Last month at a SundAI hackathon, my team built a prototype for an app called iClaw. The goal was to develop an AI agent using Apple Intelligence. I've since continued hacking away at this idea when I had time, and now I'm releasing it on https://geticlaw.com with code on GitHub.

Apple Intelligence is a really poor model choice for an AI agent. It's not great at extracting information (it often injects preambles like "Here's the page..."), it struggles with following directions, and it gets overwhelmed when you ask it to choose from more than 3 available tools. But it's also a great choice because it comes pre-installed on many Macs without any configuration. It's also incredibly fast compared to just about every other 2-4B model I've run on my MacBook Air.

iClaw is very experimental and will make mistakes, but it's designed around safety. iClaw lives in the App Sandbox where it only has access to the permissions you grant it. All tools calls that create or delete (files, emails, calendar events) require explicit consent. You can also fully disable any tool, so they can't be called.

It was fun pushing the limits of what the 3B Apple Foundation Model (AFM) could do. I trained a LoRA adapter for better instruction following, and to "learn" a DSL for rendering custom widgets. I built, re-built, and re-re-built a 40+ tool library routing system using text classifiers and a multi-step decision framework. I built a Safari Extension that allows iClaw to access what's on your browser (I envision use cases like data extraction, form filling, or agentic navigation).

In all honesty, iClaw isn't ready for prime time. My ultimate goal was to distribute on the Mac App Store, which I perceived as a "safety & trust" signal. iClaw tries to do too much, so it easily gets lost. But I'm also surprised at just how much is possible, even without a purpose-built model. Check it out at https://geticlaw.com and GitHub, and let me know what you think.