Drizz
Mobile tests that write, run, and fix themselves
Drizz launched on May 19, 2026 with 345 votes, #51 of 20,538 launches that month and more than 99% of that year's launches.
In plain words
Drizz is an AI-powered mobile test automation platform that generates test cases from plain English descriptions. Users describe what they want to test, and the platform executes tests on real devices using Vision AI, automatically creating reusable test cases without requiring scripting or manual maintenance. It handles dynamic user interfaces, integrates with CI/CD pipelines, and is designed for development teams seeking automated end-to-end mobile testing coverage.
written from the facts on this page · September 2026
From the sources
Drizz is an AI-powered mobile test automation platform built around intent-based testing. Simply describe what you want to test in plain English, Drizz executes it on a real device using Vision AI and automatically authors a reusable test case. No scripting, no flaky selectors, no manual maintenance. It adapts to dynamic UIs, integrates with your CI/CD pipeline, and gives your team reliable end-to-end coverage without the overhead.
Drizz is a Vision AI powered mobile app testing agent. Write tests in plain English, execute on real iOS and Android devices, and eliminate selector maintenance for good. Book a demo.
Just tell Drizz what to test, in plain English. Its AI agent writes the tests, runs them on real iPhones and Androids, and fixes them when your UI changes. Every step shown, every change yours to approve. Selector-based tests take weeks to author and break with every UI change. Self-healing automation fixes this. When 1 in 7 tests fail randomly, automation becomes noise. Vision AI reduces this flakiness drastically Traditional tools can't handle dynamic UIs, leaving login, payments & checkout untested. Vision AI covers critical flows. Enterprise-grade vision AI that tests like a human, without the flakiness, maintenance, or slow authoring of traditional tools. Connect your app. Write tests…from drizz.dev
Does a similar job
all alternatives →

QuashFeb 2026 · quashbugs.com · ▲151Hey HN, I’ve been working on Quash, a mobile-first QA tool where you can write test flows in plain English and have an agent execute them on real Android devices. We’ve finally shipped a downloadable desktop app (macOS, Intel + Apple Silicon), and you can try it now. No managed “demo” environment – it runs tests against your own apps and devices. (Can connect any device locally as well)

Finalrun – Spec-driven testing using English and vision for mobile appsApr 2026 · github.com · ▲28I wanted to test mobile apps in plain English instead of relying on brittle selectors like XPath or accessibility IDs. 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. The bigger problem showed up around how tests are defined and maintained. 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. I then tried generating tests directly from…
- DADeltix – AI Driven TestingAug 2026 · app.deltix.ai · ▲54
Write a task in plain English. An AI agent runs it on a simulator on your Mac and tells you if a real user could complete it. Save the successful run as a regression check you can replay later.
More ai this month
the category →



Paintings by AI models: programs run through a simulation of oil paint.
AI · 20d ago · stillwet.art

PixVerse R2▲381A real-time world model you can explore and change
AI · 28d ago · world.pixverse.video
Source: Product Hunt launch ↗
Launched alongside, May 2026
the whole month →

Parallel agents, diff reviewer, and multi-model comparisons
Dev tools · May 2026 · kilo.ai



Hey HN, Henry here from Cactus. We open-sourced Needle, a 26M parameter function-calling (tool use) model. It runs at 6000 tok/s prefill and 1200 tok/s decode on consumer devices. We were always frustrated by the little effort made towards building agentic models that run on budget phones, so we conducted investigations that led to an observation: agentic experiences are built upon tool calling, and massive models are overkill for it. Tool calling is fundamentally retrieval-and-assembly (match query to tool name, extract argument values, emit JSON), not reasoning. Cross-attention…
Life & fun · May 2026 · github.com

🌱 Private, quiet space for thinking. Simple app for .md files. - zakirullin/files.md
Dev tools · May 2026 · github.com