ShipIT for GitHub Management

Hacker News

Hi, I'm Federico and I'm creating a Lightweight Project Management tool for Github. This MVP is being tested from some friends and myself. There are 3 major features that I'm testing. * Notes: To enhance the ability to create issues and product backlog from user feedback, ideas or simply "online post-its". * Board: To automate Sw iteration from issues and pull-requests. This is where progress is done. * Suggestions: To recommend actions from your product backlog and board. This are suggestions made by ShipIT that improve how you develop, deploy and test features. Even adding resources like blog posts and articles about important topics. I'd love to hear your feedback. Thanks! https://ship-it-app.herokuapp.com/

Visit product View on Hacker NewsAug 9, 2019

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Actual performance

2points

1comments

Did not reach leaderboard

Launch Intel predictions

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Product HuntOn track for Day 1 leaderboard · Strong signals: user, project management, notes · Missing: mac, agents, macos

80%80% 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 · Missing: supports, reddit linkedin, podcasting

54%54% 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: ide, io · Missing: https docs, excited, just released

43%43% 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 · Missing: mobile apps, ios, personal

36%36% 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

33%33% 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

14%14% 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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