Al Merose

Yo, space is old hat. We’re naming AI models after elements now blog.google/innovation-a...

Gemini 4 Argon: our next era of frontier intelligence blog.google

Sneak peek: I implemented a 3 layer MLP that predicts the fashion MNIST dataset using SQL (with my own autograd impl inside the database). Apparently einsum notation is better expressed as a JOIN. github.com/xqlsystems/x...

xarray-sql/benchmarks/nn.py at claude/xarray-sql-mnist-demo · xqlsystems/xarray-sql github.com

Maybe a troll, but also maybe a union one day: concernedaistaff.org

Coalition of Concerned AI Staff concernedaistaff.org

Not yet merged, but I added autograd to my array database experiment. I’m building a new NN framework primarily in SQL. github.com/xqlsystems/x...

Differentiable SQL: grad()/jvp()/vjp() over xarray and any DataFusion table by alxmrs · Pull Request #192 · xqlsystems/xarray-sql github.com

Oy vey. Better to avoid that direction. I’m constraining myself to a $20/mo plan for my side projects. Instead, I’m trying this with my sub agents: github.com/xqlsystems/e...

Sequence agent work around the review frontier by alxmrs · Pull Request #26 · xqlsystems/einfold github.com

All of our code is open source, and contributions are welcome: github.com/m2lines/Samu... We're working on fitting our model training into a (free-tier) colab notebook, so anyone can help advance climate science: github.com/m2lines/Samu...

GitHub - m2lines/Samudra github.com

All of our code is open source, and contributions are welcome: github.com/m2lines/Samu... We're working on fitting our model training into a (free-tier) colab notebook, so anyone can help advance climate science: github.com/m2lines/Samu...

Colab quickstart by YuanYuan98 · Pull Request #722 · m2lines/Samudra github.com

Banger paper alert: This uses the P=NP complexity theory to attempt to answer a question about gravity, using the difficulty of the NP-hard problem to argue that gravity must be quantum. What a cool proof by contradiction! arxiv.org/html/2606.14...

Semiclassical Gravity Efficiently Solves 𝖭𝖯-Complete Problems arxiv.org

Exciting new paper from ECMWF: ML weather forecasts directly from observations arxiv.org/abs/2606.19093

AIFS-DOP: End-to-End Medium-Range Weather Prediction from Observations Alone with Machine Learning arxiv.org

I’ve been really happy running the Bonsai class of models on my ancient 1080 Ti GPU. I can’t wait to try out the v2 27B. prismml.com/news/bonsai-...

PrismML — Introducing Bonsai 2 27B: Near-Lossless Compression in a 9x Smaller Footprint prismml.com

I'd like to announce that at @openathena.ai, @jder.bsky.social and I helped @m2lines.bsky.social release Samudra 2. We scaled this neural ocean emulator to train on 16x the size of data in bytes on the same hardware budget. We can now skillfully predict 8 years of the ocean on…

medium.com

For file type, there is at least one type of social media built around it: images > instagram, video > youtube, vertical video > tiktok, audio > clubhouse, safetensors > huggingface, executable files > github

I’m like 6 months to a year behind using all sorts of agentic coding patterns (I just asked Claude to delegate impl to subagents, etc.) and honestly (surprisingly?), I think I’m better off for it.

Agentic engineering feels like “problem engineering” to me. Coding becomes defining goals and constraints for agents to optimize towards. Like all coding, this requires taste and discernment.

Rust is ideal for agentic programming because large compile times give you an opportunity to think and reflect

Open source project idea: give that old phone in your drawer new life by installing Prism ML’s LLM on it to make it a stateful agent.

I don’t understand why there is so much agentic tooling. I feel very productive with just Claude equipped with the gh CLI, using standard GH features like issues and comments. While I use prompt patterns, I feel most cases require sufficient nuance that I don’t want to capture something in a skill.

I’m happy to be working on open source science and language models because the biggest safety risks of AI, imo, stem from concentrations of power — in corporations, governments, and their intersection.

If there is one contribution that I hope to make to computer science, it’s that relational algebra is as important as linear algebra or calculus is to machine learning.

Just got Jev and I can’t wait to spend my non-delirious part of my parental leave exploiting its strengths for a provable privacy and AI safety system.

Why learn math or really any subject when an LLM will always give you the answer? Well, how else will you be able to make really subtle jokes and puns in a conversation? Bonus: the people that laugh might be folks you’d like to know better.