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📢 TRUST LAYER ADDED: Snowball just put the engine room on display. Presenting the Snowball Command Center; Live dashboard Burn engine: running Fee claimer: running Market-making core: running 10 sec refresh, 24/7 surveillance. Transparency hits different when it’s operational, not theoretical.

Snowball

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28,852 views • 7 months ago •via X (Twitter)

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Robinhood just turned on its own blockchain Robinhood Crypto Chain went live on mainnet today, a Layer 2 built on Arbitrum, purpose-built for tokenized real-world assets. Tokenized Stock Tokens are now tradable 24/7 through Robinhood Wallet in 120+ countries, with those shares also usable as DeFi collateral. Robinhood Earn also launched, letting users lend $USDG for an estimated 7% yield through Morpho. Whether that specific lending market actually runs on Robinhood Chain itself isn't confirmed. Everything else announced today, wider perp futures in Europe, UK crypto trading, Canada going live, a new AI trading tool, is separate from the chain. General business expansion running alongside it, not built on it. $HOOD up nearly 8% on the day, still about 30% off its October high.

Robinhood just turned on its own blockchain Robinhood Crypto Chain went live on mainnet today, a Layer 2 built on Arbitrum, purpose-built for tokenized real-world assets. Tokenized Stock Tokens are now tradable 24/7 through Robinhood Wallet in 120+ countries, with those shares also usable as DeFi collateral. Robinhood Earn also launched, letting users lend $USDG for an estimated 7% yield through Morpho. Whether that specific lending market actually runs on Robinhood Chain itself isn't confirmed. Everything else announced today, wider perp futures in Europe, UK crypto trading, Canada going live, a new AI trading tool, is separate from the chain. General business expansion running alongside it, not built on it. $HOOD up nearly 8% on the day, still about 30% off its October high.

BSCN

22,145 views • 2 months ago

Ray Dalio is right about one thing: Bitcoin forces you to think harder. But zoom out. 1) “Bitcoin has no privacy.” Bitcoin is pseudonymous, not anonymous. That’s by design. Transparency is what makes it auditable, trust-minimized, and globally verifiable. Privacy isn’t binary — it’s a spectrum. Second-layer solutions like Lightning Network improve transactional privacy, and self-custody + best practices eliminate counterparty surveillance. If your definition of “privacy” is “opaque like the banking system,” then yes — Bitcoin is different. It replaces institutional secrecy with mathematical transparency. 2) “Central banks don’t want to buy Bitcoin.” Correct. Central banks also didn’t want the internet, stablecoins, or gold leaving their vaults. Bitcoin isn’t competing for central bank approval. It’s competing as neutral collateral in a world of weaponized fiat. When sovereign debt hits structural limits, assets without counterparty risk win. That’s why individuals, institutions, ETFs, and even nation-states accumulate it — regardless of central bank preferences. 3) “Quantum computing issues.” If quantum breaks Bitcoin’s cryptography, it breaks the entire global financial system first — SWIFT, online banking, military communications. Bitcoin can upgrade via consensus long before that scenario materializes. Cryptography evolves. That’s not a flaw; that’s software. 4) “Relatively small and controlled market.” Every monetization process starts small. Gold was once a niche commodity. The internet was once “small and controlled.” Bitcoin’s market cap reflects 15 years of monetization — with no CEO, no marketing budget, and no state backing. And “controlled”? Try censoring a decentralized network running across tens of thousands of nodes worldwide. Dalio views Bitcoin through a macro-hedge lens. But Bitcoin isn’t just an asset. It’s: •Programmatic scarcity (21M hard cap) •Final settlement without intermediaries •Borderless value transfer •A hedge against monetary debasement The real question isn’t whether central banks want Bitcoin. It’s whether individuals want money that can’t be inflated, frozen, or diluted. History suggests they do.

Ray Dalio is right about one thing: Bitcoin forces you to think harder. But zoom out. 1) “Bitcoin has no privacy.” Bitcoin is pseudonymous, not anonymous. That’s by design. Transparency is what makes it auditable, trust-minimized, and globally verifiable. Privacy isn’t binary — it’s a spectrum. Second-layer solutions like Lightning Network improve transactional privacy, and self-custody + best practices eliminate counterparty surveillance. If your definition of “privacy” is “opaque like the banking system,” then yes — Bitcoin is different. It replaces institutional secrecy with mathematical transparency. 2) “Central banks don’t want to buy Bitcoin.” Correct. Central banks also didn’t want the internet, stablecoins, or gold leaving their vaults. Bitcoin isn’t competing for central bank approval. It’s competing as neutral collateral in a world of weaponized fiat. When sovereign debt hits structural limits, assets without counterparty risk win. That’s why individuals, institutions, ETFs, and even nation-states accumulate it — regardless of central bank preferences. 3) “Quantum computing issues.” If quantum breaks Bitcoin’s cryptography, it breaks the entire global financial system first — SWIFT, online banking, military communications. Bitcoin can upgrade via consensus long before that scenario materializes. Cryptography evolves. That’s not a flaw; that’s software. 4) “Relatively small and controlled market.” Every monetization process starts small. Gold was once a niche commodity. The internet was once “small and controlled.” Bitcoin’s market cap reflects 15 years of monetization — with no CEO, no marketing budget, and no state backing. And “controlled”? Try censoring a decentralized network running across tens of thousands of nodes worldwide. Dalio views Bitcoin through a macro-hedge lens. But Bitcoin isn’t just an asset. It’s: •Programmatic scarcity (21M hard cap) •Final settlement without intermediaries •Borderless value transfer •A hedge against monetary debasement The real question isn’t whether central banks want Bitcoin. It’s whether individuals want money that can’t be inflated, frozen, or diluted. History suggests they do.

Asaf · Satoshi Signal ⚡ @SatoshiSignal

22,503 views • 6 months ago

A video of a Chinese programmer’s OpenClaw farm just leaked online. Rows of Apple machines running Clawdbot agents 24/7. The video disappeared shortly after it was posted. But people managed to trace one of the accounts connected to the system on Polymarket. $1,200 → $1.52M. 42,730 predictions. Look at the profit curve above. Mostly Bitcoin markets. But the system isn’t predicting the future. It’s exploiting latency. The structure looks like this: > monitor BTC price from Binance and Coinbase > detect when Polymarket odds lag behind the real market > enter before the order book updates > close once probabilities adjust Then the system scales. Machine 1 → BTC 5m markets Machine 2 → ETH markets Machine 3 → weather markets Machine 4 → sports markets Most trades last only minutes. Just automation. People are still debating who will win. The bots are already trading the odds.

A video of a Chinese programmer’s OpenClaw farm just leaked online. Rows of Apple machines running Clawdbot agents 24/7. The video disappeared shortly after it was posted. But people managed to trace one of the accounts connected to the system on Polymarket. $1,200 → $1.52M. 42,730 predictions. Look at the profit curve above. Mostly Bitcoin markets. But the system isn’t predicting the future. It’s exploiting latency. The structure looks like this: > monitor BTC price from Binance and Coinbase > detect when Polymarket odds lag behind the real market > enter before the order book updates > close once probabilities adjust Then the system scales. Machine 1 → BTC 5m markets Machine 2 → ETH markets Machine 3 → weather markets Machine 4 → sports markets Most trades last only minutes. Just automation. People are still debating who will win. The bots are already trading the odds.

