Turbolytics.io Engineering Health Metrics Based on Pull Requests

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Hello! My name is Danny and I am working on spinning up turbolytics.io to help software engineering managers better understand their team performance! Our first product is the Engineering Health Report which provides a snapshot of team health, based on GitHub pull request metrics. I've been in the DevOps space for nearly 13 years now (even before I learned of the term DevOps :)) and have a passion for helping teams move smoother! This is an example of one of our reports executed on sourcegraph/sourcegraph github repo: https://turbolytics.io/reports/public/?uuid=90207e16-0226-47... For comparison we created a GitHub top stars industry index which benchmarks 9 of the most popular GitHub projects (based on stars): https://turbolytics.io/reports/enghealth/indexes/os-top-star... What's astonishing to me about the index is that these huge open source projects integrate 95% of code in <= 11 days, with 95% of code <= 999 lines! ---- I'd love your feedback on the report, it's usefulness and the approach! I very much consider this an MVP and would greatly appreciate any feedback. ---- This is a side project to my Full Time job so I intentionally chose "report" based product for the MVP since it was much easier to develop. There are a number of competitors in the space, but most offer "raw" analytics of these metrics, without actionable insights, leaving it up to the customer to understand what the metric means and how they can improve it. Most competitors charge huge sums of money to get simple cycle time metrics. ---- Technically the site is deployed as an SPA using next.js hosted on cloudflare pages. The backend is in django and is hosted in AWS. Thank you for looking

Visit product View on Hacker NewsFeb 9, 2023

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

91%91% 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

86%86% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

Hacker NewsStrong engagement from HN community · Strong signals: open source, ide, io · Missing: https docs, excited, just released

58%58% 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

35%35% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

26%26% 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

17%17% 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

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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