Eric Schmidt shares his weekend habit that led to billion-dollar decisions https://t.co/YZ2AC5h9ut

Charlie Munger: “You climb as [high] as you can by just advancing one inch at a time. That's the secret of life.” “There is no more guaranteed way to make people hate you than to fail them in their reasonable expectations.” https://t.co/OjZasohXRS

Tried https://t.co/SWCtioJSxo today and it’s one of the more interesting consumer agent interfaces I’ve seen. Feels closer to OpenClaw / harness than a typical chatbot. You get an LLM + voice interface and a pretty complete agent surface: persistent chat, heartbeat-based API actions, and connectors into SMS, Social Accounts, Virtuals ACP, and more. Most importantly, there's x402 plugin for access to discovery for paid APIs https://t.co/NHWw835a0D

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Ricky, an amazing 71-year-old man, bought his first Tesla solely for the Tesla Self-Driving software. Without it, he would have to rely on someone else to drive for him. The Elon Musk effect on humanity. https://t.co/4KvpZEdmE3

@flowersslop Alec is amazing, but he says himself in his paper that he was simply taking our existing work, and moving it to a new arch and scaling it. https://t.co/gVS6j8XF3e

@jrosenfeld13 @seb_ruder Thanks Jason - if folks read Alec's paper they'd know this, since he was generous with his credit! :) https://t.co/PHLt8XZoNF

AI robots doing real household tasks 🤯 not just demos anymore. #robots #Ai 💕 https://t.co/QgBisvyXbS

Want to talk to the past? Here is an LLM "trained entirely from scratch on a corpus of over 28,000 Victorian-era British texts published between 1837 and 1899, drawn from a dataset made available by the British Library." Quite different from an LLM roleplaying a Victorian. https://t.co/5jl7SyJjAP

Very cool work, I wonder what other eras have a large enough corpus for training? https://t.co/ZQTVQZjKbA

Full house here at the Multimodal Hackathon sponsored by @GoogleDeepMind ! https://t.co/j7PBAb6ATC

This seems really bad but also like it was exactly the goal of systematically dismantling our federal government's standards & protections. https://t.co/djUpcqAPWA

@BEBischof https://t.co/uzwR8l4lWe

AI robots doing real household tasks 🤯 not just demos anymore. #robots #Ai 💕 https://t.co/QgBisvyXbS

Want to build a deep research agent with Gemini in 90 minutes? @ivanleomk and I did this yesterday live to celebrate him joining @GoogleDeepMind! Some of the things we covered 👇 Amazing to see see Ivan hit the ground running with the DevRel team and can't wait to see what he builds with @OfficialLoganK, @DynamicWebPaige, @_philschmid, @osanseviero, and the whole team 🤗

New paper: "Self-Distillation of Hidden Layers for Self-Supervised Representation Learning" We introduce Bootleg — a simple twist on I-JEPA/MAE that dramatically improves self-supervised representations. The idea: MAE predicts pixels (stable but low-level). I-JEPA predicts final-layer embeddings (high-level but unstable). Bootleg bridges the two by predicting representations from multiple hidden layers of the teacher network — early, middle, and late — simultaneously. Why it works: early layers provide stimulus-driven grounding that prevents collapse; deep layers provide semantic targets; and the information bottleneck of compressing all abstraction levels through masked patches forces the encoder to build richer representations. The method is quite simple on top of I-JEPA: extract targets from evenly-spaced blocks, z-score and concatenate, widen the predictor's final layer. That's it. Frozen probe results (no fine-tuning): ImageNet-1K: 76.7% with ViT-B (+10pp over both I-JEPA and MAE) iNaturalist-21: 58.3% with ViT-B (+17pp over I-JEPA, +15pp over MAE) ADE20K segmentation: 30.9% mIoU with ViT-B (+11pp over I-JEPA, +6pp over MAE) Cityscapes segmentation: 35.9% mIoU with ViT-B (+11pp over I-JEPA, +5pp over MAE) Gains hold across ViT-S, ViT-B, and ViT-L. Single-view, batch-size independent — no augmentation stack, no multi-crop, no contrastive loss, no large compute requirements. Our study is just on images, but this change can be readily deployed to MAE and JEPA models across all domains. https://t.co/PXJlRV4I6w

