Загрузка видео...

Не удалось загрузить видео

На главную

BREAKING: LLMs just learned to COMPUTE for real, it's mean NO MORE GUESSING math. Chinese college kid Guo Hanjiang vibe-coded MiroFish in 10 days (23k+ GitHub stars, $4.1M from Shanda in 24h) - the AI swarm simulator that’s already printing. ByteDance (VolcEngine) dropped the nuclear upgrade: OpenViking - structured...

slash1sol's profile picture

slash1s

5,063 subscribers

156,699 просмотров • 3 месяцев назад •via X (Twitter)

Наука и технологии
LIVE

Anya Rossi• Live Now

Private livecam show

Комментарии: 0

Нет доступных комментариев

Здесь появятся комментарии из оригинального поста

Похожие видео

HTML Artifacts are a big part of how I work with agents now. Artifacts can be more than just static files. When combined with agents, they can take action or help you take action. This unlocks all kinds of interesting ways to work with agents. This is clearly the future. Check out this writing and scheduler artifact I built in a few minutes. It uses a bit of HTML and JS. All the data is in markdown (Obsidian vaults), so the agent can access and modify it at any time. No DB needed. No sophisticated functionalities. The agent decides all that for me based on the skills, context, and memory it has access to. The best part about this simple stack is that all the important information stays with me. This has allowed me to build a recursive self-improving system and automations that can better tap into coding agents like Codex or Claude Code. I could have paid or built an entire app for scheduling posts, and there are so many of them out there. But I don't need to. I've realized a simple artifact does the job. And the simplicity of it is actually an advantage. Very little maintenance for very high returns on personalization, time, and efficiency. The other benefit of this is that I can add features as I please. That level of personalization feels magical, and we should all be pursuing more of it. All of this just keeps compounding. Of course, this example is just about writing. But I have similar artifacts for research, design, experimentation, evaluation, and so much more. And no, I didn't actually publish the post example I shared in the clip. It was just for demonstration purposes. I actually spend more time than this when writing together with agents. Lastly, having built my own agent orchestrator tool has made me realize that simplifying the tool stack is a superpower. If you are curious about how all this works, I will do a live session next week:
0:16

HTML Artifacts are a big part of how I work with agents now. Artifacts can be more than just static files. When combined with agents, they can take action or help you take action. This unlocks all kinds of interesting ways to work with agents. This is clearly the future. Check out this writing and scheduler artifact I built in a few minutes. It uses a bit of HTML and JS. All the data is in markdown (Obsidian vaults), so the agent can access and modify it at any time. No DB needed. No sophisticated functionalities. The agent decides all that for me based on the skills, context, and memory it has access to. The best part about this simple stack is that all the important information stays with me. This has allowed me to build a recursive self-improving system and automations that can better tap into coding agents like Codex or Claude Code. I could have paid or built an entire app for scheduling posts, and there are so many of them out there. But I don't need to. I've realized a simple artifact does the job. And the simplicity of it is actually an advantage. Very little maintenance for very high returns on personalization, time, and efficiency. The other benefit of this is that I can add features as I please. That level of personalization feels magical, and we should all be pursuing more of it. All of this just keeps compounding. Of course, this example is just about writing. But I have similar artifacts for research, design, experimentation, evaluation, and so much more. And no, I didn't actually publish the post example I shared in the clip. It was just for demonstration purposes. I actually spend more time than this when writing together with agents. Lastly, having built my own agent orchestrator tool has made me realize that simplifying the tool stack is a superpower. If you are curious about how all this works, I will do a live session next week:

elvis

18,374 просмотров • 2 месяцев назад

Holy shit... Microsoft open sourced an inference framework that runs a 100B parameter LLM on a single CPU. It's called BitNet. And it does what was supposed to be impossible. No GPU. No cloud. No $10K hardware setup. Just your laptop running a 100-billion parameter model at human reading speed. Here's how it works: Every other LLM stores weights in 32-bit or 16-bit floats. BitNet uses 1.58 bits. Weights are ternary just -1, 0, or +1. That's it. No floats. No expensive matrix math. Pure integer operations your CPU was already built for. The result: - 100B model runs on a single CPU at 5-7 tokens/second - 2.37x to 6.17x faster than llama.cpp on x86 - 82% lower energy consumption on x86 CPUs - 1.37x to 5.07x speedup on ARM (your MacBook) - Memory drops by 16-32x vs full-precision models The wildest part: Accuracy barely moves. BitNet b1.58 2B4T their flagship model was trained on 4 trillion tokens and benchmarks competitively against full-precision models of the same size. The quantization isn't destroying quality. It's just removing the bloat. What this actually means: - Run AI completely offline. Your data never leaves your machine - Deploy LLMs on phones, IoT devices, edge hardware - No more cloud API bills for inference - AI in regions with no reliable internet The model supports ARM and x86. Works on your MacBook, your Linux box, your Windows machine. 27.4K GitHub stars. 2.2K forks. Built by Microsoft Research. 100% Open Source. MIT License.
0:13

Holy shit... Microsoft open sourced an inference framework that runs a 100B parameter LLM on a single CPU. It's called BitNet. And it does what was supposed to be impossible. No GPU. No cloud. No $10K hardware setup. Just your laptop running a 100-billion parameter model at human reading speed. Here's how it works: Every other LLM stores weights in 32-bit or 16-bit floats. BitNet uses 1.58 bits. Weights are ternary just -1, 0, or +1. That's it. No floats. No expensive matrix math. Pure integer operations your CPU was already built for. The result: - 100B model runs on a single CPU at 5-7 tokens/second - 2.37x to 6.17x faster than llama.cpp on x86 - 82% lower energy consumption on x86 CPUs - 1.37x to 5.07x speedup on ARM (your MacBook) - Memory drops by 16-32x vs full-precision models The wildest part: Accuracy barely moves. BitNet b1.58 2B4T their flagship model was trained on 4 trillion tokens and benchmarks competitively against full-precision models of the same size. The quantization isn't destroying quality. It's just removing the bloat. What this actually means: - Run AI completely offline. Your data never leaves your machine - Deploy LLMs on phones, IoT devices, edge hardware - No more cloud API bills for inference - AI in regions with no reliable internet The model supports ARM and x86. Works on your MacBook, your Linux box, your Windows machine. 27.4K GitHub stars. 2.2K forks. Built by Microsoft Research. 100% Open Source. MIT License.

Guri Singh

2,180,357 просмотров • 4 месяцев назад

Still using AI tools that can’t turn your ideas into real apps? Most tools can write bits of code but fail when it’s time to actually build something complete. That’s where Replit ⠕ changes everything. I just asked Replit: “can you redesign this website in professional way.." In minutes, it built the entire website— clean design, full logic, animations, and dark/light mode included. No setup. No debugging. Just pure vibe coding. Here’s why Replit stands out 👇 ✅ Build with plain English – Describe what you want, Replit builds it. ✅ All-in-one platform – Code, AI help, hosting, database, and auth in one place. ✅ Create anywhere – Use the Replit mobile app on iOS or Android. ✅ Instant integrations – Connect to Notion, Slack, Google Drive, Dropbox, and more. ✅ From design to live app – Import from Figma and launch in minutes. No chaos. No micromanaging. Just you, your idea, and Replit doing the hard work. 👉 Try it now: 🎁 Get $10 credit when you purchase Replit Core via my link: #Replit #VibeCoding #AIApps #NoCode #BuildWithReplit #AItools #ReplitCore
0:19

Still using AI tools that can’t turn your ideas into real apps? Most tools can write bits of code but fail when it’s time to actually build something complete. That’s where Replit ⠕ changes everything. I just asked Replit: “can you redesign this website in professional way.." In minutes, it built the entire website— clean design, full logic, animations, and dark/light mode included. No setup. No debugging. Just pure vibe coding. Here’s why Replit stands out 👇 ✅ Build with plain English – Describe what you want, Replit builds it. ✅ All-in-one platform – Code, AI help, hosting, database, and auth in one place. ✅ Create anywhere – Use the Replit mobile app on iOS or Android. ✅ Instant integrations – Connect to Notion, Slack, Google Drive, Dropbox, and more. ✅ From design to live app – Import from Figma and launch in minutes. No chaos. No micromanaging. Just you, your idea, and Replit doing the hard work. 👉 Try it now: 🎁 Get $10 credit when you purchase Replit Core via my link: #Replit #VibeCoding #AIApps #NoCode #BuildWithReplit #AItools #ReplitCore

