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ShipShipShip – Open-source tool for changelog and user feedback

Hacker News

ShipShipShip – Open-source tool for changelog and user feedback

Hello ! I was frustrated because every time I released a new feature on my website, users couldn't see it easily. So I created ShipShipShip, a self-hostable tool that creates a changelog page where users can see what has been released, what you're working on, and give their feedback. The repo : https://github.com/GauthierNelkinsky/ShipShipShip It's still very fresh and have a ton of features in mind to implement, so all feedback and suggestions are appreciated

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Actual performance

1points
2comments
Did not reach leaderboard

Launch Intel predictions

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Product HuntOn track for Day 1 leaderboard · Strong signals: user, new, open · Missing: mac, agents, macos
77%77% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
Indie HackersFits the IH revenue-focused audience · Strong signals: created · Missing: supports, reddit linkedin, podcasting
58%58% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: io · Missing: https docs, excited, just released
44%44% 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: users · Missing: mobile apps, ios, personal
40%40% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: host, users · Missing: plus, platform, intuitive
38%38% 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
8%8% 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
1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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