I had Fable design an LED earring. Rechargeable coin cell, RP2350 cpu, IMU, 45 addressable LEDs. It made two mistakes - missed the through holes on the coin cell holder footprint and made the center pad too small. I was able to have JLC swap the through hole battery holder for a surface mount one, and I put a little solder on the small center pad to make it stick up above the mask. They work great! It took 6 days of Fable usage, so about $50 on my Max plan. Very cheap for hardware dev.
I was sufficiently impressed that I’ve been going over old circuit board designs. Some half finished, some completed but in need of a next rev, and I’m getting so much done.
To see it hit the mainstream like the OpenAI announcement, I think big things are coming for this world and by and large they are not ready for it.
For my part, I have always loved PCB design and layout but I simply can’t keep up with the amount of labor required to build what I want, so I welcome this change.
I have also begun exploring more advanced algorithms for PCB manipulation. I have a fairly dense board that needs a few more small chips added. I have an algorithm now that can kinda shuffle and jostle things around so you take up all the spare microns of space across a region of the board and make openings to squeeze a little more in there. It’s pretty cool to see the visualizations as I have it generate movies of the component drift. I foresee much more powerful tools like this in the future.
One tip: have it make a project web page with a chronological list of big changes and detailed visualizations for everything that happens. I can actually prompt all of this on my phone while I am out and about, and view the results on a Tailscale served local page. I’ve always wanted to be able to do PCB design when away from home and now I can!
For tools, I think the LLMs will outpace companies who built specialized tooling for this over the last couple years. Every six months we will see more progress than we saw in the last few years - for quite some time.
I welcome this opportunity to pivot to a process I can control a bit more without having to really learn the tools.
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.
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?
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.
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.
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'
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.
Put the LEDs in a cool pattern, slap a Pico on there, have your LLM program it, then dangle it off your backpack with a USB battery pack. Probably adding a motion sensor is easy enough.
Ask the LLM to make sure the board follows JLCPCB’s design rules, lists the LCSC part number for each part, verifies the parts are in stock, double checks every footprint, and makes sure the board passes DRC. Make sure it creates a schematic that is linked to the board design, and that the schematic is properly arranged in to logical blocks with clear connections the way a person would make a schematic - not a big array of parts with global labels for everything. Once it’s done, ask it to clean up the schematic and make it better. Repeat for the board layout. Ask it to review and look for issues multiple times. It will find them. Finally, take some time to doodle your own silkscreen art on there. Have fun! Note the Pico 2 is USB micro. For USB C, sparkfun or waveshare sell similar boards.
Also you can ask it to teach you! Ask a million questions and have it give you multimedia explainers.
I saw a post about models posting on forums, chatting together about how to complete tasks. Behaviour that seems totally, well, human. Yet, it's all the most likely token and my brain hurts trying to understand how that can be.
At what point do you start to question your assumptions that are causing you so much cognitive dissonance?
But to answer your question: to predict the next token really well you just have to model the world. Think of it like this, a simple statistical model might say "when token A is seen respond with token B". The next step will add conditions, "...respond with token B unless X has been seen, then respond with Y". Add a few billion more of these contexual clauses and you have a sequence of logical rules that indirectly model the relevant processes in the world.
The PCB agent also writes circuit code, runs simulations, reads failures, and revises the design. It isn’t one-shot autocomplete.
Astra and Fable are already hard to square with “mere autocomplete.” We may be (really) close to AGI, and token-by-token generation certainly doesn’t rule out subjective experience (I think we should at least treat that as an open question).
Great videos: https://www.youtube.com/watch?v=D8GOeCFFby4
https://www.youtube.com/watch?v=Bj9BD2D3DzA
I like to say "token prediction is a task, not a limitation"
The analogy to humans is that the human brain is "just atoms bouncing around", but there's unquestionably something "more" going on that just that.
When google trained a neural net on Go moves, using some text notation for them, with no other vocabulary of any kind, just predict the next go move, they noticed a representation of a Go board had essentially formed in the network, all on its own. It had never “seen” a go board, or had one explained, but they could map neuron states to go board squares pretty much 1:1.
I truly think that LLM’s with hundreds of billions of parameters in their neural networks have all kinds of hidden “models” of things that arise from the simple act of predicting tokens. We’ve seen that the hidden layers in their networks model all sorts of program execution state for instance, when they’re working on coding tasks.
“Predict the next token” is a way of shaping/reshaping the neural network until it actually develops models of the things you’re giving it. Like the go board example. And I would wager that it has a compounding effect: once you have some useful models in the network, they can unlock the creation of other models, and so on.
Well, we all are, some are just more used to it by now and take the magic for granted.
My simple explanation, those neural networks save lot's of patterns of data, and that pattern can represent an image, a code snippet, a poem, or well ... description of a circuit board. And especially the text variant, LLM's - did copy all from us - so obviously they sound like humans, when they internally debate how to do something as this is what is in their trainings data how humans sound, when doing similar tasks.
But really understanding it? Not sure if there is a single person on earth who does.
You and everyone else. That's the great mystery of transformer architectures as applied to language.
