Show HN: Copperhead – Cursor for circuit boards
copperhead.sh
copperhead.sh
Flux.ai being the incumbent Silixon based out of the UK have been showing demos Quilter and DeepPCB for layout The recent OpenAI demo...
I went the other way and pre-designed sub circuits with a set of rules that allows simple merge assembly, unfortunately I didn't have the time or budget to continue with it, but I took the constrained approach as with hardware, things can't be 99%.
The one thing I will say, is that my experience of hardware engineers is very gatekeepy.
Any questions about the use of AI for any form of automation on forums/Reddit get shot down instantly and the OP's told 'skill issue' more or less. In my opinion, just as with software, most work is simple and should be automated, there will always be a need for the real engineering when it comes to high specification work, high volume optimisation and hardware debug (although Claude code is pretty handy at driving a Jlink)
Stay away from Reddit and forums if you want to see where the open minded people are looking. There are a lot of communities where EEs are experimenting with newer technologies and sharing their results, with realistic appraisals of what can be done.
You also need to understand the history of autorouting technology for PCBs. Autorouters with auto layout have been around in many forms for years. Their output never compares to a skilled EE doing it manually. The gap between autorouter output and skilled operator output is even bigger than the gap between a good developer and average AI codegen slop.
I’m interested in this space, but even the Fable/Astra or dedicated tools like Quilter, Flux, and others have a very, very long way to go for boards more complex than simple hobby boards.
I do think this is going to be good for hobby people doing simple PCBs though
https://en.wikipedia.org/wiki/Travelling_salesman_problem
While physics informed models do exist, they are still going to burn a lot of compute to generate failure modes people didn't know were possible. =3
If anything, it's closer to the bin packing problem. The cost function isn't expressed in terms of lowest cost between nodes, but instead fitting all of the traces with the lowest board layer count while still meeting stuff like EMI crosstalk guidelines. Then there'll be some traces that are very constrained (think DRAM or SERDES links) that have to be length matched, but those are pretty much invariably point to point links that already are heavily automated.
Traces being, say 20% longer than they need to be for an optimal solution doesn't really change the performance of the board in the vast majority of cases since they'll be some of the lowest resistance components of the netlist anyway, as well as the fact that the PCB is normally oversized for the number of traces needed as it fulfills structural/mechanical needs as well as the netlist needs.
And on top of that, because the optimal solution probably is NP-hard, once again, the industry doesn't look for optimal, only good enough. If layout techs in Altium were regularly solving NP-hard problems, computer science would be a different place. The only goal I see is to meet that bar without hiring layout techs.
Impedance matching requires you know what you are doing, and Altium will only take you so far. For the price, Altium/Protel has always been a poor deal for what you get software wise.
Even free Qspice (a more modern Analog Devices LTSpice) is a far better option:
https://www.qorvo.com/design-hub/calculators-simulation/qspi...
>the industry doesn't look for optimal, only good enough
Indeed, that is why your iPhone still weighs 3.7 kg. =3
Pretty much every high end board is designed under Altium. QSpice is a different tool for a different purpose.
And like I said, those few cases were the length actually matters tend to be heavily assisted with automation as it is.
> Indeed, that is why your iPhone still weighs 3.7 kg. =3
I guarantee you that an iPhone board isn't an optimal solution, simply good enough.
DFM almost certainly guarantees an iPhone is both form, and cost optimized. Jobs was very clear early on in the product development that the EE work had to be miniaturized due to the energy density possible setting volumetric minimums on the battery technology at that time.
> simply good enough.
A common philosophy for people that make cost optimized low-end products. Some people love their 3.7kg phone, as it comes with a stylish shoulder strap. =3
Optimal means there aren't any optimizations possible remaining.
Are you seriously going to suggest that there aren't any potential optimizations left in the iPhone's mainboard?
I often recommend folks get a mini thermal camera for their phone... as inspecting for issues in electrical, hydraulic, or mechanical systems gets a fair bit easier/safer. =3
It is a named problem, and can't be misappropriated like the $9Tn worth of FOSS code "AI" scraped and compacted for isomorphic plagiarism tokens. =3
Auto-routers have been around far longer than even smartphones. Somewhat functional in the trivial problem domains, and always useless where design choices mattered.
