Project Canary — MOVE Fellowship Foundation
The foundational phase of the MOVE Fellowship at Handshake AI (Sept–Oct 2025), focused on large-scale task generation to train and refine frontier AI models.
Overview
Project Canary was a community-driven initiative where subject-matter experts contributed training tasks across 15 academic domains. The goal: generate high-quality, diverse training data that would push frontier models toward deeper domain expertise.
My Contribution
As a core contributor in the Computer Science domain, I:
- Generated and reviewed tasks covering algorithms, data structures, systems design, machine learning, and software engineering
- Contributed to over 15,000 tasks across the fellowship
- Focused on tasks requiring PhD-level reasoning — problems that couldn't be solved by simply retrieving information
Impact
Quality Metrics
The most significant achievement was improving task quality:
| Metric | Before | After |
|---|---|---|
| Review 1 approval rate | 10% | 40% |
| Tasks completed | — | 15,000+ |
| Domains covered | — | 15 |
A 4× improvement in first-review approval rates meant significantly less rework, faster iteration, and higher-quality training data reaching the model.
Lessons Learned
- Domain expertise matters: Generic annotators produce generic data. PhD-level contributors produce training signal that actually moves the needle on hard problems.
- Quality over quantity: A single well-crafted reasoning task is worth more than a hundred trivial ones.
- Review feedback loops: Tight review cycles with specific feedback accelerate quality improvement dramatically.
Legacy
Project Canary laid the groundwork for Project Orion — the specialized refinement phase that followed, where the broad data generation shifted to targeted reasoning, safety, and red-teaming work.