Greptile says it now runs live app tests with GPT-6 Astra to catch bugs code review alone would miss.
I am not buying that computer use is ready to replace QA at scale without numbers.
Co-founder and CEO Daksh Gupta sells it as the first time you can automate a user trying to break your app, so large companies ship with confidence.
The demo is telling, an iPhone viewport hides a button and the model has to see it, not infer it.
Gupta argues a human QA firm is impractically slow for the pace models now allow, which is true for speed and troubling for coverage.
Speed without a false positive rate, a catch rate, or a before and after revert curve is a story, not a metric.
Greptile also leans on Daybreak for security and on Codex for rework, with a Knowledge Base exposed through a Model Context Protocol server that thousands of customers have connected according to the company.
The claim that context cuts reverts and saves inference sounds right, but Greptile shares no numbers and no definition of revert.
That follows our Oct 26 note that Greptile shows how not to build review bots, and this new push trades static review for live simulation.
Even GPT-6 Astra is pitched as OpenAI's most capable model for demanding work across reasoning, coding, computer use and research, which raises cost and latency questions Greptile does not answer.
Greptile says it uses Codex everywhere internally and sees Ultrafast as the lever to turn ideas into software as fast as enterprise constraints allow.
The concession is real, indexing a codebase so agents follow existing patterns does reduce churn, and connecting that context to Codex is the right plumbing.
If you are a founder eyeing this, ask for a controlled pull request set with measured catch, miss and flake rates before you retire any checks.
