anthropic.com/research/small…

Introducing Nested Learning: A new ML paradigm for continual learning that views models as nested optimization problems to enhance long context processing. Our proof-of-concept model, Hope, shows improved performance in language modeling. Learn more: goo.gle/47LJrzI

Understand the limits of vibe-coding. This article covers where AI-driven coding is useful for prototypes and where it falls short for production-grade systems that require accountability. By Elisha Rosensweig and Eitan Wagner towardsdatascience.com/human-wont-rep…

Hey Siri, play Heroes by David Bowie.

Partha Sarkar shares practical insights on GraphRAG, discussing design principles, limitations, and best practices for building scalable retrieval systems. towardsdatascience.com/do-you-really-…

We have a long history of model architecture innovations, and we're continuing to explore new ideas. Here's some early work that shows promising results on extending the context length over which a model can reason over.

> random youtube guy > begins 2 hour video with "today i'm bored" > continues to create ML library from scratch in C > types everything manually at 150 wpm in neovim > trains mnist classification model with his library > "that is pretty cool" > chuckles > leaves just imagine

need to learn hardware

BREAKING: machine trained on 70T words to sound exactly like a human sounds just like human

"It's not possible." "No, it's necessary."

the state of AI evaluation agent: send_msg_to_user("{}") leaderboard: 🥇 rdi.berkeley.edu/blog/trustwort…