February 17, 2026
Faster code, hotter takes
Quamina and Claude, Case 1
AI speeds up a coder’s pet project — the comments set the internet on fire
TLDR: A developer says an AI helper (Claude) made his open‑source library run about twice as fast. The comments explode into a brawl over whether AI help is fair or ethical and whether AI harms the planet, with one user dismissing climate worries as “scare mongering.”
A 47‑year coding veteran shared a “no drama” story: a friend used Claude (an AI assistant) on his open‑source project Quamina, and the code got roughly 2x faster. He tried to keep it chill—no polemics, just results. The internet said “lol, no.” The comments instantly turned a performance tweak into a culture war about AI in open source.
One crowd is cheering the scoreboard—fewer slowdowns, more speed, merge it yesterday. Another crowd says AI help is a “dick move” without consent from authors or training data contributors—echoing the author’s own setup that the debate is toxic. The rabbit holes multiply fast: user homarp drops a breadcrumb to the author’s follow‑up “conclusion” post, fanning the flames with a quiet “see also” link. Then boxed swings in hard on the climate angle, calling AI’s environmental fears “scare mongering” and pointing to a chart to argue electricity demand isn’t exploding link. Meanwhile, meme lords joke that Claude just unlocked a “turbo mode” for JSON and that two PRs beat a dozen whiteboard sessions. The vibe: one small speed win, one giant comment war—credit, consent, and carbon footprints all crashing the party.
Key Points
- •Rob Sayre applied Claude to the Quamina codebase and submitted a stream of PRs starting mid-January.
- •Most of the PRs were accepted and merged, resulting in Quamina running about twice as fast on several benchmarks.
- •Quamina is a Go library for pattern matching over JSON, centered on AddPattern() and MatchesForEvent APIs.
- •The library relies on finite automata (NFA/DFA), with matching speed only weakly dependent on number of patterns.
- •Performance discussion emphasizes algorithm choice and minimizing allocations in Go, particularly via slice capacity and reuse.