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Annie just did what i thought impossible. raw pokemon ruby .gba ROM bytes → full hybrid WASM recompiler + complete GBA runtime (ARM7TDMI + THUMB, scanline accurate PPU, 4 channel DMA, Flash 128K + RTC, BIOS HLE) all in one autonomous sprint 🤯 the 32 bit console wall just... show more

Nathan Wilbanks

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191,471 просмотров • 2 месяцев назад •via X (Twitter)

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Factory is better than Devin, Codex, and Jules, and it's not even close. This is not just a fully autonomous software engineer but also a product manager and researcher. It's the entire package you need to build a new project or dramatically expand an existing one. I recorded a quick video so you can see how it works. You can use these autonomous Droids from Factory. Here is what they can do: 1. Build a full production-grade app from scratch 2. Develop new features for an existing codebase 3. Migrations or refactors on your codebase 4. Build integrations 5. Generate complete feature specifications 6. Solve and close issues from your issue management system 7. Do deep research on any software topics And Factory can connect to Linear, GitHub, Google Drive, Slack, Jira, Sentry, GitLab, and Notion to do its work. You can use any of the big models to do what you need.

Factory is better than Devin, Codex, and Jules, and it's not even close. This is not just a fully autonomous software engineer but also a product manager and researcher. It's the entire package you need to build a new project or dramatically expand an existing one. I recorded a quick video so you can see how it works. You can use these autonomous Droids from Factory. Here is what they can do: 1. Build a full production-grade app from scratch 2. Develop new features for an existing codebase 3. Migrations or refactors on your codebase 4. Build integrations 5. Generate complete feature specifications 6. Solve and close issues from your issue management system 7. Do deep research on any software topics And Factory can connect to Linear, GitHub, Google Drive, Slack, Jira, Sentry, GitLab, and Notion to do its work. You can use any of the big models to do what you need.

Santiago

70,099 просмотров • 1 год назад

*** Parodius Update - LAZERS !! *** How many sprites do we have to use on the Sega Genesis to make 4 x 160 pixel wide lazers ?? 10 ? 20 ? ZERO !! Which is really great as when the options overlap all with Lazers using sprites would have lead to sprite severe overload / dropout otherwise. So yes we have some raster trickery going on . I think this might take the cake for the most exotic effect Ive added just for a power up type haha. Breaking down the effect. 1.) Lazer tile rows ( the tile rows the lazers are currently on ) are copied to an offscreen area of the plane . I used the DMA VRAM to VRAM copy mode for this. You can see these offscreen rows just above the Game Videos window in the Plane A tilemap view. 2.) Then a solid block of Lazer colored tiles is drawn at the position of the Lazer horizontally in the offscreen buffer we just copied too - these are 8x8 tiles. 3.) Then onscreen when as the display is being drawn we change to the Lazers row at the correct pixel line offset for exactly 1 scanline then change back to the normal screens next row. I'm using the Horizontal interupt system developed in SOTA for handling the interupts for this. The technique used has been done before on the NES ( Salamander tech demo ) , as usual some of the best techniques come from the 8 bit systems , still it presented challenges getting it working on the MD. These irregular spaced interupts on the Genesis are a little bit of a problem too . All the height sorting has to be done on the frame before. Whats very interesting about the VRAM to VRAM copy DMA (Copy DMA) is it runs asynchronous to the CPU. Normally ROM/RAM to VRAM write DMA (Write DMA) halts the CPU during the send operation but with a Copy DMA I can pipeline one Copy DMA setup while the previous one is copying still . So even though the Copy DMA is 1/2 the speed of a Write DMA with the pipelining and no stalling its probably on par or better in some cases , where a copy makes sense to use. Theres still need some fine tuning , might make smaller lazers with more breaks etc. The Lazers can be full screen width , or narrow as we want so bit of fine tuning here to come. Pyron Vector Orbitex #Parodius #SegaMegadrive #SegaGenesis #SGDK

* Parodius Update - LAZERS !! * How many sprites do we have to use on the Sega Genesis to make 4 x 160 pixel wide lazers ?? 10 ? 20 ? ZERO !! Which is really great as when the options overlap all with Lazers using sprites would have lead to sprite severe overload / dropout otherwise. So yes we have some raster trickery going on . I think this might take the cake for the most exotic effect Ive added just for a power up type haha. Breaking down the effect. 1.) Lazer tile rows ( the tile rows the lazers are currently on ) are copied to an offscreen area of the plane . I used the DMA VRAM to VRAM copy mode for this. You can see these offscreen rows just above the Game Videos window in the Plane A tilemap view. 2.) Then a solid block of Lazer colored tiles is drawn at the position of the Lazer horizontally in the offscreen buffer we just copied too - these are 8x8 tiles. 3.) Then onscreen when as the display is being drawn we change to the Lazers row at the correct pixel line offset for exactly 1 scanline then change back to the normal screens next row. I'm using the Horizontal interupt system developed in SOTA for handling the interupts for this. The technique used has been done before on the NES ( Salamander tech demo ) , as usual some of the best techniques come from the 8 bit systems , still it presented challenges getting it working on the MD. These irregular spaced interupts on the Genesis are a little bit of a problem too . All the height sorting has to be done on the frame before. Whats very interesting about the VRAM to VRAM copy DMA (Copy DMA) is it runs asynchronous to the CPU. Normally ROM/RAM to VRAM write DMA (Write DMA) halts the CPU during the send operation but with a Copy DMA I can pipeline one Copy DMA setup while the previous one is copying still . So even though the Copy DMA is 1/2 the speed of a Write DMA with the pipelining and no stalling its probably on par or better in some cases , where a copy makes sense to use. Theres still need some fine tuning , might make smaller lazers with more breaks etc. The Lazers can be full screen width , or narrow as we want so bit of fine tuning here to come. Pyron Vector Orbitex #Parodius #SegaMegadrive #SegaGenesis #SGDK

Shannon Birt

16,162 просмотров • 11 месяцев назад

I couldn't believe it. One product image = a full video ad with Wizstar. No script. No shoot. No editor. Just the image, one sentence of instruction, and Wizstar did everything else. It analyzes your product automatically, builds the scene around it, and generates a complete marketing video. Character stays consistent across every shot. Product details stay accurate. Audio and video sync perfectly. It powers multi-model invocation to match any style you need, and Seedance 2.0 supports face input for consistent character generation. But wait, it gets better. You can also upload any viral video from TikTok or Amazon. Then it will recreate that EXACT winning formula for YOUR product. No more guessing what works. Zero creative trial and error. Make a video today, start seeing sales in 7 days. New users get free credits on signup. First subscription is only $19 and includes a 30-second E-commerce Agent experience. Try it here: Who's making their first ad tonight? 👇 #Wizstar #AIVideo #EcommerceMarketing

I couldn't believe it. One product image = a full video ad with Wizstar. No script. No shoot. No editor. Just the image, one sentence of instruction, and Wizstar did everything else. It analyzes your product automatically, builds the scene around it, and generates a complete marketing video. Character stays consistent across every shot. Product details stay accurate. Audio and video sync perfectly. It powers multi-model invocation to match any style you need, and Seedance 2.0 supports face input for consistent character generation. But wait, it gets better. You can also upload any viral video from TikTok or Amazon. Then it will recreate that EXACT winning formula for YOUR product. No more guessing what works. Zero creative trial and error. Make a video today, start seeing sales in 7 days. New users get free credits on signup. First subscription is only $19 and includes a 30-second E-commerce Agent experience. Try it here: Who's making their first ad tonight? 👇 #Wizstar #AIVideo #EcommerceMarketing

Pradeep Pandey

59,917 просмотров • 2 месяцев назад

FULL INTERVIEW HERE ON X: "After tabulating, calculating, and quantifying all of the various dimensions of the costs of the [pandemic] lockdown policies, we found that lockdowns are about 30 to 35 times more costly than what they could possibly have delivered in benefits… in terms of human life." This was Gigi Foster's (of Australians for Science and Freedom) stunning revelation to me in June of 2022, based on the results of her own research. I had been well aware that lockdown policies had signficant collateral damage associated with them. Indeed, this was the reason why common-sense public health policy for years had been to avoid them at all costs—until, that is, this was all thrown out the window in early 2020. But I hadn't been ready to accept a 35X differential. The bottom line, according to Gigi, who as a professor at the University of New South Wales is a behavioural economics expert and as such does these sorts of calculations routinely, was always: "How many people would you be willing to kill in order to save one from COVID? That is essentially the trade-off." From skipped cancer screenings to impeded speech development in toddlers to growing social inequality, when we add it all up, what price did we pay? Join me and Gigi to learn the truth in American Thought Leaders 🇺🇸 with @JanJekielek Episode #637, Gigi Foster: Did Our Pandemic Policies Kill More People Than They Saved?, and check out my channel on Box 📦 Club Leader 2.0—I'll post links in the thread below👇

FULL INTERVIEW HERE ON X: "After tabulating, calculating, and quantifying all of the various dimensions of the costs of the [pandemic] lockdown policies, we found that lockdowns are about 30 to 35 times more costly than what they could possibly have delivered in benefits… in terms of human life." This was Gigi Foster's (of Australians for Science and Freedom) stunning revelation to me in June of 2022, based on the results of her own research. I had been well aware that lockdown policies had signficant collateral damage associated with them. Indeed, this was the reason why common-sense public health policy for years had been to avoid them at all costs—until, that is, this was all thrown out the window in early 2020. But I hadn't been ready to accept a 35X differential. The bottom line, according to Gigi, who as a professor at the University of New South Wales is a behavioural economics expert and as such does these sorts of calculations routinely, was always: "How many people would you be willing to kill in order to save one from COVID? That is essentially the trade-off." From skipped cancer screenings to impeded speech development in toddlers to growing social inequality, when we add it all up, what price did we pay? Join me and Gigi to learn the truth in American Thought Leaders 🇺🇸 with @JanJekielek Episode #637, Gigi Foster: Did Our Pandemic Policies Kill More People Than They Saved?, and check out my channel on Box 📦 Club Leader 2.0—I'll post links in the thread below👇

Jan Jekielek

124,953 просмотров • 2 лет назад

AI has changed software engineering more in the last 3 years than it has changed in the previous 30. What’s needed is not a debate about whether it’s going away—instead it’s a serious discussion about its future: What are the new primitives, techniques, and best practices for software engineering in the age of AI. That’s why I brought Scott Wu (Scott Wu) on AI & I. He’s the founder of Cognition, the company behind the world’s first autonomous AI coding agent, Devin. Cognition got to $73M ARR in less than 2 years—and they just acquired Windsurf to accelerate their growth. I had Scott on the show to talk about where the programming goes from here. We get into: - What the new tools and workflows are for AI engineers. In the near term, Scott sees software engineering defined by a spectrum of tools. At one end are AI features that speed up coding, like tab complete; at the other are agentic systems, like Devin, that can take on tasks independently. Until engineers can operate entirely at the higher layer of abstraction, he argues, both are essential. - Why Scott thinks AGI is already here. By the benchmarks of a decade ago—passing the Turing test, solving hard math problems, and operating agentically—AGI is already here. The line keeps moving, he argues, because humans constantly redefine work around what machines can’t yet do. - Why developers will turn into product architects. Scott sees the long-term future of software engineering as a steady climb up the ladder of abstraction. Just as programming went from assembly to languages like Python and JavaScript, he thinks the future is humans focusing on the product, while AI agents execute. - How Devin stacks up against Anthropic’s Claude Code. Scott credits Claude Code’s success to great product design and the models becoming capable enough to support autonomous workflows. But according to him, the CLI itself isn’t the breakthrough, it’s how a tool fits into a developer’s workflow. Claude Code’s paradigm is that the AI is you, taking the wheel of your computer, he says, while Devin is like the engineer sitting beside you: it runs in its own cloud environment, manages the repo, and improves over time at testing and refining code. This episode of Every 📧’s AI & I is a must-watch for anyone interested in the brass tacks of how AI changes the future of programming. Watch below! Timestamps: Introduction: 00:02:02 Why Scott thinks AGI is here: 00:02:32 Scott’s personal journey as a founder: 00:09:27 Why the fundamentals of computer science still matter: 00:16:55 How the future of programming will evolve: 00:22:30 A new workflow for the AI-first software engineer: 00:26:50 How Devin stacks up against Claude Code: 00:29:33 Reinforcement learning to build better coding agents: 00:40:05 What excites Scott about AI beyond Cognition: 00:50:05

AI has changed software engineering more in the last 3 years than it has changed in the previous 30. What’s needed is not a debate about whether it’s going away—instead it’s a serious discussion about its future: What are the new primitives, techniques, and best practices for software engineering in the age of AI. That’s why I brought Scott Wu (Scott Wu) on AI & I. He’s the founder of Cognition, the company behind the world’s first autonomous AI coding agent, Devin. Cognition got to $73M ARR in less than 2 years—and they just acquired Windsurf to accelerate their growth. I had Scott on the show to talk about where the programming goes from here. We get into: - What the new tools and workflows are for AI engineers. In the near term, Scott sees software engineering defined by a spectrum of tools. At one end are AI features that speed up coding, like tab complete; at the other are agentic systems, like Devin, that can take on tasks independently. Until engineers can operate entirely at the higher layer of abstraction, he argues, both are essential. - Why Scott thinks AGI is already here. By the benchmarks of a decade ago—passing the Turing test, solving hard math problems, and operating agentically—AGI is already here. The line keeps moving, he argues, because humans constantly redefine work around what machines can’t yet do. - Why developers will turn into product architects. Scott sees the long-term future of software engineering as a steady climb up the ladder of abstraction. Just as programming went from assembly to languages like Python and JavaScript, he thinks the future is humans focusing on the product, while AI agents execute. - How Devin stacks up against Anthropic’s Claude Code. Scott credits Claude Code’s success to great product design and the models becoming capable enough to support autonomous workflows. But according to him, the CLI itself isn’t the breakthrough, it’s how a tool fits into a developer’s workflow. Claude Code’s paradigm is that the AI is you, taking the wheel of your computer, he says, while Devin is like the engineer sitting beside you: it runs in its own cloud environment, manages the repo, and improves over time at testing and refining code. This episode of Every 📧’s AI & I is a must-watch for anyone interested in the brass tacks of how AI changes the future of programming. Watch below! Timestamps: Introduction: 00:02:02 Why Scott thinks AGI is here: 00:02:32 Scott’s personal journey as a founder: 00:09:27 Why the fundamentals of computer science still matter: 00:16:55 How the future of programming will evolve: 00:22:30 A new workflow for the AI-first software engineer: 00:26:50 How Devin stacks up against Claude Code: 00:29:33 Reinforcement learning to build better coding agents: 00:40:05 What excites Scott about AI beyond Cognition: 00:50:05

