# Show HN: profiler-md converts performance profiles to Markdown > Source: > Published: 2026-11-15 16:04:51+00:00 Hi HN! profiler-md is a CLI and JS library that converts performance profiles to Markdown across 17 formats (e.g. pprof), 29 profilers (e.g. py-spy), 22 languages (e.g. Rust), and 4 profile types (call stack profiles, call graphs, heap snapshots, and traces). Earlier this year I tried using AI agents for performance optimization. They weren't very effective because they frequently: - Speculated about why something was slow from just reading the code - Made changes, measured the wall time before vs after, and struggled to distinguish real improvements from noise - Analyzed raw profiles using one-off scripts, which were often subtly incorrect The issue was that the agents couldn't read a flamegraph like I could. In a flamegraph, I could see what was slow, and when I made changes, I could see whether the targeted work was eliminated. The agents needed a feedback loop like that. So I wrote a small converter from Node CPU profiles to Markdown. The agents were much more effective when using it, and I realized I also liked reading the Markdown myself! I felt this had potential, and I use many different programming languages at work, so I decided to build profiler-md. I also wrote an agent skill [1], which I've successfully used to optimize profiler-md itself and its pprof-format dependency (a 78x speedup on a large pprof file [2] and a 3.3x speedup more broadly [3]). I got to solve some fun problems for this project: - Making it so that adding a new format, profiler, or profile type is as simple as implementing an interface and adding to a registry. I also have project skills for this! - Resolving weirdly differing profiler semantics (e.g. around what line numbers mean [4] [5] [6] [7]) by setting up the right interface boundaries. - Matching functions across profiles for the Markdown diff output. A profile doesn't contain the full code, so this required some heuristics. I evaluated the algorithm by measuring recall (99.9%) and precision (99.7%) on diffs from real version upgrades. - Designing a call tree renderer that maximizes metrics such as how many of the hottest functions it shows and how much of the profile's self value it covers [8]. I'd love to hear what you think. Try it out and let me know if you have any feedback or questions! Full disclosure: I work at Anthropic, but this personal project is unrelated to my employer. [1] [https://github.com/TomerAberbach/profiler-md/#skill](https://github.com/TomerAberbach/profiler-md/#skill) [2] [https://github.com/DataDog/pprof-format/pull/77](https://github.com/DataDog/pprof-format/pull/77) [3] [https://github.com/DataDog/pprof-format/pull/107](https://github.com/DataDog/pprof-format/pull/107) [4] [https://github.com/rbspy/rbspy/issues/409](https://github.com/rbspy/rbspy/issues/409) [5] [https://github.com/oven-sh/bun/issues/29240](https://github.com/oven-sh/bun/issues/29240) [6] [https://github.com/google/pprof-nodejs/issues/361](https://github.com/google/pprof-nodejs/issues/361) [7] [https://github.com/NixOS/nix/blob/16fa212a96192479593620e9b9...](https://github.com/NixOS/nix/blob/16fa212a96192479593620e9b928733aaf7a2a6d/doc/manual/source/advanced-topics/eval-profiler.md?plain=1#L25-L27) [8] [https://github.com/TomerAberbach/profiler-md/blob/a1ee95e4b5...](https://github.com/TomerAberbach/profiler-md/blob/a1ee95e4b5adde2d7fd06592df30d55153130dea/scripts/evaluate-call-tree/index.ts#L5-L25) Comments URL: [https://news.ycombinator.com/item?id=50507976](https://news.ycombinator.com/item?id=50507976) Points: 1 # Comments: 0