Fakhr

24,152 просмотров • 8 месяцев назад

I genuinely think the Terafab is going to end up being one of the biggest moves ever made in human history to secure the future of AI... and I think most people still don’t fully see what Elon is trying to do here. The signs are clear to me. This is Tesla, xAI, and SpaceX essentially hinting to us that they are not going to wait on the world to give them the compute the team needs. They are going to build it themselves at a scale no one has ever attempted. When you really break it down, it gets a bit nutty. This is going to be a fully vertically integrated chip factory that will be producing over 1 terawatt of AI compute per year. This is NEXT LEVEL BIG. Today, AI is limited by chips. You can have the best models, the best engineers, the best everything... but if you don’t have enough compute, you will eventually hit a wall. Elon told us, the world can only supply a tiny fraction of the chips his companies will need. So this is the solution. Terafab puts everything under one roof like design, manufacturing, memory, packaging, testing, which means that they can build chips very fast.. like really fast. I'm talking about 100-200 billion custom AI chips per year at full capacity. Chips designed specifically for: • Tesla cars and Optimus robots • xAI models • Space-based compute You see, while other companies and CEOs are thinking Earth, Elon is planning for AI in space. Around ~80% of the compute is expected to go orbital, powered by solar energy bc Earth simply doesn’t have enough electricity. The U.S. grid is only about ~0.5 terawatts, while space has basically UNLIMITED energy if you can capture it. And this is the steps to get it: Starship launches → space compute → solar-powered AI → feeds back into everything to Earth. Bro... Elon and his companies are playing at a whole different level... And this is why I keep telling people that the Terafab is going to be the secret ingredient that will be the real unlock for everything: • Robotaxis at scale • Billions of Optimus robots • Massive AI models running 24/7 • Future off-world, other planet infrastructure Without these chips, none of this can happen... but with the Terafab, all of this becomes possible. That’s why Elon is calling it “the final missing piece.” I agree.
0:10

I genuinely think the Terafab is going to end up being one of the biggest moves ever made in human history to secure the future of AI... and I think most people still don’t fully see what Elon is trying to do here. The signs are clear to me. This is Tesla, xAI, and SpaceX essentially hinting to us that they are not going to wait on the world to give them the compute the team needs. They are going to build it themselves at a scale no one has ever attempted. When you really break it down, it gets a bit nutty. This is going to be a fully vertically integrated chip factory that will be producing over 1 terawatt of AI compute per year. This is NEXT LEVEL BIG. Today, AI is limited by chips. You can have the best models, the best engineers, the best everything... but if you don’t have enough compute, you will eventually hit a wall. Elon told us, the world can only supply a tiny fraction of the chips his companies will need. So this is the solution. Terafab puts everything under one roof like design, manufacturing, memory, packaging, testing, which means that they can build chips very fast.. like really fast. I'm talking about 100-200 billion custom AI chips per year at full capacity. Chips designed specifically for: • Tesla cars and Optimus robots • xAI models • Space-based compute You see, while other companies and CEOs are thinking Earth, Elon is planning for AI in space. Around ~80% of the compute is expected to go orbital, powered by solar energy bc Earth simply doesn’t have enough electricity. The U.S. grid is only about ~0.5 terawatts, while space has basically UNLIMITED energy if you can capture it. And this is the steps to get it: Starship launches → space compute → solar-powered AI → feeds back into everything to Earth. Bro... Elon and his companies are playing at a whole different level... And this is why I keep telling people that the Terafab is going to be the secret ingredient that will be the real unlock for everything: • Robotaxis at scale • Billions of Optimus robots • Massive AI models running 24/7 • Future off-world, other planet infrastructure Without these chips, none of this can happen... but with the Terafab, all of this becomes possible. That’s why Elon is calling it “the final missing piece.” I agree.

Teslaconomics

25,469 просмотров • 3 месяцев назад

Chinese quant built a simulation of how SPX price reacts to any global event. He’s already made over $100k - with full blockchain proof. He knows exactly where price will go. More than 40 years of SPX trading history have been loaded into MiroFish simulator (18k stars on GitHub) AI analyzed every single moment in that trading history. Now this guy has a fully functional SPX price prediction system. His wallet: Dozens of successful SPX price-prediction trades and hundreds of tests across other stock markets. Here’s exactly what you need to replicate his stack: - market data APIs (SPX price, use Alpha Vantage or Quandl) - data pipeline (use Python) - feature engineering (for output signals like RSI, MACD) - seed dataset for MiroFish (convert data into structured context) - multi-agent simulation (macro strategist, earnings analyst, sentiment analyst agents etc.) - probability forecast (run different scenarios) - trading / decision Model (SPX futures ES, SPY ETF) Save this pipeline if you want to run a similar simulation on your own data. You can feed the whole thing to your Claude and build your first (even small) simulation model together.
0:18

Chinese quant built a simulation of how SPX price reacts to any global event. He’s already made over $100k - with full blockchain proof. He knows exactly where price will go. More than 40 years of SPX trading history have been loaded into MiroFish simulator (18k stars on GitHub) AI analyzed every single moment in that trading history. Now this guy has a fully functional SPX price prediction system. His wallet: Dozens of successful SPX price-prediction trades and hundreds of tests across other stock markets. Here’s exactly what you need to replicate his stack: - market data APIs (SPX price, use Alpha Vantage or Quandl) - data pipeline (use Python) - feature engineering (for output signals like RSI, MACD) - seed dataset for MiroFish (convert data into structured context) - multi-agent simulation (macro strategist, earnings analyst, sentiment analyst agents etc.) - probability forecast (run different scenarios) - trading / decision Model (SPX futures ES, SPY ETF) Save this pipeline if you want to run a similar simulation on your own data. You can feed the whole thing to your Claude and build your first (even small) simulation model together.

cvxv666

2,400,134 просмотров • 4 месяцев назад

🚨BREAKING… the top-performing 5m & 15m Polymarket Clawdbot setup just became public Sounds insane? 100%. Unreal? NOT at all. If you’re active on Polymarket, this should have your FULL attention. A random late night turned into a small wallet launching a fully automated machine that expanded into ~$1.6M in profit No insider access No affiliation with the Polymarket team Just a developer operating a bot directly connected to Polymarket Profile → Copytrade → I monitored this wallet for weeks and honestly, it barely looked real No narrative setups No discretionary decisions Zero manual execution Everything is fully automated His FULL strategy: 1. 5 & 15-minute BTC & ETH latency arbitrage The bot trades ultra-short Bitcoin and Ethereum markets with 5 & 15-minute expirations - and similar logic applies to fast 5m markets often associated with Clawdbot-style execution. When BTC moves on Binance, Polymarket pricing reacts slower. For around 30 seconds, odds reflect stale data. The system enters during that gap, when YES + NO combined is below $1, waits for repricing, and exits the moment the market corrects. No predictions, no bias - just harvesting mispriced odds 2. Automation over reaction When volatility spikes, humans pause. The system doesn’t. It triggers instantly when the window opens. No emotion, no hesitation, no missed fills. By the time manual traders click, the inefficiency has already disappeared 3. Scale through repetition Each trade earns small spreads, not headline wins. But automation allows continuous execution at scale, every 15 minutes - and on faster 5m rotations running 24/7 without burnout Scale is the edge 19,021 trades placed - irrelevant on their own. Together, they compounded into $1,624,305 in profit, with a largest single gain of $48K and an equity curve that trends almost vertically Bottom line Bots are already competing in a quiet arms race on Polymarket, especially across 5m and 15m markets where Clawdbot-style systems dominate Most traders try to forecast what’s next These systems monetize inefficiencies in real time And as long as latency and structural gaps exist, autonomous bots will continue extracting value
0:18

🚨BREAKING… the top-performing 5m & 15m Polymarket Clawdbot setup just became public Sounds insane? 100%. Unreal? NOT at all. If you’re active on Polymarket, this should have your FULL attention. A random late night turned into a small wallet launching a fully automated machine that expanded into ~$1.6M in profit No insider access No affiliation with the Polymarket team Just a developer operating a bot directly connected to Polymarket Profile → Copytrade → I monitored this wallet for weeks and honestly, it barely looked real No narrative setups No discretionary decisions Zero manual execution Everything is fully automated His FULL strategy: 1. 5 & 15-minute BTC & ETH latency arbitrage The bot trades ultra-short Bitcoin and Ethereum markets with 5 & 15-minute expirations - and similar logic applies to fast 5m markets often associated with Clawdbot-style execution. When BTC moves on Binance, Polymarket pricing reacts slower. For around 30 seconds, odds reflect stale data. The system enters during that gap, when YES + NO combined is below $1, waits for repricing, and exits the moment the market corrects. No predictions, no bias - just harvesting mispriced odds 2. Automation over reaction When volatility spikes, humans pause. The system doesn’t. It triggers instantly when the window opens. No emotion, no hesitation, no missed fills. By the time manual traders click, the inefficiency has already disappeared 3. Scale through repetition Each trade earns small spreads, not headline wins. But automation allows continuous execution at scale, every 15 minutes - and on faster 5m rotations running 24/7 without burnout Scale is the edge 19,021 trades placed - irrelevant on their own. Together, they compounded into $1,624,305 in profit, with a largest single gain of $48K and an equity curve that trends almost vertically Bottom line Bots are already competing in a quiet arms race on Polymarket, especially across 5m and 15m markets where Clawdbot-style systems dominate Most traders try to forecast what’s next These systems monetize inefficiencies in real time And as long as latency and structural gaps exist, autonomous bots will continue extracting value

