Userdoc – My new startup after selling my development agency

Hacker News

Hey HN, my wife and I ran a software development agency for 8 years, and sold it recently to work on some products related to our experience, and Userdoc is our first product :) Userdoc is a requirements management tool with AI assistance, allowing you to easily create user stories, personas, and user journeys with AI assistance. We found managing requirements (especially on large systems) was quite tricky, so Userdoc acts as the source of truth for your software requirements, and integrates with your project management tools to sync your stories, but keep them centrally in Userdoc as living documentation. Would love any feedback, there's a quick video on the website!

Visit product View on Hacker NewsFeb 6, 2023

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

92%92% 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: user, new, project management · Missing: mac, agents, macos

88%88% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

39%39% 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

35%35% 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 · Strong signals: video · Missing: mobile apps, ios, personal

31%31% 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

13%13% 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

1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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