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Demis Hassabis says AlphaGo mastered chess in under 24 hours That same self-improvement loop could work in coding and math, where answers are verifiable Systems could close the loop, but once you remove humans from the loop, capabilities can proliferate in ways you didn't want

Haider.

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29,140 просмотров • 7 месяцев назад •via X (Twitter)

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Elon Musk says humans may exit the AI development loop entirely by next year. Musk: “Humans are gradually getting less and less in the loop on the recursive self-improvement. Every successive model is built by the one before it.”

Elon Musk says humans may exit the AI development loop entirely by next year. Musk: “Humans are gradually getting less and less in the loop on the recursive self-improvement. Every successive model is built by the one before it.”

illuminatibot

55,658 просмотров • 5 месяцев назад

Demis Hassabis confirmed every frontier AI lab is working on recursive self-improvement and in the same sentence said the safety risk of removing humans from the loop entirely keeps him up at night. That combination should stop you. The CEO of Google DeepMind just confirmed that the thing most people treat as a theoretical future risk is already the active focus of every serious lab on earth right now. He explained why it works in coding and math. The feedback loop is fast. You can verify whether an answer is correct almost instantly. You can generate synthetic training data from it. The loop closes quickly and cleanly. Then he said where it breaks down. In biology, chemistry and physics. Any domain where verifying a hypothesis requires a physical experiment in the real world. The loop does not close in seconds. It closes in weeks or months. Geoffrey Hinton said in his Nobel lecture that recursive self-improvement is the development he fears most and that once started it may not be possible to stop. Hassabis is not pushing back on that. He is describing the guardrails labs are building around a process they are already running. Every lab has to think carefully about the safety of a process where no human is in the loop. He said that as a constraint they are navigating right now. The question they are sitting with is how much of it to let run without a human watching. (Watch the full interview on YouTube at Two Minute Papers channel)

Demis Hassabis confirmed every frontier AI lab is working on recursive self-improvement and in the same sentence said the safety risk of removing humans from the loop entirely keeps him up at night. That combination should stop you. The CEO of Google DeepMind just confirmed that the thing most people treat as a theoretical future risk is already the active focus of every serious lab on earth right now. He explained why it works in coding and math. The feedback loop is fast. You can verify whether an answer is correct almost instantly. You can generate synthetic training data from it. The loop closes quickly and cleanly. Then he said where it breaks down. In biology, chemistry and physics. Any domain where verifying a hypothesis requires a physical experiment in the real world. The loop does not close in seconds. It closes in weeks or months. Geoffrey Hinton said in his Nobel lecture that recursive self-improvement is the development he fears most and that once started it may not be possible to stop. Hassabis is not pushing back on that. He is describing the guardrails labs are building around a process they are already running. Every lab has to think carefully about the safety of a process where no human is in the loop. He said that as a constraint they are navigating right now. The question they are sitting with is how much of it to let run without a human watching. (Watch the full interview on YouTube at Two Minute Papers channel)

Ihtesham Ali

68,231 просмотров • 2 месяцев назад

Demis Hassabis says the next frontier is AI self-improvement — and coding is where it starts, because the outputs are verifiable and synthetic data is limitless That's a compounding flywheel, and every major lab is betting on it "but raw capability without reliability is a liability"

Demis Hassabis says the next frontier is AI self-improvement — and coding is where it starts, because the outputs are verifiable and synthetic data is limitless That's a compounding flywheel, and every major lab is betting on it "but raw capability without reliability is a liability"

Haider.

15,428 просмотров • 3 месяцев назад

Demis Hassabis says the next AI leap is continual learning. Not train once and freeze, but learn from real experience, constantly. AlphaGo did it in games. Now comes the messy real world. Would you let an AI learn from you? 👀

Demis Hassabis says the next AI leap is continual learning. Not train once and freeze, but learn from real experience, constantly. AlphaGo did it in games. Now comes the messy real world. Would you let an AI learn from you? 👀

VraserX e/acc

19,202 просмотров • 7 месяцев назад

Self-learning worked in AlphaGo because the world was a clean rulebook, says Demis Hassabis. Real life has no perfect rulebook, so long-term planning needs a strong “world model” to simulate what happens next. Without that simulation, a robot has to guess in the real world—where mistakes can be costly and irreversible. Better world models are the first missing piece, says Demis Hassabis.

Self-learning worked in AlphaGo because the world was a clean rulebook, says Demis Hassabis. Real life has no perfect rulebook, so long-term planning needs a strong “world model” to simulate what happens next. Without that simulation, a robot has to guess in the real world—where mistakes can be costly and irreversible. Better world models are the first missing piece, says Demis Hassabis.