Kirill

17,739 views • 5 months ago

Run Gemma 4 26B MoE on 8GB VRAM with 250k context at 20+ tokens/sec If you own any 8GB VRAM graphics card, stop what you are doing. Local AI just had its absolute "Holy Shit" moment for budget hardware. Yesterday, I benchmarked Unsloth Gemma 4 12B Q4_K_XL on an 8GB card. The community went wild but immediately demanded more: "Can we run a 25B+ model on budget GPUs?" Today, I’m delivering exactly that. I am running a massive 26B parameter Mixture of Experts (MoE) model locally on a standard 8GB VRAM setup with 250k full native context!. If you own an RTX 3060, 3070, 4060, or any budget GPU with 8GB of VRAM, the local AI paradigm has completely changed. The performance metrics are astonishing: - 20 tokens/sec flat decode throughput. - Stable, flat decode speed even with massive prompts. - I threw a 60k token prompt at it, and it still clocked in at 20 TPS without dropping a single frame. # What about prefill? Yes, Time To First Token (TTFT) is slightly high when swallowing massive contexts. But with a solid 200 tokens/sec prefill speed, the wait is barely noticeable and highly usable. And this is running completely without Multi Token Prediction (MTP) active. How is this possible? It’s the magic of Google's new QAT (Quantization Aware Training) quants for Gemma 4. The model weight file (unsloth gemma-4-26B-A4B-it-qat-UD-Q4_K_XL.gguf) is only 13.2 GB, making it the ultimate local powerhouse. # The Test Setup: CPU: Intel Core i7 RAM: 16GB System RAM GPU: NVIDIA GeForce RTX 4060 Laptop GPU (8GB VRAM) # The Secret Sauce (The -cmoe Flag) To make this work properly on any 8GB card, you must use the -cmoe (CPU MoE) flag in llama.cpp. This flag isolates the heavy MoE expert weights directly to system memory (CPU/RAM) while letting your GPU focus strictly on the Attention layers and the KV Cache. It prevents VRAM spillage and holds the throughput rock solid. # The flags: -m "gemma-4-26B-A4B-it-qat-UD-Q4_K_XL.gguf" -cmoe -c 248000 -v Once running, just open the UI on localhost and toggle the new reasoning lightbulb icon in the text input box to watch the model perform multi step thinking. Are you still running smaller models, or are you ready to scale up your budget local setups? Let's discuss in the replies

Run Gemma 4 26B MoE on 8GB VRAM with 250k context at 20+ tokens/sec If you own any 8GB VRAM graphics card, stop what you are doing. Local AI just had its absolute "Holy Shit" moment for budget hardware. Yesterday, I benchmarked Unsloth Gemma 4 12B Q4_K_XL on an 8GB card. The community went wild but immediately demanded more: "Can we run a 25B+ model on budget GPUs?" Today, I’m delivering exactly that. I am running a massive 26B parameter Mixture of Experts (MoE) model locally on a standard 8GB VRAM setup with 250k full native context!. If you own an RTX 3060, 3070, 4060, or any budget GPU with 8GB of VRAM, the local AI paradigm has completely changed. The performance metrics are astonishing: - 20 tokens/sec flat decode throughput. - Stable, flat decode speed even with massive prompts. - I threw a 60k token prompt at it, and it still clocked in at 20 TPS without dropping a single frame. # What about prefill? Yes, Time To First Token (TTFT) is slightly high when swallowing massive contexts. But with a solid 200 tokens/sec prefill speed, the wait is barely noticeable and highly usable. And this is running completely without Multi Token Prediction (MTP) active. How is this possible? It’s the magic of Google's new QAT (Quantization Aware Training) quants for Gemma 4. The model weight file (unsloth gemma-4-26B-A4B-it-qat-UD-Q4_K_XL.gguf) is only 13.2 GB, making it the ultimate local powerhouse. # The Test Setup: CPU: Intel Core i7 RAM: 16GB System RAM GPU: NVIDIA GeForce RTX 4060 Laptop GPU (8GB VRAM) # The Secret Sauce (The -cmoe Flag) To make this work properly on any 8GB card, you must use the -cmoe (CPU MoE) flag in llama.cpp. This flag isolates the heavy MoE expert weights directly to system memory (CPU/RAM) while letting your GPU focus strictly on the Attention layers and the KV Cache. It prevents VRAM spillage and holds the throughput rock solid. # The flags: -m "gemma-4-26B-A4B-it-qat-UD-Q4_K_XL.gguf" -cmoe -c 248000 -v Once running, just open the UI on localhost and toggle the new reasoning lightbulb icon in the text input box to watch the model perform multi step thinking. Are you still running smaller models, or are you ready to scale up your budget local setups? Let's discuss in the replies

Alok

292,770 views • 3 months ago

An Anthropic engineer caught my screen at a café in SF. “Are you seriously running Claude against live markets?” Yes. I walked him through it. 214 trades 74% WR +$9,437 / 19 days Copy it: Stack is simple: – historical market + wallet dataset – CLI for fast market access – execution layer with strict filters No predictions. First repo: The model ranks wallets by exit efficiency. Metric that matters: captured_value / expected_value > 0.70 If a wallet can’t realize its edge — it’s noise. Signals only trigger on repeated structure. Second repo: Position sizing = Kelly. f* = (p*b - q) / b Most cycles: idle. When conditions align: deploy capital. Three trades: +7c, +22c, +16c — all closed within hours He looked at the repos, paused: “We had internal experiments like this.” There’s no secret. Just discipline. Markets don’t pay for being right. They pay for exiting well.

An Anthropic engineer caught my screen at a café in SF. “Are you seriously running Claude against live markets?” Yes. I walked him through it. 214 trades 74% WR +$9,437 / 19 days Copy it: Stack is simple: – historical market + wallet dataset – CLI for fast market access – execution layer with strict filters No predictions. First repo: The model ranks wallets by exit efficiency. Metric that matters: captured_value / expected_value > 0.70 If a wallet can’t realize its edge — it’s noise. Signals only trigger on repeated structure. Second repo: Position sizing = Kelly. f* = (p*b - q) / b Most cycles: idle. When conditions align: deploy capital. Three trades: +7c, +22c, +16c — all closed within hours He looked at the repos, paused: “We had internal experiments like this.” There’s no secret. Just discipline. Markets don’t pay for being right. They pay for exiting well.

