#r

Hadley Wickham details his workflow for generating slide illustrations with Gemini's image model, including bananarama, his own R package for scripting reproducible image generation from a YAML file, and why he thinks AI is best suited to mood-setting illustrations rather than photos or diagrams.

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.

A Substack essay exploring how CRAN's reverse dependency checks — which block a package update if it breaks any downstream package — embody a "monorepo mindset" that makes breaking-change authors responsible for their entire dependency graph.

Contrasts CRAN's culture of author-owned migrations with PyPI/npm's publish-at-will model. The author argues CRAN's approach, while slower for individual releases, produces better outcomes for the ecosystem — a point reinforced by their experience at Databricks, where centralizing breaking changes (rather than federating them to consumers) led to faster, more complete migrations with fewer regressions. Particularly pointed for Python ecosystems where library churn and silent breakage are routine.

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.

Hadley Wickham refines the agent definition by clarifying that tools run in the harness, not the model — and uses math as the concrete example. LLMs are confidently wrong at arithmetic, but harnesses can supply a calculator tool. Also covers how web chat harnesses quietly provide web search, page fetch, memory, and image generation.

Hadley Wickham demystifies coding agents (Claude Code, Cursor, Codex) by building a minimal one in R with ellmer. The core is just six tools — read file, write file, edit file, list files, search, run command — plus a system prompt. Also covers path safety and why a targeted edit tool beats full file rewrites.