Hey! I can't see your links to the LEDs or the QT Py, but I'm assuming they're probably neopixels as you mentioned "IC" LEDs. Drag your parts into flux and just ask copilot how to connect them together properly. Just to speculate on your project, you probably want some sort of gate buffer for the neopixel signal. And just ensure whatever power source you are using (be it a 5V source or a battery or buck boost converter) can handle the total current draw of those LEDs. I believe they have a wide operating voltage, but also just double check that if you are using a constant voltage source (which you probably are), it isn't over the maximum forward voltage of the LEDs. You can ask in detail with copilot by the way!
Just wanted to echo some more resources to get you oriented. The second video shows actual examples of how to leverage copilot in your exact scenario.
Flux End to End tutorial: https://youtu.be/Nz-XvvlozK4
AI Architecture Exploration: https://youtu.be/iGJOzVf0o7o
It's a weird feeling knowing that Altium isn't a pure ECAD company anymore. Like sure, the team is all the same and will build an EDA tool, but somewhere higher up, there will be a skew towards the automotive industry.
I used Altium in my previous job in high voltage and we were amonst the first to use the Creepage feature. It reminded me that there's niche features to be developed for every industry, and there's a future where they resource heavily on automotive. On one hand, $6B seems like enough to go around for everyone but on the other hand, Renesas would only spend $6B if they saw it making/saving them $10B+.
I'd like them to speak more on the long term vision and focus of the product; I think they've done a good job at serving EEs so far and if they'll still hold that position or get pulled away into one loud market. Regardless, that's like in 5-10 years time; I'd be impressed if anything changes in the next 2 years.
Full disclosure, I'm now working at flux.ai but I don't think that doesn't really changes the news for me; I understand Altium is still used by many EE companies who I can still sympathize with, and we're humbly comin' after them :P
PCB Design is a broad "field." Depends on what you're building. I think people place too much of an emphasis on the tool you use instead of the skillsets you need. At the end of the day, in 99% of cases, we're integrating highly complex, yet modular systems built by semiconductor companies.
Expand your design capabilities by understanding these semiconductor black boxes that are available to you in your field (power electronics, audio, RF, etc.) As another user mentioned, read up on design references! And datasheet graphs, and try to get your hands on closely related previous designs. As EEs, we should talk more about design patterns and primitives like software engineers see it.
But of course, I understand why there's a big discussion about tooling, it's like picking the first language to start programming in. Imo, if you're starting out in PCB design, pick a tool that doesn't get in the way with your learning; something simple but also capable when you progress. The software engineering choice would be python, and I think flux.ai is the closest thing we get to that. But still, if you learn one, you can use the rest! We're all constrained by a pretty standardized manufacturing process so the functions you perform in all these tools will be very similar.
expert user of flux here. I've been waiting for their implementation of impedance control for a while.
One key perspective I want to bring up is the impact this has to new engineers who run into differential impedance for the first time (e.g. wanting to slap on a USB connector because they're so ubiquitous). My first experience with routing diff pairs was in Altium, where you can do anything under the sun, but it was really confusing and I had to become an expert first by stitching together information from various YT tutorials and application notes.
Flux designed this with the noob and pro in mind. The pro can configure their part to their hearts content just like they can in Altium and all those impedance rules/calculations are packaged nicely for anybody to reuse. Some may call this cheating, but if I had my own design with USB working, I would be so motivated to learn those inner workings.
I love teaching PCB design! As with you, I'm also really curious about AI's contributions to learning. My opinion is that the automation of search and execution increases the impact one person can make so more time can be spent on connecting the dots at a higher level. Whether or not education catches up in time is another story haha.
It's pretty open ended right now but it's undeniable that a huge educational gap there.
I'll interpret "right" as in giving correct information. I found that it depends on the type of question asked; if it's somewhat general information such as design concepts and design topologies, it impressively nails it 99% of the time for my limited testing period. The main area it gets things wrong relates to very specific information that has only one source: e.g. datasheet specs. I heard the team already has plans to mitigate hallucinations on this front.
Also, I find myself having a conversation as I would with a real person, so it's less of spitting out the right answer for any technical question I might have, but more of a design buddy to consult when you're blocked on your search through the massive electronics solution space. Of course, this is a very preliminary use case and I can't wait to see what other people use it for.
Holy cow this is awesome! The integration of project context with an LLM enabled more than the sum of its parts when it comes to information access.
When designing circuits for fast iterations, you're constantly managing risk and time expenditure; this leads to copying a lot of "flight heritage" circuits so to speak.
With the LLM, I'm able to get highly condensed starting points for better circuits when it comes to simplicity, understandability, or performance. As of right now, all I have to do is fact check the solution which is much easier than synthesizing one myself.
I'm citing the example where I got it to design me a viable high gain analog frontend that meets my gain, bandwidth, noise, and biasing requirements.
Just wanted to echo some more resources to get you oriented. The second video shows actual examples of how to leverage copilot in your exact scenario. Flux End to End tutorial: https://youtu.be/Nz-XvvlozK4 AI Architecture Exploration: https://youtu.be/iGJOzVf0o7o