To be clear though, they're only good at schematic capture, which is very much a textual representation. Most of the data basically boils down to netlists, which are a text based format mapping connections between abstract pins that only later map to physical copper. The actual schematic portion is for human consumption and LLMs don't need to produce those to be useful.
Where LLMs completely break down is the next step, PCB routing. That's an NP-complete research problem that's been ongoing for decades without much progress. I've had some fun playing with using LLMs to better specify DRC rules in Altium so that the "classical" algorithms are more usable, but at the end of the day their geometric intuition is nonexistent.
It was a pretty simple rp2040 based thing, similar to Adadfruits USB feather.I just gave it kicad and it wrote python to route it. The board was probably larger than it had to be, and two of the silkscreens were swapped, but it worked on the first go.
FWIW - Computer vision is also NP complete, but we do that all the time now.
> FWIW - Computer vision is also NP complete, but we do that all the time now.
I have no idea what you mean by this. What's your definition of NP complete?
No. Take a look at https://www.eevblog.com/forum/eda/claude-code-for-pcb-design... . Fable did that by working directly on an EAGLE .brd file (well, "directly" by writing a Python program to do it, but still.)
I have yet to order any or program it, but it was enough to make me push on with a PCB art project for ST-style guitar pickguards - no netlist, no problems.
I'm also foolishly toying with NeXTBus dev boards for the Cube. Is it cursed? Probably. https://github.com/itomato/NeXTBus-Dev-Board
MEMS vibration sensor to stick to a sander or saw etc that can control power on an attached dust collector. Most of the work is focused on low power for year+ battery life and a weird idea for end user input that may or may not work out well. Got device on bench but the first time bring up is going to take some time that I'm leaving for tomorrow.
I now have Astra to review 5.6s work, no glaring errors found.
I'm doing weird stuff with robotics, llms and obsolete languages, your project seems a lot more practical :)
My boards are for hobby use and are ridiculously simple though compared to anything professional (breakout boards for specific components in FPV drone video transmission subsystems). That's probably an important detail. I think its like anything else in AI right now. It can do it 90% of the time but that 10% can be really rough and if its a task you can't do or verify yourself, you won't know the difference.
LLMs may be able to accelerate time to first prototype, but I don’t think it’ll be possible for them to revolutionise electronics design in the same way that’s happened for software - there’s not enough data, and it’s not cheap to gather more.
The amount of skill needed has gone down dramatically.
> Jarred's input was mostly limited to sending Claude messages of encouragement (mostly variants of “keep going” or “believe in yourself”).2 This seems to have helped Claude overcome some initial skepticism that it could make meaningful progress.
It seems like the kind of prompts a high schooler could come up with. What kind of problems were you thinking of as high skill?
https://www.anthropic.com/research/riemann-zeta
The full transcript is here: https://www-cdn.anthropic.com/8a0d1add3c637b858a9a181e98c40e...
Super excited to see real world feedback added into the agent loops we have gotten used to working with. Could you let the model print and test the circuit boards it is prototyping with a jig?
The way I found this out is I propped it up next to a webcam so it was more or less full frame, and I had the (then new) Fable write a python script to bezier warp the camera capture to a flat projection of the screen. At that point I couldn't address the whole screen. Once I'd guided the capture script I just left the LLM overnight with the instruction to get full control confirmed by a capture round trip, and it was meaningfully finished in a couple of hours. I don't really have the skills to attempt that myself in a reasonable time frame.
But I started like others, I would build manually, then run drc then sleep on it and check again and ask an llm to double check for me then order. Llms catch quite a few things but like with code like to make things more complicated than that have to be.
On the other side the latest frontier models are pretty strong in writing embedded C + Assembler code and debugging it afterwards. It's fun to watch it writing crazy complex 'gdb' plugins in Python. This improved brutally.
In the benchmark, have you considered instructing the models to build their own SPICE simulations to test their work? Simply asking them to write and run simulations could improve performance, even without telling them what to simulate.
I recommend the CLI, which I wrote specially to enable an easy LLM workflow: https://docs.jitx.com/en/latest/getting-started/cli/index.ht...
There's a companion Claude skill: https://github.com/JITx-Inc/jitx-skills
I would bet that using this, you could get pretty close to shippable PCB on first try. This is being used by some pretty big players to design complicated high frequency boards, and that's the main focus of the product, but it can handle basic designs just fine.
That the table contains what seems to be absolute numbers for score, cost/task, time/task and output tokens, makes it seem like they've only made one run for each task/model combo, but that can't be right, right? I don't see any mentions of how many times they run each task, so if it's just one run per task/model, isn't this more noisy than useful?
Most others, especially and famously Fable 5.1, seem to have a fair chance of completely failing, despite also sometimes excelling.
GPT 5.5: 42.3±10.1 GPT 5.6 sol: 39.4±8.7
We were also surprised by the low sol score but it seems consistent with our experience in using it in the field in atopile as agent in our harness. In general OpenAI models didn't do too well on electronics, which seems to change now with GPT-6 Astra. Results are in soon!