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My dad looked at my screen and said what is this, a hacker game? It was a Polymarket bot making $400 while he was standing behind me. He sat down. I explained. Polymarket runs on one equation. Softmax. The same math Claude uses to pick the next word. C(q) = b · ln Σ e^(qi/b) 93% of traders do not know this formula exists. They look at 40 cents and think cheap. My bot looks at 40 cents and calculates the theoretical price is 58 cents. That is an 18-cent edge per share. Two more formulas do the rest: f = (p·b − q) / b. Kelly Criterion. P(H|E) = P(E|H)·P(H) / P(E). Bayes. I gave Claude all 4 and said find every contract the market has wrong. $600 to $10,190. 425 trades. Still running. 61.2% win rate. Wrong 39% of the time. Does not matter. The sizing formula makes sure wins pay more than losses cost. 87% of wallets lose money trading against this math. The 4 formulas are on Wikipedia. The code fits in one file. The only edge: my bot reads them at 3 AM when the market is mispriced and nobody is looking. My dad still calls it a hacker game. But he stopped asking when I am getting a real job. I built the entire framework: Softmax arbitrage detection layer Kelly Criterion position sizing Bayesian probability recalibration Claude integration for autonomous execution 3 AM mispricing scanner The system runs 24/7. Finds where the math disagrees with the crowd. Executes before the edge compresses. No prediction. No gut feel. Just formulas. You only need Claude + device + 1 hour per day. Giving this free for 24 hours. To get it: 1. Comment the word money 2. Like and retweet this 3. Follow me Himanshu Kumar so I can DM you Save this post. Deploy the formula system this week. Start with $600. Scale on evidence.
0:44

My dad looked at my screen and said what is this, a hacker game? It was a Polymarket bot making $400 while he was standing behind me. He sat down. I explained. Polymarket runs on one equation. Softmax. The same math Claude uses to pick the next word. C(q) = b · ln Σ e^(qi/b) 93% of traders do not know this formula exists. They look at 40 cents and think cheap. My bot looks at 40 cents and calculates the theoretical price is 58 cents. That is an 18-cent edge per share. Two more formulas do the rest: f = (p·b − q) / b. Kelly Criterion. P(H|E) = P(E|H)·P(H) / P(E). Bayes. I gave Claude all 4 and said find every contract the market has wrong. $600 to $10,190. 425 trades. Still running. 61.2% win rate. Wrong 39% of the time. Does not matter. The sizing formula makes sure wins pay more than losses cost. 87% of wallets lose money trading against this math. The 4 formulas are on Wikipedia. The code fits in one file. The only edge: my bot reads them at 3 AM when the market is mispriced and nobody is looking. My dad still calls it a hacker game. But he stopped asking when I am getting a real job. I built the entire framework: Softmax arbitrage detection layer Kelly Criterion position sizing Bayesian probability recalibration Claude integration for autonomous execution 3 AM mispricing scanner The system runs 24/7. Finds where the math disagrees with the crowd. Executes before the edge compresses. No prediction. No gut feel. Just formulas. You only need Claude + device + 1 hour per day. Giving this free for 24 hours. To get it: 1. Comment the word money 2. Like and retweet this 3. Follow me Himanshu Kumar so I can DM you Save this post. Deploy the formula system this week. Start with $600. Scale on evidence.

Himanshu Kumar

13,333 görüntüleme • 3 ay önce

losers spend money on club tables and take home zero girls buying bottles at liv miami trying to impress models who make $500/night when you could be recruiting them for onlyfans with 0% apr tables and take 90% of their revenue... $300k/month from girls you met at clubs here's the onlyfans mafia system destroying simps: every weekend desperate mfs drop $50k cash on tables models pretend to care for 3 hours everyone goes home alone money wasted meanwhile smart operators run different game: THE CREDIT CARD TABLE HACK: get $150k in business funding at 0% apr book owner's tables at tier-1 miami clubs never spend cash - everything on cards earn 300k points while recruiting cards to get approved for today: - chase ink preferred: $30k typical - amex business gold: $50k typical - capital one spark: $40k typical - wells fargo business: $30k typical $150k at 0% in 30 days if you're not stupid THE RECRUITMENT PSYCHOLOGY: table attracts 10-15 models per night they think you're a whale you're not you're a businessman the pitch that converts 40%: "i manage content creators everything's handled - photographer, editor, marketing creators keep 10% pure profit most make $3-5k/month passive here's my portfolio..." show them your roster: girl #1: was bartending at bodega now: $50k/month on OF her take: $5000 your take: $45,000 girl #2: was bottle service at e11even now: $31k/month her take: $3,100 your take: $27,900 they all say yes THE MIAMI CLUB RANKING: recruit here in exact order: 1. liv (fontainebleau) - international models 2. e11even (24/7) - party girls who need money 3. story (south beach) - college girls 4. space (downtown) - underground scene 5. basement (edition) - high-end escorts transitioning wednesday-thursday better than weekends less competition from actual rich guys girls more desperate for attention THE ONLYFANS ASSEMBLY LINE: week 1: professional shoot ($500) week 2: launch with 50 posts ready week 3: tiktok spam campaign week 4: instagram reels push month 2: optimize pricing month 3: $15-30k/month steady your only job: - recruit - manage photographers - collect 90% THE BUSINESS MODEL MATH: monthly costs: - tables: $40k (on 0% cards) - photographer: $8k - editors: $5k (philippines) - shoot apartment: $4k total overhead: $57k 20 girls at $15k average: $300k your 90% cut: $270k monthly profit: $213k started with credit cards ending with empire THE SCALE FORMULA: month 1-3: recruit 20 girls month 4-6: optimize content month 7-12: $300k/month automated year 2: expand to NYC/LA year 3: sell for $20m to PE fund 3-year exit from credit cards THE DARK PSYCHOLOGY: these girls could do this alone but they won't they need leadership need someone to blame when dad finds out need the infrastructure you're not exploiting you're organizing they were already selling bottle service now they're CEOs making 10x more THE EXACT RECRUITMENT SCRIPT: "hey i know this is random you're exactly the type my agency represents we manage exclusive content creators everything's handled professionally creators keep 10% pure profit most hit $3-5k/month within 90 days here's my card, let's talk monday" success rate: 40% they all call THE CREDIT TO CASH CONVERSION: $150k in business cards approved liquidate through: - plastiq for "rent": 2.85% fee - paypal "consulting": 2.9% fee - square "services": 2.75% fee $150k credit becomes $145k cash fund entire operation at 0% pay minimums from profits THE COMPETITION ELIMINATION: other "managers" take 50% and provide nothing you take 90% but provide everything: - professional content - daily posting - fan management - marketing strategy girls make more with you at 10% than alone at 100% that's why they stay THE EXIT REALITY: building "talent management agency" 30 active models = $500k/month revenue $6m annual agencies sell for 3-5x exit value: $18-30 million from credit cards to 8 figures in 36 months you're either buying bottles like a sucker or building an empire with bank money choose your side Get $100K at 0% APR guaranteed Link in bio → Scale With Credit
0:28

losers spend money on club tables and take home zero girls buying bottles at liv miami trying to impress models who make $500/night when you could be recruiting them for onlyfans with 0% apr tables and take 90% of their revenue... $300k/month from girls you met at clubs here's the onlyfans mafia system destroying simps: every weekend desperate mfs drop $50k cash on tables models pretend to care for 3 hours everyone goes home alone money wasted meanwhile smart operators run different game: THE CREDIT CARD TABLE HACK: get $150k in business funding at 0% apr book owner's tables at tier-1 miami clubs never spend cash - everything on cards earn 300k points while recruiting cards to get approved for today: - chase ink preferred: $30k typical - amex business gold: $50k typical - capital one spark: $40k typical - wells fargo business: $30k typical $150k at 0% in 30 days if you're not stupid THE RECRUITMENT PSYCHOLOGY: table attracts 10-15 models per night they think you're a whale you're not you're a businessman the pitch that converts 40%: "i manage content creators everything's handled - photographer, editor, marketing creators keep 10% pure profit most make $3-5k/month passive here's my portfolio..." show them your roster: girl #1: was bartending at bodega now: $50k/month on OF her take: $5000 your take: $45,000 girl #2: was bottle service at e11even now: $31k/month her take: $3,100 your take: $27,900 they all say yes THE MIAMI CLUB RANKING: recruit here in exact order: 1. liv (fontainebleau) - international models 2. e11even (24/7) - party girls who need money 3. story (south beach) - college girls 4. space (downtown) - underground scene 5. basement (edition) - high-end escorts transitioning wednesday-thursday better than weekends less competition from actual rich guys girls more desperate for attention THE ONLYFANS ASSEMBLY LINE: week 1: professional shoot ($500) week 2: launch with 50 posts ready week 3: tiktok spam campaign week 4: instagram reels push month 2: optimize pricing month 3: $15-30k/month steady your only job: - recruit - manage photographers - collect 90% THE BUSINESS MODEL MATH: monthly costs: - tables: $40k (on 0% cards) - photographer: $8k - editors: $5k (philippines) - shoot apartment: $4k total overhead: $57k 20 girls at $15k average: $300k your 90% cut: $270k monthly profit: $213k started with credit cards ending with empire THE SCALE FORMULA: month 1-3: recruit 20 girls month 4-6: optimize content month 7-12: $300k/month automated year 2: expand to NYC/LA year 3: sell for $20m to PE fund 3-year exit from credit cards THE DARK PSYCHOLOGY: these girls could do this alone but they won't they need leadership need someone to blame when dad finds out need the infrastructure you're not exploiting you're organizing they were already selling bottle service now they're CEOs making 10x more THE EXACT RECRUITMENT SCRIPT: "hey i know this is random you're exactly the type my agency represents we manage exclusive content creators everything's handled professionally creators keep 10% pure profit most hit $3-5k/month within 90 days here's my card, let's talk monday" success rate: 40% they all call THE CREDIT TO CASH CONVERSION: $150k in business cards approved liquidate through: - plastiq for "rent": 2.85% fee - paypal "consulting": 2.9% fee - square "services": 2.75% fee $150k credit becomes $145k cash fund entire operation at 0% pay minimums from profits THE COMPETITION ELIMINATION: other "managers" take 50% and provide nothing you take 90% but provide everything: - professional content - daily posting - fan management - marketing strategy girls make more with you at 10% than alone at 100% that's why they stay THE EXIT REALITY: building "talent management agency" 30 active models = $500k/month revenue $6m annual agencies sell for 3-5x exit value: $18-30 million from credit cards to 8 figures in 36 months you're either buying bottles like a sucker or building an empire with bank money choose your side Get $100K at 0% APR guaranteed Link in bio → Scale With Credit

hunter

15,066 görüntüleme • 9 ay önce

An investing gem by Joe Greenblatt! One of the most comprehensive Investing lectures ever given. Here are my 6 favorite takeaways from this genius talk: 1. Not "Value" but "Valuation" Investing Low P/B or low P/S investing is what Morningstar labels Value Investing. That approach hasn't worked well for quite some time now. Momentum investing did work well over the last decade. But will it continue? Nobody knows. But there's one thing that'll always work: Valuation Investing. Investing based on sound valuation work. 2. Is Outperformance as an Active Investor Still Possible? There are so many smart people working in finance and asset management. There are more and more computers and AI systems. Is active Investing dead? Simple Answer: From 1997-2000, the S&P 500 doubled. From 2000-2002, it halved. From 2002-2007, it doubled. From 2007-2009, it halved. From 2009 to today, it multiplied sixfold. And that's just the index. Individual stocks were even more volatile. -> People are still crazy. There's still lots of opportunity. 3. Differentiate for Superior Performance Superior performance comes from differentiation. The main reason why so many people fail to outperform is because they fish in the same water. If you only look for S&P 500 stocks, where is the superior performance supposed to come from? The further you get away from the most famous stocks, the higher the chance for different performance. Yes, also for underperformance. Buying things right matters more than ever, then. 4. Valuation Look for "absolute cheap" in combination with "relative cheap." When assessing the absolute cheapness of a company, Greenblatt focuses on the FCF yield. FCF Yield: Free Cash Flow (per share) / Market Price (per share) Only after you've assessed a company "absolute cheap," you can also check for relative cheapness by comparing it to competitors within the industry. 5. Valuation is like Gravity If you're right with your valuation of the company, the stock price will follow, sooner or later. If you buy overvalued companies, >99% of them will come down. Only <1% will grow so significantly that you don't lose money on them. The problem is that people voluntarily look for those opportunities. They don't want beaten and off-the-path opportunities that are undervalued. They want Tesla to grow into an enormous valuation and then say:" I told you so!" And maybe Tesla is the one outlier out of 100. But why bet on that when there are so many less risky bets out there? 6. The Fallacy of Diversification The fact that people think you need to own at least 30 stocks shows that they didn't understand the idea of thinking like an owner. No one would call someone who owns six different businesses in your hometown a speculator. In the stock market, they do, because they think about pieces of paper and tickers on their screen. Not about businesses...
59:27

An investing gem by Joe Greenblatt! One of the most comprehensive Investing lectures ever given. Here are my 6 favorite takeaways from this genius talk: 1. Not "Value" but "Valuation" Investing Low P/B or low P/S investing is what Morningstar labels Value Investing. That approach hasn't worked well for quite some time now. Momentum investing did work well over the last decade. But will it continue? Nobody knows. But there's one thing that'll always work: Valuation Investing. Investing based on sound valuation work. 2. Is Outperformance as an Active Investor Still Possible? There are so many smart people working in finance and asset management. There are more and more computers and AI systems. Is active Investing dead? Simple Answer: From 1997-2000, the S&P 500 doubled. From 2000-2002, it halved. From 2002-2007, it doubled. From 2007-2009, it halved. From 2009 to today, it multiplied sixfold. And that's just the index. Individual stocks were even more volatile. -> People are still crazy. There's still lots of opportunity. 3. Differentiate for Superior Performance Superior performance comes from differentiation. The main reason why so many people fail to outperform is because they fish in the same water. If you only look for S&P 500 stocks, where is the superior performance supposed to come from? The further you get away from the most famous stocks, the higher the chance for different performance. Yes, also for underperformance. Buying things right matters more than ever, then. 4. Valuation Look for "absolute cheap" in combination with "relative cheap." When assessing the absolute cheapness of a company, Greenblatt focuses on the FCF yield. FCF Yield: Free Cash Flow (per share) / Market Price (per share) Only after you've assessed a company "absolute cheap," you can also check for relative cheapness by comparing it to competitors within the industry. 5. Valuation is like Gravity If you're right with your valuation of the company, the stock price will follow, sooner or later. If you buy overvalued companies, >99% of them will come down. Only <1% will grow so significantly that you don't lose money on them. The problem is that people voluntarily look for those opportunities. They don't want beaten and off-the-path opportunities that are undervalued. They want Tesla to grow into an enormous valuation and then say:" I told you so!" And maybe Tesla is the one outlier out of 100. But why bet on that when there are so many less risky bets out there? 6. The Fallacy of Diversification The fact that people think you need to own at least 30 stocks shows that they didn't understand the idea of thinking like an owner. No one would call someone who owns six different businesses in your hometown a speculator. In the stock market, they do, because they think about pieces of paper and tickers on their screen. Not about businesses...

Daniel Mahncke

247,527 görüntüleme • 3 yıl önce

3 steps story. I gave Claude $40 and it made me $8,409 in a week on Polymarket I typed a prompt. Claude wrote the bot. I funded it. $40 in. $8,409 out. 7 days. Someone else did the same thing and turned it into $294,127 in 26 days. And the prompt I used is something I've never shared publicly. Until now. This bro is a great example of how MY system looks at a scale: Profile → 0x0006af12cd4dacc450836a0e1ec6ce47365d8c63 $294,127.72 all-time. 2,226 predictions. $14,800 biggest win. Joined March 2026. 26.9K views. Still mostly unwatched. While I was developing my own script I copytraded this AI to see how profitable this strategy is, made $520 from my $10 lol Copytrade here: And every single position is the same structure. "ETH Up or Down - March 7, 11:45AM" → $1,895 in → $16,695 out +780% "BTC Up or Down - March 21, 11:20PM" → $1,186 in → $11,919 out. +904% This isn't trading. It's one equation running on loop while you sleep. Here's exactly what I typed into Claude: The prompt had 4 instructions: 1. Map temporal bias windows "Analyze 24 months of BTC and ETH hourly data. Find which specific time windows have statistically significant directional bias above 65%. Ignore all other windows." The crowd prices every window as 50/50. They're not. Claude found the ones that aren't. 2. Build Kelly sizing "Size each position using fractional Kelly. Scale conviction to statistical confidence. Never flat-size. " f* = (p × b - q) / b That's why entries look random - $259, $886, $25,002. They're not random. They're mathematically weighted. 3. Add a volatility filter "Skip any window where 24h realized volatility exceeds 1.4x the 30-day average. Patterns break in chaos." The bot doesn't fire blindly. It waits for clean mathematical conditions. 4. Bayesian self-updating "After each settled trade, update win probability for that window. The model sharpens itself." Every loss makes the next trade smarter. Every win increases conviction. Claude wrote the entire execution script in 23 minutes. I funded it with $40. It placed its first trade 4 minutes later. $40 → $8,409. One week. The wallet above ran the same logic for 26 days → $294,127. This wallet had 26.9K views before this post. Tomorrow it'll have 10x that. The people who moved first always win. Always. FOLLOW while this is still a quiet room.
0:23

3 steps story. I gave Claude $40 and it made me $8,409 in a week on Polymarket I typed a prompt. Claude wrote the bot. I funded it. $40 in. $8,409 out. 7 days. Someone else did the same thing and turned it into $294,127 in 26 days. And the prompt I used is something I've never shared publicly. Until now. This bro is a great example of how MY system looks at a scale: Profile → 0x0006af12cd4dacc450836a0e1ec6ce47365d8c63 $294,127.72 all-time. 2,226 predictions. $14,800 biggest win. Joined March 2026. 26.9K views. Still mostly unwatched. While I was developing my own script I copytraded this AI to see how profitable this strategy is, made $520 from my $10 lol Copytrade here: And every single position is the same structure. "ETH Up or Down - March 7, 11:45AM" → $1,895 in → $16,695 out +780% "BTC Up or Down - March 21, 11:20PM" → $1,186 in → $11,919 out. +904% This isn't trading. It's one equation running on loop while you sleep. Here's exactly what I typed into Claude: The prompt had 4 instructions: 1. Map temporal bias windows "Analyze 24 months of BTC and ETH hourly data. Find which specific time windows have statistically significant directional bias above 65%. Ignore all other windows." The crowd prices every window as 50/50. They're not. Claude found the ones that aren't. 2. Build Kelly sizing "Size each position using fractional Kelly. Scale conviction to statistical confidence. Never flat-size. " f* = (p × b - q) / b That's why entries look random - $259, $886, $25,002. They're not random. They're mathematically weighted. 3. Add a volatility filter "Skip any window where 24h realized volatility exceeds 1.4x the 30-day average. Patterns break in chaos." The bot doesn't fire blindly. It waits for clean mathematical conditions. 4. Bayesian self-updating "After each settled trade, update win probability for that window. The model sharpens itself." Every loss makes the next trade smarter. Every win increases conviction. Claude wrote the entire execution script in 23 minutes. I funded it with $40. It placed its first trade 4 minutes later. $40 → $8,409. One week. The wallet above ran the same logic for 26 days → $294,127. This wallet had 26.9K views before this post. Tomorrow it'll have 10x that. The people who moved first always win. Always. FOLLOW while this is still a quiet room.

Frogify

19,985 görüntüleme • 5 ay önce

⬜️ BONDI, SYDNEY (Stabbing) 🔪⬜️ 👉🏼 THREAD 🪡 🧵 👈🏼 1) John Singleton's Daughter has been taken into Witness Protection 'IMO' to testify against her Father for the Rape & Murder of Children = DARUK BOYS HOME. 2) Woman 55yrs Old Died = 5:5 Military Comms 3) We were all asking “Where were the Security Guards” ? 💥 Then they give us a PAKASTANI that started his 1st Day on the Job (Give me a BREAK) 4) The HERO that held the Baby had NO BLOOD 🩸 on him. 💥 IF HE WAS INVOLVED he wouldn’t be able to just walk away. HE WOULD BE TAKEN IN FOR QUESTIONING 🙋‍♂️ 5) The accused Stabber had an AUSTRALIAN JERSEY & SHORTS 🩳 ON 😂 (Looked Middle Eastern but wasn’t, which stinks of a PLANNED ATTACK from The GOVERNMENT / MK ULTRA. I AM ALSO TOLD FROM A 1ST RESPONDENT that Deceased Bodies go BLUE or GREY. The supposed person with the KNIFE that was SHOT is the WRONG colour for a DECEASED PERSON, that's lost that AMOUNT OF BLOOD. 6) Lady Died, her Baby was 9 Months old but didn’t die. IT TAKES “9 MONTHS” for a Baby to be BORN. 💥 This sounds like JESUS V’s LUCIFER 23/4 = PASSOVER UNLEAVEN BREAD FEAST 28/4 = FEAST FIRST FRUITS 💥 The stabbing took place on a “ 13th “ 💥 The Illuminati are known to hide the 13th floor on HIGHRISES ETC ( 9 instances of 13 ) Look it up 📚 7) Then you’ve got the Police HERO 👮‍♂️ at the end. 💥 So YOU FORGET what the POLICE 👮‍♂️ 👮‍♂️ 👮‍♂️ 🔫 Did to us DURING COVID. 💥Closure of BORDERS & FORCED VACCINES 💉 🔥 POLICE ➡️ THE CROWN 👸 ➡️ ROTHSCHILDS 🍀 Lucky for us THE CORPORATION is BANKRUPT 🍀 We return 🔜 to COMMON LAW & CONSTITUTIONAL LAW. 🔥WE TOOK DOWN THE FOLLOWING 🔥 THE ROYAL FAMILY THE VATICAN THE CENTRAL BANKS (Rothschilds & Co) WASHINGTON DC & LONDON CITY 🌆 HOLLYWOOD & EPSTEIN ISLAND BLEW UP ALL THE UNDERGROUND TUNNELS WHERE THEY WERE CHILD & HUMAN TRAFFICKING, ORGAN HARVESTING, ADRENOCHROME. What you thought were EARTHQUAKES over these last 4 yrs are actually UNDEGROUND TUNNELS being blown up as a part of GESARA / NESARA LAW in the new system, once we return to the CONSTITUTION. 💥 You’ve got a FAKE WAR in ISRAEL / IRAN MILITARY ARE IN CONTROL OF THIS OPERATION !!! POWER COMES BACK TO THE PEOPLE THE EVIL 👿 WILL SOON BE 6ft UNDER !!! ⬜️⬜️⬜️⬜️⬜️ #Bondi #BondiJuction #BondiJunctionShoppingCentre #JohnSingleton #Australia #Sydney
8:02

⬜️ BONDI, SYDNEY (Stabbing) 🔪⬜️ 👉🏼 THREAD 🪡 🧵 👈🏼 1) John Singleton's Daughter has been taken into Witness Protection 'IMO' to testify against her Father for the Rape & Murder of Children = DARUK BOYS HOME. 2) Woman 55yrs Old Died = 5:5 Military Comms 3) We were all asking “Where were the Security Guards” ? 💥 Then they give us a PAKASTANI that started his 1st Day on the Job (Give me a BREAK) 4) The HERO that held the Baby had NO BLOOD 🩸 on him. 💥 IF HE WAS INVOLVED he wouldn’t be able to just walk away. HE WOULD BE TAKEN IN FOR QUESTIONING 🙋‍♂️ 5) The accused Stabber had an AUSTRALIAN JERSEY & SHORTS 🩳 ON 😂 (Looked Middle Eastern but wasn’t, which stinks of a PLANNED ATTACK from The GOVERNMENT / MK ULTRA. I AM ALSO TOLD FROM A 1ST RESPONDENT that Deceased Bodies go BLUE or GREY. The supposed person with the KNIFE that was SHOT is the WRONG colour for a DECEASED PERSON, that's lost that AMOUNT OF BLOOD. 6) Lady Died, her Baby was 9 Months old but didn’t die. IT TAKES “9 MONTHS” for a Baby to be BORN. 💥 This sounds like JESUS V’s LUCIFER 23/4 = PASSOVER UNLEAVEN BREAD FEAST 28/4 = FEAST FIRST FRUITS 💥 The stabbing took place on a “ 13th “ 💥 The Illuminati are known to hide the 13th floor on HIGHRISES ETC ( 9 instances of 13 ) Look it up 📚 7) Then you’ve got the Police HERO 👮‍♂️ at the end. 💥 So YOU FORGET what the POLICE 👮‍♂️ 👮‍♂️ 👮‍♂️ 🔫 Did to us DURING COVID. 💥Closure of BORDERS & FORCED VACCINES 💉 🔥 POLICE ➡️ THE CROWN 👸 ➡️ ROTHSCHILDS 🍀 Lucky for us THE CORPORATION is BANKRUPT 🍀 We return 🔜 to COMMON LAW & CONSTITUTIONAL LAW. 🔥WE TOOK DOWN THE FOLLOWING 🔥 THE ROYAL FAMILY THE VATICAN THE CENTRAL BANKS (Rothschilds & Co) WASHINGTON DC & LONDON CITY 🌆 HOLLYWOOD & EPSTEIN ISLAND BLEW UP ALL THE UNDERGROUND TUNNELS WHERE THEY WERE CHILD & HUMAN TRAFFICKING, ORGAN HARVESTING, ADRENOCHROME. What you thought were EARTHQUAKES over these last 4 yrs are actually UNDEGROUND TUNNELS being blown up as a part of GESARA / NESARA LAW in the new system, once we return to the CONSTITUTION. 💥 You’ve got a FAKE WAR in ISRAEL / IRAN MILITARY ARE IN CONTROL OF THIS OPERATION !!! POWER COMES BACK TO THE PEOPLE THE EVIL 👿 WILL SOON BE 6ft UNDER !!! ⬜️⬜️⬜️⬜️⬜️ #Bondi #BondiJuction #BondiJunctionShoppingCentre #JohnSingleton #Australia #Sydney

