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Very cool! Can you reveal how much this custom board cost you?
I built a project board nearly identical to this one a few years ago, and this seems about right. I don't think the displays themselves are included in the 130 euros. They can vary a lot in price depending on spec and supplier.
I sent in a larger, slightly more complex board to JLCPCB over the weekend and it was $175 for 5 boards with components and assembly. Shipping, tariffs (ouch), and sales taxes added another ~$100 or so.
I built a project nearly identical to this one so knowing the level of complexity required, I'm not surprised it worked. AI is indeed raising the floor. Even simple hobby designs were once out of reach, but now we can "YOLO it and build stuff fast without necessarily knowing about the details" as the author puts it.
That said, without experience it's easy to pierce the veil of "simple circuitry" by accident, just like in XKCD: Tasks[1].
Looking at preview of the author's next project at the end of the article, I'm not so sure the same strategy will work. The board is probably two orders of magnitude more complex. Without knowing the circuit details, I see many red flags (randomly placed ics and decoupling, likely wrong length matching, random thick traces indicating lack of foresight on power distribution, backwards connectors, the probably unnecessary use of BGA footprints, etc).
[1]: https://xkcd.com/1425/
This seems to fall into the same category I've seen with basically all agent driven software as well. That is to say, "Amazing that someone with no experience could do this, but not interesting at all in its own right". Call me when AI is laying out the next TSMC node.
As someone who does both hardware design (mostly high precision analog electronics) and software engineering, the difference between both fields in AI capability is striking. Models like Fable and Astra can provide lots of interesting insights into the design of specialty electronics when prompted correctly. Yet they can hardly put that knowledge to work if instructed to generate a schematic or lay out a PCB.
Of course, for readers here with a solid grasp on how LLM's operate it's not exactly surprising. Still, I feel this is exactly the kind of thing that drives home that the way we reason about (general) intelligence does not map cleanly to LLM operation, no matter how advanced the current models are.
If anything, it is surprising that the LLM is so bad at stealing a ready-made RP2350 design! There are plenty of easy-to-find tutorials like [0] and [1] that handhold you through the entire design process, and a simple search will give you dozens of design files for open-source boards.
At this level of board complexity you almost have to try to make it completely non-functional, so although it is nice to see it in practice I am not exactly surprised that a LLM with a decent bunch of assistance managed to do it. But there's quite a gap between the equivalent of poorly copying a "Hello World" from StackOverflow and making actual production-ready boards.
[0]: https://pip-assets.raspberrypi.com/categories/1214-rp2350/do...
[1]: https://jlcpcb.com/blog/how-to-design-layout-with-rp2350
It’s the training data. Finished work isn’t training data because parts are sooooo different, but maybe if the finished designs were properly labeled into the training pipeline it might work? Or the iterative work itself might need to be the training corpus.
Maybe it's not properly benchmarked.
A standard benchmark using kicad to create or fix electronic designs would go a long way in motivating and measuring improvement.
Would you be willing to share the full design files? Considering most of the PCB is well-trodden territory, it would be quite interesting to see the details what it actually came up with!
I'm still waiting on my order of 15 PCBs done with Sol - a mini-BT-keyboard that I hope will be comparable to the Psion 5 / Gemini PDA ones, using Kailh PG1316S switches. I still need to design and manufacture a housing (I do have a lot of hobbyist experience with this though), but the idea is to have a magsafe-connectable keyboard I can clip onto my Pixel and make use of the new Linux subsystem (or Termux) and do some productive stuff on the go. It's just been manufactured by JLC but not shipped yet. Funny enough, production itself was ~$70, then $80 for shipping, taxes and fees (tarriffs?).
Instead of directly generating Kicad, I chose to let it write "generators" in Python that output Kicad (S-Exps!) pcb and project - with mixed results - but I can generate a version with/without per-key Neopixels and customize certain parts of the board, including debug-stuff. I also _think_ this makes it easier to resume working on this in new sessions but not sure. Otoh, I never actually looked at the Python (vibed...) so it's still a lot of hard-coding.
I also landed on and used KicadRoutingTools after trying FreeRouting, combined with some minimal "auto-placing" code for larger ICs and such that are placed with certain constraints. My routing runs are way slower though - on the order of minutes.
I always wanted to try atopile - that might be a good match for AI/PCB work. The only thing that really held me back was the lack of routing.
