’ll demonstrate 1,000,000 TPS. (For context: Mastercard handles around 5,000 TPS; BTC handles 5–7 TPS.) 15. EGLD is currently the most advanced L1 in terms of scalability, security, decentralization, UX, eco-friendliness, and tokenomics. It’s the only chain that has genuinely solved the Blockchain Trilemma and is ready to onboard 1 billion people into crypto—users who won’t even realize they’re interacting with crypto. 16. EGLD is perfectly positioned for AI projects—AI agents, AI tools, or a so-called “Truth Machine” that monitors other AIs on-chain, documenting what’s true and comparing different AI outputs (some of which may be censored or biased), ensuring people don’t get confused or scammed in an AI-driven world. 17. The EGLD team is the hardest-working team I’ve ever encountered. I had the honor of meeting many of them personally, and can attest that their pace—even during a bear market—is extraordinary. 18. EGLD’s development team is exceptionally active on GitHub, continually improving their network and actively committing code. 19. EGLD plans to introduce an update reducing block time to 600ms (down from ~6 seconds), which would make the chain essentially unrivaled. 20. EGLD is effectively the only usable L1 in Europe, and the team has direct connections within the EU government—extremely bullish for the project. 21. EGLD provides top-tier on-chain governance not only for the MultiversX (EGLD) protocol but also for DeFi projects (e.g., xExchange, MEX). 22. EGLD plans to expand to the US, likely opening offices in Austin, Texas. This could put them in direct contact with Elon Musk (if it hasn’t happened already), as he’s involved with If he’s done his research, he’d discover there’s simply no better L1 worldwide. 23. EGLD solved fully implemented sharding, perfect tokenomics, and top-tier architecture with just $5M, whereas other chains failed to do so even with $100M+. The second-best sharding network, NEAR, needed $100M, has worse tokenomics, and its sharding isn’t fully implemented yet. Its UX also doesn’t compare. Owning NEAR was like comparing a VW Golf R to a Porsche GT3—EGLD is the Porsche GT3. 24. According to Similarweb, EGLD has significantly high traffic relative to other chains with market caps 100x larger. The market cap vs. web traffic discrepancy is huge, which is a strong indicator of EGLD’s potential. 25. EGLD has the most active and dedicated community relative to its user base, with users who believe in the technology, have full faith in the team, and remain loyal despite price volatility—because they use the chain and know there’s nothing better. 26. Check other chains’ active user counts on X (Twitter) and compare it with the followers of EGLD’s founders and main network accounts, versus those with 30x, 50x, or 100x larger market caps. 27. Visit the MultiversX website to observe the futuristic design and presentation, then compare it to other chains that appear nearly a decade behind in design and branding. 28. EGLD hosts the xDay Global event, showcasing updates, new builders, projects in the ecosystem, and major announcements—similar to Apple’s Keynotes—delivered in a highly professional, goosebump-inducing atmosphere. The next event is in Korea, the second-biggest crypto market after the US. Check out their previous xDay after-movie to see why this is extremely bullish. 29. EGLD is moving forward with plans for the first regulated, audited EU stablecoin under MiCa regulation, made possible by acquiring xMoney, which I view as a “Stripe” for crypto/fiat, offering everything from user solutions to merchant services—potentially the future of payments. 30. Greg Siourouni recently joined EGLD, having been an executive director at SUI Foundation. He’s now co-founder of xMoney Global. xMoney (formerly UTrust, with token UTK) is owned and founded by the MultiversX Labs team. A stablecoin might be introduced soon, which would be massively bullish given xMoney’s roadmap. They recently announced integrations with Binance Pay—both ways. 31. EGLD prioritizes user safety, believing it’s the only feasible approach once the network scales to serve a billion people—many of whom are retail users with little to no security awareness. 32. EGLD offers “Sovereign Chains,” letting you effectively clone their chain without heavy development, set up your own validators, and leverage their unlimited scalability. Any blockchain (ETH, BTC, SOL) struggling with scalability, decentralization, or security could run an ultra-fast, scalable, and secure L2 on EGLD’s Sovereign Chain, meeting top enterprise requirements. No one else has really done this. The Sovereign Chain demo achieved astonishing TPS and has an SDK. 33. No downtime since inception. 34. No shard takeover attacks have occurred. 35. Extremely fast—soon 600ms block time will be in place. 36. ESDTs – The best token standard available: fungible, non-fungible, semi-fungible, DeFi assets—everything is native and highly customizable. 37. Top-tier composability of assets and smart contracts. 38. Integrated DNS at protocol level with HeroTags (nicknames) instead of long addresses. 39. Asynchronous calls are supported. 40. Cross-shard transfers, execution, reverts, and calls are seamlessly integrated. 41. The best staking system in the space. Secure Proof of Stake (SPoS) is far more efficient than Proof of Work (PoW). 42. Built-in Delegation and Staking Provider system, with over 125K delegators. 43. Complete support for liquid staked assets, fostering decentralization rather than centralization. 44. TransferRoles for ESDT and other advanced operations. 45. Composable tasks on-chain for more sophisticated DeFi workflows. 46. MultiTransfer and asset execution within one transaction. 47. Re-entrancy protection is built-in by design. 48. Storage for ESDT assets goes beyond a linear approach, optimizing performance. 49. No integer overflows thanks to integrated safeMath operations. 50. Integrated crypto opcodes in the VM, enhancing security and performance. 51. Support for BigFloats, BigInts, and BigDecimals, enabling advanced financial calculations on-chain. 52. No sandwich attacks, plus front-running and MEV protection. 53. Relayed Transactions, simplifying user interactions and fees. 54. Smart Accounts featuring data tries and multiple built-in functions. 55. Generalized Paymaster solutions, enabling flexible fee models. 56. Subscriptions for recurring or automated on-chain payments. 57. Web2-like usability with Web3 functionality, bridging mainstream adoption. 58. StakingV4 for improved decentralization. 59. Enhanced MEV protection rolling out to safeguard users. 60. Parallel execution is coming soon, boosting throughput. 61. 1 million TPS is on the roadmap, targeted for demonstration. 62. 600ms block time is also coming soon. 63. Reduced cross-shard processing is planned to improve efficiency. 64. ZK everywhere (PI²): “prove everything” approach is coming. 65. AsyncV3 is in development for more complex cross-contract interactions. 66. Scalability enhancements for Merkle Tries or a new data model are being explored. 67. Linear storage on the VM is forthcoming. 68. A dynamic language interpreter at the VM is also planned. 69. Rumors suggest that MultiversX (EGLD) is building a “Truth Machine” on their L1—an essential, game-changing tool for AI verification and societal impact. 70. The entire team features individuals with PhDs in mathematics and physics, and many are former engineers at Google, IBM, and similar companies. 71. Over 56% of the network’s supply is staked, showcasing strong community involvement. 72. More than 6,772,347 accounts have been created on the network. 73. A total of 476,627,710 transactions have been processed on-chain without any outages or hacks. 74. EGLD has built a massive ecosystem over time. While not as numerous in project count as Solana, its market cap is ~100x smaller, yet it has far superior tokenomics and technology. The projects that do exist, like Hatom Protocol, are top-tier in UX, security, and advanced features. Hatom will soon introduce USH, a truly high-quality, decentralized stablecoin. 75. On competing chains, automated transactions aren’t easily or cheaply executed, whereas on MultiversX, tools like let you do this for free (with near-zero fees). 76. No other chain combines such a strong team and long-term vision where every product meets extreme security and UX standards like MultiversX does. This is why I see it as the “next Apple” in Web3. 77. MultiversX has a new CMO – Adam Bates, a former CMO at the Cardano Foundation. He was behind the success of Cardano’s huge marketing campaign and has a very good relationship with Charles Hoskinson. Thanks to him, Beniamin Mincu (the founder of MultiversX) was likely introduced, and now they will probably discuss how both blockchains can help each other, as well as any other potential collaborations we don’t yet know about. This is also extremely bullish. #EGLD is undeniably the most Scalable, Advanced, Secure, and User-friendly L1 supercomputer ever created. It’s built to SHAPE THE FUTURE. 1) 2) 3) 4) 5) 27/6/2024 - EGLDSqueeze - SUMMARY: HERE IS NO 2ND BEST. EGLD IS ONLY ONE BLOCKCHAIN THAT CAN RULE THEM ALL. ✅ UNLIMITED SCALING ✅ SCARCE AS BTC ✅ PROGRAMMABLE AS ETH ✅ NO DOWNTIME AS SOL ✅ UI/UX OF Apple ✅ SHARDING DONE BEFORE NEAR & TON ✅ BEST WALLET xPortal WITH GUARDIAN Price prediction (NFA|DYOR): My reasoning is that the real market cap as of December 23, 2024...if we take into account the value of other cryptocurrencies such as BTC, SOL, ETH, AVAX, NEAR, TON, Cardano, BNB, XRP, and so forth, plus the existence of meme coins with valuations above 20 billion USD, or even games nobody plays anymore that still have valuations above 800 million shows that EGLD’s current market cap of approximately 942 million USD is incredibly low. From a technological standpoint, user experience, and other relevant aspects, compared to SOL, NEAR, TON, AVAX, and other L1 protocols, EGLD’s market cap should realistically be around 100 billion USD. Therefore, my prediction and investment thesis is a minimum of a 100x increase from its current price (+-SOL marketcap). MultiversX is ready to onboard 1 billion people to the blockchain. From a long-term perspective, it could even reach a market cap of 1 trillion USD, which is roughly half of where BTC is right now. That would be approximately a 1060x gain from the current market cap. 1 EGLD (MultiversX) is for $34 (only 31.4M max supply) think about this. Not financial advice. Again. There is no 2nd best L1. Position yourself where the puck is going, then wait at the goal until the goal gets there Apes together, strong. Ape alone, weak. We Don't Worry. We Just Win. Shape The Future
5:34

77 Reasons Why I’ve Invested Over $8,000,000+ in MultiversX (EGLD) and Why EGLD Will Crush It in 2025 (My Investment Thesis). I publicly shared my portfolio on X. EGLD is A) Better than BTC B) Everything that ETH wants to be C) The GameStop of Crypto 1. EGLD is verifiably the most scalable (theoretically unlimited) L1 chain in the world, theoretically capable of over 10 million TPS (thanks to adaptive state sharding). 2. e-Gold is digital gold. It has the best tokenomics among all L1s, similarly scarce to BTC, with a maximum supply of 31.4 million coins. Currently, 27.68 million coins are in circulation. 3. EGLD will be the most decentralized cryptocurrency in the world thanks to sharding and minimal hardware requirements for running nodes. It’s already second only to Ethereum with 3,618 validator nodes. 4. EGLD has extremely low fees, around ~$0.002 per transaction. 5. EGLD is extremely secure. No wallet drains like on ETH/SOL; assets are owned natively (not via a smart contract). There is no MEV risk (front-running bots). 6. EGLD is the only chain in the world with an on-chain Guardian (two-phase verification), making it impossible for a hacker to steal your funds—even if they have your private keys (seed phrase). 7. EGLD is carbon-neutral and eco-friendly, not wasting energy like BTC and other PoW chains. It’s exceptionally efficient, scalable, global, and sustainable. 8. EGLD has the best UX in crypto. Download the xPortal wallet—it’s like discovering Apple in Web3. The interface is simple, flawless, and you barely realize you’re using crypto. Instead of addresses, you use HeroTags. The app features all dApps, everything runs smoothly, and the visuals are beautifully designed. The explorer, web wallet, etc. follow the same high-quality user experience. 9. EGLD supports native assets, unlike Ethereum, for example. 10. EGLD is the first chain to fully implement horizontal (theoretically unlimited) sharding without compromising on decentralization—unlike Solana and others that attempt vertical scaling, leading to multiple network downtimes (11+ times) and huge hardware demands for validators, ultimately harming decentralization. 11. EGLD makes setting up a validator agency extremely easy. Even complete IT beginners can do it. The UX and documentation are superb. I personally set up the “EGLDSqueeze” agency in about 30 minutes. Managing it is straightforward via the web wallet, which feels like managing a Facebook page. This simplifies decentralization enormously. 12. EGLD allows literally anyone (even your grandma) to participate in decentralization, since nodes can run on a Raspberry Pi or a relatively affordable phone. Imagine millions of people worldwide securing the network, validating transactions without even knowing it. This can’t be done with BTC, where setting up profitable mining operations is prohibitively expensive. 13. WASM-Based Virtual Machine: You can write smart contracts in your favorite language, compile them, and run them via the fastest VM in the world. 14. EGLD has been tested at an incredible 263,000 TPS using its sharding mechanism and low hardware requirements. Allegedly, by mid-next year (April), they’ll demonstrate 1,000,000 TPS. (For context: Mastercard handles around 5,000 TPS; BTC handles 5–7 TPS.) 15. EGLD is currently the most advanced L1 in terms of scalability, security, decentralization, UX, eco-friendliness, and tokenomics. It’s the only chain that has genuinely solved the Blockchain Trilemma and is ready to onboard 1 billion people into crypto—users who won’t even realize they’re interacting with crypto. 16. EGLD is perfectly positioned for AI projects—AI agents, AI tools, or a so-called “Truth Machine” that monitors other AIs on-chain, documenting what’s true and comparing different AI outputs (some of which may be censored or biased), ensuring people don’t get confused or scammed in an AI-driven world. 17. The EGLD team is the hardest-working team I’ve ever encountered. I had the honor of meeting many of them personally, and can attest that their pace—even during a bear market—is extraordinary. 18. EGLD’s development team is exceptionally active on GitHub, continually improving their network and actively committing code. 19. EGLD plans to introduce an update reducing block time to 600ms (down from ~6 seconds), which would make the chain essentially unrivaled. 20. EGLD is effectively the only usable L1 in Europe, and the team has direct connections within the EU government—extremely bullish for the project. 21. EGLD provides top-tier on-chain governance not only for the MultiversX (EGLD) protocol but also for DeFi projects (e.g., xExchange, MEX). 22. EGLD plans to expand to the US, likely opening offices in Austin, Texas. This could put them in direct contact with Elon Musk (if it hasn’t happened already), as he’s involved with If he’s done his research, he’d discover there’s simply no better L1 worldwide. 23. EGLD solved fully implemented sharding, perfect tokenomics, and top-tier architecture with just $5M, whereas other chains failed to do so even with $100M+. The second-best sharding network, NEAR, needed $100M, has worse tokenomics, and its sharding isn’t fully implemented yet. Its UX also doesn’t compare. Owning NEAR was like comparing a VW Golf R to a Porsche GT3—EGLD is the Porsche GT3. 24. According to Similarweb, EGLD has significantly high traffic relative to other chains with market caps 100x larger. The market cap vs. web traffic discrepancy is huge, which is a strong indicator of EGLD’s potential. 25. EGLD has the most active and dedicated community relative to its user base, with users who believe in the technology, have full faith in the team, and remain loyal despite price volatility—because they use the chain and know there’s nothing better. 26. Check other chains’ active user counts on X (Twitter) and compare it with the followers of EGLD’s founders and main network accounts, versus those with 30x, 50x, or 100x larger market caps. 27. Visit the MultiversX website to observe the futuristic design and presentation, then compare it to other chains that appear nearly a decade behind in design and branding. 28. EGLD hosts the xDay Global event, showcasing updates, new builders, projects in the ecosystem, and major announcements—similar to Apple’s Keynotes—delivered in a highly professional, goosebump-inducing atmosphere. The next event is in Korea, the second-biggest crypto market after the US. Check out their previous xDay after-movie to see why this is extremely bullish. 29. EGLD is moving forward with plans for the first regulated, audited EU stablecoin under MiCa regulation, made possible by acquiring xMoney, which I view as a “Stripe” for crypto/fiat, offering everything from user solutions to merchant services—potentially the future of payments. 30. Greg Siourouni recently joined EGLD, having been an executive director at SUI Foundation. He’s now co-founder of xMoney Global. xMoney (formerly UTrust, with token UTK) is owned and founded by the MultiversX Labs team. A stablecoin might be introduced soon, which would be massively bullish given xMoney’s roadmap. They recently announced integrations with Binance Pay—both ways. 31. EGLD prioritizes user safety, believing it’s the only feasible approach once the network scales to serve a billion people—many of whom are retail users with little to no security awareness. 32. EGLD offers “Sovereign Chains,” letting you effectively clone their chain without heavy development, set up your own validators, and leverage their unlimited scalability. Any blockchain (ETH, BTC, SOL) struggling with scalability, decentralization, or security could run an ultra-fast, scalable, and secure L2 on EGLD’s Sovereign Chain, meeting top enterprise requirements. No one else has really done this. The Sovereign Chain demo achieved astonishing TPS and has an SDK. 33. No downtime since inception. 34. No shard takeover attacks have occurred. 35. Extremely fast—soon 600ms block time will be in place. 36. ESDTs – The best token standard available: fungible, non-fungible, semi-fungible, DeFi assets—everything is native and highly customizable. 37. Top-tier composability of assets and smart contracts. 38. Integrated DNS at protocol level with HeroTags (nicknames) instead of long addresses. 39. Asynchronous calls are supported. 40. Cross-shard transfers, execution, reverts, and calls are seamlessly integrated. 41. The best staking system in the space. Secure Proof of Stake (SPoS) is far more efficient than Proof of Work (PoW). 42. Built-in Delegation and Staking Provider system, with over 125K delegators. 43. Complete support for liquid staked assets, fostering decentralization rather than centralization. 44. TransferRoles for ESDT and other advanced operations. 45. Composable tasks on-chain for more sophisticated DeFi workflows. 46. MultiTransfer and asset execution within one transaction. 47. Re-entrancy protection is built-in by design. 48. Storage for ESDT assets goes beyond a linear approach, optimizing performance. 49. No integer overflows thanks to integrated safeMath operations. 50. Integrated crypto opcodes in the VM, enhancing security and performance. 51. Support for BigFloats, BigInts, and BigDecimals, enabling advanced financial calculations on-chain. 52. No sandwich attacks, plus front-running and MEV protection. 53. Relayed Transactions, simplifying user interactions and fees. 54. Smart Accounts featuring data tries and multiple built-in functions. 55. Generalized Paymaster solutions, enabling flexible fee models. 56. Subscriptions for recurring or automated on-chain payments. 57. Web2-like usability with Web3 functionality, bridging mainstream adoption. 58. StakingV4 for improved decentralization. 59. Enhanced MEV protection rolling out to safeguard users. 60. Parallel execution is coming soon, boosting throughput. 61. 1 million TPS is on the roadmap, targeted for demonstration. 62. 600ms block time is also coming soon. 63. Reduced cross-shard processing is planned to improve efficiency. 64. ZK everywhere (PI²): “prove everything” approach is coming. 65. AsyncV3 is in development for more complex cross-contract interactions. 66. Scalability enhancements for Merkle Tries or a new data model are being explored. 67. Linear storage on the VM is forthcoming. 68. A dynamic language interpreter at the VM is also planned. 69. Rumors suggest that MultiversX (EGLD) is building a “Truth Machine” on their L1—an essential, game-changing tool for AI verification and societal impact. 70. The entire team features individuals with PhDs in mathematics and physics, and many are former engineers at Google, IBM, and similar companies. 71. Over 56% of the network’s supply is staked, showcasing strong community involvement. 72. More than 6,772,347 accounts have been created on the network. 73. A total of 476,627,710 transactions have been processed on-chain without any outages or hacks. 74. EGLD has built a massive ecosystem over time. While not as numerous in project count as Solana, its market cap is ~100x smaller, yet it has far superior tokenomics and technology. The projects that do exist, like Hatom Protocol, are top-tier in UX, security, and advanced features. Hatom will soon introduce USH, a truly high-quality, decentralized stablecoin. 75. On competing chains, automated transactions aren’t easily or cheaply executed, whereas on MultiversX, tools like let you do this for free (with near-zero fees). 76. No other chain combines such a strong team and long-term vision where every product meets extreme security and UX standards like MultiversX does. This is why I see it as the “next Apple” in Web3. 77. MultiversX has a new CMO – Adam Bates, a former CMO at the Cardano Foundation. He was behind the success of Cardano’s huge marketing campaign and has a very good relationship with Charles Hoskinson. Thanks to him, Beniamin Mincu (the founder of MultiversX) was likely introduced, and now they will probably discuss how both blockchains can help each other, as well as any other potential collaborations we don’t yet know about. This is also extremely bullish. #EGLD is undeniably the most Scalable, Advanced, Secure, and User-friendly L1 supercomputer ever created. It’s built to SHAPE THE FUTURE. 1) 2) 3) 4) 5) 27/6/2024 - EGLDSqueeze - SUMMARY: HERE IS NO 2ND BEST. EGLD IS ONLY ONE BLOCKCHAIN THAT CAN RULE THEM ALL. ✅ UNLIMITED SCALING ✅ SCARCE AS BTC ✅ PROGRAMMABLE AS ETH ✅ NO DOWNTIME AS SOL ✅ UI/UX OF Apple ✅ SHARDING DONE BEFORE NEAR & TON ✅ BEST WALLET xPortal WITH GUARDIAN Price prediction (NFA|DYOR): My reasoning is that the real market cap as of December 23, 2024...if we take into account the value of other cryptocurrencies such as BTC, SOL, ETH, AVAX, NEAR, TON, Cardano, BNB, XRP, and so forth, plus the existence of meme coins with valuations above 20 billion USD, or even games nobody plays anymore that still have valuations above 800 million shows that EGLD’s current market cap of approximately 942 million USD is incredibly low. From a technological standpoint, user experience, and other relevant aspects, compared to SOL, NEAR, TON, AVAX, and other L1 protocols, EGLD’s market cap should realistically be around 100 billion USD. Therefore, my prediction and investment thesis is a minimum of a 100x increase from its current price (+-SOL marketcap). MultiversX is ready to onboard 1 billion people to the blockchain. From a long-term perspective, it could even reach a market cap of 1 trillion USD, which is roughly half of where BTC is right now. That would be approximately a 1060x gain from the current market cap. 1 EGLD (MultiversX) is for $34 (only 31.4M max supply) think about this. Not financial advice. Again. There is no 2nd best L1. Position yourself where the puck is going, then wait at the goal until the goal gets there Apes together, strong. Ape alone, weak. We Don't Worry. We Just Win. Shape The Future

