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Kanbanish, yet another kanban board

Hacker News

Kanbanish, yet another kanban board

I finally just rage-built the Kanban board I've wanted for years, mostly for retros and other structured discussions: https://www.kanbanish.com Features that I wanted, which I couldn't find all together in any other apps: - No signups at all - Anyone can edit anything on the board - Comments, voting, emoji reactions - The ability to vote multiple times on the same card (useful for Lean Coffee) - A bunch of templates for common retro/discussion formats - Export the board contents to plain text or markdown for meeting notes

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Actual performance

5points
Did not reach leaderboard

Launch Intel predictions

Analyze your own launch →
Product HuntOn track for Day 1 leaderboard · Strong signals: apps, notes, plain · Missing: mac, agents, macos
74%74% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: io · Missing: https docs, excited, just released
69%69% 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 · Strong signals: apps · Missing: mobile apps, ios, personal
31%31% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
29%29% 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
25%25% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
Indie HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting
23%23% predicted probability of success on Indie Hackers, 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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