Best of luck solving a named problem. =3
If that could solve NP-hard problems, computer science would be a very different field.
The LLM would learn the heiristics (example: data lines first, power lines later, etc.) and would request the autorouter to do that routing, then take the image output and request a different part (depth-first). If later no routing is posible with that configuration, after some retries it could try another way.
It wouldn't solve complex boards, and engineers are always needed for short comings (and even if not, for research), but I wouldn't say this is something different than poetry, music or pixel art, LLMs can imitate although most of the times in a uncanny way.
https://en.wikipedia.org/wiki/Travelling_salesman_problem
While physics informed models do exist, they are still going to burn a lot of compute to generate failure modes people didn't know were possible. =3
Still, if LLMs are capable of writing working code, art and solving math problems, they qre definitely capable of doing suboptimal routing (with some algorithmic aid)
That is the leap in logic, as an LLM doesn't think/create/feel or understand. It simply copies billions of patterns, and finds the closest fit.
> writing working code,
You mean a compacted collage of stolen codified work from real people. It will require a continuous parasitic relationship of user data to remain coherent. Given the firms have already stolen everything, it is unlikely to improve much as weights are refined.
> art
Again, a statistical output of salient feature clusters mimicking a humans output is not a creative process. For example, a glass-blower only sees the glowing art-form in its true state before it is annealed into its final form. Or a painter undergoes a creative process to find meaning, and abandons the work when there is nothing left to add.
Emotional projection onto an algorithmic output is simply recognizing the millions of peoples aesthetic choices stolen, and compacted with some degree of lost granularity. It is peoples art forms, but no creative thought or intent behind the results.
> solving math problems,
LLM are very good at context search, but require persistent data streams to mine for weighting relevant vector proximity. Again, everything that could be stolen, has already been scraped.
Only user input data remains in a sea of slop, and the process is degenerative given most human chat-users appear to be losing 17% cognitive function. As people discover the intelligence campaign against users is not in their interest.. paying companies to rob you and your friends makes less sense.
Being a sentient turnip, I am probably unaware of such things. ymmv =3
It is not, it doesn't matter if a brain's neurons or a prediction algorithm managed to get the solution if it has done so in a (sort of) reliable way.
https://en.wikipedia.org/wiki/Duck_test
Errors are made by both machines and humans, so that's not a differenting factor.
What is clear is that the human element will be more valued on art as time goes on.
LLM are very good at brute force context search spaces, and people see patterns in nonsense even when it is nebulous. Similar to the Newton's Apple story people are fond of telling each other. =3
We all just muddle through life, predicting the next token. That's it. That's all there is. And it's enough.
The same reason every human understands how Einstein brushed his teeth in the morning. Best regards =3
This is laughable when you consider that semiconductor design is the least "trivial" problem domain, and it happens entirely in RTL. There's basically zero human intervention when it comes to layout: even floorplanning is being automated nowadays.
https://en.wikipedia.org/wiki/Clock_domain_crossing
> floorplanning is being automated nowadays.
Procedural generation or parametric design is not the same, as the behavior and constraints were rationally engineered by people.
LLM would simply look at the outputs during training, abstract it as looking similar to a potato-chip factory, and generate a nonsense answer some fool assumes is appropriate.
Humans are wired to see meaningful patterns in chaotic systems even when they aren't really there. Neuromorphic computing may create real "AI" someday, but it almost certainly won't be from LLM cults. =3
We can find local optima, but there's no real way to prove a given design is the best of all possible designs. We can only find designs which work and meet the given criteria.
One can prove that a design is correct, but it requires the same class of compute as an LLM to properly simulate. Circuit boards are hellishly complicated once you start simulating EM and RF responses.
As for how a good engineer does this, it's the same way pilots can fly helicopters: intuition. It's a skill built up from lots of reading the rules and theory, and lots of practice. One gets a feel for how the various fields and energies are moving.
Which, when you think about it, is just about how neural nets learn, isn't that neat?
I think the real answer here is that machine learning is absolutely capable of producing satisfactorily correct circuit boards. In principle. But I think the problem space is far too large for today's ML techniques and verification/iteration is just way too expensive. Maybe in another few years, I just don't think we have enough compute for it yet.
I don’t know why you’re trying to make this so hard.
The problem is NP hard whether I do it or a machine does it.
