Blindsight, a Scala Logging API

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I've released Blindsight 1.0.0. Blindsight is a Scala logging API that is based around structured logging, fluent logging, semantic logging, flow logging, context aware logging, conditional logging, and other useful things. It's the result of between two to four years of thinking about logging stuff. I've written up why I wrote a(nother) logging library instead of just sticking to SLF4J: https://tersesystems.com/blog/2020/05/26/why-i-wrote-a-logging-library/ Blindsight documentation is here: https://tersesystems.github.io/blindsight/index.html

Visit product View on Hacker NewsMay 27, 2020

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Product HuntOn track for Day 1 leaderboard · Strong signals: context · 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.

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

60%60% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

47%47% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

31%31% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

18%18% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

2%2% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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