Original Summary

Ask the GitHub CLI codebase where Title is used. ripgrep hands back 431 lines. Six of them are right. It found the answer and then it made me go get it. Across six questions like that its recall was perfect every single time and its precision ran between 1.2 and 20 percent. Then there is the case that made me sure this was worth building. In a TypeScript repo the compiler names 16 reference lines where the declaration's own name is never written on the line. Kin finds 14 of them. Every name only text search finds 0, because the string is not there to match. Kin is an open source version control system that stores the call graph as repository data you commit and branch, not an index sitting beside Git. The CLI and an MCP server query the same graph, so my agent and I work from the same record. Then I measured it instead of trusting it. Public repo pinned to one commit, the Go compiler grading the answers rather than my own tool, the protocol hashed before any query ran, three runs required to come back identical. On callers of a uniquely named function Kin returned 105 call sites with none wrong, where text search returned those same 105 among 2,095 wrong ones. The part that surprised me most is what it does to context. To answer one of these questions from text search you hand a model about 9,553 tokens of matches and surrounding source. To answer it from Kin you hand it 833. Same question, both contain the answer. It is a public alpha, so there is plenty still being worked on, and the direction is very positive. Apache-2.0, macOS and Linux. Try it with one command. npx -y @kinlab/kin setup --intent agent --no-interactive The site is https://kinlab.ai and the source is at https://github.com/firelock-ai/kin   submitted by   /u/troyjr4103 [link]   [comments]

  • 情报分类：技术学习与提效
  • 分类依据：内容涉及技术、AI、软件工具或工程实践
  • 信息来源：Reddit · SideProject
  • 发布时间：2026/9/18 13:41:41