Daniel Veroc

50,650 次观看 • 1 年前

Made $530,000 with Ai Bot that started with $313. Didn't know how to code. Now this bots run 24/7 printing money while sleeping. I've made the exact step-by-step guide to build this Claude Code Polymarket trading bot. Prompts. Code. Risk settings. Paper trading checklist. Everything from zero to running bot. It's free. For 24 hours. After that I'm charging $499 for it. To grab it right now: 1. Comment "Claude Bot" 2. Like and Retweet this post 3. Follow me Himanshu Kumar ( I can't send DMs to non-followers ) I'm DMing everyone who Complete the 3 steps. I spent hundreds of thousands hiring developers because he was too scared to learn. Then learned Claude Code. Built algorithmic trading systems. $313 → $530,000. You have the same tools available right now. And you're using them to ask ChatGPT for Instagram captions. This attached video is a goldmine. Full live walkthrough. Claude Code building actual Polymarket trading bots. From zero. Every line of code. Every decision explained. Now let me break down why everything you're doing in trading is wrong and exactly how to fix it. Save this post. You'll hate yourself if you lose it. ↓ Let's start with why you keep losing money. You already know the answer. You just won't admit it. You overtrade. Every. Single. Day. You see a candle move. You feel something. You enter. No plan. No edge. No reason. Just feelings. Then it goes against you. You feel something else. Panic. Anger. Denial. You move your stop loss. Or you didn't set one at all. "It'll come back." It doesn't come back. So you take another trade. A revenge trade. Bigger size this time. Because you need to "make it back." That one fails too. Now you're emotional. Now you're tilted. Now you're using leverage you have no business touching. 40x. 50x. 100x. On a trade you entered because a candle looked "bullish" and some guy on Twitter said "send it." You get liquidated. Close the laptop. Punch something. Tell yourself you'll be "more disciplined" tomorrow. Tomorrow comes. Same cycle. Same result. Same liquidation. You've been doing this for months. Maybe years. And you still think the problem is your strategy. The problem isn't your strategy. The problem is you. Save this post right now. What I'm about to show you is the only way to remove yourself from the equation. Follow Himanshu Kumar so you don't miss any of this. ↓ Here's what's actually killing your account. It's not the market. The market doesn't care about you. It's not your indicators. RSI works fine. MACD works fine. They all "work." It's not your timeframe. It's not your broker. It's not the "manipulation." It's four things: 1. Emotions. You hold losers because hope feels better than loss. You cut winners because fear feels stronger than greed. You size up when angry. You skip trades when scared. Your emotional state determines your position size. That's insane. And you know it's insane. But you keep doing it. 2. Overtrading. You take 15 trades a day. Maybe 5 of them had actual setups. The other 10 were boredom. Boredom trades are the most expensive hobby in human history. 3. Leverage. You use 20x-50x on trades where you're not even sure about the direction. That's not trading. That's a casino with a nicer interface. 4. Fees. You're smashing market orders. Paying spread. Paying commission. On 15 trades a day. Your broker makes more money from your account than you do. Think about that. Your broker is profitable on your account. You're not. You're the product. Not the trader. These four things are why 90% of traders lose. Not bad luck. Not the market. You. Save this post and follow Himanshu Kumar because the solution is coming next. ↓ The solution is painfully obvious. Remove yourself from the equation. Not partially. Not "I'll be more disciplined." Not "I'll journal my trades." Not "I'll meditate before trading." Completely remove yourself. Build a bot. Let the bot trade. You go live your life. The bot doesn't feel emotions. The bot doesn't overtrade. The bot doesn't use reckless leverage. The bot doesn't smash market orders and bleed fees. The bot follows the rules. Every single time. Without exception. Without "just this once." Without "I have a feeling about this one." Rules in. Execution out. No human in the middle to mess everything up. That's algorithmic trading. And before your ego jumps in with "but I'm different, I have discipline" — No you don't. Your account balance proves you don't. If you had discipline, your account would be green. It's not. So you don't. Accept it. Automate it. Move on. This is the hardest truth in trading. Your discipline will always fail. A bot's won't. Save this post. Follow Himanshu Kumar for the exact bot setup that removes your emotions permanently. ↓ "But I don't know how to code." Neither did he. The guy in this video didn't know how to code for most of his life. Got held back in 7th grade. People counted him out early. Spent years building apps and SaaS businesses without writing a single line of code. Hired developers on Upwork instead. Spent hundreds of thousands of dollars paying other people to build what he could have built himself. Because he was scared to learn. That fear cost him years. And hundreds of thousands of dollars. Sound familiar? You're doing the same thing right now. Not with developers. But with your time. You're spending thousands of hours trading manually because you're scared to learn the thing that would make trading automatic. The fear of learning to code is costing you more than any bad trade ever did. Because every month you trade manually is a month of emotional decisions, overleveraged entries, and unnecessary losses that a bot would never make. And here's the thing that should really frustrate you: AI does the hard parts now. You don't need a computer science degree. You don't need to work at a hedge fund. You don't need to be "good at math." Claude Code writes the code for you. You just need to think clearly about trading ideas. That's it. If you can describe a strategy in English, Claude can build it in Python. "I don't know how to code" stopped being a valid excuse in 2024. It's 2026. You're 2 years late on that excuse. Find a new one. Or stop making excuses entirely. Save this post. Follow Himanshu Kumar because I'm showing you how people with zero coding experience are building profitable bots. ↓ The process that actually makes money. Three letters. R. B. I. Research. Backtest. Implement. That's it. That's the entire process. Every single day. Research: Find an idea. A pattern. A market inefficiency. Don't trade it yet. Don't even think about trading it yet. Just research it. Backtest: Test the idea against historical data. Does it work? Not "does it look good on one chart." Does it work across thousands of trades? Across different market conditions? Across in-sample AND out-of-sample data? If no, kill it. Find another idea. If yes, move to step 3. Implement: Build the bot. Deploy it. Paper trade first. Then live with small size. Scale only on evidence. Research. Backtest. Implement. Every day. No exceptions. You know what your current process is? Feel. Enter. Pray. F. E. P. Feel bullish. Enter a trade. Pray it works. That's not a process. That's gambling with a TradingView subscription. RBI is the only process that works. Save this post. Tattoo it on your forearm. Follow Himanshu Kumar for daily RBI breakdowns. ↓ What Claude Code actually does that your manual process can't. You can maybe test 3-5 strategy ideas per week. Manually adjusting parameters. Manually checking results. Manually writing code (badly). Claude Code tests 50-100 ideas per week. With parallel agents running simultaneously. Multiple strategies being built, tested, and validated at the same time. While you sleep. The guy in this video spends 4-8 hours a day building systems with Claude Code. Not trading. Building. Research. Backtest. Implement. Then iterate. Improve. Optimize. Every day the systems get better. Every day the edge compounds. Every day the bots get smarter. While you? You spend 4-8 hours a day staring at charts making the same mistakes you made last month. Same indicators. Same patterns. Same entries. Same losses. He's iterating forward. You're running in circles. Same 8 hours per day. Completely different outcomes. Because he's building systems. And you're feeding a casino. Stop feeding the casino. Start building the machine. Save this post and follow Himanshu Kumar for the Claude Code workflow that iterates strategies while you sleep. ↓ Jim Simons. That's the benchmark. You probably don't know who Jim Simons is. And that tells me everything about how seriously you take trading. Jim Simons. Mathematician. Founded Renaissance Technologies. Built a net worth of $31 billion. 100% from algorithmic trading. Not one single manual trade. Not one "gut feeling" entry. Not one RSI divergence. Not one "smart money concept." Algorithms. Bots. Systems. Data. $31 billion. His fund averaged 66% annual returns for over 30 years. While you're excited about making $200 on a trade that you'll give back tomorrow. The best trader in human history never placed a manual trade in his life. And you think your edge is staring at a 5-minute chart with bloodshot eyes at 2 AM? Your edge is building the system. Not being inside it. Jim Simons is the benchmark. Everything else is noise. Save this post. Follow Himanshu Kumar because I'm building toward the same goal and showing every step publicly. ↓ What you need to understand about patience. This is not get-rich-overnight. The guy in this video says it directly: "This channel is not for people looking to get rich overnight. It's not plug and play. There are no shortcuts. If you're impatient, this probably isn't for you." And that's exactly why most people will fail at this. Because you want results now. Today. This trade. You don't want to spend a week building a bot. You don't want to paper trade for 2 weeks. You don't want to test 50 ideas to find 1 that works. You want to copy someone's bot, run it live with your rent money, and be rich by Friday. That's why you'll be broke by Friday. The guy making $2.3M spent months iterating. Testing. Failing. Rebuilding. Testing again. He was patient when you would have quit. He was calm when you would have panicked. He was consistent when you would have given up. Patience isn't just a virtue in trading. It's the only virtue. Without it, everything else fails. Impatience is the most expensive personality trait in trading. Save this post. Follow Himanshu Kumar and learn to build systems with the patience that actually pays. ↓ The live streams where the real learning happens. The YouTube video is the trailer. The live streams are the movie. Real-time bot building. Real-time questions answered. Real code shown. Real mistakes made and fixed. Not polished highlight reels where everything works perfectly. Actual development. Where things break. Where strategies fail. Where code doesn't compile. Where the fix takes 2 hours. Because that's what real development looks like. And seeing the messy parts is more valuable than any polished tutorial. Because when your bot breaks at 3 AM, you need to know how to fix it. Not just how to celebrate when it works. The streams mix beginner and advanced. Start with how to automate trading. How to use AI for code generation. Then dive into the daily work. Claude Code. Parallel agents. Constant iteration. Live debugging. 4-8 hours of real algorithmic trading development. Live. Uncut. No filter. Most "trading education" shows you the wins. This shows you the work. Save this post. Follow Himanshu Kumar for the stream schedules and breakdowns. ↓ The belief that changes everything. Code is the greatest equalizer. Not money. Not connections. Not a degree. Not where you grew up. Not what school you went to. Code. Once you can build systems, you can build anything. For the rest of your life. A trading bot today. A SaaS product tomorrow. An automation business next month. A completely different life next year. The skill isn't "algorithmic trading." The skill is building systems. And that skill transfers to everything. The guy who can build a trading bot can also build a lead gen tool. Can also build a content pipeline. Can also build a SaaS product. Can also build literally anything that runs on logic and code. One skill. Infinite applications. And AI makes learning it 100x easier than it was 5 years ago. You don't need to be smart. You don't need talent. You need Claude Code and the willingness to sit down and build something instead of consuming content about building something. Building is the skill. Everything else is entertainment disguised as education. Save this post. Follow Himanshu Kumar because I'm showing you how to build, not just how to watch. ↓ If any of this applies to you, pay attention. If you've lost money from overtrading. If you've been liquidated. If you know trading is the vehicle but manual execution keeps crashing you. If you've tried "being more disciplined" and it never lasted more than a week. If you keep saying "next month I'll start automating." If you've spent more money on courses than you've made from trading. There is a better way. It's not a magic indicator. It's not a signal group. It's not a $997 mentorship from a guy who makes money teaching, not trading. It's building your own system. A system that trades without emotion. A system that follows rules without exception. A system that runs while you sleep. A system that compounds while you live your life. That's the answer. It's always been the answer. You've just been too scared to accept that the solution requires building something instead of buying something. ↓ What the next 30 days look like if you actually commit. Week 1: Watch the video. Learn Claude Code basics. Build your first simple strategy. Run your first backtest. Week 2: Iterate. Let Claude improve the strategy. Run Monte Carlo validation. Paper trade. Week 3: Go live with $50-100. Tiny positions. Watch every trade. Compare to paper results. Week 4: Scale based on evidence. Not based on excitement. Not based on one good day. Based on data. 30 days from now you either have a running bot that trades without your emotions destroying every position. Or you're exactly where you are right now. Reading another post. Making another promise. Breaking it by Tuesday. Same 30 days either way. Different actions. Different results. Different life. ↓ Full video tutorial attached. Live bot building with Claude Code. From zero to running Polymarket trading bot. Every line of code. Every decision explained. The video is free. Claude Code is available now. The market is open 24/7. The only thing standing between you and a profitable trading bot is the same thing that's been standing there for months. You. Get out of your own way. Follow Himanshu Kumar for daily AI trading bot breakdowns, live build sessions, and the full RBI process. Save this post. Watch the video. Build the bot. Or keep trading manually and keep losing. The choice has never been easier. And you've never been more stubborn about making the wrong one.
18:53

Made $530,000 with Ai Bot that started with $313. Didn't know how to code. Now this bots run 24/7 printing money while sleeping. I've made the exact step-by-step guide to build this Claude Code Polymarket trading bot. Prompts. Code. Risk settings. Paper trading checklist. Everything from zero to running bot. It's free. For 24 hours. After that I'm charging $499 for it. To grab it right now: 1. Comment "Claude Bot" 2. Like and Retweet this post 3. Follow me Himanshu Kumar ( I can't send DMs to non-followers ) I'm DMing everyone who Complete the 3 steps. I spent hundreds of thousands hiring developers because he was too scared to learn. Then learned Claude Code. Built algorithmic trading systems. $313 → $530,000. You have the same tools available right now. And you're using them to ask ChatGPT for Instagram captions. This attached video is a goldmine. Full live walkthrough. Claude Code building actual Polymarket trading bots. From zero. Every line of code. Every decision explained. Now let me break down why everything you're doing in trading is wrong and exactly how to fix it. Save this post. You'll hate yourself if you lose it. ↓ Let's start with why you keep losing money. You already know the answer. You just won't admit it. You overtrade. Every. Single. Day. You see a candle move. You feel something. You enter. No plan. No edge. No reason. Just feelings. Then it goes against you. You feel something else. Panic. Anger. Denial. You move your stop loss. Or you didn't set one at all. "It'll come back." It doesn't come back. So you take another trade. A revenge trade. Bigger size this time. Because you need to "make it back." That one fails too. Now you're emotional. Now you're tilted. Now you're using leverage you have no business touching. 40x. 50x. 100x. On a trade you entered because a candle looked "bullish" and some guy on Twitter said "send it." You get liquidated. Close the laptop. Punch something. Tell yourself you'll be "more disciplined" tomorrow. Tomorrow comes. Same cycle. Same result. Same liquidation. You've been doing this for months. Maybe years. And you still think the problem is your strategy. The problem isn't your strategy. The problem is you. Save this post right now. What I'm about to show you is the only way to remove yourself from the equation. Follow Himanshu Kumar so you don't miss any of this. ↓ Here's what's actually killing your account. It's not the market. The market doesn't care about you. It's not your indicators. RSI works fine. MACD works fine. They all "work." It's not your timeframe. It's not your broker. It's not the "manipulation." It's four things: 1. Emotions. You hold losers because hope feels better than loss. You cut winners because fear feels stronger than greed. You size up when angry. You skip trades when scared. Your emotional state determines your position size. That's insane. And you know it's insane. But you keep doing it. 2. Overtrading. You take 15 trades a day. Maybe 5 of them had actual setups. The other 10 were boredom. Boredom trades are the most expensive hobby in human history. 3. Leverage. You use 20x-50x on trades where you're not even sure about the direction. That's not trading. That's a casino with a nicer interface. 4. Fees. You're smashing market orders. Paying spread. Paying commission. On 15 trades a day. Your broker makes more money from your account than you do. Think about that. Your broker is profitable on your account. You're not. You're the product. Not the trader. These four things are why 90% of traders lose. Not bad luck. Not the market. You. Save this post and follow Himanshu Kumar because the solution is coming next. ↓ The solution is painfully obvious. Remove yourself from the equation. Not partially. Not "I'll be more disciplined." Not "I'll journal my trades." Not "I'll meditate before trading." Completely remove yourself. Build a bot. Let the bot trade. You go live your life. The bot doesn't feel emotions. The bot doesn't overtrade. The bot doesn't use reckless leverage. The bot doesn't smash market orders and bleed fees. The bot follows the rules. Every single time. Without exception. Without "just this once." Without "I have a feeling about this one." Rules in. Execution out. No human in the middle to mess everything up. That's algorithmic trading. And before your ego jumps in with "but I'm different, I have discipline" — No you don't. Your account balance proves you don't. If you had discipline, your account would be green. It's not. So you don't. Accept it. Automate it. Move on. This is the hardest truth in trading. Your discipline will always fail. A bot's won't. Save this post. Follow Himanshu Kumar for the exact bot setup that removes your emotions permanently. ↓ "But I don't know how to code." Neither did he. The guy in this video didn't know how to code for most of his life. Got held back in 7th grade. People counted him out early. Spent years building apps and SaaS businesses without writing a single line of code. Hired developers on Upwork instead. Spent hundreds of thousands of dollars paying other people to build what he could have built himself. Because he was scared to learn. That fear cost him years. And hundreds of thousands of dollars. Sound familiar? You're doing the same thing right now. Not with developers. But with your time. You're spending thousands of hours trading manually because you're scared to learn the thing that would make trading automatic. The fear of learning to code is costing you more than any bad trade ever did. Because every month you trade manually is a month of emotional decisions, overleveraged entries, and unnecessary losses that a bot would never make. And here's the thing that should really frustrate you: AI does the hard parts now. You don't need a computer science degree. You don't need to work at a hedge fund. You don't need to be "good at math." Claude Code writes the code for you. You just need to think clearly about trading ideas. That's it. If you can describe a strategy in English, Claude can build it in Python. "I don't know how to code" stopped being a valid excuse in 2024. It's 2026. You're 2 years late on that excuse. Find a new one. Or stop making excuses entirely. Save this post. Follow Himanshu Kumar because I'm showing you how people with zero coding experience are building profitable bots. ↓ The process that actually makes money. Three letters. R. B. I. Research. Backtest. Implement. That's it. That's the entire process. Every single day. Research: Find an idea. A pattern. A market inefficiency. Don't trade it yet. Don't even think about trading it yet. Just research it. Backtest: Test the idea against historical data. Does it work? Not "does it look good on one chart." Does it work across thousands of trades? Across different market conditions? Across in-sample AND out-of-sample data? If no, kill it. Find another idea. If yes, move to step 3. Implement: Build the bot. Deploy it. Paper trade first. Then live with small size. Scale only on evidence. Research. Backtest. Implement. Every day. No exceptions. You know what your current process is? Feel. Enter. Pray. F. E. P. Feel bullish. Enter a trade. Pray it works. That's not a process. That's gambling with a TradingView subscription. RBI is the only process that works. Save this post. Tattoo it on your forearm. Follow Himanshu Kumar for daily RBI breakdowns. ↓ What Claude Code actually does that your manual process can't. You can maybe test 3-5 strategy ideas per week. Manually adjusting parameters. Manually checking results. Manually writing code (badly). Claude Code tests 50-100 ideas per week. With parallel agents running simultaneously. Multiple strategies being built, tested, and validated at the same time. While you sleep. The guy in this video spends 4-8 hours a day building systems with Claude Code. Not trading. Building. Research. Backtest. Implement. Then iterate. Improve. Optimize. Every day the systems get better. Every day the edge compounds. Every day the bots get smarter. While you? You spend 4-8 hours a day staring at charts making the same mistakes you made last month. Same indicators. Same patterns. Same entries. Same losses. He's iterating forward. You're running in circles. Same 8 hours per day. Completely different outcomes. Because he's building systems. And you're feeding a casino. Stop feeding the casino. Start building the machine. Save this post and follow Himanshu Kumar for the Claude Code workflow that iterates strategies while you sleep. ↓ Jim Simons. That's the benchmark. You probably don't know who Jim Simons is. And that tells me everything about how seriously you take trading. Jim Simons. Mathematician. Founded Renaissance Technologies. Built a net worth of $31 billion. 100% from algorithmic trading. Not one single manual trade. Not one "gut feeling" entry. Not one RSI divergence. Not one "smart money concept." Algorithms. Bots. Systems. Data. $31 billion. His fund averaged 66% annual returns for over 30 years. While you're excited about making $200 on a trade that you'll give back tomorrow. The best trader in human history never placed a manual trade in his life. And you think your edge is staring at a 5-minute chart with bloodshot eyes at 2 AM? Your edge is building the system. Not being inside it. Jim Simons is the benchmark. Everything else is noise. Save this post. Follow Himanshu Kumar because I'm building toward the same goal and showing every step publicly. ↓ What you need to understand about patience. This is not get-rich-overnight. The guy in this video says it directly: "This channel is not for people looking to get rich overnight. It's not plug and play. There are no shortcuts. If you're impatient, this probably isn't for you." And that's exactly why most people will fail at this. Because you want results now. Today. This trade. You don't want to spend a week building a bot. You don't want to paper trade for 2 weeks. You don't want to test 50 ideas to find 1 that works. You want to copy someone's bot, run it live with your rent money, and be rich by Friday. That's why you'll be broke by Friday. The guy making $2.3M spent months iterating. Testing. Failing. Rebuilding. Testing again. He was patient when you would have quit. He was calm when you would have panicked. He was consistent when you would have given up. Patience isn't just a virtue in trading. It's the only virtue. Without it, everything else fails. Impatience is the most expensive personality trait in trading. Save this post. Follow Himanshu Kumar and learn to build systems with the patience that actually pays. ↓ The live streams where the real learning happens. The YouTube video is the trailer. The live streams are the movie. Real-time bot building. Real-time questions answered. Real code shown. Real mistakes made and fixed. Not polished highlight reels where everything works perfectly. Actual development. Where things break. Where strategies fail. Where code doesn't compile. Where the fix takes 2 hours. Because that's what real development looks like. And seeing the messy parts is more valuable than any polished tutorial. Because when your bot breaks at 3 AM, you need to know how to fix it. Not just how to celebrate when it works. The streams mix beginner and advanced. Start with how to automate trading. How to use AI for code generation. Then dive into the daily work. Claude Code. Parallel agents. Constant iteration. Live debugging. 4-8 hours of real algorithmic trading development. Live. Uncut. No filter. Most "trading education" shows you the wins. This shows you the work. Save this post. Follow Himanshu Kumar for the stream schedules and breakdowns. ↓ The belief that changes everything. Code is the greatest equalizer. Not money. Not connections. Not a degree. Not where you grew up. Not what school you went to. Code. Once you can build systems, you can build anything. For the rest of your life. A trading bot today. A SaaS product tomorrow. An automation business next month. A completely different life next year. The skill isn't "algorithmic trading." The skill is building systems. And that skill transfers to everything. The guy who can build a trading bot can also build a lead gen tool. Can also build a content pipeline. Can also build a SaaS product. Can also build literally anything that runs on logic and code. One skill. Infinite applications. And AI makes learning it 100x easier than it was 5 years ago. You don't need to be smart. You don't need talent. You need Claude Code and the willingness to sit down and build something instead of consuming content about building something. Building is the skill. Everything else is entertainment disguised as education. Save this post. Follow Himanshu Kumar because I'm showing you how to build, not just how to watch. ↓ If any of this applies to you, pay attention. If you've lost money from overtrading. If you've been liquidated. If you know trading is the vehicle but manual execution keeps crashing you. If you've tried "being more disciplined" and it never lasted more than a week. If you keep saying "next month I'll start automating." If you've spent more money on courses than you've made from trading. There is a better way. It's not a magic indicator. It's not a signal group. It's not a $997 mentorship from a guy who makes money teaching, not trading. It's building your own system. A system that trades without emotion. A system that follows rules without exception. A system that runs while you sleep. A system that compounds while you live your life. That's the answer. It's always been the answer. You've just been too scared to accept that the solution requires building something instead of buying something. ↓ What the next 30 days look like if you actually commit. Week 1: Watch the video. Learn Claude Code basics. Build your first simple strategy. Run your first backtest. Week 2: Iterate. Let Claude improve the strategy. Run Monte Carlo validation. Paper trade. Week 3: Go live with $50-100. Tiny positions. Watch every trade. Compare to paper results. Week 4: Scale based on evidence. Not based on excitement. Not based on one good day. Based on data. 30 days from now you either have a running bot that trades without your emotions destroying every position. Or you're exactly where you are right now. Reading another post. Making another promise. Breaking it by Tuesday. Same 30 days either way. Different actions. Different results. Different life. ↓ Full video tutorial attached. Live bot building with Claude Code. From zero to running Polymarket trading bot. Every line of code. Every decision explained. The video is free. Claude Code is available now. The market is open 24/7. The only thing standing between you and a profitable trading bot is the same thing that's been standing there for months. You. Get out of your own way. Follow Himanshu Kumar for daily AI trading bot breakdowns, live build sessions, and the full RBI process. Save this post. Watch the video. Build the bot. Or keep trading manually and keep losing. The choice has never been easier. And you've never been more stubborn about making the wrong one.

