Core Automation @coreauto
Building systems to automate and optimize coreauto.com San Francisco, CA Joined January 2026-
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Power of friendship
choose a lab where you genuinely like the people if they're sufficiently agent-pilled you'll mostly be hanging with them while agents work
Creativity, Inc in the age of AI
Some news: 5 months ago I joined @coreauto, a small research lab focused on new ways for models to learn. I’m also hiring four interns to do something my younger self would've loved. It started with a pretty unusual project. We set up a handful of small online businesses, and
Deep learning is still not solved. It has been great to see the creativity of the community and we all have more work to do to understand the universe. Thank you for being in this journey together ❤️
Over 15,000 submissions to One Layer Deeper. Nobody solved the problem as intended, but there are some interesting ideas for learning reusable operations and composing them into deeper computations coreauto.com/blog/what-the-…
@captainokirk Yes, but not very soon. The path from new research to a product takes a bit more time than just building a product.
Rohan vagueposting likely improved the proprietary deep learning tech by a decade
Project Wallfacer: I am going to stop vague posting about better than gradient descent because 10k agents, 88hrs and 300k gb300s can figure it out. Note: I would be okay if that happens and result is public, otherwise feels too powerful tech.
How our data shaped neural architecture discovery, and how automation can reshape the future coreauto.com/blog/how-our-d…
Soon in corechat near you
We have little time here but may be doable in 126 days
Congrats to @srush_nlp for increasing the amount he's going to owe me when he loses this bet in 126 days 🤗🤗🤗
We should take it seriously. The main limitation is that there are actually very few neolabs putting serious compute into fundamental research instead of doing rlaas or open weight transformers, but there are few who are doing it, are determined and moving fast.
How seriously should we take the possibility of one of the neolabs making some wild algorithmic breakthrough that puts it ahead of OpenAI and Anthropic?
@infoxiao @clankrmedia This could be us
How to live your life: Meeting Mondays Thinking Tuesdays Work Wednesdays Trying Hard THursdays Automation Fridays Sleeping Saurdays CUDA Sundays And repeat!
A prompt that launched a thousand training runs
Even in natural language you need to continually learn
learned this from the youth: if something sucks, you no longer say it's ass. you say it's "cheeks" e.g. "this model is cheeks"
AI discourse often focuses on scaling because of eye popping numbers spent on datacenter compute. Credit assignment is very difficult Most people from outside big labs and even many inside get this credit assignment wrong. It’s worth doing a simple thought experiment: It’s may 2020. GPT-3 paper just got released. We have two diverging timelines: a) we have the same algorithmic progress that we did since gpt3, but we cannot spend more compute on training models than was spent on gpt3 b) we keep scaling up gpt3 and we spend as much compute as we did on gpt5.6, but on gpt3 training system Reality is that model from timeline a) beats model from timeline b) on every axis and it’s not even close
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