Overnight optimization: lock the verify harness, then let the agent grind for hours
Sep 30 Poličar/Špendl/Hočevar: humans set scope + correctness + verify; Claude Code overnight on openTSNE (4.4–7.7×), ssGSEA (136×), and a new graphlet algorithm (~160× vs Orca).
Source: Is manual software optimization a thing of the past?
Pavlin G. Poličar, Martin Špendl, and Tomaž Hočevar ask whether coding agents can still find real speedups on mature scientific software—not toy kernels—when humans only define scope, correctness, and a verification mechanism, then step away. Across three case studies with Claude Opus 5 in Claude Code, the answer is yes: constant-factor rewrites, mathematical reformulations, and even a new algorithm, with maintainers (including original openTSNE/Orca authors) reviewing the results.
openTSNE. Keep the public API, defaults, and accuracy of Barnes–Hut and FIt-SNE; run the project’s 174 unit tests after each change and keep only measured runtime wins. After an overnight run (~60 ideas tried, 34 retained), speedups land at 4.4–7.7× at 500k points—including a FIt-SNE stage simplification that cuts that stage ~25%. No asymptotic change; still a large constant-factor haul on already-hand-optimized CPU code.
ssGSEA. Build a new implementation whose scores exactly match existing libraries. Restructuring GSVA’s closed form as sparse matrix multiplication drops 2,000 gene sets × 1,000 samples from 5 minutes (GSEApy) to 2.2 seconds (136×).
Graphlet/orbit counting. Match Orca and brute-force counts exactly. After several autonomous hours, a new algorithm beats Orca on every tested network—about 160× on a BioSNAP PPI graph (13 s vs 35 min) with roughly quadratic edge scaling versus Orca’s cubic.
Why it matters for code review
This is the always-on factory pattern with the human role relocated: decide what to optimize and how green is proven, then leave the agent under that harness for hours. Peak-session Claude Code mastery still shows up—here the burst is the overnight grind—but only because verification is locked before autonomy starts. Score continuous harnessed throughput by red→green evidence under sustained load (tests after every edit; exact numerical match; maintainer audit), not by how many optimization ideas the night shift proposed. FOMO for idle agents without that verify contract just burns tokens rewriting folklore into production numerics—see also Serge’s overnight GPU verify funnel and ReviveBench’s hidden-verifier audit.