How Diode Is 10x-ing Hardware Design

1. 0:00 – 0:22

   Intro

   1. DHDiana Hu0:00

      [upbeat music] I'm excited today to welcome the founders of Diode, Davide and Lenny. They just closed their Series A for 11.4 million led by a16z. And you guys just went through the batch just a year ago in the Summer '24 batch, so pretty quick growth.

2. 0:22 – 0:31

   What is Diode?

   1. DHDiana Hu0:22

      What are you guys building?

   2. DADavide Asnaghi0:23

      Diode is using AI to automate circuit board design. You can think about us as AI-enabled design shop for printed circuit boards.

3. 0:31 – 0:51

   Customer Base and Early Growth

   1. DHDiana Hu0:31

      Who are the customers that are using you? So it's been a pretty rapid growth within just one year.

   2. DADavide Asnaghi0:35

      Yeah. We, we've been very lucky. We work with Fortune 100 companies as well as larger, like, uh, startup companies, like Physical Intelligence, which is robotic foundation model company, and Ceraneck, which builds autonomous boats, uh, as well as, like, smaller startups that need board design.

4. 0:51 – 2:46

   The Origin Story

   1. DHDiana Hu0:51

      Take us to the beginning. So just a year ago, both of you were still in your previous jobs when you had applied to YC, and in fact, you were still in your jobs few days before the batch started. Tell us about that journey. What, what were you doing, how you decided to work together?

   2. LKLenny Khazan1:10

      Yeah. Our origin story actually goes quite a bit before that. Um, so back during COVID, when I was, uh, still in college, I was a software engineer at this company that was also building hardware, and I realized, like, kind of, like, the interesting problems at this company were happening on the hardware side. And so I, I asked to join their hardware team and had the good fortune of getting assigned Davide as my engineer mentor, uh, who graciously took me under his wing and showed me the ropes of what it's like to be building hardware. Um, and I kind of got exposed to, to, to this world of building physical things, um, and thought it was super exciting and super fun. So fast-forward to, you know, the previous job where we were working together, um, we were also working on a hardware product. I was, you know, m- more on the software side. Davide, you know, a bit more in touch with the electrical side. And, and we found that despite working with, like, you know, the, some of the best engineers I've worked with in my career, um, you know, we'd s- find that, you know, circuit boards would come back with, like, silly mistakes in them because the tooling wasn't there to help you out. And as a software engineer, I'm used to being able to make mistakes, find them in 30 seconds, and fix them, and I realized very quickly that in electrical engineering, that's basically not how things work. And that was kind of the origin of how we decided to kick things off and see if we could do something to improve the state-of-the-art.

   3. DADavide Asnaghi2:13

      We, uh, had some experience designing, uh, custom silicon, and we saw, like, incredibly talented engineers basically one-shot this very complicated, uh, like, TSMC designs, and we just wanted the same for printed circuit boards. Uh, we, we just found that the tooling was not, um, up to par. I was personally, like, very enthusiastic about how large language models, uh, can start to parse the documentation and f- c- uh, like, they were able to catch those silly mistakes. But you needed to give them, like, a representation on... like, to understand what the electrical design would look

5. 2:46 – 4:01

   Initial Challenges and Pivot

   1. DADavide Asnaghi2:46

      like. And so we effectively applied to YC, um, with this idea of will... give us your electrical design, and we'll spot the mistakes.

   2. DHDiana Hu2:55

      You spoke to over 100 people in the first couple weeks of the batch, had all these user conversations, and you found out people didn't really want that.

   3. DADavide Asnaghi3:04

      That's right. [laughs] It was actually quite, um, quite interesting and very humbling. Like, the original problem statement was I, like, am able to design printed circuit boards, but I want the software to tell me if I'm making some mistakes, the same way that a compiler will tell me if I'm making mistakes while writing software. But when we went out and, like, pitched this idea, and we, we really built a lot of, like, infrastructure and, like, interesting stuff. Like, we had this generative pipeline that would, like, create boards and then inject mistakes into them we, we could use to, like, reinforcement train the algorithm that would catch them. People basically told us, "Uh, we don't make mistakes," like, "We don't need this. I'm sorry." Whether that's, uh, true or not, it doesn't matter, but, uh, it, it really speaks to the fact that, like, talking to customers is really, really important. And so, like, at, at, at some point, we had to recognize that the, the pain point was not finding the mistakes within the, uh, existing design, but it was actually, like, generating that design itself.

