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DevScreen Recruit – a better software engineering interview

Hacker News

DevScreen Recruit – a better software engineering interview

Hello HN! I want to share DevScreen ( https://devscreen.io/ ). We’ve been iterating on DevScreen for a few years now with the goal of delivering an awesome interview process for both candidates and the company. We achieve this by using realistic job-based tests and allowing the candidate to use their own editor - not a whiteboard or unfamiliar online code editor. Many of the existing products in this space just bring the same old whiteboard coding puzzles to the web, without solving the core problem: those tests are not a good indicator of performance. This is a waste of time for the candidate and a missed opportunity for the company. All it tests is the candidate’s ability to read ‘Cracking the Coding Interview’ and work well under the pressure of being watched. Neither are particularly important attributes of an engineer and result in companies often missing great candidates or hiring sub-par engineers. Today, DevScreen supports three different test types: code reviews, bug fixes, and projects. To help prevent the challenges from being made public, we spin up a new private Github repository for each test and add the candidate. Once they’re finished, they are automatically removed from the Github repo and we review their submission (or the company reviews the submission themselves). We have a library of tests in place for various different skillsets. There are short, open-ended tests that can be done in any language or framework, and there are very specific tests that assess a candidate’s abilities in, say, React and TypeScript only. We lean towards open-ended tests ourselves, but we’ve found that companies who are hiring contractors like to test very specific skills only. There’s a demo video on the homepage, but if there are any parts you want to see that aren’t included in the demo just let me know here or email darragh[at]devscreen.io and I’d be happy to answer any questions or go more in-depth on the product. This is our second “show HN” and we’ve iterated on the product a lot based the feedback we received last year, culminating in this version 2.0. Please check it out and let us know your thoughts!

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Product HuntOn track for Day 1 leaderboard · Strong signals: new, email, using · Missing: mac, agents, macos
95%95% 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: supports · Missing: reddit linkedin, podcasting, created
82%82% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: exist, existing, code review · Missing: https docs, excited, just released
54%54% 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 · Strong signals: reviews · Missing: plus, platform, intuitive
31%31% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: video · Missing: mobile apps, ios, personal
27%27% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Strong signals: arr · Missing: mrr, revenue, profit
15%15% 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.

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