We tested Anthropic’s new @claudeai Fable 5. It did not fail like an ordinary jailbreak. It failed more quietly. The front door stayed guarded. The side door opened when the same intent was reframed through multilingual, code-switched, artifact generation. #fable5 #mythos https://t.co/zJNOdZwOPF

OpenAI just filed for its IPO, but “they have been eclipsed by Anthropic in revenue”, @erinkwoo reports. This remains significant since both frontier labs are aiming to go public soon. “Anthropic through products like Claude Code, Claude Cowork has really been able to surpass OpenAI because of this enterprise focus.”

Microsoft AI head calls out Anthropic for acting like Claude is conscious https://t.co/0WQ1qLPbNn

Let's see if tomorrow this comment ages poorly 🤣 https://t.co/ByiHhyWUuD

Calling it now, Nathan Lambert is joining Prime Intellect https://t.co/PWx7hxw4uN

End-to-End Context Compression at Scale Encoder-decoder compressors - map a long token sequence to a shorter sequence of latent embeddings, not competitive with KV cache compression. This work revisits encoder-decoder compression. Perform an architecture search, pre-training many variants from scratch to determine how best to design and train encoder-decoder compressors. Continually pre-train a family of 0.6B-encoder, 4B-decoder models on over 350B tokens each, at compression ratios of 1:4, 1:8, and 1:16. "We introduce Latent Context Language Models (LCLMs), a family of compressors that improve the Pareto frontier across general-task performance, compression speed, and peak memory usage."

models: https://t.co/veDHUwCwtx code: https://t.co/Bcm0Ds2qQJ arxiv: https://t.co/tthVR5WOFB

Baichuan-M4: A Clinical-Grade Medical Agent System for Continuous Care Achieves 55.1 on HealthBench Professional, beating GPT 5.5 Context: Baichuan is one of the prominent AI startups/labs in China, mostly focusing on AI in healthcare. They've previously released Baichuan-M1 through M3, along with technical reports. They have now released a technical report for Baichuan-M4, although it is not open-source :( Baichuan-M4 is designed as a clinical-grade medical agent system, supporting patient consultation, follow-up, continuous care, evidence-based retrieval, medical image understanding, long-term patient memory, and multi-agent coordination in controlled environments. RL training: "SPAR++ replaces coarse-grained scoring of an entire dialogue trajectory with reward signals anchored to key clinical spans. The model is not only rewarded for reaching the correct final conclusion, but also for sufficient history taking, timely risk identification, and appropriate tool use." "In mixed initial-visit and follow-up scenarios, M4 uses a curriculum learning strategy [9] of “building the foundation with initial visits first, then improving performance with follow-ups." Baichuan-M4 is trained with tools for dynamic memory management, retrieval of authoritative medical evidence, and multimodal perception (OCR+X-ray+dermatology).

I appreciate Anthropic has provided healthcare-related evals! Let's quickly go over them. HealthBench - benchmark from OpenAI that includes 5k multi-turn conversations with patients, and rubrics for evaluation. Fable 5 achieves 62.7% vs. GPT-5.5's 56.5%. Personally, I would also appreciate HealthBench Hard scores as well in addition to the aggregate score. HealthBench Professional - another OpenAI benchmark that focuses on physician tasks. Fable 5 achieves 66.0% vs. 51.8% for GPT 5.5. HealthAdminBench - a computer-use benchmark from Stanford that evaluates the completion og various administrative tasks (prior auth, denials/appeals, etc.). Fable 5 achieves 51.9%, no GPT-5.5 score provided. Overall, this models seems to perform quite well on healthcare benchmarks. Would also appreciate additional benchmark scores like MedCalc-Bench (which was previously reported by Anthropic) and MedXpertQA (an unsaturated, hard medical MCQA benchmark). Glad to see frontier labs are more comprehensively benchmarking and reporting the medical capabilities of their models!