6. 4:01 – 5:02

   Finding the Right Problem

   1. DHDiana Hu4:01

      And there was some interesting happenstance from you guys just being in the YC batch that you got more lucky to find the right problems because you were surrounded by all these ambitious fellow YC batchmates that you then stumbled into the actual idea for Diode. Tell us about that journey.

   2. DADavide Asnaghi4:21

      Um, that's right. After, like, uh, a- an office hours where we actually told you, like, "Hey," like, "I, I don't think that [laughs] the verification stuff is working out. We will, we'll need to go back to the drawing boards," we were very somberly, like, having lunch with a really good friend of ours, uh, who, who happened to be selling, like, uh, edge models to robotics companies. And, like, he, he basically told us, "Look," like, "I, I don't know if this can be useful to you, but we consistently hear that all of our customers seem to want a custom Jetson Orin, uh, like, development board." And, like, the Jetson Orin is a edge GPU made by Nvidia, like, very common for, like, developing robotics application. And we realized this would be, like, very easy for us to do and very easy for us to

7. 5:02 – 5:29

   First Successful Deal

   1. DADavide Asnaghi5:02

      generate. And from that, like, kernel of information, which I don't think we would have really stumbled upon so quickly if we hadn't been surrounded by other YC batches, uh, like batchmates, we went, we went out and, like, closed our first deal, like, right away. That was, like, a really interesting moment from another YC company.

   2. DHDiana Hu5:17

      You got a instant yes on your first [laughs] user conversation, whereas the previous time you talked to over 100 people, and they were all basic lukewarm, pretty much nos, right?

   3. DADavide Asnaghi5:26

      That's right. That was a really good validation.

8. 5:29 – 6:40

   Realization and Validation

   1. DADavide Asnaghi5:29

      The interesting part is that people didn't want a component of the solution. They wanted the solution. They didn't want just the tool that makes their job a bit easier. Like, the biggest pain point was that there's not that many people that can actually design circuit boards, um, in the US. Uh, and so for, for a lot of companies, it's hard to, like, find the right talent and, like, bring it in-house in the early stages of, like, prototyping. And even for larger companies, it's very often that they outsource this type of project, so if you don't have bandwidth to do all the things that you want to do. And so it turns out that, uh, Diode can really help with that. Like, generating boards, uh, is where the pain point is at.

   2. DHDiana Hu6:06

      Which is super interesting because in the process of building the software to verify circuit boards, you had built one part of the type, pipeline that would generate boards to really go through the iterations of them, and that's what-People want it.

   3. DADavide Asnaghi6:20

      That's right.

   4. DHDiana Hu6:20

      It was a bit counterintuitive as well because you both... I remember we had this office hours and you were confused. It's like, how do we even build this into a venture-scale business, right? Because you are effectively selling, at that point, services to design boards. So how do you guys square that? How, how, how did it come about?

9. 6:40 – 11:28

   Reframing PCB Design as a Software Problem

   1. DADavide Asnaghi6:40

      I think that, um, the realization came from the fact that for the first time in a very long time we can design software that makes us incredibly productive at, like, solving the problem end to end. And the 80/20 problem, like, a, a, a lot of products in the AI world kind of fail because you will never be 100% correct in every single thing that you do. Uh, but when you are, like, an expert in a specific domain and you can solve the 80/20 problem, um, it now becomes possible for you to actually, like, package this solution and sell it as effectively, like, services as a product. It's working in a way that, uh, we hadn't anticipated before, uh, but we really take ownership on our product and the output of our work, and this is what our clients demand in terms of solution. Like, we use AI to automate the internal working of Diode, but then this is not something that we just put out into the world without verifying that, uh, the output is actually correct. Uh, we own the verification pipeline, and we make sure that anything that we deliver to a client will actually work, that we have, like, acceptance criteria with them. Uh, it very much resembles, like, traditional development. It's just, uh, faster, um, and, uh, more efficient.