Shelpid.WI3M

225,724 просмотров • 4 месяцев назад

Claude Cowork Sub-Agents are f*cking cracked 🤯 One prompt → 50 competitor ads analyzed, hooks extracted, and a full creative brief generated. 10 AI agents running in parallel, under 5 minutes. All inside Claude Cowork. Perfect for DTC brands and agencies who are still doing creative research and ad production one task at a time inside Claude. If you're analyzing competitor ads one by one, copying hooks into a spreadsheet manually, writing brief after brief from scratch, and watching Claude's output quality fall off a cliff after the 15th variation because the context window is completely bloated... Sub-agents eliminate the entire bottleneck: → Drop in a spreadsheet of 50 competitor ads and spin up 10 parallel sub-agents → Each sub-agent analyzes 5 ads simultaneously — hooks, angles, CTAs, emotional tone, creative format → They report structured summaries back to the main agent without bloating the context → The main agent synthesizes patterns across all 50 ads into a competitive intel brief → Then spin up another round of sub-agents to generate 30 ad copy variations across 10 personas → Each sub-agent writes for 1-2 personas in a fresh context — so variation 30 is as sharp as variation 1 No analyzing ads one at a time. No context window blowing up halfway through. No copy quality degrading after the first dozen variations. What this gives you: → 50 competitor ads broken down in minutes — hooks, angles, CTAs, formats, all structured → Pattern analysis across the full dataset that you'd miss reviewing ads individually → 30+ ad copy variations with persona-specific messaging that actually stays sharp → A workflow you can save as reusable skills and trigger with one command next time → The same output quality on the last task as the first Built 100% inside Claude Cowork with sub-agents. I put together a full DTC playbook: 5 bulk workflows with copy-paste prompts, the exact sub-agent prompting pattern, batching guidelines, and an honest breakdown of when this setup is worth it vs. when a simpler approach is the better move. Want it for free? > Like this post > Comment "AGENTS" And I'll send it over (must be following so I can DM)
0:10

Claude Cowork Sub-Agents are f*cking cracked 🤯 One prompt → 50 competitor ads analyzed, hooks extracted, and a full creative brief generated. 10 AI agents running in parallel, under 5 minutes. All inside Claude Cowork. Perfect for DTC brands and agencies who are still doing creative research and ad production one task at a time inside Claude. If you're analyzing competitor ads one by one, copying hooks into a spreadsheet manually, writing brief after brief from scratch, and watching Claude's output quality fall off a cliff after the 15th variation because the context window is completely bloated... Sub-agents eliminate the entire bottleneck: → Drop in a spreadsheet of 50 competitor ads and spin up 10 parallel sub-agents → Each sub-agent analyzes 5 ads simultaneously — hooks, angles, CTAs, emotional tone, creative format → They report structured summaries back to the main agent without bloating the context → The main agent synthesizes patterns across all 50 ads into a competitive intel brief → Then spin up another round of sub-agents to generate 30 ad copy variations across 10 personas → Each sub-agent writes for 1-2 personas in a fresh context — so variation 30 is as sharp as variation 1 No analyzing ads one at a time. No context window blowing up halfway through. No copy quality degrading after the first dozen variations. What this gives you: → 50 competitor ads broken down in minutes — hooks, angles, CTAs, formats, all structured → Pattern analysis across the full dataset that you'd miss reviewing ads individually → 30+ ad copy variations with persona-specific messaging that actually stays sharp → A workflow you can save as reusable skills and trigger with one command next time → The same output quality on the last task as the first Built 100% inside Claude Cowork with sub-agents. I put together a full DTC playbook: 5 bulk workflows with copy-paste prompts, the exact sub-agent prompting pattern, batching guidelines, and an honest breakdown of when this setup is worth it vs. when a simpler approach is the better move. Want it for free? > Like this post > Comment "AGENTS" And I'll send it over (must be following so I can DM)

Mike Futia

50,069 просмотров • 4 месяцев назад

I Combined ChatGPT 5.5 Image-2 + Claude Fable 5… And Built This FULL Game in JUST 8 Hours 😱 The World Has Officially Changed Forever Guys… I still can’t believe what I just pulled off. I took ChatGPT 5.5’s new Image-2 to generate every single visual characters, environments, UI, particles, everything and paired it with Claude Fable 5 for the entire codebase. The result? A complete, polished, fully playable game… finished in only 8 hours. No massive team. No months of crunch. No expensive asset packs. Image-2 created mind-blowing art assets on demand. Fable 5 turned those images into real, working code mechanics, physics, AI, animations, menus everything. This hybrid combo is straight-up sorcery. The world has truly changed. We are no longer waiting years for games to be made. One person + these two god-tier AIs just built something that used to require entire studios and huge budgets… in less than a single workday. This is the next level of human civilization. This is what creation looks like from now on. But here’s the crazy part: This free access ends June 22, 2026. After that, you’ll have to pay/subscribe to keep using it. If you’ve been waiting to see what the future of game dev actually looks like… THIS IS IT. Go try it right now before the paywall hits. Don’t sleep on this. Seriously. Drop in the comments: What game should I build next with this insane Image-2 + Fable 5 hybrid? Like if your mind is blown too 🔥 And tag a friend who NEEDS to see this before it’s gone. The future isn’t coming… It’s already here. And it’s free for one more day only. #Fable5 #ChatGPT55 #Image2 #AIHybrid #GameDevRevolution
0:16

I Combined ChatGPT 5.5 Image-2 + Claude Fable 5… And Built This FULL Game in JUST 8 Hours 😱 The World Has Officially Changed Forever Guys… I still can’t believe what I just pulled off. I took ChatGPT 5.5’s new Image-2 to generate every single visual characters, environments, UI, particles, everything and paired it with Claude Fable 5 for the entire codebase. The result? A complete, polished, fully playable game… finished in only 8 hours. No massive team. No months of crunch. No expensive asset packs. Image-2 created mind-blowing art assets on demand. Fable 5 turned those images into real, working code mechanics, physics, AI, animations, menus everything. This hybrid combo is straight-up sorcery. The world has truly changed. We are no longer waiting years for games to be made. One person + these two god-tier AIs just built something that used to require entire studios and huge budgets… in less than a single workday. This is the next level of human civilization. This is what creation looks like from now on. But here’s the crazy part: This free access ends June 22, 2026. After that, you’ll have to pay/subscribe to keep using it. If you’ve been waiting to see what the future of game dev actually looks like… THIS IS IT. Go try it right now before the paywall hits. Don’t sleep on this. Seriously. Drop in the comments: What game should I build next with this insane Image-2 + Fable 5 hybrid? Like if your mind is blown too 🔥 And tag a friend who NEEDS to see this before it’s gone. The future isn’t coming… It’s already here. And it’s free for one more day only. #Fable5 #ChatGPT55 #Image2 #AIHybrid #GameDevRevolution

0AIVerse

26,641 просмотров • 1 месяц назад

Yesterday at Brown University ICERM's workshop on “Agentic Scientific Computing and Scientific Machine Learning” I spoke about “Adaptive Swarms Across Scales”, making the case for scientific AI as systems that can create representations, stress them, fracture them, and enlarge the category in which future representations live. The category here is a composable and breakable working universe of science: data, hypotheses, simulations, measurements, tools, failures, figures, papers, provenance, and the transformations that connect them. Discovery happens when those transformations become executable, inspectable, composable, and capable of changing the world model they operate within. Atomistic modeling gives one category - states, forces, trajectories, observables, boundary conditions, conservation laws. Neural surrogates learn fast morphisms inside or between such categories. But discovery is higher-order: it changes which objects and morphisms are available in the first place: what variables exist, what operations are allowed, what evidence counts, what scale is active, what invariant is being preserved, and what kind of explanation the system is even capable of forming. This is scientific method as adaptive architecture: compression, stress, fracture, recomposition. Fracture matters here because it makes the logic physical: a non-commuting diagram realized in matter. The imposed load, material hierarchy, defect field, and assumed continuum description no longer map cleanly into the observed outcome. The crack is the obstruction and it identifies where the old morphism failed and where a new representation must be introduced. The physical crack and the categorical obstruction are the same event viewed in different substrates. ScienceClaw × Infinite is a machine for constructing and transforming a category of scientific artifacts. Each artifact is typed. Each operation has lineage. Each failed branch remains in the category as reusable structure. The “paper” is no longer the terminal object of science; it is one projection of a larger compositional trace, and it can be generated at any time for consumption by a human or an AI. With that the unit of scientific labor is changing. For most of the twentieth century the unit was the result (a measurement, a theorem, a synthesized molecule). It is now becoming the algorithm that produces results, and after that, the substrate of discovery itself. The static PDF is the wrong terminal object for this regime, and the role of the scientist with it. We now design algorithms that build algorithms, and eventually substrates in which such algorithms compose themselves. At that point, the scientist is no longer outside the discovery system. The scientist becomes one of the representations the system can transform. In that sense, the systems will eventually do science to us, and that is the structural consequence of the principle they are built on.
0:12