Unfortunately all public open source work halted 6 months ago and they pivoted to a browser based IDE product to monetize: https://atopile.io/blog/atopile-v16
Thanks :(
I followed them for a while before the AI craze started. It looked promising back then, but I wasnt willing to invest time and attention back then on something so new.
Good thing I didn’t
That sounds like a cool project both for your pixel and the keyboard! Keep us posted how it goes!
AI is exceptionally good at RP2040/RP2350/ESP32-based boards, we've ordered ~15 unique designs, mostly RP2040-based that were designed and autorouted with AI using tscircuit and usually OpenAI models.
Currently the major mistakes that AI makes: 1) Improperly rotated connectors (backwards USB port) 2) Endless routing loops for complex boards 3) Really huge/unreadable single page schematics
I'm extremely bullish on AI for PCB design, but there is a huge, huge tooling gap. We basically need to soup up all our collective design rule checks so that AI can work in corrective loops. It needs to check for things like "opamp schematic layout conventional" and "trace width closely matches reference design for chip". One thing that makes me super excited is supply-chain resiliency when chip/subcircuit-swapping becomes super easy
The machine designed the circuit board in two hours, which was impressive, and the human understood it in never, which is becoming traditional.
This LED star PCB was 95% generated by Codex: https://www.youtube.com/shorts/8a52LjovhcE
Was non-trivial (e.g. days and days of cumulative work) but worked with Codex to build up a parametric generator where I could adjust the star size, number of rings, leds per ring. It did all the LED-to-LED routing, panel layout with mouse bites, placed decoupling caps. At the time Codex wasn't as good at spatial reasoning, I expect that Astra would do this much faster.
4 x layer PCB with GND and 5V inner layers. WS2816B LEDs (not available in strips yet). 3D printed a frame so that two stars could be opposite each other (video shows only one side). ESP32 S3 on a 3.3v to 5v shifter board sandwiched in between. ESP32 wifi antenna pokes through and sits on a keep out section. Received control signals from a central controller via ESP Now.
Bought to us by Fable 5 and the more-experienced colleague who foresaw potential issues.
If it's functional: that's a breakthrough.
But: the PCB itself is not looking beautiful in terms of routing and a lot of schematic details yet. Horrible to to be more honest. But that's a minor issue.
I'm not overly shocked. 90% of creating PCBs is copying reference schematics from vendor datasheets and connecting them. That's LLM territory. This mostly failed for now as they just did pdf2text and killed all documents.
The last 10% are brutal (HF, EMC, high frequency multi-layer designs, etc.). But I managed LLMs to automatically create ngspice models using vision onto datasheets.. lol
Shameless plug for the slop coded CLI I use in my workflow: https://github.com/akiselev/datasheet-cli
It just sends the PDF to Gemini Flash and asks it to extract semi structured data (semi structured in the sense that it's not fully structured extraction, but its a JSON file to feed into the next step of the LLM design process).
Works pretty well in general, though I should update to the latest Gemini version.
That's pretty cool! How reliable has the footprint extraction been?
It would be an interesting step to get Claude (or other LLM) to do order the PCBs itself on the JLCPCB website.
We're nearly at the stage where you can describe what a device should do, let it design the circuit (using available components on JLCPCB), design a PCB and enclosure then order PCBs, assembly and 3d printed enclosure and get it sent to you for final assembly.
There's a plugin for Kicad https://github.com/bouni/kicad-jlcpcb-tools
I've been working on several PCB projects, and agents are very helpful for automating many parts of the workflow. They're excellent for schematics. Complex routing, though, is still a human's job for now; I've watched Freerouting fall on its face many times. If you're doing six-layer high-speed DDR routing, AI won't solve that for you today.
However checkout EDA tools https://github.com/l3wi/claude-eda/ https://github.com/drandyhaas/KiCadRoutingTools
Yeah I've built about a dozen boards using GPT-sol 5.6 and my favorite is my Pi CM5 carrier board, to replace a laptop board in an old Sony Vaio p laptop. https://x.com/kanwisher/status/2089629411364745351?s=20
In our company, we are doing high speed PCB designs with FPGAs, memories, ADCs, DACs etc. We are not yet using AI much in our PCB design workflow because our designs seem to be too complex for the AI solutions we have tried. Are there any areas where people are successfully incorporating AI to their PCB design workflow, even with complex, high speed designs? Schematic/layout review? Routing? Footprint creation?