I’ve been working on an open-source project called NavixMind. Honestly, I built this because I am fascinated by the rapid evolution of "High Agency" AI. I love the direction desktop tools like Cowork, Moltbot, or Claude Code are taking, and I wanted to see if we could bring that same magic to a mobile environment.

It’s an exciting time to be building. I wanted to explore what happens when you give an LLM actual "hands" on a mobile device—a way to manipulate files and execute logic right where the data lives.

The "Local Runtime" Advantage: Most mobile AI apps run on a "Remote Runtime" (in the cloud). While they are amazing for chat, bringing the runtime to the device unlocks a new class of workflows that feel almost magical: - The "Fit-to-Size" Loop (Iterative Feedback): "Compress this video so I can send it via email (under 25MB), keeping the best possible quality." --- NavixMind: Runs an ffmpeg loop locally, checking size and adjusting bitrate parameters in real-time until the condition is met.

- The "Slicer" Loop: "Take this long recording, split it into 10-minute MP3 segments, and zip them." --- NavixMind: Does it in-place, on-device. No massive uploads required.

- The "Briefing" Loop: "Check my calendar. Generate a separate PDF summary for every meeting tomorrow." --- NavixMind: Accesses local APIs and generates files instantly.

Self-Optimization: One piece of additional magic is Self-Improvement. The agent can analyze a conversation and update its own system prompt to "learn" from the interaction. It doesn't rewrite the app binary (the code is static), but it evolves its own instruction set. This saves iterations the next time you ask for a similar task.

The Stack: Flutter (UI) + Chaquopy (Python) + FFmpeg. In theory, a similar stack could be built for iOS in the future, but I started with Android for the flexibility. The code is open source (Apache 2.0). It's a passion project, so expect some rough edges, but I’d love to hear what you think about the architecture.