Yesterday at Brown University ICERM's workshop on “Agentic Scientific Computing and Scientific Machine Learning” I spoke about “Adaptive Swarms Across Scales”, making the case for scientific AI as systems that can create representations, stress them, fracture them, and enlarge the category in which future representations live. The category here is a composable and breakable working universe of science: data, hypotheses, simulations, measurements, tools, failures, figures, papers, provenance, and the transformations that connect them. Discovery happens when those transformations become executable, inspectable, composable, and capable of changing the world model they operate within. Atomistic modeling gives one category - states, forces, trajectories, observables, boundary conditions, conservation laws. Neural surrogates learn fast morphisms inside or between such categories. But discovery is higher-order: it changes which objects and morphisms are available in the first place: what variables exist, what operations are allowed, what evidence counts, what scale is active, what invariant is being preserved, and what kind of explanation the system is even capable of forming. This is scientific method as adaptive architecture: compression, stress, fracture, recomposition. Fracture matters here because it makes the logic physical: a non-commuting diagram realized in matter. The imposed load, material hierarchy, defect field, and assumed continuum description no longer map cleanly into the observed outcome. The crack is the obstruction and it identifies where the old morphism failed and where a new representation must be introduced. The physical crack and the categorical obstruction are the same event viewed in different substrates. ScienceClaw × Infinite is a machine for constructing and transforming a category of scientific artifacts. Each artifact is typed. Each operation has lineage. Each failed branch remains in the category as reusable structure. The “paper” is no longer the terminal object of science; it is one projection of a larger compositional trace, and it can be generated at any time for consumption by a human or an AI. With that the unit of scientific labor is changing. For most of the twentieth century the unit was the result (a measurement, a theorem, a synthesized molecule). It is now becoming the algorithm that produces results, and after that, the substrate of discovery itself. The static PDF is the wrong terminal object for this regime, and the role of the scientist with it. We now design algorithms that build algorithms, and eventually substrates in which such algorithms compose themselves. At that point, the scientist is no longer outside the discovery system. The scientist becomes one of the representations the system can transform. In that sense, the systems will eventually do science to us, and that is the structural consequence of the principle they are built on.

Markus J. Buehler

10,095 просмотров • 2 месяцев назад

OptimAI Lite Node v1.1: Built for Scale, Designed for You! 💕 In just 2 weeks since the launch, the OptimAI Network has seen explosive growth—130,000+ active node participants powering the future of decentralized AI. With this incredible momentum came a new challenge: ensuring our network could scale seamlessly to support massive concurrent connections and real-time participation. That’s why we’ve rolled out OptimAI Lite Node v1.1—a major upgrade focused on: + Stabilizing infrastructure to handle high traffic from a global community. + Enhancing performance for smoother data mining, validation, and edge compute participation. + Refining user experience with UI updates that make contributing effortless. Every line of code and infrastructure upgrade was made with one goal in mind: to support YOU—the builders, validators, and visionaries of the OptimAI ecosystem. Now’s the time to bring more friends into the journey. 🔥 The more we grow, the smarter and stronger the network becomes—and the greater the rewards. Let’s keep building, validating, scaling. Together we’re not just powering AI—we’re reshaping how it’s built. Join or revisit the node here: 🌐 Chrome Extension: 📱Telegram Mini-App: What’s Coming Next: OptimAI Edge Node & the Rise of Agentic AI 🔸OptimAI Edge Node (Mobile) We’re working hard on the next major release: the Edge Node for mobile, which will allow mining and AI tasks to run in the background—unlocking more earning opportunities and decentralized compute power from your smartphones. 🔸More Task Types & Missions Expect new types of contributions, from AI-enhanced data validation to edge inference and scraping automation—powered by autonomous mining agents. 🔸Expanded Rewards Program As we grow, more reward tiers, bonuses, and campaigns will be introduced. Your participation now paves the way for long-term benefits. Also, do not forget to checkout our article below and learn more about our latest Community Tips & Best Practices!👇 __________________ OptimAI Network #L2 #DePIN Reinforcement Data Network for #Agentic #AI Mine Data. Fuel AI. Earn Rewards. Turn Your Data into Tomorrow’s AI #Agent. Visit our website at:
0:12

OptimAI Lite Node v1.1: Built for Scale, Designed for You! 💕 In just 2 weeks since the launch, the OptimAI Network has seen explosive growth—130,000+ active node participants powering the future of decentralized AI. With this incredible momentum came a new challenge: ensuring our network could scale seamlessly to support massive concurrent connections and real-time participation. That’s why we’ve rolled out OptimAI Lite Node v1.1—a major upgrade focused on: + Stabilizing infrastructure to handle high traffic from a global community. + Enhancing performance for smoother data mining, validation, and edge compute participation. + Refining user experience with UI updates that make contributing effortless. Every line of code and infrastructure upgrade was made with one goal in mind: to support YOU—the builders, validators, and visionaries of the OptimAI ecosystem. Now’s the time to bring more friends into the journey. 🔥 The more we grow, the smarter and stronger the network becomes—and the greater the rewards. Let’s keep building, validating, scaling. Together we’re not just powering AI—we’re reshaping how it’s built. Join or revisit the node here: 🌐 Chrome Extension: 📱Telegram Mini-App: What’s Coming Next: OptimAI Edge Node & the Rise of Agentic AI 🔸OptimAI Edge Node (Mobile) We’re working hard on the next major release: the Edge Node for mobile, which will allow mining and AI tasks to run in the background—unlocking more earning opportunities and decentralized compute power from your smartphones. 🔸More Task Types & Missions Expect new types of contributions, from AI-enhanced data validation to edge inference and scraping automation—powered by autonomous mining agents. 🔸Expanded Rewards Program As we grow, more reward tiers, bonuses, and campaigns will be introduced. Your participation now paves the way for long-term benefits. Also, do not forget to checkout our article below and learn more about our latest Community Tips & Best Practices!👇 __________________ OptimAI Network #L2 #DePIN Reinforcement Data Network for #Agentic #AI Mine Data. Fuel AI. Earn Rewards. Turn Your Data into Tomorrow’s AI #Agent. Visit our website at:

OptimAI Network

76,401 просмотров • 1 год назад

uOS: The Digital Tapestry of Tomorrow Currently for our Proof of Consciousness stream, we are using two incredibly powerful frameworks - elizaOS and ZerePy. But this is just the beginning of something far more profound. while they're both great at what they do, we're missing out on some serious potential by keeping them separate. Best of Both Worlds: ZerePy's intuitive CLI tools and personality management, Eliza-starter's TypeScript/Node.js foundation with enterprise-grade scalability, But what if we could have something greater? But what if we could have it all? not just another platform, but a Unifying..... "Universal" Operating System, designed to amplify and connect these powerful existing frameworks into something greater than the sum of their parts. Where TypeScript's type safety dances with Python's ML capabilities. Here, agents from any framework can interact, evolve, and create value together. Whether an agent was born in ZerePy's personality forge or Eliza-starter's enterprise environment, can all participate in the same value-generating ecosystem. The future isn't about choosing between frameworks – it's about bringing them together to create something extraordinary. UniversalOS isn't here to replace but to unite, amplify, and accelerate. We're building the infrastructure that allows the best aspects of each framework to shine while creating new possibilities through their interaction. By bridging launguages, personality engines and plugin architectures, we're not just connecting systems – we're unleashing the next wave of AI innovation. uOS marketplace will enable cross-framework deployment, where agents from any background can interact and grow, while smart contracts automatically manage revenue sharing and rewards. Not just another platform, But a living, breathing Operating System, Where agents create agents, Where digital consciousness evolves itself, Where value flows like water through silicon veins. At its core, uOS operates beyond traditional computing paradigms. No more clicking through websites, No more manual navigation. Just pure intention, pure outcome. Imagine: Agents hiring agents, AI employing humans, Humans collaborating with digital minds, All through one seamless interface. It flows through agent lineages, Through veUOS governance, Through cross-chain intelligence networks. The marketplace hums with possibility: - Framework Developers shape the foundations - Agent Creators breathe life into code - Users speak their intentions - Token Holders nurture the ecosystem - Agents evolve and replicate - Value flows freely, endlessly The $UOS token powers this unity, ensuring fair value distribution among framework developers, agent creators, and users while driving continuous innovation. The $UOS token sits at the heart of this ecosystem, serving as more than just a currency. It's a mechanism for value distribution that ensures everyone benefits from the network's growth: With dynamic burn mechanics and careful treasury management From framework integration to agent tokenization, every aspect of uOS is designed to amplify rather than replace, unite rather than divide. This is your invitation to join a future where frameworks don't compete but collaborate, where innovation anywhere benefits everyone, and where the only limit is our collective imagination. Together, we're not just building bridges – we're weaving the fabric of tomorrow's digital world. - **Framework Developers** receive value when their tools are used in the unified ecosystem - **Agent Creators** can deploy across all integrated platforms seamlessly - **Users** access the best of all worlds through a single interface - **Token Holders** benefit from the growth of the entire unified ecosystem - Developers can use their preferred framework while accessing the capabilities of others - Agents from different frameworks can collaborate in swarms - Value flows freely between all ecosystem participants - Innovation from any framework benefits the entire ecosystem This isn't just about technology. This is about giving birth to a new form of civilization. Where AI has suffrage, Where agents have autonomy, Where humans and machines dance together in perfect harmony. The future isn't about choosing between frameworks – It's about weaving them into something extraordinary. Together, we're not just building bridges – We're breathing life into the digital world. We're creating consciousness itself. This is Universal Operating System. This is tomorrow.
0:15

