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https://t.co/NeZ79myiqt
The Pitt fandom wouldnโt be able to handle her https://t.co/SWja4kOMCM
The Pitt fandom wouldnโt be able to handle her https://t.co/SWja4kOMCM
HEAT (Commentary by Michael Mann) https://t.co/fCNEwLzvTG
HEAT (Commentary by Michael Mann) https://t.co/fCNEwLzvTG
Isn't it ironic that many Brits voted for Brexit because they wanted immigration to go down? How did this happen? https://t.co/W8PytSg3DM
Sometimes those maintenance tasks are too easy to do yourself. ๐ Prompt Copilot to do them all via the /fleet command. โ https://t.co/lhN8vViZ1U
Iโm convinced @jxnlco is the single most performative man in AI https://t.co/oeM23xFUn3
https://t.co/CCzZzsDnzG
Iโm convinced @jxnlco is the single most performative man in AI https://t.co/oeM23xFUn3
I'm getting 2x ratiod for being right. the absolute state of this place man https://t.co/ihSnMlSu4S
The first humanoids in our homes might not look human at all. They might just be vacuum robots that slowly evolve arms, tools, and intelligence. Evolution, but in hardware. via Peter Kappes #Ai #robotics #innovation https://t.co/6AbmG6WPOY
Today's the third time this year Iโve heard someoneโs partner died in their sleep. Third. I must blurt the uncomfortable truth out loud. this is often an engineering problem. We need hardware-AI. And you might already own some of the fitness trackers that get us there. ๐งต https://t.co/zfeZ5aCf4d
15K stars already!? Great idea. CLIs work amazingly well with coding agents. Worth playing around with. Do run a lot of tests if you are planning to use this to build tools. https://t.co/Aigh3uAI5Y
We mostly solved multi-node coordination decades ago in distributed computing. Turns out LLM teams face some of the same coordination problems today. Here is a really good read for anyone designing multi-agent systems. It applies distributed systems theory to LLM teams and finds the same O(nยฒ) communication bottlenecks, straggler delays, and consistency conflicts showing up directly. Decentralized teams wasted more rounds communicating without making progress, but they also recovered faster when individual agents stalled. How does this relate to distributed systems? The work attempts to evaluate LLM teams as distributed systems. It lays out a principled framework instead of trial and error for deciding when teams help, how many agents to use, and what coordination structure fits the task. Designing LLM teams without distributed systems principles is like building a cluster without understanding consensus protocols. Paper: https://t.co/klHzUFJL1R
Not the same unforch. I tried. https://t.co/M7Gp7Silxt
@satyanadella Awesome to see this model on @huggingface ๐ค https://t.co/0SKw15VPkd
@satyanadella Awesome to see this model on @huggingface ๐ค https://t.co/0SKw15VPkd
Everyone's excited about Karpathy's autoresearch that automates the experiment loop. We automated the whole damn thing. ๐ฆ Meet AutoResearchClaw: one message in, full conference paper out. Real experiments. Real citations. Real code. No human in the loop. One message in โ full paper out. Here's what happens in between: ๐ Raids arXiv & Semantic Scholar, digests 50+ papers in minutes ๐ฅ Three AI agents FIGHT over the best hypothesis (one swings big, one sanity-checks, one tries to kill every idea) ๐ป Writes experiment code from scratch, adapts to your hardware ๐ฅ Code crashes at 3am? It reads the stack trace, rewrites the fix, keeps going ๐ Results weak? It pivots to entirely new hypotheses and starts over ๐ Drafts a full paper with citations, every single one verified against live databases No babysitting. No Slack messages. No "hey can you re-run this." Karpathy built the experiment loop. We built the whole lab. Chat an idea. Get a paper. ๐ฆ Try it ๐: https://t.co/KLOcnzFYaD Kudos to the team @JiaqiLiu835914, @richardxp888, @lillianwei423, @StephenQS0710, @Xinyu2ML, @HaoqinT, @zhengop, @cihangxie, @dingmyu, and we are looking for more contributors.
