AI for science at @PeriodicLabs. Formerly, building AI climate models at Google. I also contribute to the scientific Python ecosystem (Xarray, NumPy, JAX).
Things I didn’t expect from becoming a dad:
- I’m now in a secret club with most of the world’s adult population
- I’ve been magically transformed into a morning person!
- Babies are genuinely lots of fun 😊
To keep the enthusiasm contained -- my other recent extraordinary experience with Fable was when it wrote a CRUD app where the cost of create was _quadratic_ in the size of the database.
Frontier model capabilities are shockingly uneven!
I just had an extraordinary experience with Fable. It implemented a new solver for the NeuralGCM dynamical core in an afternoon, a project I previously would have scoped as weeks to months of effort.
We are hiring at @periodiclabs for physical scientists who can help us curate the datasets to make AI extraordinary at science. I think a background in software engineer, computational science or simulation would be especially appropriate here.
At @periodiclabs we are looking for brilliant new colleagues who excel at bridging AI and science. The role's main focus is advanced evals and training data/tasks. But you will also get involved in both internal science and AI work. Apply/spread the word!
jobs.ashbyhq.com/periodic-labs/…
I just had an extraordinary experience with Fable. It implemented a new solver for the NeuralGCM dynamical core in an afternoon, a project I previously would have scoped as weeks to months of effort.
It's hard to imagine better coders than Fable and GPT-5.6, but astonishingly they still lack taste at high-level design. They eagerly build perfectly constructed piles of slop.
I'm not sure more RL can fix this. The data is too sparse and the feedback cycles are too slow.