💙 XRP 🦋 DigiGold 💙

144,718 görüntüleme • 2 yıl önce

Here’s my full Rivian R2 Performance review, covering features, design, some things I liked and didn't like, going off-roading, and more: At the end of my weeklong test, it dawned on me that the R2 Performance is a competitor to the Model Y Performance. I say "dawned" because the two vehicles couldn’t feel more different in their overall ethos. The R2 feels at home off-road. It soaks up bumps well, the suspension is well tuned, and it feels like an adventure vehicle. The Model Y Performance feels at home on-road, tackling corners and eating miles with FSD. Exterior: A boxy design might not be the most efficient, but I love the look. I'm a fan of the oval shaped front lights. The side profile and rear also both look great. There is a F1-style light at the top when braking. The 5.2 cu ft frunk is large for a vehicle in this class. The 9.6" ground clearance (3" more than Model Y) gives you a commanding view of what's ahead, but you do get a decent amount of body roll even in firm suspension mode. Fit and finish was good. The 20" Black Sand wheels with all-terrain tires look sweet. Fit and finish was good overall, and the 20-inch Black Sand wheels with all-terrain tires look sweet Interior: I'm a fan of the design. I’ve seen some people say the interior feels cheap, but I mostly disagree. The main touch points use nice textile, wood, and vegan leather trim. One material choice I don’t like, though, is the A- and B-pillars. Instead of textile, Rivian uses a hard, textured plastic designed to look like fabric. It’s also slightly lighter than the all-black headliner, which makes it more noticeable. The entire rear cargo area, aside from the floor, and the tailgate trim are also covered in hard plastic. Whether that’s a good or bad thing depends on how you use the vehicle. It’s easier to clean and probably better suited for hauling dirty gear, but textile or fabric like you get in the Model Y would do a better job absorbing sound and preventing cargo from sliding around and making noise. The front seats are extremely comfortable, and the lumbar support is great. It's easier to spend hours in them. The heated seats and steering wheel get nice and toasty. The cooled seats are some of the best I've ever experienced, but they are also the loudest. You can hear them at 60 mph. You can also feel like motor humming a bit, although that disappears when the vehicle is moving at speed. The sound system is an improvement over the R1S, but I’d still like deeper, more powerful bass, clearer highs, and better overall tuning for a more immersive sound. The dual glove box/storage setup is fantastic. Since both compartments are lockable, I’d use them all the time. The R2 in general has a ton of storage and little cubbies, with 90 cu ft of total storage space. The dual wireless phone chargers are also some of the best implemented I've seen. They use MagSafe, so your phones stay securely in place even when you’re throwing the R2 around off-road, and there is a little area for the phone camera bumps so the phone is flush. The infotainment system is fast and responsive, aside from the occasional minor lag, and the graphics look crisp. I absolutely love the huge navigation view that fills the screen. I wish I could do this in my Model Y. The Gauges section is also really cool, giving you real-time information like battery and motor temps. The Autonomy system works well, but it’s fairly limited in its current form. Think of it as advanced adaptive cruise control with lane keeping and the ability to change lanes on command. It can’t currently handle stop signs, traffic lights, or city driving. RJ has said an FSD (Supervised) like system is coming later this year, so we’ll see. The current R2s use Rivian’s older Gen 2 autonomy hardware. The next-generation Gen 3 hardware arrives next year and is expected to bring a significant increase in capability. It won’t be retrofittable to Gen 2 vehicles, though, so that’s something buyers should keep in mind. The second row has plenty of legroom and headroom. I was a little surprised that it doesn’t have acoustic windows, but it’s still pretty quiet back there. The full glass roof is nice and doesn’t make the cabin feel excessively hot. The interior lighting is VERY bright, which I love. It does a great job illuminating the entire cabin at night. Efficiency: I had the 20" all-terrain wheels, which are rated for 307 miles of range. Over my weeklong test, I averaged about 250–270 miles of range, or 12–19% below the stated range. During a 50-minute trip, with 85% highway and 15% non-highway driving and no crazy acceleration, I averaged 2.83 miles per kWh at an average speed of ~60 mph. That equates to 249 miles of range from a full charge to empty, despite being in Conserve mode. I had a few other trips with similar numbers. Will be interesting to see the what the winter range is like. If you're someone who doesn't go on long trips often or mostly stays in your area and charges at home, these efficiency numbers probably won't matter to you. Final thoughts: I won’t write out every thought here, you can check out my full review video below for that, but overall, the R2 is a great vehicle. It’s a very capable off-roader while still being a comfortable daily driver. The lack of an FSD-like system is a deal-breaker for me personally since I use FSD for virtually all of my miles. It's the most luxurious and mind-blowing vehicle feature on the market today, but that might not be a deal-breaker for other people, and that’s okay. The fact there there are good EVs available is great for consumers. Thanks again Rivian for letting me test the R2 for a week!
29:49

Here’s my full Rivian R2 Performance review, covering features, design, some things I liked and didn't like, going off-roading, and more: At the end of my weeklong test, it dawned on me that the R2 Performance is a competitor to the Model Y Performance. I say "dawned" because the two vehicles couldn’t feel more different in their overall ethos. The R2 feels at home off-road. It soaks up bumps well, the suspension is well tuned, and it feels like an adventure vehicle. The Model Y Performance feels at home on-road, tackling corners and eating miles with FSD. Exterior: A boxy design might not be the most efficient, but I love the look. I'm a fan of the oval shaped front lights. The side profile and rear also both look great. There is a F1-style light at the top when braking. The 5.2 cu ft frunk is large for a vehicle in this class. The 9.6" ground clearance (3" more than Model Y) gives you a commanding view of what's ahead, but you do get a decent amount of body roll even in firm suspension mode. Fit and finish was good. The 20" Black Sand wheels with all-terrain tires look sweet. Fit and finish was good overall, and the 20-inch Black Sand wheels with all-terrain tires look sweet Interior: I'm a fan of the design. I’ve seen some people say the interior feels cheap, but I mostly disagree. The main touch points use nice textile, wood, and vegan leather trim. One material choice I don’t like, though, is the A- and B-pillars. Instead of textile, Rivian uses a hard, textured plastic designed to look like fabric. It’s also slightly lighter than the all-black headliner, which makes it more noticeable. The entire rear cargo area, aside from the floor, and the tailgate trim are also covered in hard plastic. Whether that’s a good or bad thing depends on how you use the vehicle. It’s easier to clean and probably better suited for hauling dirty gear, but textile or fabric like you get in the Model Y would do a better job absorbing sound and preventing cargo from sliding around and making noise. The front seats are extremely comfortable, and the lumbar support is great. It's easier to spend hours in them. The heated seats and steering wheel get nice and toasty. The cooled seats are some of the best I've ever experienced, but they are also the loudest. You can hear them at 60 mph. You can also feel like motor humming a bit, although that disappears when the vehicle is moving at speed. The sound system is an improvement over the R1S, but I’d still like deeper, more powerful bass, clearer highs, and better overall tuning for a more immersive sound. The dual glove box/storage setup is fantastic. Since both compartments are lockable, I’d use them all the time. The R2 in general has a ton of storage and little cubbies, with 90 cu ft of total storage space. The dual wireless phone chargers are also some of the best implemented I've seen. They use MagSafe, so your phones stay securely in place even when you’re throwing the R2 around off-road, and there is a little area for the phone camera bumps so the phone is flush. The infotainment system is fast and responsive, aside from the occasional minor lag, and the graphics look crisp. I absolutely love the huge navigation view that fills the screen. I wish I could do this in my Model Y. The Gauges section is also really cool, giving you real-time information like battery and motor temps. The Autonomy system works well, but it’s fairly limited in its current form. Think of it as advanced adaptive cruise control with lane keeping and the ability to change lanes on command. It can’t currently handle stop signs, traffic lights, or city driving. RJ has said an FSD (Supervised) like system is coming later this year, so we’ll see. The current R2s use Rivian’s older Gen 2 autonomy hardware. The next-generation Gen 3 hardware arrives next year and is expected to bring a significant increase in capability. It won’t be retrofittable to Gen 2 vehicles, though, so that’s something buyers should keep in mind. The second row has plenty of legroom and headroom. I was a little surprised that it doesn’t have acoustic windows, but it’s still pretty quiet back there. The full glass roof is nice and doesn’t make the cabin feel excessively hot. The interior lighting is VERY bright, which I love. It does a great job illuminating the entire cabin at night. Efficiency: I had the 20" all-terrain wheels, which are rated for 307 miles of range. Over my weeklong test, I averaged about 250–270 miles of range, or 12–19% below the stated range. During a 50-minute trip, with 85% highway and 15% non-highway driving and no crazy acceleration, I averaged 2.83 miles per kWh at an average speed of ~60 mph. That equates to 249 miles of range from a full charge to empty, despite being in Conserve mode. I had a few other trips with similar numbers. Will be interesting to see the what the winter range is like. If you're someone who doesn't go on long trips often or mostly stays in your area and charges at home, these efficiency numbers probably won't matter to you. Final thoughts: I won’t write out every thought here, you can check out my full review video below for that, but overall, the R2 is a great vehicle. It’s a very capable off-roader while still being a comfortable daily driver. The lack of an FSD-like system is a deal-breaker for me personally since I use FSD for virtually all of my miles. It's the most luxurious and mind-blowing vehicle feature on the market today, but that might not be a deal-breaker for other people, and that’s okay. The fact there there are good EVs available is great for consumers. Thanks again Rivian for letting me test the R2 for a week!

Sawyer Merritt

222,289 görüntüleme • 8 gün önce

Seedance 2.5 just got an upgrade with 1080p only on Higgsfield... you can now ONE SHOT commercials like this here's exactly how to do it (with a gift at then end): 1/ write the prompt as a timestamped breakdown > split the spot second by second > put the exact dialogue inside each window in quotes, the model speaks it word for word with lip sync > match the generation length to your timestamps, a 21 second script generated at 15 compresses and the delivery desyncs 2/ composition is named - never hoped for > name the camera: "handheld front camera, chest-up framing, natural micro shakes" for UGC, "35mm, slow push-in, product centered" for a produced spot > name the light: "golden hour through the windshield" or "ring light with slight reflections in the eyes" > name the grade: "high contrast, cool tones, warm skin" > whatever you leave unstated gets invented and locked for the whole clip 3/ characters hold when you anchor them > generate a first frame image before any video: real skin texture, visible pores, one fixed imperfection like freckles so drift becomes instantly visible > feed it in as the reference and open every prompt with "the same person as the reference, identical face, hair and outfit" > the @ system locks it harder: @.character for the face, @.style for the look, @.audio for the voice > then write the micro behaviors: a glance away and back, a pre-line breath, fingers adjusting grip... small involuntary movement is what reads human, and you get it by naming it 4/ sound is written - not defaulted > end every prompt with an audio block: "clear phone-mic voice with light room tone" for UGC, "clean studio voice, no echo" for a produced spot > name the music under the dialogue: "soft upbeat synth instrumental running quietly underneath" > one delivery word for the read: "delivery: fed up" produces a performance, an adjective stack produces nothing 5/ sharp text is quoted text > write the exact label or on-screen text in quotes with its style > unquoted text gets invented typography that garbles between frames > print brand names big, a bold label survives every shot while a small tag melts 6/ the consistency laws > repeat the product description verbatim in every prompt, faces anchor but products drift > count objects scene-wide: "exactly one bottle in the entire scene, no duplicate on any surface" > close the wardrobe: "small gold studs, no other jewellery, no rings, no watch" > every state change happens across a cut: the swatch on her hand in shot one, blended in shot two, no clip contains the transition... the viewer's brain supplies it and the shortcut for UGC: take an ad that already converted, ask gemini for a 1:1 timestamped breakdown of everything on screen, swap in your product and script -> that breakdown is your prompt set 9:16 for shortform, 16:9 for the spot, generate straight at 1080p and ship RT + reply to this post and i'll send you my full guide to make your own creatives
0:29

Seedance 2.5 just got an upgrade with 1080p only on Higgsfield... you can now ONE SHOT commercials like this here's exactly how to do it (with a gift at then end): 1/ write the prompt as a timestamped breakdown > split the spot second by second > put the exact dialogue inside each window in quotes, the model speaks it word for word with lip sync > match the generation length to your timestamps, a 21 second script generated at 15 compresses and the delivery desyncs 2/ composition is named - never hoped for > name the camera: "handheld front camera, chest-up framing, natural micro shakes" for UGC, "35mm, slow push-in, product centered" for a produced spot > name the light: "golden hour through the windshield" or "ring light with slight reflections in the eyes" > name the grade: "high contrast, cool tones, warm skin" > whatever you leave unstated gets invented and locked for the whole clip 3/ characters hold when you anchor them > generate a first frame image before any video: real skin texture, visible pores, one fixed imperfection like freckles so drift becomes instantly visible > feed it in as the reference and open every prompt with "the same person as the reference, identical face, hair and outfit" > the @ system locks it harder: @.character for the face, @.style for the look, @.audio for the voice > then write the micro behaviors: a glance away and back, a pre-line breath, fingers adjusting grip... small involuntary movement is what reads human, and you get it by naming it 4/ sound is written - not defaulted > end every prompt with an audio block: "clear phone-mic voice with light room tone" for UGC, "clean studio voice, no echo" for a produced spot > name the music under the dialogue: "soft upbeat synth instrumental running quietly underneath" > one delivery word for the read: "delivery: fed up" produces a performance, an adjective stack produces nothing 5/ sharp text is quoted text > write the exact label or on-screen text in quotes with its style > unquoted text gets invented typography that garbles between frames > print brand names big, a bold label survives every shot while a small tag melts 6/ the consistency laws > repeat the product description verbatim in every prompt, faces anchor but products drift > count objects scene-wide: "exactly one bottle in the entire scene, no duplicate on any surface" > close the wardrobe: "small gold studs, no other jewellery, no rings, no watch" > every state change happens across a cut: the swatch on her hand in shot one, blended in shot two, no clip contains the transition... the viewer's brain supplies it and the shortcut for UGC: take an ad that already converted, ask gemini for a 1:1 timestamped breakdown of everything on screen, swap in your product and script -> that breakdown is your prompt set 9:16 for shortform, 16:9 for the spot, generate straight at 1080p and ship RT + reply to this post and i'll send you my full guide to make your own creatives

Machina

12,785 görüntüleme • 1 ay önce

🎣 The Hook Skill Save this to write better hooks. What it does : (recommend watching the video on 2x speed instead⏩) Takes a brief (the brief defines the angle) Writes 3 hooks — same angle, different framing Scores each, rewrites anything weak, shows the rewrite All 3 ship as the ad set — Meta picks the winner via performance Gets better every Monday by checking what actually worked Pulls from your Memory & Context: Brand voice Audience Identity Mechanism Emotional Triggers What's won, what's flopped(Learnings) KILL list For me that's Notion — works with Obsidian, Claude Projects, MD folders Casts the person on camera before writing anything: Who she is (identity) Her age Where she is What she's doing What she's wearing What she's holding If anything's missing, the skill asks before generating Then asks one question: Would this exact person actually say this line out loud? Writes 3 hooks — same angle, different framing. One brief = one angle = three hooks. Want a different angle? Write a different brief. Opening styles in the menu: Controversy — a take that contradicts experts Bold opinion — a strong take that picks a side Raw confession — specific, emotional, hero-framed Riddle question — comedian/philosopher style only Mid-thought yapper — "Soooo my OBGYN told me…" Reaction — "This is wild —…" Dismissed quote — leads with the exact phrase someone said Stack — three things in fast sequence (brand names, power words, symptoms, body zones) Direct scene — a visible moment another person reacted to Authority declarative — when the speaker is the expert on camera THE HERO RULE — most important check. The audience is ALWAYS the hero. Protagonist mirrors the audience. Problem is ALWAYS external — hormones, doctors, biology, marketing lies Product is the tool the hero used Husband, doctor, friends are WITNESSES to her win — never the source of her validation Audience should want to BE her, not pity her Other rules every hook clears: 3-second rule — by 3 seconds of speech, the viewer is either curious or has a clear promise Vague ≠ curiosity — real curiosity from controversy, bold takes, raw confession, riddle questions. "Can you believe / Would you believe" are fake curiosity, auto-rewritten. Body/action specificity — "He stopped what he was doing" / "She noticed" / "Hasn't done that in years" → rewrite. "Staring at my butt" / "grabbed me by the waist" / "told me I look like I've been hitting the gym" → ship. Declarative beats interrogative — "He told me" > "He asked me." Always go bolder. Lead with the secret — open with what she KNOWS or HAS that no one else in the scene knows. Witness's reaction lands second as proof. Power-word stacks must be clear — three words alone is confusing. Make the category obvious (symptoms, body parts, etc.) and name what they are in the next sentence. Bridge between sentences — two sentences can't sit in a vacuum. Use a connector ("and for some reason today," "but somehow," "yet today," "— and") that references sentence 1 from inside sentence 2. Scores on 50 points. 5 dimensions, 0–10 each: Stops the scroll Creates curiosity Promises something specific Fits the brand and the person (Hero Rule lives here) Fits the format and what's winning Any dim under 8 → rewrites the hook. Shows the rewrite. Re-scores. Minimum two internal rewrite passes before anything is delivered. The first version you see is already polished. All three hooks ship as the ad set. Meta picks via performance data. Self-improving: Every Monday it pulls hooks that actually shipped Matches scores to real Meta performance Recalibrates the rubric if any dim stops predicting winners That's the whole skill. Comment HOOK and I'll send you the skill. Must be following.
8:32

🎣 The Hook Skill Save this to write better hooks. What it does : (recommend watching the video on 2x speed instead⏩) Takes a brief (the brief defines the angle) Writes 3 hooks — same angle, different framing Scores each, rewrites anything weak, shows the rewrite All 3 ship as the ad set — Meta picks the winner via performance Gets better every Monday by checking what actually worked Pulls from your Memory & Context: Brand voice Audience Identity Mechanism Emotional Triggers What's won, what's flopped(Learnings) KILL list For me that's Notion — works with Obsidian, Claude Projects, MD folders Casts the person on camera before writing anything: Who she is (identity) Her age Where she is What she's doing What she's wearing What she's holding If anything's missing, the skill asks before generating Then asks one question: Would this exact person actually say this line out loud? Writes 3 hooks — same angle, different framing. One brief = one angle = three hooks. Want a different angle? Write a different brief. Opening styles in the menu: Controversy — a take that contradicts experts Bold opinion — a strong take that picks a side Raw confession — specific, emotional, hero-framed Riddle question — comedian/philosopher style only Mid-thought yapper — "Soooo my OBGYN told me…" Reaction — "This is wild —…" Dismissed quote — leads with the exact phrase someone said Stack — three things in fast sequence (brand names, power words, symptoms, body zones) Direct scene — a visible moment another person reacted to Authority declarative — when the speaker is the expert on camera THE HERO RULE — most important check. The audience is ALWAYS the hero. Protagonist mirrors the audience. Problem is ALWAYS external — hormones, doctors, biology, marketing lies Product is the tool the hero used Husband, doctor, friends are WITNESSES to her win — never the source of her validation Audience should want to BE her, not pity her Other rules every hook clears: 3-second rule — by 3 seconds of speech, the viewer is either curious or has a clear promise Vague ≠ curiosity — real curiosity from controversy, bold takes, raw confession, riddle questions. "Can you believe / Would you believe" are fake curiosity, auto-rewritten. Body/action specificity — "He stopped what he was doing" / "She noticed" / "Hasn't done that in years" → rewrite. "Staring at my butt" / "grabbed me by the waist" / "told me I look like I've been hitting the gym" → ship. Declarative beats interrogative — "He told me" > "He asked me." Always go bolder. Lead with the secret — open with what she KNOWS or HAS that no one else in the scene knows. Witness's reaction lands second as proof. Power-word stacks must be clear — three words alone is confusing. Make the category obvious (symptoms, body parts, etc.) and name what they are in the next sentence. Bridge between sentences — two sentences can't sit in a vacuum. Use a connector ("and for some reason today," "but somehow," "yet today," "— and") that references sentence 1 from inside sentence 2. Scores on 50 points. 5 dimensions, 0–10 each: Stops the scroll Creates curiosity Promises something specific Fits the brand and the person (Hero Rule lives here) Fits the format and what's winning Any dim under 8 → rewrites the hook. Shows the rewrite. Re-scores. Minimum two internal rewrite passes before anything is delivered. The first version you see is already polished. All three hooks ship as the ad set. Meta picks via performance data. Self-improving: Every Monday it pulls hooks that actually shipped Matches scores to real Meta performance Recalibrates the rubric if any dim stops predicting winners That's the whole skill. Comment HOOK and I'll send you the skill. Must be following.

Jakob Counts | Creative Strategist

11,638 görüntüleme • 4 ay önce

Thought experiment for people regarding the concept of Absolute Time. Absolute means NO EXCEPTIONS. We are NOT looking back in time when we see galaxies and stars. We are NOT looking back in time 1.25 seconds when we see the moon. We're Not looking back in time 3 minutes when we see Mars. We're Not looking back in time 8.33 minutes when we see the Sun. If an astronaut lit a matchstick on Mars, the distant observer would see predator heat waves at the top of the matchstick in real-time while the matchstick started to blacken towards the astronaut's fingers. But there would be no orange light from that chemical reaction or flame seen. If the matchstick burnt out before the packet of orange light from that particular chemical reaction made it to Earth… then the distant observer would just see a disembodied orange flash of light with a lag. But the Earth-bound observer would never actually see the flame associated with the orange wavelength it put out. The wavelength of color emitted by the flame is not a recording of reality. If the orange light is 650 Thz, that means there are 650 trillion individual and separate bursts of orange light pulsating in 1 second. NOT that "the same light" is "waving" 650 trillion times a second and that light is a recording of reality. There are 650 trillion brand-new lights flashing in 1 second. Each Hertz is a brand-new emission and packet unto itself. Time does not re-emit 650 trillion times a second, nor is a photon a particle or a packet of reality acting like the frame of a reel of footage. The orange light that already left the flame will continue to propagate out until it meets the electrons making up the distant observer. But remember, it is never the same light within that packet. And the electrons making up the observer will absorb all of those different lights within that packet and re-emit brand-new lights that produce the product of illumination. A photon is a massless packet of energy, spherically expanding at the rate of c from the source it comes from. Illumination is the result of that energy being absorbed and RE-emitted by any other electrons that did not output that primary packet. But time is not associated with the same light. It's never the same light and time does not re-emit between packets. Time is not relative. Do NOT allow your mind Carte Blanche to think along the lines of relative time. DROP IT for this thought experiment. We are thinking along the lines of ABSOLUTE TIME/ Galilean VARIANCE. What does absolute time mean? It means time is constant in ALL frames of reference. Any frequency shifts between atomic clocks IS a literal change in the speed of light. But relativity forbids the speed of light from Ever changing, so relativity (Lorentz INVARIANCE) invented the concept of the 4th dimension and space-time. Because Relativity doesn't allow light speed to shift.. they interpret the same frequency shift between atomic clocks as being conclusive, irrefutable evidence that time and reality itself shifts. Rather than say it's just that ONE clock being affected by Earth's gravity and the oscillation of that ONE cesium clock is being altered compared to other clocks. People don't realize that in relativity... time dilation is SYMMETRICAL! Not even most relativists know their own theory. If Clock A and Clock B are synchronized and together... and then they accelerate apart... Einstein said Clock A would see Clock B as being slower itself. And Clock B would view Clock A as slower than ITself. NOT that only one observer would see back in time and the other would see forward or not at all. No... BOTH observers are supposed to see each other BACK in time relative to each other according to Einstein and the consequence of the math. It doesn't make ANY sense!! But relativists toss that part of time dilation under their 4th dimensional rug. The rug is woven from threads of gold that only the smart people can see apparently. So... here's a thought experiment/ Gedankenexperiment for absolute time. NO paradoxes... no nonsense or confusion. What do you see in a club? You see disco lights changing and a color wheel effect. You see everyone in REAL-TIME. Not just because they are so close to the lights. Light is not a recording of reality. Light is simply color. Illuminating reality in a certain color. Just because you can't see something yet or the color hasn't reached you doesn't mean it isn't happening in real-time. Zoom out and look at the people in the club through binoculars a mile away. You are still looking at them in real-time. The colors are shifting with a delay in the club. Now look at the club through a telescope from the surface of the moon. You're still looking at the people in real-time. But now there is a lag of the colors shifting by 1.25 seconds because it takes light 1.25 seconds to travel from the Earth to the moon. Now look at the club through an even bigger telescope from the surface of Mars. You're still looking at the people in real-time, but now there is a lag of the colors shifting by 3 minutes because it takes light 3 minutes to travel from Earth to Mars. fr you are a third hypothetical observer zoomed out and watching the person from Mars AND seeing the club on Earth... you're still seeing everything happening in real-time as well. But you see the colored wavepackets traveling with a delay to the observer on Mars. And the disco color wheel effect is just lagging before it affects the observer from Mars and the observer on the Moon. It doesn't matter how far you zoom out! There is only now to observe. But there WILL be a lag and delay for a given color/wavepacket to reach distant observers. But all points in space are already illuminated by other starlight. So if you're too far away... you'll just see the club in real-time but without any disco lights. Just see them in white light because that's the source already illuminating the scene. This is where it gets the most difficult because people think light itself is a recording of reality that replays a scene from where it came from. But another punch in the gut of relativity is that in order to see REFLECTED light... that would require a TWO-WAY transit. Which means the light would have to be sent out... record the scene of a distant event and then RETURN in order to REplay the event. Which means it would take 6 minutes to see the club from Mars by that logic and 2.5 seconds to see the club from the moon by that logic. The difference in tick rates between clocks has NOTHING to do with time dilation. Wait.. what?! How can that be? Because a clock itself doesn't represent all of time and reality. The difference between clocks is a "Transverse relative time shift." If the only light in the universe was from the lighter… the only way a distant observer would be able to see the astronaut on Mars is if the astronaut held down the button of the lighter for longer than 3 minutes. It takes 3 minutes for the packet of light to travel from Mars to Earth. The distant observer would never be able to see Mars, unless the light stretched from Mars all the way to Earth, and illuminated the path between Mars and Earth. And that would take 3 minutes for the boundary and first part of that wave packet to reach Earth. But if the distant observer wanted to observe Mars in real-time… then that packet of light would have to be on for longer than 3 minutes. So if the astronaut on Mars flicked the lighter at 12:00, the distant observer on Earth wouldn't see anything until 12:03. If the light was on for 3 minutes and 10 seconds, and the distant observer is 3 light minutes away... the distant observer would be able to see Mars in real-time for 10 seconds starting at 12:03. In the 20 second video clip of the rotating planet with shifting colors... just imagine you're a couple light minutes or light seconds away. You're still seeing the planet spin in real-time. But there is simply a delay of switching colors. You are Not looking back in time. It's just a color wheel effect from a great distance away. That's it!! There are many major flaws which tarnish people's critical thinking on this thought experiment. 1. Light does NOT ricochet or bounce. Electrons absorb, emit and re-emit ALL electromagnetic radiation. The electrons, making up the glass of a mirror will absorb the incoming light and re-emit a brand-new light as an equal and opposite reaction. NOT that "the same light" bounced off the mirror and continued on within the same frame of reference.  2. Light is NOT a recording of reality. 3. It is NOT the same light being observed from a source. It's never the same light. Each Hertz is a new light. Think of half of a sine wave as being its own emission. On an oscilloscope, a stimulus generates a peak which initiates an equal and opposite trough. Or vice versa. That repeating process is not "the same light." If you cut and paste that sine wave to another sine wave, the boundary between the waves will always be in phase. (thus refraction) 4. The speed of light is NOT the same in ALL frames of reference, no matter what. 5. Light is NOT made of particles and waves that flip back-and-forth. 6. Time is NOT connected to the speed of light. Time remains constant regardless if you accelerate towards or away from a clock. The clocks themselves will indeed be off! But that's an affect on the electrons making up the atomic clock affecting the oscillation of the isotope which is ASSUMED to ALWAYS be the same. So ANY difference in oscillation is treated as a literal distortion in space-time. 7. Space and time are not linked at all. That is a mathematical artifice under Lorentz invariance. Time is relative under Lorentz invariance. But time is absolute under Galilean VARIANCE. When people hear or see the word GALILEAN... their brains switch to auto pilot to "aether theory" and "classical physics." What people don't realize is that aether theory used Galilean INVARIANCE. Rather than space-time being used as an excuse to explain the difference in frequencies between atomic clocks... it was originally aether being used as an excuse to keep the speed of light the same. But None of those things are valid! We are thinking under the framework of Galilean VARIANCE! Completely new revolutionary model returning to Isaac Newton and Classical physics but without the corpuscular theory (particle) theory for light... without a particle-wave duality... without an aether... without a 4th dimension. Just good ol elementary math within 3D Euclidean space. Everything happening in real-time, right now. This reformulation of Galilean transformations was offered by Dr. Edward Dowdye in 1991 called The Extinction Shift Principle. Effectivity as opposed to Relativity. If light required a two-way transit, in order to travel out… Record an event, and travel back to replay the recording…  then it would take 6 minutes to see the astronaut on Mars instead of 3.  Remember… They say the SAME light is a recording, and must travel there and travel back in order to REplay. Relativity says time is relative: t' ≠ t time is NOT the same from all frames of reference) and t = tₒ / √1 - v²/c² but Galilean Variance says time is not relative: t' = t (Time IS the same from all frames of reference) and τ_tr = τₒ / √1 - v²/c² Relativity says c' = c (The velocity of light is the same from all frames of reference) but Galilean variance says c' ≠ c (The velocity of light is NOT the same from all frames of reference) and that c' = c ± v (The velocity of light in one frame of reference is dependent upon the velocity of the light source relative to an observer in another frame of reference. Whether that light source is approaching or receding away from that observer) Relativity says E = mc² (Energy and mass are universally equivalent and literally interchangeable under All conditions.) but Galilean variance says E = Δmc² = mₒc² (Energy changes in a system are the result from changes in mass. mₒ represents the original mass. Mass and energy do not literally interchange. There is an equivalence, not an interchange.) The Rebirth of Classical Physics: Time, Light & Gravity Star light and illumination: Flicking a Lighter on Mars visual example:
0:21

