Quicklog.io makes reading your logs faster, easier, more precise

Hacker News

I've been working on this side-project for a while and finally got around to launching it on ProductHunt[0]. Please give some feedback on the product itself or its landing page[1] or better yet use the free plan. It basically invents a log level higher than INFO and shows you just those logs from all your sources. You can filter by Zipkin trace/span ids or by any number of application-defined 'key:value' tags (e.g. user:1234). You can even just use the key:value tags from all your services and see relevant logs even if you haven't implemented distributed tracing. Provides Java, JavaScript, Go client libraries and a REST API. [0] https://www.producthunt.com/posts/quicklog-io [1] https://quicklog.io

Visit product View on Hacker NewsNov 7, 2018

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Actual performance

2points

Did not reach leaderboard

Launch Intel predictions

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

72%72% 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

59%59% predicted probability of success on Indie Hackers, 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

48%48% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

39%39% 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

39%39% 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

12%12% 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.

Correct prediction on native model

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