Text editor and light-weight publishing platform

Hacker News

Hello HN! There is an obvious abundance of choice when it comes to text editors, but they all seem to be either too simple and minimal, or overly complex, distracting and difficult to use. We are trying to strike the right balance between a 'complete' feature set (with some unique features you won't find anywhere else – e.g. the multi-leaf view or our real-real-time chat), while keeping the UI clutter-free with a strong separation of style from content. I've submitted Kraa here before and received actionable feedback – hoping for the same with this latest major update to the app. Some demo examples (no login required): - Real-real-time Chat: https://kraa.io/hackernews - Blog article: https://kraa.io/kraa/examples/echolibrary - Long-form story: https://kraa.io/kraa/examples/insidekick - Magazine: https://kraa.io/weeklyinspiration No account required to try it out!

Visit product View on Hacker NewsAug 28, 2026

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Actual performance

2points

1comments

Did not reach leaderboard

Launch Intel predictions

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

74%74% 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: new · Missing: mac, agents, macos

65%65% 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, io · Missing: https docs, excited, just released

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

nativeThis product was originally launched on this platform.

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

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: para · Missing: mobile apps, ios, personal

43%43% 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 · Strong signals: chat · Missing: web3, crypto, cryptocurrency

2%2% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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