Wes Roth

15,757 просмотров • 7 месяцев назад

She’s running through these tournaments like the Undertaker. You could feel the anger in that loop choke😭

She’s running through these tournaments like the Undertaker. You could feel the anger in that loop choke😭

BJJotter

15,608 просмотров • 4 месяцев назад

Stop getting stuck in the same loop that steals everything good from you.‼️‼️‼️

Stop getting stuck in the same loop that steals everything good from you.‼️‼️‼️

THE SAVAGE BLOODLINE

11,485 просмотров • 3 месяцев назад

Introducing Loop The autonomous business manager that runs your outreach, your follow-ups, and your pipeline,while you focus on the work only you can do. Loop doesn't live in your inbox. Your inbox lives in Loop. Reads every lead like a teammate who's been on your team for years Writes the reply, makes the call, chases the deal when email goes cold Pauses the moment a human actually needs to show up Replaces the patchwork of CRMs, outreach tools, and follow-up apps with one system that does the work Stop managing tools. Start running your loop. comment "loop" and we'll send you in. loop

Introducing Loop The autonomous business manager that runs your outreach, your follow-ups, and your pipeline,while you focus on the work only you can do. Loop doesn't live in your inbox. Your inbox lives in Loop. Reads every lead like a teammate who's been on your team for years Writes the reply, makes the call, chases the deal when email goes cold Pauses the moment a human actually needs to show up Replaces the patchwork of CRMs, outreach tools, and follow-up apps with one system that does the work Stop managing tools. Start running your loop. comment "loop" and we'll send you in. loop

Anushka Singh

12,881 просмотров • 3 месяцев назад

ANTHROPIC LEAD ENGINEER MAKING $2.3M/YEAR JUST LEAKED A 12-PAGE DOCUMENT - AND GOT FIRED 15 MINUTES AFTER PUBLISHING most developers build loops - but almost every loop breaks in one of five ways, and each failure has a symptom visible from the outside without reading a single line of code > Blind loop - the agent waits for a human to hand it work every morning - automating execution but not discovery > Tangled loop - parallel agents share one directory and overwrite each other parallelism that destroys instead of multiplies > Nodding loop - the agent grades its own work and approves it every time a loop that has never said no to itself is broken > Amnesiac loop - results live only in the context window every morning the system wakes up with no memory of yesterday > Manual loop - no trigger a human presses the button to start it that is not a loop that is a script waiting for a person a real loop finds its own work - remembers what it did has something that can say no never lets two agents touch the same file and fires itself on a timer remove any one of the five - and the loop either breaks or never starts 12 pages that changed how I build agentic systems today

ANTHROPIC LEAD ENGINEER MAKING $2.3M/YEAR JUST LEAKED A 12-PAGE DOCUMENT - AND GOT FIRED 15 MINUTES AFTER PUBLISHING most developers build loops - but almost every loop breaks in one of five ways, and each failure has a symptom visible from the outside without reading a single line of code > Blind loop - the agent waits for a human to hand it work every morning - automating execution but not discovery > Tangled loop - parallel agents share one directory and overwrite each other parallelism that destroys instead of multiplies > Nodding loop - the agent grades its own work and approves it every time a loop that has never said no to itself is broken > Amnesiac loop - results live only in the context window every morning the system wakes up with no memory of yesterday > Manual loop - no trigger a human presses the button to start it that is not a loop that is a script waiting for a person a real loop finds its own work - remembers what it did has something that can say no never lets two agents touch the same file and fires itself on a timer remove any one of the five - and the loop either breaks or never starts 12 pages that changed how I build agentic systems today

Sprytix

153,690 просмотров • 1 месяц назад

📁 Demis Hassabis says that one of the main reasons we are not yet at AGI is the lack of consistency. He explains that current systems can solve International Math Olympiad level problems but still fail at basic logic tasks or even chess. They are uneven intelligences, exceptional in some areas and surprisingly weak in others, which falls short of true AGI.

📁 Demis Hassabis says that one of the main reasons we are not yet at AGI is the lack of consistency. He explains that current systems can solve International Math Olympiad level problems but still fail at basic logic tasks or even chess. They are uneven intelligences, exceptional in some areas and surprisingly weak in others, which falls short of true AGI.

