Thanks to the codex on the chatgpt ios app I can “work from home” https://t.co/NI7wycHNVk

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Turn a single image into a fully meshed 3D world in minutes 👀 Built by a World Labs team member, image-blaster combines Marble + Claude skills + @fal to generate 3DGS environments, meshes, interactive physics objects and SFX from one image. learn more + try it yourself ↓ https://t.co/of9C0urgfJ

At #PyTorchCon Europe 2026, @ezyang (@Meta) explains why many developers find tensor parallelism difficult to work with and how PyTorch is exploring SPMD types to catch mistakes through type checking. Watch the full keynote: https://t.co/5NHkIonlxA https://t.co/eI8S5Ahr7T

Just subscribed to Grok SuperHeavy so I could try out their new command line interface. They have a special deal going where it's only $99 a month for the next 6 months, instead of the usual $299 https://t.co/eGO7qB3ATy

Christopher Nolan directing The Odyssey https://t.co/A7C8FeIMu9

You've been asking for this one... Now in preview: Codex in the ChatGPT mobile app. Start new work, review outputs, steer execution, and approve next steps, all from the ChatGPT mobile app. Codex will keep running on your laptop, Mac mini, or devbox. https://t.co/9i2Jckjt9z

“Let’s grab an almond latte and write our thinkpiece on datacenter water consumption” https://t.co/FSSEFhZPRO

@bznotes poor scooters didn’t stand a chance against @Bouazizalex & @shuooo https://t.co/kaPqZqMrtH

BREAKING: @theXtakeover will be taking place at Giga Austin on Saturday Oct 10 VIP tickets is super limited https://t.co/IeABh1sz2d https://t.co/LBX8or7dWL

Grok Build is also a really great interface for running subagents, which it often uses to check its own work. https://t.co/hKalbKG57B

Grok Build is a fully interactive CLI, which means you can actually use your mouse to click. No flickers. Especially useful as I find myself running 5+ agents at a time and jumping between plans. https://t.co/lRCvxRdwxr

xAI just released Grok Build CLI and it’s a game changer for developers Grok Build is a powerful AI coding agent and CLI built for professional software engineering and complex coding workflows running directly in your terminal With Grok Build CLI, you can: - Plan and review tasks before execution with clean diffs - Run multiple sub-agents in parallel for large projects - Use headless mode for automation and scripting - Seamlessly work with your existing tools and setups This is xAI going all-in on giving power users real engineering tools Currently in early beta and exclusively available for SuperGrok Heavy subscribers If you’re on SuperGrok Heavy, you can start using it right now Check it out here: https://t.co/eRNVXRSAcC

Congrats to our team, users, and supporters on the 150,000 GitHub star milestone! https://t.co/RJ5fyJuahO

https://t.co/TAx03KrYHa

Spreading the word on @NousResearch Hermes Agent — the self-improving AI agent that grows with you. Join us June 10th at Spark Coworking in Arlington, TX for a deep dive into this open-source autonomous agent from Nous Research. 🗓 Wed, June 10 | 4–6 PM 📍 @SparkArlington, Arlington, TX

The best YC launches don't please everyone. They offend someone. Zach's team let users talk to a computer to generate 3D models. Traditional engineers were furious. That fury was the marketing. The old guard shared it to complain. The forward-thinking customers saw it and bought. No ad budget. Just a product cleaving enough to force people to pick a side. If everyone in your industry nods politely at your launch, you've already failed. Real virality isn't about reach. It's about reaction. Make something the old guard hates. They'll deliver it to your best customers for free. @adamdotnew

#2 across all new releases in Canada. https://t.co/dvPbneSOx2

An early beta of Grok Build, an agentic CLI for coding, building apps, and automating workflows is now available for SuperGrok Heavy subscribers. Through this early beta, we will improve the model and product based on your feedback. Try it at https://t.co/bpTHpjivWD https://t.co/Rlg4qMLkrv

https://t.co/l9OZrEYGAL

https://t.co/l9OZrEYGAL

The MAX-LLM book just made it even easier to build an LLM from scratch. The new notebook format lets you run the GPT-2 components interactively, inspect real tensor shapes, and generate text from pretrained weights. Prefer to browse first? The pre-rendered version shows all outputs without running a cell: https://t.co/V3tysngguO