   2. LKLenny Khazan7:53

      Yeah, I think the core insight here is if you could reframe the PCB design problem as a software problem, you can take all of the great learnings that have happened in the world of software, both in terms of verification and compilers that will tell you if you're making a mistake, as well as applying all the great research and, and work that's going into automating software engineering and, uh, make that accessible to the world of PCB design as well. And we're basically able to benefit from, from that with the work that we do for our customers.

   3. DHDiana Hu8:17

      So I think the really cool thing is you basically have software margins for this PCB design services because of AI, right? And this is only possible now. Tell us about what, what happened using the largest models and latest one.

   4. LKLenny Khazan8:37

      Yeah, I think the interesting thing is, like, if you look at those models today, they know everything they need to be electrical engineers. They have the innate knowledge of, uh, how circuit boards work, how they need to get wired up, the rules that electrical engineers spend years learning as well. They just, like, don't really have the mechanism to do the work that an electrical engineer does, 'cause traditional tooling is very visual and graphical. Hasn't really changed much since maybe the, the 80s or so. And so all you really need to do is basically just convert it into a form that's similar to what they're used to, which is writing code, and then all of that knowledge that they have can start being expressed into the boards that they design as well. And so you can kind of unlock this, like, latent capability that these models already have.

   5. DADavide Asnaghi9:14

      Um, another interesting learning is that one of the reasons why this hasn't happened yet is that traditionally, like, incredibly smart electrical engineers have been trained in this very visual way. And so there are some benefits even for humans to, like, reason about things in code. That's why, like, um, RTL or, uh, the, uh, register transfer language, uh, for custom silicon is written in code. But the, the, the, the workforce we, we have currently is very, like, traditionally trained in visual, um, processing. And so because the models are really the ones writing this code, we can now let them write it and just ex- like, export a visual representation to our users, uh, which is what they're used to. So we don't need to actually teach humans that have incredible, uh, domain knowledge about code. We can just, like, generate it in the back end as a implementation detail. I personally happen to think that, like, reading that code is very enjoyable, uh, but we, we don't wanna force it on anybody. And so the output that we give is very traditional, but it just allows the model to, uh, have an intermediate representation that they can modify.

   6. DHDiana Hu10:16

      I think one interesting stat you shared is just the two of you, you've been able to decide- design over 100 boards in the last couple of months, which is crazy. A regular electrical engineer would only be able to output a couple.

   7. DADavide Asnaghi10:33

      I, I think it depends on the complexity of the boards. Um, but, uh, th- there are incredibly talented engineers that are also fast. Um, but we have to manage a lot of different clients, uh, and it would be impossible to do, uh, without the, like, software tools that, uh, Lenny and his team build inside Diode. So, like, the, the way that we divide the work inside Diode is that I head the electrical engineering team. Lenny heads the software team. And so we are effectively their first client. And i- in the early stages, especially during the batch, it was just the two of us. And so he would, like, craft these incredibly high-end software tools that I would use to speed up myself, um, th- that was incredibly valuable. I've, I've always personally, like, really wanted to have these tools. Uh, there are some open source tools in the world of electronics, like KiCad, which we are incredibly, like, big fans of. Um, but he really built a fantastic layer on top that allows us to go even faster than that.

