Google CEO, Sundar Pichai: "If you don't learn to how to orchestrate agents now, you'll spend 2027 catching up to people who started today" In 30 minutes he explains why the best engineers stopped writing code and started running agents. Watch the interview, then save the exact setup below 👇

Je suis choqué comment ça rend bien :D

One of the worst feelings in life is knowing you're meant for more and doing nothing about it.

Introducing Gemini 4 Argon – our new frontier model. It’s built for complex workflows across coding, enterprise knowledge work, and cybersecurity defense – rolling out today to a set of trusted testers through our Fairwind Program.

HN drops truth-bombs.

If you don’t prioritize, everything seems urgent and important. If you define the single most important task for each day, almost nothing seems urgent or important.

We’re sharing a solution to the Navier-Stokes Millennium Prize Problem, one of the deepest problems at the frontier of mathematics. The proof was produced by a group of agents, using an OpenAI next-generation model significantly more capable than GPT-6 Astra. The problem concerns whether the description of smooth three-dimensional fluid motion modeled by the Navier-Stokes equations can break down. It has remained unresolved for roughly 90 years.

I'm 42. Trust me on this: The answer to life, the universe and everything is being in the right place at the right time.

/show-me is a phenomenal skill Makes PR descriptions extremely easy to read Basically a toolbox of "nice ways to look at code" Nice work, @dexhorthy github.com/humanlayer/skills…

Typing code is absolutely over.

GPT-6 Astra is here. We hope it will begin to enable a new generation of entrepreneurship, scientific discovery, and building. We believe it is the best model in the world for computer use, professional work, science, coding, cybersecurity, and more. It took us some extra time to ensure that we could meet the safety and alignment standards required for this capability level, but we think you’ll find it worth the wait. It scores 98% on FrontierMath Tier 4, 99.9% on ARC-AGI 3, and 100% on ExploitBench.

I want to share how I have developed celld - because it's a bit different and it's working well for me. You might have noticed that the github.com/denoland/celld repo is pretty bare - this is because it's an export of a much larger private repo: denoland/celld.dev This larger repo contains: an llm-wiki with an enormous amount of design docs, a large test suite, a spec written in TLA+, the code for the website, scripts for spinning up fleets of celld instances on various clouds and running tests against them, and a speculative centralized managed control plane that I might someday release as a commercial product. My llm-wiki is modified to fit my workflow - diff here gist.github.com/ry/c56dfa7b1… I find llm-wiki extremely useful for gathering context. Reviewing wiki/design/ is a primary development activity. celld is open source, but the development happens in private. The test suite and design docs are private. Why do this? First of all, I don't need the public following my every commit - it's oversharing. Secondly, in the age of coding agents, I don't really need external contributors. I'll always appreciate a patch, but most properly reported issues are one-shottable solutions. Third, it's just a bit of IP that might be valuable someday. I do a lot of experiments with celld on Vultr with a VPC. I'll spin up between 2 and 10 VMs at a time, run some experiment, and usually try to tell some story via a chart of some sort (one below). My own coding agent setup is ssh/tmux to mac mini, with various codex and claude sessions running. I don't use an enormous number - something like 2-4 at a time. (Why a mac mini? idk someone just gave me it)

I did not expect it to be THIS fun watching robot olympics 😂