I built an Android OS in the browser
MobileGym is a verifiable and highly parallel simulation platform for mobile GUI agent research — programmatic state verification, structured state snapshots, identical-state replication, and scalable online RL across 28 mobile apps.
I built an Android OS in the browser launched on May 30, 2026 with 28 votes, #837 of 20,538 launches that month and more than 95% of that year's launches.
In plain words
I built an Android OS in the browser is a web-based recreation of Android OS that runs entirely in a browser window. Launched in May 2026, it appeals to developers, tech enthusiasts, and anyone curious about operating system design. The project stands out by bringing a full mobile operating system environment to desktop browsers, eliminating the need for emulators or virtual machines while maintaining authentic Android functionality and interface elements.
written from the facts on this page · September 2026
From the sources
After burning through tens of billions of tokens, I built an Android-like OS that runs entirely in the browser. Over the past few months, we built MobileGym: a fully browser-based, Android-like simulation environment implemented in TypeScript + React. It currently includes 28 simulated apps, including WeChat, Alipay, Xiaohongshu/RED, bilibili, X, Reddit, WeChat Read, China Railway 12306, Tencent Meeting, Spotify, and eBay, and others
Snapshot · time-travel · inject cross-app data while the simulator keeps running. Use the built-in demo model, or connect your own OpenAI-compatible vision endpoint. MobileGym is a verifiable and highly parallel simulation platform for mobile GUI agent research — the first to make online RL training and deterministic evaluation feasible on real-world daily apps , long a structural blind spot of real-device pipelines. It covers 28 mobile apps (12 daily + 16 system) in the browser. Across the released validation suite, programmatic state judges show no false accept/reject cases over 416 parameterized task templates (vs. 10.2% misjudgment when the same real-device trajectories are scored by a…from mobilegym.dev
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I wrote the book Building Mobile Apps at Scale2021 · mobileatscale.com · ▲357The guide for building large, iOS, and Android native apps - with the challenges and common solutions across the industry.

Muscle-Mem, a behavior cache for AI agents2025 · github.com · ▲226Hi HN! Erik here from Pig.dev, and today I'd like to share a new project we've just open sourced: Muscle Mem is an SDK that records your agent's tool-calling patterns as it solves tasks, and will deterministically replay those learned trajectories whenever the task is encountered again, falling back to agent mode if edge cases are detected. Like a JIT compiler, for behaviors. At Pig, we built computer-use agents for automating legacy Windows applications (healthcare, lending, manufacturing, etc). A recurring theme we ran into was that businesses already had RPA (pure-software scripts), and…

- WEWebGPU enables local LLM in the browser – demo site with AI chat2025 · andreinwald.github.io · ▲145
Browser LLM demo working on JavaScript and WebGPU. WebGPU is already supported in Chrome, Safari, Firefox, iOS (v26) and Android. Demo, similar to ChatGPT https://andreinwald.github.io/browser-llm/ Code https://github.com/andreinwald/browser-llm - No need to use your OPENAI_API_KEY - its local model that runs on your device - No network requests to any API - No need to install any program - No need to download files on your device (model is cached in browser) - Site will ask before downloading large files (llm model) to browser cache - Hosted on Github…
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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…
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