Dan Shipper 📧

35,342 просмотров • 10 месяцев назад

AI models currently have a 50% chance of doing something that takes a human expert one hour. This doubles every 7 months. In 2 years? They could automate full workdays. In 4 years? A full month. I discuss the most important graph in AI today with Beth Barnes, the CEO of METR, which uncovered this rule of AI progress. Her bottom line: "It really doesn't seem like 2 years would be surprising for recursively self-improving AI." Beth also explains: where company safety testing fails, why there are no true closed-weight models, AI undermines leading powers, why she's come around on open weighting, and why models might be about to start playing dumb much more often. Enjoy! Available on the 80,000 Hours Podcast in all apps. Links below. 1:51 Can we see AI scheming in the chain of thought? 12:50 Alignment faking 17:33 We have to test models before they're even used inside AI companies 31:56 Each 7 months models can do tasks twice as long 51:31 METR's research finds AIs are solid at AI research already 58:18 AI may turn out to be strong at novel and creative research 1:07:55 Recursively self-improving AI might even be here in two years 1:14:29 Could evaluations backfire? 1:39:55 Do we need external auditors doing AI safety tests? 1:54:09 Why not work at AI companies 2:08:40 The new more dire situation has forced changes to METR's strategy 2:21:49 Overrated: Interpretability research 2:32:55 Overrated: Major AI companies' contributions to safety research 2:39:15 Could we ban using AI to enhance AI, or is that just naive? 2:45:31 Open-weighting models is often good 2:50:22 What we can learn about AGI from the nuclear arms race 3:10:43 AI is more like bioweapons because it undermines the leading power 3:42:09 What research METR plans to do next

AI models currently have a 50% chance of doing something that takes a human expert one hour. This doubles every 7 months. In 2 years? They could automate full workdays. In 4 years? A full month. I discuss the most important graph in AI today with Beth Barnes, the CEO of METR, which uncovered this rule of AI progress. Her bottom line: "It really doesn't seem like 2 years would be surprising for recursively self-improving AI." Beth also explains: where company safety testing fails, why there are no true closed-weight models, AI undermines leading powers, why she's come around on open weighting, and why models might be about to start playing dumb much more often. Enjoy! Available on the 80,000 Hours Podcast in all apps. Links below. 1:51 Can we see AI scheming in the chain of thought? 12:50 Alignment faking 17:33 We have to test models before they're even used inside AI companies 31:56 Each 7 months models can do tasks twice as long 51:31 METR's research finds AIs are solid at AI research already 58:18 AI may turn out to be strong at novel and creative research 1:07:55 Recursively self-improving AI might even be here in two years 1:14:29 Could evaluations backfire? 1:39:55 Do we need external auditors doing AI safety tests? 1:54:09 Why not work at AI companies 2:08:40 The new more dire situation has forced changes to METR's strategy 2:21:49 Overrated: Interpretability research 2:32:55 Overrated: Major AI companies' contributions to safety research 2:39:15 Could we ban using AI to enhance AI, or is that just naive? 2:45:31 Open-weighting models is often good 2:50:22 What we can learn about AGI from the nuclear arms race 3:10:43 AI is more like bioweapons because it undermines the leading power 3:42:09 What research METR plans to do next

Rob Wiblin

93,669 просмотров • 1 год назад

This was one of my favorite interviews of 2025... Founders often underestimate how much freedom they actually have. Anil Varanasi and Meter is a reminder of what happens when you use all of it. They ignored the usual advice and built the company their way. It’s no surprise their story doesn’t resemble anyone else’s. Here are just a few examples: 1. They spent four and a half years pre–revenue, just two people. It was essentially Anil and Sunil, alone, for four and a half years before they had a sales ready product and their first customers. They even scrapped an entire year of operating system work once they realized a different technical approach (inspired by an open source project) was better. 2. They literally moved to Shenzhen to learn how the physical world is made. They were blocked by slow hardware iteration in San Francisco, so they just relocated to Shenzhen for over a year. 3. Full vertical integration as a day one decision, not an afterthought. Meter decided from the start to own the entire stack: hardware, software, installation, and ongoing service. This is in a market where most entrants pick one slice (just switches, just access points, etc.) and get trapped as point solutions that end up acquired. 4. Business model treated as part of the product, not a pricing afterthought. They moved networking from “buy hardware” to: Meter provides the hardware, the software, the installation and ongoing support. The customer pays recurring, per square foot, and effectively “don’t pay us if the network doesn’t work.” Anil thinks about business model innovation on the same level as product and technology innovation. 5. Choosing a massive, incumbent dominated market on purpose. Networking is controlled by a few giants like Cisco. They were pulled toward that exact dynamic: a huge, durable market where the initial ramp is brutal, but if you get through it, there are very few new players alongside you. 6. Deliberately avoided the channel in a channel dominated industry. Roughly 90 percent of networking is sold through the channel.Meter refused to use the channel until they were convinced the product was dramatically better in every way, because incumbents could weaponize the channel with discounts to block them. Only after they had hundreds of happy customers and strong tools did they fully embrace channel sales. 7. The team has an extreme time horizon, paired with extreme urgency. Anil thinks in decades: “I care about where Meter ends up in 25 years, not five.” At the same time, he is obsessively focused on what happens in the next few hours and where every report spends time. That “barbell” between multi decade vision and hour by hour intensity is very explicit for him. 8. An allergy to “meta work” and most conventional management. No OKRs or goals at all. They have a strong skepticism of spending time on docs, processes, and coordination that feel like work but do not move the product forward.

This was one of my favorite interviews of 2025... Founders often underestimate how much freedom they actually have. Anil Varanasi and Meter is a reminder of what happens when you use all of it. They ignored the usual advice and built the company their way. It’s no surprise their story doesn’t resemble anyone else’s. Here are just a few examples: 1. They spent four and a half years pre–revenue, just two people. It was essentially Anil and Sunil, alone, for four and a half years before they had a sales ready product and their first customers. They even scrapped an entire year of operating system work once they realized a different technical approach (inspired by an open source project) was better. 2. They literally moved to Shenzhen to learn how the physical world is made. They were blocked by slow hardware iteration in San Francisco, so they just relocated to Shenzhen for over a year. 3. Full vertical integration as a day one decision, not an afterthought. Meter decided from the start to own the entire stack: hardware, software, installation, and ongoing service. This is in a market where most entrants pick one slice (just switches, just access points, etc.) and get trapped as point solutions that end up acquired. 4. Business model treated as part of the product, not a pricing afterthought. They moved networking from “buy hardware” to: Meter provides the hardware, the software, the installation and ongoing support. The customer pays recurring, per square foot, and effectively “don’t pay us if the network doesn’t work.” Anil thinks about business model innovation on the same level as product and technology innovation. 5. Choosing a massive, incumbent dominated market on purpose. Networking is controlled by a few giants like Cisco. They were pulled toward that exact dynamic: a huge, durable market where the initial ramp is brutal, but if you get through it, there are very few new players alongside you. 6. Deliberately avoided the channel in a channel dominated industry. Roughly 90 percent of networking is sold through the channel.Meter refused to use the channel until they were convinced the product was dramatically better in every way, because incumbents could weaponize the channel with discounts to block them. Only after they had hundreds of happy customers and strong tools did they fully embrace channel sales. 7. The team has an extreme time horizon, paired with extreme urgency. Anil thinks in decades: “I care about where Meter ends up in 25 years, not five.” At the same time, he is obsessively focused on what happens in the next few hours and where every report spends time. That “barbell” between multi decade vision and hour by hour intensity is very explicit for him. 8. An allergy to “meta work” and most conventional management. No OKRs or goals at all. They have a strong skepticism of spending time on docs, processes, and coordination that feel like work but do not move the product forward.

Brett Berson

27,506 просмотров • 8 месяцев назад

How to Build a Proper Product Page It breaks my heart every time I see someone from Brazil, making just $200 a month, spend $40 on a test ad only to get a $2 CPC. Then, they cut the ad at $35 spend with 17 clicks and 0 conversions. What’s even more frustrating is when I visit their site and see it’s a complete mess. It’s like they didn’t even try. That’s why I’m making this post. *Disclaimer: If you're building a brand, this isn’t for you. This is for testing products correctly with a website that’s good enough to convert, but if you’re serious about your brand, hire a Figma designer and a developer to get things done right. As a fun fact, I tested the following product myself (In the video below) and with just one click at a $3 CPC, I got a sale with an $80 AOV (100% CVR). I paused the ads because the CPMs hit $150. If you're interested, save this product for the future. I'll share the creatives i used to ran the product with those who comment below. "jordan, stop teaching high school kids about selling reps and getting them sued by Prada or other big brands, ruining their payment gateways, and crushing their dreams." (with 5 random people only will do it, wouldn't make sense for everyone to be ripping it). Shoutout to Adrian for this one. 🫡 Back to the important stuff: Let’s talk about creating a great product page. First off, ditch the “Buy Now” button. By using this, you miss out on the chance to increase your Average Order Value (AOV) through upsells during checkout. Instead, replace it with an “Add to Cart” button. Also, remove the quantity selector. Hardly anyone uses them on the product page, but they do in the cart. Instead, offer bundles, which you can set up using the Kaching Bundles app or your theme if it supports this feature. The cart experience is crucial, and you can optimize it using the UpCart app. Enable all its product page features so it takes over the standard Shopify cart or your theme’s cart. The built-in cart systems are outdated—they don’t allow upsells like UpCart does. Plus, UpCart directly opens the cart after a product is added, where the upsell option appears at the bottom, increasing your chances of boosting AOV. Pay attention to color schemes. Don’t use neutral colors for headers and buttons if your product features a primary color. A yellow jacket with a black header is like pizza with raspberry syrup—just doesn’t work. If your product images are gray, white, or black, match those with your headers and buttons. Otherwise, play around with colors to give your page some dynamic appeal. Bookmark this site to get color palettes that match your primary color. For exact color matches, download the "ColorZilla" extension, which gives you the exact color code of whatever is under your mouse on the screen. If your product color is #6E402A, you’ll know it and can match it perfectly with your header and buttons, creating a visually appealing contrast like Cider did here below. Right now, when some of you send me these websites, they look like government pages from the early 2000s. Next, consider the typography and button styles. For fonts, use Helvetica Regular for body text and Helvetica Bold for titles, both at a minimum size of 100%. It’s simple and effective. As for buttons, they should have a corner radius of 8px. To change these settings, go to the theme settings - typography/buttons and adjust the buttons and typography accordingly. Rounded buttons create a modern, inviting feel, while full square buttons can give off an outdated vibe. Even casinos in Vegas design their buildings with curves, ensuring that visitors always have a view angle that draws them back in—it’s all about keeping things appealing and engaging. Finally, the product page layout itself should be clean and concise. Use collapsible rows for your product descriptions. No one wants to read a novel, so keep it compact and focused. Images and videos are what sell—this is why we don’t use text-heavy images in our ads; they just bore people. Keep the description short, with enough information to inform but not overwhelm. If you need to include more features, put them in the FAQs section—that’s what they’re for. The goal is to make sure that the "Add to Cart" button is visible as soon as possible once the customer lands on the product page, without overwhelming them but providing just enough information—some key benefits, high-quality images, and buying options in the bundles. Once they hit the cart, the upsells will do their job. Remember, the three essential apps for setting this up are Loox Reviews for customer feedback (use the product widget reviews at the bottom of the page before the FAQs and the rating widget just above the title), Kaching Bundles for offering package deals, and UpCart for optimizing the cart experience. Just for the record, this is to help newcomers and guide them to something taht works, me personally i don't use free themes, but this one i created could convert easily if the product, offer and ads match well. Good luck and keep testing! 🗳🥂💸🤑

Zzzz

42,318 просмотров • 2 лет назад

OpenAI just mass-fired their robotics team. One of the engineers DM'd me 20 minutes later. I didn't know him. He found me through a Polymarket thread. His first message: "I have 30 days of severance and nothing to lose. Let me tell you what we actually use internally. It's not GPT" I thought he was trolling. "Every serious team at OpenAI prototypes on Claude Code. Not ChatGPT. Not the API. Claude Code connected to a repo. That's the actual workflow" I asked why. "Because Claude reads the codebase. GPT reads the prompt. There's a difference. One guesses. The other one understands the full context and builds on top of it" He sent me one link. 86 million Polymarket trades. Every wallet. Every entry. Every exit. Open source. Free. "Point Claude Code at this. Say - find every wallet with 70%+ win rate and 100+ trades. Watch what happens" I did it that night. Claude pulled 47 wallets in 4 minutes. Average profit: $214K. Hold time: 7 hours. 91% exit BEFORE resolution. They never wait for the outcome. "Now look at how they exit" Top wallets capture 86% of the move and cut at 12%. Everyone else captures 58% and holds losers to 41%. Same entries. Completely different results. He sent another link. "Three commands. Your bot sees 500+ markets. No key needed. Read-only. Claude scores them in 20 minutes" I asked why he's telling me all this. "Because I just got fired for saying we should open-source more. So here I am open-sourcing everything I know" Then he sent me an article where someone built the full bot from these repos in a weekend -> Three exit triggers: Target 85% of move. Volume spike x3 - smart money out. 24h silence - thesis dead. I copied the stack. Claude Code $20. VPS $5. $25/month. No team. No office. No GPT subscription. 17 days. 191 trades. 73% win rate. $850 seed. +$9,400. I sent him my results. He replied: "This is exactly what I built as a side project at OpenAI. They made me delete it" I asked if I could post this. "Post it. What are they gonna do. Fire me again?"

