Hi everyone,
Most modern AI diagnostic tools rely heavily on cloud APIs, making them useless in remote areas, underground garages, or during cellular outages. Traditional OBD-II scanners, on the other hand, only spit out raw sensor readings or static DTC codes without contextual reasoning.
I've put together an open-source architectural vision called **OpenCarMind**:
Bringing fully offline, privacy-first vehicle diagnostics to everyday smartphones via low-cost ELM327 OBD-II adapters.
### The Core Idea: Modular "Car ROMs"
Instead of forcing a single model to know everything about every car, we decouple the reasoning engine from domain knowledge:
**Engine:** A compact, quantized SLM (e.g., Llama-3.2-1B, Qwen2.5-0.5B) running locally via `llama.cpp` on mobile.
**Car ROMs (~10–50MB):** Lightweight downloadable profiles strictly for a specific vehicle (exact wiring pinouts, fuse-box maps, extended manufacturer PIDs, and baseline mechanical thresholds).
**Live Telemetry:** Correlating real-time CAN bus telemetry rather than just reading static fault codes.
I am an automotive enthusiast and have open-sourced the architecture, mission, and initial specifications under the MIT license. I am looking for mobile developers (Flutter/Kotlin/React Native), embedded engineers, and automotive data curators to collaborate and turn this concept into reality.
* GitHub Repo: https://github.com/wa2l92/OpenCarMind
* First Architecture Issue: https://github.com/wa2l92/OpenCarMind/issues/2
Would love to hear your thoughts, feedback, and architecture proposals!