Thought experiment for people regarding the concept of Absolute Time. Absolute means NO EXCEPTIONS. We are NOT looking back in time when we see galaxies and stars. We are NOT looking back in time 1.25 seconds when we see the moon. We're Not looking back in time 3 minutes when we see Mars. We're Not looking back in time 8.33 minutes when we see the Sun. If an astronaut lit a matchstick on Mars, the distant observer would see predator heat waves at the top of the matchstick in real-time while the matchstick started to blacken towards the astronaut's fingers. But there would be no orange light from that chemical reaction or flame seen. If the matchstick burnt out before the packet of orange light from that particular chemical reaction made it to Earth… then the distant observer would just see a disembodied orange flash of light with a lag. But the Earth-bound observer would never actually see the flame associated with the orange wavelength it put out. The wavelength of color emitted by the flame is not a recording of reality. If the orange light is 650 Thz, that means there are 650 trillion individual and separate bursts of orange light pulsating in 1 second. NOT that "the same light" is "waving" 650 trillion times a second and that light is a recording of reality. There are 650 trillion brand-new lights flashing in 1 second. Each Hertz is a brand-new emission and packet unto itself. Time does not re-emit 650 trillion times a second, nor is a photon a particle or a packet of reality acting like the frame of a reel of footage. The orange light that already left the flame will continue to propagate out until it meets the electrons making up the distant observer. But remember, it is never the same light within that packet. And the electrons making up the observer will absorb all of those different lights within that packet and re-emit brand-new lights that produce the product of illumination. A photon is a massless packet of energy, spherically expanding at the rate of c from the source it comes from. Illumination is the result of that energy being absorbed and RE-emitted by any other electrons that did not output that primary packet. But time is not associated with the same light. It's never the same light and time does not re-emit between packets. Time is not relative. Do NOT allow your mind Carte Blanche to think along the lines of relative time. DROP IT for this thought experiment. We are thinking along the lines of ABSOLUTE TIME/ Galilean VARIANCE. What does absolute time mean? It means time is constant in ALL frames of reference. Any frequency shifts between atomic clocks IS a literal change in the speed of light. But relativity forbids the speed of light from Ever changing, so relativity (Lorentz INVARIANCE) invented the concept of the 4th dimension and space-time. Because Relativity doesn't allow light speed to shift.. they interpret the same frequency shift between atomic clocks as being conclusive, irrefutable evidence that time and reality itself shifts. Rather than say it's just that ONE clock being affected by Earth's gravity and the oscillation of that ONE cesium clock is being altered compared to other clocks. People don't realize that in relativity... time dilation is SYMMETRICAL! Not even most relativists know their own theory. If Clock A and Clock B are synchronized and together... and then they accelerate apart... Einstein said Clock A would see Clock B as being slower itself. And Clock B would view Clock A as slower than ITself. NOT that only one observer would see back in time and the other would see forward or not at all. No... BOTH observers are supposed to see each other BACK in time relative to each other according to Einstein and the consequence of the math. It doesn't make ANY sense!! But relativists toss that part of time dilation under their 4th dimensional rug. The rug is woven from threads of gold that only the smart people can see apparently. So... here's a thought experiment/ Gedankenexperiment for absolute time. NO paradoxes... no nonsense or confusion. What do you see in a club? You see disco lights changing and a color wheel effect. You see everyone in REAL-TIME. Not just because they are so close to the lights. Light is not a recording of reality. Light is simply color. Illuminating reality in a certain color. Just because you can't see something yet or the color hasn't reached you doesn't mean it isn't happening in real-time. Zoom out and look at the people in the club through binoculars a mile away. You are still looking at them in real-time. The colors are shifting with a delay in the club. Now look at the club through a telescope from the surface of the moon. You're still looking at the people in real-time. But now there is a lag of the colors shifting by 1.25 seconds because it takes light 1.25 seconds to travel from the Earth to the moon. Now look at the club through an even bigger telescope from the surface of Mars. You're still looking at the people in real-time, but now there is a lag of the colors shifting by 3 minutes because it takes light 3 minutes to travel from Earth to Mars. fr you are a third hypothetical observer zoomed out and watching the person from Mars AND seeing the club on Earth... you're still seeing everything happening in real-time as well. But you see the colored wavepackets traveling with a delay to the observer on Mars. And the disco color wheel effect is just lagging before it affects the observer from Mars and the observer on the Moon. It doesn't matter how far you zoom out! There is only now to observe. But there WILL be a lag and delay for a given color/wavepacket to reach distant observers. But all points in space are already illuminated by other starlight. So if you're too far away... you'll just see the club in real-time but without any disco lights. Just see them in white light because that's the source already illuminating the scene. This is where it gets the most difficult because people think light itself is a recording of reality that replays a scene from where it came from. But another punch in the gut of relativity is that in order to see REFLECTED light... that would require a TWO-WAY transit. Which means the light would have to be sent out... record the scene of a distant event and then RETURN in order to REplay the event. Which means it would take 6 minutes to see the club from Mars by that logic and 2.5 seconds to see the club from the moon by that logic. The difference in tick rates between clocks has NOTHING to do with time dilation. Wait.. what?! How can that be? Because a clock itself doesn't represent all of time and reality. The difference between clocks is a "Transverse relative time shift." If the only light in the universe was from the lighter… the only way a distant observer would be able to see the astronaut on Mars is if the astronaut held down the button of the lighter for longer than 3 minutes. It takes 3 minutes for the packet of light to travel from Mars to Earth. The distant observer would never be able to see Mars, unless the light stretched from Mars all the way to Earth, and illuminated the path between Mars and Earth. And that would take 3 minutes for the boundary and first part of that wave packet to reach Earth. But if the distant observer wanted to observe Mars in real-time… then that packet of light would have to be on for longer than 3 minutes. So if the astronaut on Mars flicked the lighter at 12:00, the distant observer on Earth wouldn't see anything until 12:03. If the light was on for 3 minutes and 10 seconds, and the distant observer is 3 light minutes away... the distant observer would be able to see Mars in real-time for 10 seconds starting at 12:03. In the 20 second video clip of the rotating planet with shifting colors... just imagine you're a couple light minutes or light seconds away. You're still seeing the planet spin in real-time. But there is simply a delay of switching colors. You are Not looking back in time. It's just a color wheel effect from a great distance away. That's it!! There are many major flaws which tarnish people's critical thinking on this thought experiment. 1. Light does NOT ricochet or bounce. Electrons absorb, emit and re-emit ALL electromagnetic radiation. The electrons, making up the glass of a mirror will absorb the incoming light and re-emit a brand-new light as an equal and opposite reaction. NOT that "the same light" bounced off the mirror and continued on within the same frame of reference.  2. Light is NOT a recording of reality. 3. It is NOT the same light being observed from a source. It's never the same light. Each Hertz is a new light. Think of half of a sine wave as being its own emission. On an oscilloscope, a stimulus generates a peak which initiates an equal and opposite trough. Or vice versa. That repeating process is not "the same light." If you cut and paste that sine wave to another sine wave, the boundary between the waves will always be in phase. (thus refraction) 4. The speed of light is NOT the same in ALL frames of reference, no matter what. 5. Light is NOT made of particles and waves that flip back-and-forth. 6. Time is NOT connected to the speed of light. Time remains constant regardless if you accelerate towards or away from a clock. The clocks themselves will indeed be off! But that's an affect on the electrons making up the atomic clock affecting the oscillation of the isotope which is ASSUMED to ALWAYS be the same. So ANY difference in oscillation is treated as a literal distortion in space-time. 7. Space and time are not linked at all. That is a mathematical artifice under Lorentz invariance. Time is relative under Lorentz invariance. But time is absolute under Galilean VARIANCE. When people hear or see the word GALILEAN... their brains switch to auto pilot to "aether theory" and "classical physics." What people don't realize is that aether theory used Galilean INVARIANCE. Rather than space-time being used as an excuse to explain the difference in frequencies between atomic clocks... it was originally aether being used as an excuse to keep the speed of light the same. But None of those things are valid! We are thinking under the framework of Galilean VARIANCE! Completely new revolutionary model returning to Isaac Newton and Classical physics but without the corpuscular theory (particle) theory for light... without a particle-wave duality... without an aether... without a 4th dimension. Just good ol elementary math within 3D Euclidean space. Everything happening in real-time, right now. This reformulation of Galilean transformations was offered by Dr. Edward Dowdye in 1991 called The Extinction Shift Principle. Effectivity as opposed to Relativity. If light required a two-way transit, in order to travel out… Record an event, and travel back to replay the recording…  then it would take 6 minutes to see the astronaut on Mars instead of 3.  Remember… They say the SAME light is a recording, and must travel there and travel back in order to REplay. Relativity says time is relative: t' ≠ t time is NOT the same from all frames of reference) and t = tₒ / √1 - v²/c² but Galilean Variance says time is not relative: t' = t (Time IS the same from all frames of reference) and τ_tr = τₒ / √1 - v²/c² Relativity says c' = c (The velocity of light is the same from all frames of reference) but Galilean variance says c' ≠ c (The velocity of light is NOT the same from all frames of reference) and that c' = c ± v (The velocity of light in one frame of reference is dependent upon the velocity of the light source relative to an observer in another frame of reference. Whether that light source is approaching or receding away from that observer) Relativity says E = mc² (Energy and mass are universally equivalent and literally interchangeable under All conditions.) but Galilean variance says E = Δmc² = mₒc² (Energy changes in a system are the result from changes in mass. mₒ represents the original mass. Mass and energy do not literally interchange. There is an equivalence, not an interchange.) The Rebirth of Classical Physics: Time, Light & Gravity Star light and illumination: Flicking a Lighter on Mars visual example:

TheRealVerbz (Jason Verbelli)

24,471 görüntüleme • 2 yıl önce

Okay, everyone is talking about AI video models right now, but honestly, most of the “comparisons” out there aren’t real comparisons at all. One video uses a different prompt. Someone tweaks the settings. Someone edits out the bad parts. And then people just decide which model is better? That never sat right with me. So I tested HappyHorse 1.1 and Kling 3.0 the same way I’d test any tool I was seriously considering for my work: the same prompt, the same reference images, the same duration, and no edits to hide the flaws. I wasn’t trying to prove that one model is better across the board. I simply wanted to see how each would handle the exact same challenge. 1. Lip-sync & speech This one's easy to judge honestly. You don't need to go frame by frame, just watch both videos side by side. Does the mouth actually match the words? Does the timing feel off or natural? Do the expressions hold up when the camera's in close? Small detail, but it tells you a lot fast. 2. Character & scene consistency This is where it gets interesting. Making one good-looking shot isn't hard anymore, keeping that same character looking like themselves across a bunch of shots is the real test. I used the same multi-angle reference set for both models and watched how they handled scene changes: face, clothes, props, where the character's standing, all of it. HappyHorse 1.1 was just noticeably more consistent here. One moment that stood out: in a crash scene where the character ends up injured on the ground, the difference isn't obvious at first glance, you really have to look closely. But HappyHorse kept him reacting, hand raised, blood visible, expression still "alive," like he was actually processing what just happened. Kling 3.0 showed him lying still, with no visible movement or reaction in that same moment. It's subtle, but it's a real example of logic and consistency holding up frame to frame, not just shot to shot. 3. Complex motion No cutting corners on this one, I wanted continuous movement. Sports, dancing, fast action, stuff that really shows whether a model understands weight, momentum, balance, how a body recovers after moving. These are the shots that expose problems you'd never catch in something static. Watching both side by side, continuously, tells you way more than any writeup could. 4. Camera control Both models got the same timestamped storyboard and the same camera directions. Then I just watched to see if they actually followed it. Here's a good example: push in, orbit around, crane up, then pull out. One continuous move. Watch closely and you'll see exactly where one model loses track of the subject or the motion gets weird, while the other stays right where it's supposed to be the whole time. That's basically the difference between a shot you keep and one you have to regenerate for the fifth time. 5. Price & workflow Price only means anything if you're comparing like for like, same output, same duration, same quality and resolution, same number of generations. But honestly I think the better question isn't "which one's cheaper," it's which one gets you more usable footage for the same money. For me that's not just about credits either, it's about how many tries it takes before I get something I actually want to keep. Where this actually matters: ads and e-commerce This is the stuff that made the biggest difference for me. When you're making product shots or ad content, you need a model that just does what you tell it, not one you have to wrestle with. HappyHorse 1.1 strictly executes your planned frames, you're setting the exact lens, the subject position, the camera's job, shot by shot. For ad work that means way fewer regenerations and getting from storyboard to finished cut a lot faster. Proof over opinions Here's what I kept coming back to. Saying "the motion's better" or "the camera control's better" doesn't really mean anything unless people can see it for themselves. That's why I think comparisons need continuous split-screen playback, identical prompts, clear labels, visible transitions, matching settings, and an honest breakdown of cost. Just let the footage speak, people can usually tell within a few seconds anyway. What I actually took away from this Both models have real strengths, I'm not saying one does everything better. But for the kind of work I do, including ad and e-commerce stuff, HappyHorse 1.1 just needed fewer compromises from me. Less regenerating shots, less fighting continuity issues, less trying to wrangle the camera back on track. Doesn't mean Kling 3.0 is bad, it's a solid model. It just means HappyHorse 1.1 got me to something production-ready faster, with less wasted time. And at the end of the day that's the thing I actually care about. p.s. links to try HappyHorse 1.1 and the community Discord are in the first reply below.
0:30

Okay, everyone is talking about AI video models right now, but honestly, most of the “comparisons” out there aren’t real comparisons at all. One video uses a different prompt. Someone tweaks the settings. Someone edits out the bad parts. And then people just decide which model is better? That never sat right with me. So I tested HappyHorse 1.1 and Kling 3.0 the same way I’d test any tool I was seriously considering for my work: the same prompt, the same reference images, the same duration, and no edits to hide the flaws. I wasn’t trying to prove that one model is better across the board. I simply wanted to see how each would handle the exact same challenge. 1. Lip-sync & speech This one's easy to judge honestly. You don't need to go frame by frame, just watch both videos side by side. Does the mouth actually match the words? Does the timing feel off or natural? Do the expressions hold up when the camera's in close? Small detail, but it tells you a lot fast. 2. Character & scene consistency This is where it gets interesting. Making one good-looking shot isn't hard anymore, keeping that same character looking like themselves across a bunch of shots is the real test. I used the same multi-angle reference set for both models and watched how they handled scene changes: face, clothes, props, where the character's standing, all of it. HappyHorse 1.1 was just noticeably more consistent here. One moment that stood out: in a crash scene where the character ends up injured on the ground, the difference isn't obvious at first glance, you really have to look closely. But HappyHorse kept him reacting, hand raised, blood visible, expression still "alive," like he was actually processing what just happened. Kling 3.0 showed him lying still, with no visible movement or reaction in that same moment. It's subtle, but it's a real example of logic and consistency holding up frame to frame, not just shot to shot. 3. Complex motion No cutting corners on this one, I wanted continuous movement. Sports, dancing, fast action, stuff that really shows whether a model understands weight, momentum, balance, how a body recovers after moving. These are the shots that expose problems you'd never catch in something static. Watching both side by side, continuously, tells you way more than any writeup could. 4. Camera control Both models got the same timestamped storyboard and the same camera directions. Then I just watched to see if they actually followed it. Here's a good example: push in, orbit around, crane up, then pull out. One continuous move. Watch closely and you'll see exactly where one model loses track of the subject or the motion gets weird, while the other stays right where it's supposed to be the whole time. That's basically the difference between a shot you keep and one you have to regenerate for the fifth time. 5. Price & workflow Price only means anything if you're comparing like for like, same output, same duration, same quality and resolution, same number of generations. But honestly I think the better question isn't "which one's cheaper," it's which one gets you more usable footage for the same money. For me that's not just about credits either, it's about how many tries it takes before I get something I actually want to keep. Where this actually matters: ads and e-commerce This is the stuff that made the biggest difference for me. When you're making product shots or ad content, you need a model that just does what you tell it, not one you have to wrestle with. HappyHorse 1.1 strictly executes your planned frames, you're setting the exact lens, the subject position, the camera's job, shot by shot. For ad work that means way fewer regenerations and getting from storyboard to finished cut a lot faster. Proof over opinions Here's what I kept coming back to. Saying "the motion's better" or "the camera control's better" doesn't really mean anything unless people can see it for themselves. That's why I think comparisons need continuous split-screen playback, identical prompts, clear labels, visible transitions, matching settings, and an honest breakdown of cost. Just let the footage speak, people can usually tell within a few seconds anyway. What I actually took away from this Both models have real strengths, I'm not saying one does everything better. But for the kind of work I do, including ad and e-commerce stuff, HappyHorse 1.1 just needed fewer compromises from me. Less regenerating shots, less fighting continuity issues, less trying to wrangle the camera back on track. Doesn't mean Kling 3.0 is bad, it's a solid model. It just means HappyHorse 1.1 got me to something production-ready faster, with less wasted time. And at the end of the day that's the thing I actually care about. p.s. links to try HappyHorse 1.1 and the community Discord are in the first reply below.

Chubby♨️

19,569 görüntüleme • 25 gün önce

My first test with the new Gemini Deep Think 3 🔥🔥🔥 Build a complete Three.js scene in a single HTML file that renders a fully 3D interior room indistinguishable from a classical oil painting hanging in a museum. The Painterly Rendering System Write custom GLSL shaders that replace all standard rendering with oil paint simulation. Every pixel must feel painted by hand. The system needs these layers working together. Brushstroke normals. Generate a procedural brushstroke normal map using layered directional noise at varying scales. Large bold strokes for walls and floors following the plane direction. Small delicate strokes for fine details like metal and glass. Circular strokes for rounded objects. The brushstrokes must catch sidelight and cast tiny shadows into their grooves exactly like real impasto paint on canvas. Paint thickness. Use parallax occlusion mapping to give highlights genuine physical thickness. Where the original painter would load their brush with white or yellow to hit a bright highlight, the paint should visibly sit above the surface. In dark shadow areas, the paint should appear thinner, letting canvas weave show through slightly. Color palette. Restrict the entire scene to a historical oil palette. Titanium white, naples yellow, yellow ochre, raw sienna, burnt sienna, burnt umber, raw umber, ivory black, vermillion used sparingly, and a muted blue-grey. No modern saturated colors. All color mixing should feel subtractive and warm. Edge treatment. No hard edges anywhere in the scene. Object silhouettes must soften and blur slightly as if the painter's brush feathered where one form meets another. Implement this as a screen-space edge-detection pass that blurs based on depth discontinuity and overlays brushstroke texture at boundaries. Canvas texture. The entire final image must have a linen canvas weave overlay rendered with its own normal map that interacts with the scene lighting. As you orbit, the canvas tooth should glint differently. This sells the illusion more than anything else. Varnish layer. Apply a post-processing pass that simulates aged oil varnish. A warm amber tint that is slightly uneven, thicker in corners and thinner in center. A subtle gloss reflection that shifts as you move the camera. Very fine craquelure, hairline crack patterns, visible only when you zoom in close. The Scene. A Dutch Golden Age Study. Model everything procedurally with no external assets. A small intimate room with rough plastered walls in thick paint, warm grey-ochre. A single tall window on the left wall. Leaded glass with thick mullions letting in one dominant shaft of warm light. The window glass should have slight imperfections like bubbles and waviness visible in the paint treatment. A heavy dark wood table positioned center-left. On the table place a brass candlestick with a half-melted candle where the wax drips are modeled and painted with naples yellow impasto highlights. A pewter plate with a half-peeled lemon, the peel curling off the edge of the plate, the exposed fruit flesh a jewel of thick yellow paint catching light. A partially unfolded letter with a broken red wax seal. A small glass of dark wine catching a single highlight. A dark velvet cloth draped from the table edge falling in heavy folds to the floor. The velvet should have that characteristic oil painting treatment where shadows go almost black and the fabric catches light in soft broken highlights. The floor is wide dark wooden planks painted with long horizontal brushstrokes. Against the back wall, barely visible in shadow, a tall wooden cabinet with a few old leather-bound books leaning against each other. A single beam of light from the window cuts diagonally across the scene illuminating floating dust motes painted as soft tiny dots of naples yellow, not CG particles. The rest of the room falls into rich warm shadow. Lighting One dominant directional light from the left simulating window light. Warm, strong, with soft VSM shadows. A very subtle fill from the right in cool blue-grey at perhaps 5% intensity. No ambient light. The shadows should be genuinely dark and warm. This is Vermeer lighting. The contrast between the luminous light-struck areas and the deep velvety shadows is what makes the painting breathe. Post-Processing Chain Render pass. Painterly edge softening pass. Canvas texture overlay pass. Varnish and aging pass. Heavy vignette like a dark gallery frame encroaching. Very subtle bloom only on the brightest impasto highlights. Film grain that mimics canvas tooth texture rather than photographic noise. Interaction OrbitControls with very slow damping at 0.02 and limited orbit range so you can look around the painting but not flip it upside down. Slow zoom. The feeling should be like leaning closer to a painting in a museum and discovering more detail. The Standard When someone opens this file and takes a screenshot, people should genuinely argue whether it is a photograph of a real oil painting or a digital render.
0:36

My first test with the new Gemini Deep Think 3 🔥🔥🔥 Build a complete Three.js scene in a single HTML file that renders a fully 3D interior room indistinguishable from a classical oil painting hanging in a museum. The Painterly Rendering System Write custom GLSL shaders that replace all standard rendering with oil paint simulation. Every pixel must feel painted by hand. The system needs these layers working together. Brushstroke normals. Generate a procedural brushstroke normal map using layered directional noise at varying scales. Large bold strokes for walls and floors following the plane direction. Small delicate strokes for fine details like metal and glass. Circular strokes for rounded objects. The brushstrokes must catch sidelight and cast tiny shadows into their grooves exactly like real impasto paint on canvas. Paint thickness. Use parallax occlusion mapping to give highlights genuine physical thickness. Where the original painter would load their brush with white or yellow to hit a bright highlight, the paint should visibly sit above the surface. In dark shadow areas, the paint should appear thinner, letting canvas weave show through slightly. Color palette. Restrict the entire scene to a historical oil palette. Titanium white, naples yellow, yellow ochre, raw sienna, burnt sienna, burnt umber, raw umber, ivory black, vermillion used sparingly, and a muted blue-grey. No modern saturated colors. All color mixing should feel subtractive and warm. Edge treatment. No hard edges anywhere in the scene. Object silhouettes must soften and blur slightly as if the painter's brush feathered where one form meets another. Implement this as a screen-space edge-detection pass that blurs based on depth discontinuity and overlays brushstroke texture at boundaries. Canvas texture. The entire final image must have a linen canvas weave overlay rendered with its own normal map that interacts with the scene lighting. As you orbit, the canvas tooth should glint differently. This sells the illusion more than anything else. Varnish layer. Apply a post-processing pass that simulates aged oil varnish. A warm amber tint that is slightly uneven, thicker in corners and thinner in center. A subtle gloss reflection that shifts as you move the camera. Very fine craquelure, hairline crack patterns, visible only when you zoom in close. The Scene. A Dutch Golden Age Study. Model everything procedurally with no external assets. A small intimate room with rough plastered walls in thick paint, warm grey-ochre. A single tall window on the left wall. Leaded glass with thick mullions letting in one dominant shaft of warm light. The window glass should have slight imperfections like bubbles and waviness visible in the paint treatment. A heavy dark wood table positioned center-left. On the table place a brass candlestick with a half-melted candle where the wax drips are modeled and painted with naples yellow impasto highlights. A pewter plate with a half-peeled lemon, the peel curling off the edge of the plate, the exposed fruit flesh a jewel of thick yellow paint catching light. A partially unfolded letter with a broken red wax seal. A small glass of dark wine catching a single highlight. A dark velvet cloth draped from the table edge falling in heavy folds to the floor. The velvet should have that characteristic oil painting treatment where shadows go almost black and the fabric catches light in soft broken highlights. The floor is wide dark wooden planks painted with long horizontal brushstrokes. Against the back wall, barely visible in shadow, a tall wooden cabinet with a few old leather-bound books leaning against each other. A single beam of light from the window cuts diagonally across the scene illuminating floating dust motes painted as soft tiny dots of naples yellow, not CG particles. The rest of the room falls into rich warm shadow. Lighting One dominant directional light from the left simulating window light. Warm, strong, with soft VSM shadows. A very subtle fill from the right in cool blue-grey at perhaps 5% intensity. No ambient light. The shadows should be genuinely dark and warm. This is Vermeer lighting. The contrast between the luminous light-struck areas and the deep velvety shadows is what makes the painting breathe. Post-Processing Chain Render pass. Painterly edge softening pass. Canvas texture overlay pass. Varnish and aging pass. Heavy vignette like a dark gallery frame encroaching. Very subtle bloom only on the brightest impasto highlights. Film grain that mimics canvas tooth texture rather than photographic noise. Interaction OrbitControls with very slow damping at 0.02 and limited orbit range so you can look around the painting but not flip it upside down. Slow zoom. The feeling should be like leaning closer to a painting in a museum and discovering more detail. The Standard When someone opens this file and takes a screenshot, people should genuinely argue whether it is a photograph of a real oil painting or a digital render.

