Daksh Gupta, Co-founder of AI Code Review Company Greptile: Using AI to Simulate User Actions Expands Defect Detection from Reading Code to Running Applications
Greptile co-founder Daksh Gupta explains why static code review cannot cover all defect types, and how GPT-6 Astra's computer use capabilities enable automated behavioral testing by simulating user interactions (adjusting viewport dimensions, checking rendering results). Effectiveness claims are self-reported by vendor clients, with no independent benchmark verification.
A button disappears under an iPhone viewport. Static code review would not catch this issue—the code logic is correct, the developer’s intent is met, but the rendering result is wrong. Daksh Gupta, co-founder and CEO of Greptile, believes this marks the boundary of AI code review: reading code can check logic, but some defects must be exposed by actually running the application.
Greptile is a company that builds AI agents for code review and testing. Gupta says validating a pull request involves multiple layers: whether the code logic is correct, whether it implements the developer’s intent, whether there are security vulnerabilities, and whether it breaks existing functionality. Some of this validation can only be done by running the application.
GPT-6 Astra’s computer use capability gives Greptile a new path. The specific approach is: start a development server for a pull request, resize the browser to an iPhone-sized viewport, and then simulate a user moving through the application. The model’s visual understanding allows it to inspect screen content and spot hidden buttons.
"A QA firm testing all your changes is just impractically slow considering how fast we’re able to ship code with these models," Gupta says. His judgment is that computer use capability has reached a level where it can automate the process of "a user experiencing your application and trying to break it," thereby giving large companies more confidence in shipping.
Greptile also provides codebase documentation to Codex via a Model Context Protocol server. Gupta states that providing context reduces the fallback rate of AI-generated code, saving customers time and inference costs. However, this metric is based on internal tracking, with no public methodology or third-party verification.