I'm building an app to replace Overleaf and Notion

Hacker News

Hi HN, Since 2019, I’ve been working on a writing platform designed for creating complex documents (e.g., theses). I personally use it for everything as it also allows to classify documents in categories so you can organize them efficiently. As of a few months ago, the app is also available in the browser, and you can now invite coworkers to collaborate on a document in real time. The app is somewhat inspired by LyX. It offers an intuitive, modern editor, but users don’t need to know any LaTeX. When it’s time to export, they can choose from a range of templates (IEEE paper, thesis, etc.). A few highlights: - It uses a custom-built block editor that performs well with large documents. Each block is its own contenteditable element (instead of having one massive contenteditable for the whole document) - If you prefer plain text - you can insert a Markdown block and write using Markdown instead - Built-in citation management - Support for cross-references and footnotes - Mermaid diagrams, inline LaTeX equations, and display math are all supported - "To-do" sections help you stay organized while writing You can try it out here: https://www.monsterwriter.com/

Visit product View on Hacker NewsJun 19, 2025

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

83%83% 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.

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

72%72% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

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

AppSumoStrong fit for a featured deal · Strong signals: platform, intuitive, efficient · Missing: plus, reviews, host

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

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

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

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

14%14% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Strong signals: real time, collaborate · 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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