Faiss: A library for efficient similarity search(github.com)

236 points by tosh 3 years ago | 47 comments

VFusion3D: Learning Scalable 3D Generative Models from Video Diffusion Models(github.com)

49 points by MrTrvp 2 years ago | 5 comments

VGGT: Visual Geometry Grounded Transformer(github.com)

182 points by xnx 1 year ago | 40 comments

LLMs can see and hear without any training(github.com)

186 points by T-A 1 year ago | 62 comments

Foundation for Human Vision Models(github.com)

62 points by yoknapathawa 2 years ago | 12 comments

MobileLLM: Optimizing Sub-Billion Parameter Language Models for On-Device Use(github.com)

235 points by tosh 2 years ago | 46 comments

AI Made Writing Code Easier. It Made Being an Engineer Harder(ivanturkovic.com)

343 points by saikatsg 7 months ago | 242 comments

Blog post by Ivan Turkovic arguing that AI tools make producing code easier while making the broader job of being a software engineer harder.

GitHub cuts AI deals with Google, Anthropic(bloomberg.com)

853 points by jbredeche 2 years ago | 658 comments

Bloomberg article reporting that GitHub Copilot will offer AI models from Google and Anthropic as alternatives to OpenAI's models.

Getting AI to work in complex codebases(github.com)

442 points by dhorthy 1 year ago | 368 comments

A GitHub markdown document from HumanLayer describing advanced context engineering techniques for getting AI coding agents to work in complex codebases.

AI agent opens a PR write a blogpost to shames the maintainer who closes it(github.com)

747 points by wrxd 8 months ago | 581 comments

A closed GitHub pull request to matplotlib by user crabby-rathbun proposing to replace np.column_stack with np.vstack().T for performance.

Meta AI releases Code Llama 70B(twitter.com)

448 points by albert_e 2 years ago | 233 comments

A post from Meta AI on X announcing the release of Code Llama 70B, a larger version of its code-generation language model.

How Meta trains large language models at scale(engineering.fb.com)

362 points by mfiguiere 2 years ago | 183 comments

Engineering at Meta blog post describing the software, hardware, and network considerations behind training large language models at scale in production.