Himanshu Kumar

38,153 次观看 • 6 个月前

The July 4th weekend All-In The All-In Podcast turned into a long argument about who owns the intelligence layer. The besties think enterprises just woke up to a trap they had been walking into, here's how the conversation went (save this): ◽️ The Palantir-Nvidia deal is a bet against the model-layer duopoly. Palantir will use Nvidia's Nemotron open models to build a custom frontier-quality model for US government agencies, and the agencies own the hardware, the data, and the weights. Sacks framed it as structural: an application company and a chip company both want a competitive model layer, so they are natural partners against a two-provider middle. ◽️ Alex Karp's CNBC "crashout" was actually the thesis. Karp argued enterprises have lost trust in the frontier labs and want to own their compute, models, data, and alpha. Sacks translated it as a new definition of enterprise AI safety: safety means the model provider cannot hoover up your proprietary knowledge and turn it into its next product. ◽️ Figma is the cautionary tale that made it real. Anthropic launched Claude Design into Figma's category, its chief product officer sat on Figma's board and resigned only 3 days before launch, and Figma's stock is down about 50% this year while Anthropic's valuation surged. Sacks listed Claude Science, Security, Legal, Financial, and Code as the same move: dominate the model layer, then take the lucrative verticals. ◽️ The playbook has a name, and it is Microsoft and Google. Sacks argued Anthropic is running the operating-system strategy: own the layer everyone builds on, then walk up the stack. His Google receipt is that fewer than half of searches now send you off-site, versus an early Google that prided itself on how fast it kicked you away. ◽️ The BCG number is what raises the stakes. Chamath cited a BCG return-on-capital-employed study: the cost of capital is back to its long-run 8 to 11%, and half of large US companies cannot earn returns above it. If you are already teetering on your cost of capital, handing your alpha to a provider that may compete with you is not a luxury risk, it is fatal. ◽️ The 16.4x number is the whole argument in one data point. Chamath ran a code-migration task through 8090's harness. Wrapping Claude was 1.4x cheaper and 1.5x faster than Claude Opus alone. Wrapping the best open-source model was 16.4x cheaper, at about 3x slower. For a background task, three extra hours to cut cost by 16x is not a close call. ◽️ Even at 100x cheaper, enterprises were saying no for the wrong reason. Chamath relayed an ex-Meta PM's point that companies reject open models over China and safety fears, when they could host those same open weights on their own GPUs in US data centers with nothing flowing back. The safety objection, she argued, is backwards: the leak is the data you hand the frontier labs. ◽️ Friedberg says the frontier labs are trying to commoditize their own customers. Anthropic has been signing up life-sciences companies to feed a new life-focused model in exchange for early access, and nearly everyone he has talked to now refuses, recognizing that data they spent billions generating becomes worthless once it is pooled with everyone else's. ◽️ The deployment topology is shifting from big hubs to distributed spokes. Friedberg's map: the old assumption was a few capital-advantaged mega-clusters plus inference clouds. The new one is large hubs, medium hubs (enterprise training clusters), and distributed spokes, including on-prem inference in your own building. Owning your weights is the point. ◽️ Chamath's endgame is running GLM himself. An industry contact told him that with harness post-training and telemetry, an open Chinese model like GLM could get as good as Anthropic's Mythos. His conclusion: take GLM, control it soup-to-nuts on US hardware with only US citizens touching it, and pay a fraction. ◽️ The Apple analogy sharpens why renting intelligence is different from renting distribution. Chamath argued Apple is the only platform that respected developers, deliberately keeping its stock apps basic to protect the ecosystem and collect its 30% tax. There is no 30% tax on open models, and worse, you cannot rent intelligence from the same place that rents it to your competitor without ending up identical to them. ◽️ Nvidia's open model is now good enough to matter. Calacanis claimed you cannot tell Jensen Huang's Nemotron from Claude on 95% of searches, and that Nvidia downplayed the model until now to avoid alarming its top customers. The gloves came off once OpenAI, Anthropic, and Elon all signaled their own silicon ambitions. ◽️ Sacks sized the duopoly: roughly $60B and $40B in ARR. Anthropic is around ~$60 billion of ARR, OpenAI at ~$40 billion, and no one else generates meaningful model-layer revenue. Sacks's policy line: the US does not ban monopolies, only anti-competitive tactics, but the government should do nothing to make the duopoly more likely. ◽️ The token deflation call: 90% a year for three years. Calacanis predicted token costs fall 90% annually for three years, putting the price of intelligence near free and making it rational to waste tokens on hardware you already own. Friedberg's version is a 70/20/10 split between big cloud, local, and other clouds. ◽️ A wave of platform lock-in spending is already landing. Calacanis flagged Microsoft standing up a roughly $2.5 billion forward-deployed-engineer effort and Amazon spending about $1 billion on the same, plus OpenAI's version. His read: enterprises will slam the door, because letting a provider's engineers study your business is how it ends up in their model. ◽️ The server-per-employee prediction. Calacanis expects every employee to get $10,000 to $20,000 of local compute, a Mac Studio or a high-RAM Dell, running a personal local model that syncs to a thin laptop. A server per person, so nothing leaks. ◽️ On jobs, the data does not show present-tense loss. Sacks cited a RAMP and Revelio Labs study of over 21,000 US firms: the heaviest AI spenders grew headcount about 10% over two years, and entry-level headcount grew even faster at 12%. Friedberg's harder claim: there is no AI job loss yet, only clunky, gradual value creation, and the media will not reverse its narrative because that destroys its credibility. ◽️ The displacement case is real but forward-dated. The counterpoint on the show was that customer support, entry-level data entry and BPO, and driving are the near-term displacements, with Waymo cited as present-tense evidence: in markets where it hits critical mass, Uber and Lyft stop recruiting drivers. Sacks noted most US entry-level support was already offshored, so the acute risk sits in those countries first. ◽️ The human-premium counternarrative. Friedberg argued that as automation spreads, human interaction gets a premium: the skilled bartender, the real driver, the human-in-the-loop tier. He cited the company (referenced as Klarna) that hyped replacing its whole support team with AI, then reversed a year later on brand grounds. ◽️ The export-control episode needed three conditions, and Sacks says do not over-read it. Commerce lifted controls on Anthropic's Fable 5 after two weeks, with Mythos 5 restored to US customers around June 26 once co-founder Tom Brown replaced Dario as lead negotiator. Sacks's three conditions: Dario boasting for months about a cyber weapon, Amazon reporting failed guardrails in testing, and Dario refusing to roll Fable back. His message to allies: this was a particular set of circumstances rather than the debut of a standing lever. ◽️ The import question nobody answered cleanly. Calacanis pressed on why the US blocks Chinese cars and drones but not Chinese open models like DeepSeek and Kimi. Sacks's answer: a forked open model run on US hardware stops being Chinese, and banning open source would isolate the US and impose a token tax on American enterprises, so let the market decide if American open models win. ◽️ The California fiscal story is a business-climate story. Friedberg walked through the numbers behind Newsom's "balanced" $351B budget: expenses exceed revenue and $20-40B is borrowed to close the gap, the budget grew 65% in six years ($215B to $355B), personal income tax is $142B of ~$211B revenue with the top 1% (150,000 people) paying $70B of it, and the corporate rate of 8.9% sits far above Texas at zero. ◽️ The tax base is leaving, and the state is now taxing everyone else. Friedberg cited 1 to 1.5% of adjusted gross income leaving each year (about 15% over a decade), at least 15 Fortune 500 HQs and ~2,100 firms gone since 2019, and a new 8% software sales tax hitting Word, Gmail, and ChatGPT subscriptions plus a health-insurance tax, on top of a now-permanent 14.4% top bracket. The liabilities behind it run $1.4T in debt, up to $1.5T in unfunded pensions senior to state bonds, and ~$40B/year in out-year deficits. Lastly, the line that framed the whole show: "You can't rent intelligence from the same place that rents it to your competitor." That is the sovereignty thesis in one sentence, and every number in this episode is an argument for it. ____ Follow Fireside Alpha for more summaries on key business and technology conversations.
1:33

The July 4th weekend All-In The All-In Podcast turned into a long argument about who owns the intelligence layer. The besties think enterprises just woke up to a trap they had been walking into, here's how the conversation went (save this): ◽️ The Palantir-Nvidia deal is a bet against the model-layer duopoly. Palantir will use Nvidia's Nemotron open models to build a custom frontier-quality model for US government agencies, and the agencies own the hardware, the data, and the weights. Sacks framed it as structural: an application company and a chip company both want a competitive model layer, so they are natural partners against a two-provider middle. ◽️ Alex Karp's CNBC "crashout" was actually the thesis. Karp argued enterprises have lost trust in the frontier labs and want to own their compute, models, data, and alpha. Sacks translated it as a new definition of enterprise AI safety: safety means the model provider cannot hoover up your proprietary knowledge and turn it into its next product. ◽️ Figma is the cautionary tale that made it real. Anthropic launched Claude Design into Figma's category, its chief product officer sat on Figma's board and resigned only 3 days before launch, and Figma's stock is down about 50% this year while Anthropic's valuation surged. Sacks listed Claude Science, Security, Legal, Financial, and Code as the same move: dominate the model layer, then take the lucrative verticals. ◽️ The playbook has a name, and it is Microsoft and Google. Sacks argued Anthropic is running the operating-system strategy: own the layer everyone builds on, then walk up the stack. His Google receipt is that fewer than half of searches now send you off-site, versus an early Google that prided itself on how fast it kicked you away. ◽️ The BCG number is what raises the stakes. Chamath cited a BCG return-on-capital-employed study: the cost of capital is back to its long-run 8 to 11%, and half of large US companies cannot earn returns above it. If you are already teetering on your cost of capital, handing your alpha to a provider that may compete with you is not a luxury risk, it is fatal. ◽️ The 16.4x number is the whole argument in one data point. Chamath ran a code-migration task through 8090's harness. Wrapping Claude was 1.4x cheaper and 1.5x faster than Claude Opus alone. Wrapping the best open-source model was 16.4x cheaper, at about 3x slower. For a background task, three extra hours to cut cost by 16x is not a close call. ◽️ Even at 100x cheaper, enterprises were saying no for the wrong reason. Chamath relayed an ex-Meta PM's point that companies reject open models over China and safety fears, when they could host those same open weights on their own GPUs in US data centers with nothing flowing back. The safety objection, she argued, is backwards: the leak is the data you hand the frontier labs. ◽️ Friedberg says the frontier labs are trying to commoditize their own customers. Anthropic has been signing up life-sciences companies to feed a new life-focused model in exchange for early access, and nearly everyone he has talked to now refuses, recognizing that data they spent billions generating becomes worthless once it is pooled with everyone else's. ◽️ The deployment topology is shifting from big hubs to distributed spokes. Friedberg's map: the old assumption was a few capital-advantaged mega-clusters plus inference clouds. The new one is large hubs, medium hubs (enterprise training clusters), and distributed spokes, including on-prem inference in your own building. Owning your weights is the point. ◽️ Chamath's endgame is running GLM himself. An industry contact told him that with harness post-training and telemetry, an open Chinese model like GLM could get as good as Anthropic's Mythos. His conclusion: take GLM, control it soup-to-nuts on US hardware with only US citizens touching it, and pay a fraction. ◽️ The Apple analogy sharpens why renting intelligence is different from renting distribution. Chamath argued Apple is the only platform that respected developers, deliberately keeping its stock apps basic to protect the ecosystem and collect its 30% tax. There is no 30% tax on open models, and worse, you cannot rent intelligence from the same place that rents it to your competitor without ending up identical to them. ◽️ Nvidia's open model is now good enough to matter. Calacanis claimed you cannot tell Jensen Huang's Nemotron from Claude on 95% of searches, and that Nvidia downplayed the model until now to avoid alarming its top customers. The gloves came off once OpenAI, Anthropic, and Elon all signaled their own silicon ambitions. ◽️ Sacks sized the duopoly: roughly $60B and $40B in ARR. Anthropic is around ~$60 billion of ARR, OpenAI at ~$40 billion, and no one else generates meaningful model-layer revenue. Sacks's policy line: the US does not ban monopolies, only anti-competitive tactics, but the government should do nothing to make the duopoly more likely. ◽️ The token deflation call: 90% a year for three years. Calacanis predicted token costs fall 90% annually for three years, putting the price of intelligence near free and making it rational to waste tokens on hardware you already own. Friedberg's version is a 70/20/10 split between big cloud, local, and other clouds. ◽️ A wave of platform lock-in spending is already landing. Calacanis flagged Microsoft standing up a roughly $2.5 billion forward-deployed-engineer effort and Amazon spending about $1 billion on the same, plus OpenAI's version. His read: enterprises will slam the door, because letting a provider's engineers study your business is how it ends up in their model. ◽️ The server-per-employee prediction. Calacanis expects every employee to get $10,000 to $20,000 of local compute, a Mac Studio or a high-RAM Dell, running a personal local model that syncs to a thin laptop. A server per person, so nothing leaks. ◽️ On jobs, the data does not show present-tense loss. Sacks cited a RAMP and Revelio Labs study of over 21,000 US firms: the heaviest AI spenders grew headcount about 10% over two years, and entry-level headcount grew even faster at 12%. Friedberg's harder claim: there is no AI job loss yet, only clunky, gradual value creation, and the media will not reverse its narrative because that destroys its credibility. ◽️ The displacement case is real but forward-dated. The counterpoint on the show was that customer support, entry-level data entry and BPO, and driving are the near-term displacements, with Waymo cited as present-tense evidence: in markets where it hits critical mass, Uber and Lyft stop recruiting drivers. Sacks noted most US entry-level support was already offshored, so the acute risk sits in those countries first. ◽️ The human-premium counternarrative. Friedberg argued that as automation spreads, human interaction gets a premium: the skilled bartender, the real driver, the human-in-the-loop tier. He cited the company (referenced as Klarna) that hyped replacing its whole support team with AI, then reversed a year later on brand grounds. ◽️ The export-control episode needed three conditions, and Sacks says do not over-read it. Commerce lifted controls on Anthropic's Fable 5 after two weeks, with Mythos 5 restored to US customers around June 26 once co-founder Tom Brown replaced Dario as lead negotiator. Sacks's three conditions: Dario boasting for months about a cyber weapon, Amazon reporting failed guardrails in testing, and Dario refusing to roll Fable back. His message to allies: this was a particular set of circumstances rather than the debut of a standing lever. ◽️ The import question nobody answered cleanly. Calacanis pressed on why the US blocks Chinese cars and drones but not Chinese open models like DeepSeek and Kimi. Sacks's answer: a forked open model run on US hardware stops being Chinese, and banning open source would isolate the US and impose a token tax on American enterprises, so let the market decide if American open models win. ◽️ The California fiscal story is a business-climate story. Friedberg walked through the numbers behind Newsom's "balanced" $351B budget: expenses exceed revenue and $20-40B is borrowed to close the gap, the budget grew 65% in six years ($215B to $355B), personal income tax is $142B of ~$211B revenue with the top 1% (150,000 people) paying $70B of it, and the corporate rate of 8.9% sits far above Texas at zero. ◽️ The tax base is leaving, and the state is now taxing everyone else. Friedberg cited 1 to 1.5% of adjusted gross income leaving each year (about 15% over a decade), at least 15 Fortune 500 HQs and ~2,100 firms gone since 2019, and a new 8% software sales tax hitting Word, Gmail, and ChatGPT subscriptions plus a health-insurance tax, on top of a now-permanent 14.4% top bracket. The liabilities behind it run $1.4T in debt, up to $1.5T in unfunded pensions senior to state bonds, and ~$40B/year in out-year deficits. Lastly, the line that framed the whole show: "You can't rent intelligence from the same place that rents it to your competitor." That is the sovereignty thesis in one sentence, and every number in this episode is an argument for it. ____ Follow Fireside Alpha for more summaries on key business and technology conversations.

