AIAny
AI Infra2024
Icon for item

Midscene

Drives UI automation from screenshots alone: describe steps in natural language and a vision model acts on what it sees, no DOM selectors. One API spans web, Android, iOS, HarmonyOS and desktop; plugs into Playwright/Vitest or runs autonomously.

Introduction

Every Selenium or Playwright suite carries the same hidden liability: it is bound to selectors that break the moment a designer renames a class or restructures a div. Midscene removes that coupling entirely — it reads the screen the way a human does, from pixels, so a test describes intent ("click the login button") rather than structure ("div.header > button:nth-child(2)").

What Sets It Apart
  • Vision-first, not DOM-first — it acts on screenshots, so one script runs against a web page, an Android app, iOS, HarmonyOS, or a desktop window without rewriting locators.
  • Natural-language primitivesaiAct performs actions, aiQuery extracts structured data, and aiAssert verifies visual state, all from plain-English instructions.
  • Bring your own model — it is a harness, not a model: plug in Qwen, Doubao, GLM, or Gemini, or self-host UI-TARS, trading cost, latency, and privacy as you see fit.
  • Two modes — drop it into an existing Playwright/Vitest pipeline, or let it run as an autonomous agent that works out the steps itself.
Who It Is For

A strong fit for teams whose UIs change fast, who test across several platforms, or who want non-engineers to author tests in prose. The tradeoff is real: every step is a model inference, so runs are slower and cost more per action than selector-based tools, and visual ambiguity can still mislead the model. If your app is a stable single web surface with a mature selector suite, classic Playwright stays cheaper and more deterministic.

Information

  • Websitegithub.com
  • OrganizationsByteDance
  • AuthorsXiao Zhou, Tao Yu, YiBing Lin
  • Published date2024/07/23

Categories

More Items

GitHub
AI Infra2025

Measures generative AI inference performance with token-level metrics (TTFT, inter-token latency), latency, and throughput under realistic traffic patterns. Provides a multiprocess engine, real-time TUI dashboard, extensible plugins, and integrations for telemetry and result uploads, aimed at inference benchmarking and capacity planning.

GitHub
AI Train2019

Train and experiment with multi-billion to trillion-parameter transformer models on large GPU clusters using GPU-optimized building blocks and reference training scripts; offers advanced parallelism and mixed-precision support for research teams and ML engineers.

GitHub

Indexes full text of visited web pages and local files on a self‑hosted server so you can search your personal knowledge from a web UI, terminal, CLI, or an AI assistant. Runs without mandatory telemetry, offers a browser extension for automatic capture, and supports optional semantic search via a configurable embeddings endpoint.