Better Interviews

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Better Interviews

Has anyone else been struggling to get a good signal on the candidates you've been interviewing? I've been interviewing Software Engineers for almost ten years now and it hasn't gotten any easier. I struggled to get a good read on each candidate and I couldn't easily compare them with each other. That is, until recently. I discovered a pretty simple tool for judging candidates. I call this tool "interview signals". They are five "yes" or "no" questions that I answer about each candidate that I'm interviewing for a role. These consistent questions help me compare candidates across a standard set of criteria. Suddenly, it becomes much easier to compare candidates to each other. There are a ton of other benefits too. I can now write up interview feedback in just a few minutes. It used to take me thirty minutes to an hour. I also have a helpful way to guide candidates through the interview. If they haven't touched on an area where I know I need a signal, I can prompt them to focus on that area. After all, my goal is to help candidates do their best in an interview, not make them fail! Better Interviews (betterinterview.club) encapsulates this idea in a simple workflow. The tool is totally free because I just want to help us make interviews better and fairer for everyone. In this spirit, I encourage you to check it out, give it a try, and let me know if it helped you make better hiring decisions. Also, as you make these interview signals, I encourage you to share them with your candidates. The idea is to set them up for success. They can best prepare to show you their skills in the areas you care most about. Thanks everyone, and good luck with your hiring!

Visit product View on Hacker NewsDec 18, 2022

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best fitHighest predicted score across all platforms for this description.

Product HuntOn track for Day 1 leaderboard · Missing: mac, agents, macos

51%51% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

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

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

40%40% 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

23%23% 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

16%16% 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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