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Distributed LLM tracing and GH PR/issue linking [Apache 2.0]

Hacker News

Distributed LLM tracing and GH PR/issue linking [Apache 2.0]

See where coding agents struggle, what tools eat tokens and how it all links to GitHub PRs & issues. I open-sourced a tool that streams data from all your dev machines, VMs & sandboxes into S3. You can run your own analysis from there or use the bundled TUI. Why I built this: I run a team of ~10 AGI-pilled engineers and struggle to keep track of how the work we do aligns to the milestones/projects we look in planning. synty helps me identify areas of work that are getting a lot of agent/human attention but they are not accounted for in planning and we use this data to justify re-factoring/documentation efforts to remove friction for agents

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Actual performance

3points
Did not reach leaderboard

Launch Intel predictions

Analyze your own launch →
Product HuntOn track for Day 1 leaderboard · Strong signals: mac, agents, agent · Missing: macos, cursor, claude
93%93% 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.
TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal
46%46% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: ide, io · Missing: https docs, excited, just released
39%39% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
22%22% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
22%22% predicted probability of success on AppSumo, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto
1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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