Perfetto2LLM - A tool to pass system traces to an LLM

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I work with perfetto traces for ML/GPU optimization a lot. Copy pasting trace info to LLM is not easy. As usually the traces are gzip compressed, have very large file sizes. Also there is no way to select a certain section and sending it to LLM easily (can maybe write a query to do this but not ergonomic). So vibe coded this tool to quickly select the kernels/slices/threads I want to ask about and one click to get a text/json/markdown in my clipboard to paste for LLMs. I think an MCP server for this might also be useful (tried a few but all miss certain things i wanted so chose to just build this quickly)

Visit product View on Hacker NewsDec 31, 2025

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Product HuntOn track for Day 1 leaderboard · Strong signals: mcp, code · Missing: mac, agents, macos

75%75% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting

54%54% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

Hacker NewsStrong engagement from HN community · Strong signals: io · Missing: https docs, excited, just released

53%53% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

TrustMRRLess likely to generate early MRR · Strong signals: way · Missing: mobile apps, ios, personal

47%47% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive

40%40% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue

16%16% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto

0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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