Task priority score for software development

Hacker News

https://github.com/joelparkerhenderson/task_priority_score We work on projects where we want to prioritize tasks, issues, incidents, etc. We want to use a shared language that is easy to understand for software developers and also for business executives. The link above is our work in progress, on a simple way to rank tasks using clear language and clear examples. The scores are simple: P0 is emergency, P1 is must have, P2 is should have, etc. We use this for agile development, scrum teams, kanban boards, and XP product management. I'm seeking feedback please for areas to improve, adjust, add, and constructive criticism. I'm especially seeking feedback from high-performing startup. Thank you!

Visit product View on Hacker NewsOct 20, 2018

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5points

1comments

Did not reach leaderboard

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Product HuntOn track for Day 1 leaderboard · Strong signals: tasks, using · Missing: mac, agents, macos

66%66% 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

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38%38% 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: way · Missing: mobile apps, ios, personal

30%30% 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

25%25% 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

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

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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