Emily

20,940 görüntüleme • 7 ay önce

🧵 ASAP Rocky vs Drake — A DEEPER LOOK (Receipts, Not Revisionism) ASAP Rocky recently went on The Joe Budden Podcast and claimed the reason he dissed Drake on “Stole My Flow” and other songs on the (Don’t Be Dumb) project is because Drake has been “taking shots at him for years” while he stayed silent. That narrative doesn’t hold up when you look at the full history. This has been back and forth tension for nearly half a decade, with Rocky repeatedly firing first musically, visually, and symbolically and Drake responding in kind. 📌 The Real Starting Point (2021) •May 2021: ASAP Rocky & Rihanna confirm their relationship in GQ •June 2021: Rocky previews “D.M.B (Dat’s My B*tch)” in a Klarna ad •Extended version leaks shortly after •The intro and bars clearly frame Rihanna as a “prize won,” widely interpreted as a Drake sub •Video rollout goes viral with Rihanna as the love interest, doubling down on the flex September 3, 2021 •Drake drops Certified Lover Boy •Multiple bars are read as indirect responses to Rocky’s rollout September 13, 2021 •ASAP Rocky & Rihanna attend the Met Gala •Rocky wears a quilted blanket outfit, which many saw as a calculated visual jab toward Drake 2022 •Pregnancy announcement 4 months later first child is born •8 days later, Rocky officially drops the D.M.B video, once again starring Rihanna •Drake responds months later on Her Loss, continuing the subliminal exchange 📌 Escalation Era (2023) •Rihanna performs at the Super Bowl, visibly pregnant again •Shortly after: •ASAP Rocky previews “Stole My Flow” at Rolling Loud •The clip goes viral immediately •Rocky then drops “Riot”, another track interpreted as a Drake jab Drake Responds •October 6, 2023: Drake releases For All The Dogs •Tracks like “Fear of Heights” and “Another Late Night” contain clear subs •Drake even trolls Rocky’s Rolling Loud appearance by wearing colorful hair clips, mirroring Rocky’s look Rocky’s Counter •Puma x F1 partnership rollout featuring Rocky and Rihanna •Rocky positions himself as a fashion forward mogul while taking subtle cultural shots 📌 Diss Cycle Goes Public (2024) •Rocky jumps on Future & Metro Boomin’s Drake diss project •Track: “Show of Hands” •Drake fires back on “Family Matters”: “Rakim talkin’ shit again…” •Drake follows with 100 Gigs for Your Head Top •Rocky responds with “Tailor Swif” and “Hijack” •Drake answers again with: •No Face •SOD •Circadian Rhythm •Rocky doubles down with another Puma F1 campaign, leaning into optics instead of direct records 📌 Optics, Timing & Strategy (2025) •Rolling Loud: Rocky performs multiple Drake sub tracks like Helicopter •Met Gala 2025: •ASAP & Rihanna announce another pregnancy • Rocky does late night appearance with Anna wintour on Seth meyers •Rihanna drops a Smurfs soundtrack song produced by the same producer behind Drake’s “Nokia” •Lyrics hint at déjà vu 👀 Drake’s Response •Headlines Wireless Festival solo •Sends an unspoken message: I don’t need Rolling Loud or a stacked lineup •July 4, 2025: Drake drops “What Did I Miss?” •Same day: Rocky drops “Pray4DaGang” •July 25, 2025: Drake drops “Which One” viewed as a Rihanna centered response 📌 The Album Delay & Final Drop (2026) •Rocky teases Don’t Be Dumb for nearly three years •Avoids direct release windows where Drake might step on the moment •January 2026: •Rocky finally drops the album •Includes the same Drake diss tracks previewed years earlier •Then goes on podcasts claiming Drake dissed him “out of nowhere” 🎯 Final Take This wasn’t: ❌ sudden ❌ unprovoked ❌ one sided It was: ✔️ long term ✔️ strategic ✔️ mutually escalated ASAP Rocky has been sending shots through music, fashion, relationships, timing, and optics. Drake has been responding consistently and directly. That’s hip hop Trying to reframe it as“I stayed quiet and Drake attacked me”
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🧵 ASAP Rocky vs Drake — A DEEPER LOOK (Receipts, Not Revisionism) ASAP Rocky recently went on The Joe Budden Podcast and claimed the reason he dissed Drake on “Stole My Flow” and other songs on the (Don’t Be Dumb) project is because Drake has been “taking shots at him for years” while he stayed silent. That narrative doesn’t hold up when you look at the full history. This has been back and forth tension for nearly half a decade, with Rocky repeatedly firing first musically, visually, and symbolically and Drake responding in kind. 📌 The Real Starting Point (2021) •May 2021: ASAP Rocky & Rihanna confirm their relationship in GQ •June 2021: Rocky previews “D.M.B (Dat’s My B*tch)” in a Klarna ad •Extended version leaks shortly after •The intro and bars clearly frame Rihanna as a “prize won,” widely interpreted as a Drake sub •Video rollout goes viral with Rihanna as the love interest, doubling down on the flex September 3, 2021 •Drake drops Certified Lover Boy •Multiple bars are read as indirect responses to Rocky’s rollout September 13, 2021 •ASAP Rocky & Rihanna attend the Met Gala •Rocky wears a quilted blanket outfit, which many saw as a calculated visual jab toward Drake 2022 •Pregnancy announcement 4 months later first child is born •8 days later, Rocky officially drops the D.M.B video, once again starring Rihanna •Drake responds months later on Her Loss, continuing the subliminal exchange 📌 Escalation Era (2023) •Rihanna performs at the Super Bowl, visibly pregnant again •Shortly after: •ASAP Rocky previews “Stole My Flow” at Rolling Loud •The clip goes viral immediately •Rocky then drops “Riot”, another track interpreted as a Drake jab Drake Responds •October 6, 2023: Drake releases For All The Dogs •Tracks like “Fear of Heights” and “Another Late Night” contain clear subs •Drake even trolls Rocky’s Rolling Loud appearance by wearing colorful hair clips, mirroring Rocky’s look Rocky’s Counter •Puma x F1 partnership rollout featuring Rocky and Rihanna •Rocky positions himself as a fashion forward mogul while taking subtle cultural shots 📌 Diss Cycle Goes Public (2024) •Rocky jumps on Future & Metro Boomin’s Drake diss project •Track: “Show of Hands” •Drake fires back on “Family Matters”: “Rakim talkin’ shit again…” •Drake follows with 100 Gigs for Your Head Top •Rocky responds with “Tailor Swif” and “Hijack” •Drake answers again with: •No Face •SOD •Circadian Rhythm •Rocky doubles down with another Puma F1 campaign, leaning into optics instead of direct records 📌 Optics, Timing & Strategy (2025) •Rolling Loud: Rocky performs multiple Drake sub tracks like Helicopter •Met Gala 2025: •ASAP & Rihanna announce another pregnancy • Rocky does late night appearance with Anna wintour on Seth meyers •Rihanna drops a Smurfs soundtrack song produced by the same producer behind Drake’s “Nokia” •Lyrics hint at déjà vu 👀 Drake’s Response •Headlines Wireless Festival solo •Sends an unspoken message: I don’t need Rolling Loud or a stacked lineup •July 4, 2025: Drake drops “What Did I Miss?” •Same day: Rocky drops “Pray4DaGang” •July 25, 2025: Drake drops “Which One” viewed as a Rihanna centered response 📌 The Album Delay & Final Drop (2026) •Rocky teases Don’t Be Dumb for nearly three years •Avoids direct release windows where Drake might step on the moment •January 2026: •Rocky finally drops the album •Includes the same Drake diss tracks previewed years earlier •Then goes on podcasts claiming Drake dissed him “out of nowhere” 🎯 Final Take This wasn’t: ❌ sudden ❌ unprovoked ❌ one sided It was: ✔️ long term ✔️ strategic ✔️ mutually escalated ASAP Rocky has been sending shots through music, fashion, relationships, timing, and optics. Drake has been responding consistently and directly. That’s hip hop Trying to reframe it as“I stayed quiet and Drake attacked me”

Cousin Tino ™️

42,850 görüntüleme • 8 ay önce

It printed. I gave Claude the gold trading strategy from a $250K Polymarket wallet and it rebuilt the entire system. $4,298 profit later I knew I need to share this. Gold and silver prices + UP/DOWN = under radar gem. I've never seen anyone explain this publicly I didn't thought I would post it, but here's the entire breakdown of this strategy for you So you can lock the f in and try to copy it for yourself: Albert1953. Joined June 2022. 2.5K views. $248,326.77 all-time. 3,338 predictions. $35,300 biggest win. Profile → 0x777fae71d2ff9ec48a1213d48ba1d9d91024a1bb I found this wallet at 2AM scrolling pages of Polymarket nobody reads. Opened the positions. Stared at the screen. Not a single standard crypto trade. "Will Silver hit HIGH $120 by end of June?" → bought No at 61.3¢ → now 89.9¢. +46% "Will Gold hit HIGH $5,500 by end of June?" → bought No at 22.1¢ → now 75.7¢. +242% "Will Gold settle above $6,200 in June?" → bought No at 75.4¢ → now 94.2¢. +24% $106,300 still sitting in active positions. All green. All commodities. Before I built my own version I copytraded this wallet for 48 hours to stress-test the logic. $80 in. Didn't touch it. Went to sleep. Woke up to $4,298. I alwas stress-test wallets I find by copy trading here: Here's exactly how to build this with Claude yourself: 1. Commodity ceiling/floor probability mapping Open Claude. Type this: "Analyze 36 months of Gold, Silver and WTI Crude Oil price data. For each asset identify the statistical probability of hitting specific price ceilings and floors within 30, 60 and 90 day windows. Flag every current Polymarket commodity market where the implied probability differs from historical probability by more than 25%." The crowd prices Gold hitting $5,500 by June as a 22% chance. Three years of data says it's closer to 8%. That 14% gap is the entire edge. 2. Mean reversion Kelly sizing "Size every position using fractional Kelly weighted by mean reversion strength. Assets further from their historical range get larger positions. Assets near historical midpoints get smaller positions." f* = (p × b - q) / bThat's why he has $16,244 on Silver NOT hitting $120 but only $2,806 on Silver settling above $115. The math knows which prediction is more extreme. Extreme predictions = fatter edge = bigger Kelly size. 3. Macro correlation filter "Before entering any commodity position check current correlation between: DXY dollar index, 10-year Treasury yield, and the target commodity. If macro conditions are actively moving against the position's thesis - skip the entry entirely." Gold and Silver don't move in isolation. They move with dollar strength and interest rates. The bot only fires when macro confirms the statistical edge, not fights it. 4. Multi-month compounding structure "Prioritize end-of-month and end-of-quarter settlement markets over weekly markets. Longer settlement windows allow mean reversion to play out fully without noise interference." This is why every position is June settlement. Not next week. Not April. June. Long enough for the math to be right even if the market is temporarily wrong. Albert1953 has been running this logic since June 2022. 3,338 predictions. $248,326 profit. $106K still active and green. 2,500 people have seen this wallet in 4 years. You're one of them now. - You found this while it's still quiet. The next commodity wallet I find will be even quieter. FOLLOW before that changes.
0:21

It printed. I gave Claude the gold trading strategy from a $250K Polymarket wallet and it rebuilt the entire system. $4,298 profit later I knew I need to share this. Gold and silver prices + UP/DOWN = under radar gem. I've never seen anyone explain this publicly I didn't thought I would post it, but here's the entire breakdown of this strategy for you So you can lock the f in and try to copy it for yourself: Albert1953. Joined June 2022. 2.5K views. $248,326.77 all-time. 3,338 predictions. $35,300 biggest win. Profile → 0x777fae71d2ff9ec48a1213d48ba1d9d91024a1bb I found this wallet at 2AM scrolling pages of Polymarket nobody reads. Opened the positions. Stared at the screen. Not a single standard crypto trade. "Will Silver hit HIGH $120 by end of June?" → bought No at 61.3¢ → now 89.9¢. +46% "Will Gold hit HIGH $5,500 by end of June?" → bought No at 22.1¢ → now 75.7¢. +242% "Will Gold settle above $6,200 in June?" → bought No at 75.4¢ → now 94.2¢. +24% $106,300 still sitting in active positions. All green. All commodities. Before I built my own version I copytraded this wallet for 48 hours to stress-test the logic. $80 in. Didn't touch it. Went to sleep. Woke up to $4,298. I alwas stress-test wallets I find by copy trading here: Here's exactly how to build this with Claude yourself: 1. Commodity ceiling/floor probability mapping Open Claude. Type this: "Analyze 36 months of Gold, Silver and WTI Crude Oil price data. For each asset identify the statistical probability of hitting specific price ceilings and floors within 30, 60 and 90 day windows. Flag every current Polymarket commodity market where the implied probability differs from historical probability by more than 25%." The crowd prices Gold hitting $5,500 by June as a 22% chance. Three years of data says it's closer to 8%. That 14% gap is the entire edge. 2. Mean reversion Kelly sizing "Size every position using fractional Kelly weighted by mean reversion strength. Assets further from their historical range get larger positions. Assets near historical midpoints get smaller positions." f* = (p × b - q) / bThat's why he has $16,244 on Silver NOT hitting $120 but only $2,806 on Silver settling above $115. The math knows which prediction is more extreme. Extreme predictions = fatter edge = bigger Kelly size. 3. Macro correlation filter "Before entering any commodity position check current correlation between: DXY dollar index, 10-year Treasury yield, and the target commodity. If macro conditions are actively moving against the position's thesis - skip the entry entirely." Gold and Silver don't move in isolation. They move with dollar strength and interest rates. The bot only fires when macro confirms the statistical edge, not fights it. 4. Multi-month compounding structure "Prioritize end-of-month and end-of-quarter settlement markets over weekly markets. Longer settlement windows allow mean reversion to play out fully without noise interference." This is why every position is June settlement. Not next week. Not April. June. Long enough for the math to be right even if the market is temporarily wrong. Albert1953 has been running this logic since June 2022. 3,338 predictions. $248,326 profit. $106K still active and green. 2,500 people have seen this wallet in 4 years. You're one of them now. - You found this while it's still quiet. The next commodity wallet I find will be even quieter. FOLLOW before that changes.