Lawyers for Trump's ICE provided false information to justify arresting and detaining thousands of people who had attended immigration courts. https://t.co/jgTto0GZBn

My ideal timeline: Growing up in the 90s, discovering neural nets, scaling laws, and building an artificial consciousness. https://t.co/FQMunONGm3

Segawaさん@segawachobbies が開発中の、オープンソースverキボウちゃんをテスターとして一足先に作ってみました！ 4つのサーボだけで表情豊かに動き、首回りの動きを実現するロットエンドが面白いです。 ※データは公開前です。今後の動向はSegawaさんの投稿を要チェック👀 https://t.co/DFUG0VLIKk

The happiest person I knew killed themselves four years ago. The more I tell people that I’m anxious or neurotic the more I realize deep down I’m doing pretty good for myself There’s so much friction in trying to mask everything https://t.co/PQTT6CLquZ

Day 2 went amazing My first $1k MRR from a single app I have no words You made my small dreams come true I will start working on all the small bugs you guys found in the app And probably gonna release a lifetime version because a lot of you guys requested that Let’s keep building This was Day 207 ✅ X: 7950 🥳

👀 https://t.co/TbuVOvL3QO

👀 https://t.co/TbuVOvL3QO

こんにちは、日本の皆さん。 OpenAIで働いている劉嘉欣（りゅう かきん）です。お会いできてとても嬉しいです。 AIエージェントの構築やCodexについて質問があれば、いつでもオンラインで気軽に声をかけてください。 近いうちに日本を訪れて、皆さんのお話をもっと聞けるのを楽しみにしています。 https://t.co/lkow1wxfcX

The perfect camping mug? https://t.co/PQmz69VVyr https://t.co/XfMShMvHD5

NEW research from NVIDIA. Post-training agents with RL is powerful but expensive. Every parameter update needs full multi-turn rollouts with environment interactions, making end-to-end RL prohibitively costly for long-horizon agentic tasks. This research offers a practical middle ground. The work introduces PivotRL, a framework that operates on existing SFT trajectories to combine the computational efficiency of SFT with the out-of-domain retention of end-to-end RL. Instead of exhaustive full-trajectory rollouts, PivotRL identifies pivots, informative intermediate turns where sampled actions show mixed outcomes, and trains only on those high-signal moments. Standard SFT degrades OOD performance by -9.83 points on average. PivotRL stays near zero (+0.21) while achieving +14.11 average in-domain gains over the base model versus +9.94 for SFT. On SWE-Bench, PivotRL reaches competitive accuracy with E2E RL using 4x fewer rollout turns and 5.5x less wall-clock time. The method is already deployed in production as the workhorse for NVIDIA's Nemotron-3-Super-120B agentic post-training. Paper: https://t.co/sIBLUpyfMD Learn to build effective AI agents in our academy: https://t.co/1e8RZKs4uX

Finally got a chance to play around with @karpathy's LLM Council. I built it as a plugin inside of Claude Code. Hooked it up with OpenRouter for models. The AskUserQuestion tool came in handy to select the council and chairman. This is my first test, but I agree with Karpathy that the concept of LLM ensembles can be used beyond models that offer perspectives on interesting questions. I feel like this could have really cool applications in agentic coding. More on that soon. I built this as a plugin, so next I will be exploring other user cases around agentic coding, like evaluation, tool building, designing, and research. If there is enough interest, I will clean it up and push it out as an open plugin.

@chamath @karpathy It exists in our plugin: https://t.co/5hNgew17xQ Highly flexible and super simple to use. You can use an LLM council in Claude Code to select different models that provide different perspectives. https://t.co/JJq8YI1uV5

Augmental sent me a MouthPad^ A bluetooth mouse for your tongue?! 🤯 Make it a controller? 👅🎮 https://t.co/XY5TNuvwZ2

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Augmental sent me a MouthPad^ A bluetooth mouse for your tongue?! 🤯 Make it a controller? 👅🎮 https://t.co/XY5TNuvwZ2

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he's on his way to a full recovery! https://t.co/9N1WjZITUD

i often think about this.. https://t.co/YMhKgLRD1G

i often think about this.. https://t.co/YMhKgLRD1G