Open-source product documentation platform

Hacker News

Vrite is meant to be an open-source, collaborative developer content platform for everything from technical blogs and product documentation to internal knowledge bases. The idea is to provide a great "hybrid" editing experience (combining code and WYSIWYG approach) to create an editing environment that's extensible and can be used for various technical needs, by people of varying technical proficiency. Since the last time I posted this project here ( https://news.ycombinator.com/item?id=36324281 ) I followed on the recieved feedback, adding: - GitHub synchronization (for existing projects using docs-as-code approach); - Self-hosting support via Docker Compose; - Command palette with semantic search (also accessible via API); - Variants (for i18n support); - New "Element" block in the editor, allowing for custom markup (via XML-like syntax) that's then transformed to structured JSON; - Other features, like more customization, better commenting support and new dashboard view (list) Check it out and let me know what you think. For basic look, use the following credentials: - Email: hello@vrite.io - Password: Hello_2023 This account will be reset every few hours.

Visit product View on Hacker NewsOct 13, 2023

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Product HuntOn track for Day 1 leaderboard · Strong signals: dock, new, email · Missing: mac, agents, macos

94%94% 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.

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

86%86% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

75%75% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal

40%40% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Strong signals: platform, host · Missing: plus, intuitive, reviews

39%39% 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

12%12% 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.

Incorrect prediction on native model

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