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Show HN: Finalrun – Spec-driven testing using English and vision for mobile apps
I wanted to test mobile apps in plain English instead of relying on brittle selectors like XPath or accessibility IDs.<p>With a vision-based agent, that part actually works well. It can look at the screen, understand intent, and perform actions across Android and iOS.<p>The bigger problem showed up around how tests are defined and maintained.<p>When test flows are kept outside the codebase (written manually or generated from PRDs), they quickly go out of sync with the app. Keeping them updated becomes a lot of effort, and they lose reliability over time.<p>I then tried generating tests directly from the codebase (via MCP). That improved sync, but introduced high token usage and slower generation.<p>The shift for me was realizing test generation shouldn’t be a one-off step. Tests need to live alongside the codebase so they stay in sync and have more context.<p>I kept the execution vision-based (no brittle selectors), but moved test generation closer to the repo.<p>I’ve open sourced the core pieces:<p>1. generate tests from codebase context 2. YAML-based test flows 3. Vision-based execution across Android and iOS<p>Repo: <a href="https://github.com/final-run/finalrun-agent" rel="nofollow">https://github.com/final-run/finalrun-agent</a> Demo: <a href="https://youtu.be/rJCw3p0PHr4" rel="nofollow">https://youtu.be/rJCw3p0PHr4</a><p>In the Demo video, you’ll see the "post-development hand-off." An AI builds a feature in an IDE, and Finalrun immediately generates and executes a vision-based test for it verifying the feature developed by AI.
Finalrun addresses the critical 'brittle selector' and 'out-of-sync' problems in mobile testing using a vision-based agent integrated directly into the developer workflow. While the technical approach is highly relevant to the current AI-driven development shift, the project is in its infancy with limited public traction and faces a crowded market of AI-testing incumbents.