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Get out your pitchforks, I wrote a book on screening technical talent

Hacker News

Get out your pitchforks, I wrote a book on screening technical talent

I know that many of you hate automated hiring tests. Common complaints seen on HN: - Automated tests focus on arbitrary algorithms, and don’t actually assess how well you create a user-loving mobile/web/scalable/whatever app. - No one does their daily work under stopwatch pressure, so why should a test be timed? - I have a long resume, why do I need to take a test? With that said, I just released a free ebook Evidence-Based Hiring to address these concerns and explain how and why automated testing can benefit both candidates and companies. Hint: it is all about creating short questions for a specific skill. I’d love to get your feedback on the book. You can download a free HTML, PDF or EPUB version here: https://www.testdome.com/evidence-based-hiring/book/ebh.html Entire source in markdown and build scripts for Pandoc are on GitHub: https://github.com/ZSvedic/EBH-book Full disclosure: I’m a founder of TestDome, an online testing company. I started TestDome, in part, because the only thing worse than a terrible hiring test is using resumes to screen technical talent.

Visit product View on Hacker NewsMay 17, 2020

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4points

Did not reach leaderboard

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

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nativeThis product was originally launched on this platform.

Product HuntUnlikely to reach the leaderboard · Strong signals: user, using, plain · Missing: mac, agents, macos

45%45% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal

32%32% 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

31%31% 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

18%18% 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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