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Stempad – Fast Online Scientific Writing

Hacker News

Stempad – Fast Online Scientific Writing

I'm building an online text-editor editor to write and save scientific documents fast. Here's a video of how it works: https://youtu.be/Hyk8CvCdEWE As an engineering student, I hated that handwriting was the only viable way to do fast or impromptu scientific writing. It would be the only way to take quick notes in class or in a lab, write an assignment, or create a presentation. Here's a few things I witnessed in academia: * Unsuitable editors: Students attempt to resort to text editors unoptimized for science, such as Notion or Word, to take notes and write assignments. * Slow or expensive software: Students, teachers, and researchers using high-friction and high-cost tools for writing * Messy class notes: Professors upload pictures of hastily handwritten class notes as supplementary material The list could go on. I believe that the ability to quickly document scientific ideas with a keyboard would be a huge QOL improvement for anyone learning or doing science. I recently launched the ability to export Stempad documents to LaTex. I tested it by rewriting part of a paper I found online (Metabolic scaling in small life forms by Marc E. Ritchie & Christopher P. Kempes) and exporting it. You can try the editor and export yourself using the post url. The export button is on the top right of the page. In case you want to see the result directly, this was it: https://www.overleaf.com/read/zjccqbjdyhtc#6e146c Feedback is really appreciated! If anyone thinks they might find Stempad useful, let me know and I'd love to get in touch.

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Indie HackersFits the IH revenue-focused audience · Strong signals: latex · Missing: supports, reddit linkedin, podcasting
94%94% 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: using, notes · Missing: mac, agents, macos
73%73% predicted probability of success on Product Hunt, 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
49%49% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
47%47% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: video, way · Missing: mobile apps, ios, personal
46%46% 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
16%16% 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.

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