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docs: Opus 4.8 benchmark re-validation + release-notes headline (#1399)

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README Benchmark Results re-run 2026-07-21 on the current build (Rust
kernel + this cycle's resolution overhaul), Claude Opus 4.8, 7 repos,
median of 4 runs/arm: 89% fewer tool calls, 60% cheaper, 69% fewer
tokens, 20% faster on average, file reads 0-vs-1..24 on ALL seven repos.
Per-repo floor effects reported honestly (excalidraw/alamofire wall,
okhttp cost wash). Cost note updated to match the measured data.
Changelog [Unreleased] headline now co-leads with near-instant sync.

Co-authored-by: Claude Fable 5 <noreply@anthropic.com>
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Colby Mchenry
2026-07-21 13:47:10 -05:00
committed by GitHub
co-authored by Claude Fable 5
parent f8e6f0066c
commit 0f1096e238
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CHANGELOG.md
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@@ -9,9 +9,9 @@ and adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0.html).
## [Unreleased]
# ⚡ The Rust engine release
# ⚡ The Rust engine release — with near-instant sync
**This release rebuilds CodeGraph's parsing engine as a native Rust kernel and overhauls the resolution pipeline around it — the largest performance upgrade in the project's history, with every graph verified byte-for-byte identical to the previous engine.**
**This release rebuilds CodeGraph's parsing engine as a native Rust kernel, overhauls the resolution pipeline around it, and makes the live graph effectively instant: a save now reaches the graph in well under a second, even on a 27,000-file repository. It is the largest performance upgrade in the project's history — and every graph is verified byte-for-byte identical to the previous engine.**
- **Native Rust parsing for 20 languages** — TypeScript, JavaScript (+TSX/JSX), Java, Python, Go, C, C++, Rust, C#, Ruby, PHP, Swift, Kotlin, Scala, Dart, R, Lua, and Luau now parse in a compiled Rust kernel (Metal and CUDA ride the C++ path). Platforms without a prebuilt binary, and individual files with syntax errors, fall back to the previous engine automatically — same graph either way, proven on repositories from small libraries to the Linux kernel.
- **Adaptive to your machine** — CodeGraph sizes its parse workers, resolver pool, and caches from what the system actually has: real core counts (container/cgroup-aware, not the host's), honest available memory on macOS and Linux, and measured per-project resolution cost. A big workstation gets the full parallel pipeline; a 2-core VPS gets a pipeline tuned to finish reliably instead of running out of memory — the Linux kernel (70k files) indexes to completion on a 2-core, 6GB machine in under 12 minutes — down from 26 at the start of this cycle.