Across the winners
What AI did, and what stayed human
Four winning projects show a useful division of labor: AI interprets or proposes; people encode the rules that validate results and control execution. We picked four winners from OpenAI Build Week and The Gemma 4 Good Hackathon where the boundary between AI and human-defined rules was especially clear in the code.
Drawn from verified findings in our published coverage. These are implementation observations, not new runtime or safety tests.
AirBridge for Windows · OpenAI Build Week
An assistant with defined permissions
Read the verified finding- What AI did
- AirBridge’s assistant reasons about the audio setup and can request tools.
- What AI didn’t control
- A local catalog classifies every tool as read-only, reversible, confirmation-required, or forbidden. Code checks that policy before anything runs.
- What AI did
- Sentinel asks the model for a structured semantic review and a proposed probe plan.
- What AI didn’t control
- The host rejects mismatched finding IDs, citations outside the supplied evidence, and probe plans that do not fit the tool’s schema. A model answer has to clear those checks.
Project Rosie · The Gemma 4 Good Hackathon
An order form made from case data
Read the verified finding- What AI did
- Project Rosie initially asked Gemma to write the synthesis specification.
- What AI didn’t control
- The published code history shows a switch to a template: all 26 variables come from case data, and catalog numbers are constants. The model’s prose no longer determines that document.
- What AI did
- Gilbeot asks Gemma to locate an arrow’s tip and tail and describe the direction.
- What AI didn’t control
- When the horizontal coordinates differ by at least 0.05, code calculates the direction and corrects a conflicting word in the sentence. This checks consistency; it does not prove the model located the arrow correctly.
The takeaway
Give AI a job with a boundary.
Strong projects often use AI selectively. The model supplies an interpretation or proposal; people decide which rules govern permission, validation, and execution, then put those rules in code. These examples make that division visible and testable.