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Devscreen.io – realistic tests for software engineering interviews

Hacker News

Devscreen.io – realistic tests for software engineering interviews

Hi HN! I wanted to share a project that I’ve been working on for the past year: DevScreen ( https://devscreen.io ). I built DevScreen to give companies a way to use more realistic interview assessments to software engineers in a way that works for both the company and the candidate. I created this because of my own experiences as both an interviewer and candidate. Many companies don’t assess relevant skills during an interview. This is a waste of time for the candidate and a missed opportunity for the company. At the extreme end of the scale, you have companies asking candidates to invert a binary search tree on a whiteboard. All that this assesses 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. We spin up a new private Github Repository which the candidate will work on in their own time to complete the task. Once they’re finished, they are automatically removed from the Github repo and the company is notified so they can review the submission. The results can be sent to the relevant ATS application, if applicable. We have a small library of tests but allow private tests that are custom to each company also. I have built a demo version of the app with sample data hardcoded on the front end to give you a feel for how it works without needing to sign up for an account. This will probably suit those of you who prefer to get stuck in rather than read about it (which, I suspect, is the majority of the HN community!). That’s available from the home page or here: https://demo-app.devscreen.io/ . If there are any parts you want to see that aren’t included in the demo just let me know on the chat on the website or email support [at] devscreen.io and I’d be happy to show any part of the product you want to see. For example, only logged in users can create an interview or users. This is solely to prevent abuse of the system as we send emails to candidates to tell them that their test is ready. I want to be up front that it is still in beta, but we do have beta customers using it so I figured it was a good time to share and get feedback from the community and beta customers.

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Product HuntOn track for Day 1 leaderboard · Strong signals: user, new, email · 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, created · Missing: reddit linkedin, podcasting, latex
84%84% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: code review, io · Missing: https docs, excited, just released
55%55% 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, users · Missing: plus, platform, intuitive
27%27% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: users, way · 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 · Missing: arr, mrr, revenue
15%15% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Strong signals: chat · Missing: web3, crypto, cryptocurrency
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

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