Show HN: Tilth v0.3 – 17% cheaper AI code navigation (279 runs, 3 Claude models)

3 pointsjahala1mo ago0 comments

tilth gives AI agents structural code intelligence (tree-sitter definitions, callee resolution, smart outlining) via MCP. I benchmarked it on 21 code navigation tasks across 4 real repos (Express, FastAPI, Gin, ripgrep).

-> https://github.com/jahala/tilth

Results: Sonnet 4.5 — 26% cheaper per correct answer (79% → 86% accuracy). Opus 4.6 — 14% cheaper (and the only model+mode combo to crack the hardest task). Haiku 4.5 — 82% cheaper when forced to use tilth (69% → 100% accuracy at $0.04/answer).

We measure “cost per correct answer” — what you’d expect to spend before getting a usable answer under retry. A wrong answer isn’t a cheap success.

Interesting finding: smarter models adopt MCP tools voluntarily (Sonnet 95%, Opus 94%), but Haiku ignores them (9%). Instruction tuning didn’t help. Removing the overlapping built-in tools did.

https://github.com/jahala/tilth/blob/main/benchmark/README.m...

PS: I dont have the budget to run the benchmark a lot with Opus, so if any token whales has capacity to run some benchmarks, please feel free to PR results.

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Show HN: Tilth v0.3 – 17% cheaper AI code navigation (279 runs, 3 Claude models)

3 pointsjahala1mo ago0 comments

-> https://github.com/jahala/tilth

We measure “cost per correct answer” — what you’d expect to spend before getting a usable answer under retry. A wrong answer isn’t a cheap success.

Interesting finding: smarter models adopt MCP tools voluntarily (Sonnet 95%, Opus 94%), but Haiku ignores them (9%). Instruction tuning didn’t help. Removing the overlapping built-in tools did.

https://github.com/jahala/tilth/blob/main/benchmark/README.m...

PS: I dont have the budget to run the benchmark a lot with Opus, so if any token whales has capacity to run some benchmarks, please feel free to PR results.