gptdriver

AI native mobile testing ‧ backed by @ycombinator ‧ 👉

[gptdriver @gptdriver]

5/ Read the Full Case Study
We’re excited to have collaborated with Duolingo on this journey, and we’d love for you to check out the full case study: https://t.co/umZ8gEc1YD

How is your team integrating AI into software testing? Let’s discuss.

[gptdriver @gptdriver]

4/ Key Impact & Learnings
70% reduction in manual regression testing efforts
A more flexible, scalable testing framework
AI-driven automation can empower QA teams to focus on higher-value tasks

This wouldn’t have been possible without great partners like Duolingo and the support of @ycombinator

[gptdriver @gptdriver]

3/ The Power of AI-Driven Testing
Initially, rigid automation approaches weren’t working. Instead, we helped Duolingo reframe test cases as goal-oriented prompts, allowing GPT Driver to intelligently navigate workflows rather than relying on predefined button clicks. This shift dramatically improved test reliability.

[gptdriver @gptdriver]

We’re excited to share that Duolingo has published a case study on how @gptdriver helped them reduce manual regression testing by 70%.

[gptdriver @gptdriver]

2/ The GPT Driver Solution

We partnered with Duolingo to implement GPT Driver, our AI-powered testing tool that translates natural language instructions into real-time, automated test execution. The results:
- Anyone—regardless of coding expertise—could run tests
- Test cases were generated in hours instead of weeks
- The system adapted dynamically to UI changes

[gptdriver @gptdriver]

1/ The Challenge: Scaling QA Without Slowing Innovation

Duolingo ships updates at an incredible pace. But with constant changes come potential regressions, requiring extensive manual testing. Their QA team needed a solution that could:
- Automate regression testing without brittle scripts
- Adapt to frequent UI changes
- Be accessible to non-technical team members

[gptdriver @gptdriver]

7/ Purpose-Built AI for QA

We built GPT-Driver—an AI-native testing agent designed for automation, performance, and CI/CD integration. Unlike Operator, it’s built for both web and mobile, ensuring reliability at scale.

Would you trust AI for test automation? Share your thoughts below.

[gptdriver @gptdriver]

3/ Why Operator Fails for QA:

- Frequent human intervention: It often pauses for confirmation, making true automation impossible.
- Web-only limitations: No support for mobile app testing or gestures.
-Blocked by sites: Runs in a remote browser, which many real-world apps reject.

[gptdriver @gptdriver]

6/ Operator is impressive for general browsing automation but isn’t built for rigorous software testing. It lacks autonomy, configurability, and stability—key for scaling QA workflows.

So what’s the alternative?

[gptdriver @gptdriver]

2/ The Hype vs. Reality

Operator is designed to mimic human browsing behavior—clicking, filling forms, and navigating sites. OpenAI calls 2025 “the year of agents.”

Sounds perfect for testing, right? Not exactly.

[gptdriver @gptdriver]

1/ OpenAI's Operator: A Game-Changer for QA? Not Quite.

OpenAI is pushing “autonomous agents” hard, positioning Operator as the future of AI-driven automation. But when we tested it for web and app QA, it fell short. Here’s why it won’t replace your test automation stack anytime soon.

[gptdriver @gptdriver]

7/ How does your team handle mobile UI testing?
Do engineers own it, or do you share the load? Reply with your thoughts—we’d love to hear!

[gptdriver @gptdriver]

1/ Should Mobile Engineers Own E2E Tests? Maybe Not.

Mobile teams struggle with UI test ownership. A director of mobile engineering at a $30B+ fintech company shared how engineers managing E2E tests (XCUITest, Espresso) create bottlenecks. The challenge? Making test creation easier & less engineering-dependent. Let’s dive in.

[gptdriver @gptdriver]

6/ Testing should reflect how features work, not just how they’re built. The right tools/processes let teams share responsibility—boosting productivity without overloading engineers.