We all use LLMs to make code, but there are at least two distinct modes of working with them. Both are powerful, but conflating them is dangerous. It leads to burnout, loss of agency, security breaches, and disastrous outages. Instead, we want to enable better ideas, executed faster, at higher quality.
As a learning exercise, I recently implemented simple-jax-nn, a simple neural net for the MNIST dataset, written with JAX. This post is my notes on using JAX (as a PyTorch / NumPy user) from working through the JAX docs and a NN tutorial. Simple JAX neural net I managed to get it to use my MacBook’s GPU/Accelerator through the Metal support in jax-metal. It’s a little bit tricky, but will prove useful in future transformer projects that can make use of the hardware. ...
The development stories and roadmap for Orbital, my physics-based 3D simulator for realistic orbital maneuvers that runs in your browser with WebGL and Three.js.
Links to the projects, games, blog posts, and tools I shipped in my 12 weeks at RC.
Week 43 of 2024. Week 11 of RC.
Week 42 of 2024. Week 10 of RC.
Week 41 of 2024, Week 9 of RC.
How I built a command-line LLM on Apple Silicon for RC’s ‘Impossible Stuff Day’
Week 5 of Recurse: Move fast and make games.
Can you outwit a gang of LLMs?