moretitle

[Z.ai @Zai_org]

Intelligence should be open, accessible, and ready to build with, empowering every developer, everywhere.

GLM-5.2 is now available to all GLM Coding Plan users, including Lite, Pro, Max, and Team plans.
https://t.co/AedZACyzej

As our new flagship model, GLM-5.2 delivers powerful coding capabilities, usable 1M-context support, and continued strengths in long-horizon tasks.

API and Chatbot services will launch next week. The model will also be officially open-sourced next week under the MIT License.

The future of AI is open, and it belongs to the people.

[manopsi @manopsi]

ข่าวใหญ่วงการ medical AI ผลวิจัยลงใน Nature Medicine พบว่า general purpose models อย่าง Opus, GPT, Gemini เก่งกว่า clinical AI ในทุกการทดสอบ แถมยัง hallucinate น้อยกว่าด้วย

การพัฒนาของ frontier models ใหญ่ไปไกลและเร็วกว่า niche models มากจนเจ้าเล็กตามไม่ทันแล้ว ยุคของ medical AI model ต่อจากนี้ต้องเน้น use case และการ integrate กับ workflow เป็นหลัก ขายจุดแข็งเรื่องความฉลาดไม่ได้อีกต่อไป
https://t.co/PnceUNkhc3

[Sydney Runkle @sydneyrunkle]

https://t.co/qV7B67dyxV

[Harrison Chase @hwchase17]

Excited to dive into this - an open source agent designed with memory/continual learning in mind

[Hunter Lovell @huntlovell]

we're adding interpreters to our agents, and now so can you!

There's a lot of interesting patterns/ agent behaviors I've seen come across the timeline that depend on some way to run code (think PTC, CodeMode, RLM), but it's been hard to filter that down into an abstraction thats easy to work with. We've spent the last couple of weeks creating an abstraction to solve just that!

Expect me to yap more about this soon - there's a lot to talk about here that was hard to fit into a single post

[Dmitrii Kovanikov @ChShersh]

For my non-tech friends, this is like Ronaldo joining Manchester City

[moretitle @moretitle_]

WOW!!

[Andrej Karpathy @karpathy]

Personal update: I've joined Anthropic. I think the next few years at the frontier of LLMs will be especially formative. I am very excited to join the team here and get back to R&D. I remain deeply passionate about education and plan to resume my work on it in time.

[Chanon N. @KillerNay]

Thaigov spending dashboard https://t.co/zSD5vW6CQI ตอนนี้ เห็นงบใช้จ่าย 41 อบจ แล้ว จริงๆ ทำได้ทุก อบต เดี๋ยวคงเปลี่ยนเป็น open source cli ให้คน ใส่ api key + run เอาขึ้นเอง ทำให้ดูว่ามันทำได้ ทีเดียวไม่ต้องอวดว่า ทำอยู่อบจ เดียว

[Fabrizio Romano @FabrizioRomano]

🚨 BREAKING: Xabi Alonso has accepted to become Chelsea next manager, HERE WE GO! 🔵🔜

The agreement is set to be completed.

#CFC prepare official announcement for the upcoming days, but Xabi said YES. 💣

[mem0 @mem0ai]

https://t.co/ffV4apO5je

[End Wokeness @EndWokeness]

Helen of Troy & Achilles 2004 vs 2026:

[Andrej Karpathy @karpathy]

This works really well btw, at the end of your query ask your LLM to "structure your response as HTML", then view the generated file in your browser. I've also had some success asking the LLM to present its output as slideshows, etc.

More generally, imo audio is the human-preferred input to AIs but vision (images/animations/video) is the preferred output from them. Around a ~third of our brains are a massively parallel processor dedicated to vision, it is the 10-lane superhighway of information into brain. As AI improves, I think we'll see a progression that takes advantage:

1) raw text (hard/effortful to read)
2) markdown (bold, italic, headings, tables, a bit easier on the eyes) <-- current default
3) HTML (still procedural with underlying code, but a lot more flexibility on the graphics, layout, even interactivity) <-- early but forming new good default
...4,5,6,...
n) interactive neural videos/simulations

Imo the extrapolation (though the technology doesn't exist just yet) ends in some kind of interactive videos generated directly by a diffusion neural net. Many open questions as to how exact/procedural "Software 1.0" artifacts (e.g. interactive simulations) may be woven together with neural artifacts (diffusion grids), but generally something in the direction of the recently viral https://t.co/z21CP5iQfu

There are also improvements necessary and pending at the input. Audio nor text nor video alone are not enough, e.g. I feel a need to point/gesture to things on the screen, similar to all the things you would do with a person physically next to you and your computer screen.

TLDR The input/output mind meld between humans and AIs is ongoing and there is a lot of work to do and significant progress to be made, way before jumping all the way into neuralink-esque BCIs and all that. For what's worth exploring at the current stage, hot tip try ask for HTML.

[JetBrains @jetbrains]

Once a powerful coding model lives inside the IDE, you're no longer just typing code – you're steering an agent, shaping its context, and deciding what's ready to ship.

That was the focus of the inaugural JetBrains x Codex Hackathon with @OpenAIDevs, and these are the results.

[vLLM @vllm_project]

🚀 Day-0 MTP support for Gemma4 now available at vLLM with ready-to-use docker image!

⚡️Enjoy up to 3x faster decoding performance to supercharge your development with zero quality degradation!

Check out the full vLLM recipes for Gemma 4 model series👇
https://t.co/IrCaaa6SIo

[luthira @luthiraabeykoon]

We implemented @karpathy 's MicroGPT fully on FPGA fabric.

No GPU.
No PyTorch.
No CPU inference loop.

Just a transformer burned into hardware, generating 50,000+ tokens/sec.

The model is small, but the idea is not: inference does not have to live only in software 👇

[mem0 @mem0ai]

https://t.co/vAimGBAdMt

[kache @yacineMTB]

you can outsource your thinking
but you cannot outsource your understanding

[Oliver @olvrgln]

Introducing Mesa: the most powerful filesystem ever built, designed specifically for enterprise AI agents.

Every team building agents eventually hits the same wall: where do the files live?

Not the chat history, the actual artifacts the agent works on.
> The contracts your agent redlined
> The claim files it updated
> The 200-page audit report it edited overnight while you were asleep

Today those documents live in a sandbox that dies in 30 minutes, an S3 bucket where concurrent writes clobber each other, or a GitHub repo that was never built to absorb agent-scale traffic.

So we built Mesa.

The world's first POSIX-compatible filesystem with built-in version control, designed from the ground up for agents. You mount it into your sandbox like any other filesystem. Your agent reads and writes files normally. Behind the scenes every change is versioned, branchable, reviewable, and rollback-able — like a codebase, for any file type.

Mesa provides
– Branches so agents work in parallel without locking
– Durable storage that survives sandbox death
– Sparse materialization so massive document sets load instantly
– Fine-grained access control per agent
– Full history for human review and audit

Design partners are running Mesa in production across legal, healthcare, GTM, business ops, and coding agents.

Private beta is open: link in the comments

[NVIDIA AI @NVIDIAAI]

Meet Nemotron 3 Nano Omni 👋

Our latest addition to the Nemotron family is the highest efficiency, open multimodal model with leading accuracy.

30B parameters. 256K context length. 🧵👇

[LangChain OSS @LangChain_OSS]

LangChain Community Spotlight: text2sql 🤖🗄️

An agentic text-to-SQL SDK built on LangChain's Deep Agents that autonomously explores schemas, writes queries, and self-corrects—achieving 100% accuracy on Spider benchmark with no RAG or pre-computed schemas.

Check it out: https://t.co/wtwjY2Tgk3