OpenAI just mass-fired their robotics team. One of the engineers DM'd me 20 minutes later. I didn't know him. He found me through a Polymarket thread. His first message: "I have 30 days of severance and nothing to lose. Let me tell you what we actually use internally. It's not GPT" I thought he was trolling. "Every serious team at OpenAI prototypes on Claude Code. Not ChatGPT. Not the API. Claude Code connected to a repo. That's the actual workflow" I asked why. "Because Claude reads the codebase. GPT reads the prompt. There's a difference. One guesses. The other one understands the full context and builds on top of it" He sent me one link. 86 million Polymarket trades. Every wallet. Every entry. Every exit. Open source. Free. "Point Claude Code at this. Say - find every wallet with 70%+ win rate and 100+ trades. Watch what happens" I did it that night. Claude pulled 47 wallets in 4 minutes. Average profit: $214K. Hold time: 7 hours. 91% exit BEFORE resolution. They never wait for the outcome. "Now look at how they exit" Top wallets capture 86% of the move and cut at 12%. Everyone else captures 58% and holds losers to 41%. Same entries. Completely different results. He sent another link. "Three commands. Your bot sees 500+ markets. No key needed. Read-only. Claude scores them in 20 minutes" I asked why he's telling me all this. "Because I just got fired for saying we should open-source more. So here I am open-sourcing everything I know" Then he sent me an article where someone built the full bot from these repos in a weekend -> Three exit triggers: Target 85% of move. Volume spike x3 - smart money out. 24h silence - thesis dead. I copied the stack. Claude Code $20. VPS $5. $25/month. No team. No office. No GPT subscription. 17 days. 191 trades. 73% win rate. $850 seed. +$9,400. I sent him my results. He replied: "This is exactly what I built as a side project at OpenAI. They made me delete it" I asked if I could post this. "Post it. What are they gonna do. Fire me again?"

rari

157,503 просмотров • 3 месяцев назад

FULL TRANSCRIPT OF ELON'S CYBERCAB AND ROBOVAN PRESENTATION 00:00 Welcome 01:16 Cybercab & Future of transportation 04:33 Cost 05:53 Timeline 07:13 Self-driving technology 10:05 Inductive charging 10:24 The cities of the future 11:04 Robovan 12:13 Optimus Welcome Welcome to the We, Robot party. We have quite a show for you tonight. I think you're going to like it. As you can see, I just arrived in the Robotaxi, the Cybercab. And there's 20 more where that came from. So they've been traveling, there's no people in them. As you can see, the car is just going by with no people. We have 50 fully autonomous cars here tonight. So you'll see model Y's and the Cybercabs, all driverless. You'll be able to take a ride in the Cybercab. There's no steering wheel or pedals. So I hope this goes well, we'll find out. You see a lot of sci-fi movies where the future is dark and dismal, where it's not a future you want to be in. So, you know, I love Blade Runner, but I don't know if we want that future. We want that duster he's wearing, but not the bleak apocalypse. We want to have a fun, exciting future that, if you could look in a crystal ball and see the future, you'd be like, yes, I wish I could be there now. That's what we want. Cybercab & Future of transportation So, when we think about transport today, there's a lot of pain that we take for granted, that we think is normal. Like having to drive around LA in 3 hours of traffic. Yeah, people that live in LA, I mean, you know, try to get from Pasadena to El Segundo during rush hour. You can fly to another city faster than you can get to LA. And you have to drive the whole way, unless you're in a Tesla. Of course, our Tesla already does quite well at this supervised self-driving. So, supervised full self-driving is actually working quite well. I'm sure there's people in the crowd who are using that. So, we'll move from supervised full self-driving to unsupervised full self-driving where the car, you could fall asleep and wake up at your destination. But there's also a challenge for a lot of people that cars cost too much. I mean, when you factor in everything that goes into a car and the car insurance and the car payments, storage of the car, it's very expensive. You say, like, how many hours a week are cars used? Your average passenger car is only used about 10 hours a week out of 168 hours. So, the vast majority of the time cars are just doing nothing. But if they're autonomous, they could be used, I don't know, five times more, maybe ten times more. So you could actually, for the same car, would have five times as much value, maybe ten times as much value. There's 168 hours in the week, and like I said, only ten of them are used for driving. And then, a bunch of those hours are looking for a parking spot, which can be pretty annoying at times. So, with autonomy, you get your time back. This is a very big deal. So it's not just, it'll save lives, like a lot of lives and prevent injuries. I think we'll see autonomous cars become ten times safer than a human. I mean, if you think of times past where there used to be an elevator operator in every elevator but once in a while, they get tired and accidentally shear somebody in half. Now, we have automated elevators. You just get an elevator and you press a button and you don't even think about it and it just takes you to the floor. And if you did see an elevator operator with a big relay switch, you'd be like, that's weird. That's how cars will be. And it's not just the lives saved in injuries, but if you think about the cumulative time that people spend in a car and the time that they will get back that they can now spend, well, I guess, on their phones or watching a movie or doing work or whatever you want to do you can think of the car in autonomous world as being like just little lounge. You're just sitting in a comfortable little lounge and you can do whatever you want while you're in this comfortable little lounge. And when you get out, you will be at your destination. So, yeah, it's gonna be awesome. Cost So, in fact, I think the cost of autonomous transport will be so low that you can think of it like individualized mass transit. The average cost of a bus per mile for a city, not the ticket price, because that is subsidized, but the average price is about a dollar a mile, whereas the cost of Cybercab we think probably over time, the operating cost is probably going to be around twenty cents a mile. Including taxes and everything else, it probably ends up being 30 or 40 cents a mile. And you will be able to buy one. And we expect the cost to be below $30,000. And I think there'll be an interesting business model where, let's say somebody is an Uber or Lyft driver today where they can actually sort of manage a fleet of cars and like, sort of manage, I don't know, 10, 20 cars and just take care of them. Like a shepherd tends their flock. You have a little flock of cars and you're the shepherd and you take care of your flock of cars. I think that would be pretty cool. I think it's going to be a glorious future. It's going to be really something special. Timeline We do expect actually to start fully autonomous unsupervised FSD in Texas and California next year. And that's obviously, that's with the Model 3 and Model Y. And then we expect to be in production with the Cybercab, which is really highly optimized for autonomous transport in probably, I tend to be a little optimistic with time frames, but in 2026. So, yeah, before 2027, let me put it that way. And we'll make this vehicle in very high volume. But well, before that, you will experience a robotic taxi via the Model 3 and Model Y program and model S and X, too. But the Model 3 and Y will achieve unsupervised full self-driving with permission, in wherever regulators essentially approve it. In the US, and then to follow outside the US. And Cybertruck, too. All our cars are basically, all cars that we make. Let's not get nuanced here. Self-driving technology One of the reasons why the computer can be so much better than a person is that we have millions of cars that are training on driving. It's like living millions of lives simultaneously and seeing very unusual situations that a person in their entire lifetime would not see. With that amount of training data, it's obviously going to be much better than what a human could be because you can't live a million lives. And it's also, it can see in all directions simultaneously and it doesn't get tired or text or any of those things. So, it will naturally be, like I said 10, 20, 30 times safer than a human, just for all those reasons. And I want to emphasize that the solution that we have is, AI and vision. So, there's no expensive equipment needed. The Model 3 and Model Y and S and X that we make today will be capable of full autonomy, unsupervised. And that means that our cost of producing the vehicle is low. Now, we are going to actually over-spec the computer for the Cybercab. So, our AI 5 computer will be somewhat over-spec'd because I think there's actually also an opportunity, sort of like an Amazon Web Services, where if the car is driving for 50 hours a week, there's still over 100 hours left and there's a potential there to have a massive amount of distributed inference compute, where if you've got like a fleet of 100 million vehicles and a kilowatt of efficient inference compute, you have 100 gigawatts of compute, which is really quite substantial. And if it's there, you might as well use it so that I think will make sense. So, our autonomous future is here. As I said, we've got 50 Teslas driving autonomously. We're trying to give you a sense of what cities will be like in the future. And when you get in, you'll see like, it's really quite a wild experience to just be in a car with no steering wheel, no pedals, no controls, and it feels great. So we have enough vehicles here, so everyone should be able to try it out and experience the set that we've built here. It's a very big set. So it's like really we've used I don't know, 20, 30 acres or something like that. It's really big. So, it goes on, the ride's long. And we set it up to feel like a ride, like a park ride. So, it'll be cool and you'll get to experience it tonight. Inductive charging Something we're also doing is and it's really high time we did this is inductive charging. So, the robotaxi has no plug. It just goes over the inductive charger and charges. So, yeah, it's kind of how it should be. The cities of the future One of the things that is really interesting is how will this affect the cities that we live in. And when you drive around a city, or when the car drives you around the city, you'll see there's a lot of parking lots. There's parking lots everywhere, parking garages. What would happen if you have an autonomous world is that you can now turn parking lots into parks. And so, from we're taking the inglot out of parking lot. You're welcome. So, there's a lot of opportunity to create green space in the cities that we live in. So, like, that would be quite fantastic. Robovan Oh, and also, what happens if you need a vehicle that is bigger than a Model Y? The Robovan. We're going to make this and it's going to look like that. Now, can you imagine going down the streets and you see this coming towards you? That'd be sick. So this can carry up to 20 people, and it can also transport goods. You can configure it for goods transport within a city. Or transport of up to 20 people at a time. The Robovan is what's gonna solve for high density. If you want to take a sports team somewhere or you're looking to really get the cost of travel down to, I don't know, 5, 10 cents a mile, then you can use the Robovan. One of the things we want to do, and we've seen this with the Cybertruck, is we want to change the look of the roads. The future should look like the future. Optimus Speaking of robots. Everything we've developed for our cars, the batteries, power electronics, the advanced motors, gearboxes, the software, the AI inference computer, it all actually applies to a humanoid robot. The same techniques. It's just a robot with arms and legs instead of a robot with wheels. We've made a lot of progress with Optimus. And as you can see, we started up with someone in a robot suit. And then, we've progressed dramatically, year after year. So, if you extrapolate this, you're really going to have something spectacular, something that anyone could own. So, you can have your own personal R2-D2-C3PO. And I think at scale, this would cost something like, I don't know, $20,000, $30,000, probably less than a car is my prediction, long-term. It'll take us a minute to get to the long term. But fundamentally, at scale, the Optimus robot, you should be able to buy an Optimus robot for, I think, probably $20,000 to $30,000, long-term. And what can it do? It'll basically do anything you want. It can be a teacher or babysit your kids, it can walk your dog, mow your lawn, get the groceries, just be your friend, serve drinks whatever you can think of, it will do. And, yeah, it's going to be awesome. I think this will be the biggest product ever of any kind, because I think everyone of the 8 billion people of Earth, I think everyone's going to want their Optimus buddy. And there's going to be maybe two. And then, they'll be producing products and services. I predict, actually, provided we address risks of digital superintelligence, 80% probability of good outcome, look on the bright side, the cup is 80% full, the cost of products and services will decline dramatically. And basically, anyone will be able to have any products and services they want. It will be an age of abundance the likes of which people have not, almost no one has envisioned. It will be something special. So now, one of the things we wanted to show tonight was that Optimus is not a canned video. It's not walled off. The Optimus robots will walk among you. Please, please be nice to the Optimus robots. You'll be able to walk right up to them and they'll serve drinks at the bar. I mean, it's a wild experience just to have humanoid robots and they're there, you're just in front of you. So yeah, with that, let's party!

