{"id":47263220,"title":"Show HN: Reduce LLM token use by ~30% with this MCP/CLI tool(Claude benchmarked)","points":2,"user":"jahala","time":1772726372,"time_ago":"5 hours ago","type":"link","content":"

Smart code reading for humans and AI agents. Tilth is what happens when you give ripgrep, tree-sitter, and cat a shared brain.\n--

v0.4.4: Added adaptive 2nd-hop impact analysis to callers search — when a function has ≤10 unique callers, tilth automatically traces callers-of-callers in a single scan. First full 26-task Opus baseline (previously 5 hard tasks only). Haiku adoption improved from 42% to 78%, flipping Haiku from a cost regression to -38% $/correct.

v0.4.5: Bumped TOKEN_THRESHOLD from 3500 to 6000 estimated tokens (~24KB), so mid-sized files return full content instead of an outline that agents then read back via 5–7 sequential --section calls. Fixed two major regressions: gin_radix_tree (+35% → ~tie) and rg_search_dispatch (+90% → -26% win). Sonnet hit 100% accuracy (52/52) and -34% $/correct overall.

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

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

-- PS: I dont have the budget to run the benchmark a lot (especially with Opus), so if any token whales has capacity to run some benchmarks, please feel free to PR results.","url":"item?id=47263220","comments":[{"id":47263298,"level":0,"user":"jahala","time":1772726601,"time_ago":"5 hours ago","content":"

All contributions are welcome, especially more benchmarks for other models!","comments":[]}],"comments_count":1}