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A podcast about machine learning and data science hosted by Katie Malone and Ben Jaffe. Approachable explanations of ML concepts without dumbing them down.
Back with new episodes on Substack. Also available on Spotify.
Community-curated weekly digest of the best R-related content — tutorials, packages, blog posts, and news. The single best way to stay current with the R ecosystem.
Reading since 2018. I'm a curator on the team. Pairs well with an AI assistant for exploring the links.
Hadley Wickham writes up a talk on his ambivalence about AI-assisted coding — the "software factory" dystopia of Steve Yegge's Gas Town versus using AI to write higher-quality code — and argues chat interfaces leave ~90% of an LLM's value on the table for programming, compared to giving it a harness to write, run, and iterate on code with a feedback loop of errors and docs.
Hadley Wickham relaunches his Substack to write about AI — a genuinely conflicted take that acknowledges both the excitement (programming accessibility, wide and shallow expertise) and the harms (copyright theft, wealth concentration, intellectual laziness) without collapsing into a take.
The excited/harm framing is what makes this worth reading — programming accessibility, voice input, and wide/shallow expertise as genuine wins, alongside copyright and wealth concentration as real costs. Rare to see someone acknowledge both sides without collapsing into a take.
Hadley Wickham builds up the definition of an agent from first principles — conversations, turns, tools, harnesses — landing on "an LLM in a harness that calls tools repeatedly in a loop." A clear technical explainer for a term that's everywhere but rarely unpacked.
Great bottom-up explainer. Hadley's definition ("LLM in a harness, calling tools in a loop") is more detailed than Simon Willison's earlier "runs tools in a loop to achieve a goal" (https://simonwillison.net/2025/Sep/18/agents/) — interesting to see how the concept has accumulated vocabulary as the field matures.