The Problem
The Portfolio Trust Crisis Is Real.
When anyone can prompt an LLM to clone an app in seconds, a perfect GitHub repository no longer guarantees an interview.
- 01
The LLM Clone Flood
Recruiters are overwhelmed by thousands of identical, tutorial-copied repositories.
- 02
The Pedigree Trap
Lacking validation metrics, companies default to filtering candidates solely by college tier.
- 03
The Silent Rejection
Talented engineers with genuine foundational skills remain buried under unread applications.
$ ls ./applicants/ | grep clone
→ 2,847 repos · 96% structural duplicates · 0 verified depth
$
Shifting the Metric from Code Ownership to Code Understanding.
ProofWork AI doesn't check if you own the code; it evaluates whether you have the engineering depth to maintain, scale, and debug it.
Dynamic Valuation
We replace passive project viewing with active, contextual repository stress-testing.
Transparent Signal
Candidates gain an un-gamable, verified layer of technical credibility to attach to applications.
Fair Sourcing
Recruiters evaluate applicants purely based on execution evidence and engineering grit.
Frequently asked
- Why are GitHub stars and commit graphs no longer reliable signals?
- Stars can be bought, forks can be automated, and commit graphs can be back-filled with scripted commits. None of these prove the author actually understands the architectural decisions in the repo — they measure activity, not comprehension.
- How does AI-generated code break traditional portfolios?
- An engineer can scaffold a polished-looking repo in minutes with an LLM and never engage with the trade-offs in it. Reviewers can no longer tell, from the code alone, whether the author can defend a single design decision.
- Why don't take-home tests solve this?
- Take-homes are slow, easy to outsource, and bias against candidates with day jobs. They also test a fresh greenfield problem rather than verifying ownership of the candidate's existing public work.
- What signal actually proves engineering ability today?
- A verifiable reasoning trace: the candidate explaining, under adversarial questioning, why their code looks the way it does. ProofWork AI generates exactly that artifact from any public repo.