Using LLMs/agents to do gap analysis and fill boilerplate doesn't rule out also reviewing the output and hand-optimising. That is how the tools should be used (if you aren't being a luddite like me and not using them at all) rather than click-and-hope vibe-coding.
The problem I see is that LLM use deters many potential contributors. I understand that in your use case this is not an issue since you prefer working code over theoretical contributors (as said makes a lot of sense), but I am noticing this in many projects that transitioned hard into an AI dependency. It puts a barrier to some people. If 99% of a project's contributions are via AI, is that project still alive?
edit: actually if you have the weird/exotic hardware you’re in the best position to do so as you can actually test whatever the coding agents would shit out.