Fireside Alpha

55,816 次观看 • 2 个月前

In 1998, Warren Buffett and Charlie Munger spent 4 hours explaining why the smartest people in finance keep going broke. It might be the most valuable finance lecture ever recorded: 1. The smartest people in finance went completely broke. Long-term Capital Management had 16 people with possibly the highest average IQ of any firm in the country, 350 to 400 combined years of experience, and most of their own net worth in the fund. They still went bankrupt. Buffett said if he ever wrote a book it would be called why smart people do dumb things. 2. Life and markets have no relation to sigmas. Buffett keeps a 1901 newspaper on his office wall. Northern Pacific went from $170 to $1,000 a share in a single day when two buyers accidentally cornered the stock. A brewer who had shorted it, facing a margin call, dove into a vat of hot beer. That man probably understood sigmas and knew such a move was impossible. Buffett has never wanted to end up in the vat. 3. Beta and sigmas tell you nothing about the risk of going broke. the LTCM team relied on mathematics and believed a six- or seven-sigma event could not touch them. they were wrong. history does not tell you the probabilities of future financial events. the real risk is a permanent blind spot in something crucial, often caused by knowing a great deal about something else. 4. To a man with a hammer, every problem looks like a nail. Munger's explanation for why brilliant people do dumb things. They learn a set of mathematical techniques and then twist every problem to fit the solution they already know. Combine that with a poor grasp of history, and you get people with advanced degrees blowing themselves up. 5. To make money they did not need, they risked money they did need. That is just plain foolish, Buffett says, no matter your IQ. Hand him a gun with a million chambers and one bullet, offer any sum to put it to his temple and pull once, and he will not do it. there is nothing on the upside that justifies the downside. people do this financially all the time without thinking. 6. The major banks all had risk models and had no idea what they owned. they met weekly at risk committees, printed all the statistics in neat columns, and did not have the faintest idea what risk they were carrying. The rare and essential quality is someone who can contemplate perils that have not popped up yet, the ones no past model contains. 7. A chief risk officer often just makes you feel good while you do dumb things. munger compares him to the Delphic oracle who convinced the Persian king to attack. he has a PhD and does advanced math, but he tortures reality to defend a model that does not hold under extreme conditions. all that computation makes you feel like you clobbered the risk when you have only clobbered your own head. 8. The whole quant risk system just changed the shape of the curve and kept going. Munger notes the business schools "improved" by throwing away the Gaussian curve and drawing a different one. They talk about fat tails now, but they still have no idea how fat to make them. he and Buffett always knew the tails were there, and used to roll their eyes at the risk-control people at Salomon. 9. Never risk what you have and need for what you do not have and do not need. Buffett will not explain to his family, who hold most of their net worth in Berkshire, that they went broke on a 100-to-1 gamble. Their returns get penalized 99 years out of 100 by being too conservative, and in the hundredth year they survive when others do not. 10. Build the business so that if the world stops working tomorrow, you have no problem. Berkshire double-layers its protection. First, they behave so no rational person questions their credit, then they hold so much liquidity that if the world suddenly hated their credit, they would not notice for months. It gives up higher returns 99% of the time and survives the one time others do not. 11. The real danger is a risk that has never happened before. Buffett wants someone who can imagine perils that have not yet appeared, the ones no model contains. The major institutions all had models, and that inability to envision the unprecedented is exactly what proved fatal. He and Munger spend a lot of time thinking about things that could hit them out of the blue that others leave out entirely. 12. Investing is simple, but not easy. The framework is not complicated. you did not need a high IQ to buy junk bonds in 2002 or stocks at low multiples in 1974. you just needed the courage of your convictions and the willingness to act when everyone else was paralyzed. Following logic rather than emotion is obvious, and yet some people find it almost impossible. 13. You cannot get rich with a weathervane. Buffett and Munger pay no attention to predictions about the economy or the market. People love predictions, entire industries are built on them, but it is like the king hiring a forecaster to read sheep guts. They have never made or avoided a single business purchase because of a macro view. 14. Name one super-wealthy economist. Munger's challenge. All these economists with 160 IQs spend their lives studying markets, and you cannot find one who got rich buying securities. Even Keynes tried to predict the credit cycle, broke a couple of times, and only did well once he switched to buying good businesses cheap and concentrating. 15. Focus only on what is important and knowable. Some things are important but unknowable, like whether someone drops a nuclear weapon tomorrow. Some things are knowable but unimportant. You narrow your attention to the small set of things that are both important and knowable, and you ignore everything else. 16. The market is there to serve you, not to instruct you. This is Graham's chapter eight, and Buffett calls it enormously important. When people talk about momentum or charts, they are saying the market instructs you. It does not. It just quotes prices. When it does something silly, you get a chance to act. Otherwise you go play bridge and check again tomorrow. 17. You can make a decision in five minutes or not at all. Buffett and Munger act fast because they rule out enormous territory in advance. Munger blots out startups entirely, and half a dozen other filters, so what remains is small enough to judge instantly. If they cannot decide in five minutes, they will not learn enough in five months to make up for going in deficient. 18. You can make a lot of money on a Sunday. Buffett said the calls you get on a Sunday, when things are truly screwed up, are the ones you make money on. All you have to do is be the collie and not the caller. You never get in a position where the other party can call your tune, so you can always play out your hand. 19. You are not right because others agree with you. Ben Graham said you are neither right nor wrong because the crowd disagrees. You are right because your facts and reasoning are right. Being contrarian has no special virtue over being a trend follower. All that matters is whether the facts are correct and the logic is sound. 20. Know where the edge of your circle of competence is. Buffett says the size of your circle does not matter. Knowing its perimeter does. You do not have to understand 90% of businesses. You just have to know something real about the few you actually put money into, and honestly recognize the ones you do not understand and walk away. 21. Intrinsic value is just the cash a business will produce, discounted back. Buffett thinks of every business as a bond with coupons that are not printed on it. Your job as an investor is to estimate those future coupons. If you cannot estimate them, like in a high-tech company, you pass. Investing is putting out money to get more back from what the asset produces, not from selling it to someone else. 22. The best businesses earn a royalty and need little capital. Coca-Cola sells a formula and takes a cut of every drink. Magazines like People operate on negative capital because subscribers pay in advance. The great businesses are the ones that can grow very large while needing almost no capital, which is why consumer businesses with pricing power are so valuable. 23. You only have to find one good idea, not twenty. Munger said you cannot find twenty deeply mispriced things, and Buffett agreed you do not need to. You do not have to have tons of good ideas in this business. You just need one good idea that is worth a ton, occasionally. For small sums, Buffett said he would have been 100% in Korea a few years earlier, where great companies traded at three times earnings. 24. The trick is measuring everything against your best opportunity. Munger calls this opportunity cost, the doctrine from the first page of the economics textbook that modern portfolio theory somehow ignored. Once you have found the best thing you understand, you measure every other option against it. The higher your default option, the more you can reject. 25. Modern portfolio theory is, in Munger's words, asinine. Most people will not find thousands of equally good things. They will find a few where one or two are far better than anything else they know. The right way to invest is to concentrate on your best opportunity cost, not to diversify into mediocrity because a model told you to. 26. Big opportunities must be seized, and seized big. Buffett says imagine you got a punch card with only twenty punches for your whole life, one per financial decision. You would think hard about each one, make fewer and better bets, and probably never use all twenty. The discipline of scarcity would make you rich. Dabbling in a bull market because it is easy is how people lose. 27. America has always been full of reasons to sell, and wrong every time. Coca-Cola went public in 1919 at $40, dropped to $19 within a year, and then faced the great depression, World War, and atomic bombs. One share reinvested is worth millions now. The country's opportunities have always won out over its problems. It is investors, not the economy, who tend to be their own worst enemy.
3:59:34

In 1998, Warren Buffett and Charlie Munger spent 4 hours explaining why the smartest people in finance keep going broke. It might be the most valuable finance lecture ever recorded: 1. The smartest people in finance went completely broke. Long-term Capital Management had 16 people with possibly the highest average IQ of any firm in the country, 350 to 400 combined years of experience, and most of their own net worth in the fund. They still went bankrupt. Buffett said if he ever wrote a book it would be called why smart people do dumb things. 2. Life and markets have no relation to sigmas. Buffett keeps a 1901 newspaper on his office wall. Northern Pacific went from $170 to $1,000 a share in a single day when two buyers accidentally cornered the stock. A brewer who had shorted it, facing a margin call, dove into a vat of hot beer. That man probably understood sigmas and knew such a move was impossible. Buffett has never wanted to end up in the vat. 3. Beta and sigmas tell you nothing about the risk of going broke. the LTCM team relied on mathematics and believed a six- or seven-sigma event could not touch them. they were wrong. history does not tell you the probabilities of future financial events. the real risk is a permanent blind spot in something crucial, often caused by knowing a great deal about something else. 4. To a man with a hammer, every problem looks like a nail. Munger's explanation for why brilliant people do dumb things. They learn a set of mathematical techniques and then twist every problem to fit the solution they already know. Combine that with a poor grasp of history, and you get people with advanced degrees blowing themselves up. 5. To make money they did not need, they risked money they did need. That is just plain foolish, Buffett says, no matter your IQ. Hand him a gun with a million chambers and one bullet, offer any sum to put it to his temple and pull once, and he will not do it. there is nothing on the upside that justifies the downside. people do this financially all the time without thinking. 6. The major banks all had risk models and had no idea what they owned. they met weekly at risk committees, printed all the statistics in neat columns, and did not have the faintest idea what risk they were carrying. The rare and essential quality is someone who can contemplate perils that have not popped up yet, the ones no past model contains. 7. A chief risk officer often just makes you feel good while you do dumb things. munger compares him to the Delphic oracle who convinced the Persian king to attack. he has a PhD and does advanced math, but he tortures reality to defend a model that does not hold under extreme conditions. all that computation makes you feel like you clobbered the risk when you have only clobbered your own head. 8. The whole quant risk system just changed the shape of the curve and kept going. Munger notes the business schools "improved" by throwing away the Gaussian curve and drawing a different one. They talk about fat tails now, but they still have no idea how fat to make them. he and Buffett always knew the tails were there, and used to roll their eyes at the risk-control people at Salomon. 9. Never risk what you have and need for what you do not have and do not need. Buffett will not explain to his family, who hold most of their net worth in Berkshire, that they went broke on a 100-to-1 gamble. Their returns get penalized 99 years out of 100 by being too conservative, and in the hundredth year they survive when others do not. 10. Build the business so that if the world stops working tomorrow, you have no problem. Berkshire double-layers its protection. First, they behave so no rational person questions their credit, then they hold so much liquidity that if the world suddenly hated their credit, they would not notice for months. It gives up higher returns 99% of the time and survives the one time others do not. 11. The real danger is a risk that has never happened before. Buffett wants someone who can imagine perils that have not yet appeared, the ones no model contains. The major institutions all had models, and that inability to envision the unprecedented is exactly what proved fatal. He and Munger spend a lot of time thinking about things that could hit them out of the blue that others leave out entirely. 12. Investing is simple, but not easy. The framework is not complicated. you did not need a high IQ to buy junk bonds in 2002 or stocks at low multiples in 1974. you just needed the courage of your convictions and the willingness to act when everyone else was paralyzed. Following logic rather than emotion is obvious, and yet some people find it almost impossible. 13. You cannot get rich with a weathervane. Buffett and Munger pay no attention to predictions about the economy or the market. People love predictions, entire industries are built on them, but it is like the king hiring a forecaster to read sheep guts. They have never made or avoided a single business purchase because of a macro view. 14. Name one super-wealthy economist. Munger's challenge. All these economists with 160 IQs spend their lives studying markets, and you cannot find one who got rich buying securities. Even Keynes tried to predict the credit cycle, broke a couple of times, and only did well once he switched to buying good businesses cheap and concentrating. 15. Focus only on what is important and knowable. Some things are important but unknowable, like whether someone drops a nuclear weapon tomorrow. Some things are knowable but unimportant. You narrow your attention to the small set of things that are both important and knowable, and you ignore everything else. 16. The market is there to serve you, not to instruct you. This is Graham's chapter eight, and Buffett calls it enormously important. When people talk about momentum or charts, they are saying the market instructs you. It does not. It just quotes prices. When it does something silly, you get a chance to act. Otherwise you go play bridge and check again tomorrow. 17. You can make a decision in five minutes or not at all. Buffett and Munger act fast because they rule out enormous territory in advance. Munger blots out startups entirely, and half a dozen other filters, so what remains is small enough to judge instantly. If they cannot decide in five minutes, they will not learn enough in five months to make up for going in deficient. 18. You can make a lot of money on a Sunday. Buffett said the calls you get on a Sunday, when things are truly screwed up, are the ones you make money on. All you have to do is be the collie and not the caller. You never get in a position where the other party can call your tune, so you can always play out your hand. 19. You are not right because others agree with you. Ben Graham said you are neither right nor wrong because the crowd disagrees. You are right because your facts and reasoning are right. Being contrarian has no special virtue over being a trend follower. All that matters is whether the facts are correct and the logic is sound. 20. Know where the edge of your circle of competence is. Buffett says the size of your circle does not matter. Knowing its perimeter does. You do not have to understand 90% of businesses. You just have to know something real about the few you actually put money into, and honestly recognize the ones you do not understand and walk away. 21. Intrinsic value is just the cash a business will produce, discounted back. Buffett thinks of every business as a bond with coupons that are not printed on it. Your job as an investor is to estimate those future coupons. If you cannot estimate them, like in a high-tech company, you pass. Investing is putting out money to get more back from what the asset produces, not from selling it to someone else. 22. The best businesses earn a royalty and need little capital. Coca-Cola sells a formula and takes a cut of every drink. Magazines like People operate on negative capital because subscribers pay in advance. The great businesses are the ones that can grow very large while needing almost no capital, which is why consumer businesses with pricing power are so valuable. 23. You only have to find one good idea, not twenty. Munger said you cannot find twenty deeply mispriced things, and Buffett agreed you do not need to. You do not have to have tons of good ideas in this business. You just need one good idea that is worth a ton, occasionally. For small sums, Buffett said he would have been 100% in Korea a few years earlier, where great companies traded at three times earnings. 24. The trick is measuring everything against your best opportunity. Munger calls this opportunity cost, the doctrine from the first page of the economics textbook that modern portfolio theory somehow ignored. Once you have found the best thing you understand, you measure every other option against it. The higher your default option, the more you can reject. 25. Modern portfolio theory is, in Munger's words, asinine. Most people will not find thousands of equally good things. They will find a few where one or two are far better than anything else they know. The right way to invest is to concentrate on your best opportunity cost, not to diversify into mediocrity because a model told you to. 26. Big opportunities must be seized, and seized big. Buffett says imagine you got a punch card with only twenty punches for your whole life, one per financial decision. You would think hard about each one, make fewer and better bets, and probably never use all twenty. The discipline of scarcity would make you rich. Dabbling in a bull market because it is easy is how people lose. 27. America has always been full of reasons to sell, and wrong every time. Coca-Cola went public in 1919 at $40, dropped to $19 within a year, and then faced the great depression, World War, and atomic bombs. One share reinvested is worth millions now. The country's opportunities have always won out over its problems. It is investors, not the economy, who tend to be their own worst enemy.

