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Here’s the list: linus-sellout-tweets.com

You can now use X Premium subscriptions in Hermes Agent, and Hermes Agent can now search X posts. x.ai/news/grok-hermes

You can now use X Premium subscriptions in Hermes Agent, and Hermes Agent can now search X posts. x.ai/news/grok-hermes

We're now on Day 4 of nonstop autonomous operations with F.03 humanoid robots running 24/7 until failure nitter.net/i/broadcasts/1OxwblMvX…

Introducing Zero The programming language for agents. I wanted a systems language that was faster, smaller, and easier for agents to use and repair. Explicit capabilities. JSON diagnostics. Typed safe fixes. Made for agents on day zero.

OPENAI 🔥: In the future, Codex will be able to control other desktop devices with the Codex installation. All your Mac Minis, your desktop station at work, or even your grandparents' old computers can form your own "Codex network". Along with the upcoming "Locked Use" setting, this feature will allow Codex to invoke Computer Use capabilities on other machines from your main device.

ANTHROPIC 🔥: Claude Mythos model has been spotted on Google Cloud Console. -claude-mythos 👀 It is hard to imagine that Anthropic would change its mind and release it publicly but they could act as a model provider for those companies who have access to the model and run their stuff on GCP.

aws.amazon.com/about-aws/wha…

Claude Mythos now appears in the Google Cloud console, which was not the case yesterday The preview label is also gone. Is Anthropic preparing for a public release? Opus 4.7 also appeared first in the Google Cloud console before its release

You can now run MagicPath as a native canvas inside Codex to design and build functional apps. It's pretty incredible. Here's how to do it 👇 Video

OPENAI 🔥: In the future, Codex will be able to control other desktop devices with the Codex installation. All your Mac Minis, your desktop station at work, or even your grandparents' old computers can form your own "Codex network". Along with the upcoming "Locked Use" setting, this feature will allow Codex to invoke Computer Use capabilities on other machines from your main device.

"AI can cost more than human workers now," per Axios

We’re having way too much fun working through your feedback. (Please, keep it coming.) Keyboard shortcuts are now customizable. Set Codex up around how you actually work, then tweak shortcuts from settings instead of adapting to our defaults. Video

codex for improving computational complexity

gotta say Codex is completely unrecognizable from 3 months ago. guys went extreme founder mode on this thing @gabrielchua was demoing this and i was like “you guys have agentic excel on mac”

BREAKING: The results are in for Slides Arena... @AnthropicAI and @Zai_org models continue to lead the way in soft-verifiable domains 1st: Opus 4.7 by @AnthropicAI 2nd: Opus 4.7 (Thinking) by @AnthropicAI 3rd: GLM 5.1 by @Zai_org Huge congrats to @AnthropicAI and @Zai_org for establishing the SOTA for Agentic Slides

World Action Models: The Next Frontier in Embodied AI The first systematic survey defining WAMs as embodied foundation models that jointly predict future states and generate actions, covering architectures, data ecosystems, and evaluation protocols.

The latest CodexBar update renders API costs wayyyy nicer. codex.bar

How to Train Diffusion LLM more efficiently? Our paper has an answer for you: Don’t Retrain, Align: Adapting Autoregressive LMs to Diffusion LMs via Representation Alignment Diffusion language models are becoming increasingly attractive: they support bidirectional generation, non-sequential decoding, and flexible editing. But training them from scratch is expensive. So a natural question is: If we already have strong pretrained autoregressive LMs, do we really need to relearn all language representations for diffusion LMs? We argue: probably not. Our view is that AR→DLM conversion should not be treated as learning language from scratch again. Much of the semantic structure is already inside the AR model. What changes is the generation order and denoising behavior. So instead of only continuing denoising training, we explicitly preserve the representation geometry of the AR model. We introduce REPR-ALIGN: during masked diffusion training, we align the hidden states of the DLM to a frozen AR teacher of the same architecture, layer by layer, using cosine similarity. No adapters. No architectural changes beyond the attention mask. Just representation alignment + masked denoising. The result: up to 4× training acceleration in our setting, with especially strong gains in low-data regimes. The main takeaway is simple: Don’t retrain the representation space from scratch. Align it, and let the model relearn the decoding path. Paper: arxiv.org/abs/2605.06885 Code: github.com/pengzhangzhi/Open… Work done with an amazing undergrad @alexisfox and advisors @Anru_Zhang @AlexanderTong7

New article: a visual tour of recent LLM architecture advances, from Gemma 4 to DeepSeek V4. I focus on long-context efficiency tweaks like KV sharing, per-layer embeddings, layer-wise attention budgets, compressed attention, and mHC. Link: magazine.sebastianraschka.co…

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