Di
Dinit – specialized init for Docker containers
Hacker NewsDinit – specialized init for Docker containers
I made an opinionated init specially for Docker containers. It supports running and managing multiple programs, signals, and their stdout and stderr all in the way more suitable for Docker containers. No more resource exhaustion from zombie processes, data loss by uncleanly terminated containers, stray daemonized processes keep running, or hidden issues because failed processes inside a container gets restarted instead of taking whole container with them.
Share cardActual performance
1points
Did not reach leaderboard
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Product HuntOn track for Day 1 leaderboard · Strong signals: dock · Missing: mac, agents, macos57%57% 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 · Strong signals: supports, started · Missing: reddit linkedin, podcasting, created55%55% 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, personal42%42% predicted probability of success on TrustMRR, 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 released39%39% 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, intuitive34%34% 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, revenue18%18% 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, crypto9%9% predicted probability of success on BetaList, based on ML models trained on real launch data.
Correct prediction on native model
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