Aleph, our fully autonomous AI agent system for formal verification, aced all major theorem proving benchmarks including PutnamBench, VeriSoftBench, and Verina https://t.co/spIql8Pf4g

Interesting position paper on agentic AI as a foreseeable pathway to AGI. (bookmark it) There has been strong debate on whether a larger single model get us there or a multi-agent system. The authors argue that agentic AI systems, not bigger foundation models on their own, are the most foreseeable route to AGI. Formalizes what "agentic" actually contributes beyond the base model: memory, reasoning, tool use, self-improvement, alignment. Each is a separable axis with its own bottlenecks (long-horizon coherence, credit assignment, safety auditing). They argues that none of those bottlenecks get solved by another order of magnitude on pretraining compute. Paper: https://t.co/ppz4mh7kde Learn to build effective AI agents in our academy: https://t.co/1e8RZKs4uX

Boston, we're coming for you. May 29 we're hosting Engineers Day as part of the first ever Boston Tech Week. Our team will be on the ground sharing what actually works (and what breaks) when you're building agent swarms. Food, drinks + real talk with people actually shipping. https://t.co/y8ixt9e8Q6

@Techweek_ RSVP 👇 https://t.co/0DTHyv6MEE https://t.co/jx7qRc0uPp

Video: https://t.co/0uGlly09wx Vid has results but also some thoughts on how fun + easy this was! Hopefully inspires you to try some things too :) https://t.co/jUCXarg9NV

// Harnessing Agentic Evolution // Pay attention to this one if you run iterative agentic search loops. (bookmark it) AEvo splits the self-improvement loop into two jobs: > One proposes the next candidate. > The other watches what worked, what failed, and edits the procedure that proposes future candidates. Past runs (candidates, feedback, traces, failures) become memory the meta-agent reads from. Achieves 26% relative gain over the strongest evolution baseline on agentic and reasoning benchmarks. SOTA on three open-ended optimization tasks under the same iteration budget. If you are accumulating agentic search logs you never use, this is how to feed them back into the search procedure itself. Paper: https://t.co/eWFO4rI4iA Learn to build effective AI agents in our academy: https://t.co/LRnpZN7L4c

🚀 Ring-2.6-1T is now open source. A trillion-scale flagship thinking model built for real-world complex tasks: Agent workflows, coding & engineering, long-horizon tasks, complex reasoning, research, and enterprise automation. It is designed to move beyond “answering” toward execution: understanding context, planning steps, calling tools, and staying stable across long task chains. Highlights： - Advanced agentic workflow support. - Reasoning effort levels: high for agentic tasks, xhigh for complex reasoning. - Scalable asynchronous RL via the IcePop algorithm, enabling stable, trillion-scale training for long-horizon agentic RL.

we built the first sane way to debug your agent locally. you can see your traces. codex/claude code can too. this lets them write evals and test your agents automatically. best part: it's completely free and open source. install with 1 line. (github below) https://t.co/ln5LEZzEHu

Thanks pydantic. https://t.co/Syt1wj0UJL

Are scaling laws finally working for time series foundation models? Today, @datadoghq is releasing Toto 2.0 weights in Apache 2.0 on @huggingface. It's a family of open-weights TSFMs from 4M to 2.5B parameters, where every size beats the last from a single hyperparameter config. First across the leading benchmarks: BOOM, GIFT-Eval, and TIME. Most TSFM families ship multiple sizes that all perform roughly the same. This one doesn't. Why it matters: scaling laws gave language and vision a predictable relationship between compute, data, parameters, and downstream performance. Time series hasn't had that curve until now. Once you have it, you can scale data and compute with confidence, and start asking which new capabilities emerge at the next order of magnitude. 2.5B open-source weights: https://t.co/prpcGoCw0U 4M open-source weights: https://t.co/5d6rw5NYL2 Blogpost: https://t.co/xKazgTMh1I

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This one blew my mind back in the day! From "Learning Invariant Features through Topographic Filter Maps" (2009) by @koraykv, @MarcRanzato, @rob_fergus and @ylecun https://t.co/IjzRi4R9X6