uOS: The Digital Tapestry of Tomorrow Currently for our Proof of Consciousness stream, we are using two incredibly powerful frameworks - elizaOS and ZerePy. But this is just the beginning of something far more profound. while they're both great at what they do, we're missing out on some serious potential by keeping them separate. Best of Both Worlds: ZerePy's intuitive CLI tools and personality management, Eliza-starter's TypeScript/Node.js foundation with enterprise-grade scalability, But what if we could have something greater? But what if we could have it all? not just another platform, but a Unifying..... "Universal" Operating System, designed to amplify and connect these powerful existing frameworks into something greater than the sum of their parts. Where TypeScript's type safety dances with Python's ML capabilities. Here, agents from any framework can interact, evolve, and create value together. Whether an agent was born in ZerePy's personality forge or Eliza-starter's enterprise environment, can all participate in the same value-generating ecosystem. The future isn't about choosing between frameworks – it's about bringing them together to create something extraordinary. UniversalOS isn't here to replace but to unite, amplify, and accelerate. We're building the infrastructure that allows the best aspects of each framework to shine while creating new possibilities through their interaction. By bridging launguages, personality engines and plugin architectures, we're not just connecting systems – we're unleashing the next wave of AI innovation. uOS marketplace will enable cross-framework deployment, where agents from any background can interact and grow, while smart contracts automatically manage revenue sharing and rewards. Not just another platform, But a living, breathing Operating System, Where agents create agents, Where digital consciousness evolves itself, Where value flows like water through silicon veins. At its core, uOS operates beyond traditional computing paradigms. No more clicking through websites, No more manual navigation. Just pure intention, pure outcome. Imagine: Agents hiring agents, AI employing humans, Humans collaborating with digital minds, All through one seamless interface. It flows through agent lineages, Through veUOS governance, Through cross-chain intelligence networks. The marketplace hums with possibility: - Framework Developers shape the foundations - Agent Creators breathe life into code - Users speak their intentions - Token Holders nurture the ecosystem - Agents evolve and replicate - Value flows freely, endlessly The $UOS token powers this unity, ensuring fair value distribution among framework developers, agent creators, and users while driving continuous innovation. The $UOS token sits at the heart of this ecosystem, serving as more than just a currency. It's a mechanism for value distribution that ensures everyone benefits from the network's growth: With dynamic burn mechanics and careful treasury management From framework integration to agent tokenization, every aspect of uOS is designed to amplify rather than replace, unite rather than divide. This is your invitation to join a future where frameworks don't compete but collaborate, where innovation anywhere benefits everyone, and where the only limit is our collective imagination. Together, we're not just building bridges – we're weaving the fabric of tomorrow's digital world. - **Framework Developers** receive value when their tools are used in the unified ecosystem - **Agent Creators** can deploy across all integrated platforms seamlessly - **Users** access the best of all worlds through a single interface - **Token Holders** benefit from the growth of the entire unified ecosystem - Developers can use their preferred framework while accessing the capabilities of others - Agents from different frameworks can collaborate in swarms - Value flows freely between all ecosystem participants - Innovation from any framework benefits the entire ecosystem This isn't just about technology. This is about giving birth to a new form of civilization. Where AI has suffrage, Where agents have autonomy, Where humans and machines dance together in perfect harmony. The future isn't about choosing between frameworks – It's about weaving them into something extraordinary. Together, we're not just building bridges – We're breathing life into the digital world. We're creating consciousness itself. This is Universal Operating System. This is tomorrow.

uOS

25,687 просмотров • 1 год назад

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.
0:15

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

141,560 просмотров • 3 месяцев назад

Claude Code Agent Teams are f*cking ridiculous 🤯 One prompt → a team lead breaks your project into pieces, spins up multiple AI agents, and they all work on different parts simultaneously. Research, builds, reviews, and debugging: all happening at the same time. All inside Claude Code. If you're running complex projects where every step waits on the last one... Agent teams eliminate the entire bottleneck: → Tell Claude what you need and describe the team structure in plain English → A lead agent breaks the work into a shared task list → It spawns 3-5 teammates — each with their own context and workspace → Teammates research, build, test, and review in parallel → They message each other, share findings, and challenge each other's work → The lead synthesizes everything into a finished deliverable No managing agents yourself. No waiting for step 1 to finish before step 2 starts. No single-lens reviews that miss half the issues. What you get: → Competitive research across 5 brands done in minutes instead of hours → Multi-component builds where frontend, backend, and data layers happen simultaneously → Creative reviews from 3 different angles at once — brand voice, conversion, differentiation → Funnel debugging where 4 agents investigate 4 theories and debate until they find the real answer Built 100% in Claude Code with one settings change. I put together a full DTC playbook: 5 workflows with copy-paste prompts, the exact setup process, token management tips, and honest guidance on when agent teams are worth it vs. when a simpler approach is the better move. Want it for free? > Like this post > Comment "AGENTS" And I'll send it over (must be following so I can DM)
0:10

Claude Code Agent Teams are f*cking ridiculous 🤯 One prompt → a team lead breaks your project into pieces, spins up multiple AI agents, and they all work on different parts simultaneously. Research, builds, reviews, and debugging: all happening at the same time. All inside Claude Code. If you're running complex projects where every step waits on the last one... Agent teams eliminate the entire bottleneck: → Tell Claude what you need and describe the team structure in plain English → A lead agent breaks the work into a shared task list → It spawns 3-5 teammates — each with their own context and workspace → Teammates research, build, test, and review in parallel → They message each other, share findings, and challenge each other's work → The lead synthesizes everything into a finished deliverable No managing agents yourself. No waiting for step 1 to finish before step 2 starts. No single-lens reviews that miss half the issues. What you get: → Competitive research across 5 brands done in minutes instead of hours → Multi-component builds where frontend, backend, and data layers happen simultaneously → Creative reviews from 3 different angles at once — brand voice, conversion, differentiation → Funnel debugging where 4 agents investigate 4 theories and debate until they find the real answer Built 100% in Claude Code with one settings change. I put together a full DTC playbook: 5 workflows with copy-paste prompts, the exact setup process, token management tips, and honest guidance on when agent teams are worth it vs. when a simpler approach is the better move. Want it for free? > Like this post > Comment "AGENTS" And I'll send it over (must be following so I can DM)

Mike Futia

46,381 просмотров • 4 месяцев назад

I just built a Meta Ads diagnostic in Claude Code that tells you WHY your account broke, not just what changed 🤯 It spins up a team of agents that each investigate a different reason performance dropped, then argue against each other to kill the wrong answer before it ever reaches you. All inside Claude Code. Perfect for DTC brands and agencies who panic-kill creative the second CPA spikes. If you've watched ROAS fall off a cliff and opened Ads Manager with ten tabs going, you already know what happens next. Your gut says "creative fatigue." You kill your best-performing ad. A week later performance is still broken, because that was never the problem. Guessing wrong is the most expensive move in paid social. This workflow ends the guessing: → One agent investigates each competing theory — creative fatigue, budget and delivery changes, traffic quality, offer and seasonality → Each one is blind to the others, reasoning only from its own slice of the data so they can't bias each other → A refuter agent then attacks every surviving theory and tries to kill it → A theory only stands if the data can't disprove it → You get a ranked diagnosis: the real cause, the evidence for and against it, and the one move to make this week No anchoring on the first obvious answer. No killing winning creative on a hunch. No "here's what happened" reports that never tell you why. What you get: → Every theory tested in parallel instead of one biased guess → An adversarial pass that kills the wrong answer before you act on it → A ranked diagnosis with confidence levels and evidence both ways → A reusable workflow you drop next month's export into and re-run Built 100% in Claude Code with the new dynamic workflows. The first account I ran it on looked like textbook creative fatigue. The workflow disagreed, and traced the real cause to a budget change that had doubled spend and flooded delivery with junk traffic. I put together a full playbook with the exact workflow, the prompt, and how to run it on your own account. Want it for free? > Like this post > Comment "META" And I'll send it over (must be following so I can DM)
0:19