I can still do a good job despite being NP hard. I believe a machine with enough compute will be able to, too.
LLM don't think because it is not real "AI", and simply conflates every isomorphic vector search proximity output.
Auto-routers have existed for decades, and they do work in the trivial design cases... but are almost always a liability where design choices matter.
Best of luck =3
NP-hard is still NP-hard... and named problems are rarely solvable with naive approaches. Best of luck =3
Where are these communities?
As for Reddit, there's a lot of "bury head in sand and problem will go away" behavior when it comes to AI. People are understandably worried about their jobs, but they're certainly not doing themselves any favors by downplaying AI or looking for "tech niches where AI isn't yet."
Copperhead is very interesting. I do some embedded systems consulting on the side, but I've never enjoyed PCB layout very much. I briefly investigated using AI for it the last time I had to do a board, but it wasn't very helpful.
I manufactured both PCBs via PCBWay (Claude also red all the specs and configured the constraints accordingly) and both work. I'm pretty impressed, especially because I've never done a PCB myself and didn't know I could.
What I was doing manually, co-working with Claude, was placing and routing (I was doing those via Kicad, then saving, then Claude was re-importing my work in his py files).
"Nothing commits without its gate."
The board houses may have questions for you that may be difficult for you to answer, but theoretically you could answer those with AI and get fully assembled boards delivered right to your doorstep.
we're also building a design house around it to close the loop with fabs, handle DFM questions and eventually let you hit "order" and receive fully assembled boards!
Look at jlcpcb or other Chinese assembly houses. If you stay within their component library it is dead easy, just upload your gerber and BOM, and you can have assembled boards within a week or so. Slightly more difficult if they have to order parts for your BOM, but still not really that hard.
Go look at the library, I can’t really tell you if it is constrained for your use case.
Its maybe more hands on than you want it to be, but you mostly just need to supervise the work. But yes, the CAD outputs go directly to the JLCPCB ordering form, or whereever you fab
It is a good idea to consult with a contract manufacturer engineering team early, and stop wasting peoples time including their own.
I have also seen people re-brand China pad-printed generic products, and claim they needed investor help buying a shipping container full of cheap stuff or outright e-waste. Careful, especially if you see multiple versions or the same product pop up at the same time. Best of luck =3
In the end is the PCB design the bottleneck to create new devices or is it mostly about available components and their price?
There are already millions of vendors all over the world making infinite variations of PCB designs for the same/similar product. You can hire a design shop in China for peanuts to make a variation as well.
Ultimately the bottleneck is the consumer does not care for infinite variations. The vast majority just want a "top rated product" at the lowest price. Which is whatever Amazon sorts their results as or whatever the Jones are buying that they need to keep up with.
The biggest driver of "unique product" (not just the 9 millionth clone product) commercial success is marketing, it is branding, it is finding a unique niche, it is timing, it is existing social & business relationships to drive sales.
https://github.com/deadbringer17/Copperbrain
They have similar names, seem to solve similar problems and both have first commit within a few days in July. One is Python and the other Typescript though and they don't seem to be forks.
Goal is to make it easy for users to remix circuits, like say you want to make a mod for a retro video game system, you can fork off of an existing mod and just change it, and we guide you through getting it printed at pcbway or jlcpcb.
I've imagined a chip running as slow as python.
I tested over the weekend and Astra can
- create and modify schematics from instructions - place components and route traces
out of the box, just via normal 'computer use'. I'm not satisfied with the routing but it isn't terrible.
Software moves so fast that its actually an anti feature. Libraries and SDKs measure lifetime support in years, but industrial hardware support *at minimum* is a decade. A lot of these new startups dont understand that established industry wont use them unless they known this shiny new system wont ve abandoned in a few years.
On the other hand, softwate devs trying to quickly make a product have something to get them going.
Yes yes, I know, leagues apart. Still though... One can dream.
Do it have extra feature? Speed? Ease of use?
I understand—these days, there are more reasons to vibe a landing page than not. To many, however, this page is going to be their first encounter with the project. While it sure is shallow to judge a book solely by its cover, in this case, the cover just blatantly screams "we haven't even tried"... Can you really blame people for not looking beyond it if even its own creator gave it a similar amount of attention? Especially in 2026, when everything preys on your attention and generating plausibly-looking low-effort projects has never been easier?