Discover

11,699 views • 4 months ago

I watched so you did not have to and here is the breakdown!!!! Nearly 2 hours. Plenty of nostalgia. Plenty of vibes. Lots of feelings. But when the net was wide open for actual clarification? Dead air. Total time Chris Albert spoke 1,501 seconds Which is 25 min and 1 sec. So on an almost 2 hour show, he spoke for about 25 minutes total. Roughly 21% of the entire show The remaining time was largely filled by the hosts themselves frequently cutting him off, reframing questions, finishing answers for him, or moving the conversation along before he ever had to sit in discomfort or explain anything fully. 0 minutes of that time addressed the major unresolved timeline and movement issues Oddly missing from this fact-based discussion No explanation for how Colin got home 20 min AFTER Chris that night, despite a four-minute head start No explanation for why Chris’s wife that went to 34 Fairview with donuts for brian Jr. pulled behind a car that was not there only an hour earlier with no explanation then, turned around, came back to get him, returned again, stayed 30 min, then left, all on the morning John was found. No mention of parents allegedly coordinating to quietly clean up a house-destroying party involving minors in which his son was one No discussion of repeated allegations of Colin being removed from bars for underage drinking. We were told this channel is about facts, not opinion. So we treated it like a data exercise. We timed everything Chris talked about, so you didn’t have to. Here’s how nearly two hours were spent 2 min saying they tried to “let the court system play out,” while explaining his New Year’s Day Twitter Space tour and condemning Sean McCabe’s comments 37 sec on moving to Canton in 1974 35 sec explaining it’s normal to work in the town you grew up in (we agree) 35 sec on Canton no longer being blue-collar and now very expensive 44 sec reminiscing about red paint stains in his childhood garage was misrepresented by the defense 1 min on town politics 15 sec explaining what a Select Board is 50 sec on handling public comment and being a “First Amendment guy… to a degree” 38 sec announcing he’s not running again 25 sec on how he keeps his cool during public comment 15 sec on pineapple pizza 52 sec explaining Colin was 17 when John died and can’t play college football with his cousin 45 sec expressing loss for how his family has been treated 10 sec referencing a blogger saying “horrible things” about Colin 26 sec blaming the media for selling and sensationalizing a story 20 sec saying he’s mind-boggled 48 sec explaining why the term “McAlberts” is dehumanizing 28 sec criticizing Yannetti’s performance was upsetting to him while stating John was his friend 42 sec explaining the fence-straddling photo was a joke 1:50 on “Nebbercracker” and a childhood yard incident 10 sec about his wife bringing donuts to a nephew for his birthday was not weird 32 sec blaming Karen for making his family look like villains 1 min on selling his house and convincing his brother to sell 24 sec saying Nebbercracker was the best part of the trial 1:12 on knowing Michael Proctor but not really knowing him 10 sec saying he was never alone with Proctor 11 sec correcting the “pin it on the girl” claim (which we agree with) 1 min praising Jillian Daniels as a great family supporter and defender 1 min explaining early lack of support made him sad 15 sec thanking the hosts 31 sec discussing starting a “Defend the Truth” media page 30 sec saying both sides were horrible and doxxed people 24 sec on feeling isolated at first 33 sec minimizing the Proctor gift as “just gratitude for solving a murder in one day” 28 sec saying he’s still friends with the O’Keefes and they eat at his pizza shop 38 sec saying Mr. O’Keefe sits with him for long periods of time 43 sec lamenting being made into villains 30 sec saying the case took years off his parents’ lives 35 sec thanking supporters and saying “the truth is on our side”

I watched so you did not have to and here is the breakdown!!!! Nearly 2 hours. Plenty of nostalgia. Plenty of vibes. Lots of feelings. But when the net was wide open for actual clarification? Dead air. Total time Chris Albert spoke 1,501 seconds Which is 25 min and 1 sec. So on an almost 2 hour show, he spoke for about 25 minutes total. Roughly 21% of the entire show The remaining time was largely filled by the hosts themselves frequently cutting him off, reframing questions, finishing answers for him, or moving the conversation along before he ever had to sit in discomfort or explain anything fully. 0 minutes of that time addressed the major unresolved timeline and movement issues Oddly missing from this fact-based discussion No explanation for how Colin got home 20 min AFTER Chris that night, despite a four-minute head start No explanation for why Chris’s wife that went to 34 Fairview with donuts for brian Jr. pulled behind a car that was not there only an hour earlier with no explanation then, turned around, came back to get him, returned again, stayed 30 min, then left, all on the morning John was found. No mention of parents allegedly coordinating to quietly clean up a house-destroying party involving minors in which his son was one No discussion of repeated allegations of Colin being removed from bars for underage drinking. We were told this channel is about facts, not opinion. So we treated it like a data exercise. We timed everything Chris talked about, so you didn’t have to. Here’s how nearly two hours were spent 2 min saying they tried to “let the court system play out,” while explaining his New Year’s Day Twitter Space tour and condemning Sean McCabe’s comments 37 sec on moving to Canton in 1974 35 sec explaining it’s normal to work in the town you grew up in (we agree) 35 sec on Canton no longer being blue-collar and now very expensive 44 sec reminiscing about red paint stains in his childhood garage was misrepresented by the defense 1 min on town politics 15 sec explaining what a Select Board is 50 sec on handling public comment and being a “First Amendment guy… to a degree” 38 sec announcing he’s not running again 25 sec on how he keeps his cool during public comment 15 sec on pineapple pizza 52 sec explaining Colin was 17 when John died and can’t play college football with his cousin 45 sec expressing loss for how his family has been treated 10 sec referencing a blogger saying “horrible things” about Colin 26 sec blaming the media for selling and sensationalizing a story 20 sec saying he’s mind-boggled 48 sec explaining why the term “McAlberts” is dehumanizing 28 sec criticizing Yannetti’s performance was upsetting to him while stating John was his friend 42 sec explaining the fence-straddling photo was a joke 1:50 on “Nebbercracker” and a childhood yard incident 10 sec about his wife bringing donuts to a nephew for his birthday was not weird 32 sec blaming Karen for making his family look like villains 1 min on selling his house and convincing his brother to sell 24 sec saying Nebbercracker was the best part of the trial 1:12 on knowing Michael Proctor but not really knowing him 10 sec saying he was never alone with Proctor 11 sec correcting the “pin it on the girl” claim (which we agree with) 1 min praising Jillian Daniels as a great family supporter and defender 1 min explaining early lack of support made him sad 15 sec thanking the hosts 31 sec discussing starting a “Defend the Truth” media page 30 sec saying both sides were horrible and doxxed people 24 sec on feeling isolated at first 33 sec minimizing the Proctor gift as “just gratitude for solving a murder in one day” 28 sec saying he’s still friends with the O’Keefes and they eat at his pizza shop 38 sec saying Mr. O’Keefe sits with him for long periods of time 43 sec lamenting being made into villains 30 sec saying the case took years off his parents’ lives 35 sec thanking supporters and saying “the truth is on our side”

Dixie Normus

17,063 views • 8 months ago

Your metabolism isn’t broken. You’re just playing the wrong game. Post-chest day pump into a full Forbici feast: their largest pepperoni pizza, cacio e pepe, Caesar salad, double protein chicken bowl, and wine. This is what metabolic flexibility actually looks like. When you carry significant muscle mass at 12% body fat, your body becomes a completely different machine. You’re not just “lean”, you’re metabolically advantaged. Think of it like this: Fat cells are storage units. Muscle cells are furnaces. The more furnaces you have running 24/7, the more fuel you can throw at them without consequence. At 12% body fat with real muscle tissue, your insulin sensitivity is optimized. Nutrients partition preferentially into muscle, not fat. Your body actually PREFERS to burn fat for fuel at rest and shuttle carbs into muscle for growth and performance. This is why I can destroy an entire Italian feast on date night without anxiety or tomorrow’s cardio “punishment.” The muscle mass creates metabolic flexibility, the ability to efficiently use whatever fuel source you give it. Most people obsess over aggressive deficits and endless cardio, treating fat loss like an emergency. Yes, these tools work. Yes, you can preserve muscle if you’re smart about it. But you’re still playing defense, constantly fighting to maintain what little muscle you have while grinding through restriction. The game isn’t just getting lean. It’s building the engine that makes being lean effortless. When you prioritize muscle acquisition FIRST, you create a physique that burns more calories at rest, handles carbs better, and allows you to eat like someone who enjoys date night. Stop majoring in the minors. Build the metabolic advantage first, then reveal what you’ve built. Your grandmother was right - you need to eat. When you have the muscle mass to support it, food becomes fuel, not the enemy.

