MonsterWriter – Write a thesis, post, or organize notes [$35–>free]

Hacker News

Hello HN community, in 2017 I started a project that would become MonsterWriter. First envisioned as a semi-structured wiki it became a writing application specialized for scientific content. It is a perfect tool if you write your thesis. While it is focused on technical content, you can still see the knowledge management spirit in it. One or two years ago, my wife joined me in my efforts and redesigned the whole project and we recently released it as MonsterWriter2. To celebrate this milestone we are giving away free lifetime licenses for the Desktop version (till Apr 28). Just use the promo code "gu0ho4q" for a 100% discount. You can find detailed instructions here: https://www.monsterwriter.app/promotion.html You can also find a short introduction video to the app on YouTube: https://www.youtube.com/watch?v=vR8i-EY_UBk We are happy to receive any kind of feedback!

Visit product View on Hacker NewsApr 18, 2024

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Actual performance

2points

Did not reach leaderboard

Launch Intel predictions

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Indie HackersFits the IH revenue-focused audience · Strong signals: started, wife · 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.

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

51%51% 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 HuntUnlikely to reach the leaderboard · Strong signals: notes, code · Missing: mac, agents, macos

49%49% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive

41%41% 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

38%38% 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

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.

Incorrect prediction on native model

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