I made an AI prompt manager to stop rewriting the same prompts

Hacker News

I made EchoStash. If you’ve ever written a great prompt, used it once, and then couldn’t find it again — I’ve been there too. I kept losing my best prompts in chats, notes, or random documents. Every time I started a new project, I ended up rewriting the same stuff or wasting time trying to remember where I saved things. So I built EchoStash — a simple tool for developers who use AI tools regularly. It helps you save, organize, and reuse your prompts. You can tag them by project or tool, and when you need something, just search. The cool part? EchoStash has AI-powered search. You don’t need to remember the exact words — just type what you’re looking for, and it’ll find the right prompt. It’s fast, minimal, and built to save you time without getting in the way. Would love feedback from devs working with LLMs or building AI workflows. --> https://www.echostash.app

Visit product View on Hacker NewsMay 29, 2025

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Indie HackersFits the IH revenue-focused audience · Strong signals: started · Missing: supports, reddit linkedin, podcasting

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

best fitHighest predicted score across all platforms for this description.

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

81%81% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

45%45% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · 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.

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

34%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, revenue

12%12% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Strong signals: chat · Missing: web3, crypto, cryptocurrency

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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