Brf.it – Extracting code interfaces for LLM context
Tree-sitter extraction cuts LLM context 50-tokens-to-8 tokens. Cursor and Cody ignore this.
Smart(er) code reading for humans and AI agents. Reduces cost per correct answer by ~40% on average. Install: cargo install tilth -or- npx tilth
Token-efficient code indexing with adaptive callers tracing cuts Claude costs by 34%.
AI engineers, LLM-powered agents, developers optimizing API costs
Cursor IDE · Continue.dev · Sourcegraph Cody
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.
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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.
Tree-sitter extraction cuts LLM context 50-tokens-to-8 tokens. Cursor and Cody ignore this.
Tree-sitter MCP cuts Claude code task costs 17–82% while improving accuracy.
44% cheaper Claude code navigation via tree-sitter definitions + call resolution.
Tree-sitter + FTS5 + MCP = tokens saved for AI agents to actually code, not search.
Cuts Claude coding tokens 58% via dependency graphs; runs local, no cloud, no account.
AST + embeddings for codebase search—but Sourcegraph Cody, Cursor, and Continue already solve this.