Sylvain B.

The project I mentioned in my last post works great, but it definitely took longer than I expected to get everything tuned. So for my next build — a new controller for a spin coater — I tried a different approach. If you haven’t seen one, a spin coater is basically a machine that spins a substrate at controlled speeds so you can lay down a thin, even film. Commercial ones are expensive, so I built my own with a drone motor, 3D‑printed parts, and an ESC. Up until now I was driving it with an RC transmitter, but I needed proper ramp‑up/ramp‑down curves and accurate RPM control. This time I used Google Gemini, and it took me about eight hours to go from idea to a fully working system: firmware, UI, PID tuning, ESC calibration, and support for pro‑level spin profiles. I shared both the prompt and the generated code in this repo. 👉 https://lnkd.in/gWu98ejp But the real story here isn’t the project — it’s the workflow that’s starting to emerge: My LLM‑Driven Engineering Workflow: • I start by having the LLM help define the module’s specs. It’s like a design review partner that never gets tired. • Once the specs look good, I ask the model to write the prompt that will generate the code. • Then I take that prompt to a second LLM and let it critique and improve it. (It usually says “this is already solid, but…” and then gives genuinely useful tweaks.) • I run the refined prompt in my code‑gen model and save that prompt as part of the project. • And yes — the model always misses something. That’s normal. I issue follow‑up prompts and keep them in a “prompt log.” • The magic happens later: when a new LLM version comes out, I can re‑run the same prompts and instantly get a better implementation without rewriting anything. And this is where things get interesting. LLMs are about to change code reuse in a big way I’m realizing that the old idea of “reusing code” is going to shift. Sometimes it’s not worth pulling in a third‑party library anymore. If the model already knows how to implement a PID controller, for example, it can generate a clean, purpose‑built version that fits your architecture better than a generic library. That’s a big deal. Because it means the thing we reuse isn’t the code — it’s the instructions that generate the code. We’re moving from: • reusing libraries → to reusing prompts • sharing code → to sharing the recipe that produces the code • maintaining a codebase → to regenerating it when the models improve The prompt becomes the durable artifact. The code becomes a snapshot — something you can always recreate, refine, or upgrade later. This feels like the beginning of a pretty big shift in how we build software.

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1 commentaire

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