Handbook for better QA in organisation(s)

Hacker News

After a bunch of recruitment interviews (some good, some...rough on the QA front), I realized how much variation there is in how companies approach quality. So, I started building a theoretical (and I wish soon practical) guide on GitHub for establishing a quality-first engineering culture: https://github.com/403-html/the-qa-architecture-handbook Have an idea for something I missed? Think I'm way off base? Jump into the repo and let's discuss it. And no, it's not for personal roadmap, for this https://roadmap.sh/qa has been created.

Visit product View on Hacker NewsAug 26, 2024

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

61%61% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

Indie HackersIH features products with proven revenue · Strong signals: created, started · Missing: supports, reddit linkedin, podcasting

49%49% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

43%43% predicted probability of success on AppSumo, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Strong signals: personal, way · Missing: mobile apps, ios, entrepreneurs

43%43% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Product HuntUnlikely to reach the leaderboard · Missing: mac, agents, macos

43%43% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue

13%13% 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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