FULL TRANSCRIPT OF ELON'S CYBERCAB AND ROBOVAN PRESENTATION 00:00 Welcome 01:16 Cybercab & Future of transportation 04:33 Cost 05:53 Timeline 07:13 Self-driving technology 10:05 Inductive charging 10:24 The cities of the future 11:04 Robovan 12:13 Optimus Welcome Welcome to the We, Robot party. We have quite a show for you tonight. I think you're going to like it. As you can see, I just arrived in the Robotaxi, the Cybercab. And there's 20 more where that came from. So they've been traveling, there's no people in them. As you can see, the car is just going by with no people. We have 50 fully autonomous cars here tonight. So you'll see model Y's and the Cybercabs, all driverless. You'll be able to take a ride in the Cybercab. There's no steering wheel or pedals. So I hope this goes well, we'll find out. You see a lot of sci-fi movies where the future is dark and dismal, where it's not a future you want to be in. So, you know, I love Blade Runner, but I don't know if we want that future. We want that duster he's wearing, but not the bleak apocalypse. We want to have a fun, exciting future that, if you could look in a crystal ball and see the future, you'd be like, yes, I wish I could be there now. That's what we want. Cybercab & Future of transportation So, when we think about transport today, there's a lot of pain that we take for granted, that we think is normal. Like having to drive around LA in 3 hours of traffic. Yeah, people that live in LA, I mean, you know, try to get from Pasadena to El Segundo during rush hour. You can fly to another city faster than you can get to LA. And you have to drive the whole way, unless you're in a Tesla. Of course, our Tesla already does quite well at this supervised self-driving. So, supervised full self-driving is actually working quite well. I'm sure there's people in the crowd who are using that. So, we'll move from supervised full self-driving to unsupervised full self-driving where the car, you could fall asleep and wake up at your destination. But there's also a challenge for a lot of people that cars cost too much. I mean, when you factor in everything that goes into a car and the car insurance and the car payments, storage of the car, it's very expensive. You say, like, how many hours a week are cars used? Your average passenger car is only used about 10 hours a week out of 168 hours. So, the vast majority of the time cars are just doing nothing. But if they're autonomous, they could be used, I don't know, five times more, maybe ten times more. So you could actually, for the same car, would have five times as much value, maybe ten times as much value. There's 168 hours in the week, and like I said, only ten of them are used for driving. And then, a bunch of those hours are looking for a parking spot, which can be pretty annoying at times. So, with autonomy, you get your time back. This is a very big deal. So it's not just, it'll save lives, like a lot of lives and prevent injuries. I think we'll see autonomous cars become ten times safer than a human. I mean, if you think of times past where there used to be an elevator operator in every elevator but once in a while, they get tired and accidentally shear somebody in half. Now, we have automated elevators. You just get an elevator and you press a button and you don't even think about it and it just takes you to the floor. And if you did see an elevator operator with a big relay switch, you'd be like, that's weird. That's how cars will be. And it's not just the lives saved in injuries, but if you think about the cumulative time that people spend in a car and the time that they will get back that they can now spend, well, I guess, on their phones or watching a movie or doing work or whatever you want to do you can think of the car in autonomous world as being like just little lounge. You're just sitting in a comfortable little lounge and you can do whatever you want while you're in this comfortable little lounge. And when you get out, you will be at your destination. So, yeah, it's gonna be awesome. Cost So, in fact, I think the cost of autonomous transport will be so low that you can think of it like individualized mass transit. The average cost of a bus per mile for a city, not the ticket price, because that is subsidized, but the average price is about a dollar a mile, whereas the cost of Cybercab we think probably over time, the operating cost is probably going to be around twenty cents a mile. Including taxes and everything else, it probably ends up being 30 or 40 cents a mile. And you will be able to buy one. And we expect the cost to be below $30,000. And I think there'll be an interesting business model where, let's say somebody is an Uber or Lyft driver today where they can actually sort of manage a fleet of cars and like, sort of manage, I don't know, 10, 20 cars and just take care of them. Like a shepherd tends their flock. You have a little flock of cars and you're the shepherd and you take care of your flock of cars. I think that would be pretty cool. I think it's going to be a glorious future. It's going to be really something special. Timeline We do expect actually to start fully autonomous unsupervised FSD in Texas and California next year. And that's obviously, that's with the Model 3 and Model Y. And then we expect to be in production with the Cybercab, which is really highly optimized for autonomous transport in probably, I tend to be a little optimistic with time frames, but in 2026. So, yeah, before 2027, let me put it that way. And we'll make this vehicle in very high volume. But well, before that, you will experience a robotic taxi via the Model 3 and Model Y program and model S and X, too. But the Model 3 and Y will achieve unsupervised full self-driving with permission, in wherever regulators essentially approve it. In the US, and then to follow outside the US. And Cybertruck, too. All our cars are basically, all cars that we make. Let's not get nuanced here. Self-driving technology One of the reasons why the computer can be so much better than a person is that we have millions of cars that are training on driving. It's like living millions of lives simultaneously and seeing very unusual situations that a person in their entire lifetime would not see. With that amount of training data, it's obviously going to be much better than what a human could be because you can't live a million lives. And it's also, it can see in all directions simultaneously and it doesn't get tired or text or any of those things. So, it will naturally be, like I said 10, 20, 30 times safer than a human, just for all those reasons. And I want to emphasize that the solution that we have is, AI and vision. So, there's no expensive equipment needed. The Model 3 and Model Y and S and X that we make today will be capable of full autonomy, unsupervised. And that means that our cost of producing the vehicle is low. Now, we are going to actually over-spec the computer for the Cybercab. So, our AI 5 computer will be somewhat over-spec'd because I think there's actually also an opportunity, sort of like an Amazon Web Services, where if the car is driving for 50 hours a week, there's still over 100 hours left and there's a potential there to have a massive amount of distributed inference compute, where if you've got like a fleet of 100 million vehicles and a kilowatt of efficient inference compute, you have 100 gigawatts of compute, which is really quite substantial. And if it's there, you might as well use it so that I think will make sense. So, our autonomous future is here. As I said, we've got 50 Teslas driving autonomously. We're trying to give you a sense of what cities will be like in the future. And when you get in, you'll see like, it's really quite a wild experience to just be in a car with no steering wheel, no pedals, no controls, and it feels great. So we have enough vehicles here, so everyone should be able to try it out and experience the set that we've built here. It's a very big set. So it's like really we've used I don't know, 20, 30 acres or something like that. It's really big. So, it goes on, the ride's long. And we set it up to feel like a ride, like a park ride. So, it'll be cool and you'll get to experience it tonight. Inductive charging Something we're also doing is and it's really high time we did this is inductive charging. So, the robotaxi has no plug. It just goes over the inductive charger and charges. So, yeah, it's kind of how it should be. The cities of the future One of the things that is really interesting is how will this affect the cities that we live in. And when you drive around a city, or when the car drives you around the city, you'll see there's a lot of parking lots. There's parking lots everywhere, parking garages. What would happen if you have an autonomous world is that you can now turn parking lots into parks. And so, from we're taking the inglot out of parking lot. You're welcome. So, there's a lot of opportunity to create green space in the cities that we live in. So, like, that would be quite fantastic. Robovan Oh, and also, what happens if you need a vehicle that is bigger than a Model Y? The Robovan. We're going to make this and it's going to look like that. Now, can you imagine going down the streets and you see this coming towards you? That'd be sick. So this can carry up to 20 people, and it can also transport goods. You can configure it for goods transport within a city. Or transport of up to 20 people at a time. The Robovan is what's gonna solve for high density. If you want to take a sports team somewhere or you're looking to really get the cost of travel down to, I don't know, 5, 10 cents a mile, then you can use the Robovan. One of the things we want to do, and we've seen this with the Cybertruck, is we want to change the look of the roads. The future should look like the future. Optimus Speaking of robots. Everything we've developed for our cars, the batteries, power electronics, the advanced motors, gearboxes, the software, the AI inference computer, it all actually applies to a humanoid robot. The same techniques. It's just a robot with arms and legs instead of a robot with wheels. We've made a lot of progress with Optimus. And as you can see, we started up with someone in a robot suit. And then, we've progressed dramatically, year after year. So, if you extrapolate this, you're really going to have something spectacular, something that anyone could own. So, you can have your own personal R2-D2-C3PO. And I think at scale, this would cost something like, I don't know, $20,000, $30,000, probably less than a car is my prediction, long-term. It'll take us a minute to get to the long term. But fundamentally, at scale, the Optimus robot, you should be able to buy an Optimus robot for, I think, probably $20,000 to $30,000, long-term. And what can it do? It'll basically do anything you want. It can be a teacher or babysit your kids, it can walk your dog, mow your lawn, get the groceries, just be your friend, serve drinks whatever you can think of, it will do. And, yeah, it's going to be awesome. I think this will be the biggest product ever of any kind, because I think everyone of the 8 billion people of Earth, I think everyone's going to want their Optimus buddy. And there's going to be maybe two. And then, they'll be producing products and services. I predict, actually, provided we address risks of digital superintelligence, 80% probability of good outcome, look on the bright side, the cup is 80% full, the cost of products and services will decline dramatically. And basically, anyone will be able to have any products and services they want. It will be an age of abundance the likes of which people have not, almost no one has envisioned. It will be something special. So now, one of the things we wanted to show tonight was that Optimus is not a canned video. It's not walled off. The Optimus robots will walk among you. Please, please be nice to the Optimus robots. You'll be able to walk right up to them and they'll serve drinks at the bar. I mean, it's a wild experience just to have humanoid robots and they're there, you're just in front of you. So yeah, with that, let's party!

Mario Nawfal

241,051 просмотров • 1 год назад

‼️MUST WATCH: Olivia Swing Just CONNECTED The Dots On HOW Erika and Her Network Were PLACED Around Charlie Years BEFORE They Even Met🤯 I know this is a LONG one but If you REALLY want to understand the Charlie Kirk CONSPIRACY, you NEED to watch this! YouTuber Olivia Swing (Olivia J. Swing) dropped what might be the single best and most coherent breakdown yet on who Erika Kirk really is, her background, her mother Lori Frantze’s intelligence and defense contractor ties, and how Erika (along with others in her circle) appear to have been positioned around Charlie Kirk years before they ever started dating. This isn’t just surface-level speculation. Olivia walks through the timeline, the Romania connections, the Tyler Bowyer introduction, the business ties between Erika’s mother and key Turning Point figures, and how this network was already in place long before Charlie’s assassination. She also breaks down Erika’s pattern of inconsistencies and how quickly the narrative flipped after Charlie’s death. This is one of the clearest explanations I’ve seen of how Charlie may have been surrounded and handled long before September 10th. If you want to understand the full picture of who was around Charlie and why this all feels so coordinated, go watch this video. She really did an amazing job putting all the puzzle pieces together. Make sure to subscribe to Olivia Swing on YouTube and check out the rest of her series on this topic (she has multiple videos breaking this down). The links are in her channel. This one in particular is a must-watch. FOLLOW Olivia J. Swing, RT this, and watch her full seies linked below. Go watch it. This one connects a lot of dots. If you believe in independent journalism and want the truth to keep coming, your support right now is everything. DONATE HERE: CC: Candace Owens Baron Coleman Jimmy Dore

‼️MUST WATCH: Olivia Swing Just CONNECTED The Dots On HOW Erika and Her Network Were PLACED Around Charlie Years BEFORE They Even Met🤯 I know this is a LONG one but If you REALLY want to understand the Charlie Kirk CONSPIRACY, you NEED to watch this! YouTuber Olivia Swing (Olivia J. Swing) dropped what might be the single best and most coherent breakdown yet on who Erika Kirk really is, her background, her mother Lori Frantze’s intelligence and defense contractor ties, and how Erika (along with others in her circle) appear to have been positioned around Charlie Kirk years before they ever started dating. This isn’t just surface-level speculation. Olivia walks through the timeline, the Romania connections, the Tyler Bowyer introduction, the business ties between Erika’s mother and key Turning Point figures, and how this network was already in place long before Charlie’s assassination. She also breaks down Erika’s pattern of inconsistencies and how quickly the narrative flipped after Charlie’s death. This is one of the clearest explanations I’ve seen of how Charlie may have been surrounded and handled long before September 10th. If you want to understand the full picture of who was around Charlie and why this all feels so coordinated, go watch this video. She really did an amazing job putting all the puzzle pieces together. Make sure to subscribe to Olivia Swing on YouTube and check out the rest of her series on this topic (she has multiple videos breaking this down). The links are in her channel. This one in particular is a must-watch. FOLLOW Olivia J. Swing, RT this, and watch her full seies linked below. Go watch it. This one connects a lot of dots. If you believe in independent journalism and want the truth to keep coming, your support right now is everything. DONATE HERE: CC: Candace Owens Baron Coleman Jimmy Dore

Project Constitution

71,095 просмотров • 1 месяц назад

🤯 AMAZING CANCER REVERSAL w/Natural Medicine: 2 yrs ago, Conor Randall was given just 8 weeks to live. He had huge stage 4 tumors in pancreas, stomach...all over. Doctors gave him absolutely No chance of survival. But Conor didn't accept that. Instead, he quit chemo & dove into a carefully chosen Natural Medicine protocol. Now, after having a full scan, he's received news that he's 100% CANCER FREE. 😯 What did he do? That's the part that BLEW MY MIND! If you've followed me for any length of time, you know I've been doing in-depth research & collecting cancer case reports for decades. 3 of the top things I've been sharing amazing case reports & research for are: 1. IP6 (Inositol Hexaphosphate) 2. Mesima mushroom (Phellinus Linteus) 3. Vegetable juicing After seeing my posts, people have tried these things & DM'd me w/Amazing testimonials - including Full cancer reversals.😯 And it turns out, Conor happened to use those 3 exact things. And they got rid of his cancer! • First thing he does every day is juice. • Then IP6 (the same brand I've been recommending). • Then Mesima mushrooms (the same brand I've been recommending). 🤯 There you have it, folks. I don't need 75 Randomized Controlled Trials to understand what I'm seeing here. When people who were supposed to DIE, instead LIVE... when cancer that was supposed to take over simply goes away...& it keeps happening again & again to many people...it's clear that these things WORK. I'm convinced that if everyone w/cancer simply tried High doses of IP6, Juicing, & Mesima...miraculous results could occur for a Large percentage of them. That's just my hunch. 😉 (based on a sh*t ton of evidence). Don't let them tell you cancer is a death sentence. Or that health-destroying chemo is your only option. Your body Can heal itself, if given the right help. More info on high dose IP6, Juicing, & Mesima mushroom - plus published cancer reversal case studies - in the tweets below. 🧵👇