Frogify

23,399 görüntüleme • 5 ay önce

🟢GIVEAWAY🟢 Best comments or memes about this whole circus + RT this post. 10 winners will each get $50💎 (For evidence, supporting materials, and context, read both articles and watch the video included in the article I posted yesterday) Housebets.com & Porchy pay your debts A few people told me they did not fully understand the first article because there were too many moving parts: leaderboard accounts, rewards, weekly dates, monthly bonus, Tequity, game categories, withdrawals, Provably Fair, seed changes, migration, support tickets, ledgers and founder messages. Fair enough. The evidence is already there, and I still recommend reading the full articles and, above all, watching the video, because the video shows the reward system failing live. But this text is the cleaner version: the full story explained in plain English, without assuming the reader knows anything about crypto casinos, leaderboards or lossback systems. From all the evidence I’ve gathered, the Housebets story is not a normal “player lost money” complaint. It looks like a full transparency failure across the whole product: leaderboard, rewards, withdrawals, game categories, Provably Fair / Tequity mapping, support, migration and founder response. Housebets sold itself as a rewards-first casino: public leaderboards, weekly/monthly bonuses, fast withdrawals, VIP treatment and Provably Fair games. But every time I asked for the records behind those systems, snapshots, ledger entries, weekly cycles, GGR/NGR, slider logs, PF seed mapping, Tequity round IDs, withdrawal approval logs, the answer became some version of “forwarded to the relevant department.” This started long before the public dispute. I was not some random angry player who appeared after one bad session. In January I was helping Housebets and giving product feedback. I literally told support on 27 January that I was “testing the website for George,” while already dealing with a non-instant withdrawal and a 100% welcome bonus that had not applied. Support even asked me for “proof about your testing job.” The same chat shows the advertised 100% Welcome Bonus, the bonus not applying, and support saying the withdrawal needed internal confirmation instead of being instant. The welcome bonus issue never looked clean. Housebets advertised a 100% Welcome Bonus up to $1,000 on first deposit; I deposited, contacted support, and the bonus did not apply. Then support effectively turned a first-deposit bonus into a second-deposit workaround because the first one had not been applied properly. On 31 January I came back after another deposit and told them the bonus still had not been applied, even though I had already followed support’s instructions. Edward replied that he had “forwarded” the concern to the team. The same 100% welcome bonus was still being advertised in March. By April, the rewards system was already showing serious problems. I had the weekly slider at 100% lossback and told support I had lost money but the weekly did not appear. Jacky said the weekly was generated every Thursday at 00:01 UTC and gave actual internal figures: GGR $6,250, Total Bonus $6,083.99, NGR $168.31. So Housebets clearly had internal calculations when it wanted to explain why something might not pay. But when I later asked for full calculations, those same numbers suddenly became impossible to produce. Then on 18–19 April, the rewards page was bugged and would not let me claim. Support could see a pending weekly bonus of $717.37, but I could not claim it from the UI. Tee said it had been forwarded to the relevant department. That $717.37 later appears in the bonus ledger as Rakeback (20 Apr) 717.37089061, so I am not saying that specific one stayed unpaid forever. The point is worse: already in April, support could see a pending weekly reward while the player-facing reward page did not work. For a casino built around rewards, that is not a small bug. That is the product. In May, the UI and account data kept failing basic trust checks. On 8 May, I deposited 400 USDT; support said it had been credited, but I could not see it, and the proposed fix was to log out, clear cookies and cache. On 16 May, I asked why total deposits and withdrawals had disappeared from the menu; support said the platform was “in continuous evolution.” On 17 May, I asked for my total deposits and withdrawals, and support said they did not have direct access to that consolidated summary and would email it. That full official ledger did not arrive. So when Housebets later defends itself with UI screenshots, remember: this was the same UI where deposits could be credited but invisible, totals disappeared, rewards pages bugged, and support could not access consolidated account totals. Withdrawals were also not what was advertised. On 16 May, I asked why a crypto withdrawal was pending if withdrawals were supposed to be instant. Tee answered: “A few withdrawals require manual approval,” then added, “Our withdrawals are typically instant but…” That matters because a few days later the withdrawal delay became real damage. On 25 May, I told support before a match that I needed the funds to place a time-sensitive bet on another site in less than 20 minutes. I explained I wanted to bet around 60k at odds of 2.55. The withdrawal did not arrive in time. Later I told them the bet won and that I missed around 90k in profit because Housebets took more than two hours despite being warned before the match started. Jacky said he would raise the compensation case to the VIP team. Nobody resolved it. This was not one delayed withdrawal either. In my formal complaint I reconstructed several withdrawal delays: 23 May 02:55 → 08:03, around 5h08m; 25 May 03:05 → 08:09, around 5h04m; 17 May 03:54 → 08:02, around 4h08m; 18 May 04:46 → 08:11, around 3h25m; 16 May 05:23 → 08:12, around 2h49m. That is not “instant withdrawal.” And if later marketing says withdrawals are much faster now, the obvious question is: if this was the faster version, what did slow look like? The Provably Fair / Tequity side was another major issue. On 17 May I asked support how to verify an old Blackjack round. I did not ask for a generic explanation of Provably Fair; I asked where I could see the server seed, client seed, nonce and result for previous games. Support sent me to bet history, mentioned RTP, gave a generic PF explanation and showed the current Dice seed screen. When I said that did not let me verify previous games, they told me to clear cookies/cache. After doing that, I saw a new client seed and nonce 1 even though I had not played with that seed pair. I asked if Housebets changes seeds on every login. Support could not answer and told me to contact VIP. That seed/session behaviour is important. I later recorded video evidence around the seed changing after clearing cookies/cache and asked for the exact mapping: Housebets account ID → Tequity/provider player ID → session/currency context → seed pair → server seed hash → revealed server seed → client seed → nonce/cursor → raw outcome → final result. Housebets cannot sell Provably Fair if the player cannot verify historical bets, and “contact VIP” is not a verification algorithm. On 24 May, I asked for raw verification data for a specific Tequity Blackjack round: Round ID e1648d60-0da1-4433-a5ab-9ae39f5302e3, Blackjack, Tequity, bet amount 11,346 USDT, client seed O3YBZF7LBu, server seed hash starting 712875.... I asked for revealed server seed, nonce, full result JSON, card draw order and verification algorithm. I also asked about an apparent duplicate-card/deck question. Tee replied: “I don’t have the answers to your questions right now, but I’m forwarding your request to the relevant department.” That same day, I asked for a full audit of six Dice bets of 11,400 USDT each, total 68,400 USDT. I requested bet IDs, provider round IDs, roll results, seed data, balance ledger, request/session logs, security logs, retry flags, provider records and a full technical reconciliation. Tee replied: “I will forward this to the relevant department.” So when I asked for raw data, the answer was not data. It was forwarding. Again. There were also many large loss clusters that required reconciliation because of those unresolved PF, Tequity, category, RTP and session questions. In my complaint I listed clusters such as 25 May 02:17–02:54 Blackjack around 169,932 USDT; 16 May 12:31–13:26 Dice around 90,571.92 USDT; 26 May 02:48–03:58 Mines around 89,199 USDT; 24 May 06:20–06:21 Dice at 68,400 USDT; 26 May 00:11–01:41 Blackjack around 59,910 USDT; 25 May 22:51–22:59 Dice around 59,576 USDT; and several more between 40k and 56k. I am not saying every losing cluster proves manipulation by itself. I am saying that when PF mapping, provider logs, RTP/HE, category mapping and seed/session behaviour are unresolved, these sequences need a real reconciliation. The leaderboard is where the story becomes very hard for Housebets to explain. Around 19–20 May, two new accounts, elmourabut and lucasmartirini, appeared and started climbing every day at a vertiginous pace. Not normal slow leaderboard growth. Not a casual player building volume over time. They were created around that period and then started rising with huge wagering in a way that looked extremely unnatural for brand new accounts. By 29 May, I was first on both weekly and monthly leaderboards, and those two accounts were directly behind me with huge volume. In the monthly leaderboard screenshots, I was around $3.33M wagered, while elmourabut was around $1.29M and lucasmartirini around $1.08M. In the weekly leaderboard, I was around $1.096M, while those two accounts were around $635k and $578k. They were not normal accounts sitting at the bottom; they were directly behind me, applying pressure. In my formal complaint I recorded that elmourabut joined on 19 May and lucasmartirini on 20 May, that they showed zero visible withdrawals, large deposits/wagering and significant card-game volume, and I asked Housebets to confirm they were not staff, test, QA, admin, house-controlled, affiliate-controlled, internally funded, promotional, bonus-only or multi-account related accounts. This matters because a leaderboard is not passive. It is gamification. It makes players defend rank. When two new accounts appear behind you with hundreds of thousands or more than a million in volume, you are pressured to keep wagering. In my case, the disputed deposit sequence from 25 May 22:23 to 26 May 02:09 totals 91,168.375326 USDT. That sequence begins with 1,000.00 at 22:23 and continues with repeated deposits until 2,879.148969 at 02:09. The video later shows why those dates matter: there were deposits coming in, no gameplay withdrawal offsetting the sequence, a balance basically at zero, and later a leaderboard prize shown as P/L. I formally asked Housebets to confirm those two leaderboard accounts were real and eligible, and also to preserve wager logs, transaction records, balance adjustment logs, account flags, leaderboard calculation snapshots, support ticket logs, Telegram/email records and internal notes. Edward said he forwarded the request. In the same thread, he added that they were “working on fixing an issue regarding the weekly bonuses,” and then said the weekly countdown was “not currently on Thursday evenings.” So the leaderboard issue and the weekly bonus issue are linked in time and support context. After that, Housebets confirmed by email that elmourabut and lucasmartirini were “legitimate and eligible accounts.” That email is the trap door. If they were legitimate and eligible, they should have remained in the leaderboard with their volume. If they were not, Housebets should never have confirmed them as legitimate and eligible. After that confirmation, the accounts disappeared from the leaderboard or stopped appearing in the positions their previous wagering required. I went back to support on 30 May and wrote: “There has been a material post-confirmation leaderboard change involving two accounts that Housebets had already confirmed as legitimate and eligible. I need the exact reason, timestamp, logs, and recalculation basis.” Edward said the matter was flagged and that I could expect a prompt response. I am still waiting for the actual explanation. Why did they disappear? My read is simple: because every hour that passed, there was more evidence around those accounts. They had been created around the same period, they were climbing at a speed that looked anything but human, they showed no visible withdrawals in the data I could see and reported, they appeared to be generating huge volume in unclear game categories, and the games/categories tied to that volume did not even make sense from the player-facing UI. When I started asking what they were actually playing, what Card meant, whether the volume was Tequity / UnOriginals / House Games, what RTP and house edge applied, and where the logs were, the questions became uncomfortable. Keeping those accounts visible became harder than removing them. So they disappeared. The game category issue made the leaderboard even more suspicious. On 30 May, I asked support why my own stats showed almost all my volume under Slots / Tragamonedas when I did not play real slots. I told them: “i dont play 3$ in unoriginals,” “i played all 3M in unoriginals,” and “ive never play slots.” I asked what “Card” was, where that game was, what RTP and house edge it had. Monica said Card was mainly Blackjack, Baccarat and Poker variants. Marcus later said the team was investigating why it showed that I mostly played slots when I had not. He could not give the exact game, RTP, HE, provider, category mapping or contribution logic. That matters because those same unclear categories were connected to leaderboard volume. If the site cannot clearly explain whether volume is Slots, Card, UnOriginals, House Games, Blackjack, Baccarat, Always 9 Baccarat or Tequity, then the leaderboard is not auditable for the player. I even asked which UnOriginals those two accounts were playing, and support told me to look at Live Bets. That is not an answer. I was not asking for gossip; I was asking what exact games generated leaderboard volume, what RTP/HE applied and whether that volume was eligible. There is also an earlier leaderboard-related precedent: Porchy had already told me in February that I would lose leaderboard places if I did not rename, because too many people were messaging support saying the site was not being fair due to my name and it “doesn’t make us look good.” That matters because it suggests leaderboard positioning was not treated as a sacred, untouchable system when public perception was involved. If leaderboard positions can be threatened for image reasons, then later claims that everything is purely automatic deserve scrutiny. Then Porchy made the leaderboard situation worse. Instead of producing logs or snapshots, he later said the leaderboard had “abusers” on it, that they were removed to help other players, and that it never affected me. Later he said they paid every single person, “even these abusers,” then called me “begging for money.” That creates a direct contradiction: Housebets confirmed the accounts as legitimate and eligible, then Porchy referred to leaderboard “abusers.” If they were abusers, why were they confirmed as legitimate and eligible? If they were eligible, why did they disappear? If they never affected me, where are the historical snapshots proving that? Once those accounts disappeared, Housebets paid the leaderboard prizes. On 1 June, the bonus ledger shows two Leaderboard entries: 5,007.46111706 and 1,001.49222341, totaling 6,008.95334047. That part was paid. But then Act Two started: the weekly and monthly rewards did not appear as separate ledger entries. The same bonus ledger shows those two 1 June entries as Leaderboard only, not Monthly Bonus, not Weekly Reload, not Lossback. The weekly timeline is a mess. On 28 May, the dashboard / UI said the weekly bonus was claimable every Thursday at 00:01 UTC, and the monthly was available on the 1st at 00:01 UTC. That same night I told support the weekly had shown as available, then reset to 6 days without paying. Later I sent screenshots and wrote: “1M wagered and 0.2$.” Jacky said he had raised the issue to the technical team. So the weekly failure was reported live, not reconstructed after the fact. The next day, 29 May, Edward said they were fixing an issue regarding weekly bonuses and that the weekly countdown was “not currently on Thursday evenings.” Then on 1 June, Spencer said the May weekly bonuses were 7th, 14th, 21st, and then due to migration the weekly moved to Monday, so there was one on the 25th on the new platform. He also said the 25 May weekly covered gameplay from 21–24 May, and that tech was looking at that plus the monthly bonus. The ledger does show a 25 May 02:10 Rakeback entry of 1,996.08334791, which likely corresponds to that 21–24 May weekly. But my major loss sequence starts about 20 hours later, on 25 May at 22:23, and continues until 26 May at 02:09. So the 25 May weekly cannot cover those losses. If weekly was still Thursday, the 25/26 losses should have been in the 28 May weekly. But the bonus ledger on 28 May shows only two tiny Rakeback entries, 0.28373945 and 0.00280958. If weekly moved to Monday because of migration, those losses should have appeared in the next weekly after 25 May. But on 1 June the ledger only shows Leaderboard entries. Then the final video shows the next Weekly Reload reaching zero, paying nothing and resetting to 6d 23h. So the same loss sequence appears to fall into no paid weekly cycle. The 4 June support conversation makes this even more ridiculous. After I recorded the weekly reset video, I asked support a very simple question: what were the last weekly dates/cycles? The dashboard / support flow again said weekly bonuses are claimable every Thursday at 00:01 UTC. Jacky confirmed: “Weekly bonuses can be claimed every Thursday at 00:01 UTC in the Rewards tab,” and added that if not claimed by the following Wednesday at 23:59 UTC, it expires. But when I asked for the exact last four dates, Jacky said he had to check with the relevant department. When I pressed again, he said, “Sorry, As I am only a CS, Let me raise your concerns to relevant department.” I asked whether support did not have the information or simply could not answer. He replied: “Do you have any other concerns?” They use weekly cycles to decide whether to pay, but support cannot explain the weekly cycle. The monthly is missing too. The dashboard / UI said the monthly bonus is based on activity and VIP level from the previous month and is available on the 1st at 00:01 UTC. In May I had more than 3,258,023.0829 wagered according to the formal complaint data. I also have proof/video that the monthly slider was set to 50/50. On 1 June, Spencer first told me I had claimed the Monthly Bonus at 1:12am BST around the same time as the monthly leaderboard reward. I immediately said I only received leaderboard prizes. Then Spencer changed the answer: “Our tech team are still actively working on issues regarding the monthly bonuses.” So first the monthly was claimed, then tech was still fixing it. The ledger still shows no Monthly Bonus entry. Housebets then seems to rely on “up overall” as a defence. But the video and ledger show why that does not work. My weekly/monthly profile later showed around +6,008 P/L with 0 deposits, 0 wagered and around 6,008 in bonuses. That number matches exactly the two 1 June Leaderboard payments. So the UI is showing leaderboard rewards as P/L. Then support used “up overall” to say I was not eligible for weekly lossback. That is not a clean lossback calculation. That is using a leaderboard reward as apparent profit to deny a lossback that should be based on actual eligible losses. There were also smaller reward-confusion issues along the way. On 22 May I asked for all pending bonuses,weekly, monthly, rakeback, level-up, anything, and support said the internal team would manually verify whether everything had been credited correctly and email me. On 24 May, I asked about level-up rewards because the reward looked like $3,500 for Pearl; support clarified it was $3,500 total across all Pearl levels, $500 per level. These are not the core issues, but they are part of the same pattern: rewards marketing, unclear UI, manual verification, emails that do not arrive, and players having to chase basic explanations. Then there is the migration. On 25 May, after the delayed withdrawal, missing VIP contact and unresolved issues, support told me my account would be moved to the new platform and that this upgrade would offer a better withdrawal process and fix many issues. Before that migration, I explicitly requested that no account data, internal data, logs, balance history, bonus history, bet history, provider records or pending issues be deleted. The response: “Your request has been relayed to the relevant department.” Again, forwarding. But if the old data is safe, Housebets should provide the old leaderboard snapshots, old weekly states, old bonus logs, old Tequity mapping and old withdrawal approval logs. The founder response did not fix anything. When Porchy finally engaged, he did not provide the records. He framed the settlement request as “so you want $100,000?” and asked whether I needed it or else I was going to post on X. I had already made clear this was not money for silence; I asked for logs, snapshots, withdrawal records, calculations and a counter-calculation if Housebets disagreed. He later referred to “abusers,” told me I was “up overall,” said “You are begging for money,” and suggested I “just do this to casinos.” Still no ledger. Still no weekly calculation. Still no monthly entry. Still no PF/Tequity mapping. Still no leaderboard snapshots. Another player also contacted me with screenshots pointing to similar categories of issues: private deals, leaderboard payout disputes, migration/account merge problems, missing history and a tiny monthly bonus despite claimed losses. I am not using that player’s case as the foundation of my claim without his full ledger, but it matters because it suggests the same type of opacity may not be isolated: private VIP/reward deals, leaderboard eligibility, monthly bonus calculations, migration and unclear history. If Housebets has private deals that affect leaderboard eligibility or rewards, it must explain how those deals interact with public leaderboards. So the overall picture is this: Housebets sold a public leaderboard and rewards system that pressured real wagering. Two new accounts appeared directly behind me with huge volume, were confirmed as legitimate and eligible, then disappeared after I asked for logs and questioned game categories. Housebets could not explain the exact games, RTP, house edge or category mapping behind the volume. The accounts were later framed by Porchy as “abusers,” contradicting the earlier eligibility confirmation. Once Housebets paid me the leaderboard prizes, those prizes were shown as P/L, and that contaminated P/L was then used to claim I was “up overall” and not eligible for lossback. At the same time, my real 25 May 22:23 → 26 May 02:09 loss sequence of 91,168.375326 USDT appears in no clean weekly cycle. The 25 May weekly covered 21–24 May according to Spencer, so it cannot cover that loss sequence. The 28 May weekly showed only tiny Rakeback entries and was already reported as broken. The 1 June ledger shows only Leaderboard entries. The later video shows Weekly Reload reaching zero, paying nothing and resetting. And when I ask support for the exact weekly calendar, they cannot answer and send it to the relevant department. The monthly is the same story. The dashboard / UI says it is based on activity and VIP. I had more than 3.25M wagered in May. Spencer first says I claimed it, then says tech is still working on monthly bonuses. The ledger shows no Monthly Bonus. If Housebets says I was not eligible, they need to show the formula, slider history, cycle, GGR/NGR, eligible loss/activity, deductions and ledger result. If they cannot, “not eligible” is just another label. And this opens another can of worms: Tequity / provider configuration. Housebets cannot hide behind “the provider” whenever something goes wrong. The player does not deposit with Tequity. The player does not withdraw from Tequity. The player does not speak to Tequity support. The player does not compete in a Tequity leaderboard. The player plays on Housebets, with a Housebets wallet, Housebets UI, Housebets rewards, Housebets leaderboard and Housebets support. 1/2
22:49

🟢GIVEAWAY🟢 Best comments or memes about this whole circus + RT this post. 10 winners will each get $50💎 (For evidence, supporting materials, and context, read both articles and watch the video included in the article I posted yesterday) Housebets.com & Porchy pay your debts A few people told me they did not fully understand the first article because there were too many moving parts: leaderboard accounts, rewards, weekly dates, monthly bonus, Tequity, game categories, withdrawals, Provably Fair, seed changes, migration, support tickets, ledgers and founder messages. Fair enough. The evidence is already there, and I still recommend reading the full articles and, above all, watching the video, because the video shows the reward system failing live. But this text is the cleaner version: the full story explained in plain English, without assuming the reader knows anything about crypto casinos, leaderboards or lossback systems. From all the evidence I’ve gathered, the Housebets story is not a normal “player lost money” complaint. It looks like a full transparency failure across the whole product: leaderboard, rewards, withdrawals, game categories, Provably Fair / Tequity mapping, support, migration and founder response. Housebets sold itself as a rewards-first casino: public leaderboards, weekly/monthly bonuses, fast withdrawals, VIP treatment and Provably Fair games. But every time I asked for the records behind those systems, snapshots, ledger entries, weekly cycles, GGR/NGR, slider logs, PF seed mapping, Tequity round IDs, withdrawal approval logs, the answer became some version of “forwarded to the relevant department.” This started long before the public dispute. I was not some random angry player who appeared after one bad session. In January I was helping Housebets and giving product feedback. I literally told support on 27 January that I was “testing the website for George,” while already dealing with a non-instant withdrawal and a 100% welcome bonus that had not applied. Support even asked me for “proof about your testing job.” The same chat shows the advertised 100% Welcome Bonus, the bonus not applying, and support saying the withdrawal needed internal confirmation instead of being instant. The welcome bonus issue never looked clean. Housebets advertised a 100% Welcome Bonus up to $1,000 on first deposit; I deposited, contacted support, and the bonus did not apply. Then support effectively turned a first-deposit bonus into a second-deposit workaround because the first one had not been applied properly. On 31 January I came back after another deposit and told them the bonus still had not been applied, even though I had already followed support’s instructions. Edward replied that he had “forwarded” the concern to the team. The same 100% welcome bonus was still being advertised in March. By April, the rewards system was already showing serious problems. I had the weekly slider at 100% lossback and told support I had lost money but the weekly did not appear. Jacky said the weekly was generated every Thursday at 00:01 UTC and gave actual internal figures: GGR $6,250, Total Bonus $6,083.99, NGR $168.31. So Housebets clearly had internal calculations when it wanted to explain why something might not pay. But when I later asked for full calculations, those same numbers suddenly became impossible to produce. Then on 18–19 April, the rewards page was bugged and would not let me claim. Support could see a pending weekly bonus of $717.37, but I could not claim it from the UI. Tee said it had been forwarded to the relevant department. That $717.37 later appears in the bonus ledger as Rakeback (20 Apr) 717.37089061, so I am not saying that specific one stayed unpaid forever. The point is worse: already in April, support could see a pending weekly reward while the player-facing reward page did not work. For a casino built around rewards, that is not a small bug. That is the product. In May, the UI and account data kept failing basic trust checks. On 8 May, I deposited 400 USDT; support said it had been credited, but I could not see it, and the proposed fix was to log out, clear cookies and cache. On 16 May, I asked why total deposits and withdrawals had disappeared from the menu; support said the platform was “in continuous evolution.” On 17 May, I asked for my total deposits and withdrawals, and support said they did not have direct access to that consolidated summary and would email it. That full official ledger did not arrive. So when Housebets later defends itself with UI screenshots, remember: this was the same UI where deposits could be credited but invisible, totals disappeared, rewards pages bugged, and support could not access consolidated account totals. Withdrawals were also not what was advertised. On 16 May, I asked why a crypto withdrawal was pending if withdrawals were supposed to be instant. Tee answered: “A few withdrawals require manual approval,” then added, “Our withdrawals are typically instant but…” That matters because a few days later the withdrawal delay became real damage. On 25 May, I told support before a match that I needed the funds to place a time-sensitive bet on another site in less than 20 minutes. I explained I wanted to bet around 60k at odds of 2.55. The withdrawal did not arrive in time. Later I told them the bet won and that I missed around 90k in profit because Housebets took more than two hours despite being warned before the match started. Jacky said he would raise the compensation case to the VIP team. Nobody resolved it. This was not one delayed withdrawal either. In my formal complaint I reconstructed several withdrawal delays: 23 May 02:55 → 08:03, around 5h08m; 25 May 03:05 → 08:09, around 5h04m; 17 May 03:54 → 08:02, around 4h08m; 18 May 04:46 → 08:11, around 3h25m; 16 May 05:23 → 08:12, around 2h49m. That is not “instant withdrawal.” And if later marketing says withdrawals are much faster now, the obvious question is: if this was the faster version, what did slow look like? The Provably Fair / Tequity side was another major issue. On 17 May I asked support how to verify an old Blackjack round. I did not ask for a generic explanation of Provably Fair; I asked where I could see the server seed, client seed, nonce and result for previous games. Support sent me to bet history, mentioned RTP, gave a generic PF explanation and showed the current Dice seed screen. When I said that did not let me verify previous games, they told me to clear cookies/cache. After doing that, I saw a new client seed and nonce 1 even though I had not played with that seed pair. I asked if Housebets changes seeds on every login. Support could not answer and told me to contact VIP. That seed/session behaviour is important. I later recorded video evidence around the seed changing after clearing cookies/cache and asked for the exact mapping: Housebets account ID → Tequity/provider player ID → session/currency context → seed pair → server seed hash → revealed server seed → client seed → nonce/cursor → raw outcome → final result. Housebets cannot sell Provably Fair if the player cannot verify historical bets, and “contact VIP” is not a verification algorithm. On 24 May, I asked for raw verification data for a specific Tequity Blackjack round: Round ID e1648d60-0da1-4433-a5ab-9ae39f5302e3, Blackjack, Tequity, bet amount 11,346 USDT, client seed O3YBZF7LBu, server seed hash starting 712875.... I asked for revealed server seed, nonce, full result JSON, card draw order and verification algorithm. I also asked about an apparent duplicate-card/deck question. Tee replied: “I don’t have the answers to your questions right now, but I’m forwarding your request to the relevant department.” That same day, I asked for a full audit of six Dice bets of 11,400 USDT each, total 68,400 USDT. I requested bet IDs, provider round IDs, roll results, seed data, balance ledger, request/session logs, security logs, retry flags, provider records and a full technical reconciliation. Tee replied: “I will forward this to the relevant department.” So when I asked for raw data, the answer was not data. It was forwarding. Again. There were also many large loss clusters that required reconciliation because of those unresolved PF, Tequity, category, RTP and session questions. In my complaint I listed clusters such as 25 May 02:17–02:54 Blackjack around 169,932 USDT; 16 May 12:31–13:26 Dice around 90,571.92 USDT; 26 May 02:48–03:58 Mines around 89,199 USDT; 24 May 06:20–06:21 Dice at 68,400 USDT; 26 May 00:11–01:41 Blackjack around 59,910 USDT; 25 May 22:51–22:59 Dice around 59,576 USDT; and several more between 40k and 56k. I am not saying every losing cluster proves manipulation by itself. I am saying that when PF mapping, provider logs, RTP/HE, category mapping and seed/session behaviour are unresolved, these sequences need a real reconciliation. The leaderboard is where the story becomes very hard for Housebets to explain. Around 19–20 May, two new accounts, elmourabut and lucasmartirini, appeared and started climbing every day at a vertiginous pace. Not normal slow leaderboard growth. Not a casual player building volume over time. They were created around that period and then started rising with huge wagering in a way that looked extremely unnatural for brand new accounts. By 29 May, I was first on both weekly and monthly leaderboards, and those two accounts were directly behind me with huge volume. In the monthly leaderboard screenshots, I was around $3.33M wagered, while elmourabut was around $1.29M and lucasmartirini around $1.08M. In the weekly leaderboard, I was around $1.096M, while those two accounts were around $635k and $578k. They were not normal accounts sitting at the bottom; they were directly behind me, applying pressure. In my formal complaint I recorded that elmourabut joined on 19 May and lucasmartirini on 20 May, that they showed zero visible withdrawals, large deposits/wagering and significant card-game volume, and I asked Housebets to confirm they were not staff, test, QA, admin, house-controlled, affiliate-controlled, internally funded, promotional, bonus-only or multi-account related accounts. This matters because a leaderboard is not passive. It is gamification. It makes players defend rank. When two new accounts appear behind you with hundreds of thousands or more than a million in volume, you are pressured to keep wagering. In my case, the disputed deposit sequence from 25 May 22:23 to 26 May 02:09 totals 91,168.375326 USDT. That sequence begins with 1,000.00 at 22:23 and continues with repeated deposits until 2,879.148969 at 02:09. The video later shows why those dates matter: there were deposits coming in, no gameplay withdrawal offsetting the sequence, a balance basically at zero, and later a leaderboard prize shown as P/L. I formally asked Housebets to confirm those two leaderboard accounts were real and eligible, and also to preserve wager logs, transaction records, balance adjustment logs, account flags, leaderboard calculation snapshots, support ticket logs, Telegram/email records and internal notes. Edward said he forwarded the request. In the same thread, he added that they were “working on fixing an issue regarding the weekly bonuses,” and then said the weekly countdown was “not currently on Thursday evenings.” So the leaderboard issue and the weekly bonus issue are linked in time and support context. After that, Housebets confirmed by email that elmourabut and lucasmartirini were “legitimate and eligible accounts.” That email is the trap door. If they were legitimate and eligible, they should have remained in the leaderboard with their volume. If they were not, Housebets should never have confirmed them as legitimate and eligible. After that confirmation, the accounts disappeared from the leaderboard or stopped appearing in the positions their previous wagering required. I went back to support on 30 May and wrote: “There has been a material post-confirmation leaderboard change involving two accounts that Housebets had already confirmed as legitimate and eligible. I need the exact reason, timestamp, logs, and recalculation basis.” Edward said the matter was flagged and that I could expect a prompt response. I am still waiting for the actual explanation. Why did they disappear? My read is simple: because every hour that passed, there was more evidence around those accounts. They had been created around the same period, they were climbing at a speed that looked anything but human, they showed no visible withdrawals in the data I could see and reported, they appeared to be generating huge volume in unclear game categories, and the games/categories tied to that volume did not even make sense from the player-facing UI. When I started asking what they were actually playing, what Card meant, whether the volume was Tequity / UnOriginals / House Games, what RTP and house edge applied, and where the logs were, the questions became uncomfortable. Keeping those accounts visible became harder than removing them. So they disappeared. The game category issue made the leaderboard even more suspicious. On 30 May, I asked support why my own stats showed almost all my volume under Slots / Tragamonedas when I did not play real slots. I told them: “i dont play 3$ in unoriginals,” “i played all 3M in unoriginals,” and “ive never play slots.” I asked what “Card” was, where that game was, what RTP and house edge it had. Monica said Card was mainly Blackjack, Baccarat and Poker variants. Marcus later said the team was investigating why it showed that I mostly played slots when I had not. He could not give the exact game, RTP, HE, provider, category mapping or contribution logic. That matters because those same unclear categories were connected to leaderboard volume. If the site cannot clearly explain whether volume is Slots, Card, UnOriginals, House Games, Blackjack, Baccarat, Always 9 Baccarat or Tequity, then the leaderboard is not auditable for the player. I even asked which UnOriginals those two accounts were playing, and support told me to look at Live Bets. That is not an answer. I was not asking for gossip; I was asking what exact games generated leaderboard volume, what RTP/HE applied and whether that volume was eligible. There is also an earlier leaderboard-related precedent: Porchy had already told me in February that I would lose leaderboard places if I did not rename, because too many people were messaging support saying the site was not being fair due to my name and it “doesn’t make us look good.” That matters because it suggests leaderboard positioning was not treated as a sacred, untouchable system when public perception was involved. If leaderboard positions can be threatened for image reasons, then later claims that everything is purely automatic deserve scrutiny. Then Porchy made the leaderboard situation worse. Instead of producing logs or snapshots, he later said the leaderboard had “abusers” on it, that they were removed to help other players, and that it never affected me. Later he said they paid every single person, “even these abusers,” then called me “begging for money.” That creates a direct contradiction: Housebets confirmed the accounts as legitimate and eligible, then Porchy referred to leaderboard “abusers.” If they were abusers, why were they confirmed as legitimate and eligible? If they were eligible, why did they disappear? If they never affected me, where are the historical snapshots proving that? Once those accounts disappeared, Housebets paid the leaderboard prizes. On 1 June, the bonus ledger shows two Leaderboard entries: 5,007.46111706 and 1,001.49222341, totaling 6,008.95334047. That part was paid. But then Act Two started: the weekly and monthly rewards did not appear as separate ledger entries. The same bonus ledger shows those two 1 June entries as Leaderboard only, not Monthly Bonus, not Weekly Reload, not Lossback. The weekly timeline is a mess. On 28 May, the dashboard / UI said the weekly bonus was claimable every Thursday at 00:01 UTC, and the monthly was available on the 1st at 00:01 UTC. That same night I told support the weekly had shown as available, then reset to 6 days without paying. Later I sent screenshots and wrote: “1M wagered and 0.2$.” Jacky said he had raised the issue to the technical team. So the weekly failure was reported live, not reconstructed after the fact. The next day, 29 May, Edward said they were fixing an issue regarding weekly bonuses and that the weekly countdown was “not currently on Thursday evenings.” Then on 1 June, Spencer said the May weekly bonuses were 7th, 14th, 21st, and then due to migration the weekly moved to Monday, so there was one on the 25th on the new platform. He also said the 25 May weekly covered gameplay from 21–24 May, and that tech was looking at that plus the monthly bonus. The ledger does show a 25 May 02:10 Rakeback entry of 1,996.08334791, which likely corresponds to that 21–24 May weekly. But my major loss sequence starts about 20 hours later, on 25 May at 22:23, and continues until 26 May at 02:09. So the 25 May weekly cannot cover those losses. If weekly was still Thursday, the 25/26 losses should have been in the 28 May weekly. But the bonus ledger on 28 May shows only two tiny Rakeback entries, 0.28373945 and 0.00280958. If weekly moved to Monday because of migration, those losses should have appeared in the next weekly after 25 May. But on 1 June the ledger only shows Leaderboard entries. Then the final video shows the next Weekly Reload reaching zero, paying nothing and resetting to 6d 23h. So the same loss sequence appears to fall into no paid weekly cycle. The 4 June support conversation makes this even more ridiculous. After I recorded the weekly reset video, I asked support a very simple question: what were the last weekly dates/cycles? The dashboard / support flow again said weekly bonuses are claimable every Thursday at 00:01 UTC. Jacky confirmed: “Weekly bonuses can be claimed every Thursday at 00:01 UTC in the Rewards tab,” and added that if not claimed by the following Wednesday at 23:59 UTC, it expires. But when I asked for the exact last four dates, Jacky said he had to check with the relevant department. When I pressed again, he said, “Sorry, As I am only a CS, Let me raise your concerns to relevant department.” I asked whether support did not have the information or simply could not answer. He replied: “Do you have any other concerns?” They use weekly cycles to decide whether to pay, but support cannot explain the weekly cycle. The monthly is missing too. The dashboard / UI said the monthly bonus is based on activity and VIP level from the previous month and is available on the 1st at 00:01 UTC. In May I had more than 3,258,023.0829 wagered according to the formal complaint data. I also have proof/video that the monthly slider was set to 50/50. On 1 June, Spencer first told me I had claimed the Monthly Bonus at 1:12am BST around the same time as the monthly leaderboard reward. I immediately said I only received leaderboard prizes. Then Spencer changed the answer: “Our tech team are still actively working on issues regarding the monthly bonuses.” So first the monthly was claimed, then tech was still fixing it. The ledger still shows no Monthly Bonus entry. Housebets then seems to rely on “up overall” as a defence. But the video and ledger show why that does not work. My weekly/monthly profile later showed around +6,008 P/L with 0 deposits, 0 wagered and around 6,008 in bonuses. That number matches exactly the two 1 June Leaderboard payments. So the UI is showing leaderboard rewards as P/L. Then support used “up overall” to say I was not eligible for weekly lossback. That is not a clean lossback calculation. That is using a leaderboard reward as apparent profit to deny a lossback that should be based on actual eligible losses. There were also smaller reward-confusion issues along the way. On 22 May I asked for all pending bonuses,weekly, monthly, rakeback, level-up, anything, and support said the internal team would manually verify whether everything had been credited correctly and email me. On 24 May, I asked about level-up rewards because the reward looked like $3,500 for Pearl; support clarified it was $3,500 total across all Pearl levels, $500 per level. These are not the core issues, but they are part of the same pattern: rewards marketing, unclear UI, manual verification, emails that do not arrive, and players having to chase basic explanations. Then there is the migration. On 25 May, after the delayed withdrawal, missing VIP contact and unresolved issues, support told me my account would be moved to the new platform and that this upgrade would offer a better withdrawal process and fix many issues. Before that migration, I explicitly requested that no account data, internal data, logs, balance history, bonus history, bet history, provider records or pending issues be deleted. The response: “Your request has been relayed to the relevant department.” Again, forwarding. But if the old data is safe, Housebets should provide the old leaderboard snapshots, old weekly states, old bonus logs, old Tequity mapping and old withdrawal approval logs. The founder response did not fix anything. When Porchy finally engaged, he did not provide the records. He framed the settlement request as “so you want $100,000?” and asked whether I needed it or else I was going to post on X. I had already made clear this was not money for silence; I asked for logs, snapshots, withdrawal records, calculations and a counter-calculation if Housebets disagreed. He later referred to “abusers,” told me I was “up overall,” said “You are begging for money,” and suggested I “just do this to casinos.” Still no ledger. Still no weekly calculation. Still no monthly entry. Still no PF/Tequity mapping. Still no leaderboard snapshots. Another player also contacted me with screenshots pointing to similar categories of issues: private deals, leaderboard payout disputes, migration/account merge problems, missing history and a tiny monthly bonus despite claimed losses. I am not using that player’s case as the foundation of my claim without his full ledger, but it matters because it suggests the same type of opacity may not be isolated: private VIP/reward deals, leaderboard eligibility, monthly bonus calculations, migration and unclear history. If Housebets has private deals that affect leaderboard eligibility or rewards, it must explain how those deals interact with public leaderboards. So the overall picture is this: Housebets sold a public leaderboard and rewards system that pressured real wagering. Two new accounts appeared directly behind me with huge volume, were confirmed as legitimate and eligible, then disappeared after I asked for logs and questioned game categories. Housebets could not explain the exact games, RTP, house edge or category mapping behind the volume. The accounts were later framed by Porchy as “abusers,” contradicting the earlier eligibility confirmation. Once Housebets paid me the leaderboard prizes, those prizes were shown as P/L, and that contaminated P/L was then used to claim I was “up overall” and not eligible for lossback. At the same time, my real 25 May 22:23 → 26 May 02:09 loss sequence of 91,168.375326 USDT appears in no clean weekly cycle. The 25 May weekly covered 21–24 May according to Spencer, so it cannot cover that loss sequence. The 28 May weekly showed only tiny Rakeback entries and was already reported as broken. The 1 June ledger shows only Leaderboard entries. The later video shows Weekly Reload reaching zero, paying nothing and resetting. And when I ask support for the exact weekly calendar, they cannot answer and send it to the relevant department. The monthly is the same story. The dashboard / UI says it is based on activity and VIP. I had more than 3.25M wagered in May. Spencer first says I claimed it, then says tech is still working on monthly bonuses. The ledger shows no Monthly Bonus. If Housebets says I was not eligible, they need to show the formula, slider history, cycle, GGR/NGR, eligible loss/activity, deductions and ledger result. If they cannot, “not eligible” is just another label. And this opens another can of worms: Tequity / provider configuration. Housebets cannot hide behind “the provider” whenever something goes wrong. The player does not deposit with Tequity. The player does not withdraw from Tequity. The player does not speak to Tequity support. The player does not compete in a Tequity leaderboard. The player plays on Housebets, with a Housebets wallet, Housebets UI, Housebets rewards, Housebets leaderboard and Housebets support. 1/2

