An open-source, collaborative, WYSIWYG Markdown editor

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Inspired by the design and UI/UX of apps like Notion, and utility of open-source apps like StackEdit, I decided to create a minimalistic, local-only WYSIWYG Markdown editor. Some features worth highlighting: - Monaco editor and Prettier integration for code snippets - Tables (apparently the holy grail of WYSIWYG editing) - Embeds (for CodePen, CodeSandbox and YouTube, most useful for HTML or JSON exports) - Accepts Markdown paste-in, and "exports"/generates HTML, Markdown and JSON outputs - Collaboration (with real-time awareness and initial commenting system, available only when logged in) - GPT-3.5 integration (only when logged-in with the corresponding extension installed) Stack used: TipTap, Solid.js, HocusPocus, Fastify, tRPC. Some notable drawbacks: - No mobile support - Collaboration available only between signed-in users, in the same workspace; - I tried my best to support most common Markdown formatting, pasting and in-editor shortcuts, though there might still be room for improvement - Self-hosting isn't easy right now, though you should be able to figure it out from the source code The editor itself is a standalone app, extracted from the larger Vrite CMS project ( https://github.com/vriteio/vrite ) which you can also test out (only with sign-in) here: https://app.vrite.io/

Visit product View on Hacker NewsJun 23, 2023

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Product HuntOn track for Day 1 leaderboard · Strong signals: apps, user, code · Missing: mac, agents, macos

86%86% 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

76%76% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

69%69% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

TrustMRRFits verified-revenue profile · Strong signals: apps, users · Missing: mobile apps, ios, personal

50%50% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

41%41% 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

15%15% 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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