Your metabolism isn’t broken. You’re just playing the wrong game. Post-chest day pump into a full Forbici feast: their largest pepperoni pizza, cacio e pepe, Caesar salad, double protein chicken bowl, and wine. This is what metabolic flexibility actually looks like. When you carry significant muscle mass at 12% body fat, your body becomes a completely different machine. You’re not just “lean”, you’re metabolically advantaged. Think of it like this: Fat cells are storage units. Muscle cells are furnaces. The more furnaces you have running 24/7, the more fuel you can throw at them without consequence. At 12% body fat with real muscle tissue, your insulin sensitivity is optimized. Nutrients partition preferentially into muscle, not fat. Your body actually PREFERS to burn fat for fuel at rest and shuttle carbs into muscle for growth and performance. This is why I can destroy an entire Italian feast on date night without anxiety or tomorrow’s cardio “punishment.” The muscle mass creates metabolic flexibility, the ability to efficiently use whatever fuel source you give it. Most people obsess over aggressive deficits and endless cardio, treating fat loss like an emergency. Yes, these tools work. Yes, you can preserve muscle if you’re smart about it. But you’re still playing defense, constantly fighting to maintain what little muscle you have while grinding through restriction. The game isn’t just getting lean. It’s building the engine that makes being lean effortless. When you prioritize muscle acquisition FIRST, you create a physique that burns more calories at rest, handles carbs better, and allows you to eat like someone who enjoys date night. Stop majoring in the minors. Build the metabolic advantage first, then reveal what you’ve built. Your grandmother was right - you need to eat. When you have the muscle mass to support it, food becomes fuel, not the enemy.

Coach Paul

22,950 views • 9 months ago

𝗧𝗵𝗲 𝗕𝗹𝗼𝗼𝗱𝗺𝗼𝗼𝗻 𝗿𝗶𝘀𝗲𝘀 𝘁𝗼𝗱𝗮𝘆. 🌑 𝗕𝗹𝗶𝗴𝗵𝘁𝗳𝗲𝗹𝗹 is officially stepping out of 𝗰𝗼𝗺𝗶𝗻𝗴 𝘀𝗼𝗼𝗻 and into 𝗽𝗹𝗮𝘆𝗮𝗯𝗹𝗲 𝗿𝗲𝗮𝗹𝗶𝘁𝘆. If you’ve been watching from the sidelines, this is your signal. This isn’t a hype brochure game 👀 it’s 𝗽𝘂𝗿𝗲 𝘁𝗮𝗰𝘁𝗶𝗰𝗮𝗹 𝗣𝘃𝗣 crafted by Blightfell + Merlyn Game Studio, deploying on Abstract. We’ve been running 𝗴𝗮𝗺𝗶𝗻𝗴 𝗻𝗶𝗴𝗵𝘁𝘀 and the gameplay just hits different. No hand-holding. 𝗥𝗲𝗮𝗱𝘀, 𝘀𝗽𝗮𝗰𝗶𝗻𝗴, 𝗰𝗼𝗺𝗯𝗼𝘀, 𝗺𝗼𝗺𝗲𝗻𝘁𝘂𝗺 𝘀𝘄𝗶𝗻𝗴𝘀… and those VC screaming moments when someone gets folded. 😭🔥 And just to be clear, I’m not watching from afar. I’m a 𝗽𝗿𝗼𝘂𝗱 𝗵𝗼𝗹𝗱𝗲𝗿 𝗼𝗳 𝗺𝘂𝗹𝘁𝗶𝗽𝗹𝗲 𝗚𝗲𝗻𝗲𝘀𝗶𝘀 𝗞𝗻𝗶𝗴𝗵𝘁𝘀 + a 𝗟𝗲𝗴𝗲𝗻𝗱𝗮𝗿𝘆 𝗗𝗿𝗲𝗮𝗺 𝗟𝗮𝗶𝗿. So yes, I am 𝗹𝗼𝗰𝗸𝗲𝗱 𝗶𝗻. 🛡️🏰 𝗧𝘂𝗿𝗻 𝗻𝗼𝘁𝗶𝘀 𝗼𝗻 𝗳𝗼𝗿 Blightfell. 𝗧𝗼𝗱𝗮𝘆 𝗶𝘀 𝗻𝗼𝘁 𝗷𝘂𝘀𝘁 𝗮𝗻 𝘂𝗽𝗱𝗮𝘁𝗲. 𝗜𝘁’𝘀 𝘁𝗵𝗲 𝗯𝗲𝗴𝗶𝗻𝗻𝗶𝗻𝗴. 𝗧𝗵𝗲 𝗵𝘂𝗻𝘁 𝗯𝗲𝗴𝗶𝗻𝘀. 🩸🌑⚔️

𝗧𝗵𝗲 𝗕𝗹𝗼𝗼𝗱𝗺𝗼𝗼𝗻 𝗿𝗶𝘀𝗲𝘀 𝘁𝗼𝗱𝗮𝘆. 🌑 𝗕𝗹𝗶𝗴𝗵𝘁𝗳𝗲𝗹𝗹 is officially stepping out of 𝗰𝗼𝗺𝗶𝗻𝗴 𝘀𝗼𝗼𝗻 and into 𝗽𝗹𝗮𝘆𝗮𝗯𝗹𝗲 𝗿𝗲𝗮𝗹𝗶𝘁𝘆. If you’ve been watching from the sidelines, this is your signal. This isn’t a hype brochure game 👀 it’s 𝗽𝘂𝗿𝗲 𝘁𝗮𝗰𝘁𝗶𝗰𝗮𝗹 𝗣𝘃𝗣 crafted by Blightfell + Merlyn Game Studio, deploying on Abstract. We’ve been running 𝗴𝗮𝗺𝗶𝗻𝗴 𝗻𝗶𝗴𝗵𝘁𝘀 and the gameplay just hits different. No hand-holding. 𝗥𝗲𝗮𝗱𝘀, 𝘀𝗽𝗮𝗰𝗶𝗻𝗴, 𝗰𝗼𝗺𝗯𝗼𝘀, 𝗺𝗼𝗺𝗲𝗻𝘁𝘂𝗺 𝘀𝘄𝗶𝗻𝗴𝘀… and those VC screaming moments when someone gets folded. 😭🔥 And just to be clear, I’m not watching from afar. I’m a 𝗽𝗿𝗼𝘂𝗱 𝗵𝗼𝗹𝗱𝗲𝗿 𝗼𝗳 𝗺𝘂𝗹𝘁𝗶𝗽𝗹𝗲 𝗚𝗲𝗻𝗲𝘀𝗶𝘀 𝗞𝗻𝗶𝗴𝗵𝘁𝘀 + a 𝗟𝗲𝗴𝗲𝗻𝗱𝗮𝗿𝘆 𝗗𝗿𝗲𝗮𝗺 𝗟𝗮𝗶𝗿. So yes, I am 𝗹𝗼𝗰𝗸𝗲𝗱 𝗶𝗻. 🛡️🏰 𝗧𝘂𝗿𝗻 𝗻𝗼𝘁𝗶𝘀 𝗼𝗻 𝗳𝗼𝗿 Blightfell. 𝗧𝗼𝗱𝗮𝘆 𝗶𝘀 𝗻𝗼𝘁 𝗷𝘂𝘀𝘁 𝗮𝗻 𝘂𝗽𝗱𝗮𝘁𝗲. 𝗜𝘁’𝘀 𝘁𝗵𝗲 𝗯𝗲𝗴𝗶𝗻𝗻𝗶𝗻𝗴. 𝗧𝗵𝗲 𝗵𝘂𝗻𝘁 𝗯𝗲𝗴𝗶𝗻𝘀. 🩸🌑⚔️

