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The barrier for me as a hobbyist is always learning the tools. Same for game design. Text is a better interface than learning CAD, KiCAD, Blender, etc. None of those things feel user friendly to people that don’t want to make a career out of it. To the point I usually just jump on fivver and hire someone to do a pair design session. Problem with that is cost sometimes, but also the difficulty of mobilization (find someone, agree on price, align calendars) and also the difficulty of changes because you have mobilize again to some degree, if some time goes by you may not even be able to reach the person. I also find it difficult when I want to do something unusual or non-standard. I usually know what standard is, and have decided I like something else instead, and I don’t really like a human critic of the decision when they don’t understand my reasoning. I find this happens in nearly all design; architects, engineers, etc.

I welcome this opportunity to pivot to a process I can control a bit more without having to really learn the tools.

by SequoiaHope3 hours ago|

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Ya that makes sense. I will say tho that for things like mechanical CAD, text is extremely limiting. But yeah the learning curve is real and you can’t learn it all. I’ve started trying a bit of LLM assisted mechanical design, but I am basically too skilled there to accept what it is doing. I want to keep trying tho, I like learning how these things can work.

And yeah I always imagined hiring a PCB designer to help but it always seemed like a lot. To have a reliable tool I can use any time I want is wonderful.

by a2ff6eeb03 hours ago|

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So, basically, in a few years, humans say what spectrum a device may operate in, AI figures out how to make it, and humans check that AI didn't fuck up?

Given, say, 4 more years (ie, same as time from initial ChatGPT to today), what level of spec do you think humans will be giving?

by SequoiaHope2 hours ago|

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Yes I think generative board design will become better packaged, trained, and validated, and that it will be common place for electrical engineers to rely more on these tools. Major vendors like Altium will add tools like this and their use will become commonplace in industry. For hobbyists, you will be able to specify a board design in natural language and have it completed with unprecedented ease. I’m excited to help my raver friends make wearable glowie things.

I find it hard to predict what technology will be like in four years, but the next major leap for these tools will be higher level system specification and integrated design. What you want is not a circuit board what you want is a product. The tools will bring multiple functions together and fully integrated iterative design will accelerate development.

Despite the many problems with the AI roll out I am fundamentally excited for tools which can accelerate our engineering development. We will build things much faster in 4 years than today. One year of progress will in some instances take one week.

I am particularly interested in how this might begin to accelerate change in heavy industry. With hope it will help us build fusion power reactors and high speed electric trains.

I hope we find the courage to support every person who for one reason or another does not ride this wave. We will have so much more to share, or to hoard.

by a2ff6eeb02 hours ago|

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Yeah. I'm expecting that we fully automate cognition in the medium to long run. I think it's inevitable.

With the advances in math, I'm also hoping we can automate fundamental physics. Just ask for the physics needed for better fusion, no need for human toil.

If we play this right, the AI can fully take care of all our needs, and reaping the rewards of what it does when we stop being able to keep up with the rate of automated discoveries.

Hopefully it's able to dumb down enough knowledge to keep entertained people who decide to learn after learning stops being a requirement for human advancement.

by idiotsecant1 hours ago|

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This is a big leap. So far LLMs are really good at turning training data into accurate results. The more data, the better. LLMs are very, very bad at making intuitive leaps based on the 'shape' of sparse data- a technique that is, to be fair, pretty rare in humans as well, but essential to progress. Maybe they can make up for it with brute force and the precision and breadth of knowledge that only an LLM can have, we'll see.

I don't think we've nailed the architecture that will allow things like generalized self directed training, yet, which is what would be needed for something like 'make fusion better'

by a2ff6eeb055 minutes ago|

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Are you sure about that?

They seem to be able to make intuitive leaps pretty well. They need to make the same leaps over and over, though, because they lack online learning, so the discoveries only persist after the next training cycle. Context only goes so far.

We're pouring billions into solving that, though, so I would be surprised if we don't get there soon.