engineering intelligence · react & react native
React decisions, with receipts.
Hundreds of libraries, conflicting takes, advice that quietly rots. react-brain digests the ecosystem continuously and turns it into context-keyed recommendations — every load-bearing fact carries a verified-on date and a primary source, and every default is a forecast graded on the public scorecard.
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recommendation of the week
Run strict TypeScript (strict:true, the TS 6 default) and export a small, deliberate public type surface from…
Read the reasoning →Quick start
$ npx -y @heart-it/react-brain doctor . # assess a repo, ranked priorities $ npx -y @heart-it/react-brain migrate . # sequenced upgrade plan $ npx -y @heart-it/react-brain review . --ci # block dead deps in CI
Organized by decision, not by article — 123 deep-dives cited as evidence, every default a dated prediction on the scorecard, and nothing asserted casually.
One corpus, every surface
# this website — press ⌘K for the console # your terminal, zero install $ npx -y @heart-it/react-brain doctor . # your coding agent — 9 MCP tools $ claude mcp add react-brain -- npx -y @heart-it/react-brain mcp # your CI — dead/deprecated dep adds fail the build, with receipts $ npx -y @heart-it/react-brain review . --base=origin/main --ci
Not another model answer
Don’t take the framing on faith — the staleness benchmark measured it: claude-sonnet-5 alone answers 70% fresh and hedges a third of the time; with the corpus injected it hits 94% fresh and barely hedges at all. Deterministic rubric, conditions labeled, reproducible with your own key.