Peace: Zero Stress Automation

Hacker News

Heya HN, I've been working on this framework because it's always too easy to do dangerous things with the deployment tools I've worked with, and I tend to feel guilty when "I tried to fix things, but it replaced the server instead of renamed the existing one", or "there's so much information that I read none of it". The framework handles the aesthetics and provides nice stuff that would never get prioritized at work, so that the next time we have to build a tool, the user experience is more advanced than "text, text everywhere". The project scope just kept growing, so here's my write up and some demo clips of where I got to. If nothing else, am sure you'll enjoy the concept.

Visit product View on Hacker NewsJan 26, 2025

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

70%70% 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

70%70% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

57%57% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

TrustMRRFits verified-revenue profile · Strong signals: way · Missing: mobile apps, ios, personal

51%51% 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

35%35% 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

21%21% 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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