Dr. W

20,491 görüntüleme • 3 ay önce

Use this prompt in OpenClaw to create your own AI agent command center that syncs up your life like Tony Stark's Jarvis in Iron Man. Adapt the specifics (agent names, data sources, branding) below to your own setup. Prompt: Build me a mission control dashboard for my OpenClaw AI agent system. Stack: Next.js 15 (App Router) + Convex (real-time backend) + Tailwind CSS v4 + Framer Motion + ShadCN UI + Lucide icons. TypeScript throughout. This is the command center where I monitor and control my autonomous AI agent(s) running on OpenClaw. The agent operates 24/7 on a Mac Mini, connected to Telegram/Discord, running cron jobs, spawning sub-agents, and reading/writing to a filesystem-based memory and state system. Dark mode only. Ultra-premium aesthetic, think Iron Man's JARVIS HUD meets a Bloomberg terminal. Subtle glass effects (backdrop-blur-xl, bg-white/[0.03]), no heavy gradients or glow. Rounded corners (16-20px on cards). Framer Motion for page transitions, stagger animations on card grids, spring physics on interactions. Mobile-first responsive. Never cookie-cutter. ## Architecture The dashboard reads live data from TWO sources: 1. **Convex**: real-time database for structured data (tasks, contacts, content drafts, calendar events, activity logs) 2. **Local API routes** (`/api/*`): read files from the agent's workspace filesystem at `~/.openclaw/workspace/` and return JSON. This is how live system state flows into the dashboard. ## Pages & Views (8 nav items, some with tab sub-views) ### 1. HOME (`/`) Dashboard overview. Grid of live status cards: - **System Health**: read from `/api/system-state` (parses `state/servers.json`). Show each service with UP/DOWN indicator, port, last check time. - **Agent Status**: read from `/api/agents` (parses `agents/registry.json` + agent workspace files). Show active agent count, healthy/unhealthy ratio, active sub-agent count from OpenClaw sessions API. - **Cron Health**: read from `/api/cron-health` (parses `state/crons.json`). Table of all scheduled jobs with name, schedule, last status (green/red dot), consecutive errors. - **Revenue Tracker**: read from `/api/revenue` (parses `state/revenue.json`). Current revenue, monthly burn, net. - **Content Pipeline**: read from `/api/content-pipeline` (parses `content/queue.md`). Kanban-style: Draft | Review | Approved | Published counts. - **Quick Stats**: total tasks, pending approvals, active sessions, uptime. All panels auto-refresh every 15 seconds. Live indicator dot + "AUTO 15S" badge in header. ### 2. OPS (`/ops`) with 3 tabs: Operations | Tasks | Calendar **Operations tab:** Full operational view. Server health table, branch status (from `state/branch-check.json`), observations feed (from `state/observations.md`), system priorities (from `shared-context/priorities.md`). **Tasks tab:** Strategic task suggestion system. API route `/api/suggested-tasks` reads/writes `state/suggested-tasks.json`. Cards grouped by category (Revenue, Product, Community, Content, Operations, Clients, Trading, Brand) with emoji headers. Each card shows title, reasoning, next action, priority badge, effort badge, approve/reject buttons. Filter bar by status and category. **Calendar tab:** Weekly calendar view from Convex `calendarEvents` table. Drag-to-create, color-coded by type, time slots. ### 3. AGENTS (`/agents`) with 2 tabs: Agents | Models **Agents tab:** Card grid of all registered agents from `/api/agents`. Each card shows name, role, model, level (L1-L4), status. Cards are CLICKABLE: expanding into a detail panel showing: - Agent personality (reads their SOUL .md) - Capabilities and rules (reads their RULES .md) - Sub-agents they can spawn - Recent outputs (reads from `shared-context/agent-outputs/`) **Models tab:** Model inventory table showing all available models, their routing (which tasks go to which model), costs, and failover chains. ### 4. CHAT (`/chat`): 2 tabs: Chat | Command **Chat tab:** Chat interface to communicate with the agent. Left sidebar shows session list (from `/api/chat-history` reading .jsonl transcript files). Main area shows messages with role-aligned bubbles (user right, assistant left), date separators, channel badges (telegram/discord/webchat). Input bar with send button + voice input (Web Speech API with SpeechRecognition). Messages sent via `/api/chat-send` which queues to a file the agent reads. **Command tab:** Quick command interface for common operations. ### 5. CONTENT (`/content`) Content pipeline management. Read from Convex `contentDrafts` table AND `/api/content-pipeline`. Show drafts in kanban columns. Each card shows title, platform target, draft text preview, status, created date. Edit/approve/reject actions. ### 6. COMMS (`/comms`) with 2 tabs: Comms | CRM **Comms tab:** Communication hub showing recent Discord digest, Telegram messages, notification history. **CRM tab:** Client pipeline kanban (Prospect → Contacted → Meeting → Proposal → Active). API route `/api/clients` reads markdown files from `clients/` directory. Each card shows client name, status, contacts, last interaction, next action. ### 7. KNOWLEDGE (`/knowledge`) with 2 tabs: Knowledge | Ecosystem **Knowledge tab:** Searchable knowledge base. Global search across all workspace files using `/api/knowledge` endpoint. **Ecosystem tab:** Product grid showing all products/apps in the ecosystem. Each card shows product name, status (Active/Development/Concept), health indicator, key metrics. Cards link to `/ecosystem/[slug]` detail pages with tabbed views (Overview, Brand, Community, Content, Legal, Product, Website, Actions). Detail pages read from `/api/ecosystem/[slug]` which parses workspace memory files. ### 8. CODE (`/code`) Code pipeline view. Shows repositories from `/api/repos` (scans ~/Desktop/Projects/ for git repos). Each repo card shows name, branch, last commit, dirty file count, language breakdown. Detail view at `/api/repos/detail` shows recent commits, file tree, open PRs. ## Navigation Top horizontal nav bar, NOT sidebar. All 8 items visible at all viewport widths. Use `flex` layout with `flex-1` items. Text size uses `clamp(0.45rem, 0.75vw, 0.6875rem)` for fluid scaling. Active item gets `text-primary bg-primary/[0.06]` static highlight (no sliding animation). Agent/app name visible at md+ breakpoints (`hidden md:inline`). Tab sub-views use a reusable `TabBar` component with pill/glass styling and Framer Motion `layoutId` transitions. Tab state stored in URL via `?tab=` search params. ## API Routes (all under `src/app/api/`) Each API route reads from the agent's workspace filesystem and returns JSON: - `/api/system-state` → reads `state/servers.json`, `state/branch-check.json` - `/api/agents` → reads `agents/registry.json`, agent SOUL .md files - `/api/agents/[id]` → reads specific agent's SOUL .md, RULES .md, outputs - `/api/cron-health` → reads `state/crons.json` - `/api/revenue` → reads `state/revenue.json` - `/api/content-pipeline` → parses `content/queue.md` (markdown with status markers) - `/api/suggested-tasks` → GET (read) / POST (approve/reject) on `state/suggested-tasks.json` - `/api/observations` → reads `state/observations.md` - `/api/priorities` → reads `shared-context/priorities.md` - `/api/chat-history` → reads .jsonl transcript files with pagination/search/channel filter - `/api/chat-send` → writes to queue file - `/api/clients` → reads markdown files from `clients/` directory - `/api/ecosystem/[slug]` → reads memory files for specific ecosystem - `/api/repos` → scans project directories for git repos - `/api/health` → returns status, uptime, memory usage, Convex connectivity All filesystem paths should be configurable via environment variable (default: `~/.openclaw/workspace/`). ## Convex Schema Define tables for: activities, calendarEvents, tasks, contacts, contentDrafts, ecosystemProducts. Include seed scripts (`convex/seed.ts`) to populate initial data. ## Key Design Rules - Mobile-first, test at 320px minimum - Font sizes 10-14px for body text, everything must fit naturally at small viewports - Cards use consistent border radius (16-20px) - Glass cards: `bg-white/[0.03] backdrop-blur-xl border border-white/[0.06]` - No heavy blur blobs or grain overlays - Stagger animations on card grids (0.05s delay per item) - Skeleton loading states for all async data - Custom scrollbar styling - Empty states with helpful messaging - All text must use Inter or system font stack - Never mix sharp and rounded corners in the same view - Premium = lighter feel, more whitespace, less visual noise ## File Structure ``` src/ app/ page.tsx, layout.tsx, providers.tsx agents/page.tsx calendar/page.tsx chat/page.tsx code/page.tsx comms/page.tsx content/page.tsx ecosystem/page.tsx, ecosystem/[slug]/page.tsx knowledge/page.tsx ops/page.tsx api/[...all routes above] components/ nav.tsx tab-bar.tsx dashboard-overview.tsx ops-view.tsx, suggested-tasks-view.tsx agents-view.tsx, models-view.tsx chat-center-view.tsx, voice-input.tsx content-view.tsx comms-view.tsx, crm-view.tsx knowledge-base.tsx, ecosystem-view.tsx code-pipeline.tsx activity-feed.tsx, calendar-view.tsx ui/ (ShadCN primitives) hooks/ lib/ convex/ schema.ts functions for each table seed.ts ``` Build the complete application. Every component, every API route, every Convex function. Production-quality code and premium design, not stubs. Dark mode only. Make it look incredibly beautiful and premium, no cookie cutter UI / AI slop.
1:14

Use this prompt in OpenClaw to create your own AI agent command center that syncs up your life like Tony Stark's Jarvis in Iron Man. Adapt the specifics (agent names, data sources, branding) below to your own setup. Prompt: Build me a mission control dashboard for my OpenClaw AI agent system. Stack: Next.js 15 (App Router) + Convex (real-time backend) + Tailwind CSS v4 + Framer Motion + ShadCN UI + Lucide icons. TypeScript throughout. This is the command center where I monitor and control my autonomous AI agent(s) running on OpenClaw. The agent operates 24/7 on a Mac Mini, connected to Telegram/Discord, running cron jobs, spawning sub-agents, and reading/writing to a filesystem-based memory and state system. Dark mode only. Ultra-premium aesthetic, think Iron Man's JARVIS HUD meets a Bloomberg terminal. Subtle glass effects (backdrop-blur-xl, bg-white/[0.03]), no heavy gradients or glow. Rounded corners (16-20px on cards). Framer Motion for page transitions, stagger animations on card grids, spring physics on interactions. Mobile-first responsive. Never cookie-cutter. ## Architecture The dashboard reads live data from TWO sources: 1. **Convex**: real-time database for structured data (tasks, contacts, content drafts, calendar events, activity logs) 2. **Local API routes** (`/api/*`): read files from the agent's workspace filesystem at `~/.openclaw/workspace/` and return JSON. This is how live system state flows into the dashboard. ## Pages & Views (8 nav items, some with tab sub-views) ### 1. HOME (`/`) Dashboard overview. Grid of live status cards: - **System Health**: read from `/api/system-state` (parses `state/servers.json`). Show each service with UP/DOWN indicator, port, last check time. - **Agent Status**: read from `/api/agents` (parses `agents/registry.json` + agent workspace files). Show active agent count, healthy/unhealthy ratio, active sub-agent count from OpenClaw sessions API. - **Cron Health**: read from `/api/cron-health` (parses `state/crons.json`). Table of all scheduled jobs with name, schedule, last status (green/red dot), consecutive errors. - **Revenue Tracker**: read from `/api/revenue` (parses `state/revenue.json`). Current revenue, monthly burn, net. - **Content Pipeline**: read from `/api/content-pipeline` (parses `content/queue.md`). Kanban-style: Draft | Review | Approved | Published counts. - **Quick Stats**: total tasks, pending approvals, active sessions, uptime. All panels auto-refresh every 15 seconds. Live indicator dot + "AUTO 15S" badge in header. ### 2. OPS (`/ops`) with 3 tabs: Operations | Tasks | Calendar **Operations tab:** Full operational view. Server health table, branch status (from `state/branch-check.json`), observations feed (from `state/observations.md`), system priorities (from `shared-context/priorities.md`). **Tasks tab:** Strategic task suggestion system. API route `/api/suggested-tasks` reads/writes `state/suggested-tasks.json`. Cards grouped by category (Revenue, Product, Community, Content, Operations, Clients, Trading, Brand) with emoji headers. Each card shows title, reasoning, next action, priority badge, effort badge, approve/reject buttons. Filter bar by status and category. **Calendar tab:** Weekly calendar view from Convex `calendarEvents` table. Drag-to-create, color-coded by type, time slots. ### 3. AGENTS (`/agents`) with 2 tabs: Agents | Models **Agents tab:** Card grid of all registered agents from `/api/agents`. Each card shows name, role, model, level (L1-L4), status. Cards are CLICKABLE: expanding into a detail panel showing: - Agent personality (reads their SOUL .md) - Capabilities and rules (reads their RULES .md) - Sub-agents they can spawn - Recent outputs (reads from `shared-context/agent-outputs/`) **Models tab:** Model inventory table showing all available models, their routing (which tasks go to which model), costs, and failover chains. ### 4. CHAT (`/chat`): 2 tabs: Chat | Command **Chat tab:** Chat interface to communicate with the agent. Left sidebar shows session list (from `/api/chat-history` reading .jsonl transcript files). Main area shows messages with role-aligned bubbles (user right, assistant left), date separators, channel badges (telegram/discord/webchat). Input bar with send button + voice input (Web Speech API with SpeechRecognition). Messages sent via `/api/chat-send` which queues to a file the agent reads. **Command tab:** Quick command interface for common operations. ### 5. CONTENT (`/content`) Content pipeline management. Read from Convex `contentDrafts` table AND `/api/content-pipeline`. Show drafts in kanban columns. Each card shows title, platform target, draft text preview, status, created date. Edit/approve/reject actions. ### 6. COMMS (`/comms`) with 2 tabs: Comms | CRM **Comms tab:** Communication hub showing recent Discord digest, Telegram messages, notification history. **CRM tab:** Client pipeline kanban (Prospect → Contacted → Meeting → Proposal → Active). API route `/api/clients` reads markdown files from `clients/` directory. Each card shows client name, status, contacts, last interaction, next action. ### 7. KNOWLEDGE (`/knowledge`) with 2 tabs: Knowledge | Ecosystem **Knowledge tab:** Searchable knowledge base. Global search across all workspace files using `/api/knowledge` endpoint. **Ecosystem tab:** Product grid showing all products/apps in the ecosystem. Each card shows product name, status (Active/Development/Concept), health indicator, key metrics. Cards link to `/ecosystem/[slug]` detail pages with tabbed views (Overview, Brand, Community, Content, Legal, Product, Website, Actions). Detail pages read from `/api/ecosystem/[slug]` which parses workspace memory files. ### 8. CODE (`/code`) Code pipeline view. Shows repositories from `/api/repos` (scans ~/Desktop/Projects/ for git repos). Each repo card shows name, branch, last commit, dirty file count, language breakdown. Detail view at `/api/repos/detail` shows recent commits, file tree, open PRs. ## Navigation Top horizontal nav bar, NOT sidebar. All 8 items visible at all viewport widths. Use `flex` layout with `flex-1` items. Text size uses `clamp(0.45rem, 0.75vw, 0.6875rem)` for fluid scaling. Active item gets `text-primary bg-primary/[0.06]` static highlight (no sliding animation). Agent/app name visible at md+ breakpoints (`hidden md:inline`). Tab sub-views use a reusable `TabBar` component with pill/glass styling and Framer Motion `layoutId` transitions. Tab state stored in URL via `?tab=` search params. ## API Routes (all under `src/app/api/`) Each API route reads from the agent's workspace filesystem and returns JSON: - `/api/system-state` → reads `state/servers.json`, `state/branch-check.json` - `/api/agents` → reads `agents/registry.json`, agent SOUL .md files - `/api/agents/[id]` → reads specific agent's SOUL .md, RULES .md, outputs - `/api/cron-health` → reads `state/crons.json` - `/api/revenue` → reads `state/revenue.json` - `/api/content-pipeline` → parses `content/queue.md` (markdown with status markers) - `/api/suggested-tasks` → GET (read) / POST (approve/reject) on `state/suggested-tasks.json` - `/api/observations` → reads `state/observations.md` - `/api/priorities` → reads `shared-context/priorities.md` - `/api/chat-history` → reads .jsonl transcript files with pagination/search/channel filter - `/api/chat-send` → writes to queue file - `/api/clients` → reads markdown files from `clients/` directory - `/api/ecosystem/[slug]` → reads memory files for specific ecosystem - `/api/repos` → scans project directories for git repos - `/api/health` → returns status, uptime, memory usage, Convex connectivity All filesystem paths should be configurable via environment variable (default: `~/.openclaw/workspace/`). ## Convex Schema Define tables for: activities, calendarEvents, tasks, contacts, contentDrafts, ecosystemProducts. Include seed scripts (`convex/seed.ts`) to populate initial data. ## Key Design Rules - Mobile-first, test at 320px minimum - Font sizes 10-14px for body text, everything must fit naturally at small viewports - Cards use consistent border radius (16-20px) - Glass cards: `bg-white/[0.03] backdrop-blur-xl border border-white/[0.06]` - No heavy blur blobs or grain overlays - Stagger animations on card grids (0.05s delay per item) - Skeleton loading states for all async data - Custom scrollbar styling - Empty states with helpful messaging - All text must use Inter or system font stack - Never mix sharp and rounded corners in the same view - Premium = lighter feel, more whitespace, less visual noise ## File Structure ``` src/ app/ page.tsx, layout.tsx, providers.tsx agents/page.tsx calendar/page.tsx chat/page.tsx code/page.tsx comms/page.tsx content/page.tsx ecosystem/page.tsx, ecosystem/[slug]/page.tsx knowledge/page.tsx ops/page.tsx api/[...all routes above] components/ nav.tsx tab-bar.tsx dashboard-overview.tsx ops-view.tsx, suggested-tasks-view.tsx agents-view.tsx, models-view.tsx chat-center-view.tsx, voice-input.tsx content-view.tsx comms-view.tsx, crm-view.tsx knowledge-base.tsx, ecosystem-view.tsx code-pipeline.tsx activity-feed.tsx, calendar-view.tsx ui/ (ShadCN primitives) hooks/ lib/ convex/ schema.ts functions for each table seed.ts ``` Build the complete application. Every component, every API route, every Convex function. Production-quality code and premium design, not stubs. Dark mode only. Make it look incredibly beautiful and premium, no cookie cutter UI / AI slop.