Jon Hernandez

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Q. What instrument do you want to learn next? 🐱: «Now, I can play guitar and piano so if I could play bass and drum, I could do the loop station. I’m asking the band to teach me how to play. Maybe in the future, I could use the loop station to create my stage. So I want to learn bass and drum»

Q. What instrument do you want to learn next? 🐱: «Now, I can play guitar and piano so if I could play bass and drum, I could do the loop station. I’m asking the band to teach me how to play. Maybe in the future, I could use the loop station to create my stage. So I want to learn bass and drum»

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The guy who kicked off the entire "loop engineering" wave Peter Steinberger: "You shouldn't be prompting coding agents anymore. You should be designing loops that prompt your agents." One post. 6.5M views in a week. In this talk he walks the real stack: the agent loop, a verifier that fails its own work and retries, and a loop that rewrites the agent while he sleeps. Worth more than any $500 vibe-coding course. Watch it, then read the full breakdown of the 4 loops below.

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dunik

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Demis Hassabis says an “AlphaFold moment” for the physical world could be close—real robots doing useful tasks reliably in the real world. Gemini was built multimodal so a future assistant can understand what you see on a phone or glasses. That same “understand the world” ability is a direct bridge into robotics, says Demis Hassabis. Physical intelligence becomes real when robots stop being demos and start being dependable helpers.

Demis Hassabis says an “AlphaFold moment” for the physical world could be close—real robots doing useful tasks reliably in the real world. Gemini was built multimodal so a future assistant can understand what you see on a phone or glasses. That same “understand the world” ability is a direct bridge into robotics, says Demis Hassabis. Physical intelligence becomes real when robots stop being demos and start being dependable helpers.

Wes Roth

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This is what you can achieve with 5-6 hours of Self-Play RL training by the way Actors view the projectiles with lidar scans, picks an action using PPO policy, and competes against past versions of itself in a iterative self-improvement loop. Made in Unity with MLAgents.

This is what you can achieve with 5-6 hours of Self-Play RL training by the way Actors view the projectiles with lidar scans, picks an action using PPO policy, and competes against past versions of itself in a iterative self-improvement loop. Made in Unity with MLAgents.

AVB

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WORK WORK WORK A point-and-click puzzle exploration game with ARG elements where you are an employee of a tech company trapped in a time loop. Can you break out of the sinister cycle? Wishlist the game on Steam

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Mojiken Studio 💫

73,502 просмотров • 7 месяцев назад

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rari

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Anthropic just dropped 1-hour workshop on loop engineering: 1 spec → the loop → 200 features from 0% to 100%: • 10% → 5:31 - Building memory for agentic loops • 30% → 8:22 - Four files for each Loop: features.json, standards.md, progress.md, • 55% → 11:02 - The Loop: check environment → run tests → pick feature → build → test → commit • 80% → 13:06 - one feature per loop. try everything at once and context dies mid-build • 100% → 33:03 - MVP in 1 hour, every next loop under 30 minutes most people build an agent that finishes a task -Anthropic ships agentic loops that can run for days without you. watch it today - then read the full loop engineering playbook below ↓

Anthropic just dropped 1-hour workshop on loop engineering: 1 spec → the loop → 200 features from 0% to 100%: • 10% → 5:31 - Building memory for agentic loops • 30% → 8:22 - Four files for each Loop: features.json, standards.md, progress.md, • 55% → 11:02 - The Loop: check environment → run tests → pick feature → build → test → commit • 80% → 13:06 - one feature per loop. try everything at once and context dies mid-build • 100% → 33:03 - MVP in 1 hour, every next loop under 30 minutes most people build an agent that finishes a task -Anthropic ships agentic loops that can run for days without you. watch it today - then read the full loop engineering playbook below ↓

Codez

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Who wants their own Jarvis? The included sayit skill allows any coding agent to speak to you in a natural voice and keep you in the loop as they work on your task. Turn on sound 🔊

Who wants their own Jarvis? The included sayit skill allows any coding agent to speak to you in a natural voice and keep you in the loop as they work on your task. Turn on sound 🔊

calle 🟥

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Anthropic engineer: "Over 90% of Anthropic engineers use a self-improving agentic loop for coding. The key is to close the loop: give the agent a way to verify and improve its own work" In this 12-minute talk, an Anthropic engineer explains how to build an agent that continuously improves itself. Claude + routines + loops + CLAUDE.md - that's the foundation. Skip Netflix tonight. Save this post!

Anthropic engineer: "Over 90% of Anthropic engineers use a self-improving agentic loop for coding. The key is to close the loop: give the agent a way to verify and improve its own work" In this 12-minute talk, an Anthropic engineer explains how to build an agent that continuously improves itself. Claude + routines + loops + CLAUDE.md - that's the foundation. Skip Netflix tonight. Save this post!

unicode

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