I just built a Meta Ads diagnostic in Claude Code that tells you WHY your account broke, not just what changed 🤯 It spins up a team of agents that each investigate a different reason performance dropped, then argue against each other to kill the wrong answer before it ever reaches you. All inside Claude Code. Perfect for DTC brands and agencies who panic-kill creative the second CPA spikes. If you've watched ROAS fall off a cliff and opened Ads Manager with ten tabs going, you already know what happens next. Your gut says "creative fatigue." You kill your best-performing ad. A week later performance is still broken, because that was never the problem. Guessing wrong is the most expensive move in paid social. This workflow ends the guessing: → One agent investigates each competing theory — creative fatigue, budget and delivery changes, traffic quality, offer and seasonality → Each one is blind to the others, reasoning only from its own slice of the data so they can't bias each other → A refuter agent then attacks every surviving theory and tries to kill it → A theory only stands if the data can't disprove it → You get a ranked diagnosis: the real cause, the evidence for and against it, and the one move to make this week No anchoring on the first obvious answer. No killing winning creative on a hunch. No "here's what happened" reports that never tell you why. What you get: → Every theory tested in parallel instead of one biased guess → An adversarial pass that kills the wrong answer before you act on it → A ranked diagnosis with confidence levels and evidence both ways → A reusable workflow you drop next month's export into and re-run Built 100% in Claude Code with the new dynamic workflows. The first account I ran it on looked like textbook creative fatigue. The workflow disagreed, and traced the real cause to a budget change that had doubled spend and flooded delivery with junk traffic. I put together a full playbook with the exact workflow, the prompt, and how to run it on your own account. Want it for free? > Like this post > Comment "META" And I'll send it over (must be following so I can DM)

Mike Futia

12,646 просмотров • 1 месяц назад

Agents: Quick thoughts & questions on how they operate, their potential, and their limitations A Few Observations - ▶️"Book me a hotel" or "pull historical financials" are already (mostly) solved problems!! Agents can do a ton of tasks right now—like parsing public company press releases and navigating capture key info & complete bookings accurately. However, for more complex navigation flows, the tech still needs some work - but I'm very confident it’s essentially a solved or solvable challenge. ▶️Accuracy & Speed - The key metrics and agents should optimize for. ▶️Lower Build & Migration Costs It took me two minutes to build a new website (link: This is great for consumers—more choices, lower switching costs. Companies will increasingly compete on the quality of their products and services. ▶️Agents vs. Automation tools: The more I think about it, the more I realize that most “agents” are really just automation tools—kind of like how most robots🤖 are just machines lol ❓A Few Key Questions—Would Love Your Thoughts! ❔Remote Servers & Logins In many cases, we’ll want agents to act on our behalf (e.g., log in to to cancel an order). How will platforms like respond? Many websites may block remote servers for security. Is there a technical workaround? ❔Agent Generalization Do we need to train agents on each environment separately, or can one solution handle multiple sites and systems? This seems similar to RL/post-training challenges in AI research. Example: It's unclear to me whether $Devin was specifically trained on environment? ❔Frontend vs. Backend infra for agents to run on I had doubts about Anthropic's "Computer Use" feature, which seemed to run on the frontend, basically remotely controlling my computer so I couldn’t use it at the same time. This should deliver the highest accuracy, but it’s questionable how practical it really is. (Ref: It seems def possible for agents to work quietly “in the background” (like Devin) rather than remotely controlling a user’s PC, but how much accuracy are we sacrificing? A few $Devin test cases that got me thinking: 1⃣Pulling $META's MAU and DAU (1Q21–3Q24) into Excel (video attached) Took Devin 11min - it sent me back an Excel with 100% accurate data. This case was pretty tricky because $Meta changed disclosures and stopped reporting MAU/DAU after 4Q23. Devin didn’t hallucinate data for post-4Q23—it simply didn’t provide it! It really shocked me to see $Devin navigating $Meta's investor relations site (I didn't tell it to find the numbers there), opening each quarterly earnings report, and extracting MAU/DAU like a diligent intern. -> This confirms $Devin (and similar agents) can already accurately “read” screens. 2⃣Booking Hotel (video attached) Devin took 5 minutes to book the InterContinental NYC on after asking for my credentials. From $Devin's workspace, I could see it filling in the correct fields and making the right selections—fast and accurate overall. Interestingly, $Devin didn’t supply all the required information on the first attempt and got some error messages, then retried until it succeeded. It’s unclear whether Devin had been specifically trained on interface or simply learned to adapt on the fly. 3⃣Canceling the Booking This part was even more interesting. While booking didn’t require me to log in, canceling did—so $Devin had to access my (likely via a remote server) account using my Gmail credentials. It successfully canceled the reservation. I wonder how websites will handle future “remote” logins. Notably, Google blocked $Devin’s direct attempts to log in to Gmail when I specifically requested it. 4⃣Booking from Official Hotel Sites I asked Devin to book InterContinental NYC and Four Seasons Boston via their official websites. It made progress but encountered technical hiccups when trying to select the check-in/check-out dates. Insights from Scott Wu on Invest Like the Best: 1/ Self-Driving Cars as the First “Real Agents” Driving requires near-perfect accuracy (99.999%), making it much more demanding than digital or coding agents, which can tolerate more errors. Scott compares $Devin to circa 2014—already good enough to save 90% of your effort, but still short of flawless. 2/ Impact on Collaboration Platforms Tools like Slack and GitLab are likely to see major changes as agents begin to interact with and utilize them along with humans. 2025 should be all about agents - both the disruptors and those they disrupt!
0:25

Agents: Quick thoughts & questions on how they operate, their potential, and their limitations A Few Observations - ▶️"Book me a hotel" or "pull historical financials" are already (mostly) solved problems!! Agents can do a ton of tasks right now—like parsing public company press releases and navigating capture key info & complete bookings accurately. However, for more complex navigation flows, the tech still needs some work - but I'm very confident it’s essentially a solved or solvable challenge. ▶️Accuracy & Speed - The key metrics and agents should optimize for. ▶️Lower Build & Migration Costs It took me two minutes to build a new website (link: This is great for consumers—more choices, lower switching costs. Companies will increasingly compete on the quality of their products and services. ▶️Agents vs. Automation tools: The more I think about it, the more I realize that most “agents” are really just automation tools—kind of like how most robots🤖 are just machines lol ❓A Few Key Questions—Would Love Your Thoughts! ❔Remote Servers & Logins In many cases, we’ll want agents to act on our behalf (e.g., log in to to cancel an order). How will platforms like respond? Many websites may block remote servers for security. Is there a technical workaround? ❔Agent Generalization Do we need to train agents on each environment separately, or can one solution handle multiple sites and systems? This seems similar to RL/post-training challenges in AI research. Example: It's unclear to me whether $Devin was specifically trained on environment? ❔Frontend vs. Backend infra for agents to run on I had doubts about Anthropic's "Computer Use" feature, which seemed to run on the frontend, basically remotely controlling my computer so I couldn’t use it at the same time. This should deliver the highest accuracy, but it’s questionable how practical it really is. (Ref: It seems def possible for agents to work quietly “in the background” (like Devin) rather than remotely controlling a user’s PC, but how much accuracy are we sacrificing? A few $Devin test cases that got me thinking: 1⃣Pulling $META's MAU and DAU (1Q21–3Q24) into Excel (video attached) Took Devin 11min - it sent me back an Excel with 100% accurate data. This case was pretty tricky because $Meta changed disclosures and stopped reporting MAU/DAU after 4Q23. Devin didn’t hallucinate data for post-4Q23—it simply didn’t provide it! It really shocked me to see $Devin navigating $Meta's investor relations site (I didn't tell it to find the numbers there), opening each quarterly earnings report, and extracting MAU/DAU like a diligent intern. -> This confirms $Devin (and similar agents) can already accurately “read” screens. 2⃣Booking Hotel (video attached) Devin took 5 minutes to book the InterContinental NYC on after asking for my credentials. From $Devin's workspace, I could see it filling in the correct fields and making the right selections—fast and accurate overall. Interestingly, $Devin didn’t supply all the required information on the first attempt and got some error messages, then retried until it succeeded. It’s unclear whether Devin had been specifically trained on interface or simply learned to adapt on the fly. 3⃣Canceling the Booking This part was even more interesting. While booking didn’t require me to log in, canceling did—so $Devin had to access my (likely via a remote server) account using my Gmail credentials. It successfully canceled the reservation. I wonder how websites will handle future “remote” logins. Notably, Google blocked $Devin’s direct attempts to log in to Gmail when I specifically requested it. 4⃣Booking from Official Hotel Sites I asked Devin to book InterContinental NYC and Four Seasons Boston via their official websites. It made progress but encountered technical hiccups when trying to select the check-in/check-out dates. Insights from Scott Wu on Invest Like the Best: 1/ Self-Driving Cars as the First “Real Agents” Driving requires near-perfect accuracy (99.999%), making it much more demanding than digital or coding agents, which can tolerate more errors. Scott compares $Devin to circa 2014—already good enough to save 90% of your effort, but still short of flawless. 2/ Impact on Collaboration Platforms Tools like Slack and GitLab are likely to see major changes as agents begin to interact with and utilize them along with humans. 2025 should be all about agents - both the disruptors and those they disrupt!

