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I was wasting hours on bad AI prompts, so I built a tool to change it

Hacker News

I was wasting hours on bad AI prompts, so I built a tool to change it

Hey HN, I’m a new solopreneur, and this is one of my first public launches. While building AI products, I kept running into the same issue - spending endless hours crafting prompts, fixing broken outputs, and worrying about security. I realized that writing prompts manually wasn’t scalable or safe. So I built ArtisMind - a tool that helps you engineer prompts, not just write them. It generates clear requirements, builds structured prompts using multiple AI models, tests them for quality and security, and even lets you add context files for better accuracy. Now I use it daily for my projects, and it’s saving me tons of time and frustration. It started as a small fix for my own pain - but I hope it helps others too. Would love your feedback, Sebastian

Visit product View on Hacker NewsOct 28, 2025

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best fitHighest predicted score across all platforms for this description.

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

82%82% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

AppSumoStrong fit for a featured deal · Missing: plus, platform, intuitive

51%51% 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

42%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: io · Missing: https docs, excited, just released

31%31% 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.

Correct prediction on native model

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