Originally posted: 2025-02-16. Last updated: 2025-02-16. View source code for this page here.
— bhlsource
A detailed walkthrough of using LLMs for both greenfield development and legacy code maintenance, with specific prompts and workflows for different scenarios.
— Economistsource
— Jeremy Howardsource
— Nathan Lambertsource
— Andrej Karpathysource
— Andrej Karpathysource
— Gary Marcussource
— Gary Marcussource
— swyxsource
— The Economistsource
— roonsource
— gwernsource
— Tyler Cowensource
— David Crawshawsource
— David Crawshawsource
— Logan Kilpatricksource
— patio11source
— Ethan Mollicksource
— Simon Willisonsource
— Simon Willisonsource
— Simon Willisonsource
— Andrej Karpathysource
— m_kesource
— François Cholletsource
Prompting can go very deep!
What are agents and how do we expect them to evolve.
A good guide to thinking about whether scaling is dead.
Not directly relevant to LLMs, but it's interesting to think at what point an LLM could produce an article like this. I feel like they're a long way off.
Hacker News discussion about companies moving from Apache Spark to DuckDB.
— Hannes Mühleisensource
— Hannes Mühleisensource
Interesting on interpretability
— Eerke Boitensource
This article made me think of LLMs as like really software with no tests, no documentation, and lots of bugs. And yet very useful.
DuckDB's approach to handling concurrent transactions for analytics workloads.
The CEO of Anthropic outlines how AI could transform the world for the better.
There are lots of examples of strange capabilities like this you'd never see in a benchmark.
— Andrej Karpathysource
— Dwarkesh Patelsource
— Wojciech Zarembasource