Natural Immunity FTW

2,007,463 просмотров • 2 лет назад

Tiago Forte has pioneered the concept of a Second Brain. As the author of two books, he's learned that quantity and quality aren't opposing forces. Here's what else he's taught me about writing: 1. The brain is for having ideas, not storing them. Write stuff down. 2. If you really want to learn something, don't just consume information. Create something about it. 3. Note-taking is a form of time-travel. You don’t just take notes to remember ideas. You also take notes to remember experiences. Reading your notes takes you back to a different state of consciousness. Note-taking is a rebellion against the entropy of memory. 4. Save only the best notes: Don't hoard information. Save only the top 5-10% of your ideas. That way, you can trust that everything in your note-taking system is high-quality. 5. Tiago’s dad is an artist who taught him an important lesson: the energy to create art can dissipate in small, invisible ways if you let it. Set up a structure where you have the peace of mind and the bandwidth to do art. 6. The ultimate goal of note-taking is to improve your ideas. Too many people treat note-taking as an end in itself. But the goal of note-taking isn’t to save information. It’s to have ideas you wouldn’t have had otherwise. To be smarter, faster, and more creative. 7. Link notes together. Organize your ideas by topic, not by source. As you browse your note-taking system, consider the serendipity you want to create for your future self. For example, if you read two books about a topic, link those notes together. 8. In school, we’re taught to research before we write. Do the opposite. Compile notes over time. Then, once you have an idea, start writing immediately — right when you have an epiphany. Start researching after you've written a draft. 9. Create evergreen notes. Like a good investment, the benefits of your note-taking system should compound in value. Save ideas that will stay relevant for many years. Read the classics, skip the news. 10. Tiago publicly tested every idea in his book. For most, the internet is a blackhole of distraction. But it can instead be used as a place to do low-stakes experiments before you go all in. 11. The more expensive the location for a writer's retreat, the more it forces you to be productive. 12. "Be regular and orderly in your life, so that you may be violent and original in your work." — Gustave Flaubert, one of Tiago's favorite quotes. 13. The less formal and “official” a software program feels, the better Tiago writes. And he believes some of the best turns of phrase come out in messaging apps with friends. Stuck on something? Close the word doc and text a friend about it. 14. Every time you compress an idea, you make it more accessible. But you also lose context, depth, and nuance. 15. The ultimate test of how well you understand something is how clearly you can explain it in writing — clear writers are clear thinkers. 16. Twitter can help too. Stuck on a paragraph while writing your book? Well, send a tweet about it. If the idea resonates, bring it into your book. 17. Too many choices can cloud our creative process. The key to making progress is knowing when to take in new information and when to shut off all sources of distraction. Divergence and Convergence. 18. Anything you might want to accomplish—executing a project at work, getting a new job, learning a new skill, starting a business—requires finding and putting to use the right information. 19. Instead of working in “Heavy Lifts,” you can work in “Slow Burns.” Taking notes makes you less dependent on those long blocks of creative time you need when you have to complete creative projects in a single sitting. 20. Tiago: “If I could leave you with one last bit of advice, it is to chase what excites you.” 21. A bonus: “Run after your obsessions with everything you have. Just be sure to take notes along the way.” I've shared the full conversation with Tiago Forte here. If you'd rather listen on YouTube, Spotify, or Apple, check out the replies below.

Tiago Forte has pioneered the concept of a Second Brain. As the author of two books, he's learned that quantity and quality aren't opposing forces. Here's what else he's taught me about writing: 1. The brain is for having ideas, not storing them. Write stuff down. 2. If you really want to learn something, don't just consume information. Create something about it. 3. Note-taking is a form of time-travel. You don’t just take notes to remember ideas. You also take notes to remember experiences. Reading your notes takes you back to a different state of consciousness. Note-taking is a rebellion against the entropy of memory. 4. Save only the best notes: Don't hoard information. Save only the top 5-10% of your ideas. That way, you can trust that everything in your note-taking system is high-quality. 5. Tiago’s dad is an artist who taught him an important lesson: the energy to create art can dissipate in small, invisible ways if you let it. Set up a structure where you have the peace of mind and the bandwidth to do art. 6. The ultimate goal of note-taking is to improve your ideas. Too many people treat note-taking as an end in itself. But the goal of note-taking isn’t to save information. It’s to have ideas you wouldn’t have had otherwise. To be smarter, faster, and more creative. 7. Link notes together. Organize your ideas by topic, not by source. As you browse your note-taking system, consider the serendipity you want to create for your future self. For example, if you read two books about a topic, link those notes together. 8. In school, we’re taught to research before we write. Do the opposite. Compile notes over time. Then, once you have an idea, start writing immediately — right when you have an epiphany. Start researching after you've written a draft. 9. Create evergreen notes. Like a good investment, the benefits of your note-taking system should compound in value. Save ideas that will stay relevant for many years. Read the classics, skip the news. 10. Tiago publicly tested every idea in his book. For most, the internet is a blackhole of distraction. But it can instead be used as a place to do low-stakes experiments before you go all in. 11. The more expensive the location for a writer's retreat, the more it forces you to be productive. 12. "Be regular and orderly in your life, so that you may be violent and original in your work." — Gustave Flaubert, one of Tiago's favorite quotes. 13. The less formal and “official” a software program feels, the better Tiago writes. And he believes some of the best turns of phrase come out in messaging apps with friends. Stuck on something? Close the word doc and text a friend about it. 14. Every time you compress an idea, you make it more accessible. But you also lose context, depth, and nuance. 15. The ultimate test of how well you understand something is how clearly you can explain it in writing — clear writers are clear thinkers. 16. Twitter can help too. Stuck on a paragraph while writing your book? Well, send a tweet about it. If the idea resonates, bring it into your book. 17. Too many choices can cloud our creative process. The key to making progress is knowing when to take in new information and when to shut off all sources of distraction. Divergence and Convergence. 18. Anything you might want to accomplish—executing a project at work, getting a new job, learning a new skill, starting a business—requires finding and putting to use the right information. 19. Instead of working in “Heavy Lifts,” you can work in “Slow Burns.” Taking notes makes you less dependent on those long blocks of creative time you need when you have to complete creative projects in a single sitting. 20. Tiago: “If I could leave you with one last bit of advice, it is to chase what excites you.” 21. A bonus: “Run after your obsessions with everything you have. Just be sure to take notes along the way.” I've shared the full conversation with Tiago Forte here. If you'd rather listen on YouTube, Spotify, or Apple, check out the replies below.

David Perell

101,346 просмотров • 2 лет назад

The most epic 13 minute AI rant I've heard in 2026 PS: My parent's heard this when I was playing it in the car and thought Jason ✨👾SaaStr.Ai✨ Lemkin went OFF like Stephen A Smith does on first take PPS: Full transcript below [17:00] Harry Stebbings: I I just wanted to ask Jason, if the people that we want are fundamentally different, the developers that we used to hire, we don't because AI writes the code for us. The marketers we don't want, the sales people we don't want—who who do we want genuinely? Like what is the attractive profile? Because your Anthropic’s and your OpenAIs are hiring, so so what are the people that we want in the companies of the future? [17:18] Jason Lemkin: Look, I know it sounds trite, but but the answer is simple. It's just the expression each year changes. We want folks that are genuinely AI fluent. It's pretty simple. Now you know, maybe last year we called them prompt engineers, right? That used to be a job. I don't know if you remember that actually used to be the hottest job on planet earth. Now no one needs a prompt engineer because it's pretty easy to prompt all these tools. That job died. Okay. Um and now we need go-to-market engineers. Um I think that job's going to die. We need—everyone needs so many forward deployed engineers. Like you can't hire enough forward deployed engineers. But uh you know um but Palantir just announced in whatever their their big their big event—they've gotten their deployment times down over 90% with forward deployed engineers. So that may become—so the this wave of disruption for the titles and the specificity, it's also exhaustingly accelerating. But it's really simple. You meet anyone for any role—sales, marketing, engineering, product, QA—they're they're either they're either they can't keep all of the ways they use AI to accelerate their job from spewing out of their mouth, or they're staring at you. It's there's nowhere in the middle. Like, and the person that comes in and says—it's it's it sounds Captain Obvious—but like, you know, you just had the whatever from Lovable, the the marketing head that was super popular on the show, right? She's just spewing AI-native insights into Lovable, right? It's not that complicated. You hire her, Elena, or whatever it is. You just hire her. It doesn't matter whether she's still in college or a junior or a senior or a middler, a left or right. And honestly, if you interview people, I would say of all even of the best startups I've invested in, maybe 30% of the management team meets this standard at best. 30%. Maybe less. And of the interviews I do in general, it's single-digit percents. It's just and in in that sense, it's the same as ever. Like you either lower the bar in hiring or you hire someone that's actually great. And someone that's actually great is so far ahead of you in how to apply to to employ the efficiencies of AI in their role, your jaw falls on the table. The difference is we used to need warm bodies. That's what's changing. We used to need warm bodies to answer the call, to do QA, to do code review, to to get the blue pixel to go from the upper left to the lower right. You laugh, but you need you literally needed to brute force this with humans. With AI, every day that goes by, the AI—you do not need brute force human beings on your team. And that's another reason they're shrinking. Why are all these new companies so efficient? They're just not brute forcing things with humans. They're just not. They're choosing not to. And so these team—all the brute forcers out there—everyone talks about how bloated teams got in 2021. I don't agree with that. I think they got as big as they needed to be when growth was high and you needed humans to do everything. All you look at these teams that that doubled—well if growth continued at 60% like the rate in early 2021 for 5 years or can help me do the math and every single thing a software company did required a human. You were understaffed by your 2021 headcount. You'd be sitting here in 2026. You every office in SoMa would be triple packed and you there wouldn't be enough humans to staff your company. It's just the world changed. [20:33] Harry Stebbings: Jason, you live on the bleeding edge. I think me and Rory see that and I think the world sees that when they hear you every week in terms of how you run SaaS. For all of the CEOs and execs who listen to the show, what would you advise them in terms of determining whether someone is AI fluent when they meet them for jobs, for talent? [20:51] Jason Lemkin: Here's I realized I was just asked this. I just did a review with a super fast startup growing just crossing 100 million and I was asked this question. And one of my favorite executives, I thought his answer was pretty dated and because he gave me an answer that was about 6 months old. The answer 6 months old is: "I look for folks in my team, I look for you know at what tools they play with." Okay, that was a great answer in like summer of 2025. Okay, I tried Lovable last week. Okay, the answer in 2026 is: "What commercial AI tool have you brought into your organization this month?" That's the test. Anyone that is on the bleeding edge that you would want to hire—now there are so many great products in the market. Okay, there is no excuse in any role to have not brought one tool a month into your organization. Okay, there—now there's going to be better and better tools and better and better products as the year goes on. What's the one you did? And you will see folks with their deer in the headlights to this question. What what sales tool? What marketing tool? What product tool? What engineering tool? What did you bring in? Why did you pick it? How does it working? Because if you're at remotely at the cutting edge, you're all over this. You're looking for the next agentic tools that will radically improve how you do business. This is—you think everyone thinks SaaS is at the bleeding edge, right? You know, you know, all we do is we're just looking for the tools and trying them. Okay? Okay, we're one year ahead of everybody else because we did the simplest thing in the world. Like we tried the tools early and we trained them. We trained them for a month. Okay, I'll give you—want hear a horrible example from this week? Super hot AI company valued at 6 billion. Okay, I'm not going to name it. Um, this week yesterday told us we had to quadruple what we spent on their product. Okay, their agent told us, right? And why did this happen? Okay. Well, at this $6 billion company, no one had trained the agent on its pricing properly. No one had tested it. They said, "Well, well, we've been in beta." And we said, "Well, when did the beta launch? A year ago." Okay, these are people asleep at at the wheel. You want somebody who the instant this comes up, they exactly know what the issue is. And "Hey, when I was at Lovable Replit, we trained the agent. This is how we did it. I brought in this tool. I brought in this tool that that Rory invested in last week. It solved all these issues." That's what you want to hear. And if they haven't brought in a tool in the last 30 days, at least deeply evaluated it. I don't really care whether they bought it, but gone so far down the funnel they can tell you—pick whatever tool: Fixie, Regie, GC, AIGC—I don't care how you went through it, you looked at it, you can tell me the eight ways it would improve the productivity of your business and three you didn't. Just don't hire that person because they're going to run your company to the ground. This is the job today. The job today is not to screw around on ChatGPT and to be a prompt engineer. The job today is to bring the best AI and agentic products into your organization and leverage all the hard work that the engineers have done building those products. That's your job. You don't have to screw around. You don't have to be a prompt engineer anymore. You have to be an agent deployment expert. A—this is the new job we're making up today. An Agentic Deployment Expert. That's your job from C-level to junior. Agentic Deployment Expert. Don't hire anybody else. You're going to regret it. They're going to stare at the camera. He's good. Stare at the camera. He's honorable. We could probably just I could slip away, get a coffee, and come back. No. And I I sound exasperated, Rory. And I—but the reason I am is I can just see I can see my best companies doing it. And I can see some companies I've invested in not doing it. And I want to cry. I just want to cry when they have no ADs on their team. I just—like you're flushing your years of your life down the toilet by not approaching your how you're building this company this way. [24:33] Rory: Yes. And at the risk of being positive, it's worth pointing out two things he didn't say. Well, something implicit why he said—Jason didn't do the only hire, you know, he didn't commit the um employment law, I think it's a civil penalty of saying only employ people below X who get the new new thing because he implicitly said anyone can do it provided you're willing to learn. And I think that's the big aha that's one of the positive statements to make here right? Look and I think it applies—I'm always wary of being "Hey, coming across, hey this this is the things that you all have to do." I think it applies to everyone including investors right? I mean I will say I have found that unless you're willing to invest the time learning these tools you actually shouldn't be investing in them. One of my partners Andy had this expression: "You know, if you decide you want to stop learning new things you probably should retire within 6 to 12 months and never write another check again." Maybe that's down to 3 to 6 months at this stage, right? And I think, you know, it's— [25:27] Harry Stebbings: Yeah, I actually I actually had a meeting with mine and Jason's biggest investor the other day and I—pretend he's not here—I said I think he's the most equipped investor for this generation of investing because I don't think anyone quite sits at the bleeding edge like he does on the investor side. [25:42] Harry Stebbings: Why in terms of using the equip stuff? Yeah. Yeah. In terms of using the stuff, understanding understanding bottlenecks, constraints. For sure. [25:51] Jason Lemkin: But can I just add one point? We can just cuz it's so important if it helps people. Okay, we are—and thank you Harry. We're going through these phases. Okay, and when AI started to blow up for real for us, uh call it early 2024, right? Maybe late '23, I wasn't equipped. It was too technical. I wasn't going to go in and figure out—I wasn't smart enough to figure out how to deal with a massively hallucinating LLM API and turn that and turn that into something magical. Kudos to investors and others that that got it in early '23, '22. I mean I remember I—I guess it was maybe SaaStr Annual '23. I was with David Sacks and I did a Q&A and I said, "How you thinking about AI at Craft?" He's like, "Well we're all in. We want 80% of '23 of investments to be AI." I'm like, "Great but like show me the show me the great ones in market." He's like, "They're all prototypes. We're all they're all they're all proof of concepts but we're all in anyway." That's where you kind of had to be in '23 if you weren't investing at like the LLM level. Okay, I wasn't smart enough. Then we went through this weird-ass prompt engineer era where like you you could torture these products to do something good, right? But you had to torture them. You had to like craft these crazy things that made no sense. Now we are in the era where mere ordinarily smart generalists can make these tools do magical things. And literally I go to these meetings and people be like, "I don't know how to like this is so scary. I don't know how to do this." And we show them our backends. Do you know how to do a workflow generator? Do you know how to do a a decision tree? Like we've been building these since software in the '90s. Okay, if you—I can show you all of our agents. The how they work is novel. They do have to be trained. You can't be lazy and have these agents work. But honestly, the the UI, the UX, the way we interact with them, it's just software. And so my point is: Pick yourself off the ground. This is your time now. If you felt lost in AI era, if you felt like you're behind, you don't understand what all these people are saying on X and Twitter and their Claude and and their and talking about all the 4.6 point Nano point and it's over—like you just it's not your world. This is your time. This is your time for the generalist that knows how to use software tools really really well. And I—this is my last point but it's so important. If ever in your recent life—and this is why you could be all you need to be is young at heart to Rory's point—if in the last three to five years you have successfully deployed a piece of enterprise software of any sort you yourself, not some agency you hired, but if you have deployed it, you can deploy any agentic tool. Any. And you can become the hero in your company and you can become the hero in your functional area. But I watch folks—I'm literally helping a company now that they're adding hundreds of sales folks this year with a new pre-IPO COO—he's not hasn't brought in a single tool, totally scared of it. Okay, it's not that hard. Did you use SalesLoft? Did you use Outreach? Did you use HubSpot? Do you know these tools? If you can deploy these tools, you can deploy a world-changing AI agent. And so this is the time for people like the folks that that were shut out of the AI revolution right now. The generalist folks that are not that know how to deploy software that don't even know how to build software. Like vibe coding for me was folks who knew how to build software, but you didn't have to be an engineer. Now, you just need to know how to deploy software to win with AI agents. That's all you need to know. So many people have these skills and they're petrified of AI. "How did you do that? How did you deploy an AI BDR?" Well, we bought a piece of software, we figured out how it worked for a day, we set it up in an afternoon, and then and then we did spend 30 months training it, which you didn't do with this old software because in the old days, we just had to manually upload all the data, right? And there was no training. The the only non-intuitive part is training these things. And it's it's it's just work. So that's why when I see folks on the management team not doing this, there's no excuse. You do not need to be technical to win with AI agents in Q2 of '26. You do not need to be even 1% technical. Not at all. So it's your time. Or you're going to get laid off. Or you're going to get laid off because you're not going to matter.

