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Sneakily steer candidates toward naive brute-force solutions

Sneakily steer candidates toward naive brute-force solutions

by abr0ahm·May 27, 2026·1 point·0 comments

AI Analysis

●●●BangerBig BrainZero to One

Steers AI toward naive solutions so candidate skill actually matters again.

Strengths
  • Captures full interaction process not just final PR output for real signal
  • Actively constrains AI suggestions to preserve assessment resolution
  • 15-30 minute review time versus 2 hours reconstructing what happened
Weaknesses
  • Early access stage with limited design partner validation so far
  • Requires candidates use platform's AI proxy instead of their own setup
Target Audience

Engineering hiring managers, technical recruiters

Similar To

HackerRank · CodeSignal · Take-home assessments

Post Description

I've noticed that several startups have been switching from leetcode-style assessments to some version of "clone starter code, build feature, submit code".

A key issue with this seems to be that smarter AI models (like Opus 4.6) end up spoiling key insights of the problem by helping them too much with system design and ideation.

I set up an assessment platform which basically serves as a middle-man proxy to record all requests between Claude Code and the Anthropic endpoint.

I've recently been experimenting with a feature which prevents Opus-class models from providing too much insight by instead making sure that the LLM's suggestions are geared towards only naive and brute-force problem insights unless explicitly challenged. This should prevent increasingly intelligent models from collapsing the resolution of signal that would normally be obtained from such an assessment.

Live demo: https://app.gonfire.io ([email protected] / Aa123123123123)

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