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InterviewKit
LeetCode is dead, let Codex conduct technical interviews
I built InterviewKit because technical interviews should reflect how engineers actually work with AI. Candidates use Codex to complete a real task, but they still have to drive the process: choosing an approach, making trade-offs, directing the code, testing it, and dealing with failures. Employers see more than the final output. They get a clearer picture of the candidate’s judgment and how they work.
Hi Product Hunt 👋
I built InterviewKit because technical interviews were starting to feel disconnected from how software is actually being built.
AI tools are becoming part of everyday engineering, but many interviews still ask candidates to pretend they don’t exist. I wanted to explore a different question:
Can this person use AI with good engineering judgment?
During an InterviewKit assessment, the candidate works with Codex on a real engineering task. They decide the approach, make the trade-offs, direct the implementation, choose what to test, and respond when something fails.
Codex follows their direction, but it doesn’t coach them, suggest the right answer, or quietly rescue weak decisions. The candidate still has to drive the work.
InterviewKit captures privacy-preserving evidence of that process so employers can evaluate more than the final code. Candidate work stays on their device until they explicitly choose to submit it, and submitted projects are evaluated in an isolated environment.
This is an early pilot, and I’m still learning what an effective AI-native interview should look like.
I’d especially love feedback from founders, engineering leaders, recruiters, and developers:
What would you want to understand about a candidate’s ability to work with AI?
About InterviewKit on Product Hunt
“LeetCode is dead, let Codex conduct technical interviews”
InterviewKit was submitted on Product Hunt and earned 3 upvotes and 1 comments, placing #147 on the daily leaderboard. I built InterviewKit because technical interviews should reflect how engineers actually work with AI. Candidates use Codex to complete a real task, but they still have to drive the process: choosing an approach, making trade-offs, directing the code, testing it, and dealing with failures. Employers see more than the final output. They get a clearer picture of the candidate’s judgment and how they work.
On the analytics side, InterviewKit competes within Career and OpenAI Day — topics that collectively have 2.2k followers on Product Hunt. The dashboard above tracks how InterviewKit performed against the three products that launched closest to it on the same day.
Who hunted InterviewKit?
InterviewKit was hunted by Abishek Muthian. A “hunter” on Product Hunt is the community member who submits a product to the platform — uploading the images, the link, and tagging the makers behind it. Hunters typically write the first comment explaining why a product is worth attention, and their followers are notified the moment they post. Around 79% of featured launches on Product Hunt are self-hunted by their makers, but a well-known hunter still acts as a signal of quality to the rest of the community. See the full all-time top hunters leaderboard to discover who is shaping the Product Hunt ecosystem.
For a complete overview of InterviewKit including community comment highlights and product details, visit the product overview.
Abishek Muthian