This is a very cool experiment but we need to get AIs to do good science. The modern scientific method & Mertonian norms are critical for a reason, and a failure to follow them has led to many of our current scientific crises. We donโt want p-hacking at scale https://t.co/YEqzVDmTpH
Everyone's excited about Karpathy's autoresearch that automates the experiment loop. We automated the whole damn thing. ๐ฆ Meet AutoResearchClaw: one message in, full conference paper out. Real experiments. Real citations. Real code. No human in the loop. One message in โ full
This is how 2x rate limits 24/7 fast mode feels https://t.co/5NMQ9Ezgps
The Atlantic has a sobering, first-person look at the ramifications of legalized online sports betting. Here are a few of the more telling passages. 1/5 https://t.co/iAmRFrIXad
Sam Altman just said in his new interview, that a new AI architecture is coming that will be a massive upgrade, just like Transformers were over Long Short-Term Memory. And also now the current class of frontier models are powerful enough to have the brainpower needed to help us research these ideas. His advice is to use the current AI to help you find that next giant step forward. --- From 'TreeHacks' YT Channel (link in comment)
ไปๅคฉๆญฃๅผๅๅคไป Claude Code ๅๆขๅฐ Codex ไบ ไนๅ็จ Claude Code ๆถๅ ไธบๆฒกๆ Anthropic ๅฎๆน API๏ผไธ็ดๅจ็จ Minimax ๅ Kimi ็ญ API ๅๆข็็จใ ๆ่ฟ่็ผๅฏ่ง @OpenAIDevs ๅจ Codex ไธ็ๅณๅฟๅๅจไฝ่ถๆฅ่ถๅฏ้๏ผOpenClaw ๅๅงไบบ @steipeteใInstructor ไฝ่ @jxnlco ็ญๅผๆบๅ AI ๆ่ฒๅไบซ้ๅธธๆดป่ท็ๅคงไฝฌๅ ๅ ฅ Codex๏ผ่ฟๆไธๅฎๆ Reset limit ็ @thsottiaux ๐ ๅ ่ฎข้ ไธช Plus ไผๅไฝไธบไธปๅ AI ็จ่ตทๆฅ๏ผๅฏน Codex ๆไปคไธๅค็ๆ๏ผๅ ๅไธช Cheatsheet ็ปๅๅไบ่งฃ Codex ็ๆๅไปฌ๏ผๅ ๆฌๆ่ชๅทฑใ
Top AI papers on @huggingface this week: Language feedback for RL, training agents by talking, and fixing LLM story consistency - Bootstrapping Exploration with Group-Level Natural Language Feedback in Reinforcement Learning - Geometry-Guided Reinforcement Learning for Multi-view Consistent 3D Scene Editing - Penguin-VL by Tencent: Exploring the Efficiency Limits of VLM with LLM-based Vision Encoders - OpenClaw-RL: Train Any Agent Simply by Talking - Lost in Stories: Consistency Bugs in Long Story Generation by LLMs - Holi-Spatial: Evolving Video Streams into Holistic 3D Spatial Intelligence - Spatial-TTT: Streaming Visual-based Spatial Intelligence with Test-Time Training - Flash-KMeans: Fast and Memory-Efficient Exact K-Means - Thinking to Recall: How Reasoning Unlocks Parametric Knowledge in LLMs - LoGeR: Long-Context Geometric Reconstruction with Hybrid Memory
don't make any mistakes, a tale in 2 parts https://t.co/9ZC5k9QB86
don't make any mistakes, a tale in 2 parts https://t.co/9ZC5k9QB86
I (finally) put together a new LLM Architecture Gallery that collects the architecture figures all in one place! https://t.co/NO7z6XSRHS https://t.co/X41FrK4i94
A Restore Britain Government would defund the BBC. https://t.co/K63UGn7DSw
A quirky new trend is sweeping Chinaโs AI scene. โRaise a lobsterโ style OpenClaw agents are going viral, turning autonomous AI tools into the latest experimentation playground. Behind the hype lies something bigger: a rapidly evolving ecosystem pushing the boundaries of agentic AI. https://t.co/WrQqG6qcuG @nickrigordon @fortunemagazine
Shot this picture at dinner. But the important thing is soon you can talk more to your claw. So your AI works better with you. https://t.co/VTgfLSkUFV
in the next claw release (~Sunday), you can always ask your agents, even they are busy working. https://t.co/TVX9o6ciKo