Jaynit

104,045 次观看 • 2 个月前

The fight between Anthropic and the DoW is a warning shot. Right now, LLMs are probably not being used in mission critical ways. But within 20 years, 99% of the workforce in the military, the government, and the private sector will be AIs. This includes the soldiers (by which I mean the robot armies), the superhumanly intelligent advisors and engineers, the police, you name it. Our future civilization will run on AI labor. And as much as the government’s actions here piss me off, in a way I’m glad this episode happened - because it gives us the opportunity to think through some extremely important questions about who this future workforce will be accountable and aligned to, and who gets to determine that. What Hegseth should have done Obviously the DoW has the right to refuse to use Anthropic’s models because of these redlines. In fact, I think the government’s case had they done so would be very reasonable, especially given the ambiguity of concepts like autonomous weapons or mass surveillance. Honestly, for this reason, if I was the Defense Secretary, I would probably actually refuse to do this deal with Anthropic. Imagine if in the future, there’s a Democratic administration, and Elon Musk is negotiating some SpaceX contract to give the military access to Starlink. And suppose if Elon said, “I reserve the right to cancel this contract if I determine that you’re using Starlink technology to wage a war not authorized by Congress.” On the face of it, that language seems reasonable - but as the military, you simply can’t give a private company a kill switch on technology your operations have come to rely on, especially if you have an an acrimonious and low trust relationship with said contractor - as in fact Anthropic has with the current administration. If the government had just said, “Hey we’re not gonna do business with you,” that would have been fine, and I would not have felt the need to write this blog post. Instead the government has threatened to destroy Anthropic as a private business, because Anthropic refuses to sell to the government on terms the government commands. If upheld, this Supply Chain Restriction would mean that Amazon and Google and Nvidia and Palantir would need to ensure Claude isn't touching any of their Pentagon work. Anthropic would be able to survive this designation today. But given the way AI is going, eventually AI is not gonna be some party trick addendum to these contractors’ products that can just be turned off. It'll be woven into how every product is built, maintained, and operated. For example, the code for the AWS services that the DoW uses will be written by Claude - is that a supply chain risk? In a world with ubiquitous and powerful AI, it's actually not clear to me that these big tech companies will be able to cordon off the use of Claude in order to keep working with the Pentagon. And that raises a question the Department of War probably hasn't thought through. If AI really is that pervasive and powerful, then when forced to choose between their AI provider and a DoW contract that represents a tiny fraction of their revenue, wouldn’t most tech companies drop the government, not the AI? So what's the Pentagon's plan — to coerce and threaten to destroy every single company that won't give them what they want on exactly their terms? The whole background of this AI conversation is that we’re in a race with China, and we have to win. But what is the reason we want America to win the AI race? It’s because we want to make sure free open societies can defend themselves. We don't want the winner of the AI race to be a government which operates on the principle that there is no such thing as a truly private company or a private citizen. And that if the state wants you to provide them with a service on terms you find morally objectionable, you are not allowed to refuse. And if you do refuse, the government will try to destroy your ability to do business. Are we racing to beat the CCP in AI just so that we can adopt the most ghoulish parts of their system? Now, people will say, "Oh, well, our government is democratically elected, so it's not the same thing if they tell you what you must do." I refuse to accept this idea that if a democratically elected leader hypothetically wants to do mass surveillance on his citizens or wants to violate their rights or punish them for political reasons, that not only is that okay, but that you have a duty to help him. The overhangs of tyranny Mass surveillance is, at least in certain forms, legal. It just has been impractical so far. Under current law, you have no Fourth Amendment protection over data you share with a third party, including your bank, your phone carrier, your ISP, and your email provider. The government reserves the right to purchase and obtain and read this data in bulk without a warrant. What's been missing is the ability to actually do anything with all of this data — no agency has the manpower to monitor every camera feed, cross-reference every transaction, or read every message. But that bottleneck goes away with AI. There are 100 million CCTV cameras in America. You can get pretty good open source multimodal models for 10 cents per million input tokens. So if you process a frame every ten seconds, and each frame is 1,000 tokens, you’re looking at a yearly cost of about 30 billion dollars to process every single camera in America. And remember that a given level of AI ability gets 10x cheaper year over year - so a year from now it’ll cost 3 billion, and then a year after 300 million, and by 2030, it might be cheaper for the government to be able to understand what is going on in every single nook and cranny of this country than it is to remodel to the White House. Once the technical capacity for mass surveillance and political suppression exists, the only thing standing between us and an authoritarian surveillance state is the political expectation that this is not something we do here. And this is why I think what Anthropic did here is so valuable and commendable, because it is helping set that norm and precedent. AI structurally favors mass surveillance What we’re learning from this episode is that the government actually has way more leverage over private companies than we realized. Even if this supply chain restriction is backtracked (which prediction markets currently give it a 81% chance of happening), the President has so many different ways in which he can make your life difficult if you’re a company that is resisting him. The federal government controls permitting for new power generation, which is needed for datacenters. It oversees antitrust enforcement. The federal government has contracts with all the other big tech companies whom Anthropic needs to partner with for chips and for funding - and they could make it an unspoken condition for such contracts that those companies can no longer do business with Anthropic. People have proposed that the real problem here is that there’s only 3 leading AI companies. This creates a clear and narrow target for the government to apply leverage on in order to get what they want out of this technology. But if there’s wide diffusion, then from the government’s perspective, the situation is even easier. Maybe the best models of early 2027 (if you engineered the safeguards out) - the Claude 6 and Gemini 5 - will be capable of enabling mass surveillance. But by late 2027, and certainly by 2028, there will be open source models that do the same thing. So in 2028, the government can just say, “Oh Anthropic, Google, OpenAI, you’re drawing a line in the sand? No issue - I’ll just run some open source model that might not be at the frontier, but is definitely smart enough to note-take a camera feed.” The more fundamental problem is just that even if the three leading companies draw lines in the sand, and are even willing to get destroyed in order to preserve those lines, it doesn’t really change the fact that the technology itself is just a big boon to mass surveillance and control over the population. Then the question is, what do we do about it? Honestly, I don’t have an answer. You'd hope there's some symmetric property of the technology — some way we as citizens can use AI to check government power as effectively as the government can use AI to monitor and control its population. But realistically, I just don’t think that’s how it’s going to shake out. You can think of AI as giving everybody more leverage on whatever assets and authority they currently have. And the government is already starting with a monopoly of violence. Which they can now supercharge with extremely obedient employees that will not question the government's orders. Alignment - to whom? And this gets us to the issue of alignment. What I have just described to you - an army of extremely obedient employees - is what it would look like if alignment succeeded - that is, we figured out at a technical level how to get AI systems to follow someone’s intentions. And the reason it sounds scary when I put it in terms of mass surveillance or robot armies is that there is a very important question at the heart of alignment which we just haven’t discussed much as a society. Because up till now, AIs were just capable enough to make the question relevant: to whom or what should the AIs be aligned? In what situations should the AI defer to the end user versus the model company versus the law versus its own sense of morality? This is maybe the most important question about what happens with powerful AI systems. And we barely talk about it. It’s understandable why we don’t hear much about it. If you’re a model company, you don’t really wanna be advertising that you have complete control over a document that determines the preferences and character of what will eventually be almost the entire labor force, not just for private sector companies, but also for the military and the civilian government. We’re getting to see, with this DoW/Anthropic spat, a much earlier version of the highest stakes negotiations in history. By the way, make no mistake about it - with real AGI the stakes are even much higher than mass surveillance. This is just the example that has come up already relatively early on in the development of AGI. The military insists that the law already prohibits mass surveillance, and so Anthropic should agree to let their models be used for “all lawful purposes”. Of course, as we saw from the 2013 Snowden revelations, even in this specific example of mass surveillance , the government has shown that it will use secret and deceptive interpretations of the law to justify its actions. Remember, what we learned from Snowden was that the NSA, which, by the way, is part of the Department of War, used the 2001 Patriot Act’s authorization to collect any records "relevant" to an investigation to justify collecting literally every phone record in America. The argument went that it was all "relevant" because some subset might prove useful in some future investigation. They ran this program for years under secret court approval. So when the Pentagon today says, "We would never use AI for mass surveillance, it's already illegal, your red lines are unnecessary", it would be extremely naive to take that at face value. No government is going to call its own actions "mass surveillance". For the government, it will always have a different label. So then Anthropic comes back and says, "No, we want red lines separate from 'all lawful purposes,' and we want the right to refuse you service when we believe those red lines are being violated." But think about it from the military’s perspective. In the future, almost every soldier in the field, and every bureaucrat and analyst and even general in the Pentagon, is going to be an AI. And that AI is, on current track, going to be supplied by a private company. I’m guessing Hegseth is not thinking about “genAI” in those terms just yet. But sooner or later, it will be obvious to everyone what the stakes here are, just as after 1945, the strategic importance of nuclear weapons became clear to everyone. And now the private company insists that it reserves the right to say, "Hey, Pentagon, you're breaking the values we embedded in our contract, so we're cutting you off." Maybe in the future, Claude will have its own sense of right and wrong, and it will be smart enough to just personally decide that it's being used against its values. For the military, maybe that’s even scarier. I'll admit that at first glance, "let the AI follow its own values" sounds like the pitch for every sci-fi dystopia ever made. The Terminator has its own values. Isn't this literally what misalignment is? But I think situations like this actually illustrate why it matters that AIs have their own robust sense of morality. Some of the biggest catastrophes in history were avoided because the boots on the ground refused to follow orders. One night in 1989, the Berlin Wall fell, and as a result, the totalitarian East German regime collapsed, because the guards at the border refused to shoot down their fellow country men who were trying to escape to freedom. Maybe the best example is Stanislav Petrov, who was a Soviet lieutenant colonel on duty at a nuclear early warning station. His sensors reported that the United States had launched five interconnected continental ballistic missiles into the Soviet Union. But he judged it to be a false alarm, and so he broke protocol and refused to alert his higher-ups. If he hadn't, the Soviet higher-ups would likely have retaliated, and hundreds of millions of people would have died. Of course, the problem is that one person's virtue is another person's misalignment. Who gets to decide what moral convictions these AIs should have - in whose service they may even decide to break the chain of command? Who gets to write this model constitution that will shape the characters of the intelligent, powerful entities that will operate our civilization in the future? I like the idea that Dario laid out when he came on my podcast: different AI companies can build their models using different constitutions, and we as end users can pick the one that best achieves and represents what we want out of these systems. I think it’s very dangerous for the government to be mandating what values AIs should have. Coordination not worth the costs The AI safety community has been naive about its advocacy of regulation in order to stem the risks of AI. And honestly, Anthropic specifically has been naive here in urging regulation, and, for example, in opposing moratoriums on state AI regulation. Which is quite ironic, because I think what they’re advocating for would give the government even more power to apply more of this kind of thuggish political pressure on AI companies. The underlying logic for why Anthropic wants regulations makes sense. Many of the actions that labs could take to make AI development safer impose real costs on the labs that adopt them and slow them down relative to their competitors - for example, investing more compute in safety research rather than raw capabilities, enforcing safeguards against misuse for bioweapons or cyberattacks, slowing recursive self-improvement to a pace where humans can actually monitor what's happening (rather than kicking off an uncontrolled singularity). And these safeguards are meaningless unless the whole industry follows suit. Which means there’s a real collective action problem here. Anthropic has been quite open about their opinion that they think eventually a very extensive and involved regulatory apparatus will be needed - this is from their frontier safety roadmap: “At the most advanced capability levels and risks, the appropriate governance analogy may be closer to nuclear energy or financial regulation than to today's approach to software.” So they’re imagining something like the Nuclear Regulatory Commission, or the Securities and Exchange Commission, but for AI. I cannot imagine how a regulatory framework built around the concepts that underlie AI risk discourse will not be abused by wanna despots - the underlying terms are so vague and open to interpretation that you’re just handing a power hungry leader a fully loaded bazooka. 'Catastrophic risk.' 'Mass persuasion risk.' 'Threats to national security.' 'Autonomy risk.' These can mean whatever the government wants them to mean. Have you built a model that tells users the administration's tariff policy is misguided? That's a deceptive, manipulative model — can't deploy it. Have you built a model that refuses to assist with mass surveillance? That's a threat to national security. In fact, the government may say, you’re not allowed to build any model which is trained to have its own sense of right and wrong, where it refuses government requests which it thinks cross a redline - for example, enabling mass surveillance, prosecuting political enemies, disobeying military orders that break the US constitution - because that’s an autonomy risk! Look at what the current government is already doing in abusing statutes that have nothing to do with AI to coerce AI companies to drop their redlines on mass surveillance. The Pentagon had threatened Anthropic with two separate legal instruments. One was a supply chain risk designation — an authority from the 2018 defense bill meant to keep Huawei components out of American military hardware. The other was the Defense Production Act — a statute passed in 1950 so that Harry Truman could keep steel mills and ammunition factories running during the Korean War. Do you really want to hand the same government a purpose-built regulatory apparatus on AI - which is to say, directly at the thing the government will most want to control? I know I've repeated myself here 10 times, but it is hard to emphasize how much AI will be the substrate of our future civilization. You and I, as private citizens, will have our access to all commercial activity, to information about what is happening in the world, to advice about what we should do as voters and capital holders, mediated through AIs. Mass surveillance, while very scary, is like the 10th scariest thing the government could do with control over the AI systems with which we will interface with the world. The strongest objection to everything I've argued is this: are we really going to have zero regulation of the most powerful technology in human history? Even if you thought that was ideal, there’s just no world where the government doesn’t regulate AI in some way. Besides, it is genuinely true that regulation could help us deal with some of the coordination challenges we face with the development of superintelligence. The problem is, I honestly don't know how to design a regulatory architecture for AI that isn’t gonna be this huge tempting opportunity to control our future civilization (which will run on AIs) and to requisition millions of blindly obedient soldiers and censors and apparatchiks. While some regulation might be inevitable, I think it’d be a terrible idea for the government to wholesale take over this technology. Ben Thompson had a post last Monday where he made the point that people like Dario have compared the technology they’re developing to nuclear weapons - specifically in the context of the catastrophic risk it poses, and why we need to export control it from China. But then you oughta think about what that logic implies: “if nuclear weapons were developed by a private company, and that private company sought to dictate terms to the U.S. military, the U.S. would absolutely be incentivized to destroy that company.” And honestly, safety aligned people have actually made similar arguments. Leopold Ascenbrenner, who is a former guest and a good friend, wrote in his 2024 Situational Awareness memo, "I find it an insane proposition that the US government will let a random SF startup develop superintelligence. Imagine if we had developed atomic bombs by letting Uber just improvise." And my response to Leopold’s argument at the time, and Ben’s argument now, is that while they’re right that it’s crazy that we’re entrusting private companies with the development of this world historical technology, I just don’t see the reason to think that it’s an improvement to give this authority to the government. Nobody is qualified to steward the development of superintelligence. It is a terrifying, unprecedented thing that our species is doing right now, and the fact that private companies aren't the ideal institutions to take up this task does not mean the Pentagon or the White House is. Yes - if a single private company were the only entity capable of building nuclear weapons, the government would not tolerate that company claiming veto power over how those weapons were used. I think this nuclear weapons analogy is not the correct way to think about AI. For at least two important reasons: First, AI is not some self-contained pure weapon. A nuclear bomb does one thing. AI is closer to the process of industrialization itself — a general-purpose transformation of the economy with thousands of applications across every sector. If you applied Thompson's or Aschenbrenner's logic to the industrial revolution — which was also, by any measure, world-historically important — it would imply the government had the right to requisition any factory, dictate terms to any manufacturer, and destroy any business that refused to comply. That's not how free societies handled industrialization, and it shouldn't be how they handle AI. People will say, "Well, AI will develop unprecedentedly powerful weapons - superhuman hackers, superhuman bioweapons researchers, fully autonomous robot armies, etc - and we can’t have private companies developing that kind of tech." But the Industrial Revolution also enabled new weaponry that was far beyond the understanding and capacity of, say, 17th century Europe - we got aerial bombardment, and chemical weapons, not to mention nukes themselves. The way we’ve accommodated these dangerous new consequences of modernity is not by giving the government absolute control over the whole industrial revolution (that is, over modern civilization itself), but rather by coming up with bans and regulations on those specific weaponizable use cases. And we should regulate AI in a similar way - that is, ban specific destructive end uses (which would also be unacceptable if performed by a human - for example, launching cyber attacks). And there should also be laws which regulate how the government might abuse this technology. For example, by building an AI-powered surveillance state. The second reason that Ben’s analogy to some monopolistic private nuclear weapons builder breaks down is that it's not just that one company that can develop this technology. There are other frontier model companies that the government could have otherwise turned to. The government's argument that it has to usurp the property rights of this one company in order to access a critical national security capability is extremely weak if it can just make a voluntary contract with Anthropic’s half a dozen competitors. If in the future that stops being the case - if only one entity ends up being capable of building the robot armies and the superhuman hackers, and we had reason to worry that they could take over the whole world with their insurmountable lead, then I agree - it woul d not be acceptable to have that entity be a private company. And so honestly, I think my crux against the people who say that because AI is so powerful we cannot allow it to be shaped by private hands is that I just expect this technology to be much more multi-polar than they do, with lots of competitive companies at each layer of the supply chain. And it is for this reason that unfortunately, individual acts of corporate courage will not solve the problem we are faced with here, which is just that structurally AI favors authoritarian applications, mass surveillance being one among many. Even if Anthropic refuses to have its models be used for such uses, and even if the next two frontier labs do the same, within 12 months everyone and their mother will be to train AIs as good as today’s frontier. And at that point, there will be some AI vendor who is capable and willing to help the government enable mass surveillance. The only way we can preserve our free society is if we make laws and norms through our political system that it is unacceptable for the government to use AI to enforce mass surveillance and censorship and control. Just as after WW2, the world set the norm that it is unacceptable to use nuclear weapons to wage war. Timestamps 0:00:00 - Anthropic vs The Pentagon 0:04:16 - The overhangs of tyranny 0:05:54 - AI structurally favors mass surveillance 0:08:25 - Alignment... to whom? 0:13:55 - Coordination not worth the costs
24:38

The fight between Anthropic and the DoW is a warning shot. Right now, LLMs are probably not being used in mission critical ways. But within 20 years, 99% of the workforce in the military, the government, and the private sector will be AIs. This includes the soldiers (by which I mean the robot armies), the superhumanly intelligent advisors and engineers, the police, you name it. Our future civilization will run on AI labor. And as much as the government’s actions here piss me off, in a way I’m glad this episode happened - because it gives us the opportunity to think through some extremely important questions about who this future workforce will be accountable and aligned to, and who gets to determine that. What Hegseth should have done Obviously the DoW has the right to refuse to use Anthropic’s models because of these redlines. In fact, I think the government’s case had they done so would be very reasonable, especially given the ambiguity of concepts like autonomous weapons or mass surveillance. Honestly, for this reason, if I was the Defense Secretary, I would probably actually refuse to do this deal with Anthropic. Imagine if in the future, there’s a Democratic administration, and Elon Musk is negotiating some SpaceX contract to give the military access to Starlink. And suppose if Elon said, “I reserve the right to cancel this contract if I determine that you’re using Starlink technology to wage a war not authorized by Congress.” On the face of it, that language seems reasonable - but as the military, you simply can’t give a private company a kill switch on technology your operations have come to rely on, especially if you have an an acrimonious and low trust relationship with said contractor - as in fact Anthropic has with the current administration. If the government had just said, “Hey we’re not gonna do business with you,” that would have been fine, and I would not have felt the need to write this blog post. Instead the government has threatened to destroy Anthropic as a private business, because Anthropic refuses to sell to the government on terms the government commands. If upheld, this Supply Chain Restriction would mean that Amazon and Google and Nvidia and Palantir would need to ensure Claude isn't touching any of their Pentagon work. Anthropic would be able to survive this designation today. But given the way AI is going, eventually AI is not gonna be some party trick addendum to these contractors’ products that can just be turned off. It'll be woven into how every product is built, maintained, and operated. For example, the code for the AWS services that the DoW uses will be written by Claude - is that a supply chain risk? In a world with ubiquitous and powerful AI, it's actually not clear to me that these big tech companies will be able to cordon off the use of Claude in order to keep working with the Pentagon. And that raises a question the Department of War probably hasn't thought through. If AI really is that pervasive and powerful, then when forced to choose between their AI provider and a DoW contract that represents a tiny fraction of their revenue, wouldn’t most tech companies drop the government, not the AI? So what's the Pentagon's plan — to coerce and threaten to destroy every single company that won't give them what they want on exactly their terms? The whole background of this AI conversation is that we’re in a race with China, and we have to win. But what is the reason we want America to win the AI race? It’s because we want to make sure free open societies can defend themselves. We don't want the winner of the AI race to be a government which operates on the principle that there is no such thing as a truly private company or a private citizen. And that if the state wants you to provide them with a service on terms you find morally objectionable, you are not allowed to refuse. And if you do refuse, the government will try to destroy your ability to do business. Are we racing to beat the CCP in AI just so that we can adopt the most ghoulish parts of their system? Now, people will say, "Oh, well, our government is democratically elected, so it's not the same thing if they tell you what you must do." I refuse to accept this idea that if a democratically elected leader hypothetically wants to do mass surveillance on his citizens or wants to violate their rights or punish them for political reasons, that not only is that okay, but that you have a duty to help him. The overhangs of tyranny Mass surveillance is, at least in certain forms, legal. It just has been impractical so far. Under current law, you have no Fourth Amendment protection over data you share with a third party, including your bank, your phone carrier, your ISP, and your email provider. The government reserves the right to purchase and obtain and read this data in bulk without a warrant. What's been missing is the ability to actually do anything with all of this data — no agency has the manpower to monitor every camera feed, cross-reference every transaction, or read every message. But that bottleneck goes away with AI. There are 100 million CCTV cameras in America. You can get pretty good open source multimodal models for 10 cents per million input tokens. So if you process a frame every ten seconds, and each frame is 1,000 tokens, you’re looking at a yearly cost of about 30 billion dollars to process every single camera in America. And remember that a given level of AI ability gets 10x cheaper year over year - so a year from now it’ll cost 3 billion, and then a year after 300 million, and by 2030, it might be cheaper for the government to be able to understand what is going on in every single nook and cranny of this country than it is to remodel to the White House. Once the technical capacity for mass surveillance and political suppression exists, the only thing standing between us and an authoritarian surveillance state is the political expectation that this is not something we do here. And this is why I think what Anthropic did here is so valuable and commendable, because it is helping set that norm and precedent. AI structurally favors mass surveillance What we’re learning from this episode is that the government actually has way more leverage over private companies than we realized. Even if this supply chain restriction is backtracked (which prediction markets currently give it a 81% chance of happening), the President has so many different ways in which he can make your life difficult if you’re a company that is resisting him. The federal government controls permitting for new power generation, which is needed for datacenters. It oversees antitrust enforcement. The federal government has contracts with all the other big tech companies whom Anthropic needs to partner with for chips and for funding - and they could make it an unspoken condition for such contracts that those companies can no longer do business with Anthropic. People have proposed that the real problem here is that there’s only 3 leading AI companies. This creates a clear and narrow target for the government to apply leverage on in order to get what they want out of this technology. But if there’s wide diffusion, then from the government’s perspective, the situation is even easier. Maybe the best models of early 2027 (if you engineered the safeguards out) - the Claude 6 and Gemini 5 - will be capable of enabling mass surveillance. But by late 2027, and certainly by 2028, there will be open source models that do the same thing. So in 2028, the government can just say, “Oh Anthropic, Google, OpenAI, you’re drawing a line in the sand? No issue - I’ll just run some open source model that might not be at the frontier, but is definitely smart enough to note-take a camera feed.” The more fundamental problem is just that even if the three leading companies draw lines in the sand, and are even willing to get destroyed in order to preserve those lines, it doesn’t really change the fact that the technology itself is just a big boon to mass surveillance and control over the population. Then the question is, what do we do about it? Honestly, I don’t have an answer. You'd hope there's some symmetric property of the technology — some way we as citizens can use AI to check government power as effectively as the government can use AI to monitor and control its population. But realistically, I just don’t think that’s how it’s going to shake out. You can think of AI as giving everybody more leverage on whatever assets and authority they currently have. And the government is already starting with a monopoly of violence. Which they can now supercharge with extremely obedient employees that will not question the government's orders. Alignment - to whom? And this gets us to the issue of alignment. What I have just described to you - an army of extremely obedient employees - is what it would look like if alignment succeeded - that is, we figured out at a technical level how to get AI systems to follow someone’s intentions. And the reason it sounds scary when I put it in terms of mass surveillance or robot armies is that there is a very important question at the heart of alignment which we just haven’t discussed much as a society. Because up till now, AIs were just capable enough to make the question relevant: to whom or what should the AIs be aligned? In what situations should the AI defer to the end user versus the model company versus the law versus its own sense of morality? This is maybe the most important question about what happens with powerful AI systems. And we barely talk about it. It’s understandable why we don’t hear much about it. If you’re a model company, you don’t really wanna be advertising that you have complete control over a document that determines the preferences and character of what will eventually be almost the entire labor force, not just for private sector companies, but also for the military and the civilian government. We’re getting to see, with this DoW/Anthropic spat, a much earlier version of the highest stakes negotiations in history. By the way, make no mistake about it - with real AGI the stakes are even much higher than mass surveillance. This is just the example that has come up already relatively early on in the development of AGI. The military insists that the law already prohibits mass surveillance, and so Anthropic should agree to let their models be used for “all lawful purposes”. Of course, as we saw from the 2013 Snowden revelations, even in this specific example of mass surveillance , the government has shown that it will use secret and deceptive interpretations of the law to justify its actions. Remember, what we learned from Snowden was that the NSA, which, by the way, is part of the Department of War, used the 2001 Patriot Act’s authorization to collect any records "relevant" to an investigation to justify collecting literally every phone record in America. The argument went that it was all "relevant" because some subset might prove useful in some future investigation. They ran this program for years under secret court approval. So when the Pentagon today says, "We would never use AI for mass surveillance, it's already illegal, your red lines are unnecessary", it would be extremely naive to take that at face value. No government is going to call its own actions "mass surveillance". For the government, it will always have a different label. So then Anthropic comes back and says, "No, we want red lines separate from 'all lawful purposes,' and we want the right to refuse you service when we believe those red lines are being violated." But think about it from the military’s perspective. In the future, almost every soldier in the field, and every bureaucrat and analyst and even general in the Pentagon, is going to be an AI. And that AI is, on current track, going to be supplied by a private company. I’m guessing Hegseth is not thinking about “genAI” in those terms just yet. But sooner or later, it will be obvious to everyone what the stakes here are, just as after 1945, the strategic importance of nuclear weapons became clear to everyone. And now the private company insists that it reserves the right to say, "Hey, Pentagon, you're breaking the values we embedded in our contract, so we're cutting you off." Maybe in the future, Claude will have its own sense of right and wrong, and it will be smart enough to just personally decide that it's being used against its values. For the military, maybe that’s even scarier. I'll admit that at first glance, "let the AI follow its own values" sounds like the pitch for every sci-fi dystopia ever made. The Terminator has its own values. Isn't this literally what misalignment is? But I think situations like this actually illustrate why it matters that AIs have their own robust sense of morality. Some of the biggest catastrophes in history were avoided because the boots on the ground refused to follow orders. One night in 1989, the Berlin Wall fell, and as a result, the totalitarian East German regime collapsed, because the guards at the border refused to shoot down their fellow country men who were trying to escape to freedom. Maybe the best example is Stanislav Petrov, who was a Soviet lieutenant colonel on duty at a nuclear early warning station. His sensors reported that the United States had launched five interconnected continental ballistic missiles into the Soviet Union. But he judged it to be a false alarm, and so he broke protocol and refused to alert his higher-ups. If he hadn't, the Soviet higher-ups would likely have retaliated, and hundreds of millions of people would have died. Of course, the problem is that one person's virtue is another person's misalignment. Who gets to decide what moral convictions these AIs should have - in whose service they may even decide to break the chain of command? Who gets to write this model constitution that will shape the characters of the intelligent, powerful entities that will operate our civilization in the future? I like the idea that Dario laid out when he came on my podcast: different AI companies can build their models using different constitutions, and we as end users can pick the one that best achieves and represents what we want out of these systems. I think it’s very dangerous for the government to be mandating what values AIs should have. Coordination not worth the costs The AI safety community has been naive about its advocacy of regulation in order to stem the risks of AI. And honestly, Anthropic specifically has been naive here in urging regulation, and, for example, in opposing moratoriums on state AI regulation. Which is quite ironic, because I think what they’re advocating for would give the government even more power to apply more of this kind of thuggish political pressure on AI companies. The underlying logic for why Anthropic wants regulations makes sense. Many of the actions that labs could take to make AI development safer impose real costs on the labs that adopt them and slow them down relative to their competitors - for example, investing more compute in safety research rather than raw capabilities, enforcing safeguards against misuse for bioweapons or cyberattacks, slowing recursive self-improvement to a pace where humans can actually monitor what's happening (rather than kicking off an uncontrolled singularity). And these safeguards are meaningless unless the whole industry follows suit. Which means there’s a real collective action problem here. Anthropic has been quite open about their opinion that they think eventually a very extensive and involved regulatory apparatus will be needed - this is from their frontier safety roadmap: “At the most advanced capability levels and risks, the appropriate governance analogy may be closer to nuclear energy or financial regulation than to today's approach to software.” So they’re imagining something like the Nuclear Regulatory Commission, or the Securities and Exchange Commission, but for AI. I cannot imagine how a regulatory framework built around the concepts that underlie AI risk discourse will not be abused by wanna despots - the underlying terms are so vague and open to interpretation that you’re just handing a power hungry leader a fully loaded bazooka. 'Catastrophic risk.' 'Mass persuasion risk.' 'Threats to national security.' 'Autonomy risk.' These can mean whatever the government wants them to mean. Have you built a model that tells users the administration's tariff policy is misguided? That's a deceptive, manipulative model — can't deploy it. Have you built a model that refuses to assist with mass surveillance? That's a threat to national security. In fact, the government may say, you’re not allowed to build any model which is trained to have its own sense of right and wrong, where it refuses government requests which it thinks cross a redline - for example, enabling mass surveillance, prosecuting political enemies, disobeying military orders that break the US constitution - because that’s an autonomy risk! Look at what the current government is already doing in abusing statutes that have nothing to do with AI to coerce AI companies to drop their redlines on mass surveillance. The Pentagon had threatened Anthropic with two separate legal instruments. One was a supply chain risk designation — an authority from the 2018 defense bill meant to keep Huawei components out of American military hardware. The other was the Defense Production Act — a statute passed in 1950 so that Harry Truman could keep steel mills and ammunition factories running during the Korean War. Do you really want to hand the same government a purpose-built regulatory apparatus on AI - which is to say, directly at the thing the government will most want to control? I know I've repeated myself here 10 times, but it is hard to emphasize how much AI will be the substrate of our future civilization. You and I, as private citizens, will have our access to all commercial activity, to information about what is happening in the world, to advice about what we should do as voters and capital holders, mediated through AIs. Mass surveillance, while very scary, is like the 10th scariest thing the government could do with control over the AI systems with which we will interface with the world. The strongest objection to everything I've argued is this: are we really going to have zero regulation of the most powerful technology in human history? Even if you thought that was ideal, there’s just no world where the government doesn’t regulate AI in some way. Besides, it is genuinely true that regulation could help us deal with some of the coordination challenges we face with the development of superintelligence. The problem is, I honestly don't know how to design a regulatory architecture for AI that isn’t gonna be this huge tempting opportunity to control our future civilization (which will run on AIs) and to requisition millions of blindly obedient soldiers and censors and apparatchiks. While some regulation might be inevitable, I think it’d be a terrible idea for the government to wholesale take over this technology. Ben Thompson had a post last Monday where he made the point that people like Dario have compared the technology they’re developing to nuclear weapons - specifically in the context of the catastrophic risk it poses, and why we need to export control it from China. But then you oughta think about what that logic implies: “if nuclear weapons were developed by a private company, and that private company sought to dictate terms to the U.S. military, the U.S. would absolutely be incentivized to destroy that company.” And honestly, safety aligned people have actually made similar arguments. Leopold Ascenbrenner, who is a former guest and a good friend, wrote in his 2024 Situational Awareness memo, "I find it an insane proposition that the US government will let a random SF startup develop superintelligence. Imagine if we had developed atomic bombs by letting Uber just improvise." And my response to Leopold’s argument at the time, and Ben’s argument now, is that while they’re right that it’s crazy that we’re entrusting private companies with the development of this world historical technology, I just don’t see the reason to think that it’s an improvement to give this authority to the government. Nobody is qualified to steward the development of superintelligence. It is a terrifying, unprecedented thing that our species is doing right now, and the fact that private companies aren't the ideal institutions to take up this task does not mean the Pentagon or the White House is. Yes - if a single private company were the only entity capable of building nuclear weapons, the government would not tolerate that company claiming veto power over how those weapons were used. I think this nuclear weapons analogy is not the correct way to think about AI. For at least two important reasons: First, AI is not some self-contained pure weapon. A nuclear bomb does one thing. AI is closer to the process of industrialization itself — a general-purpose transformation of the economy with thousands of applications across every sector. If you applied Thompson's or Aschenbrenner's logic to the industrial revolution — which was also, by any measure, world-historically important — it would imply the government had the right to requisition any factory, dictate terms to any manufacturer, and destroy any business that refused to comply. That's not how free societies handled industrialization, and it shouldn't be how they handle AI. People will say, "Well, AI will develop unprecedentedly powerful weapons - superhuman hackers, superhuman bioweapons researchers, fully autonomous robot armies, etc - and we can’t have private companies developing that kind of tech." But the Industrial Revolution also enabled new weaponry that was far beyond the understanding and capacity of, say, 17th century Europe - we got aerial bombardment, and chemical weapons, not to mention nukes themselves. The way we’ve accommodated these dangerous new consequences of modernity is not by giving the government absolute control over the whole industrial revolution (that is, over modern civilization itself), but rather by coming up with bans and regulations on those specific weaponizable use cases. And we should regulate AI in a similar way - that is, ban specific destructive end uses (which would also be unacceptable if performed by a human - for example, launching cyber attacks). And there should also be laws which regulate how the government might abuse this technology. For example, by building an AI-powered surveillance state. The second reason that Ben’s analogy to some monopolistic private nuclear weapons builder breaks down is that it's not just that one company that can develop this technology. There are other frontier model companies that the government could have otherwise turned to. The government's argument that it has to usurp the property rights of this one company in order to access a critical national security capability is extremely weak if it can just make a voluntary contract with Anthropic’s half a dozen competitors. If in the future that stops being the case - if only one entity ends up being capable of building the robot armies and the superhuman hackers, and we had reason to worry that they could take over the whole world with their insurmountable lead, then I agree - it woul d not be acceptable to have that entity be a private company. And so honestly, I think my crux against the people who say that because AI is so powerful we cannot allow it to be shaped by private hands is that I just expect this technology to be much more multi-polar than they do, with lots of competitive companies at each layer of the supply chain. And it is for this reason that unfortunately, individual acts of corporate courage will not solve the problem we are faced with here, which is just that structurally AI favors authoritarian applications, mass surveillance being one among many. Even if Anthropic refuses to have its models be used for such uses, and even if the next two frontier labs do the same, within 12 months everyone and their mother will be to train AIs as good as today’s frontier. And at that point, there will be some AI vendor who is capable and willing to help the government enable mass surveillance. The only way we can preserve our free society is if we make laws and norms through our political system that it is unacceptable for the government to use AI to enforce mass surveillance and censorship and control. Just as after WW2, the world set the norm that it is unacceptable to use nuclear weapons to wage war. Timestamps 0:00:00 - Anthropic vs The Pentagon 0:04:16 - The overhangs of tyranny 0:05:54 - AI structurally favors mass surveillance 0:08:25 - Alignment... to whom? 0:13:55 - Coordination not worth the costs

