Sahil 🤖 @SahilKumar

Two months ago I was fired by Google for creating the Google Workspace CLI. It went viral, hit #1 on Hacker News, gained thousands of GitHub stars and many thousands of actual users in just a couple days. It was an incredible, confusing journey, from directors and leaders asking what they could learn from the tool to getting grilled by legal about why the Google logo and brand colors are on the Google Workspace GitHub code repositories. I think the cause was that Workspace and certain leaders (and projects) were afraid of being disrupted. But the fear wasn't specific to my CLI, it was a broader fear in what agents meant for Workspace. Either way, the irony of my termination was the announcement at Google Cloud Next two days before I was fired that an official Workspace CLI was coming. I want this out there because it is easier for me to explain my story and it is an experience I want to fully own. It's also part of my healing. Nearly 7 years at Google was an incredible opportunity for me and I was fortunate to have wonderful teammates and a manager that fully supported me through these last few months. Thank you.

Solo-founded $2B company. Used by OpenAI, Anthropic, Replit. Solo Founders ep 13 is live with @grinich of @WorkOS. 07:49 The co-founder "blood bond" is a myth 10:55 There's only room for one Zuck at Meta 32:51 Pivots are the most traumatic thing you can do to a business 43:18 The best ideas always look like bad ideas 50:14 Sales is the ultimate expression of the company's value 53:57 The "founder pixie dust" no hire can replace 01:07:42 You only get one life. Why not swing as hard as you can?

Personal update: I've joined Anthropic. I think the next few years at the frontier of LLMs will be especially formative. I am very excited to join the team here and get back to R&D. I remain deeply passionate about education and plan to resume my work on it in time.

Agents Over Bubbles Agents are fundamentally changing the shape of demand for compute, both in terms of how they work and in terms of who will use them. They're so compelling that I no longer believe we're in a bubble. stratechery.com/2026/agents-ov…

I packaged up the "autoresearch" project into a new self-contained minimal repo if people would like to play over the weekend. It's basically nanochat LLM training core stripped down to a single-GPU, one file version of ~630 lines of code, then: - the human iterates on the prompt (.md) - the AI agent iterates on the training code (.py) The goal is to engineer your agents to make the fastest research progress indefinitely and without any of your own involvement. In the image, every dot is a complete LLM training run that lasts exactly 5 minutes. The agent works in an autonomous loop on a git feature branch and accumulates git commits to the training script as it finds better settings (of lower validation loss by the end) of the neural network architecture, the optimizer, all the hyperparameters, etc. You can imagine comparing the research progress of different prompts, different agents, etc. github.com/karpathy/autor… Part code, part sci-fi, and a pinch of psychosis :)

Introducing the Claude Marketplace, a way for enterprises to simplify their procurement of AI tools. Now in limited preview.

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Voice mode is rolling out now in Claude Code. It’s live for ~5% of users today, and will be ramping through the coming weeks. You'll see a note on the welcome screen once you have access. /voice to toggle it on!

I've published the first two chapters of a new guide to Agentic Engineering Patterns - coding practices and patterns to help get the best results out of coding agents like Claude Code and OpenAI Codex simonwillison.net/2026/Feb/23/ag…

Learn more about a very high quality time series model released by @GoogleResearch a while back at research.google/blog/a-decoder…

𝗿𝗮𝗺𝗮𝗸𝗿𝘂𝘀𝗵𝗻𝗮— 𝗲/𝗮𝗰𝗰 @techwith_ram

6 months ago

𝗔 𝗧𝗶𝗺𝗲 𝗦𝗲𝗿𝗶𝗲𝘀 𝗙𝗼𝘂𝗻𝗱𝗮𝘁𝗶𝗼𝗻 𝗠𝗼𝗱𝗲𝗹 𝗕𝘆 𝗚𝗼𝗼𝗴𝗹𝗲 This has been pre-trained on a time series corpus of 100 billion data points, & shows impressive performance on various benchmarks from diverse domains. 𝗧𝗶𝗺𝗲𝘀𝗙𝗠 𝗚𝗶𝘁𝗵𝘂𝗯 𝗽𝗮𝗴𝗲:

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August at Google DeepMind was like 🧞‍♂️ 🖼️ 🍌 🚀 🔍 🤏🏻 - Nano Banana (Gemini 2.5 Flash Image) - Gemini Embedding - Veo 3 Fast - Genie 3 - Imagen 4 Fast - Gemma 3 270M - Perch 2 - Kaggle Game Arena - Gemini API Url Context - AI Studio Builder (UI Rework, Prompt Suggestions, GitHub integration …) - AI studio UI (Model Picker, Chat, Scrolling…) and much more!

