Interceptor | Network Debugger vs Nara Mobile AI Lab

Interceptor helps you inspect HTTPS traffic for selected hosts on your own iPhone or iPad. View saved request and response headers, cookies, and available JSON bodies, and inspect supported service tokens. The app uses an iOS VPN configuration to direct selected hosts through a proxy running on your device. It does not provide a remote VPN server, change your public IP address, or protect public Wi-Fi traffic. HTTPS inspection requires you to install and trust a locally generated certificate authority certificate in iOS Settings. At startup, Interceptor explains what it can access and requires agreement to the current data-use notice before you can access the main interface. Agreement alone does not start capture, install a certificate or VPN configuration, or request notification permission. Captured traffic can contain sensitive cookies, authentication tokens, account identifiers, and request or response content. Use it only with accounts and devices you are authorized to inspect. Capture is limited by the app's configured hosts; service changes or certificate restrictions may prevent capture. New capture records are stored in the app's local shared storage. New certificates, private keys, and saved tokens use Keychain items with iCloud Keychain synchronization disabled. Interceptor does not upload captured data to a developer-operated server, sell it, use it for advertising, or automatically share it with third parties. Requests still reach their original destination services. Data you choose to copy, earlier iCloud Keychain copies, and operating-system backups are separate from this local capture flow. You can review the notice and withdraw consent in Settings → Data Use & Consent. Withdrawal stops the VPN, disables automatic reconnection and clears capture notifications, and returns to the required consent screen until you agree again. Withdrawal does not erase saved records, tokens, certificates, or private keys, or remove an installed certificate profile. Clear saved request history from Home. Optional local notifications announce captured records without including tokens or captured headers and bodies in the notification.

Nara Mobile AI Lab is a developer-focused test bench for exploring Apple built-in AI capabilities directly on iPhone. The app helps developers, builders, and technical teams understand how on-device AI performs in real app workflows before investing in a larger implementation. For the most accurate test, turn on Airplane Mode before running experiments. This helps verify whether results are coming from the device’s built-in AI capability instead of a cloud service. With Nara Mobile AI Lab, you can test practical AI workflows such as: Speech-to-text with structured JSON extraction Vision and OCR extraction from photos Prompt response testing Mock tool and callback execution User-defined tool function testing LocalKnowledge search testing Object detection using preset Core ML models Image classification using preset Core ML models Prompt to Image using Image Playground Latency, validation, manual scoring, and notes Exporting results for review or project planning The app includes built-in templates and one-time page hints so you can quickly test common developer scenarios without building a full prototype first. You can inspect raw extraction, AI output, validation status, benchmark timing, manual score, and notes. Nara Mobile AI Lab is especially useful for evaluating whether on-device AI is suitable for privacy-focused apps, offline-first workflows, mobile automation, field tools, form extraction, image analysis, local knowledge search, and AI-assisted business software. Core ML Vision Tests Nara Mobile AI Lab includes preset Core ML model tests for object detection and image classification. The current preset models include YOLOv3TinyFP16 for object detection and FastViTMA36F16 for image classification. These tests provide practical developer examples for reviewing on-device model behavior, latency, and output format. Tool Calling Tests The app includes mock tool-calling tests so developers can evaluate whether the Apple Foundation Model chooses the correct function, passes reasonable arguments, and returns a useful result. Write-style actions are mocked for safety, allowing the app to demonstrate tool-calling behavior without performing real external changes. LocalKnowledge Tests The app includes local knowledge search testing so developers can explore how user-defined local information can be used in offline-first AI workflows. Test data stays on the device unless the user manually exports it. Nara Mobile AI Lab is a testing and learning tool. AI output may be incomplete or inaccurate and should always be reviewed by the user.