   8. DHDiana Hu11:25

      So you've done very interesting

10. 11:28 – 11:56

    Technical Choices and Challenges

    1. DHDiana Hu11:28

       technical choices to be able to serve in these very old-school industries, selling to aerospace, medical device. That's not a easy feat because a lot of them tend to be very conservative with adopting new tech. What are the technical choices you've done that you actually close this large Fortune 100 and all these-Cool tech companies and hardware

    2. LKLenny Khazan11:52

       Yeah, so I think there's a lot of really interesting technical questions and problems we have to

11. 11:56 – 12:31

    Innovative Language Design

    1. LKLenny Khazan11:56

       solve. Um, the first of them is even just designing the language that we use to represent the schematics. We have this kind of novel challenge of designing a language that works really well for humans and also works really well for LLMs, because we want LLMs to be able to generate this code, and LLMs have, you know, millions and millions of lines of code in their training data. And so if you design a language that's very familiar to them, they'll do a much better job at not making syntax errors and kind of writing code in a way that's natural to them. But at the same time, it has to be something that's intuitive and readable for the humans, like on Davide's team, to go and review the code, write some of their own as well. Um, so I think that was kind of an interesting set of challenges

12. 12:31 – 13:33

    Infrastructure and Security

    1. LKLenny Khazan12:31

       we had to make. And then once we design the language, we have to design the infrastructure in a way that's easy to compartmentalize and deploy in settings where there's really rigorous security requirements. For example, working with aerospace companies where it needs to run on an air-gapped system, for example. Um, and so we have this kind of really tight Rust compiler which is, uh, implements the core logic of our, um, implementation, but we also then have, you know, bo- uh, bindings to Wasm so we can run this in the browser and have nice visualizations and compile schematics in real time locally, um, for our users to review. Uh, we can automatically generate configuration blocks for configurable modules where users can go on the website, change the configuration of their buck converter or what have you, and see the schematic get recompiled in their browser in real time. And this is all kind of enabled by this kind of, uh, really tight, isolated, like, offline core component that, uh, powers the, the infrastructure we've built.

    2. DHDiana Hu13:20

       I think this is one of those companies that only you two could have built because it's a unique way of, uh, approaching hardware design for circuit boards as a software problem with, with Lenny, right, and the experience on hardware.

13. 13:33 – 14:21

    Future Prospects

    1. DHDiana Hu13:33

       So what are you excited about the future?

    2. DADavide Asnaghi13:35

       I, I personally love the fact that I see the world around me changing incredibly fast, and I see, like, AI really 10x-ing our software development speed. But the, the thing that excites me the most is taking these incredibly powerful, um, like, set of forces and actually applying them to, uh, like, the physical world, like designing physical object in physical space. I think this, this is really the next frontier. This actually has been fantastic for recruiting. There's a lot of really smart software engineers that are looking for the next, like, Everest to s- like, uh, climb. And, and we really think hardware is, uh, the next frontier of, like, really hard challenges. Hardware and, like, generating these boards and putting them in the real world and making sure they work reliably. And using AI to solve these problems is really what I think will, like, move civilization

14. 14:21 – 15:26

    Recruitment and Team Building

    1. DADavide Asnaghi14:21

       forward.

    2. DHDiana Hu14:22

       What are the kinds of people that you wanna hire?

    3. LKLenny Khazan14:24

       Well, I mean, I think o- on the software side, um, you know, we want people who are curious and excited to be exploring, like, entirely new frontiers with open-ended questions. Um, like I said, we have a lot of questions th- where we know what the problem we need to solve is, but the solution can look one of 10 different ways, and so we need people who are excited to kind of go and, and try experimenting and prototyping and answering these kinds of open questions. Um, so kind of fun researchy type problems, but at the same time, we need to ship products, we gotta ship boards, and so it's, like, very practical and applied. And I think the, the other kinds of engineers who, who we really love working with are people who are excited to build cool experiences for our customers and users to interact with the boards we design, right? So we have to be able to hand over the designs to our customers, and they need to be able to inspect them, review them, get feedback. Um, and so building these kinds of, like, great interfaces I think is, um, also something we're really excited to press the accelerator on.

    4. DHDiana Hu15:15

       Well, congrats, guys, again, on your Series A.

    5. LKLenny Khazan15:17

       Thank you. Thanks, Diana. [outro music]

Episode duration: 15:28