AG⚡️DAO

40,208 views • 10 months ago

April 9, 2025: #ES hits 5,000, $SPY at weekly 200 EMA. News: #Trump freezes tariffs for 75+ countries for 90 days. Price spikes. October 10, 2025: $QOQ hits $600. News: Donald J. Trump slaps 100% tariffs on China. Price drops. January 11/12, 2026: #ES hits 7,000. News: #DOJ opens criminal probe into Jerome #Powell. Every example is easy to check. A coincidence happens once. I see a pattern. News often comes exactly when algos running the market need it – and I can anticipate when, not what. My content is all about algorithmic moves. ❗️Price doesn’t have to follow the news❗️ I don’t believe in global conspiracies or tin-foil hats. I just stay calm and rational. That’s probably why I see what’s hidden, even in plain sight. I could give dozens of examples, but: • If your critical thinking is sharp – that’s enough • If not – even hundreds of examples won’t convince you I’ll explain why this happens and how the mechanism works when the time comes. Not now. For now, I want to see if this content actually matters to you. I don’t want to look crazy. Hit 200 likes – and I’ll know I can share these patterns from time to time.

April 9, 2025: #ES hits 5,000, $SPY at weekly 200 EMA. News: #Trump freezes tariffs for 75+ countries for 90 days. Price spikes. October 10, 2025: $QOQ hits $600. News: Donald J. Trump slaps 100% tariffs on China. Price drops. January 11/12, 2026: #ES hits 7,000. News: #DOJ opens criminal probe into Jerome #Powell. Every example is easy to check. A coincidence happens once. I see a pattern. News often comes exactly when algos running the market need it – and I can anticipate when, not what. My content is all about algorithmic moves. ❗️Price doesn’t have to follow the news❗️ I don’t believe in global conspiracies or tin-foil hats. I just stay calm and rational. That’s probably why I see what’s hidden, even in plain sight. I could give dozens of examples, but: • If your critical thinking is sharp – that’s enough • If not – even hundreds of examples won’t convince you I’ll explain why this happens and how the mechanism works when the time comes. Not now. For now, I want to see if this content actually matters to you. I don’t want to look crazy. Hit 200 likes – and I’ll know I can share these patterns from time to time.

Denistratos

46,030 views • 7 months ago

I put together a collage of the iconic photos coming out of Iran. Wow. 47 Years of Dying is Over. Today, Iran Lives. They said they were broken. They said they were silenced. They were wrong. Watch these images closely. Look at the woman in this video—bleeding, elderly, yet standing taller than the giants who try to crush her. When she shouts, "I am not afraid. I have been dying for 47 years already," she isn’t just speaking for herself. She is the voice of a civilization that has been held hostage for nearly half a century. While the world looks away, Iranians are standing in the freezing snow—temperatures that would break most—unveiling the Imperial Lion & Sun. This isn't just a flag; it’s a reclamation of identity. It’s a return to the light. The fear has shifted. It no longer belongs to the people. It belongs to the tyrants. The Lion has awakened. The sun is rising. It’s game over. 🦁☀️ #IranRevolution #FreeIran #LionAndSun #WomenLifeFreedom #IranProtests The same damn BBC that was running 24/7 "Free Palestine" BS propaganda is now totally deaf and mute on Iranians seeking freedom from their Islamist terrorist government. Tells you everything you need to know. Thank God for X. Share. Support the people.

I put together a collage of the iconic photos coming out of Iran. Wow. 47 Years of Dying is Over. Today, Iran Lives. They said they were broken. They said they were silenced. They were wrong. Watch these images closely. Look at the woman in this video—bleeding, elderly, yet standing taller than the giants who try to crush her. When she shouts, "I am not afraid. I have been dying for 47 years already," she isn’t just speaking for herself. She is the voice of a civilization that has been held hostage for nearly half a century. While the world looks away, Iranians are standing in the freezing snow—temperatures that would break most—unveiling the Imperial Lion & Sun. This isn't just a flag; it’s a reclamation of identity. It’s a return to the light. The fear has shifted. It no longer belongs to the people. It belongs to the tyrants. The Lion has awakened. The sun is rising. It’s game over. 🦁☀️ #IranRevolution #FreeIran #LionAndSun #WomenLifeFreedom #IranProtests The same damn BBC that was running 24/7 "Free Palestine" BS propaganda is now totally deaf and mute on Iranians seeking freedom from their Islamist terrorist government. Tells you everything you need to know. Thank God for X. Share. Support the people.

Afshine Emrani MD FACC

57,691 views • 8 months ago

here's how the whole thing works. claude code doesn't care what's behind the API. it just sends requests and expects responses. so i pointed it at my own machine instead of anthropic's servers. llama-server runs the model locally. LiteLLM sits in between and translates the API format. claude code thinks it's talking to claude. it's talking to qwen on localhost. the setup: 2x 3090s, 38 layers on GPU, 10 on CPU. 128K context window. generation is only 7 tok/s but the tradeoff is worth it. 128K means the agent can hold an entire project in memory without losing context midtask. claude code alone loads a 17.5K token system prompt on every request. tool definitions, safety rules, agent behavior. that's your baseline before you even say hello. pushed as far as i could tonight. what surprised me most wasn't the speed. it was the iteration quality. first prompt gave me a working particle sim. second prompt, the model read its own 564 lines, understood the architecture, and added trails, explosions, gravity wells, bloom effects. no handholding. 4bit quantized. 45GB on two consumer cards. running a full coding agent autonomously. detailed article coming. full benchmarks, hardware breakdowns, engine debugging, code quality. everything from setup to what broke and why.

here's how the whole thing works. claude code doesn't care what's behind the API. it just sends requests and expects responses. so i pointed it at my own machine instead of anthropic's servers. llama-server runs the model locally. LiteLLM sits in between and translates the API format. claude code thinks it's talking to claude. it's talking to qwen on localhost. the setup: 2x 3090s, 38 layers on GPU, 10 on CPU. 128K context window. generation is only 7 tok/s but the tradeoff is worth it. 128K means the agent can hold an entire project in memory without losing context midtask. claude code alone loads a 17.5K token system prompt on every request. tool definitions, safety rules, agent behavior. that's your baseline before you even say hello. pushed as far as i could tonight. what surprised me most wasn't the speed. it was the iteration quality. first prompt gave me a working particle sim. second prompt, the model read its own 564 lines, understood the architecture, and added trails, explosions, gravity wells, bloom effects. no handholding. 4bit quantized. 45GB on two consumer cards. running a full coding agent autonomously. detailed article coming. full benchmarks, hardware breakdowns, engine debugging, code quality. everything from setup to what broke and why.

