X

https://x.com/i/article/2106533229775990784

Gemini 4 Argon has been added to the Gemini API docs. Hopefully we might see a public release upcoming week.

I haven’t touched Claude Code or Codex CLI in a while. The terminal era is over imo. It's the wrong interface for coding agents. Tabs are ephemeral, but context is persistent, and managing 30 tabs is pure cognitive overhead. I don’t really need an IDE like Cursor either. I rarely navigate the whole codebase anymore. The new primitive is the agent, not the file. (Codex desktop app is the best agentic UI for now. But we’re still early.)

Great advice for landing interviews, this actually works. Another one: put yourself in the shoes of the person in charge of hiring. Remember that they are hyper pressed for time, what can you do to stand out in the shortest window of time? There are many ways to stand out. Do some research and work up front. If you just DM them with “I’d love to work together without even looking at the jobs page consider yourself a time waster. Instead, research everything you can about the company and roles available, even go as far as to build something you think would solve a problem for them, make it polished and stand out to the point you’re extremely proud of it (top 1% is not hard to do these days if you put a tiny bit of extra time) There are a lot of “hacks” you can take advantage of. Be creative.

I am very uncomfortable about people trying to ascribe religious force or a surrender of human judgment to AI models, and think it is a real safety issue.

getting the band back together

"Vibecoding with cheap chinese AI model"

People at Anthropic keep telling me they can't coordinate to slow down because of anti-trust, but I've talked to leading lawyers who disagree. Meta spent $2,400,000,000 on lawyers in a single quarter fighting charges that its products hurt kids. How much has Anthropic spent on this anti-trust thing?

Here are 5 examples of inline visualizations that Computer can create directly in your thread: 1. Show me how a jet engine works in an inline 3D cutaway.

i turned my ModRetro into a smart device. by plugging it in and asking codex "is this device compatible with https://gadgets.muse.ai/"

Small bird, fast wings, Kolibri is here. 78B parameters. 3.46B active. Up to 1M tokens of context. Built in Europe. Now the weights are yours. Run it on your own hardware, under Apache 2.0.

I put Muse on my 3DS! @natfriedman @alexandr_wang I created a game running on actual Nintendo hardware allowing me to control my smart home through Muse! So cool!

Small bird, fast wings, Kolibri is here. 78B parameters. 3.46B active. Up to 1M tokens of context. Built in Europe. Now the weights are yours. Run it on your own hardware, under Apache 2.0.

Today we're unveiling Trillium Labs @trillium_labs, a new non-profit to foster the open science of frontier AI. We're building open post-training recipes and will expand into open infra to study RSI, reward-hacking, multi-agent systems, and whatever comes next. We're built around the theory of change that you need more eyes to solve hard technical problems. We have faith in the scientific methods and communities that humanity has built, and worry that AI is becoming too closed to utilize them. Trilliums are wildflowers that bloom briefly in the spring, before the forest canopies fill out. Though they are small, they lay the foundation for the cycles of growth and nourishment through the rest of the year. At Trillium Labs, the recipes will be the slow nutrients for the seasons and the model releases will be the blooms. Building an institution dedicated to this is needed because, much as nature’s trilliums are slow to expand and grow, the open-ecosystem needs time and dedicated resources to catch up. I co-founded with with a long-time friend and collaborator Tom Zick (@thesezickbeats). We're hiring (full time + student collabs/interns), we're fundraising, and we're looking for compute. Please get in touch if you're interested in helping out. Offices based in the Bay Area and Cambridge MA, remote okay. I’m in the Bay Area until for The Curve and COLM to connect with people who are interested. We’re thankful to have initial support from Halcyon Futures and Schmidt Sciences with more funding en route to enable our ambitions of scaling. Our advisors @Thom_Wolf, @HannaHajishirzi, @gneubig and @ctnzr have been instrumental to building the ecosystem that exists today, and I’m stoked to get to keep working with them.

Confirmed: @Muse can sue people in small claims court

Banger paper from Meta Superintelligence Labs. They find something super interesting and unexpected. (bookmark it) Base models with a light harness often solve more agentic tasks than their RL post-trained versions when both get enough samples. Post-trained models win on pass@1. At large K, base models frequently solve tasks the post-trained ones never solve on BFCL v4 multi-turn, ACEBench, and WebShop. This is because post-training pushes each task toward always solved or never solved. Consistency goes up, and coverage goes down. The authors call the lost test-time scalability the Sharpening Tax. Across 42 base and post-trained pairs, it shows up in most settings, grows with model size, and can be estimated from a few rollouts. Their fix, PTGS, sets the sampling temperature per prompt from its estimated difficulty during RL. It pays a smaller tax and also raises pass@1. Paper: https://academy.dair.ai/papers/sharpening-tax-in-post-training-2610.01509

98% of US households aren't paying for AI yet More charts in State of Markets II: https://www.a16z.news/p/state-of-markets-ii