I made a documentation tool for Jira projects

Hacker News

For the past 6 months, I’ve been building Docai, a tool to automate project documentation from Jira. As a dev, I’ve worked on too many projects where documentation was missing, outdated, or written too late. Most of the time, we had to dig through Jira tickets to understand what was built and why. It was painful. So I decided to fix that. Docai reads your Jira releases and generates clean, structured documentation automatically. Here’s what it does: - Generates documentation from Jira tickets (based on versions/releases) - Builds an incremental feature tree to follow the evolution of your product - Multi-language support (docs can be generated in English, French, Spanish, German… more to come) - Everything is editable — both the tree and the textual docs - PDF export for sharing outside your org - Syncs with Jira — no need to copy/paste anything We’re still early so would love your feedback and opinions. Feel free to try us out for free (free trial/no credit card needed) – and there’s a demo project so you can test the tool before connecting your Jira. Website: https://docai.so Demo video: https://www.youtube.com/watch?v=Y25e1RhkwSI

Visit product View on Hacker NewsApr 22, 2025

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Product HuntOn track for Day 1 leaderboard · Missing: mac, agents, macos

54%54% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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Hacker NewsMay not resonate with HN audience · Strong signals: ide, io · Missing: https docs, excited, just released

46%46% 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

37%37% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

31%31% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Indie HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting

30%30% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

18%18% 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

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