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Writemark, a dependency free web component for inline Markdown editing

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Writemark, a dependency free web component for inline Markdown editing

I like writing Markdown, but do not like writing it inside a plain textarea. I wanted something I could use anywhere by dropping in a single web component: ``` <writemark-editor name="body"></writemark-editor> ``` That became Writemark. It renders Markdown while you write, but Markdown remains the value you read, store, and submit. It also has source, split, and preview modes, along with slash commands, tables, task lists, code blocks, native form support, and an API for adding your own controls. There are no runtime dependencies and no required framework or built in toolbar. It is fully vibecoded. The process was very iterative. I knocked something out, tried using it, found bugs, fixed them, and repeated that cycle until I had something I liked writing in and that performed reasonably well. It did not begin as an experiment about AI generated software -- just did not want to use textareas anymore. I like Markdown, and I wanted one component that I could use anywhere without bringing along an entire editor framework. It is still very young. The parser is handwritten, the component is essentially one large JavaScript file, and I am certain there are edge cases waiting to be found. If you find one, I am happy to fix it. I have tried to give it a decent safety net. It currently has 951 Playwright checks across Chromium, Firefox, and WebKit, along with hostile input cases, sanitizer fuzzing, and differential tests against CommonMark. I built this because I wanted it for myself, but I think it turned into something kind of cool. I hope some of you enjoy it. I would love hearing what you think, especially if you try it and manage to break something. - Github: https://github.com/Brostoffed/writemark - NPM: https://www.npmjs.com/package/writemark-editor

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
84%84% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
Product HuntOn track for Day 1 leaderboard · Strong signals: single, using, code · Missing: mac, agents, macos
81%81% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: ide · Missing: https docs, excited, just released
62%62% 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 · Missing: mobile apps, ios, personal
42%42% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: host · Missing: plus, platform, intuitive
38%38% 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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