On demand GPUs

Transform any Markdown note, checklist, or section of text into a live experience users can click, select, and submit—directly in your app or docs.

Flash card & llm

I see a lot of bad system design advice. One classic is the LinkedIn-optimized “bet you never heard of queues” style of post, presumably aimed at people who are…

State-of-the-art text to speech model under 25MB

When I want garlic bread (or pizza), I make this instead:

Air-popped popcorn
Fresh garlic butter (real butter, fresh garlic, sizzled together)
A sprinkle of oregano

Fun Lean introduction

Alice's Adventures in a Differentiable Wonderland -- Volume I, A Tour of the Land
Simone Scardapane

Neural networks surround us, in the form of large language models, speech transcription systems, molecular discovery algorithms, robotics, and much more. Stripped of anything else, neural networks are compositions of differentiable primitives, and studying them means learning how to program and how to interact with these models, a particular example of what is called differentiable programming.
This primer is an introduction to this fascinating field imagined for someone, like Alice, who has just ventured into this strange differentiable wonderland. I overview the basics of optimizing a function via automatic differentiation, and a selection of the most common designs for handling sequences, graphs, texts, and audios. The focus is on a intuitive, self-contained introduction to the most important design techniques, including convolutional, attentional, and recurrent blocks, hoping to bridge the gap between theory and code (PyTorch and JAX) and leaving the reader capable of understanding some of the most advanced models out there, such as large language models (LLMs) and multimodal architectures.