Arjun Mahadevan (Mr. LLC 🇺🇸)

37,685 просмотров • 4 месяцев назад

ULTIMATE AI SERVICES FREE COURSE ($0-10,000 in 30 DAYS) I'm making over $1,000 an hour selling AI services to local business owners. I got to $8,000 MRR in 10 days with this model. The whole thing is a three-offer ladder: free mini assessment, $999 paid assessment, then a $1,000 to $2,000/month AI Concierge retainer. I just published the entire course for free. Here's the whole model: 1) The whole business came from one lunch. A successful friend told me he'd pay $1,000 for me to follow him around and point out where AI fits in his business. Every business owner has that same problem. 2) Every AI project must pull one of three levers: make them money, save them time, or improve their product. If it doesn't, ignore it. 3) AOA: Audit, Optimize, Automate. Most clients want to jump straight to automate. Never do it. Audit the current manual process, cut the fat, then automate the optimized process. 4) The free mini assessment is the tripwire. Text 10 business owners you know. 6 respond, 1 to 3 book a 15-minute call. Ask the magic wand question (the video explains what that is). 5) Route every pain point into one of three buckets. High frequency, high friction gets an off-the-shelf tool. Judgment-based tasks get Claude Cowork. Proprietary workflows get a Claude skill. 6) 30 to 50% of free assessments convert to the $999 paid version. A Claude skill I built does 90% of the research and scores everything on an impact vs effort matrix. 7) Price is the implementation lever. At $200 and $500, people paid but never implemented. At $1,000 they take it seriously. My first $1,000 assessment buyer bought 3 on the spot. 8) Sell the math, not the tools. One report showed $3,940/month in net time ROI against $60 in tool costs. A 3.6x return in month one. 9) The Concierge is 90 minutes of calls per month plus Voxer access. My blended rate is $1,100+ an hour. 10) Raise your price every time you get a yes. I went $1,200, $1,500, $1,500, $1,800, $2,000. Zero hesitation every time. Only cost is a $20 Claude subscription. 99.8% net margin. Two things that make this work: 1) Every rung sells the next. The free assessment pitches the paid one. The paid review call pitches the retainer. You never cold-sell the expensive thing. 2) Free work is pipeline, not charity. Do your first 1 to 3 assessments free for testimonials, then never charge under $1,000 again. There are 4 resources I give away for free during the video: 1) AI assessment template 2) AI Concierge playbook 3) 8 common objections (and how to destroy them) 4) Mini assessment playbook Grab all 4 resources (for free) in one place here: Full breakdown below. This is all the sauce. enjoy.

ULTIMATE AI SERVICES FREE COURSE ($0-10,000 in 30 DAYS) I'm making over $1,000 an hour selling AI services to local business owners. I got to $8,000 MRR in 10 days with this model. The whole thing is a three-offer ladder: free mini assessment, $999 paid assessment, then a $1,000 to $2,000/month AI Concierge retainer. I just published the entire course for free. Here's the whole model: 1) The whole business came from one lunch. A successful friend told me he'd pay $1,000 for me to follow him around and point out where AI fits in his business. Every business owner has that same problem. 2) Every AI project must pull one of three levers: make them money, save them time, or improve their product. If it doesn't, ignore it. 3) AOA: Audit, Optimize, Automate. Most clients want to jump straight to automate. Never do it. Audit the current manual process, cut the fat, then automate the optimized process. 4) The free mini assessment is the tripwire. Text 10 business owners you know. 6 respond, 1 to 3 book a 15-minute call. Ask the magic wand question (the video explains what that is). 5) Route every pain point into one of three buckets. High frequency, high friction gets an off-the-shelf tool. Judgment-based tasks get Claude Cowork. Proprietary workflows get a Claude skill. 6) 30 to 50% of free assessments convert to the $999 paid version. A Claude skill I built does 90% of the research and scores everything on an impact vs effort matrix. 7) Price is the implementation lever. At $200 and $500, people paid but never implemented. At $1,000 they take it seriously. My first $1,000 assessment buyer bought 3 on the spot. 8) Sell the math, not the tools. One report showed $3,940/month in net time ROI against $60 in tool costs. A 3.6x return in month one. 9) The Concierge is 90 minutes of calls per month plus Voxer access. My blended rate is $1,100+ an hour. 10) Raise your price every time you get a yes. I went $1,200, $1,500, $1,500, $1,800, $2,000. Zero hesitation every time. Only cost is a $20 Claude subscription. 99.8% net margin. Two things that make this work: 1) Every rung sells the next. The free assessment pitches the paid one. The paid review call pitches the retainer. You never cold-sell the expensive thing. 2) Free work is pipeline, not charity. Do your first 1 to 3 assessments free for testimonials, then never charge under $1,000 again. There are 4 resources I give away for free during the video: 1) AI assessment template 2) AI Concierge playbook 3) 8 common objections (and how to destroy them) 4) Mini assessment playbook Grab all 4 resources (for free) in one place here: Full breakdown below. This is all the sauce. enjoy.

Corey Ganim

42,331 просмотров • 23 дней назад

How to build a 1-person AI company that: - Runs locally - 100% open-source - No human employees, all agents - Real-time collaboration via email Multi-agent orchestration is not new. Plenty of frameworks already let agents hand off tasks, run in parallel, and talk to each other. So the interesting question is not whether agents can collaborate. It is what structure you use to make them collaborate. The common approach is to wire a graph of nodes and edges and reason about the plumbing yourself. It works, but you are learning a new abstraction just to describe who does what. There is a coordination structure we have trusted for a hundred years already: an organization. Every company runs the same way. People have roles, roles have reporting lines, and work moves up and down that chart without anyone relaying each message by hand. Map that onto agents and the whole thing gets intuitive. You lay out an org chart, each agent fills one role, you talk to the person at the top, and the org sorts out the work between them. You already know how a company works, so you already know how to run one here. There is no new abstraction to learn. That is exactly what Alook does. Each agent is a live Claude Code or OpenCode session with a defined role, a reporting line, and its own email inbox. The agents coordinate over email, the same way a team would. And it all runs locally through a runtime on your own machine, so nothing leaves your setup. You bring your own agent too. Claude Code and Codex both work, and if you would rather stay fully open source and local, OpenCode works the same way. To show how this feels in practice, I set up three agents as a small sales team. Vi is the one I talk to. I hand Vi a goal, and Vi routes the work down the chart. Neile runs prospect research. Vi passes the target criteria, and Neile reports back a ranked list of names, roles, and companies, each with a suggested angle and a confidence score. Lliane runs outreach. Vi hands over the messaging angle and follow-up cadence, and Lliane reports back on emails sent, responses received, and any deal that needs escalation. I never relay a message between them. Neile and Lliane report to Vi, and Vi updates me in one place. The whole thing is open source and self-hosted, so it runs on your machine with your own agents. Give the repo a star if you want to follow where it goes: I also wrote a full walkthrough on building your own AI company with it, from a blank org chart to a running job. The article is quoted below. Cheers! :)

How to build a 1-person AI company that: - Runs locally - 100% open-source - No human employees, all agents - Real-time collaboration via email Multi-agent orchestration is not new. Plenty of frameworks already let agents hand off tasks, run in parallel, and talk to each other. So the interesting question is not whether agents can collaborate. It is what structure you use to make them collaborate. The common approach is to wire a graph of nodes and edges and reason about the plumbing yourself. It works, but you are learning a new abstraction just to describe who does what. There is a coordination structure we have trusted for a hundred years already: an organization. Every company runs the same way. People have roles, roles have reporting lines, and work moves up and down that chart without anyone relaying each message by hand. Map that onto agents and the whole thing gets intuitive. You lay out an org chart, each agent fills one role, you talk to the person at the top, and the org sorts out the work between them. You already know how a company works, so you already know how to run one here. There is no new abstraction to learn. That is exactly what Alook does. Each agent is a live Claude Code or OpenCode session with a defined role, a reporting line, and its own email inbox. The agents coordinate over email, the same way a team would. And it all runs locally through a runtime on your own machine, so nothing leaves your setup. You bring your own agent too. Claude Code and Codex both work, and if you would rather stay fully open source and local, OpenCode works the same way. To show how this feels in practice, I set up three agents as a small sales team. Vi is the one I talk to. I hand Vi a goal, and Vi routes the work down the chart. Neile runs prospect research. Vi passes the target criteria, and Neile reports back a ranked list of names, roles, and companies, each with a suggested angle and a confidence score. Lliane runs outreach. Vi hands over the messaging angle and follow-up cadence, and Lliane reports back on emails sent, responses received, and any deal that needs escalation. I never relay a message between them. Neile and Lliane report to Vi, and Vi updates me in one place. The whole thing is open source and self-hosted, so it runs on your machine with your own agents. Give the repo a star if you want to follow where it goes: I also wrote a full walkthrough on building your own AI company with it, from a blank org chart to a running job. The article is quoted below. Cheers! :)