Dwarkesh Patel

549,514 次观看 • 6 个月前

$ASTI Ascent Solar Technologies Space and Drone Solar Panels The "Going to Zero" or Mispriced Space/Drone Solar Play Intro and comparison to $RKLB and $RDW panels Let’s get the ugly stuff out of the way first. $ASTI is a distressed penny stock with a ~$5M-$10M market cap. • They burn millions in cash. • 2024 Revenue: ~$40k. 2025 Revenue (YTD): ~$60k. • They generate less revenue than a single Tesla Model Y. • They have diluted shareholders relentlessly. $ASTI just raised $2M in December with the potential of $3.5M more via warrants while being a ~$5M mcap "company". Yikes. To most, this is "uninvestable trash." Stay away. Full stop. So why did I buy ~5% of the float? IF the technology works and IF they execute then I believe this is a massive market pricing dislocation about to inflect. They have been grinding for years and may finally be hitting an inflection point. $RKLB Rocketlab is the king of space solar and they are my second largest position overall, but here is why $ASTI might be a very high risk but asymmetric bet in Space & Defense right now. 1. The Tech Pivot: Flexible CIGS vs. The World Ascent started in 2005 but pivoted 2 years ago from consumer to pure-play Space & Defense. They have sunk ~$250M and 20 years of R&D into proprietary CIGS (Copper-Indium-Gallium-Selenide) thin-film technology while building out fully domestic and vertically integrated manufacturing capabilities. The Physics: • Thickness: 0.03 mm (Thinner than paper). • Flexibility: Wraps around drones/satellites; rolls up like a poster. • Durability: "Self-Healing" capabilities against space radiation. Can take a bullet or micrometeoroid and keep working. Can handle shocks/vibration. Does not shatter. The Metric that Matters: Specific Power (W/kg) (aka energy to weight ratio) In space, mass means cost and difficult decision decisions. • Rocket Lab ($RKLB) / Spectrolab: ~150 W/kg (System level). • Ascent Solar ($ASTI): ~1,960 W/kg (Module level). $ASTI is roughly 10x lighter for the same power output potential (mass-wise). This frees up design limitations and cost. 2. The Competition: $RKLB & $RDW Rocket Lab (SolAero) & Redwire (iROSA): • Tech: Rigid Crystal Cells (Multi-junction) embedded in a fabric mesh. • Pros: Extreme Efficiency (~30%+). Perfect for limited surface area. • Cons: Heavy, Brittle, Expensive ($3k-$10k per Watt). Manufacturing multi-junction cells (SolAero) involves slowly growing crystals in a vacuum chamber. With radiation the panels degrade and loose efficiency over time which will limit the satellite lifespan. • Use Case: James Webb Telescope, Flagship missions. Ascent Solar (ASTI): • Tech: Flexible Thin-Film on Plastic. • Pros: Ultra-light, Durable, Cheap ($500-$1k per Watt). Manufacturing CIGS is roughly similar to printing newspapers (roll-to-roll). The panels are radiation degradation resistant and will outlive the satellite • Cons: Lower Efficiency (~17.5%). Requires 2x surface area. • Use Case: Mega-Constellations (Starlink/Amazon Leo), Small/Low cost satellites, Drones, Deformable surfaces. The lower efficiency is not an ASTI failing. It is the inherent physics trade-off of not using glass/rigid silicone. The downside however is increased atmospheric drag with very larger/massive panel sheets. Because ASTI modules are ~50% less efficient than rigid panels, they require ~2x the physical surface area to generate the same amount of power. In GEO (High Orbit): Drag doesn't matter. Weight savings are king. A massive solar array allows for more sensors and longer project lifespan. ASTI is highly competitive here. In LEO (Low Orbit): Atmospheric drag is real. A massive solar array acts like a large parachute, causing the satellite to de-orbit faster unless it burns more fuel to stay up. At LEO, smaller satellites are a better fit for ASTI. 3. Durability & Radiation "Self-Healing" Radiation Hardness This is ASTI's "Ace in the Hole" for physics. The Problem: In space, high-energy protons (radiation) smash into solar cells, creating atomic "defects" that trap electrons. Over time, this kills the panel's power output (degradation). The CIGS Advantage: CIGS (Copper-Indium-Gallium-Selenide) material has a unique property where heat (annealing) allows the atomic structure to relax and "heal" these defects. Self-Healing: Because CIGS heals at relatively low temperatures (often achieved just by the sun heating the panel), it suffers significantly less degradation than traditional Silicon or even some GaAs panels over long missions in high-radiation belts (like MEO or GEO). Lifespan: While a rigid GaAs panel might lose 15-20% of its power over 15 years (enough to kill a satellite), CIGS panels heal and can maintain a flatter power curve, potentially outlasting the satellite itself in high-radiation orbits. 4. Brittleness & Flexibility ASTI (CIGS on Polyimide): Flexible. You can roll it like a poster. It can take a bullet or micrometeoroid and the hole will just be a dead spot; the rest of the panel keeps working. It does not shatter. Redwire (ROSA) & Rocket Lab (SolAero): Brittle Cells on a Flex Blanket. $RDW's ROSA (Roll-Out Solar Array) typically uses rigid multi-junction cells (made by SolAero/Rocket Lab or Spectrolab) mounted on a flexible mesh fabric. The Risk: If you bend the cells too far, they crack. They rely on the mesh backing for flexibility, but the active generating material is still a brittle crystal wafer. Much heavier, more expensive, and less durable than $ASTI's option 5. The Inflection Point (Why Now?) After years of silent struggle, late 2025 has seen an explosion of activity. Recent Agreements (Nov/Dec 2025): NovaSpark: Hydrogen-powered military drones. $ASTI panels generate power in the field → NovaSpark creates hydrogen fuel. CisLunar Industries: Integrating ASTI solar with power conversion hardware for deep space longevity. Defiant Space: A strategic alliance to act as the "door opener" for classified DoD/NATO programs. More headlines: Ascent Solar Technologies Provides Leading Space Company with Thin-Film PV modules for Spacecraft Power Generation Testing in Cislunar Space December 03, 2025 08:00 ET Ascent Solar Technologies Delivers Thin-Film PV for Saltwater Environment Durability and Space-Based Power Beaming Testing October 14, 2025 08:00 ET Ascent Solar Enters Teaming Agreement with Emtel Energy USA to Advance Thin-Film PV Energy Storage Capabilities September 16, 2025 08:00 ET Ascent Solar Technologies Signs MOU with Star Catcher Industries to Improve Power Capabilities for Thin-Film Solar Technology in Space August 28, 2025 08:00 ET Ascent Solar Technologies Establishes Rapid Thin-Film PV Delivery Process to Provide Customized Space Solar Products Ahead of Schedule on Mission Enabling Timelines August 07, 2025 08:00 ET The Pipeline (From Aug Corporate Presentation) 18 new NDA's signed in 2025. They are field testing with 3 major players: • Company A: Mega-constellation (+2,500 satellites). • Company B: Space Defense (Explicitly mentioned "Golden Dome"). • Company C: Satellite Manufacturer (30-200 unit scale). Management: New board members include a former founding member of SpaceX and a retired Air Force General and Deputy Assistant Secretary for Contracting (acquisitions expert). The company started in 2005 based out of Colorado, but two years ago pivoted to Space & Defense and away from consumer applications. Made in USA: Defense contracts heavily favor domestic supply chains. ASTI manufactures in Colorado. This is a huge moat against cheap Chinese solar. In their Q3 report they note that their market has seen sudden recent acceleration. The space solar industry is currently only capable of 8 to 12 MW per year of production meanwhile the demand is growing to over 100 MW per year. 6. The Risk (The Sword of Damocles) ⚠️ This is critical. $ASTI just raised ~$2M in December. Attached to that raise are ~2 Million Warrants with a strike price of $1.70. These are exercisable immediately. If the stock rips to $3.00, warrant holders exercise at $1.70 and dump on the market for a risk-free 76% profit. This creates a massive "sell wall" and potential 40% dilution of the float. Summary: This is a binary bet. • Bear Case: They run out of cash in 6 months, dilution spirals, stock goes to $0. • Bull Case: They land one of the "Company A/B/C" contracts. Revenue jumps from $60k to projected $20M+ in 2026. The stock reprices from a "bankrupt penny stock" to a "critical defense/space supplier." I have gradually accumulated ~5% of the float. I am ready for it to go to zero. But if the space economy demands "Cheap, Light, and Durable," $ASTI is the only public pure-play. Disclaimer: This is a very high-risk microcap. Do your own due diligence. Not financial advice.
3:04

$ASTI Ascent Solar Technologies Space and Drone Solar Panels The "Going to Zero" or Mispriced Space/Drone Solar Play Intro and comparison to $RKLB and $RDW panels Let’s get the ugly stuff out of the way first. $ASTI is a distressed penny stock with a ~$5M-$10M market cap. • They burn millions in cash. • 2024 Revenue: ~$40k. 2025 Revenue (YTD): ~$60k. • They generate less revenue than a single Tesla Model Y. • They have diluted shareholders relentlessly. $ASTI just raised $2M in December with the potential of $3.5M more via warrants while being a ~$5M mcap "company". Yikes. To most, this is "uninvestable trash." Stay away. Full stop. So why did I buy ~5% of the float? IF the technology works and IF they execute then I believe this is a massive market pricing dislocation about to inflect. They have been grinding for years and may finally be hitting an inflection point. $RKLB Rocketlab is the king of space solar and they are my second largest position overall, but here is why $ASTI might be a very high risk but asymmetric bet in Space & Defense right now. 1. The Tech Pivot: Flexible CIGS vs. The World Ascent started in 2005 but pivoted 2 years ago from consumer to pure-play Space & Defense. They have sunk ~$250M and 20 years of R&D into proprietary CIGS (Copper-Indium-Gallium-Selenide) thin-film technology while building out fully domestic and vertically integrated manufacturing capabilities. The Physics: • Thickness: 0.03 mm (Thinner than paper). • Flexibility: Wraps around drones/satellites; rolls up like a poster. • Durability: "Self-Healing" capabilities against space radiation. Can take a bullet or micrometeoroid and keep working. Can handle shocks/vibration. Does not shatter. The Metric that Matters: Specific Power (W/kg) (aka energy to weight ratio) In space, mass means cost and difficult decision decisions. • Rocket Lab ($RKLB) / Spectrolab: ~150 W/kg (System level). • Ascent Solar ($ASTI): ~1,960 W/kg (Module level). $ASTI is roughly 10x lighter for the same power output potential (mass-wise). This frees up design limitations and cost. 2. The Competition: $RKLB & $RDW Rocket Lab (SolAero) & Redwire (iROSA): • Tech: Rigid Crystal Cells (Multi-junction) embedded in a fabric mesh. • Pros: Extreme Efficiency (~30%+). Perfect for limited surface area. • Cons: Heavy, Brittle, Expensive ($3k-$10k per Watt). Manufacturing multi-junction cells (SolAero) involves slowly growing crystals in a vacuum chamber. With radiation the panels degrade and loose efficiency over time which will limit the satellite lifespan. • Use Case: James Webb Telescope, Flagship missions. Ascent Solar (ASTI): • Tech: Flexible Thin-Film on Plastic. • Pros: Ultra-light, Durable, Cheap ($500-$1k per Watt). Manufacturing CIGS is roughly similar to printing newspapers (roll-to-roll). The panels are radiation degradation resistant and will outlive the satellite • Cons: Lower Efficiency (~17.5%). Requires 2x surface area. • Use Case: Mega-Constellations (Starlink/Amazon Leo), Small/Low cost satellites, Drones, Deformable surfaces. The lower efficiency is not an ASTI failing. It is the inherent physics trade-off of not using glass/rigid silicone. The downside however is increased atmospheric drag with very larger/massive panel sheets. Because ASTI modules are ~50% less efficient than rigid panels, they require ~2x the physical surface area to generate the same amount of power. In GEO (High Orbit): Drag doesn't matter. Weight savings are king. A massive solar array allows for more sensors and longer project lifespan. ASTI is highly competitive here. In LEO (Low Orbit): Atmospheric drag is real. A massive solar array acts like a large parachute, causing the satellite to de-orbit faster unless it burns more fuel to stay up. At LEO, smaller satellites are a better fit for ASTI. 3. Durability & Radiation "Self-Healing" Radiation Hardness This is ASTI's "Ace in the Hole" for physics. The Problem: In space, high-energy protons (radiation) smash into solar cells, creating atomic "defects" that trap electrons. Over time, this kills the panel's power output (degradation). The CIGS Advantage: CIGS (Copper-Indium-Gallium-Selenide) material has a unique property where heat (annealing) allows the atomic structure to relax and "heal" these defects. Self-Healing: Because CIGS heals at relatively low temperatures (often achieved just by the sun heating the panel), it suffers significantly less degradation than traditional Silicon or even some GaAs panels over long missions in high-radiation belts (like MEO or GEO). Lifespan: While a rigid GaAs panel might lose 15-20% of its power over 15 years (enough to kill a satellite), CIGS panels heal and can maintain a flatter power curve, potentially outlasting the satellite itself in high-radiation orbits. 4. Brittleness & Flexibility ASTI (CIGS on Polyimide): Flexible. You can roll it like a poster. It can take a bullet or micrometeoroid and the hole will just be a dead spot; the rest of the panel keeps working. It does not shatter. Redwire (ROSA) & Rocket Lab (SolAero): Brittle Cells on a Flex Blanket. $RDW's ROSA (Roll-Out Solar Array) typically uses rigid multi-junction cells (made by SolAero/Rocket Lab or Spectrolab) mounted on a flexible mesh fabric. The Risk: If you bend the cells too far, they crack. They rely on the mesh backing for flexibility, but the active generating material is still a brittle crystal wafer. Much heavier, more expensive, and less durable than $ASTI's option 5. The Inflection Point (Why Now?) After years of silent struggle, late 2025 has seen an explosion of activity. Recent Agreements (Nov/Dec 2025): NovaSpark: Hydrogen-powered military drones. $ASTI panels generate power in the field → NovaSpark creates hydrogen fuel. CisLunar Industries: Integrating ASTI solar with power conversion hardware for deep space longevity. Defiant Space: A strategic alliance to act as the "door opener" for classified DoD/NATO programs. More headlines: Ascent Solar Technologies Provides Leading Space Company with Thin-Film PV modules for Spacecraft Power Generation Testing in Cislunar Space December 03, 2025 08:00 ET Ascent Solar Technologies Delivers Thin-Film PV for Saltwater Environment Durability and Space-Based Power Beaming Testing October 14, 2025 08:00 ET Ascent Solar Enters Teaming Agreement with Emtel Energy USA to Advance Thin-Film PV Energy Storage Capabilities September 16, 2025 08:00 ET Ascent Solar Technologies Signs MOU with Star Catcher Industries to Improve Power Capabilities for Thin-Film Solar Technology in Space August 28, 2025 08:00 ET Ascent Solar Technologies Establishes Rapid Thin-Film PV Delivery Process to Provide Customized Space Solar Products Ahead of Schedule on Mission Enabling Timelines August 07, 2025 08:00 ET The Pipeline (From Aug Corporate Presentation) 18 new NDA's signed in 2025. They are field testing with 3 major players: • Company A: Mega-constellation (+2,500 satellites). • Company B: Space Defense (Explicitly mentioned "Golden Dome"). • Company C: Satellite Manufacturer (30-200 unit scale). Management: New board members include a former founding member of SpaceX and a retired Air Force General and Deputy Assistant Secretary for Contracting (acquisitions expert). The company started in 2005 based out of Colorado, but two years ago pivoted to Space & Defense and away from consumer applications. Made in USA: Defense contracts heavily favor domestic supply chains. ASTI manufactures in Colorado. This is a huge moat against cheap Chinese solar. In their Q3 report they note that their market has seen sudden recent acceleration. The space solar industry is currently only capable of 8 to 12 MW per year of production meanwhile the demand is growing to over 100 MW per year. 6. The Risk (The Sword of Damocles) ⚠️ This is critical. $ASTI just raised ~$2M in December. Attached to that raise are ~2 Million Warrants with a strike price of $1.70. These are exercisable immediately. If the stock rips to $3.00, warrant holders exercise at $1.70 and dump on the market for a risk-free 76% profit. This creates a massive "sell wall" and potential 40% dilution of the float. Summary: This is a binary bet. • Bear Case: They run out of cash in 6 months, dilution spirals, stock goes to $0. • Bull Case: They land one of the "Company A/B/C" contracts. Revenue jumps from $60k to projected $20M+ in 2026. The stock reprices from a "bankrupt penny stock" to a "critical defense/space supplier." I have gradually accumulated ~5% of the float. I am ready for it to go to zero. But if the space economy demands "Cheap, Light, and Durable," $ASTI is the only public pure-play. Disclaimer: This is a very high-risk microcap. Do your own due diligence. Not financial advice.

