Weekly newsletter all about tech hiring

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https://hiringfor.tech/ Recently, I got so frustrated at the way the tech industry is hiring that I wanted to put down all my thoughts on the topic. The topics so far[0] have talked about: - False positives vs. false negatives. - Diversity in hiring. - My fundamental principle of hiring: an experienced engineer shouldn't have to prepare for an interview. Part of this project is to highlight how others in the industry feel about hiring. This means I'd love to hear both what what topics people are interested in, and what they have to say about it. If you're interested in contributing, please reach out to me. The ultimate goal is to create training material for interviewers and companies to improve their hiring process. For that to be successful, we need to hear the stories of how capable people have failed to pass interviews. Note: there are multiple ways to subscribe if you're interested in future editions, including email, LinkedIn and RSS. [0] https://hiringfor.tech/archive.html

Visit product View on Hacker NewsFeb 29, 2020

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Actual performance

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Did not reach leaderboard

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

62%62% predicted probability of success on TrustMRR, 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: including · Missing: supports, reddit linkedin, podcasting

58%58% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

57%57% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: io, including · Missing: https docs, excited, just released

48%48% 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 · Missing: plus, platform, intuitive

20%20% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

19%19% 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.

Correct prediction on native model

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