klöss

202,615 görüntüleme • 7 ay önce

#new Clancy Defense Attorney Asked Internet Blogger For Help Making Lindsay Look Innocent,Turtleboy Says Fast Fact: Kevin Reddington reportedly asked Aidan Kearney (Turtleboy) for help on the Lindsay Clancy case in July 2023, according to Turtleboy's website and social media posts Critics say it is a major ethical violation to enlist the help of a social media blogger to help create a perception of innocence about a client that Reddington was (and still is) representing. In messages shared by Kearney, Reddington praised Kearney because he 'created' Karen Read's innocence. Karen Read is the woman who was accused of klling her police officer boyfriend, but claimed she was being framed by his friends, who were also cops and the ones she claims actually k*lled him. Karen Read was acquitted of second-degree m*rder and manslaughter. 🔶On July 22, 2023 - Reddington was already on the Clancy case, and also representing Jennifer McCabe in the Read case. (McCabe was the woman that Read claims was trying to frame her) 🔶Screenshots show a Messenger account labeled Kevin Reddington writing to Kearney late at night. 🔶The message praised Kearney for being a great investigative reporter, while saying they started off on the wrong foot. 🔶The account then asked Kearney to help with the Clancy case. Other messages in the same thread detailed how Kearney created “more than reasonable doubt” for Karen Read. Kearney replied the next morning saying he was already on the Clancy case and said Patrick Clancy was the strongest person he had seen, “followed by Karen Read.” Kearney posted the messages on his website in September of 2023 under the title: 🔶"Reddington thought Read was innocent and “could use my help defending his client, Lindsay Clancy.” Critics say this is an ethical violation for a defense attorney to enlist the help of an online blogger - who was already facing legal trouble for jury intimidation - to sway public opinion about a client he is representing. So what is the jury intimidation? Well it stems from the Karen Read case. Oct. 11, 2023 – Kearney was: ➡️Arrested on multiple counts of witness intimidation / conspiracy tied to witnesses in the Karen Read trial. ➡️He was indicted Dec. 2023 - 8 counts of witness intimidation, 3 counts of conspiracy, and 5 counts of picketing a witness ➡️He Pleaded not guilty, and a trial set for December of this year. 2. He was arrested in late December when: ➡️His ex-girlfriend Lindsey Gaetani alleged he attacked her and threatened to release nude photos of her after she was summoned to a grand jury about him. ➡️He was charged with domestic assault and battery and witness intimidation. ➡️Bail on the Read case was revoked. He did about 60 days in the Norfolk County jail. ➡️Oct. 22, 2025: Charges dropped after the special prosecutor quit, and no replacement was appointed. This was not an acquittal; just a lucky break for Kearney 3. In 2024 – he was: ➡️Charged with violating an abuse-prevention order. ➡️June 12, 2025: Judge Michael Pomarole found him not guilty after a two-day bench trial. The one-year RO was not extended in Jan. 2025. 4. May 13, 2025 - third indictment Two more witness intimidation counts after he stopped at D&E Pizza and caused a scene. ➡️Pleaded not guilty. This is part of the remaining pending charges. 5. July 2026 - Atlantic County, New Jersey indictment (sealed) ➡️His lawyer Mark Bederow said NJ indicted him on a civilian complaint from a woman who contacted him about humiliating John O’Keefe’s brother. Some claim that Reddington knew Kearney's methods and wanted him to impact the jury pool in the Clancy case as well - however there is no proof of that. 🔶Another message Kearney posted is one from the Reddington account that said he believed Karen Read was innocent –to which Kearney posted with this comment: 🔶“Kevin Reddington messaged me a month or so ago to tell me thta [sic] he believes Karen Read is innocent, which by default means that his client Jennifer McCabe was involved in covering up John O'Keefe's m*rder.” Which is also very interesting. Reddington has not admitted that the messages were sent by him, but he hasn't denied it either. 🔶In April 2023 (about three months before the friendly message exchange) Reddington left a public comment on Turtleboy’s Facebook page calling him “turd boy” and saying he wanted to “dance” with him in court. They later shook hands in a courthouse, according to Kearney. In the Clancy case Reddington took aim at the lone holdout juror himself when he thanked “these jurors”. Minus one.” He said the other 11 had been “robbed by one man, for whatever his agenda was,” who “stole seven weeks” from people who “were so attentive, so beautiful, so wonderful.” Then: “I hope that guy can sleep well at night.” He accused the juror of being a bigot, gender--doxxed him and tried to get the judge to reveal his identity by holding an open court questioning. Leading many to claim jury intimidation is part of Reddington’s MO – which is why he reached out to Kearney in the first place. He saw what Kearney did for Karen Read, and he wanted him do the same thing for Clancy – again, this is a claim and not a proven fact. Jonathan Turley called Reddington’s attack on the juror “utterly reprehensible” and said Reddington “put a target on the back of a juror who committed the unpardonable sin of not being convinced by his arguments.” And now, after all this mess, the local NBC Boston report releasing searchable data about the holdout juror, the doxxing of that juror via photos, name, and details about his personal life - Judge Sullivan – in the Clancy case, has extended the lock down on the jury list indefinitely. Sullivan was petitioned by a juror to keep the list sealed, and ruled there is credible fear for the jurors. What do you think? Should a defense attorney be able to enlist the help of an online blogger to 'create' an innocent view of his client before the jury is even selected? #ChristinaAguayoNews
6:04

#new Clancy Defense Attorney Asked Internet Blogger For Help Making Lindsay Look Innocent,Turtleboy Says Fast Fact: Kevin Reddington reportedly asked Aidan Kearney (Turtleboy) for help on the Lindsay Clancy case in July 2023, according to Turtleboy's website and social media posts Critics say it is a major ethical violation to enlist the help of a social media blogger to help create a perception of innocence about a client that Reddington was (and still is) representing. In messages shared by Kearney, Reddington praised Kearney because he 'created' Karen Read's innocence. Karen Read is the woman who was accused of klling her police officer boyfriend, but claimed she was being framed by his friends, who were also cops and the ones she claims actually k*lled him. Karen Read was acquitted of second-degree m*rder and manslaughter. 🔶On July 22, 2023 - Reddington was already on the Clancy case, and also representing Jennifer McCabe in the Read case. (McCabe was the woman that Read claims was trying to frame her) 🔶Screenshots show a Messenger account labeled Kevin Reddington writing to Kearney late at night. 🔶The message praised Kearney for being a great investigative reporter, while saying they started off on the wrong foot. 🔶The account then asked Kearney to help with the Clancy case. Other messages in the same thread detailed how Kearney created “more than reasonable doubt” for Karen Read. Kearney replied the next morning saying he was already on the Clancy case and said Patrick Clancy was the strongest person he had seen, “followed by Karen Read.” Kearney posted the messages on his website in September of 2023 under the title: 🔶"Reddington thought Read was innocent and “could use my help defending his client, Lindsay Clancy.” Critics say this is an ethical violation for a defense attorney to enlist the help of an online blogger - who was already facing legal trouble for jury intimidation - to sway public opinion about a client he is representing. So what is the jury intimidation? Well it stems from the Karen Read case. Oct. 11, 2023 – Kearney was: ➡️Arrested on multiple counts of witness intimidation / conspiracy tied to witnesses in the Karen Read trial. ➡️He was indicted Dec. 2023 - 8 counts of witness intimidation, 3 counts of conspiracy, and 5 counts of picketing a witness ➡️He Pleaded not guilty, and a trial set for December of this year. 2. He was arrested in late December when: ➡️His ex-girlfriend Lindsey Gaetani alleged he attacked her and threatened to release nude photos of her after she was summoned to a grand jury about him. ➡️He was charged with domestic assault and battery and witness intimidation. ➡️Bail on the Read case was revoked. He did about 60 days in the Norfolk County jail. ➡️Oct. 22, 2025: Charges dropped after the special prosecutor quit, and no replacement was appointed. This was not an acquittal; just a lucky break for Kearney 3. In 2024 – he was: ➡️Charged with violating an abuse-prevention order. ➡️June 12, 2025: Judge Michael Pomarole found him not guilty after a two-day bench trial. The one-year RO was not extended in Jan. 2025. 4. May 13, 2025 - third indictment Two more witness intimidation counts after he stopped at D&E Pizza and caused a scene. ➡️Pleaded not guilty. This is part of the remaining pending charges. 5. July 2026 - Atlantic County, New Jersey indictment (sealed) ➡️His lawyer Mark Bederow said NJ indicted him on a civilian complaint from a woman who contacted him about humiliating John O’Keefe’s brother. Some claim that Reddington knew Kearney's methods and wanted him to impact the jury pool in the Clancy case as well - however there is no proof of that. 🔶Another message Kearney posted is one from the Reddington account that said he believed Karen Read was innocent –to which Kearney posted with this comment: 🔶“Kevin Reddington messaged me a month or so ago to tell me thta [sic] he believes Karen Read is innocent, which by default means that his client Jennifer McCabe was involved in covering up John O'Keefe's m*rder.” Which is also very interesting. Reddington has not admitted that the messages were sent by him, but he hasn't denied it either. 🔶In April 2023 (about three months before the friendly message exchange) Reddington left a public comment on Turtleboy’s Facebook page calling him “turd boy” and saying he wanted to “dance” with him in court. They later shook hands in a courthouse, according to Kearney. In the Clancy case Reddington took aim at the lone holdout juror himself when he thanked “these jurors”. Minus one.” He said the other 11 had been “robbed by one man, for whatever his agenda was,” who “stole seven weeks” from people who “were so attentive, so beautiful, so wonderful.” Then: “I hope that guy can sleep well at night.” He accused the juror of being a bigot, gender--doxxed him and tried to get the judge to reveal his identity by holding an open court questioning. Leading many to claim jury intimidation is part of Reddington’s MO – which is why he reached out to Kearney in the first place. He saw what Kearney did for Karen Read, and he wanted him do the same thing for Clancy – again, this is a claim and not a proven fact. Jonathan Turley called Reddington’s attack on the juror “utterly reprehensible” and said Reddington “put a target on the back of a juror who committed the unpardonable sin of not being convinced by his arguments.” And now, after all this mess, the local NBC Boston report releasing searchable data about the holdout juror, the doxxing of that juror via photos, name, and details about his personal life - Judge Sullivan – in the Clancy case, has extended the lock down on the jury list indefinitely. Sullivan was petitioned by a juror to keep the list sealed, and ruled there is credible fear for the jurors. What do you think? Should a defense attorney be able to enlist the help of an online blogger to 'create' an innocent view of his client before the jury is even selected? #ChristinaAguayoNews

Christina Aguayo

29,635 görüntüleme • 13 gün önce

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

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

Himanshu Kumar

38,153 görüntüleme • 6 ay önce

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

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

Dwarkesh Patel

549,514 görüntüleme • 6 ay önce

THE SEVEN SEALS The same day the Church is taken out of this world at the Rapture, judgement begins. As it was in the days of Noah, the same day Noah entered the ark, the judgement began. The same day Lot left Sodom, fire came from heaven and destroyed them all. So it is, on the same day that the Church leaves this world, at the Rapture, the Judgment begins. SEAL 1 - The Conqueror (The Revealing of the Antichrist). The Man on the White Horse. Rev 6:2 And I saw, and behold, a white horse, and he that sat thereon had a bow; and there was given unto him a crown: and he came forth conquering, and to conquer After the rapture of the Church,2 Thess.6v7 the first thing that happens in the earth is that the antichrist is revealed; a white horse and its rider. This is not the Lord Jesus- i.) “He that sat on the horse had a bow”, Jesus doesn’t use a bow, when you see Jesus, He uses a Sword. ii.) “A crown was given to him”- Jesus is King of Kings; He gives crowns. Rev.6v2 iii.) “He went forth conquering and to conquer”. The Lord Jesus is only going to battle against the Nations at the second coming, which is not yet So this is not Jesus, this is the antichrist. He had a bow, without arrows. He would conquer nations without firing a shot, he would conquer nations with peace; they would give their nations to him. Daniel 8:23-24 describes a powerful king who will arise at the end of a period of time, characterized by transgression and wickedness. This king will have a fierce appearance, understand dark schemes, and wield mighty power, not by his own means, but through extraordinary destruction. He will succeed in what he does, destroying the mighty and the holy people. SEAL 2- Conflict on Earth: Killings, Murders (The Man on the Red Horse) Daniel 6v3- 4 When He opened the second seal, I heard the second living creature saying, “Come [b]and see.” 4 Another horse, fiery red, went out. And it was granted to the one who sat on it to take peace from the earth, and that people should kill one another; and there was given to him a great sword. Once the Church is gone at the Rapture, no more peace on the earth, there would be killings. Matt.24v7,10 For nation will rise against nation, and kingdom against kingdom. And there will be famines, [a]pestilences, and earthquakes in various places. 8 All these are the beginning of sorrows. 9 “Then they will deliver you up to tribulation and kill you, and you will be hated by all nations for My name’s sake. 10 And then many will be offended, will betray one another, and will hate one another. Rev.13v15 15 He was granted power to give breath to the image of the beast, that the image of the beast should both speak and cause as many as would not worship the image of the beast to be killed. SEAL 3- Scarcity (Inflation and Famine) The Man on the Black Horse Daniel 6v5-6 When He opened the third seal, I heard the third living creature say, “Come and see.” So I looked, and behold, a black horse, and he who sat on it had a pair of scales[c] in his hand. 6 And I heard a voice in the midst of the four living creatures saying, “A [d]quart of wheat for a [e]denarius, and three quarts of barley for a denarius; and do not harm the oil and the wine. Lamentations 4v4-10 AMP [4] The tongue of the infant clings To the roof of its mouth because of thirst; The little ones ask for food, But no one gives it to them. [5] Those who feasted on delicacies Are perishing in the streets; Those reared in purple [as nobles] Embrace ash heaps. [6] For the [punishment of the] wickedness of the daughter of my people [Jerusalem] Is greater than the [punishment for the] sin of Sodom, Which was overthrown in a moment, And no hands were turned toward her [to offer help]. [7] Her princes were purer than snow, They were whiter than milk [in appearance]; They were more ruddy in body than rubies, Their polishing was like lapis lazuli (sapphire). [8] Their appearance is [now] blacker than soot [because of the prolonged famine]; They are not recognized in the streets; Their skin clings to their bones; It is withered, and it has become [dry] like wood. [9] Those killed with the sword Are more fortunate than those killed with hunger; For the hungry pine and ebb away, For the lack of the fruits of the field. [10] The hands of compassionate women Boiled their own children; They became food for them Because of the destruction of the daughter of my people [Judah]. Ezekiel 4v10-12, 17 AMP [10] The food you eat each day shall be [measured] by weight, twenty shekels, to be eaten daily at a set time. [11] You shall drink water by measure also, the sixth part of a hin; you shall drink daily at a set time. [12] You shall eat your food as barley cakes, having baked it in their sight over human dung.” [17] because bread and water will be scarce; and they will look at one another in dismay and waste away [in punishment] for their wickedness. If you think there are economic problems right now in the world, this is nothing compared to what is coming. No one would be able to solve the economic problem then, not even the antichrist. He would make promises, but would fail. This is going to be a very terrible time on earth. At this point the peace treaty is broken and the next three and a half years begins SEAL 4- Widespread Death on Earth (Death Released) A Pale(green) Horse with a rider called- Death, and Hell Followed. Rev.6v7 -8 7 When He opened the fourth seal, I heard the voice of the fourth living creature saying, “Come and see.” 8 So I looked, and behold, a pale horse. And the name of him who sat on it was Death, and Hades followed with him. And [f]power was given to them over a fourth of the earth, to kill with sword, with hunger, with death, and by the beasts of the earth. These are the judgments of God, not even the actions taken by the antichrist. The world is being punished for their rejection of Jesus Christ. Killings with the sword, refers to war. Matt 24v21 For then shall be great tribulation, such as was not since the beginning of the world to this time, no, nor ever shall be.” (KJV) The Fourth and Fifth Seals are in the second 3 and a half years, after the Peace treaty has been broken. 1 Thess. 5v3 -4 For when they shall say, peace and safety; then sudden destruction cometh upon them, as travail upon a woman with child; and they shall not escape. At this time the antichrist enters into the temple in Jerusalem and from then on, fierce persecution begins. SEAL 5- The Cry of the Tribulation Saints (Killed for their Faith) Rev 6v9-11 When He opened the fifth seal, I saw under the altar the souls of those who had been slain for the word of God and for the testimony which they held. 10 And they cried with a loud voice, saying, “How long, O Lord, holy and true, until You judge and avenge our blood on those who dwell on the earth?” 11 Then a white robe was given to each of them; and it was said to them that they should rest a little while longer, until both the number of their fellow servants and their brethren, who would be killed as they were, was completed. When a godly man dies, he goes to heaven and his soul is without a body, but is clothed with a spiritual garment until the resurrection. At the rapture, those who were dead in Christ were raised up with a glorified body, and we that are alive and remain were caught and changed into that same glorified body. The tribulation saints who were slain for their faith, they gave their lives as a sacrifice to God, they refused to take the mark of the beast, and were killed. Their souls are seen in Heaven under the Altar, without their spiritual glorified bodies, hence their cry. Vr 11 the Lord told them to rest for a while until others to be killed like them also arrive. At this time too something happens, there is more preaching of the gospel ongoing in the earth, Evangelism intensifies. Rev.14: 14 And I looked, and behold a white cloud, and upon the cloud one sat like unto the Son of man, having on his head a golden crown, and in his hand a sharp sickle. 15 And another angel came out of the temple, crying with a loud voice to him that sat on the cloud, Thrust in thy sickle, and reap: for the time is come for thee to reap; for the harvest of the earth is ripe. Here the Tribulation Saints are Raptured, which is getting to the ending part of the tribulation period. #YLWSPECIALSSeason1Phase3 #RaptureModeActivated #RaptureReady #FirstFlightGang #YearOfCompleteness
30:04

THE SEVEN SEALS The same day the Church is taken out of this world at the Rapture, judgement begins. As it was in the days of Noah, the same day Noah entered the ark, the judgement began. The same day Lot left Sodom, fire came from heaven and destroyed them all. So it is, on the same day that the Church leaves this world, at the Rapture, the Judgment begins. SEAL 1 - The Conqueror (The Revealing of the Antichrist). The Man on the White Horse. Rev 6:2 And I saw, and behold, a white horse, and he that sat thereon had a bow; and there was given unto him a crown: and he came forth conquering, and to conquer After the rapture of the Church,2 Thess.6v7 the first thing that happens in the earth is that the antichrist is revealed; a white horse and its rider. This is not the Lord Jesus- i.) “He that sat on the horse had a bow”, Jesus doesn’t use a bow, when you see Jesus, He uses a Sword. ii.) “A crown was given to him”- Jesus is King of Kings; He gives crowns. Rev.6v2 iii.) “He went forth conquering and to conquer”. The Lord Jesus is only going to battle against the Nations at the second coming, which is not yet So this is not Jesus, this is the antichrist. He had a bow, without arrows. He would conquer nations without firing a shot, he would conquer nations with peace; they would give their nations to him. Daniel 8:23-24 describes a powerful king who will arise at the end of a period of time, characterized by transgression and wickedness. This king will have a fierce appearance, understand dark schemes, and wield mighty power, not by his own means, but through extraordinary destruction. He will succeed in what he does, destroying the mighty and the holy people. SEAL 2- Conflict on Earth: Killings, Murders (The Man on the Red Horse) Daniel 6v3- 4 When He opened the second seal, I heard the second living creature saying, “Come [b]and see.” 4 Another horse, fiery red, went out. And it was granted to the one who sat on it to take peace from the earth, and that people should kill one another; and there was given to him a great sword. Once the Church is gone at the Rapture, no more peace on the earth, there would be killings. Matt.24v7,10 For nation will rise against nation, and kingdom against kingdom. And there will be famines, [a]pestilences, and earthquakes in various places. 8 All these are the beginning of sorrows. 9 “Then they will deliver you up to tribulation and kill you, and you will be hated by all nations for My name’s sake. 10 And then many will be offended, will betray one another, and will hate one another. Rev.13v15 15 He was granted power to give breath to the image of the beast, that the image of the beast should both speak and cause as many as would not worship the image of the beast to be killed. SEAL 3- Scarcity (Inflation and Famine) The Man on the Black Horse Daniel 6v5-6 When He opened the third seal, I heard the third living creature say, “Come and see.” So I looked, and behold, a black horse, and he who sat on it had a pair of scales[c] in his hand. 6 And I heard a voice in the midst of the four living creatures saying, “A [d]quart of wheat for a [e]denarius, and three quarts of barley for a denarius; and do not harm the oil and the wine. Lamentations 4v4-10 AMP [4] The tongue of the infant clings To the roof of its mouth because of thirst; The little ones ask for food, But no one gives it to them. [5] Those who feasted on delicacies Are perishing in the streets; Those reared in purple [as nobles] Embrace ash heaps. [6] For the [punishment of the] wickedness of the daughter of my people [Jerusalem] Is greater than the [punishment for the] sin of Sodom, Which was overthrown in a moment, And no hands were turned toward her [to offer help]. [7] Her princes were purer than snow, They were whiter than milk [in appearance]; They were more ruddy in body than rubies, Their polishing was like lapis lazuli (sapphire). [8] Their appearance is [now] blacker than soot [because of the prolonged famine]; They are not recognized in the streets; Their skin clings to their bones; It is withered, and it has become [dry] like wood. [9] Those killed with the sword Are more fortunate than those killed with hunger; For the hungry pine and ebb away, For the lack of the fruits of the field. [10] The hands of compassionate women Boiled their own children; They became food for them Because of the destruction of the daughter of my people [Judah]. Ezekiel 4v10-12, 17 AMP [10] The food you eat each day shall be [measured] by weight, twenty shekels, to be eaten daily at a set time. [11] You shall drink water by measure also, the sixth part of a hin; you shall drink daily at a set time. [12] You shall eat your food as barley cakes, having baked it in their sight over human dung.” [17] because bread and water will be scarce; and they will look at one another in dismay and waste away [in punishment] for their wickedness. If you think there are economic problems right now in the world, this is nothing compared to what is coming. No one would be able to solve the economic problem then, not even the antichrist. He would make promises, but would fail. This is going to be a very terrible time on earth. At this point the peace treaty is broken and the next three and a half years begins SEAL 4- Widespread Death on Earth (Death Released) A Pale(green) Horse with a rider called- Death, and Hell Followed. Rev.6v7 -8 7 When He opened the fourth seal, I heard the voice of the fourth living creature saying, “Come and see.” 8 So I looked, and behold, a pale horse. And the name of him who sat on it was Death, and Hades followed with him. And [f]power was given to them over a fourth of the earth, to kill with sword, with hunger, with death, and by the beasts of the earth. These are the judgments of God, not even the actions taken by the antichrist. The world is being punished for their rejection of Jesus Christ. Killings with the sword, refers to war. Matt 24v21 For then shall be great tribulation, such as was not since the beginning of the world to this time, no, nor ever shall be.” (KJV) The Fourth and Fifth Seals are in the second 3 and a half years, after the Peace treaty has been broken. 1 Thess. 5v3 -4 For when they shall say, peace and safety; then sudden destruction cometh upon them, as travail upon a woman with child; and they shall not escape. At this time the antichrist enters into the temple in Jerusalem and from then on, fierce persecution begins. SEAL 5- The Cry of the Tribulation Saints (Killed for their Faith) Rev 6v9-11 When He opened the fifth seal, I saw under the altar the souls of those who had been slain for the word of God and for the testimony which they held. 10 And they cried with a loud voice, saying, “How long, O Lord, holy and true, until You judge and avenge our blood on those who dwell on the earth?” 11 Then a white robe was given to each of them; and it was said to them that they should rest a little while longer, until both the number of their fellow servants and their brethren, who would be killed as they were, was completed. When a godly man dies, he goes to heaven and his soul is without a body, but is clothed with a spiritual garment until the resurrection. At the rapture, those who were dead in Christ were raised up with a glorified body, and we that are alive and remain were caught and changed into that same glorified body. The tribulation saints who were slain for their faith, they gave their lives as a sacrifice to God, they refused to take the mark of the beast, and were killed. Their souls are seen in Heaven under the Altar, without their spiritual glorified bodies, hence their cry. Vr 11 the Lord told them to rest for a while until others to be killed like them also arrive. At this time too something happens, there is more preaching of the gospel ongoing in the earth, Evangelism intensifies. Rev.14: 14 And I looked, and behold a white cloud, and upon the cloud one sat like unto the Son of man, having on his head a golden crown, and in his hand a sharp sickle. 15 And another angel came out of the temple, crying with a loud voice to him that sat on the cloud, Thrust in thy sickle, and reap: for the time is come for thee to reap; for the harvest of the earth is ripe. Here the Tribulation Saints are Raptured, which is getting to the ending part of the tribulation period. #YLWSPECIALSSeason1Phase3 #RaptureModeActivated #RaptureReady #FirstFlightGang #YearOfCompleteness

