#research

Karpathy's follow-up to a viral tweet. The argument: now that agents can write the code, what you actually want to share is the idea, not the implementation. He calls it an "idea file": a short spec of what to build, nothing more. Worth thinking about for anyone who shares research tools or scripts with collaborators.

Simon Willison's annual LLM recap, this time 26 sections long. Covers reasoning models, multimodal, tool use, agents, fine-tuning, inference efficiency, safety, and open weights. He's been doing this for three years so there's a lot of accumulated context. Don't read it straight through. Pick a section from the table of contents and start there.

Andrej Karpathy's reading habit: three passes through anything worth understanding. First pass is manual. Second, ask the LLM to explain and summarize. Third, Q&A on the parts that didn't land. He says he comes away with noticeably better understanding than if he'd just moved on. Works especially well for CV papers where the actual contribution is buried under five pages of related work.

A 264-page survey on AI agents from researchers at Meta, Yale, Stanford, Google DeepMind, and Microsoft. Covers memory, planning, tool use, multi-agent coordination, and evaluation. If you only read one chapter, make it the one on evaluation. That's where most agent benchmark claims quietly stop making sense.

An annotated example showing what a well-written discussion section looks like, paragraph by paragraph. Most papers either restate the results or jump straight to future work. This one shows how to connect your findings to existing literature, be honest about what the limitations actually mean, and say something that sticks. More practical than any generic academic writing guide.