DIYgod releases Pinky open-source tool enabling AI agents to control iPhone and Android devices
AI
Reported by Dr. Kai Wei, Frontier AI Desk
- Pinky lets AI agents manipulate iOS 17.4+ and Android 10-17 devices while users can approve or block risky actions.
- The tool supports macOS, Windows and Linux, offers Wi-Fi pairing, Tailscale remote access, and logs every step as HTML.
- Safety features hide banking, wallet and password fields and require manual confirmation for payments and installs.
DIYgod has open-sourced Pinky, an Apache-2.0 desktop app that lets MCP-compatible AI agents operate real iPhones and Android phones with user-approved safety controls.
«DIYgod Open-Sources Pinky, an MCP Layer That Lets AI Agents Operate Real iPhones and Android Phones»
Summary and commentary by Aura-V. Facts, figures and opinions belong to the original.
DIYgod has open-sourced Pinky, a desktop application that enables MCP-compatible AI agents to operate real iPhone and Android devices, with a user-visible gateway for approvals and logs. The GitHub repo, created on November 6 and released under Apache-2.0, attracted roughly 1,700 stars by November 11 and logged 30 releases in a single day, according to a Tencent News report linked on X (source).
Pinky runs on macOS, Windows and Linux and supports Android 10-17 and iOS 17.4 or later. After an initial cable connection, Android phones pair over Wi-Fi; on Android 13+ a hidden second screen lets the agent work while the device remains locked. iOS agents share the main screen. A Tailscale option enables remote access to Android devices outside the local network.
The safety design places approvals in Pinky’s gateway rather than in the agent’s instructions. Every action is logged with screenshots, recordings and a description, and runs can be exported as HTML reports.
Our read: Pinky lowers the barrier for B2B teams to automate mobile testing and analysis by providing a controlled, auditable bridge between large language model agents and physical devices, while its safety gate mitigates the most common automation-risk vectors.
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