Userdoc – The source of truth for your project requirements

Hacker News

Hey HN, My wife and I have ran a software consultancy for 8 years, and we found that after sales and staff - our biggest challenge was capturing requirements, confirming requirements, and having a standard way for everyone to access the source of truth (both clients and staff). We found important info was being kept solely on a JIRA ticket, and completely disappeared when the ticked moved to "Done". Requirements were being confirmed in many places. E.g. verbally, in emails, in project management tools, etc. This led to miscommunication and a lack of traceability. And although I had always believed in working software over comprehensive documentation - I found with larger projects and complex business rules, keeping the functionality in your head was a full-time job. So we've built Userdoc - a requirements management system to gather, collaborate on, and confirm your software project requirements. An opinionated requirements management system, leveraging standard agile concepts (user stories, user personas, and user journeys) to build a knowledge base for your software project. There's a video demo on the website which runs through the core functionality. Would love to hear your feedback, and more than happy to answer any questions. https://userdoc.fyi

Visit product View on Hacker NewsNov 23, 2022

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Product HuntOn track for Day 1 leaderboard · Strong signals: user, email, project management · Missing: mac, agents, macos

88%88% 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 · Strong signals: wife · Missing: supports, reddit linkedin, podcasting

85%85% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

46%46% 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

42%42% 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, way · Missing: mobile apps, ios, personal

39%39% 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

10%10% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

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