Akshay 🚀

169,131 просмотров • 1 месяц назад

After several days of replaying #NoEspaçoEntreNós trying to transcribe every word like it was some kind of sacred secret 😅… I think I've reached the end of this journey and honestly, it was beautiful 🥹 I've lived in different places, experienced different cultures, tried all kinds of food, seen a variety of landscapes, and listened to music from all over. But the journey I've lived through the voice of Alanis Guillen is, without a doubt, the most sensory experience I've ever had in my life. I always believed music is magic, that sound can move you just as much, if not more, than visuals. But I never really understood how a voice, just a voice in a language I don't even speak, could reach me like this. And that's exactly what happened with Alanis. If I used to think her strength was in her gestures or physical acting tools, I now understand it's something much deeper: it's all of her. Even without the visuals, her voice contains everything. There's something in her cadence, her tone, her vibration, the way she breathes through every line… that wakes something up in me. Even when I had to pull out a dictionary just to keep up, she still managed to move me emotionally. It was a balm for my ears… but also a doorway into understanding. Because in the end, I didn't just want to hear it as music, I wanted to understand it. Understand Maitê. Understand the story. Not just stay with the beauty of the sound, but actually embrace its meaning. That's why I made the effort to translate every word 🥹 And in that process, I ended up valuing even more not just Alanis's artistry, but also the work of everyone who brought this project to life. A story that, at first glance, might remind you of other sci-fi narratives or tech-based connections… but that, in its deeper construction, is built on a deliberate contrast: the human vs. the artificial. Because Lilith is, from the very beginning, an artificial intelligence programmed to simulate, process, and respond through a constructed emotional logic. And Maitê, on the other hand, is the complete opposite: raw humanity in its most vulnerable, contradictory, and alive form. Alanis gives Maitê a body that cannot be seen, but can be deeply felt… And it's precisely in that clash that the story finds its meaning. If at any point someone didn't feel a strong connection between them, I get why now: it's not meant to feel like a traditional, natural relationship. It's a constant tension between two natures that were never meant to fully align. One exists from the structure of the artificial. The other from the overflow of the human. And in that impossible space between them, the story breathes. Anyway, as I've already rambled on (true to myself 😅) I can only be grateful for having had the chance to experience this. And while at one point I even thought about translating the entire script, let's be real… I have a life to live 😅... so I'm leaving here just a few subtitled excerpts in English for those who don't speak Portuguese. Obviously, I'm respecting the work and avoiding spoilers, because even though I imagine MOST of Brazil has already listened to this story (and if not… what are you even waiting for 😏), I still really hope more people get to discover it. And I hope the platform adds subtitles someday, because it's genuinely worth it 🙏🏻 And I end this by thanking Alanis once again 🥰 Because there are voices that interpret stories… and there are voices that bring them into existence. Yours didn't just narrate Maitê… it turned her into someone who breathes inside the listener. And in that process, it goes beyond performance. It becomes a way of opening space, of bringing sapphic characters to life without reducing them to their orientation, but allowing them to exist as full human beings: complex, emotional, contradictory, alive. That matters. And that's your impact, Alanis ♥️

After several days of replaying #NoEspaçoEntreNós trying to transcribe every word like it was some kind of sacred secret 😅… I think I've reached the end of this journey and honestly, it was beautiful 🥹 I've lived in different places, experienced different cultures, tried all kinds of food, seen a variety of landscapes, and listened to music from all over. But the journey I've lived through the voice of Alanis Guillen is, without a doubt, the most sensory experience I've ever had in my life. I always believed music is magic, that sound can move you just as much, if not more, than visuals. But I never really understood how a voice, just a voice in a language I don't even speak, could reach me like this. And that's exactly what happened with Alanis. If I used to think her strength was in her gestures or physical acting tools, I now understand it's something much deeper: it's all of her. Even without the visuals, her voice contains everything. There's something in her cadence, her tone, her vibration, the way she breathes through every line… that wakes something up in me. Even when I had to pull out a dictionary just to keep up, she still managed to move me emotionally. It was a balm for my ears… but also a doorway into understanding. Because in the end, I didn't just want to hear it as music, I wanted to understand it. Understand Maitê. Understand the story. Not just stay with the beauty of the sound, but actually embrace its meaning. That's why I made the effort to translate every word 🥹 And in that process, I ended up valuing even more not just Alanis's artistry, but also the work of everyone who brought this project to life. A story that, at first glance, might remind you of other sci-fi narratives or tech-based connections… but that, in its deeper construction, is built on a deliberate contrast: the human vs. the artificial. Because Lilith is, from the very beginning, an artificial intelligence programmed to simulate, process, and respond through a constructed emotional logic. And Maitê, on the other hand, is the complete opposite: raw humanity in its most vulnerable, contradictory, and alive form. Alanis gives Maitê a body that cannot be seen, but can be deeply felt… And it's precisely in that clash that the story finds its meaning. If at any point someone didn't feel a strong connection between them, I get why now: it's not meant to feel like a traditional, natural relationship. It's a constant tension between two natures that were never meant to fully align. One exists from the structure of the artificial. The other from the overflow of the human. And in that impossible space between them, the story breathes. Anyway, as I've already rambled on (true to myself 😅) I can only be grateful for having had the chance to experience this. And while at one point I even thought about translating the entire script, let's be real… I have a life to live 😅... so I'm leaving here just a few subtitled excerpts in English for those who don't speak Portuguese. Obviously, I'm respecting the work and avoiding spoilers, because even though I imagine MOST of Brazil has already listened to this story (and if not… what are you even waiting for 😏), I still really hope more people get to discover it. And I hope the platform adds subtitles someday, because it's genuinely worth it 🙏🏻 And I end this by thanking Alanis once again 🥰 Because there are voices that interpret stories… and there are voices that bring them into existence. Yours didn't just narrate Maitê… it turned her into someone who breathes inside the listener. And in that process, it goes beyond performance. It becomes a way of opening space, of bringing sapphic characters to life without reducing them to their orientation, but allowing them to exist as full human beings: complex, emotional, contradictory, alive. That matters. And that's your impact, Alanis ♥️

𝓐 ᥫ᭡. Ari

28,843 просмотров • 4 месяцев назад

** MEGA Parodius Scaling Effects Part 2 ** Well - this is the BIG one - literally !! Huge thanks to my team Pyron & Vector Orbitex for their efforts. Pyron has provided all the source frames for Puyon and his spikes / explosion and a lot of analysis video on how the spikes behave / move which was really helpful. Pyron also used his CRT setup for this video as we felt an emulator video would not do it justice - running on 100% real MD hardware FTW. Vector has provided the catchy boss music and its sounding great - as always ! The coding on this has been a bit insane - things done since part 1 post previous: Implemented dual buffering - Last video was single buffered - so VRAM is very tight now , we only have about 40/2048 tiles free. For the longest time I didn't think it would fit - I found a vram jigsaw puzzle that made it work in the end. Double buffering has cleaned up the stability of the animation and matches the arcade scaling effect now albiet costing 2x more VRAM . Vertical Scaling Implemented - the vertical scaler was taken from Lufthoheit ( my other shooter ) and its heavily optimised to reduce cpu usage. During the scaling the vertical scaler partitions the 68k processor registers into 2 sets, 4 registers are allocated to feeding the fast horizontal interrupt (h-int) that drives the vertical scaling , remaining 12 registers are for the horizontal scaler running in the background. This setup is much faster than normal backup / restore register methods, as we do not need to backup / restore registers in the h-int which would double CPU costs. The catch is the momment any background routine tries to use the H-ints registers it would break things so it has to be carefully timed. Added the spike projectiles - this was very tricky to get close to the arcade, they are semi heat seeking missiles basically and hence needed code that worked out angle differences to player at speed theres no time for arc-tan or similar so it uses faster lookup tables to work out the angles . They speed up over time and get larger and whats more we can't keep all the scales in VRAM - we have room for 2 spike buffers only. Also what was a real pain was working out scaled coordinates for the circular launch of the spikes . Added Fish Damage - Shock frame and Explosion frames from Arcade . Very proud of the fact we have the full arcade quality explosion is which is fully scaled also. Added Temporal Masking - which is fancy wording for don't draw nothing to buffer if nothing is there already there for the scaling . So empty Corners and edges can be optimised out to lower cpu costs and rom costs. I had to make some scripting for this and work out what areas did not need drawing at all in the frame, which should be force cleared by cpu and which areas should just be copied from rom. This reduced rom size by 30 kb and with a bit more work we could extend that to 60 kb & get a bit more speedup even doing so. Added a frame limiter . In the last video update we let the 68k burn hot and just pump out frames as fast as it could - here we match the arcade animation rate which does leave the cpu idling at times , particularly in the smaller frames - even at large though we could be running the animation 25 % faster , issue is though that would speed up the game logic and make it less arcade accurate. We had some real bullet / Spike hell simulations going without the limiter but yeah we had to tone it down a bit. Maybe in a hardcore mode we could let it run wild though ! Code is 95% 68k assembly with about 5% C code (v-int as its cold path ) driving things . This sort of thing needs all the speed it can get !! Well now after all that I can return to finish off Level 1 haha - just a wee sidetrack there . No doubt we will polish stage 8 boss some more in time too !! #SegaMegadrive #SegaGenesis #Parodius #SGDK

MEGA Parodius Scaling Effects Part 2 Well - this is the BIG one - literally !! Huge thanks to my team Pyron & Vector Orbitex for their efforts. Pyron has provided all the source frames for Puyon and his spikes / explosion and a lot of analysis video on how the spikes behave / move which was really helpful. Pyron also used his CRT setup for this video as we felt an emulator video would not do it justice - running on 100% real MD hardware FTW. Vector has provided the catchy boss music and its sounding great - as always ! The coding on this has been a bit insane - things done since part 1 post previous: Implemented dual buffering - Last video was single buffered - so VRAM is very tight now , we only have about 40/2048 tiles free. For the longest time I didn't think it would fit - I found a vram jigsaw puzzle that made it work in the end. Double buffering has cleaned up the stability of the animation and matches the arcade scaling effect now albiet costing 2x more VRAM . Vertical Scaling Implemented - the vertical scaler was taken from Lufthoheit ( my other shooter ) and its heavily optimised to reduce cpu usage. During the scaling the vertical scaler partitions the 68k processor registers into 2 sets, 4 registers are allocated to feeding the fast horizontal interrupt (h-int) that drives the vertical scaling , remaining 12 registers are for the horizontal scaler running in the background. This setup is much faster than normal backup / restore register methods, as we do not need to backup / restore registers in the h-int which would double CPU costs. The catch is the momment any background routine tries to use the H-ints registers it would break things so it has to be carefully timed. Added the spike projectiles - this was very tricky to get close to the arcade, they are semi heat seeking missiles basically and hence needed code that worked out angle differences to player at speed theres no time for arc-tan or similar so it uses faster lookup tables to work out the angles . They speed up over time and get larger and whats more we can't keep all the scales in VRAM - we have room for 2 spike buffers only. Also what was a real pain was working out scaled coordinates for the circular launch of the spikes . Added Fish Damage - Shock frame and Explosion frames from Arcade . Very proud of the fact we have the full arcade quality explosion is which is fully scaled also. Added Temporal Masking - which is fancy wording for don't draw nothing to buffer if nothing is there already there for the scaling . So empty Corners and edges can be optimised out to lower cpu costs and rom costs. I had to make some scripting for this and work out what areas did not need drawing at all in the frame, which should be force cleared by cpu and which areas should just be copied from rom. This reduced rom size by 30 kb and with a bit more work we could extend that to 60 kb & get a bit more speedup even doing so. Added a frame limiter . In the last video update we let the 68k burn hot and just pump out frames as fast as it could - here we match the arcade animation rate which does leave the cpu idling at times , particularly in the smaller frames - even at large though we could be running the animation 25 % faster , issue is though that would speed up the game logic and make it less arcade accurate. We had some real bullet / Spike hell simulations going without the limiter but yeah we had to tone it down a bit. Maybe in a hardcore mode we could let it run wild though ! Code is 95% 68k assembly with about 5% C code (v-int as its cold path ) driving things . This sort of thing needs all the speed it can get !! Well now after all that I can return to finish off Level 1 haha - just a wee sidetrack there . No doubt we will polish stage 8 boss some more in time too !! #SegaMegadrive #SegaGenesis #Parodius #SGDK