SeyiB

15,925 görüntüleme • 1 yıl önce

What if I told you ripple:native just moved closer to a financial universe doing $17.5 TRILLION in FX and interest-rate derivatives every single day? I’m not talking about some random prediction. I’m talking about BIS Working Paper No. 1374. This is going to be a long read, because the headline barely scratches the surface. Four of the five authors work at the Bank for International Settlements, and instead of only mentioning XRP Ledger in theory, the researchers actually built, tested and published an open-source XRPL-based prototype. That distinction matters. This is a research implementation, not a production BIS deployment. But the technical choice itself is what caught me. The researchers needed a public blockchain that could help prove official economic and financial data had not been altered. They chose XRP Ledger. And they explained why: low fees, fast finality, developer resources and existing research around its consensus system. This wasn’t somebody adding an XRP logo to a presentation. They built the gateway. They created XRPL transactions. They used institutional anchoring wallets. They put cryptographic proofs inside transaction memos. They linked publisher identities to XRPL addresses. They retrieved those transactions again during verification. Then they measured how the system performed. Median publication latency came in around 3–5 seconds. Verification took around 1–2 seconds. That is where my brain immediately went beyond the headline. Because what exactly were they trying to verify? The kind of information the entire financial system runs on. -Inflation. -GDP. -Interest rates. -Banking statistics. -Debt information. -Financial-stability data. -Regulatory reporting. Imagine a central bank publishes an inflation number. Today that number gets copied everywhere. -Websites. -News terminals. -Databases. -Screenshots. -AI models. -Trading systems. Once it spreads across the internet, how does another machine independently prove that the number it received is exactly what the institution originally published? That is the problem BIS researchers were attacking. Their model creates a cryptographic fingerprint of the official dataset. Individual statistical series can receive fingerprints too. Those hashes are combined through a Merkle tree. A final Merkle root gets anchored to XRPL. The underlying economic data do not need to be dumped onto the blockchain. XRPL simply keeps the proof. Think of it like this: The official institution publishes the document. XRPL holds the tamper-proof receipt. Someone changes even one part of the underlying file? The cryptographic fingerprint changes. Now a bank, regulator, investor, trading engine or AI agent can check: Is this the original data? Has it been changed? Did it really come from the institution claiming to publish it? And that second part is where this paper gets even more serious. The BIS prototype combines the data proof with a W3C Verifiable Credential for the publisher. The publisher’s cryptographic identity is connected to an XRPL address. The paper even uses the format: did:xrpl: So you are not only verifying the information. You are verifying who published it. Now picture a financial world where machines can check both automatically. A central bank publishes CPI. A model receives it. Before touching money, the software checks XRPL. Correct file. Correct publisher. No alteration. Then it acts. That sounds simple until you realize what financial markets actually do with official data. -Rates move. -Currencies move. -Bond prices move. -Derivatives reprice. -Collateral requirements change. -Loans reset. -Inflation-linked instruments adjust. -Portfolio risk changes. And this is where BIS Working Paper 1374 stops being a boring statistics paper for me. Because the authors themselves discuss putting verified information beside digital financial assets. They specifically mention: -CBDCs -stablecoins -tokenized deposits -derivatives. That one section changes the entire way I look at this. The vision is not simply: “Put a hash on a blockchain.” It becomes: verified economic information + digital money + tokenized assets + automated execution. Now remember what Ripple has been building around XRPL. -Multi-Purpose Tokens. -Credentials. -Permissioned Domains. -Permissioned DEX infrastructure. -Confidential Transfers. -Stablecoins. -Institutional lending. -Tokenized collateral. -FX. -Onchain credit. And Ripple has repeatedly positioned XRP across payments, liquidity and credit. Now put those pieces beside what the BIS researchers are exploring. An official institution needs an identity. XRPL can represent identity and credentials. A regulated participant needs permission to enter a market. XRPL is building permissioned infrastructure. A bond needs trustworthy economic information. The BIS prototype shows one way that information can be authenticated through XRPL. A financial asset needs a digital representation. XRPL is being built for tokenization. A transaction needs money. Stablecoins and tokenized deposits can provide the cash side. Then all those different assets need liquidity. That is where ripple:native becomes much more interesting to me. But before getting there, look at the scale surrounding BIS itself. The BIS does not process the world’s $9.6 trillion of daily FX transactions. It measures that market through its Triennial Central Bank Survey. That distinction matters. According to the numbers in the context here: global OTC FX turnover = $9.6 TRILLION every day. Then add: OTC interest-rate derivatives turnover = $7.9 TRILLION every day. Together: $17.5 TRILLION per day. Just the FX number annualized across roughly 250 trading days comes to around: $2.4 QUADRILLION per year. That is the financial universe BIS research sits over. -Currencies. -Banks. -Central banks. -FX swaps. -Rates. -Derivatives. -Cross-border capital. -Collateral. -Dollar funding. And researchers inside that institution just chose XRP Ledger for an actual technical prototype. That is why I keep telling people not to reduce this to transaction fees. Yes, the worked example uses an XRPL Payment transaction. Yes, the reference cost is only: 10 drops = 0.00001 XRP. Yes, transaction fees on XRPL are destroyed. So if this kind of anchoring eventually ran on mainnet, publishing data itself would consume XRP. But that is not the part that gets me excited. The fee is intentionally tiny. The much bigger question is: What happens when verified information starts triggering financial activity on the same broader infrastructure? The paper itself talks about: inflation-linked products perpetual futures tokenized financial instruments derivative settlement interest payments automated compliance and even: automated monetary-policy applications. Now we are talking about information causing money to move. Imagine an inflation-linked bond. The government publishes inflation. That release gets cryptographically anchored. The bond checks the proof. The CPI number is verified. The contract adjusts what is owed. Digital cash settles the payment. No one has to manually copy a number from a website into another system. No one has to blindly trust a third-party data feed. The financial instrument can verify the economic input itself. That is the idea I keep coming back to: self-verifying finance. And the researchers even discuss using the XRPL EVM-compatible sidechain for more advanced applications where data verification and programmable financial execution exist in the same broader ecosystem. They mention: access controls, permissioning, automated compliance, multisignature requirements, oracle integration, programmable validation. Now connect that with Ripple’s institutional roadmap. Credentials can prove who a participant is. Permissioned Domains can define who belongs inside a regulated environment. Tokenized assets can represent financial instruments. RLUSD can represent digital dollar liquidity. Lending can make those assets productive. XRP can provide native network resources and, where economically useful, liquidity between fragmented assets. That is a very different picture of XRPL than the one people were arguing about years ago. It is not simply: “Can XRP send a payment quickly?” The question becomes: Can XRPL sit underneath parts of a machine-readable financial system? And Working Paper 1374 just gave that question much more weight for me. There is another section that barely gets discussed. The architecture is not limited to one data publisher. The researchers designed a multi-publisher system. Different institutions can create their own Merkle roots. Those roots can be combined into one larger super-root. One XRPL transaction can anchor that shared proof. Yet each publisher remains independently accountable for its own data. Now imagine the participants. Central Bank A. Central Bank B. Regulator C. Statistical Office D. International Organization E. One public verification system. Different publishers. Independent cryptographic accountability. That begins to resemble infrastructure for cross-border public-sector data exchange. And the paper’s own conclusion talks about trustworthy exchange among: national statistical offices central banks international organizations. Then look at who already uses the statistical standard the paper builds around. SDMX is sponsored by institutions including: BIS European Central Bank Eurostat International Monetary Fund OECD United Nations World Bank Group International Labour Organization. That does not mean those institutions are adopting XRPL. But it tells you something important about the design philosophy. The researchers did not create a blockchain system that requires the existing financial world to throw everything away. They designed it to sit underneath an existing institutional standard. That matters a lot. Because the easiest technology to adopt is often the technology that does not force everyone to rebuild from zero. Existing systems can continue publishing. XRPL can provide the cryptographic proof underneath. Then comes BIS Open Tech. The paper says the open-source reference implementation is being released as a prototype through BIS Open Tech and the SDMX community. That means other institutions can inspect it. Reuse it. Modify it. Build on it. This is how technical ideas can spread inside serious institutions. Not through hype. Through code. Documentation. Standards. Reuse. That is the kind of adoption path I pay attention to. Then there is the AI angle. This is where the whole thesis becomes almost unfairly interesting. The authors explicitly discuss AI agents. An AI system receives economic information. Instead of blindly trusting what it scraped from somewhere, it can ask: Is this data authentic? It checks the XRPL proof. Valid? Continue. Invalid? Do nothing. Now compare that with what Ripple launched in June 2026: the XRPL AI Starter Kit, designed around autonomous agents making payments with XRP and RLUSD. Two completely separate directions suddenly sit beside each other. BIS research: AI verifies information through XRPL. Ripple ecosystem: AI moves value through XRPL. Now imagine both ideas eventually meeting. An agent receives official inflation data. It verifies the release cryptographically. It recalculates risk. It reprices a bond. It adjusts collateral. It changes an FX position. It executes a payment. It settles in RLUSD. It routes through XRP where XRP is the best available liquidity path. That is machine-native finance. And now go back to the scale. The BIS 2025 Triennial Survey says: $9.6T/day FX. The dollar appears on one side of 89% of FX trades. The euro is involved in 28.9%. The Japanese yen in 16.8%. FX swaps alone are around $4T every day. Then another $7.9T/day exists in OTC interest-rate derivatives turnover. Think about what happens if only part of those markets becomes tokenized. Digital USD deposits. Digital EUR deposits. Tokenized JPY. RLUSD. CBDCs. Tokenized Treasuries. Interest-rate derivatives. FX derivatives. Collateral. Money-market instruments. The first problem is getting the assets onchain. The second is verifying the information those assets depend on. The third is moving liquidity between all the different forms of value. This BIS paper attacks the second problem using XRPL. Ripple has spent years attacking the first and third. That is why the combination gets my attention. And you do not need XRPL to capture the whole market for the numbers to become enormous. For scale only: 0.1% of $9.6T daily FX turnover = $9.6B per day. 1% = $96B per day. Again, that is not a forecast. It shows what even tiny percentages mean when the underlying market is measured in trillions every day. And that is only FX. It does not include the additional $7.9T/day of interest-rate derivatives turnover BIS measures. This is where the XRP liquidity thesis changes from a crypto argument into a market-structure argument. Suppose the future has hundreds of tokenized currencies and financial products. Every possible pair cannot maintain perfect direct liquidity. USD token / EUR token. EUR token / JPY token. JPY token / RLUSD. RLUSD / Treasury token. Treasury token / derivative. Derivative / deposit token. The combinations explode. A common intermediate asset becomes useful whenever routing through it provides a better market. That is where XRP’s role becomes interesting. Not replacing the dollar. Not replacing the euro. Not replacing CBDCs. Not replacing bank deposits. Connecting liquidity between them when that route makes economic sense. Now imagine the system is automated. No trader needs to shout: “Use XRP.” Software looks at: price, spread, depth, settlement, availability. If the XRP path wins, the software uses XRP. That is the outcome I care about. Machine-selected liquidity. And if those transactions grow large enough, the XRP market itself has to change. Institutional market makers need inventory. Liquidity providers need inventory. Prime brokers need financing capacity. Order books need deeper capital. Large transactions need to clear without huge price impact. That is where the price thesis becomes different from retail speculation. If XRP ever helps support institutional flows inside markets measured in trillions per day, the relevant question is not: “How many retail holders bought today?” It becomes: How much dollar liquidity does the XRP market need to represent? That is an entirely different valuation conversation. There is one more thing I think people are missing. BIS Working Paper 1374 does not only talk about SDMX statistics. The researchers say the same architecture can extend to: XBRL regulatory filings FINREP COREP and other forms of structured official information. Now imagine banks submitting regulatory reports that receive immutable XRPL proofs. The bank cannot quietly change an old filing later. The regulator can verify the exact version. Auditors can verify it. Another authority can verify it. AI software can consume it. One system can prove both: who submitted the data and whether it changed. That gives XRPL a potential role far beyond payments. It starts touching the information layer of finance. And this is why the line “BIS used XRP Ledger” actually undersells the paper. What happened is more specific. Researchers inside BIS took a real institutional problem. They selected XRPL. They built a working implementation. They measured performance. They published the code direction. Then they explored how authenticated data could coexist with: CBDCs, stablecoins, tokenized deposits, derivatives, AI agents, automated financial instruments. That is what I am bullish on. Not a logo. Not a rumor. Not a screenshot. Technical work. And when I look at the direction Ripple is independently pushing XRPL, the overlap is hard for me to ignore. Trusted identities. Verified information. Regulated participants. Tokenized assets. Digital money. Automated execution. Credit. Collateral. FX. Liquidity. AI. Put together, the long-term architecture can look like this: Official institutions publish information. XRPL anchors the proof. Banks and regulators verify it. AI consumes it. Tokenized instruments use it. Stablecoins and tokenized deposits provide cash. Institutional markets execute trades. XRP supplies native network resources and can supply cross-asset liquidity where the route makes sense. That is not simply a faster payment network. That starts looking like part of a digital financial operating system. And then remember where this conversation is happening. Inside the research world of the institution that measures: $9.6 trillion of FX turnover every day plus $7.9 trillion of interest-rate derivatives turnover every day. A combined: $17.5 TRILLION DAILY. No, that is not XRPL volume. No, BIS does not process those trades. The significance is that BIS researchers just tested XRP Ledger while working inside the institutional world surrounding markets of that size. That is the fact. And now I’m asking the question that matters to me as an ripple:native holder: What happens if XRPL earns even a small role inside the tokenized version of that financial system? Because 0.1% of a trillion-dollar market is not small. And this market is not one trillion. It is trillions every single day. That is why Working Paper 1374 changed the scale of the conversation for me. For years, people asked whether XRP could become part of the future financial system. Now researchers inside the BIS have taken XRP Ledger, built institutional infrastructure on it, and explicitly discussed a future combining trusted information with digital money and programmable financial assets. We are still at the prototype stage. But for me, the direction is the real story. The next financial system will need trusted data, tokenized assets, automated execution and deep liquidity. XRPL is now showing up in all four conversations. And XRP sits natively underneath the network where those pieces can eventually meet. $17.5T a day. Now look at your ripple:native bag again. Enough?
0:26

What if I told you ripple:native just moved closer to a financial universe doing $17.5 TRILLION in FX and interest-rate derivatives every single day? I’m not talking about some random prediction. I’m talking about BIS Working Paper No. 1374. This is going to be a long read, because the headline barely scratches the surface. Four of the five authors work at the Bank for International Settlements, and instead of only mentioning XRP Ledger in theory, the researchers actually built, tested and published an open-source XRPL-based prototype. That distinction matters. This is a research implementation, not a production BIS deployment. But the technical choice itself is what caught me. The researchers needed a public blockchain that could help prove official economic and financial data had not been altered. They chose XRP Ledger. And they explained why: low fees, fast finality, developer resources and existing research around its consensus system. This wasn’t somebody adding an XRP logo to a presentation. They built the gateway. They created XRPL transactions. They used institutional anchoring wallets. They put cryptographic proofs inside transaction memos. They linked publisher identities to XRPL addresses. They retrieved those transactions again during verification. Then they measured how the system performed. Median publication latency came in around 3–5 seconds. Verification took around 1–2 seconds. That is where my brain immediately went beyond the headline. Because what exactly were they trying to verify? The kind of information the entire financial system runs on. -Inflation. -GDP. -Interest rates. -Banking statistics. -Debt information. -Financial-stability data. -Regulatory reporting. Imagine a central bank publishes an inflation number. Today that number gets copied everywhere. -Websites. -News terminals. -Databases. -Screenshots. -AI models. -Trading systems. Once it spreads across the internet, how does another machine independently prove that the number it received is exactly what the institution originally published? That is the problem BIS researchers were attacking. Their model creates a cryptographic fingerprint of the official dataset. Individual statistical series can receive fingerprints too. Those hashes are combined through a Merkle tree. A final Merkle root gets anchored to XRPL. The underlying economic data do not need to be dumped onto the blockchain. XRPL simply keeps the proof. Think of it like this: The official institution publishes the document. XRPL holds the tamper-proof receipt. Someone changes even one part of the underlying file? The cryptographic fingerprint changes. Now a bank, regulator, investor, trading engine or AI agent can check: Is this the original data? Has it been changed? Did it really come from the institution claiming to publish it? And that second part is where this paper gets even more serious. The BIS prototype combines the data proof with a W3C Verifiable Credential for the publisher. The publisher’s cryptographic identity is connected to an XRPL address. The paper even uses the format: did:xrpl: So you are not only verifying the information. You are verifying who published it. Now picture a financial world where machines can check both automatically. A central bank publishes CPI. A model receives it. Before touching money, the software checks XRPL. Correct file. Correct publisher. No alteration. Then it acts. That sounds simple until you realize what financial markets actually do with official data. -Rates move. -Currencies move. -Bond prices move. -Derivatives reprice. -Collateral requirements change. -Loans reset. -Inflation-linked instruments adjust. -Portfolio risk changes. And this is where BIS Working Paper 1374 stops being a boring statistics paper for me. Because the authors themselves discuss putting verified information beside digital financial assets. They specifically mention: -CBDCs -stablecoins -tokenized deposits -derivatives. That one section changes the entire way I look at this. The vision is not simply: “Put a hash on a blockchain.” It becomes: verified economic information + digital money + tokenized assets + automated execution. Now remember what Ripple has been building around XRPL. -Multi-Purpose Tokens. -Credentials. -Permissioned Domains. -Permissioned DEX infrastructure. -Confidential Transfers. -Stablecoins. -Institutional lending. -Tokenized collateral. -FX. -Onchain credit. And Ripple has repeatedly positioned XRP across payments, liquidity and credit. Now put those pieces beside what the BIS researchers are exploring. An official institution needs an identity. XRPL can represent identity and credentials. A regulated participant needs permission to enter a market. XRPL is building permissioned infrastructure. A bond needs trustworthy economic information. The BIS prototype shows one way that information can be authenticated through XRPL. A financial asset needs a digital representation. XRPL is being built for tokenization. A transaction needs money. Stablecoins and tokenized deposits can provide the cash side. Then all those different assets need liquidity. That is where ripple:native becomes much more interesting to me. But before getting there, look at the scale surrounding BIS itself. The BIS does not process the world’s $9.6 trillion of daily FX transactions. It measures that market through its Triennial Central Bank Survey. That distinction matters. According to the numbers in the context here: global OTC FX turnover = $9.6 TRILLION every day. Then add: OTC interest-rate derivatives turnover = $7.9 TRILLION every day. Together: $17.5 TRILLION per day. Just the FX number annualized across roughly 250 trading days comes to around: $2.4 QUADRILLION per year. That is the financial universe BIS research sits over. -Currencies. -Banks. -Central banks. -FX swaps. -Rates. -Derivatives. -Cross-border capital. -Collateral. -Dollar funding. And researchers inside that institution just chose XRP Ledger for an actual technical prototype. That is why I keep telling people not to reduce this to transaction fees. Yes, the worked example uses an XRPL Payment transaction. Yes, the reference cost is only: 10 drops = 0.00001 XRP. Yes, transaction fees on XRPL are destroyed. So if this kind of anchoring eventually ran on mainnet, publishing data itself would consume XRP. But that is not the part that gets me excited. The fee is intentionally tiny. The much bigger question is: What happens when verified information starts triggering financial activity on the same broader infrastructure? The paper itself talks about: inflation-linked products perpetual futures tokenized financial instruments derivative settlement interest payments automated compliance and even: automated monetary-policy applications. Now we are talking about information causing money to move. Imagine an inflation-linked bond. The government publishes inflation. That release gets cryptographically anchored. The bond checks the proof. The CPI number is verified. The contract adjusts what is owed. Digital cash settles the payment. No one has to manually copy a number from a website into another system. No one has to blindly trust a third-party data feed. The financial instrument can verify the economic input itself. That is the idea I keep coming back to: self-verifying finance. And the researchers even discuss using the XRPL EVM-compatible sidechain for more advanced applications where data verification and programmable financial execution exist in the same broader ecosystem. They mention: access controls, permissioning, automated compliance, multisignature requirements, oracle integration, programmable validation. Now connect that with Ripple’s institutional roadmap. Credentials can prove who a participant is. Permissioned Domains can define who belongs inside a regulated environment. Tokenized assets can represent financial instruments. RLUSD can represent digital dollar liquidity. Lending can make those assets productive. XRP can provide native network resources and, where economically useful, liquidity between fragmented assets. That is a very different picture of XRPL than the one people were arguing about years ago. It is not simply: “Can XRP send a payment quickly?” The question becomes: Can XRPL sit underneath parts of a machine-readable financial system? And Working Paper 1374 just gave that question much more weight for me. There is another section that barely gets discussed. The architecture is not limited to one data publisher. The researchers designed a multi-publisher system. Different institutions can create their own Merkle roots. Those roots can be combined into one larger super-root. One XRPL transaction can anchor that shared proof. Yet each publisher remains independently accountable for its own data. Now imagine the participants. Central Bank A. Central Bank B. Regulator C. Statistical Office D. International Organization E. One public verification system. Different publishers. Independent cryptographic accountability. That begins to resemble infrastructure for cross-border public-sector data exchange. And the paper’s own conclusion talks about trustworthy exchange among: national statistical offices central banks international organizations. Then look at who already uses the statistical standard the paper builds around. SDMX is sponsored by institutions including: BIS European Central Bank Eurostat International Monetary Fund OECD United Nations World Bank Group International Labour Organization. That does not mean those institutions are adopting XRPL. But it tells you something important about the design philosophy. The researchers did not create a blockchain system that requires the existing financial world to throw everything away. They designed it to sit underneath an existing institutional standard. That matters a lot. Because the easiest technology to adopt is often the technology that does not force everyone to rebuild from zero. Existing systems can continue publishing. XRPL can provide the cryptographic proof underneath. Then comes BIS Open Tech. The paper says the open-source reference implementation is being released as a prototype through BIS Open Tech and the SDMX community. That means other institutions can inspect it. Reuse it. Modify it. Build on it. This is how technical ideas can spread inside serious institutions. Not through hype. Through code. Documentation. Standards. Reuse. That is the kind of adoption path I pay attention to. Then there is the AI angle. This is where the whole thesis becomes almost unfairly interesting. The authors explicitly discuss AI agents. An AI system receives economic information. Instead of blindly trusting what it scraped from somewhere, it can ask: Is this data authentic? It checks the XRPL proof. Valid? Continue. Invalid? Do nothing. Now compare that with what Ripple launched in June 2026: the XRPL AI Starter Kit, designed around autonomous agents making payments with XRP and RLUSD. Two completely separate directions suddenly sit beside each other. BIS research: AI verifies information through XRPL. Ripple ecosystem: AI moves value through XRPL. Now imagine both ideas eventually meeting. An agent receives official inflation data. It verifies the release cryptographically. It recalculates risk. It reprices a bond. It adjusts collateral. It changes an FX position. It executes a payment. It settles in RLUSD. It routes through XRP where XRP is the best available liquidity path. That is machine-native finance. And now go back to the scale. The BIS 2025 Triennial Survey says: $9.6T/day FX. The dollar appears on one side of 89% of FX trades. The euro is involved in 28.9%. The Japanese yen in 16.8%. FX swaps alone are around $4T every day. Then another $7.9T/day exists in OTC interest-rate derivatives turnover. Think about what happens if only part of those markets becomes tokenized. Digital USD deposits. Digital EUR deposits. Tokenized JPY. RLUSD. CBDCs. Tokenized Treasuries. Interest-rate derivatives. FX derivatives. Collateral. Money-market instruments. The first problem is getting the assets onchain. The second is verifying the information those assets depend on. The third is moving liquidity between all the different forms of value. This BIS paper attacks the second problem using XRPL. Ripple has spent years attacking the first and third. That is why the combination gets my attention. And you do not need XRPL to capture the whole market for the numbers to become enormous. For scale only: 0.1% of $9.6T daily FX turnover = $9.6B per day. 1% = $96B per day. Again, that is not a forecast. It shows what even tiny percentages mean when the underlying market is measured in trillions every day. And that is only FX. It does not include the additional $7.9T/day of interest-rate derivatives turnover BIS measures. This is where the XRP liquidity thesis changes from a crypto argument into a market-structure argument. Suppose the future has hundreds of tokenized currencies and financial products. Every possible pair cannot maintain perfect direct liquidity. USD token / EUR token. EUR token / JPY token. JPY token / RLUSD. RLUSD / Treasury token. Treasury token / derivative. Derivative / deposit token. The combinations explode. A common intermediate asset becomes useful whenever routing through it provides a better market. That is where XRP’s role becomes interesting. Not replacing the dollar. Not replacing the euro. Not replacing CBDCs. Not replacing bank deposits. Connecting liquidity between them when that route makes economic sense. Now imagine the system is automated. No trader needs to shout: “Use XRP.” Software looks at: price, spread, depth, settlement, availability. If the XRP path wins, the software uses XRP. That is the outcome I care about. Machine-selected liquidity. And if those transactions grow large enough, the XRP market itself has to change. Institutional market makers need inventory. Liquidity providers need inventory. Prime brokers need financing capacity. Order books need deeper capital. Large transactions need to clear without huge price impact. That is where the price thesis becomes different from retail speculation. If XRP ever helps support institutional flows inside markets measured in trillions per day, the relevant question is not: “How many retail holders bought today?” It becomes: How much dollar liquidity does the XRP market need to represent? That is an entirely different valuation conversation. There is one more thing I think people are missing. BIS Working Paper 1374 does not only talk about SDMX statistics. The researchers say the same architecture can extend to: XBRL regulatory filings FINREP COREP and other forms of structured official information. Now imagine banks submitting regulatory reports that receive immutable XRPL proofs. The bank cannot quietly change an old filing later. The regulator can verify the exact version. Auditors can verify it. Another authority can verify it. AI software can consume it. One system can prove both: who submitted the data and whether it changed. That gives XRPL a potential role far beyond payments. It starts touching the information layer of finance. And this is why the line “BIS used XRP Ledger” actually undersells the paper. What happened is more specific. Researchers inside BIS took a real institutional problem. They selected XRPL. They built a working implementation. They measured performance. They published the code direction. Then they explored how authenticated data could coexist with: CBDCs, stablecoins, tokenized deposits, derivatives, AI agents, automated financial instruments. That is what I am bullish on. Not a logo. Not a rumor. Not a screenshot. Technical work. And when I look at the direction Ripple is independently pushing XRPL, the overlap is hard for me to ignore. Trusted identities. Verified information. Regulated participants. Tokenized assets. Digital money. Automated execution. Credit. Collateral. FX. Liquidity. AI. Put together, the long-term architecture can look like this: Official institutions publish information. XRPL anchors the proof. Banks and regulators verify it. AI consumes it. Tokenized instruments use it. Stablecoins and tokenized deposits provide cash. Institutional markets execute trades. XRP supplies native network resources and can supply cross-asset liquidity where the route makes sense. That is not simply a faster payment network. That starts looking like part of a digital financial operating system. And then remember where this conversation is happening. Inside the research world of the institution that measures: $9.6 trillion of FX turnover every day plus $7.9 trillion of interest-rate derivatives turnover every day. A combined: $17.5 TRILLION DAILY. No, that is not XRPL volume. No, BIS does not process those trades. The significance is that BIS researchers just tested XRP Ledger while working inside the institutional world surrounding markets of that size. That is the fact. And now I’m asking the question that matters to me as an ripple:native holder: What happens if XRPL earns even a small role inside the tokenized version of that financial system? Because 0.1% of a trillion-dollar market is not small. And this market is not one trillion. It is trillions every single day. That is why Working Paper 1374 changed the scale of the conversation for me. For years, people asked whether XRP could become part of the future financial system. Now researchers inside the BIS have taken XRP Ledger, built institutional infrastructure on it, and explicitly discussed a future combining trusted information with digital money and programmable financial assets. We are still at the prototype stage. But for me, the direction is the real story. The next financial system will need trusted data, tokenized assets, automated execution and deep liquidity. XRPL is now showing up in all four conversations. And XRP sits natively underneath the network where those pieces can eventually meet. $17.5T a day. Now look at your ripple:native bag again. Enough?

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