Sudo su

37,623 views • 6 months ago

As a graphics engine coder I think when you look at a flickering bug like this one in the video below it’s not immediately obvious what is going on. The key here is observation - to study this flickering/bugged render carefully - what do we see? Firstly for me it was very obvious that nearly all of the scene shadows were flashing on and off - but (but!) there was a secondary issue where some buildings and parts of the sky were also flashing purple. Hmmmm. Interesting. I initially thought then this might be two separate bugs - but because the sky purple element could only based on full screen post fx and not 3D rendering I looked at this first with a few GPU captures to step through all our post processing to find the rendering stage which made these pixels turn purple: When I did this I found the colour 3D texture LUT grading that makes our different biomes have unique colour palettes was going very wrong - colours near 0 or 1 were wrapping and making the purple elements that we see in the said sky and base parts. The only way this could happen was if the texture was corrupt (which it was not) or if the 3D texture sampling was wrapping and not clamped as intended. That was the Eureka moment - because if the post fx had the wrong texture sampler then the disappearing shadows which also require an exact texture sampler for comparing depth might be also wrong because of the same kind of texture sampling issue! So with this idea that the engine was using the wrong texture samplers, but only in very high draw call scenes like the big base here I the looked at some engine limits and found the bug very quickly - a circular dx12 descriptor buffer for samplers running out over multiple frames, reusing the wrong data for new scenes inflight. Hence the flickering, as the GPU randomly got wrong samplers for some post textures or shadow depth. Easy to fix with triple limits for future expansion and also adding an assert/debug spam in case this limit is ever reached again - QA testers would see this message and report if they ever saw a flicker with this style of bug. My bug and my bad from 2017 porting NMS to DX12 without foreseeing how massively complex bases and our game would grow.

As a graphics engine coder I think when you look at a flickering bug like this one in the video below it’s not immediately obvious what is going on. The key here is observation - to study this flickering/bugged render carefully - what do we see? Firstly for me it was very obvious that nearly all of the scene shadows were flashing on and off - but (but!) there was a secondary issue where some buildings and parts of the sky were also flashing purple. Hmmmm. Interesting. I initially thought then this might be two separate bugs - but because the sky purple element could only based on full screen post fx and not 3D rendering I looked at this first with a few GPU captures to step through all our post processing to find the rendering stage which made these pixels turn purple: When I did this I found the colour 3D texture LUT grading that makes our different biomes have unique colour palettes was going very wrong - colours near 0 or 1 were wrapping and making the purple elements that we see in the said sky and base parts. The only way this could happen was if the texture was corrupt (which it was not) or if the 3D texture sampling was wrapping and not clamped as intended. That was the Eureka moment - because if the post fx had the wrong texture sampler then the disappearing shadows which also require an exact texture sampler for comparing depth might be also wrong because of the same kind of texture sampling issue! So with this idea that the engine was using the wrong texture samplers, but only in very high draw call scenes like the big base here I the looked at some engine limits and found the bug very quickly - a circular dx12 descriptor buffer for samplers running out over multiple frames, reusing the wrong data for new scenes inflight. Hence the flickering, as the GPU randomly got wrong samplers for some post textures or shadow depth. Easy to fix with triple limits for future expansion and also adding an assert/debug spam in case this limit is ever reached again - QA testers would see this message and report if they ever saw a flicker with this style of bug. My bug and my bad from 2017 porting NMS to DX12 without foreseeing how massively complex bases and our game would grow.

Martin Griffiths

72,828 views • 1 year ago

NEVER open Meta Ads Manager to launch a campaign again. 8 ads. 4 minutes. Straight from a Google Drive folder. → No more 264 clicks to ship 8 ads → No more 2 hours of execution tax per launch → No more pasting UTMs 8 times and hoping nothing breaks → No more writing headlines and CTAs by hand → No more crashes on the 4th ad upload Just one message → autonomous Meta campaign launches running through Agent Mode. Here's how it works: → Drive Pull Engine (pulls 8 creatives straight from a folder, no manual upload) → Voice-Matched Copy Generation (headlines, CTAs, primary text in your brand voice) → UTM Application Layer (applies per-campaign tags automatically, no paste errors) → Ad Set Structuring (duplicates winning structures, applies new creatives) → Preview + Approval Gate (nothing goes live without you seeing it) → Direct Meta Deployment (Ads Manager never opens) Built on AI-first infrastructure. Runs on natural language, not clicks. 4-minute launches vs 2-hour manual builds. The execution tax is dead. I put the 20 exact prompts I use to run a full Meta account through Agent Mode (launch, pause, scale, bulk URL swap, UTM fix) into a playbook. Reply "AGENT" + repost, I'll DM the playbook + Agent Mode access.

Aryan Mahajan

23,906 views • 4 months ago

Introducing the Agent Virtual Machine (AVM) Think V8 for agents. AI agents are currently running on your computer with no unified security, no resource limits, and no visibility into what data they're sending out. Every agent framework builds its own security model, its own sandboxing, its own permission system. You configure each one separately. You audit each one separately. You hope you didn't miss anything in any of them. The AVM changes this. It's a single runtime daemon (avmd) that sits between every agent framework and your operating system. Install it once, configure one policy file, and every agent on your machine runs inside it - regardless of which framework built it. The AVM enforces security (91-pattern injection scanner, tool/file/network ACLs, approval prompts), protects your privacy (classifies every outbound byte for PII, credentials, and financial data - blocks or alerts in real-time), and governs resources (you say "50% CPU, 4GB RAM" and the AVM fair-shares it across all agents, halting any that exceed their budget). One config. One audit command. One kill switch. The architectural model is V8 for agents. Chrome, Node.js, and Deno are different products but they share V8 as their execution engine. Agent frameworks bring the UX. The AVM brings the trust. Where needed, AVM can also generate zero-knowledge proofs of agent execution via 25 purpose-built opcodes and 6 proof systems, providing the foundational pillar for the agent-to-agent economy. AVM v0.1.0 - Changelog - Security gate: 5-layer injection scanner with 91 compiled regex patterns. Every input and output scanned. Fail-closed - nothing passes without clearing the gate. - Privacy layer: Classifies all outbound data for PII, credentials, and financial info (27 detection patterns + Luhn validation). Block, ask, warn, or allow per category. Tamper-evident hash-chained log of every egress event. - Resource governor: User sets system-wide caps (CPU/memory/disk/network). AVM fair-shares across all agents. Gas budget per agent - when gas runs out, execution halts. No agent starves your machine. - Sandbox execution: Real code execution in isolated process sandboxes (rlimits, env sanitization) or Docker containers (--cap-drop ALL, --network none, --read-only). AVM auto-selects the tier - agents never choose their own sandbox. - Approval flow: Dangerous operations (file writes, shell commands, network requests) trigger interactive approval prompts. 5-minute timeout auto-denies. Every decision logged. - CLI dashboard: hyperspace-avm top shows all running agents, resource usage, gas budgets, security events, and privacy stats in one live-updating screen. - Node.js SDK: Zero-dependency hyperspace/avm package. AVM.tryConnect() for graceful fallback - if avmd isn't running, the agent framework uses its own execution path. OpenClaw adapter example included. - One config for all agents: ~/.hyperspace/avm-policy.json governs every agent framework on your machine. One file. One audit. One kill switch.

