Core Automation @coreauto
Building systems to automate and optimize coreauto.com San Francisco, CA Joined January 2026-
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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
Power of friendship is one of few durable moats
Power of friendship was always going to be one of the new pretraining axis
We are very proud to co-organize open participation competition inviting scientists from around the world on one of the deep learning problems that is still open: blog.tilderesearch.com/blog/one-layer…
In deep learning, deeper is almost always better. If it isn’t, that means you’re just not very good at optimization.
One Layer Deeper is officially live! The motivating idea is that some models just don’t want to learn. Most optimizer benchmarks ask how quickly you can train a given model, but baked into that question is the assumption that it can be trained (well) at all. We think this
CoreWarming party!
We had the @coreauto housewarming party a couple of weeks back. It was tons of fun and should hopefully give you a better idea of where we're spending some of our time improving model architectures and systems 00:00: Introduction 00:40: @MillionInt (@coreauto), Building the
Nice!
An exciting talk on Long-Running Agents by @MillionInt @coreauto (previously @OpenAI ) #agenticaisummit
Get up and try harder. Age of research is waiting for you
watching my loss not even move an inch on my 999th attempt at one layer deeper competition by @tilderesearch x @coreauto
Losing to hardware lottery is a skill issue
I present to you the ultimate showdown. In our left corner, we have the researchers complaining about the hardware lottery. In our right corner, we have the infrastructure guys like below, with the exact opposite argument. Let the battle be fair but harsh!
Thank you!
Very insightful conversation with the founders of @coreauto - a pretraining maximalist and an RL maximalist. Got a little glimpse of what they think the next step could be in terms of model architecture. A couple of points they made that I really liked - - The models are
So what’s coming after transformer?
Two of the people most responsible for scaling the transformer are now betting on a next act. @MillionInt ran the Reasoning 🍓 team at OpenAI. @_arohan_ was a pre-training lead on Gemini after years at Google Brain and Anthropic. They just started @coreauto to find what comes
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