Storygenie, a Tool for Better Stories

Hacker News

Hi HN, I'm Aron, I'm 26 and a software engineer working with different scrum teams for 6 years. I really enjoy working with the scrum process, however, most product owners care relatively less if stories are well written and have no problems refining them over and over again. That's where I saw potential: I built (yet another, I have to say regarding shownew) a website that uses the OpenAI API to generate scrum stories based on a project description and a short idea description. It works fairly well, but I would love to hear feedback from others than co-employees, and this is the only place I know to get some valuable early feedback. The website does not require any sign up, has no paid model, and I am really doing this "advertisement" only to get some feedback of any kind. The landing page is https://storygenie.io , the app itself is also directly accessible in a demo format under https://app.storygenie.io/demo :) Thank you for any feedback and greetings from Germany! Aron

Visit product View on Hacker NewsFeb 18, 2023

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Indie HackersFits the IH revenue-focused audience · 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: model, new, openai · Missing: mac, agents, macos

76%76% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

51%51% 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

31%31% 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

30%30% 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

11%11% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Strong signals: paid · 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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