Open-source, lightweight tracer for debugging LLMs applications

Hacker News

This allows you to set up logging traces with minimal effort on any python codebase and store it into a mongo db database or a local directory. I'm leveraging it mostly for developing on language models, and use the data for a downstream evaluation and debugging. Since I'm didn't want to rely on any of the opinionated frameworks (Langchain with Langsmith, etc), I thought a simpler plug-and-play solution would suffice. I have a lot of features in mind for the roadmap -- let me know if you'd like to add to it and feel free to drop in any suggestions. There is also a quick Streamlit app added to stare at the results; I'm not a Streamlit pro and preemptively apologize for any UI issues.

Visit product View on Hacker NewsAug 22, 2023

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Launch Intel predictions

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

77%77% 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.

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

66%66% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

Indie HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting

45%45% predicted probability of success on Indie Hackers, 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.

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

35%35% predicted probability of success on TrustMRR, 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

1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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