Something I just told a founder: Stay as small as you can for as long as you can. People who come to visit your office should always be surprised that such an important company has so few employees.

@anythingai @anythingai

Earlier this year, a 17-year-old high school student named Hannah Cairo solved a 40-year-old mystery about how waves behave, surprising and exciting mathematicians. @KSHartnett reports: quantamagazine.org/at-17-hannah-c…

I’m a 19 y/o founder from Germany, and I cracked how Google’s Text Diffusion model works! There are three main approaches on this topic:

Figure is officially 3 years old 🎉 Over the last 3 years, we’ve designed three generations of robots, deployed commercially, and built the best team ever assembled Think about what this looks like 3 years from now

The new (computer use) agent in town.

Simular @SimularAI

a year ago

🚨 New SOTA on OS-World: 41.4% success rate! Agent S + Gemini 2.5 Pro outperforms the previous SOTA held by Agent S + Claude 3.7 (34.5%) — once again, we beat ourselves. And we’re now significantly ahead of OpenAI (32.6%) and Anthropic (26%). 🔽

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This is interesting as a first large diffusion-based LLM. Most of the LLMs you've been seeing are ~clones as far as the core modeling approach goes. They're all trained "autoregressively", i.e. predicting tokens from left to right. Diffusion is different - it doesn't go left to right, but all at once. You start with noise and gradually denoise into a token stream. Most of the image / video generation AI tools actually work this way and use Diffusion, not Autoregression. It's only text (and sometimes audio!) that have resisted. So it's been a bit of a mystery to me and many others why, for some reason, text prefers Autoregression, but images/videos prefer Diffusion. This turns out to be a fairly deep rabbit hole that has to do with the distribution of information and noise and our own perception of them, in these domains. If you look close enough, a lot of interesting connections emerge between the two as well. All that to say that this model has the potential to be different, and possibly showcase new, unique psychology, or new strengths and weaknesses. I encourage people to try it out!

Inception @_inception_ai

a year ago

We are excited to introduce Mercury, the first commercial-grade diffusion large language model (dLLM)! dLLMs push the frontier of intelligence and speed with parallel, coarse-to-fine text generation.

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New 3h31m video on YouTube: "Deep Dive into LLMs like ChatGPT" This is a general audience deep dive into the Large Language Model (LLM) AI technology that powers ChatGPT and related products. It is covers the full training stack of how the models are developed, along with mental models of how to think about their "psychology", and how to get the best use them in practical applications. We cover all the major stages: 1. pretraining: data, tokenization, Transformer neural network I/O and internals, inference, GPT-2 training example, Llama 3.1 base inference examples 2. supervised finetuning: conversations data, "LLM Psychology": hallucinations, tool use, knowledge/working memory, knowledge of self, models need tokens to think, spelling, jagged intelligence 3. reinforcement learning: practice makes perfect, DeepSeek-R1, AlphaGo, RLHF. I designed this video for the "general audience" track of my videos, which I believe are accessible to most people, even without technical background. It should give you an intuitive understanding of the full training pipeline of LLMs like ChatGPT, with many examples along the way, and maybe some ways of thinking around current capabilities, where we are, and what's coming. (Also, I have one "Intro to LLMs" video already from ~year ago, but that is just a re-recording of a random talk, so I wanted to loop around and do a lot more comprehensive version of this topic. They can still be combined, as the talk goes a lot deeper into other topics, e.g. LLM OS and LLM Security) Hope it's fun & useful! youtube.com/watch?v=7xTGNN…

🚀 Thrilled to launch Verbis(verbis.ai), an open-source MacOS app leveraging GenAI for secure, local data processing. Your workplace search assistant now on your own machine! 🛡️ GitHub: github.com/verbis-ai/verb… Discord: discord.gg/GDdQ7D3J2E #GenAI #OpenSource