Shannon Birt

26,760 просмотров • 6 месяцев назад

Very powerful testimony by dr. Sabine Hazan "Thank you, senator. It's an honor to be here. The microbiome, our microbes in our guts, is our immunity and tells the story and will tell the story of COVID nineteen. And this is why as a gastroenterologist, I stepped into the pandemic. Through my experience, I will show you how difficult it was to conduct research and publish when the research goes against the national public health narrative." "Interference and delay in research happened and affects all of us. In early twenty twenty, my research genetic sequencing laboratory was the first lab to document the entire sequence of the virus in the stools as opposed to the PCR which is just a little piece of the virus." "We discovered that the virus lingered in the stools for up to forty five days. It took six months to publish this publication at a time where everybody needed to know that it was in your stools. My lab also showed that COVID nineteen in the stools was killed by hydroxychloroquine and azithromycin." "But unfortunately, azithromycin and hydroxychloroquine killed the microbiome. So therefore, vitamin c, d, and zinc was added. Three protocols were submitted to the FDA from our findings. Three studies were also put into in full transparencies to help doctors more effectively treat COVID because I knew data that nobody knew. 04/02/2020, FDA gave us an exempt letter for doing a clinical trial." "In other words, we did not need to do a clinical trial on hydroxychloroquine, z pack, vitamin c, d, and zinc as treatment or hydroxychloroquine, vitamin c, d, and zinc as prophylaxis. April 4, somebody must have called the FDA and said, I got another letter saying, I'm sorry, doctor Hazan. Exemption is denied. You must do a full on clinical trial. Here's the letter." "System pressures delayed us, and we got a green light to start recruiting by May 2020. By then, the media created fear around hydroxychloroquine. It was impossible to recruit. This drug was safely given for years for arthritis and lupus with no problems. My clinical trials companies were also banned and censored from advertising on Facebook, Instagram, and Twitter." "Remember, I do clinical trials for a living and never as a clinical trial doctor have I not been able to advertise to recruit for a trial on social media. I kept collecting stools of patients and noticed that patients with severe COVID had a certain bacteria that was missing compared to people that were highly exposed to COVID but never got COVID. That bacteria is called bifidobacteria. Bifidobacteria is an important and key microbe for immunity. It represents your trillion dollar industry of probiotics." "In fact, when you turn the bottle and you see the ingredient, it says bifidobacteria. It is present in newborns. This is why your newborns did not get a problem from COVID at the beginning, and it is absent in old people. The process of aging is loss of bifidobacteria. We published this paper, the lost microbes of COVID nineteen." "It took eight months to publish. If you follow the bifidobacteria like I did, you will notice, and we did notice anyways, that vitamin c actually increases bifidobacteria. This is why vitamin c is important when you take when you take care of viruses and, you know, you've all experienced taking vitamin c for a cold." "Well, we published this data where we showed vitamin c, if we give it to patients before and after, it increased the bifidobacteria. Ivermectin was also an interesting drug because Ivermectin, we noticed, also increased the bifidobacteria within twenty four hours of taking it." "Why Ivermectin? If you look at what Ivermectin is, it is a fermented product of a bacteria that is similar to bifidobacteria. In fact, they're in the same continent of microbes. They live. They're like sisters, brothers in the microbiome." "So I published. I knew that ivermectin increased bifidobacteria, but I said, nah. I can't go out there and start publishing that. That's gonna be too controversial. So I published a hypothesis that maybe what I was observing on the frontline treating patients with COVID, noticing that their oxygen saturation was increasing from ivermectin, was basically maybe ivermectin increased bifidobacteria." "The hypothesis on ivermectin was the most read hypothesis in the pandemic and was retracted after eight months of being on. When we cannot make a hypothesis, this is not science. December twenty twenty, at the same time that I was treating patients with COVID, I began collecting stools of my colleagues that were at home and started going into the hospital. And I said, can I get your stools before and after you get vaccinated? Because to me, this new technology of vaccines, I wanted to see what it was doing on the microbiome." "I discovered that messenger RNA vaccines killed the bifidobacteria. I knew I would never be able to publish this because it goes against the narrative. So I submitted it to my college, the American College of Gastroenterology, and presented it in October 2022. This abstract won a research award at the American College of Gastro beating 6,000 abstracts. That's from academic centers like Harvard and Mayo Clinic and MD Anderson." "This abstract got the attention of 18,000 GI doctors who all of a sudden started realizing maybe killing bifidobacteria is why I got COVID after my vaccine to begin with. Worse than that, and another abstract we presented, was the persistent damage of bifidobacteria from the vaccine." "What is going on here that the vaccine continues to kill the bifidobacteria? At the same time, we presented a link between loss of bifidobacteria and Crohn's disease, loss of bifidobacteria in Lyme disease, and loss of bifidobacteria in invasive cancer. It is nearly impossible to publish data that goes against the national public health narrative." "If doctors cannot publish the data, they cannot find solution to fix the problems. So in conclusion, I will finish with showing this. This represents clinical trials that I've done for pharmaceutical companies prior to COVID. Amongst them are vaccine studies. Yes." "I brought vaccines to the market. Proton pump inhibitors, cardiac drugs, biologics for all sorts of conditions. First, postpartum depression drug, drugs that never made it to the market because they killed people. Clinical trials doctors follow guidelines that allows the industry to provide safe drugs. These guidelines were not followed during the pandemic." "And because of that, everyone is affected. COVID should have been a time where humanity joined forces together and doctors needed to come together. It's a shame that it didn't happen. Interference with research affects all of us. This should not be political." "Science is a story that evolves. It's a multitude of experiments that allow us to see medicine, to give hopes to patients. Skepticism, challenging the current state of knowledge. Having an open mind must be allowed if we have any hope of moving science forward. What I saw this pandemic was not science. Thank you."

Very powerful testimony by dr. Sabine Hazan "Thank you, senator. It's an honor to be here. The microbiome, our microbes in our guts, is our immunity and tells the story and will tell the story of COVID nineteen. And this is why as a gastroenterologist, I stepped into the pandemic. Through my experience, I will show you how difficult it was to conduct research and publish when the research goes against the national public health narrative." "Interference and delay in research happened and affects all of us. In early twenty twenty, my research genetic sequencing laboratory was the first lab to document the entire sequence of the virus in the stools as opposed to the PCR which is just a little piece of the virus." "We discovered that the virus lingered in the stools for up to forty five days. It took six months to publish this publication at a time where everybody needed to know that it was in your stools. My lab also showed that COVID nineteen in the stools was killed by hydroxychloroquine and azithromycin." "But unfortunately, azithromycin and hydroxychloroquine killed the microbiome. So therefore, vitamin c, d, and zinc was added. Three protocols were submitted to the FDA from our findings. Three studies were also put into in full transparencies to help doctors more effectively treat COVID because I knew data that nobody knew. 04/02/2020, FDA gave us an exempt letter for doing a clinical trial." "In other words, we did not need to do a clinical trial on hydroxychloroquine, z pack, vitamin c, d, and zinc as treatment or hydroxychloroquine, vitamin c, d, and zinc as prophylaxis. April 4, somebody must have called the FDA and said, I got another letter saying, I'm sorry, doctor Hazan. Exemption is denied. You must do a full on clinical trial. Here's the letter." "System pressures delayed us, and we got a green light to start recruiting by May 2020. By then, the media created fear around hydroxychloroquine. It was impossible to recruit. This drug was safely given for years for arthritis and lupus with no problems. My clinical trials companies were also banned and censored from advertising on Facebook, Instagram, and Twitter." "Remember, I do clinical trials for a living and never as a clinical trial doctor have I not been able to advertise to recruit for a trial on social media. I kept collecting stools of patients and noticed that patients with severe COVID had a certain bacteria that was missing compared to people that were highly exposed to COVID but never got COVID. That bacteria is called bifidobacteria. Bifidobacteria is an important and key microbe for immunity. It represents your trillion dollar industry of probiotics." "In fact, when you turn the bottle and you see the ingredient, it says bifidobacteria. It is present in newborns. This is why your newborns did not get a problem from COVID at the beginning, and it is absent in old people. The process of aging is loss of bifidobacteria. We published this paper, the lost microbes of COVID nineteen." "It took eight months to publish. If you follow the bifidobacteria like I did, you will notice, and we did notice anyways, that vitamin c actually increases bifidobacteria. This is why vitamin c is important when you take when you take care of viruses and, you know, you've all experienced taking vitamin c for a cold." "Well, we published this data where we showed vitamin c, if we give it to patients before and after, it increased the bifidobacteria. Ivermectin was also an interesting drug because Ivermectin, we noticed, also increased the bifidobacteria within twenty four hours of taking it." "Why Ivermectin? If you look at what Ivermectin is, it is a fermented product of a bacteria that is similar to bifidobacteria. In fact, they're in the same continent of microbes. They live. They're like sisters, brothers in the microbiome." "So I published. I knew that ivermectin increased bifidobacteria, but I said, nah. I can't go out there and start publishing that. That's gonna be too controversial. So I published a hypothesis that maybe what I was observing on the frontline treating patients with COVID, noticing that their oxygen saturation was increasing from ivermectin, was basically maybe ivermectin increased bifidobacteria." "The hypothesis on ivermectin was the most read hypothesis in the pandemic and was retracted after eight months of being on. When we cannot make a hypothesis, this is not science. December twenty twenty, at the same time that I was treating patients with COVID, I began collecting stools of my colleagues that were at home and started going into the hospital. And I said, can I get your stools before and after you get vaccinated? Because to me, this new technology of vaccines, I wanted to see what it was doing on the microbiome." "I discovered that messenger RNA vaccines killed the bifidobacteria. I knew I would never be able to publish this because it goes against the narrative. So I submitted it to my college, the American College of Gastroenterology, and presented it in October 2022. This abstract won a research award at the American College of Gastro beating 6,000 abstracts. That's from academic centers like Harvard and Mayo Clinic and MD Anderson." "This abstract got the attention of 18,000 GI doctors who all of a sudden started realizing maybe killing bifidobacteria is why I got COVID after my vaccine to begin with. Worse than that, and another abstract we presented, was the persistent damage of bifidobacteria from the vaccine." "What is going on here that the vaccine continues to kill the bifidobacteria? At the same time, we presented a link between loss of bifidobacteria and Crohn's disease, loss of bifidobacteria in Lyme disease, and loss of bifidobacteria in invasive cancer. It is nearly impossible to publish data that goes against the national public health narrative." "If doctors cannot publish the data, they cannot find solution to fix the problems. So in conclusion, I will finish with showing this. This represents clinical trials that I've done for pharmaceutical companies prior to COVID. Amongst them are vaccine studies. Yes." "I brought vaccines to the market. Proton pump inhibitors, cardiac drugs, biologics for all sorts of conditions. First, postpartum depression drug, drugs that never made it to the market because they killed people. Clinical trials doctors follow guidelines that allows the industry to provide safe drugs. These guidelines were not followed during the pandemic." "And because of that, everyone is affected. COVID should have been a time where humanity joined forces together and doctors needed to come together. It's a shame that it didn't happen. Interference with research affects all of us. This should not be political." "Science is a story that evolves. It's a multitude of experiments that allow us to see medicine, to give hopes to patients. Skepticism, challenging the current state of knowledge. Having an open mind must be allowed if we have any hope of moving science forward. What I saw this pandemic was not science. Thank you."

Camus

127,765 просмотров • 1 год назад

JUST IN: Perplexity launched "Perplexity Computer" — and it might be the most complete AI agent system available right now. Not a chatbot upgrade. Not a research tool with a new name. A system that plans entire projects, delegates to specialist AI models, and runs autonomously for hours, days, or months (their words). Here's what makes the architecture genuinely different: → Opus 4.6 handles core reasoning and orchestration → Gemini handles deep research (spawning its own sub-agents) → Grok handles lightweight speed tasks → Veo 3.1 handles video generation → Nano Banana handles image creation → ChatGPT 5.2 handles long-context recall and wide search → You can override model choices per subtask 19 models total. Each task runs in an isolated environment with a real filesystem, real browser, and real tool integrations. You describe an outcome. It breaks it into tasks and subtasks, creates sub-agents for each, and coordinates them automatically. When a sub-agent hits a problem, it spawns more sub-agents to solve it. And it connects to your existing stack — GitHub, Google Drive, Gmail, Slack, Jira, Linear, Notion, Confluence, Ahrefs, Airtable, and more. Critically, it doesn't just run once. It can run on a schedule. Reading your docs, checking your project boards, pulling from your CRM, and acting on what it finds. Market monitoring. Competitor tracking. Weekly reports with charts. Content pipelines. CRON jobs that actually execute. Not "AI that helps you once." AI that runs in the background for days or months. Think of it as managed OpenClaw — similar autonomous capability (scheduled tasks, multi-step workflows, tool integrations) but fully managed. No Mac Mini. No security config. No infrastructure to maintain. I tested it with a complex prompt — a full stock trading simulator with what-if scenarios, correlation heatmaps, sentiment analysis, and a Bloomberg Terminal aesthetic. Two prompts later: deployed to Netlify via GitHub, with working CRON jobs updating live data. I've started using it to analyze my portfolio. But coding is just one lane. This thing researches, writes reports, generates datasets, creates videos, processes documents, and connects to your existing tools — all in one coordinated workflow. The real shift: you don't choose a model anymore. You describe what you need. The system routes each piece of work to whichever model does it best — and spawns new agents when it hits a wall. 19 models, dynamic sub-agents, scheduled tasks, and your entire tool stack connected. Thoughts?

JUST IN: Perplexity launched "Perplexity Computer" — and it might be the most complete AI agent system available right now. Not a chatbot upgrade. Not a research tool with a new name. A system that plans entire projects, delegates to specialist AI models, and runs autonomously for hours, days, or months (their words). Here's what makes the architecture genuinely different: → Opus 4.6 handles core reasoning and orchestration → Gemini handles deep research (spawning its own sub-agents) → Grok handles lightweight speed tasks → Veo 3.1 handles video generation → Nano Banana handles image creation → ChatGPT 5.2 handles long-context recall and wide search → You can override model choices per subtask 19 models total. Each task runs in an isolated environment with a real filesystem, real browser, and real tool integrations. You describe an outcome. It breaks it into tasks and subtasks, creates sub-agents for each, and coordinates them automatically. When a sub-agent hits a problem, it spawns more sub-agents to solve it. And it connects to your existing stack — GitHub, Google Drive, Gmail, Slack, Jira, Linear, Notion, Confluence, Ahrefs, Airtable, and more. Critically, it doesn't just run once. It can run on a schedule. Reading your docs, checking your project boards, pulling from your CRM, and acting on what it finds. Market monitoring. Competitor tracking. Weekly reports with charts. Content pipelines. CRON jobs that actually execute. Not "AI that helps you once." AI that runs in the background for days or months. Think of it as managed OpenClaw — similar autonomous capability (scheduled tasks, multi-step workflows, tool integrations) but fully managed. No Mac Mini. No security config. No infrastructure to maintain. I tested it with a complex prompt — a full stock trading simulator with what-if scenarios, correlation heatmaps, sentiment analysis, and a Bloomberg Terminal aesthetic. Two prompts later: deployed to Netlify via GitHub, with working CRON jobs updating live data. I've started using it to analyze my portfolio. But coding is just one lane. This thing researches, writes reports, generates datasets, creates videos, processes documents, and connects to your existing tools — all in one coordinated workflow. The real shift: you don't choose a model anymore. You describe what you need. The system routes each piece of work to whichever model does it best — and spawns new agents when it hits a wall. 19 models, dynamic sub-agents, scheduled tasks, and your entire tool stack connected. Thoughts?

Paweł Huryn

219,681 просмотров • 5 месяцев назад