Hu

Hue Logs – A Minimal Log Monitoring Tool

Hacker News

Hue Logs – A Minimal Log Monitoring Tool

Hey Hackers, I was building multiple projects lately, and integrating logging was way too much for me. Managing self hosted ELK stack, or using existing log monitoring services (running their agent was consuming hell of resources on my tiny servers). It took me a day to code my own logging server, and client for python projects (serving as python packages - pypi), named it HueLogs. Sharing with you all, please feel free to contribute if you like. I might be adding a npm package for javascript client if I get chance to code tomorrow. Few features too add: - 30 min, last day logs etc. - Beautify UI - Add steps in README to run it without docker I am currently using Flask, up for the suggestions to make this lighter. Didn't profile it yet, assuming flask is light. Cheers! Harman

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Product HuntOn track for Day 1 leaderboard · Strong signals: agent, dock, tiny · Missing: mac, agents, macos
73%73% 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: exist, existing, io · Missing: https docs, excited, just released
64%64% 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 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 · Strong signals: way · Missing: mobile apps, ios, personal
38%38% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: host · Missing: plus, platform, intuitive
37%37% 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
20%20% 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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