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. ...
A sorcerer’s guide to wielding chaotic and powerful magicks.
Embracing the exponentials and vibecoding an entire 3D game in a few days with Aider, o3-mini, and Claude 3.7 sonnet.
Notes on the interconnection of generative AI’s two leading paradims.
How I learned Svelte and outsmarted Vercel to make a delightful frontend to OpenAI’s DALL-E … just for my mom.
Week 44 of 2024. Week 12 of RC.
Week 43 of 2024. Week 11 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’