Au

Auto-updating docs with every product release

Hacker News

Auto-updating docs with every product release

Hey HN, Every time software is released, documentation goes out-of-date. I was a PM for 3 years and by far the worst part was the bookkeeping required to draft updates to documentation, marketing materials, sales decks and demo videos every time a new feature was shipped. It was a huge time sink for myself and our engineers. Product teams want to focus on making the best products possible, and maintaining these materials is therefore an afterthought. Unfortunately, they are critical for a business to operate. Bad docs lead to increase in support workload and higher churn. Today this problem is solved by hiring dedicated roles to maintain these assets (tech writers, sales enablement), which is expensive and adds processes. With LLMs, we think the same can now be achieved for less. That’s why we’re building AutoDocs, a tool that automatically finds which parts of your documentation need updating, every time a product is changed. We ingest JIRA / Linear releases, search for internal context to understand the change, and then flag the documentation that needs updating. We’ll next start automatically drafting updates to docs, and then move into other content such as help centres, sales decks and product videos. Our mission is to make it 10x easier to have a full set of assets that stays in sync with the codebase. We’d love to hear your honest feedback!

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Launch Intel predictions

Analyze your own launch →
Product HuntOn track for Day 1 leaderboard · Strong signals: new, context, code · Missing: mac, agents, macos
93%93% 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
72%72% predicted probability of success on Indie Hackers, 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
45%45% 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
39%39% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
29%29% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
17%17% 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
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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