Introducing the Agent Virtual Machine (AVM) Think V8 for agents. AI agents are currently running on your computer with no unified security, no resource limits, and no visibility into what data they're sending out. Every agent framework builds its own security model, its own sandboxing, its own permission system. You configure each one separately. You audit each one separately. You hope you didn't miss anything in any of them. The AVM changes this. It's a single runtime daemon (avmd) that sits between every agent framework and your operating system. Install it once, configure one policy file, and every agent on your machine runs inside it - regardless of which framework built it. The AVM enforces security (91-pattern injection scanner, tool/file/network ACLs, approval prompts), protects your privacy (classifies every outbound byte for PII, credentials, and financial data - blocks or alerts in real-time), and governs resources (you say "50% CPU, 4GB RAM" and the AVM fair-shares it across all agents, halting any that exceed their budget). One config. One audit command. One kill switch. The architectural model is V8 for agents. Chrome, Node.js, and Deno are different products but they share V8 as their execution engine. Agent frameworks bring the UX. The AVM brings the trust. Where needed, AVM can also generate zero-knowledge proofs of agent execution via 25 purpose-built opcodes and 6 proof systems, providing the foundational pillar for the agent-to-agent economy. AVM v0.1.0 - Changelog - Security gate: 5-layer injection scanner with 91 compiled regex patterns. Every input and output scanned. Fail-closed - nothing passes without clearing the gate. - Privacy layer: Classifies all outbound data for PII, credentials, and financial info (27 detection patterns + Luhn validation). Block, ask, warn, or allow per category. Tamper-evident hash-chained log of every egress event. - Resource governor: User sets system-wide caps (CPU/memory/disk/network). AVM fair-shares across all agents. Gas budget per agent - when gas runs out, execution halts. No agent starves your machine. - Sandbox execution: Real code execution in isolated process sandboxes (rlimits, env sanitization) or Docker containers (--cap-drop ALL, --network none, --read-only). AVM auto-selects the tier - agents never choose their own sandbox. - Approval flow: Dangerous operations (file writes, shell commands, network requests) trigger interactive approval prompts. 5-minute timeout auto-denies. Every decision logged. - CLI dashboard: hyperspace-avm top shows all running agents, resource usage, gas budgets, security events, and privacy stats in one live-updating screen. - Node.js SDK: Zero-dependency hyperspace/avm package. AVM.tryConnect() for graceful fallback - if avmd isn't running, the agent framework uses its own execution path. OpenClaw adapter example included. - One config for all agents: ~/.hyperspace/avm-policy.json governs every agent framework on your machine. One file. One audit. One kill switch.

Varun

142,992 views • 5 months ago

Internet legends rarely get a second act. LimeWire might be one of them. If you were around in the early web era, you remember the name. LimeWire wasn’t just software. It’s where many people discovered music, shared files, and learned how the internet actually worked. Now LimeWire is building something very different. LimeWire Network is its decentralized storage layer on BNB Chain, built to compete with traditional cloud providers while staying permissionless and token powered. And the traction is already there. The ecosystem has crossed 5M+ users with over 400 TB stored, even before full decentralization. One monthly report recorded: • 13.77M new files uploaded • 497 TB shared • 2.1M active users Real usage. The $LMWR token powers payments, rewards node operators, and keeps incentives aligned across the network. Most decentralized storage projects fight for adoption. LimeWire starts with something you cannot fabricate quickly: global recognition. People already trust the name. That familiarity lowers the barrier to entry and creates a real expansion advantage. Legacy awareness paired with live infrastructure and token utility is uncommon. Running on BNB Chain also gives the network room to scale if growth continues. The real signal is the transition. LimeWire is no longer just part of internet history. It is being rebuilt as infrastructure. Worth watching closely. Are you paying attention to LimeWire because of the nostalgia, the network growth, or the long term role of LMWR? What pulled you in first?

Internet legends rarely get a second act. LimeWire might be one of them. If you were around in the early web era, you remember the name. LimeWire wasn’t just software. It’s where many people discovered music, shared files, and learned how the internet actually worked. Now LimeWire is building something very different. LimeWire Network is its decentralized storage layer on BNB Chain, built to compete with traditional cloud providers while staying permissionless and token powered. And the traction is already there. The ecosystem has crossed 5M+ users with over 400 TB stored, even before full decentralization. One monthly report recorded: • 13.77M new files uploaded • 497 TB shared • 2.1M active users Real usage. The $LMWR token powers payments, rewards node operators, and keeps incentives aligned across the network. Most decentralized storage projects fight for adoption. LimeWire starts with something you cannot fabricate quickly: global recognition. People already trust the name. That familiarity lowers the barrier to entry and creates a real expansion advantage. Legacy awareness paired with live infrastructure and token utility is uncommon. Running on BNB Chain also gives the network room to scale if growth continues. The real signal is the transition. LimeWire is no longer just part of internet history. It is being rebuilt as infrastructure. Worth watching closely. Are you paying attention to LimeWire because of the nostalgia, the network growth, or the long term role of LMWR? What pulled you in first?

RYU

26,099 views • 7 months ago

The future of housework just leaked on GitHub and nobody is talking about it. knox byte just open sourced a framework that coordinates swarms of Unitree G1 humanoid robots to clean your entire house on their own. It's called ARGOS. You tell it "clean the bedroom" in plain English and 2+ G1 robots split the room into zones, sweep in parallel, and sync up for the tasks that need four hands like making the bed or moving furniture. The Claude API decomposes your sentence into a task graph. An auction system makes every robot bid on every task based on distance, battery, and current load. The cheapest robot wins. Cooperative jobs go to the cheapest team. Here's what makes this different from every demo video Boston Dynamics keeps teasing: → 12 cleaning tasks baked in sweeping, mopping, wiping, vacuuming, taking out trash, making the bed, changing sheets, moving furniture, sorting items → 3 policy architectures running underneath OpenVLA-7B for language tasks, Diffusion Policy for floor coverage, ACT for dexterous bimanual work → Train it on your own footage record yourself cleaning, run one command, it extracts poses, builds a LeRobot dataset, and LoRA fine-tunes the policy → PEFA protocol for cooperative work Propose, Execute, Feedback, Adjust. If one robot fails halfway through making the bed, the team replans and retries → Full MuJoCo simulation so you test policies before pushing them to real hardware → Silver and cyan terminal dashboard that shows live fleet status, zone maps, task queues, and battery levels in real time The G1 robots talk to each other over CycloneDDS mesh using Unitree's native SDK. No cloud. No middleware. The whole thing runs on a Jetson Orin inside each robot. The wildest part is the training pipeline. Drop cleaning videos into a folder, run argos train ingest, and the framework does the entire pipeline frame extraction, pose estimation, action labeling, HDF5 dataset, fine-tune, evaluate in sim, deploy to robot. One command per stage. Unitree G1s already exist. The framework to make them clean your house just hit GitHub. 52 stars. MIT License. 100% Opensource.

