NitroIQ – A Jira plugin for optimizing scrum teams

Hacker News

Hi HN! We've just launched a JIRA plugin that helps technology and product leaders optimize sprint performance on scrum boards (kanban support is coming soon). This is a tool that I've personally always wanted in my arsenal growing and mentoring engineering teams, and had to always hack together with spreadsheets and API calls. The aim of NitroIQ was to not only present JIRA data in a clean and beneficial way, but to provide advice, scorecards, and "prescriptions" to help leaders fine-tune their teams and also find where the bottlenecks in process are. NitroIQ homepage and docs: https://nitroiq.io/ Atlassian Marketplace page: https://marketplace.atlassian.com/apps/1232080?tab=overview&... Any and all feedback, comments, and discussions are more than welcome!

Visit product View on Hacker NewsSep 26, 2023

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

65%65% 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: apps · Missing: mac, agents, macos

62%62% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

TrustMRRFits verified-revenue profile · Strong signals: personal, apps, way · Missing: mobile apps, ios, entrepreneurs

54%54% predicted probability of success on TrustMRR, 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

50%50% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

AppSumoMay struggle as an AppSumo deal · Strong signals: soon, calls · Missing: plus, platform, intuitive

28%28% 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.

Incorrect prediction on native model

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