My lightweight prompt management and analytics tool.

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I’ve been working on a side project that helps compare and analyze AI prompts. You can test prompts side-by-side, collect ratings, and see which one performs best. This came out of a chatbot project I built at work last year. We discovered how sensitive LLMs are to small prompt changes and with our setup being storing prompts in a `.ts` file checked into the repo, it meant analysing effectiveness of prompts was impossible. Existing tools I tried were either overloaded with data or locked me into their own models. So over the last few months I’ve spent my very limited free time (new Dad) building something simpler: - Compare prompts with real user feedback. - Clean analytics without information overload. - Bring your own OpenAI key and choose any of their LLM models. It’s up and running and free to try (signup required, no credit cards though). This is no where near a finished product in terms of the website design, it’s a bit rough and ready since design is not my thing. Feedback on the idea and direction would be really appreciated. https://testune.xyz

Visit product View on Hacker NewsSep 18, 2025

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

85%85% 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.

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

53%53% 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, intuitive

48%48% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

33%33% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue

14%14% 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.

Incorrect prediction on native model

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