The future of housework just leaked on GitHub and nobody is talking about it. knox byte just open sourced a framework that coordinates swarms of Unitree G1 humanoid robots to clean your entire house on their own. It's called ARGOS. You tell it "clean the bedroom" in plain English and 2+ G1 robots split the room into zones, sweep in parallel, and sync up for the tasks that need four hands like making the bed or moving furniture. The Claude API decomposes your sentence into a task graph. An auction system makes every robot bid on every task based on distance, battery, and current load. The cheapest robot wins. Cooperative jobs go to the cheapest team. Here's what makes this different from every demo video Boston Dynamics keeps teasing: → 12 cleaning tasks baked in sweeping, mopping, wiping, vacuuming, taking out trash, making the bed, changing sheets, moving furniture, sorting items → 3 policy architectures running underneath OpenVLA-7B for language tasks, Diffusion Policy for floor coverage, ACT for dexterous bimanual work → Train it on your own footage record yourself cleaning, run one command, it extracts poses, builds a LeRobot dataset, and LoRA fine-tunes the policy → PEFA protocol for cooperative work Propose, Execute, Feedback, Adjust. If one robot fails halfway through making the bed, the team replans and retries → Full MuJoCo simulation so you test policies before pushing them to real hardware → Silver and cyan terminal dashboard that shows live fleet status, zone maps, task queues, and battery levels in real time The G1 robots talk to each other over CycloneDDS mesh using Unitree's native SDK. No cloud. No middleware. The whole thing runs on a Jetson Orin inside each robot. The wildest part is the training pipeline. Drop cleaning videos into a folder, run argos train ingest, and the framework does the entire pipeline frame extraction, pose estimation, action labeling, HDF5 dataset, fine-tune, evaluate in sim, deploy to robot. One command per stage. Unitree G1s already exist. The framework to make them clean your house just hit GitHub. 52 stars. MIT License. 100% Opensource.

Guri Singh

27,404 views • 3 months ago

My dad called me at 7 AM on a Sunday. I need you to look at something. Do not tell your mother. I thought he was sick. He sent me a screenshot. A Claude terminal. Live Polymarket odds. Green numbers. +$14,200. He is 56. Retired electrician. Calls WiFi the internet box. Dad what the fuck is this? I found an article about connecting Claude to Polymarket. Told Claude to install it. One prompt. Four commands. 45 tools running on his old Dell laptop. Live orderbooks. Volume spikes. Closing deadlines. All inside Claude. He does not know what MCP means. Does not care. Every morning at 5 AM. Before mom wakes up. Reading glasses and black coffee. He asks Claude: What is closing today with mispriced odds? Claude cross-references markets in one call. Spots where the price disagrees with the news. He does not out-execute bots. He out-waits them. Quarter Kelly. $500 from his fishing fund. Last 60 days: 680 trades 90.2% win rate +$14,200 Best trade: Claude alert pinged him during Sunday football. Volume spike on a geopolitics market. Entry $0.09. Resolved $1.00. +$2,800. Mom thought he was checking the score. He was not checking the score. Last week he booked her a cruise. Said he had been saving since January. He has not saved anything since January. When are you telling her? When I hit $25K. I am buying the lake house. Dad you do not even know what an API is. I do not need to. The machine knows. He still calls Claude the machine. The machine prints money. I built the entire framework he used: Claude MCP setup Polymarket mispricing detection Quarter Kelly position sizing Volume spike alerts Cron-based market scanning The system runs 24/7. Finds mispriced odds before the crowd adjusts. Executes before the edge compresses. No emotions. No FOMO. Just math. 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 mispricing system this week. Start with $500. Scale on evidence.

My dad called me at 7 AM on a Sunday. I need you to look at something. Do not tell your mother. I thought he was sick. He sent me a screenshot. A Claude terminal. Live Polymarket odds. Green numbers. +$14,200. He is 56. Retired electrician. Calls WiFi the internet box. Dad what the fuck is this? I found an article about connecting Claude to Polymarket. Told Claude to install it. One prompt. Four commands. 45 tools running on his old Dell laptop. Live orderbooks. Volume spikes. Closing deadlines. All inside Claude. He does not know what MCP means. Does not care. Every morning at 5 AM. Before mom wakes up. Reading glasses and black coffee. He asks Claude: What is closing today with mispriced odds? Claude cross-references markets in one call. Spots where the price disagrees with the news. He does not out-execute bots. He out-waits them. Quarter Kelly. $500 from his fishing fund. Last 60 days: 680 trades 90.2% win rate +$14,200 Best trade: Claude alert pinged him during Sunday football. Volume spike on a geopolitics market. Entry $0.09. Resolved $1.00. +$2,800. Mom thought he was checking the score. He was not checking the score. Last week he booked her a cruise. Said he had been saving since January. He has not saved anything since January. When are you telling her? When I hit $25K. I am buying the lake house. Dad you do not even know what an API is. I do not need to. The machine knows. He still calls Claude the machine. The machine prints money. I built the entire framework he used: Claude MCP setup Polymarket mispricing detection Quarter Kelly position sizing Volume spike alerts Cron-based market scanning The system runs 24/7. Finds mispriced odds before the crowd adjusts. Executes before the edge compresses. No emotions. No FOMO. Just math. 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 mispricing system this week. Start with $500. Scale on evidence.

Himanshu Kumar

18,287 views • 2 months ago

Out of Ryan Cohen’s entire interview, the line that should be burned into everyone’s brain is this: “If I were running the business, it would be making a lot more money.” That is the whole thesis. Not hype. Not memes. Not nostalgia. A founder-operator looked at eBay, saw one of the most recognized marketplaces on earth, saw billions being spent with barely any user growth, saw a company sitting on trust, traffic, collectibles, payments, authentication, and global commerce infrastructure… Basically said: This asset is wildly under-earning. That is why this matters. GameStop is not trying to buy eBay because it wants a bigger logo. Ryan Cohen is saying eBay should be worth much more, and he believes he can turn it into something worth hundreds of billions of dollars. GameStop’s proposal is reportedly $125 per share, valuing eBay around $56 billion, with Cohen prepared to push the offer directly to shareholders if needed. That is the biggest takeaway. He is not talking like a meme-stock mascot. He is talking like an owner. Cut the waste. Fix the machine. Use GameStop’s stores as physical commerce nodes. Build authentication, fulfillment, live commerce, collectibles, and marketplace trust into one ecosystem. The wild part? I believe him. Because Ryan Cohen has already shown what he does when people call a business dead. He cuts the fat. He protects the balance sheet. He ignores the noise. He lets the work speak. Wall Street keeps asking what GameStop’s plan is. Maybe the plan is simple: Find underperforming assets with massive cultural value, put them under owner-led discipline, and make them print. eBay should be making a lot more money. Ryan Cohen just said the quiet part out loud.

MD

60,905 views • 4 months ago