AI/AIZENME
📱 AUTOMATION · Agents & IoT Level: Intermediate ⏱ 25 MIN Free & Open Source

Build an Autonomous iPhone Agent with $7 ESP32: AirPlay Eyes & USB HID Touch Hands

A viral open-source mobile automation project with thousands of GitHub stars! It achieves autonomous iPhone operation without jailbreaks, enterprise certificates, or intrusive MDM profiles. Using a $7 ESP32-S3 board: the host PC receives high-resolution, low-latency iPhone screen feeds via Apple AirPlay (the agent’s "eyes"), while a vision model computes coordinates and commands the ESP32 over serial to inject hardware-level USB HID multitouch and keyboard events (the agent’s "hands"). Perfect for automated QA, social workflows, and routine mobile tasks.

CORE TOOLS ESP32-S3 (硬件), Python 3.10+, py_airplay / scrcpy, Vision LLM (DeepSeek-V3/GPT-4o), Arduino IDE
BEST FOR Hardware geeks, mobile QA engineers, e-commerce automation developers, accessibility creators, AI hardware enthusiasts
REQUIREMENTS ESP32-S3 board with native USB, OTG adapter for iPhone, host PC (Win/macOS/Linux), Python 3.10+, multimodal vision API credentials
✦ WHY THIS WORKFLOW

iOS sandboxing prevents traditional software automation without risky jailbreaks or fragile enterprise certificates. Pairing a hardware ESP32 HID controller with native AirPlay screen mirroring creates a 100% unblockable, non-invasive physical automation bridge.

✦ Interactive Workflow Animation Preview

Step-by-Step Action Checklist

0 / 4 Completed
READY-TO-USE PROMPT TEMPLATE
Open Official Tool / Repo ↗
I am building a jailbreak-free iPhone automation setup using an ESP32-S3 hardware HID bridge and a Multimodal Vision LLM.
Please provide:
1) [ESP32 USB-HID Firmware Protocol]: Arduino/ESP-IDF snippet parsing serial commands (CLICK x y, SWIPE x1 y1 x2 y2 duration, TYPE text) and translating them to native USB HID digitizer packets;
2) [Vision LLM UI Navigation Prompt]: System prompt for multimodal models receiving raw iOS screenshots and outputting deterministic JSON actions (target bounding box, thought reasoning, and exact touch coordinates);
3) [Human-Like Anti-Bot Smoothing Implementation]: Python implementation of cubic bezier curve touch path interpolation, natural tap jittering, and randomized dwell times to avoid platform risk flags.
Use Cases & Tags:
E-commerce analysts automating cross-app price comparison and stock checks via genuine hardware-level touch inputsMobile QA engineers running robust cross-app end-to-end regression tests on production iPhones without enterprise certs or jailbreaksPower users automating daily check-instransit reservationsand routine morning information harvesting hands-freeAccessibility copilots allowing voice-driven hands-free navigation of native iOS apps for physically impaired individuals#esp32#airplay#usb-hid