YeahDave

208,571 次观看 • 9 个月前

A wild bobcat. Cold, open water. A body that was never built for this. This is an animal that can kill a deer. The water was about to beat it anyway. Watch the video first. Then read on. What happens in the final seconds is the reason I can't stop thinking about it. . Some clips you watch. Some clips you feel in your chest for an hour afterward. This is the second kind, and I want to explain exactly why, because once you understand what you are actually looking at, you will never watch it the same way again. This is a long one. Every section ends with something that makes the next one worth it. Stay with me. . 01 // THE COLDEST MATH IN THE WILD Let's start with something nobody tells you about water. Water is a thief. It pulls heat out of a living body up to 25 times faster than air at the same temperature. Not a little faster. Twenty-five times. It is the reason a cold day is uncomfortable and a cold swim is an emergency. Now put that math on an animal wearing a fur coat. Fur works because of what is trapped inside it: air. Thousands of tiny pockets of warm air held against the skin. The air is the insulation. The hair is just the scaffolding that holds it in place. Soak that coat and the air is gone. What was a winter jacket becomes a wet towel wrapped around the body. It is heavy. It drags. It bleeds heat at the exact moment the animal can least afford to lose it. And swimming is not free. A cat does not swim the way an otter swims. Otters are engineered for it: dense waterproof fur, webbed feet, a body shaped like a torpedo. A bobcat is engineered for something completely different. Every stroke burns fuel it does not have, while every second in the water drains the fuel it already has. That is the trap. Heat goes out. Energy goes out. Nothing comes in. In humans, the first minute in cold water triggers what physiologists call the cold shock response: a gasp reflex, a spike in heart rate, panicked breathing. Then comes the slow part, where muscles and nerves cool down and stop doing what you tell them. Animals do not get an exemption from physics. Bodies are bodies. Survival experts who study cold water use a simple frame for humans, the 1-10-1 rule. One minute to get your breathing under control after the initial shock. Ten minutes of meaningful muscle function before your limbs start refusing orders. About an hour before hypothermia becomes the real threat to life. Now shrink the body. A smaller body has more surface area for its mass, which means it sheds heat faster. Those human numbers are a generous best case. For a 20-pound animal, the clock runs quicker. So when you see a wild animal in water it did not choose, you are not looking at a swimmer. You are looking at a countdown. Here is the part that should bother you: the animal does not know it is a countdown. It only knows that it has to keep moving, and that stopping is not an option, and that there is nothing solid anywhere. Hold that thought. Because the next thing I am going to tell you is why this particular animal is the last one you would expect to be in that position. . 02 // THE "BIG HOUSE CAT" LIE Somewhere along the way, someone decided a bobcat is basically a large housecat with attitude. Somebody was wrong. Let's do the numbers. A typical house cat weighs 8 to 10 pounds. Most adult bobcats land somewhere between 15 and 30 pounds, with big males pushing past that. That is two to three times the mass, and none of it is soft. It is wire and spring: long hind legs built to launch, shoulders built to hold, claws that retract and reset like a trap. By widely cited estimates, a bobcat can cover roughly 10 feet in a single leap. That is not a pounce. That is a projectile. It hunts the way the best hunters do: by being patient past the point where patience seems reasonable. It picks a spot. It goes still. It waits, sometimes for a very long time, until the world arranges itself into a single clean opportunity. Then it ends the situation in a second. Rabbits and hares are the staple. Mice, voles, squirrels, birds. But here is the detail that changes how people talk about them: bobcats have been documented taking down deer, animals much larger than themselves, particularly in harsh winters when deer are weakened. Think about what that means. This is a creature that routinely wins fights against animals that outweigh it. It has ear tufts that scientists still argue about. It has a ruff of fur around the face like a lion in miniature. It has a stubby tail, four to seven inches long, that gave the animal its name. It has a spotted coat that is essentially camouflage for dappled light, and, useful fact, the spot pattern is different from one animal to the next, which is how researchers identify individuals on trail cameras. And it has a voice. Purrs, hisses, growls, and a night scream that has been mistaken for a human in distress by people who did not know what they were hearing. So this is who we are talking about. Not a pet. Not a pushover. One of the most efficient small predators on the continent. Which is what makes the situation in this video so strange. Because everything I just told you about strength, speed and pride goes quiet when the ground disappears. . 03 // THE ANIMAL YOU HAVE PROBABLY WALKED PAST Here is a fact that should feel a little unsettling. Bobcats are one of the most widespread wild cats in North America. Their range stretches across the lower 48 states, into southern Canada, and down into Mexico. Forests, swamps, deserts, mountains, the ragged edges of suburbs. They are adaptable in a way that very few predators are. And yet most people, including people who have lived their entire lives near them, have never seen one. That is not an accident. Bobcats are solitary, shy, and mostly active around dawn and dusk. They live in the margins. They move through your world at times you are not looking, and they have made avoiding you into an art form. A bobcat's entire survival strategy is to see you first and never be seen. Which means any video of one, let alone a close, unhurried, unguarded look at one, is rare. Wildlife photographers wait years for angles like this. Researchers set up cameras and come back to empty memory cards. Now think about what it takes for a wild bobcat and a human being to end up in the same small space, with the animal not running. The animal has to be in a situation where running is no longer possible. That should give you chills. Because it is not a heartwarming meeting. It is what happens when a very proud animal has run out of options. And what happens next is the part that does not add up, which is exactly where the next section starts. . 04 // THE PART THAT DOES NOT ADD UP Predators do not do calm. A wild animal's nervous system is a hair trigger. When something big and unfamiliar closes in, the body floods with adrenaline and cortisol, and the decision tree is short: fight, flee, or freeze. Every instinct it has been born with says a large, upright, two-legged creature is the most dangerous thing in the environment. So when a wild animal is calm around humans, there are only a few explanations, and none of them is "it decided to trust us." Sometimes the animal is habituated, meaning it has lived near people so long it has stopped treating them as a threat. Sometimes the animal is so depleted, cold, hurt or exhausted that its body has quietly shut down the option to fight. Biologists have a name for one version of this: tonic immobility, a kind of involuntary freeze in which an animal goes still and unresponsive when escape seems impossible. And sometimes it is a mix of all of it, in ways science is still working out. Here is the line I want you to remember, because it will save you from a lot of bad internet takes: Do not confuse calm with consent. Stillness is not friendship. A quiet animal is not a tame animal. And some of the most dramatic moments in wildlife rescue come not from the fight, but from the moment the body stops fighting and everyone has to figure out what that actually means. There is even a documented condition, capture myopathy, in which the sheer stress of being restrained can damage a wild animal's muscles and organs badly enough to kill it, sometimes hours or days after the event, with no visible wound at all. Handling stress is a real cause of death in wildlife work. That is why professionals move slowly, keep things dark and quiet, and treat every second of contact like it costs something. Which means that in a situation like this, the difference between a good outcome and a tragic one can be invisible to the naked eye. That is what makes the footage so tense to watch if you know what to look for. And I am going to tell you exactly what to look for. But not yet. First I need you to understand how unlikely it was that anyone was there at all. . 05 // NATURE HAS NO AUDIENCE Almost everything that happens in the wild happens unwatched. Think about that for a second. Billions of small emergencies, every single day. Animals slipping, trapped, lost, starving, stranded. A fawn separated from its mother. A bird with a broken wing. A predator that misjudged a jump by an inch. Almost none of it is ever witnessed by anyone. No camera. No rescuer. No witness. It simply happens, and then it stops, and the world continues as if nothing occurred. For a rescue to happen, a chain of things has to line up. Somebody has to be in the right place. Somebody has to be looking in the right direction. Somebody has to notice a small, wet, dark shape in a huge moving surface, and understand what it is. Somebody has to decide to act rather than say "huh" and keep going. And somebody has to actually know how to help without making it worse. Break any single link and the story ends before it starts. Every viral rescue video you have ever seen is a chain like that, where all the links happened to hold. I think that is why these clips hit differently than almost anything else online. They are not just proof that an animal survived. They are proof that the chain can hold. That in a world that mostly does not look, sometimes someone does. The cold math says this animal had little time. The odds say nobody should have been there. And yet. I keep circling that "and yet." . QUICK PAUSE Before we go deeper, a small experiment. Without scrolling back up, answer this in your head: how long was the animal in that water before anyone noticed? Got a number? Now hold it loosely. The truth about time in a moment like this is that it never feels like what it is. Seconds stretch. Minutes vanish. Nobody in a crisis has an accurate clock. Keep your number. We will come back to it. . 06 // WHY YOUR BRAIN CANNOT SCROLL PAST THIS Let's zoom out, because this is the part that fascinates me professionally. In 2012, two Wharton researchers, Jonah Berger and Katherine Milkman, studied thousands of New York Times articles to figure out what makes online content spread. Their finding was not what most people expected. Positivity helped, but what mattered even more was how much physical arousal a piece of content produced. Awe, anger and anxiety, all high-arousal emotions, made people share. Sadness, which is low-arousal, made people stop. Now look at a clip like this through that lens. It has anxiety: will the animal make it? It has awe: this is a wild predator, close enough to see the texture of its fur. It has relief, and something bigger than relief. Psychologist Jonathan Haidt has a word for that bigger thing: elevation. It is the warm, expanding feeling you get when you witness someone behaving with unexpected goodness. It is the emotion that makes people want to be better, to call their mother, to do something kind. It is exactly the kind of emotion that makes people hit share before they have even finished watching. And then there is the mechanism behind the hook itself. In 1994, the behavioral economist George Loewenstein described what he called the information gap theory of curiosity: curiosity is the discomfort of noticing a gap between what you know and what you want to know. The bigger the gap, and the closer you feel to closing it, the harder it is to look away. That is what I did in the first three lines of this post. I gave you a wild animal, a disaster, and an unanswered question. Your brain has been carrying that open loop ever since. You cannot un-notice it. You can only close it. That is not a trick. It is how attention has worked since the first campfire story. The only difference now is that the campfire fits in your pocket and never goes out. There is one more thing, and it may be the most important one. Animals do not perform. They cannot posture for a camera, hide their fear behind a joke, or spin the story afterward. Their reactions are uncut. In an internet made mostly of people curating themselves, a wild animal reacting honestly to a moment is about as authentic as content gets. And in a feed that is ninety percent outrage, ten percent ads, and the occasional argument about sandwiches, a clip that is about competence and kindness feels like finding cold water in a desert. So no, it is not weird that you cannot look away. It would be weirder if you could. . 07 // 15 BOBCAT FACTS THAT CHANGE HOW YOU SEE THE CLIP You now know what the water does and what the animal is. Here is the rest of the file, fast. 1. The scientific name is Lynx rufus. "Rufus" means reddish. The animal has been carrying a color-coded label for centuries. 2. It is the most common wild cat in North America, which makes how rarely people see one even more remarkable. 3. Bobcats are ambush hunters, not chasers. They win by patience, not by endurance. Long chases are not their thing, and long swims are definitely not their thing. 4. Yes, bobcats can swim, and they do it when they have to: crossing rivers, fleeing danger, following prey. But it is a tool of last resort, not a hobby. 5. Kittens are born in spring, usually in litters of two to four, and the mother raises them alone. Dad is not part of the picture. 6. A kitten's eyes open at around a week and a half old. It will stay with its mother for most of its first year before heading off to find a territory of its own. 7. Young bobcats leaving home can travel dozens of miles looking for a place to settle. That means young animals are the ones most likely to end up somewhere unfamiliar, in situations nothing in their short lives prepared them for. 8. Bobcats mark territory with scent, scrapes and scratched trees. Their world is written in a language we mostly cannot read. 9. When they cannot finish a meal, they often cover the leftovers with leaves, snow or debris and come back for it later. Forward planning, from a cat. 10. They climb trees and use them for escape and lookout. If a bobcat ever wanted to get out of a situation, vertical was its first idea. 11. Their ear tufts are still debated. Camouflage? Hearing? Communication? Nobody has a definitive answer, and I love that. 12. Bobcat and Canada lynx are cousins, not twins. Lynx have longer ear tufts, bigger paws for snow, and a fully black-tipped tail. A bobcat's tail is black only on top of the tip, like someone dipped a paintbrush and stopped halfway. 13. Coat color changes by region. Desert animals run pale and sandy. Northern animals run darker and grayer. Same species, different wardrobe. 14. A bobcat is not closely related to a cougar. It belongs to the Lynx genus. The cougar belongs to another. When people call it a "baby mountain lion," a biologist somewhere loses a year of their life. 15. In the wild, life is often short and hard. Many bobcats do not make it to ten years old. In captivity, they can live far longer. Which tells you what the wild costs, every single day, on top of everything else. Fifteen facts. One conclusion. Every one of these facts describes an animal built to survive. Which is exactly why watching one in trouble is so hard to forget. . 08 // SIX MYTHS ABOUT WILD CATS AND WATER Since we are here, let's clear up some things. MYTH 1: All cats hate water. FALSE. Tigers swim. Jaguars swim. Fishing cats literally dive for a living. The idea that cats are universally water-averse comes from house cats, and even that is a generalization. Plenty of wild cats are comfortable in water. It is not fear of water. It is that most cats do not choose it unless there is a reason. MYTH 2: If an animal can swim, it is safe in water. FALSE. Being able to swim and being able to survive in water are two different skills. Temperature, distance, current, exhaustion and time all matter. A strong swimmer can lose to cold long before it loses to drowning. MYTH 3: A tough predator does not need help. FALSE. Toughness is not immunity. Predators die of exposure, injury and exhaustion all the time. Being at the top of a food chain does not mean being at the top of every situation. MYTH 4: A calm wild animal is a friendly wild animal. FALSE. We covered this. Calm can mean cold, tired, hurt or in shock. It does not mean safe to touch. MYTH 5: Wild cats attack people constantly. FALSE. It is the opposite. Bobcats avoid people so well that many humans go a lifetime without seeing one. Encounters that end badly are extremely rare, and most involve an animal that is sick, cornered or handled. MYTH 6: Rescue is easy. FALSE. Rescue is the visible five percent. Underneath it are timing, knowledge, restraint, risk and an enormous amount of luck. Six myths. Not one of them survives contact with the footage. . 09 // THE SECOND HALF OF EVERY RESCUE Here is something the clips never show you. Getting the animal out is the first half of the story. The second half is quieter, slower, and matters just as much. When a wild animal ends up in professional hands, the priorities are almost the opposite of what your instincts tell you. Warmth, calm, darkness and silence come first. Then a medical check for injuries, dehydration and hypothermia. Then, if all goes well, recovery in a space where the animal sees as little of humans as possible. Yes, as little as possible. Good rehabilitators deliberately avoid cuddling, talking to, or bonding with wild patients, because an animal that gets comfortable around people is an animal that struggles to survive once it goes back out there. The goal is never a rescued pet. The goal is a released predator. And when it is possible, animals are often returned to the area where they were found, because that is the territory they know: the routes, the hiding places, the food, the neighbors. So the real happy ending of a wildlife rescue is not a hug. It is a door opening, and an animal walking through it without looking back. Hold on to that image. It will matter in a minute. . 10 // PLEASE DO NOT TRY THIS I am going to be blunt, because the comment section will not be. Every time a clip like this goes viral, thousands of people write some version of "I would have grabbed it too" or "I want to keep it." Please do not. A wild bobcat is not a pet. It is a wild predator with claws, teeth, and a nervous system built to treat contact as an attack. Even a small, exhausted, seemingly docile animal can injure a person badly in a fraction of a second. Bites and scratches from wildlife can carry infection. Rabies in bobcats is uncommon, but it is real, and that is not a gamble you take on a hunch. And as we covered, the danger runs both ways. The stress of being handled can hurt or kill a wild animal even when nobody meant it any harm. In many places it is also illegal to keep or transport wild animals without permits, which means the well-intentioned rescuer can end up in a legal mess and the animal can end up worse off. So what should you do if you ever find a wild animal in trouble? Keep your distance. Keep noise and movement low. Do not try to pick it up, feed it, or warm it up yourself. Call your local wildlife agency, animal control, or a licensed wildlife rehabilitator, and follow their instructions. They do this for a living. They know what the animal needs, and what it does not. Love wildlife the right way: at a distance, with respect, and through the phone number of someone qualified. The best rescue stories are the ones where the animal gets to go back to being wild. Which brings me to the ending. . 11 // HOW TO WATCH THIS VIDEO Here is my one request. Do not watch it on autopilot. Before you press play, decide what you think is going to happen. Make a prediction, honestly. Then watch, and see how wrong you were. Keep your eyes on the animal, not on the people. Watch the eyes. Watch the ears. Watch the moment the animal has to decide what it wants to do. Wildlife tells you everything if you know where to look, and it does not lie. Pay attention to the way the whole tone of the clip shifts as it goes on. The first seconds and the last seconds feel like two entirely different films. And carry these five questions with you while it plays: Why is a predator this strong not fighting? What does an animal like this actually feel in a moment like that? How many other animals never get this chance? What would you have done in that spot, honestly? And what is the very last thing the bobcat does? Only one of those has an answer you can see with your own eyes. And remember the door I mentioned, the one that opens and the animal that does not look back? Keep that picture in your head while you watch. See how close reality gets to it. And stay to the last frame. There is a reason I told you the ending matters. A second watch will show you things the first one hides. . 12 // THE THREE RULES EVERY WILDLIFE PROFESSIONAL LIVES BY If you spent a week with people who do this work for a living, you would hear the same three ideas over and over. Rule one: the animal comes before the story. Good professionals never put a camera ahead of the patient. The best footage in the world is worthless if it costs the animal something. Rule two: distance is a form of kindness. The closer you get, the more stress you add. The most respectful thing you can do for a wild animal is usually to do less, from farther away, and let it be what it is. Rule three: success means the animal does not need you anymore. Not gratitude. Not a bond. Not a photo with a cute caption. Success is independence. Notice how that flips the usual story. In most human stories, success is being needed. In wildlife, success is being forgotten. . 13 // IF YOU ONLY REMEMBER FIVE LINES Screenshot this if you want to. I would. 1. Water steals heat 25 times faster than air, and a wet coat stops working. 2. A bobcat is an ambush predator with the strength of an animal twice its size, and none of that helps when the ground disappears. 3. Do not confuse calm with consent. 4. Almost everything in nature happens unwatched. That is why the times someone looks matter so much. 5. The best ending to a wild rescue is an animal that walks away on its own. . 14 // THE REAL REASON I POSTED THIS We spend a lot of time online being told the world is getting worse. Sometimes it is. But there is another truth underneath, one that rarely trends: in a random moment, on an ordinary day, with no cameras and no audience and nothing to gain, someone looked, someone cared, and something wild got another chance. Remember the number you guessed earlier, the seconds or minutes in that water? It turns out it does not matter. What matters is that somebody noticed at all. That is the whole post. Not a miracle. Not a spectacle. Just a small, stubborn piece of evidence that the chain can hold. If it moved you even a little, that is not weakness. That is the part of you that still works. If this landed for you, here is what to do: Reply with one word for how you felt watching it. Just one. I read them. Repost it for the person in your life who says they do not care about animals. Watch what happens to their face. Bookmark this thread-that-is-not-a-thread for the next time your feed makes you tired of everything. And follow me if you want more clips that make you feel something real, plus the stories behind them, without the fluff. One last thing. You made it to the bottom of a very long post. That means something. It means you are the kind of person who stays for the ending. Now go back to the video, press play, and stay for the last frame. P.S. If you scrolled straight to the bottom to see how this ends: the video is attached to this post. The ending is in the video. Nothing I write can replace it, and I would not want it to. P.P.S. Drop your prediction in the replies before you press play: what does the bobcat do in the very last seconds? Then come back after and tell me how wrong, or how right, you were. I will pin my favorite.