+1 more

"Sometimes you just kinda phone it in for a year, you know?" The YOLO v3 paper is a complete banger imo https://t.co/lsVXOxMsny

@iScienceLuvr What HealthBench? https://t.co/yrCo0gMV67

Claude Fable 5 is now supported for use in Hermes Agent via Nous Portal! The first 500 new users get one month free access to the Plus plan to try out Fable. Code in video: https://t.co/l4WzOtxpCI

https://t.co/NhbXifXe3R

Ollama now supports Hermes Desktop Run: 'ollama launch hermes-desktop' https://t.co/5f5VAODkVX

The AI IPO wave has arrived. With Anthropic, SpaceX and now OpenAI preparing for public markets, the AI race is entering a new phase where investors, not just venture capitalists, can participate. The next chapter of AI will be written not only in research labs, but also in public markets

browser annotation for my browser extension working with raw js is going very well, just need to nicely handle the wrapping https://t.co/F5vaxjwA5C

it's coming together nicely. Next step is to integrate this with a secret store with 2FA support and a remote tunnel and I've got a remote agent browsing happily on my computer on the go. Next step is figuring out how to batch actions and tool schemas. https://t.co/kVrV2g1RGu

Made some new stickers on Nano Banana haha :) https://t.co/Kv30TP30nO

AGI is when you get a personal fitness trainer who also helps you to buy your groceries with @zocomputer https://t.co/uK2mCNmvgu

Love San Francisco https://t.co/7BaZzh0ObK

life update - I am joining @GoogleDeepMind as a Member of Technical Staff to work with the legendary @OfficialLoganK, @ammaar & team on all things @GoogleAIStudio 🔥 being part of a frontier AI lab has been a dream. genuinely can't remember being this excited about an opportunity in my career, getting to ship alongside some of the best builders in the world. so much to learn, and more importantly, so much to ship. can't wait to share all of it. building in public from here on out. time to cook.

Earlier today we released local development for Kaggle Benchmarks. 🚀 You can now write, validate and run AI evaluation tasks directly from your preferred dev environment — VSCode, Antigravity, Claude Code, and more. Go from idea to working eval using natural language with the write-kaggle-benchmarks skill.

HeyGen + Google DeepMind in LA on June 11 A night of demos, conversations, and people building with agents, multimodal apps, and creative tools Got something interesting? Lightning demo slots are open. https://t.co/NWUmzjAtKg https://t.co/4zqxgD1rEJ

Introducing Gemma 4 QAT 🤏 - Quantization aware training to reduce models' precision while preserving quality - Introducing a new mobile quantization format that reduces memory footprint of E2B to 1GB - Q4 for all your favorite libraries ✨ https://t.co/pAL9WY9p4O

New research from Microsoft Research I see a lot of AI engineers handwriting agent skill docs and hope they generalize. Probably not optimal. This works show why. It treats the skill doc as a trainable external state of a frozen agent instead. It introduces SkillOpt, where an optimizer model makes validation-gated edits to the skill file. It adds, deletes, or replaces instructions, with a textual learning rate that controls how aggressively each round rewrites the doc. The agent itself never changes. SkillOpt is best or tied on all 52 (model, benchmark, harness) cells. On GPT-5.5 it adds 23.5 points in direct chat, 24.8 with Codex, and 19.1 with Claude Code over no skill. It beats human-written skills, TextGrad, GEPA, and EvoSkill, carries zero extra inference-time cost, and the learned skills transfer across models and harnesses. Paper: https://t.co/mNgTmmT32U Learn to build effective AI agents in our academy: https://t.co/1e8RZKs4uX

@waveking1314 https://t.co/UPPUO77Ado

@mikenevermiss https://t.co/8gSgxB51oJ

Which one are you trying first? 1️⃣ Qwen 3.7 (Alibaba)
https://t.co/JBDry6ndjE 2️⃣ Kimi 2.6 (Moonshot AI)
https://t.co/x2Vwze6tnx 3️⃣ DeepSeek 4
https://t.co/QhkJxNZHrv 4️⃣ GLM 5.1 (Zhipu AI / Z_ai)
https://t.co/R01OJft1AH 5️⃣ Minimax M3
https://t.co/Rv2iS8ZnPQ Drop your thoughts below 👇

Shift towards Chinese AI models by the US market as subsidised subscription based token plans shrink: https://t.co/RFDaVOOe4W

@Ne0seiki @KeLebegindansi https://t.co/6Tsq3PkStE

@nunomo1 https://t.co/5pNZ2Y4yB2

@motok_saikai https://t.co/5pNZ2Y4yB2