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Engineer uses open-weight AI coding agents to build PineTime smart watch face in hours
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A software engineer demonstrated rapid firmware prototyping on a $27 open-source smart watch using AI coding assistants instead of manual development.
This experiment shows how AI coding agents can lower the barrier to embedded development for engineers who lack domain experience. The approach trades some precision for speed, making it practical for hobbyist or exploratory work but not yet for production-grade firmware.
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AI coding agents generated a functional watch face from a reference image in hours, though with layout inaccuracies requiring manual correction.
The $27 PineTime’s open firmware, documentation, and simulator enabled fast iteration with AI assistance but revealed hardware limitations for complex designs.
Open-weight models on OpenCode provided a low-cost alternative to proprietary AI tools, though with higher token usage and less autonomous refinement.
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A software engineer used open-weight AI coding agents to build a custom watch face for the PineTime, a $27 open-source smart watch. The process involved feeding the AI a reference image of a Casio-style watch face and iteratively refining the generated code. While the AI produced a functional prototype quickly, it struggled with precise text positioning and layout, requiring manual intervention to correct overlaps and alignment issues. This demonstrates how AI can accelerate embedded development but still falls short of pixel-perfect accuracy without human oversight.
The PineTime’s open firmware (InfiniTime) and well-documented toolchain made it an ideal platform for AI-assisted development. The watch’s simulator (InfiniSim) provided a fast feedback loop, allowing the AI to test changes without deploying to hardware. However, the project also exposed the device’s hardware constraints: a full-screen background image took 10 minutes to transfer via Bluetooth and caused slow screen refreshes. These limitations highlight the trade-offs between rapid prototyping and performance when using AI to generate firmware for low-cost devices.
The engineer used open-weight models (Kimi K3 & K2.6, DeepSeek v4 Pro & Flash) on OpenCode instead of proprietary tools like Claude or Fable. While this approach reduced costs, it required more manual guidance to compensate for the models’ less autonomous refinement capabilities. The project’s success relied on breaking tasks into smaller, well-scoped steps and providing concrete feedback to the AI. This suggests that open-weight models can be effective for embedded prototyping but may demand more hands-on engineering effort than closed-source alternatives.
The experiment underscores the potential of AI coding agents to democratize firmware development for engineers without deep embedded experience. The low-stakes, hobbyist nature of the project allowed for rapid iteration and experimentation, contrasting with the slower, more deliberate pace of production-grade firmware development. However, the need for manual corrections and the hardware’s limitations indicate that AI-assisted development is not yet a drop-in replacement for traditional methods in resource-constrained environments.
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