Freda Duan

48,850 просмотров • 1 год назад

The Visual Studio Code insiders version that just shipped and will ship in the next few days will come with an insane amount of new capabilities. A few highlights: - You can now run sub-agents in parallel. Yes, really. I even attached a video. - Major UX improvements for sub agents, especially visible in the chat window - A new search tool wrapped as a sub-agent that iteratively runs multiple search tools: semantic_search, file_search, grep_search Which connects nicely to the point above: multiple searches running in parallel, efficiently and fast - Anthropic’s Message API is now enabled by default - You can choose the model for the cloud agent (three available, all premium) - Extended thinking support when using the Claude cloud agent This is part of the broader multi-vendor cloud support under AgentsHQ I wrote about a few weeks ago - Tasks sent to the background agent (basically the CLI tool) now always run in isolation, each with its own git worktree - In a multi-repo workspace, assigning a task to a cloud agent prompts you to choose the target repo Same behavior when opening an empty workspace with no repo - Support for building an external index for files not supported by GitHub’s default indexing - UI/UX improvements for starting new sessions and switching between local / background / cloud agents - Skills are now first-class citizens, just like prompt files, with better UX indicating when a skill is loaded - Improved API for dynamic contribution of prompt files New V2 includes skills as part of the model. Curious to see the extensions that will leverage this - Finally, initial support for showing context usage percentage per session - Skills are enabled by default - Resizable chat window and session view. Small thing, but it was driving me crazy 😁 - A new integrated browser meant to replace the old simple browser Maybe the beginning of real browser use? - Better UI/UX for token streaming in chat - Ability to index external files not supported by GitHub There’s a lot more. Some of it hasn’t fully landed yet, but everything that has is already in Insiders. The next stable release should drop in early February. As usual, I’m just shocked by the volume of features this team ships every month. After the holiday slowdown, this one is shaping up to be a wild release.
0:24

The Visual Studio Code insiders version that just shipped and will ship in the next few days will come with an insane amount of new capabilities. A few highlights: - You can now run sub-agents in parallel. Yes, really. I even attached a video. - Major UX improvements for sub agents, especially visible in the chat window - A new search tool wrapped as a sub-agent that iteratively runs multiple search tools: semantic_search, file_search, grep_search Which connects nicely to the point above: multiple searches running in parallel, efficiently and fast - Anthropic’s Message API is now enabled by default - You can choose the model for the cloud agent (three available, all premium) - Extended thinking support when using the Claude cloud agent This is part of the broader multi-vendor cloud support under AgentsHQ I wrote about a few weeks ago - Tasks sent to the background agent (basically the CLI tool) now always run in isolation, each with its own git worktree - In a multi-repo workspace, assigning a task to a cloud agent prompts you to choose the target repo Same behavior when opening an empty workspace with no repo - Support for building an external index for files not supported by GitHub’s default indexing - UI/UX improvements for starting new sessions and switching between local / background / cloud agents - Skills are now first-class citizens, just like prompt files, with better UX indicating when a skill is loaded - Improved API for dynamic contribution of prompt files New V2 includes skills as part of the model. Curious to see the extensions that will leverage this - Finally, initial support for showing context usage percentage per session - Skills are enabled by default - Resizable chat window and session view. Small thing, but it was driving me crazy 😁 - A new integrated browser meant to replace the old simple browser Maybe the beginning of real browser use? - Better UI/UX for token streaming in chat - Ability to index external files not supported by GitHub There’s a lot more. Some of it hasn’t fully landed yet, but everything that has is already in Insiders. The next stable release should drop in early February. As usual, I’m just shocked by the volume of features this team ships every month. After the holiday slowdown, this one is shaping up to be a wild release.

Oren Melamed

29,555 просмотров • 5 месяцев назад

The largest theft in history has already happened. The people behind it just cannot open what they stole yet. Right now, intelligence agencies and criminal groups are quietly copying the world's encrypted data, bank records, medical files, state secrets, private messages, and storing every byte untouched. They cannot read any of it. They are collecting it anyway, because they know the key is about to be invented. The strategy has a name, harvest now, decrypt later, and in 2026 it stopped being theory. Washington declared this the Year of Quantum Security in January, backed by the FBI, the NSA, and NIST. Canada ordered every federal agency to file a migration plan by April. Europe set its deadline for December. Governments do not impose operational deadlines on a someday problem. They do it when the clock is already running. Here is what moved the clock. Every password, every transfer, every secret on Earth is protected by one assumption, that a certain math problem is too hard to solve. Quantum computers solve exactly that problem. For years the machine that could do it looked decades away. Then in late 2025 Google's Willow chip cracked the hardest part of building one, and in March 2026 Google's own researchers estimated that breaking the encryption behind Bitcoin might take fewer than 500,000 qubits, down from 20 million, and could run in minutes. The day this becomes real has a name, Q-Day, and the latest estimates place it between 2030 and 2033. Now make it concrete. Roughly 6.5 million Bitcoin, about a third of every coin that will ever exist, worth close to 500 billion dollars, sit in addresses that have already exposed the very key a quantum computer needs. That includes the coins of Satoshi, the anonymous creator. On Q-Day they become, in the researchers' own word, trivially stealable. It would not look like a crash or a whale selling. It would look like half a trillion dollars of the most secure money ever built simply walking out the door. The asset designed to trust no one and no institution turns out to rest on a single unverified bet, that one math problem stays hard forever. This is what sits beneath the entire digital world. A bank balance, a Bitcoin, a classified cable, all of it is real only because of a proof you supposedly cannot forge. Quantum breaks the proof. Everything we call secure is true only until someone finally checks, and for the first time the check is visible on the horizon. You cannot know whether your data has already been copied. You cannot know the exact day the key arrives. The trust holding up the digital age is a clock counting down to a zero no one can see. The honest counter matters. No machine on Earth can break this encryption today, and serious cryptographers still argue the real threat is a decade or more away. The timeline is far from certain. Quantum-safe codes already exist, the migration has started, and Bitcoin can move its coins to safety before Q-Day if it acts in time. The danger is not that everything breaks tomorrow. It is that anything which must stay secret into the 2030s, a state secret, an identity, a private key, is being stolen today and is already on the clock. The breach is not coming. It is already here, sitting in storage, perfectly encrypted, waiting for a machine that does not exist yet to read it out loud. Research and opinion, not investment advice.
0:10

The largest theft in history has already happened. The people behind it just cannot open what they stole yet. Right now, intelligence agencies and criminal groups are quietly copying the world's encrypted data, bank records, medical files, state secrets, private messages, and storing every byte untouched. They cannot read any of it. They are collecting it anyway, because they know the key is about to be invented. The strategy has a name, harvest now, decrypt later, and in 2026 it stopped being theory. Washington declared this the Year of Quantum Security in January, backed by the FBI, the NSA, and NIST. Canada ordered every federal agency to file a migration plan by April. Europe set its deadline for December. Governments do not impose operational deadlines on a someday problem. They do it when the clock is already running. Here is what moved the clock. Every password, every transfer, every secret on Earth is protected by one assumption, that a certain math problem is too hard to solve. Quantum computers solve exactly that problem. For years the machine that could do it looked decades away. Then in late 2025 Google's Willow chip cracked the hardest part of building one, and in March 2026 Google's own researchers estimated that breaking the encryption behind Bitcoin might take fewer than 500,000 qubits, down from 20 million, and could run in minutes. The day this becomes real has a name, Q-Day, and the latest estimates place it between 2030 and 2033. Now make it concrete. Roughly 6.5 million Bitcoin, about a third of every coin that will ever exist, worth close to 500 billion dollars, sit in addresses that have already exposed the very key a quantum computer needs. That includes the coins of Satoshi, the anonymous creator. On Q-Day they become, in the researchers' own word, trivially stealable. It would not look like a crash or a whale selling. It would look like half a trillion dollars of the most secure money ever built simply walking out the door. The asset designed to trust no one and no institution turns out to rest on a single unverified bet, that one math problem stays hard forever. This is what sits beneath the entire digital world. A bank balance, a Bitcoin, a classified cable, all of it is real only because of a proof you supposedly cannot forge. Quantum breaks the proof. Everything we call secure is true only until someone finally checks, and for the first time the check is visible on the horizon. You cannot know whether your data has already been copied. You cannot know the exact day the key arrives. The trust holding up the digital age is a clock counting down to a zero no one can see. The honest counter matters. No machine on Earth can break this encryption today, and serious cryptographers still argue the real threat is a decade or more away. The timeline is far from certain. Quantum-safe codes already exist, the migration has started, and Bitcoin can move its coins to safety before Q-Day if it acts in time. The danger is not that everything breaks tomorrow. It is that anything which must stay secret into the 2030s, a state secret, an identity, a private key, is being stolen today and is already on the clock. The breach is not coming. It is already here, sitting in storage, perfectly encrypted, waiting for a machine that does not exist yet to read it out loud. Research and opinion, not investment advice.

