Musk says SpaceX will use the best model per task, including Claude Opus 5.5

Important note regarding Grok @Bot: Going forward, @SpaceX will use the best back end model for any given task, including Claude Opus 5.5, MidJourney, Suno and other leading APIs. Whatever is most likely to give you the best outcome.

Gemini 4 Argon is here. It’s available globally today for developers and enterprises in the paid tier of the Gemini API and the Gemini platform, for Ultra subscribers in Google Antigravity, and Ultra and Pro subscribers in the Gemini app and Search. Hope you all enjoy it! https://t.co/2jyAgqoylU

We were able to fine tune an open weight model (Qwen3.5-9B) on proprietary internal data at @coinbase, and it out performed frontier models, at much lower cost and speed, for a specialized task (fraud prevention in this case). I think we'll see more of this pattern inside companies. The frontier models are amazing generalists. But increasingly you'll see specialist models out perform on more narrow tasks, either working on teams, or by themselves. We're making it easy to fine tune models internally for our employees. We even have a skill for it in our internal harness that makes it easy to spin these up and start training them: "/fine-tune-and-serve-llm I want to fine-tune Qwen for payments risk review. help me get started" I imagine we'll have some backend services that could be replaced entirely by this new architecture.

AI cracking unsolvable math problems while being kinda mid at writing is a much-needed W for humanities majors after a rough 21st century.

literal hardest role to hire for rn and every ai startup wants someone > chronically online (knows trends) > has taste & can create (new media) > understands ai & technical > can execute with quality (operator) such a small niche of folks who can do this

BREAKING: The AI boom will most likely destroy massive amounts of capital in the US in the longterm, as cheaper, open-source Chinese models capture market share, per Jefferies.

With everyone busily using LLMs + Blender/Three.js to make 3D models, I thought I'd try a different geometric representation. This dragon isn't a mesh, NeRF, 3DGS, or video model: it's 27kb of GLSL code (generated via LLM), defining a closed-form implicit surface. 🧵 [1/n] https://t.co/o0fMwGk7Id

Fields medallist Hugo Duminil-Copin was deeply shaken by OpenAI’s release of more than 700 AI-generated mathematical manuscripts, including work on 16 questions directly relevant to his research. “On Tuesday night, I cried, and it woke up my daughter” “They have annihilated my field of research.” https://t.co/RQliNgxjS8

I no longer see any compelling reason to believe that taste is some permanent boundary LLMs won’t cross. It’s already happening.

Introducing SubQ Snap, the first long-context decision model for real enterprise work. Snap is the only decision model or LLM where compute per token stays constant as context grows. • 384K tokens of multimodal context, 12x larger than Jev. • 61.7 on Decision Index v0.2.1 vs Jev’s 57.9, outperforming on 13 of 16 enterprise benchmarks. •Processes 100K tokens and returns a decision in under one second. •$0.05/MM input tokens. Output is free. Available today to anyone. The first 10,000 sign-ups get 100M free input tokens. We’ll likely hit capacity fast and will add users as we add compute. Compute offers welcome 🙂. Latency will be variable as we tune the platform to this new model and category of users. Model speeds are 80ms for minimal context, 250ms for 32k, 660ms for 100k, and 2.5 seconds for 384k. Visit: https://t.co/Nc76iJF2a8

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AI consultant and strategist is the second-fastest growing role in the US, per LinkedIn

The compute needed for the stage of AI we’re about to enter is going to be insane. Personal agents, agent swarms defending enterprises, agents that review all of our code for security issues, agents that process nearly all enterprise data in workflows, background agents working on tasks 24/7, and on and on. This will take a mix of inference volume that is going to keep growing by orders of magnitude, as well as infrastructure that agents need alongside the tokens. They need computers, networking, file systems, and so on. We’re only in the early stages of what this buildout is going to look like.

True, this is a very big deal