Hey Matthias, I'm curious whether/how you validate copilot output suggestions. Software bugs from generative AI can at least be reasonably tested for by developers, but it's certainly much more difficult to catch subtle bugs in hardware.
It’s something we spend a lot of time thinking about. On one side we want to prevent hallucinations but on the other we want to keep the LLMs creativeness
We are experimenting with a bunch of different approaches here already
Preventing hallucinations and therefore improving correctness is fairly simple by providing the model with factual data sources which is something we are working on
The challenge is to balance that so that the model is also still creative when you want it to…but we are making progress here too
If you are interested ping us in our slack and we can add you to our beta tester channel to get access to our experiments in this space
Who is the target audience? It seems like experienced EEs would know most of this stuff. Complete beginners would just not have sufficient context. Is there some middle of the road electronics hobbyists that you are targeting?
We have found engineers across the spectrum to find huge value
From the engineers that design the motherboard of the phone in your pocket to students and hobbyists
Use case go from learning/explanation to brainstorming, component research, figuring out the match for signal filters, to triaging of issues and so on.