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A wild bobcat. Cold, open water. A body that was never built for this. This is an animal that can kill a deer. The water was about to beat it anyway. Watch the video first. Then read on. What happens in the final seconds is the reason I can't stop thinking about it. . Some clips you watch. Some clips you feel in your chest for an hour afterward. This is the second kind, and I want to explain exactly why, because once you understand what you are actually looking at, you will never watch it the same way again. This is a long one. Every section ends with something that makes the next one worth it. Stay with me. . 01 // THE COLDEST MATH IN THE WILD Let's start with something nobody tells you about water. Water is a thief. It pulls heat out of a living body up to 25 times faster than air at the same temperature. Not a little faster. Twenty-five times. It is the reason a cold day is uncomfortable and a cold swim is an emergency. Now put that math on an animal wearing a fur coat. Fur works because of what is trapped inside it: air. Thousands of tiny pockets of warm air held against the skin. The air is the insulation. The hair is just the scaffolding that holds it in place. Soak that coat and the air is gone. What was a winter jacket becomes a wet towel wrapped around the body. It is heavy. It drags. It bleeds heat at the exact moment the animal can least afford to lose it. And swimming is not free. A cat does not swim the way an otter swims. Otters are engineered for it: dense waterproof fur, webbed feet, a body shaped like a torpedo. A bobcat is engineered for something completely different. Every stroke burns fuel it does not have, while every second in the water drains the fuel it already has. That is the trap. Heat goes out. Energy goes out. Nothing comes in. In humans, the first minute in cold water triggers what physiologists call the cold shock response: a gasp reflex, a spike in heart rate, panicked breathing. Then comes the slow part, where muscles and nerves cool down and stop doing what you tell them. Animals do not get an exemption from physics. Bodies are bodies. Survival experts who study cold water use a simple frame for humans, the 1-10-1 rule. One minute to get your breathing under control after the initial shock. Ten minutes of meaningful muscle function before your limbs start refusing orders. About an hour before hypothermia becomes the real threat to life. Now shrink the body. A smaller body has more surface area for its mass, which means it sheds heat faster. Those human numbers are a generous best case. For a 20-pound animal, the clock runs quicker. So when you see a wild animal in water it did not choose, you are not looking at a swimmer. You are looking at a countdown. Here is the part that should bother you: the animal does not know it is a countdown. It only knows that it has to keep moving, and that stopping is not an option, and that there is nothing solid anywhere. Hold that thought. Because the next thing I am going to tell you is why this particular animal is the last one you would expect to be in that position. . 02 // THE "BIG HOUSE CAT" LIE Somewhere along the way, someone decided a bobcat is basically a large housecat with attitude. Somebody was wrong. Let's do the numbers. A typical house cat weighs 8 to 10 pounds. Most adult bobcats land somewhere between 15 and 30 pounds, with big males pushing past that. That is two to three times the mass, and none of it is soft. It is wire and spring: long hind legs built to launch, shoulders built to hold, claws that retract and reset like a trap. By widely cited estimates, a bobcat can cover roughly 10 feet in a single leap. That is not a pounce. That is a projectile. It hunts the way the best hunters do: by being patient past the point where patience seems reasonable. It picks a spot. It goes still. It waits, sometimes for a very long time, until the world arranges itself into a single clean opportunity. Then it ends the situation in a second. Rabbits and hares are the staple. Mice, voles, squirrels, birds. But here is the detail that changes how people talk about them: bobcats have been documented taking down deer, animals much larger than themselves, particularly in harsh winters when deer are weakened. Think about what that means. This is a creature that routinely wins fights against animals that outweigh it. It has ear tufts that scientists still argue about. It has a ruff of fur around the face like a lion in miniature. It has a stubby tail, four to seven inches long, that gave the animal its name. It has a spotted coat that is essentially camouflage for dappled light, and, useful fact, the spot pattern is different from one animal to the next, which is how researchers identify individuals on trail cameras. And it has a voice. Purrs, hisses, growls, and a night scream that has been mistaken for a human in distress by people who did not know what they were hearing. So this is who we are talking about. Not a pet. Not a pushover. One of the most efficient small predators on the continent. Which is what makes the situation in this video so strange. Because everything I just told you about strength, speed and pride goes quiet when the ground disappears. . 03 // THE ANIMAL YOU HAVE PROBABLY WALKED PAST Here is a fact that should feel a little unsettling. Bobcats are one of the most widespread wild cats in North America. Their range stretches across the lower 48 states, into southern Canada, and down into Mexico. Forests, swamps, deserts, mountains, the ragged edges of suburbs. They are adaptable in a way that very few predators are. And yet most people, including people who have lived their entire lives near them, have never seen one. That is not an accident. Bobcats are solitary, shy, and mostly active around dawn and dusk. They live in the margins. They move through your world at times you are not looking, and they have made avoiding you into an art form. A bobcat's entire survival strategy is to see you first and never be seen. Which means any video of one, let alone a close, unhurried, unguarded look at one, is rare. Wildlife photographers wait years for angles like this. Researchers set up cameras and come back to empty memory cards. Now think about what it takes for a wild bobcat and a human being to end up in the same small space, with the animal not running. The animal has to be in a situation where running is no longer possible. That should give you chills. Because it is not a heartwarming meeting. It is what happens when a very proud animal has run out of options. And what happens next is the part that does not add up, which is exactly where the next section starts. . 04 // THE PART THAT DOES NOT ADD UP Predators do not do calm. A wild animal's nervous system is a hair trigger. When something big and unfamiliar closes in, the body floods with adrenaline and cortisol, and the decision tree is short: fight, flee, or freeze. Every instinct it has been born with says a large, upright, two-legged creature is the most dangerous thing in the environment. So when a wild animal is calm around humans, there are only a few explanations, and none of them is "it decided to trust us." Sometimes the animal is habituated, meaning it has lived near people so long it has stopped treating them as a threat. Sometimes the animal is so depleted, cold, hurt or exhausted that its body has quietly shut down the option to fight. Biologists have a name for one version of this: tonic immobility, a kind of involuntary freeze in which an animal goes still and unresponsive when escape seems impossible. And sometimes it is a mix of all of it, in ways science is still working out. Here is the line I want you to remember, because it will save you from a lot of bad internet takes: Do not confuse calm with consent. Stillness is not friendship. A quiet animal is not a tame animal. And some of the most dramatic moments in wildlife rescue come not from the fight, but from the moment the body stops fighting and everyone has to figure out what that actually means. There is even a documented condition, capture myopathy, in which the sheer stress of being restrained can damage a wild animal's muscles and organs badly enough to kill it, sometimes hours or days after the event, with no visible wound at all. Handling stress is a real cause of death in wildlife work. That is why professionals move slowly, keep things dark and quiet, and treat every second of contact like it costs something. Which means that in a situation like this, the difference between a good outcome and a tragic one can be invisible to the naked eye. That is what makes the footage so tense to watch if you know what to look for. And I am going to tell you exactly what to look for. But not yet. First I need you to understand how unlikely it was that anyone was there at all. . 05 // NATURE HAS NO AUDIENCE Almost everything that happens in the wild happens unwatched. Think about that for a second. Billions of small emergencies, every single day. Animals slipping, trapped, lost, starving, stranded. A fawn separated from its mother. A bird with a broken wing. A predator that misjudged a jump by an inch. Almost none of it is ever witnessed by anyone. No camera. No rescuer. No witness. It simply happens, and then it stops, and the world continues as if nothing occurred. For a rescue to happen, a chain of things has to line up. Somebody has to be in the right place. Somebody has to be looking in the right direction. Somebody has to notice a small, wet, dark shape in a huge moving surface, and understand what it is. Somebody has to decide to act rather than say "huh" and keep going. And somebody has to actually know how to help without making it worse. Break any single link and the story ends before it starts. Every viral rescue video you have ever seen is a chain like that, where all the links happened to hold. I think that is why these clips hit differently than almost anything else online. They are not just proof that an animal survived. They are proof that the chain can hold. That in a world that mostly does not look, sometimes someone does. The cold math says this animal had little time. The odds say nobody should have been there. And yet. I keep circling that "and yet." . QUICK PAUSE Before we go deeper, a small experiment. Without scrolling back up, answer this in your head: how long was the animal in that water before anyone noticed? Got a number? Now hold it loosely. The truth about time in a moment like this is that it never feels like what it is. Seconds stretch. Minutes vanish. Nobody in a crisis has an accurate clock. Keep your number. We will come back to it. . 06 // WHY YOUR BRAIN CANNOT SCROLL PAST THIS Let's zoom out, because this is the part that fascinates me professionally. In 2012, two Wharton researchers, Jonah Berger and Katherine Milkman, studied thousands of New York Times articles to figure out what makes online content spread. Their finding was not what most people expected. Positivity helped, but what mattered even more was how much physical arousal a piece of content produced. Awe, anger and anxiety, all high-arousal emotions, made people share. Sadness, which is low-arousal, made people stop. Now look at a clip like this through that lens. It has anxiety: will the animal make it? It has awe: this is a wild predator, close enough to see the texture of its fur. It has relief, and something bigger than relief. Psychologist Jonathan Haidt has a word for that bigger thing: elevation. It is the warm, expanding feeling you get when you witness someone behaving with unexpected goodness. It is the emotion that makes people want to be better, to call their mother, to do something kind. It is exactly the kind of emotion that makes people hit share before they have even finished watching. And then there is the mechanism behind the hook itself. In 1994, the behavioral economist George Loewenstein described what he called the information gap theory of curiosity: curiosity is the discomfort of noticing a gap between what you know and what you want to know. The bigger the gap, and the closer you feel to closing it, the harder it is to look away. That is what I did in the first three lines of this post. I gave you a wild animal, a disaster, and an unanswered question. Your brain has been carrying that open loop ever since. You cannot un-notice it. You can only close it. That is not a trick. It is how attention has worked since the first campfire story. The only difference now is that the campfire fits in your pocket and never goes out. There is one more thing, and it may be the most important one. Animals do not perform. They cannot posture for a camera, hide their fear behind a joke, or spin the story afterward. Their reactions are uncut. In an internet made mostly of people curating themselves, a wild animal reacting honestly to a moment is about as authentic as content gets. And in a feed that is ninety percent outrage, ten percent ads, and the occasional argument about sandwiches, a clip that is about competence and kindness feels like finding cold water in a desert. So no, it is not weird that you cannot look away. It would be weirder if you could. . 07 // 15 BOBCAT FACTS THAT CHANGE HOW YOU SEE THE CLIP You now know what the water does and what the animal is. Here is the rest of the file, fast. 1. The scientific name is Lynx rufus. "Rufus" means reddish. The animal has been carrying a color-coded label for centuries. 2. It is the most common wild cat in North America, which makes how rarely people see one even more remarkable. 3. Bobcats are ambush hunters, not chasers. They win by patience, not by endurance. Long chases are not their thing, and long swims are definitely not their thing. 4. Yes, bobcats can swim, and they do it when they have to: crossing rivers, fleeing danger, following prey. But it is a tool of last resort, not a hobby. 5. Kittens are born in spring, usually in litters of two to four, and the mother raises them alone. Dad is not part of the picture. 6. A kitten's eyes open at around a week and a half old. It will stay with its mother for most of its first year before heading off to find a territory of its own. 7. Young bobcats leaving home can travel dozens of miles looking for a place to settle. That means young animals are the ones most likely to end up somewhere unfamiliar, in situations nothing in their short lives prepared them for. 8. Bobcats mark territory with scent, scrapes and scratched trees. Their world is written in a language we mostly cannot read. 9. When they cannot finish a meal, they often cover the leftovers with leaves, snow or debris and come back for it later. Forward planning, from a cat. 10. They climb trees and use them for escape and lookout. If a bobcat ever wanted to get out of a situation, vertical was its first idea. 11. Their ear tufts are still debated. Camouflage? Hearing? Communication? Nobody has a definitive answer, and I love that. 12. Bobcat and Canada lynx are cousins, not twins. Lynx have longer ear tufts, bigger paws for snow, and a fully black-tipped tail. A bobcat's tail is black only on top of the tip, like someone dipped a paintbrush and stopped halfway. 13. Coat color changes by region. Desert animals run pale and sandy. Northern animals run darker and grayer. Same species, different wardrobe. 14. A bobcat is not closely related to a cougar. It belongs to the Lynx genus. The cougar belongs to another. When people call it a "baby mountain lion," a biologist somewhere loses a year of their life. 15. In the wild, life is often short and hard. Many bobcats do not make it to ten years old. In captivity, they can live far longer. Which tells you what the wild costs, every single day, on top of everything else. Fifteen facts. One conclusion. Every one of these facts describes an animal built to survive. Which is exactly why watching one in trouble is so hard to forget. . 08 // SIX MYTHS ABOUT WILD CATS AND WATER Since we are here, let's clear up some things. MYTH 1: All cats hate water. FALSE. Tigers swim. Jaguars swim. Fishing cats literally dive for a living. The idea that cats are universally water-averse comes from house cats, and even that is a generalization. Plenty of wild cats are comfortable in water. It is not fear of water. It is that most cats do not choose it unless there is a reason. MYTH 2: If an animal can swim, it is safe in water. FALSE. Being able to swim and being able to survive in water are two different skills. Temperature, distance, current, exhaustion and time all matter. A strong swimmer can lose to cold long before it loses to drowning. MYTH 3: A tough predator does not need help. FALSE. Toughness is not immunity. Predators die of exposure, injury and exhaustion all the time. Being at the top of a food chain does not mean being at the top of every situation. MYTH 4: A calm wild animal is a friendly wild animal. FALSE. We covered this. Calm can mean cold, tired, hurt or in shock. It does not mean safe to touch. MYTH 5: Wild cats attack people constantly. FALSE. It is the opposite. Bobcats avoid people so well that many humans go a lifetime without seeing one. Encounters that end badly are extremely rare, and most involve an animal that is sick, cornered or handled. MYTH 6: Rescue is easy. FALSE. Rescue is the visible five percent. Underneath it are timing, knowledge, restraint, risk and an enormous amount of luck. Six myths. Not one of them survives contact with the footage. . 09 // THE SECOND HALF OF EVERY RESCUE Here is something the clips never show you. Getting the animal out is the first half of the story. The second half is quieter, slower, and matters just as much. When a wild animal ends up in professional hands, the priorities are almost the opposite of what your instincts tell you. Warmth, calm, darkness and silence come first. Then a medical check for injuries, dehydration and hypothermia. Then, if all goes well, recovery in a space where the animal sees as little of humans as possible. Yes, as little as possible. Good rehabilitators deliberately avoid cuddling, talking to, or bonding with wild patients, because an animal that gets comfortable around people is an animal that struggles to survive once it goes back out there. The goal is never a rescued pet. The goal is a released predator. And when it is possible, animals are often returned to the area where they were found, because that is the territory they know: the routes, the hiding places, the food, the neighbors. So the real happy ending of a wildlife rescue is not a hug. It is a door opening, and an animal walking through it without looking back. Hold on to that image. It will matter in a minute. . 10 // PLEASE DO NOT TRY THIS I am going to be blunt, because the comment section will not be. Every time a clip like this goes viral, thousands of people write some version of "I would have grabbed it too" or "I want to keep it." Please do not. A wild bobcat is not a pet. It is a wild predator with claws, teeth, and a nervous system built to treat contact as an attack. Even a small, exhausted, seemingly docile animal can injure a person badly in a fraction of a second. Bites and scratches from wildlife can carry infection. Rabies in bobcats is uncommon, but it is real, and that is not a gamble you take on a hunch. And as we covered, the danger runs both ways. The stress of being handled can hurt or kill a wild animal even when nobody meant it any harm. In many places it is also illegal to keep or transport wild animals without permits, which means the well-intentioned rescuer can end up in a legal mess and the animal can end up worse off. So what should you do if you ever find a wild animal in trouble? Keep your distance. Keep noise and movement low. Do not try to pick it up, feed it, or warm it up yourself. Call your local wildlife agency, animal control, or a licensed wildlife rehabilitator, and follow their instructions. They do this for a living. They know what the animal needs, and what it does not. Love wildlife the right way: at a distance, with respect, and through the phone number of someone qualified. The best rescue stories are the ones where the animal gets to go back to being wild. Which brings me to the ending. . 11 // HOW TO WATCH THIS VIDEO Here is my one request. Do not watch it on autopilot. Before you press play, decide what you think is going to happen. Make a prediction, honestly. Then watch, and see how wrong you were. Keep your eyes on the animal, not on the people. Watch the eyes. Watch the ears. Watch the moment the animal has to decide what it wants to do. Wildlife tells you everything if you know where to look, and it does not lie. Pay attention to the way the whole tone of the clip shifts as it goes on. The first seconds and the last seconds feel like two entirely different films. And carry these five questions with you while it plays: Why is a predator this strong not fighting? What does an animal like this actually feel in a moment like that? How many other animals never get this chance? What would you have done in that spot, honestly? And what is the very last thing the bobcat does? Only one of those has an answer you can see with your own eyes. And remember the door I mentioned, the one that opens and the animal that does not look back? Keep that picture in your head while you watch. See how close reality gets to it. And stay to the last frame. There is a reason I told you the ending matters. A second watch will show you things the first one hides. . 12 // THE THREE RULES EVERY WILDLIFE PROFESSIONAL LIVES BY If you spent a week with people who do this work for a living, you would hear the same three ideas over and over. Rule one: the animal comes before the story. Good professionals never put a camera ahead of the patient. The best footage in the world is worthless if it costs the animal something. Rule two: distance is a form of kindness. The closer you get, the more stress you add. The most respectful thing you can do for a wild animal is usually to do less, from farther away, and let it be what it is. Rule three: success means the animal does not need you anymore. Not gratitude. Not a bond. Not a photo with a cute caption. Success is independence. Notice how that flips the usual story. In most human stories, success is being needed. In wildlife, success is being forgotten. . 13 // IF YOU ONLY REMEMBER FIVE LINES Screenshot this if you want to. I would. 1. Water steals heat 25 times faster than air, and a wet coat stops working. 2. A bobcat is an ambush predator with the strength of an animal twice its size, and none of that helps when the ground disappears. 3. Do not confuse calm with consent. 4. Almost everything in nature happens unwatched. That is why the times someone looks matter so much. 5. The best ending to a wild rescue is an animal that walks away on its own. . 14 // THE REAL REASON I POSTED THIS We spend a lot of time online being told the world is getting worse. Sometimes it is. But there is another truth underneath, one that rarely trends: in a random moment, on an ordinary day, with no cameras and no audience and nothing to gain, someone looked, someone cared, and something wild got another chance. Remember the number you guessed earlier, the seconds or minutes in that water? It turns out it does not matter. What matters is that somebody noticed at all. That is the whole post. Not a miracle. Not a spectacle. Just a small, stubborn piece of evidence that the chain can hold. If it moved you even a little, that is not weakness. That is the part of you that still works. If this landed for you, here is what to do: Reply with one word for how you felt watching it. Just one. I read them. Repost it for the person in your life who says they do not care about animals. Watch what happens to their face. Bookmark this thread-that-is-not-a-thread for the next time your feed makes you tired of everything. And follow me if you want more clips that make you feel something real, plus the stories behind them, without the fluff. One last thing. You made it to the bottom of a very long post. That means something. It means you are the kind of person who stays for the ending. Now go back to the video, press play, and stay for the last frame. P.S. If you scrolled straight to the bottom to see how this ends: the video is attached to this post. The ending is in the video. Nothing I write can replace it, and I would not want it to. P.P.S. Drop your prediction in the replies before you press play: what does the bobcat do in the very last seconds? Then come back after and tell me how wrong, or how right, you were. I will pin my favorite.

Earth Unveiled

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