Log collector that runs on a $4 VPS

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Hey guys, I'm building erlog to try and solve problems with logging. While trying to add logs to my application, I couldn't find any lightweight log platform which was easy to set up without adding tons of dependencies to my code, or configuring 10,000 files. ErLog is just a simple go web server which batch inserts json logs into an sqlite3 server. Through tuning sqlite3 and batching inserts, I find I can get around 8k log insertions/sec which is fast enough for small projects. This is just an MVP, and I plan to add more features once I talk to users. If anyone has any problems with logging, feel free to leave a comment and I'd love to help you out.

Visit product View on Hacker NewsFeb 11, 2023

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AppSumoStrong fit for a featured deal · Strong signals: platform, users · Missing: plus, intuitive, reviews

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

best fitHighest predicted score across all platforms for this description.

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

52%52% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

49%49% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

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

Hacker NewsMay not resonate with HN audience · Strong signals: 000, io · Missing: https docs, excited, just released

40%40% 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

17%17% 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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