Winner Signal

Winner Signal connects AI-assisted work to evidence that its outputs function, get used, and create value—developing better signals for AI evaluation and training beyond benchmark pass rates. Builders share a demo and public GitHub repository, optionally adding reviewed prompts and coding-agent session histories. Gemini 4 Argon reviews video claims alongside commit-pinned code and supplied traces, producing timestamped findings, source references, actor/action/outcome summaries, and follow-up tests. An evidence layer preserves the review inputs and checks their integrity. Our own project demonstrates a curated backtrace from video claims to actual build decisions, failed tests, fixes, and commits. A live review of our uploaded demo, pinned code, and a curated session excerpt returned five video claims and six trace findings with a verified evidence seal. The backtrace displayed in the workspace is curated, not an automatically reconstructed full execution history. A public demo gallery gives builders initial viewers. Optional owner-authorized YouTube data provides a separate video-response signal; it does not prove product value or agent capability. The research objective is to discover which real-world signals are useful for evaluating and improving the agents that built the artifacts. Training gains have not yet been demonstrated. Live app: https://winner-signal.vercel.app Built during this hackathon on a reused, pinned Apache-2.0 AERead evidence-sealing component.

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