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I Built a Simple Prompt Manager to Organize AI Prompts with Windsurf

Hacker News

I Built a Simple Prompt Manager to Organize AI Prompts with Windsurf

Hey HN, Vibe coding or not, that's the question ;) I'm trying to be an indie hacker in my spare time. I'm a sysadmin / DevOps / SRE. I was tired of searching for my prompts in my notepad, so I built a small SaaS with Django to manage them. The idea is simple: store my prompts and add tags to each one to find them quickly. I'd really appreciate any feedback if you give it a try (and maybe spot some bugs?) :) Cheers, Guillaume.

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Product HuntOn track for Day 1 leaderboard · Strong signals: coding · Missing: mac, agents, macos
75%75% 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
69%69% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
AppSumoStrong fit for a featured deal · Missing: plus, platform, intuitive
50%50% predicted probability of success on AppSumo, 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
34%34% 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
28%28% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Strong signals: saas · Missing: arr, mrr, revenue
22%22% 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
4%4% predicted probability of success on BetaList, based on ML models trained on real launch data.

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