The technical screen
for the AI era.
See how candidates actually build with AI — every prompt, every decision, every tool.
spize Challenge ready. Work normally — we're watching.
✓ Session active · 1h 30m remaining
Finding your next AI engineer sucks.
Your team is stuck doing interviews and reviewing take-homes — and 84% of devs use AI in ways you'll never see.
LeetCode
Tests memorization, not real work.
Take-homes
You see the output, never the process.
"No AI" rules
Tests a workflow nobody uses anymore.
Three candidates. Same challenge.
Assessment: Recreate HackerNews · 90 min · React + Node · 3 completed
Hover the top candidate to see their prompt timeline →
From real work to hiring signal.
Implement a commenting system similar to HackerNews for our Discussions section. Needs nested replies, upvoting, and real-time updates. Reference HN's UX for threading depth.
Build a functional HackerNews clone — auth, stories, voting, nested comments. Tests the exact skills from ENG-347 but in a standalone challenge.
Use real work. Or let our agent create it.
Point Spize at your actual Linear stories, GitHub issues, or Jira tickets. Our agent analyzes the work your team has done and generates a standalone assessment that tests the same skills — without exposing your codebase.
Or bring your own challenge. Either way, candidates get something that actually matters to your team.
Invite candidates with a single token.
Each candidate gets a unique, expiring token. One command to start — no accounts, no setup, no IDE restrictions. They use their own tools.
They work. We observe.
Every prompt, every tool switch, every pause, every edit. Our agent captures it all silently — no screen recording, no webcam, just AI interaction data.
Compare. Decide. Hire.
Side-by-side review with signal scores, prompt timelines, and AI usage profiles. Then each candidate defends their decisions in The Defense.
What We Surface
Not just what they built — how they built it.
Novel Techniques
Creative approaches that separate good engineers from great ones. Research-first, multi-tool orchestration, unconventional prompting strategies.
AI Proficiency
Vibing or orchestrating? Do they lead the AI with intent, or just accept whatever comes back?
Error Correction
When the AI hallucinates — do they catch it, fix it, or ship it?
Security Instincts
Do they think about secrets and trust boundaries — or ship whatever compiles?
The Defense
Code is done. Now AI interrogates every decision — generated from their actual session data.
You spent the first 4 minutes researching existing HackerNews repos instead of coding. What specifically were you looking for, and how did it change your architecture?
I wanted to see how others handled the comment threading — recursive CTEs vs. adjacency list vs. materialized paths. Found two repos using materialized paths which confirmed my instinct. Saved me from over-engineering the schema.
At 0:20, Claude generated auth middleware with a generic catch(err) block. You rewrote it to handle TokenExpiredError and InvalidSignatureError separately. Why wasn't the generic handler sufficient?
Expired tokens need a 401 with a refresh hint. Invalid signatures are a potential attack — that's a 403, log it, and maybe rate-limit the IP. Lumping them together means you can't distinguish between a user who needs to re-login and someone probing your auth.
You also moved the JWT secret from a hardcoded string to an environment variable in the same edit. Was that in response to the AI's code, or something you planned from the start?