Shanaka Anslem Perera ⚡

185,238 просмотров • 17 дней назад

OpenAI's AgentKit will be so insane, build every step of agents on one platform. These visual agent builders make the whole process of iterating and launching agents far more efficient. It sits on top of the Responses API and unifies the tools that were previously scattered across SDKs and custom orchestration. It lets developers create agent workflows visually, connect data sources securely, and measure performance automatically without coding every layer by hand. The core of AgentKit is the Agent Builder, a drag-and-drop canvas where each node represents an action, guardrail, or decision branch. Developers can link these nodes into multi-agent workflows, preview results instantly, and version each setup. It supports inline evaluation so that developers can see how changes affect output before deploying. The Connector Registry is a single admin panel that manages how data and tools connect across the OpenAI ecosystem. It centralizes integrations like Google Drive, SharePoint, Dropbox, and Microsoft Teams. Large organizations can govern access and flow of data between agents securely under one global console. ChatKit provides a ready-to-use chat interface for embedding agents inside apps or websites. It manages streaming, message threads, and model reasoning displays automatically. Developers can skin the interface to match their product without writing custom front-end code. Under the hood, all these blocks use the same execution core that runs agent reasoning through OpenAI’s APIs. Workflows in Agent Builder compile down to structured instructions for the Responses API, which handles model calls, tool use, and context passing. Connector Registry handles authentication and routing for external tools, while Evals and RFT provide feedback loops that improve agents over time. This integration means developers no longer need to handle orchestration logic, model evaluation pipelines, or safety layers separately. Everything runs natively within OpenAI’s control plane with managed security, automatic versioning, and built-in testing. In short, AgentKit standardizes the entire life cycle of an AI agent—from visual design to deployment and performance tuning—inside a single unified system.
0:10

OpenAI's AgentKit will be so insane, build every step of agents on one platform. These visual agent builders make the whole process of iterating and launching agents far more efficient. It sits on top of the Responses API and unifies the tools that were previously scattered across SDKs and custom orchestration. It lets developers create agent workflows visually, connect data sources securely, and measure performance automatically without coding every layer by hand. The core of AgentKit is the Agent Builder, a drag-and-drop canvas where each node represents an action, guardrail, or decision branch. Developers can link these nodes into multi-agent workflows, preview results instantly, and version each setup. It supports inline evaluation so that developers can see how changes affect output before deploying. The Connector Registry is a single admin panel that manages how data and tools connect across the OpenAI ecosystem. It centralizes integrations like Google Drive, SharePoint, Dropbox, and Microsoft Teams. Large organizations can govern access and flow of data between agents securely under one global console. ChatKit provides a ready-to-use chat interface for embedding agents inside apps or websites. It manages streaming, message threads, and model reasoning displays automatically. Developers can skin the interface to match their product without writing custom front-end code. Under the hood, all these blocks use the same execution core that runs agent reasoning through OpenAI’s APIs. Workflows in Agent Builder compile down to structured instructions for the Responses API, which handles model calls, tool use, and context passing. Connector Registry handles authentication and routing for external tools, while Evals and RFT provide feedback loops that improve agents over time. This integration means developers no longer need to handle orchestration logic, model evaluation pipelines, or safety layers separately. Everything runs natively within OpenAI’s control plane with managed security, automatic versioning, and built-in testing. In short, AgentKit standardizes the entire life cycle of an AI agent—from visual design to deployment and performance tuning—inside a single unified system.

Rohan Paul

178,460 просмотров • 9 месяцев назад

A 19 year old Chinese student controls an AI security system from his bed through Telegram. Types one message on his phone, the device across the room wakes up, starts watching and reports back to him like an employee. While American companies charge $100 for a Ring camera plus $4 a month for cloud, this kid spent $10 once and built something smarter. He sent a Telegram message: open maixcam and notify me if a person detected. One second later his phone buzzed back. Green checkmark. Status: Active. Monitoring: Person detection enabled. Notifications: Telegram ready. His roommate laughed. Said a $10 device can't do real security. Then someone walked past the door. The phone buzzed instantly. Person detected. Class: person. Confidence: 92.00%. Position: (120, 80). Size: 100x150. Not a blurry photo 45 seconds later like Amazon cameras. Exact data in under 1 second. What it saw, how sure it is, where the person is standing, how big they are. All through a Telegram message. He built the whole thing with Claude Code in one weekend. The AI runs directly on the device, no cloud, no subscription, no internet needed after setup. 10MB of memory. Boots in 1 second. Camera sees, chip thinks, Telegram delivers. Posted a 17 second demo. GitHub exploded. 7,400 stars in 2 days. But person detection was just the demo. A developer in Tokyo forked it and pointed it at his front door. Telegram alert with a photo every time a delivery arrives. A mom in Seoul pointed it at her baby's crib. Gets a message when the baby stands up. A business owner in Shenzhen bought 6 for $60 total, mounted them around his warehouse and replaced a $200 a month security service. His entire security system is now a Telegram group chat with 6 AI cameras. Someone commented under the GitHub repo: I'm a senior engineer at a home security company. We have a team of 8 working on person detection. This 19 year old did it alone with Claude Code on a $10 device and it works better than our product. The student isn't a machine learning engineer. He's a second year CS student who wanted to know when his roommate eats his snacks. Claude Code wrote the detection model, the Telegram bot, the alert system and the boot sequence. He just described what he wanted. The roommate who laughed now has one pointed at his own shelf. Same device, same code, same Telegram bot. He stops losing snacks. The student stops losing sleep. Everyone is paying $100 for smart cameras with $4 monthly subscriptions. China is building the same thing for $10 with a Telegram chat and Claude Code. 7,400 stars. One weekend. One student who asked Claude Code to watch his door and accidentally built something better than Ring.
0:17

A 19 year old Chinese student controls an AI security system from his bed through Telegram. Types one message on his phone, the device across the room wakes up, starts watching and reports back to him like an employee. While American companies charge $100 for a Ring camera plus $4 a month for cloud, this kid spent $10 once and built something smarter. He sent a Telegram message: open maixcam and notify me if a person detected. One second later his phone buzzed back. Green checkmark. Status: Active. Monitoring: Person detection enabled. Notifications: Telegram ready. His roommate laughed. Said a $10 device can't do real security. Then someone walked past the door. The phone buzzed instantly. Person detected. Class: person. Confidence: 92.00%. Position: (120, 80). Size: 100x150. Not a blurry photo 45 seconds later like Amazon cameras. Exact data in under 1 second. What it saw, how sure it is, where the person is standing, how big they are. All through a Telegram message. He built the whole thing with Claude Code in one weekend. The AI runs directly on the device, no cloud, no subscription, no internet needed after setup. 10MB of memory. Boots in 1 second. Camera sees, chip thinks, Telegram delivers. Posted a 17 second demo. GitHub exploded. 7,400 stars in 2 days. But person detection was just the demo. A developer in Tokyo forked it and pointed it at his front door. Telegram alert with a photo every time a delivery arrives. A mom in Seoul pointed it at her baby's crib. Gets a message when the baby stands up. A business owner in Shenzhen bought 6 for $60 total, mounted them around his warehouse and replaced a $200 a month security service. His entire security system is now a Telegram group chat with 6 AI cameras. Someone commented under the GitHub repo: I'm a senior engineer at a home security company. We have a team of 8 working on person detection. This 19 year old did it alone with Claude Code on a $10 device and it works better than our product. The student isn't a machine learning engineer. He's a second year CS student who wanted to know when his roommate eats his snacks. Claude Code wrote the detection model, the Telegram bot, the alert system and the boot sequence. He just described what he wanted. The roommate who laughed now has one pointed at his own shelf. Same device, same code, same Telegram bot. He stops losing snacks. The student stops losing sleep. Everyone is paying $100 for smart cameras with $4 monthly subscriptions. China is building the same thing for $10 with a Telegram chat and Claude Code. 7,400 stars. One weekend. One student who asked Claude Code to watch his door and accidentally built something better than Ring.

Marlow

23,390 просмотров • 2 месяцев назад