I recreated the Apple Lisa computer inside an FPGA [video](youtube.com)

119 points by cyrc 5 months ago | 39 comments

Build a Superscalar 8-Bit CPU (YouTube Playlist) [video](youtube.com)

116 points by lrsjng 1 year ago | 14 comments

Pijul: Version-Control Post-Git [video](youtube.com)

178 points by todsacerdoti 3 years ago | 128 comments

What we learned from C++ atomics and memory model standardization [video](youtube.com)

27 points by matt_d 2 years ago | 2 comments

William Latham – Art and the Computer (1990) [video](youtube.com)

8 points by hyperific 8 months ago | 1 comments

How/Why to Sweep Async Tasks Under a Postgres Table [video](youtube.com)

28 points by surprisetalk 2 years ago | 13 comments

We were wrong about GPUs(fly.io)

834 points by mxstbr 1 year ago | 529 comments

Fly.io blog post reflecting on how the company's bet on offering GPU machines to developers did not pan out as expected.

PeerTube is a free, decentralized and federated video platform(github.com)

592 points by doener 3 months ago | 274 comments

GitHub repository for PeerTube, an AGPL-licensed ActivityPub-federated video streaming platform that uses P2P delivery in the browser.

Andrej Karpathy: Software in the era of AI [video](youtube.com)

1382 points by sandslash 1 year ago | 757 comments

YouTube video from Y Combinator of Andrej Karpathy's talk "Software Is Changing (Again)" about how AI and LLMs are reshaping software.

GPUs Go Brrr(hazyresearch.stanford.edu)

1056 points by nmstoker 2 years ago | 243 comments

Stanford Hazy Research blog post on what GPU hardware wants for efficient AI kernels, introducing the ThunderKittens CUDA kernel library.

What every developer should know about GPU computing(codeconfessions.substack.com)

422 points by Anon84 3 years ago | 149 comments

A Substack primer by Abhinav Upadhyay explaining GPU architecture and computing concepts for software developers familiar mainly with CPUs.

How to Think About GPUs(jax-ml.github.io)

361 points by alphabetting 1 year ago | 107 comments

Part 12 of Google's "How To Scale Your Model" book, a deep dive into how NVIDIA GPUs work and are networked, and what that means for LLMs compared to TPUs.