17 days after open-sourcing Patternflow — first community pattern, v2 build guide, and the AI gacha problem

Hey! Some of you might remember Patternflow — ESP32-S3, 128×64 LED matrix, 4 knobs, a device for generative patterns. 17 days ago I open-sourced it (the Open Source Hero badge still doesn't feel real, thank you). Here's where it's gone since then. A few things happened I didn't expect. Someone actually made a pattern. This is the part I keep going back to. A few days ago someone took the JS pattern template, wrote their own, and sent it to me. I helped port it to C++, and we posted it as a collab on our Instagram. Flashing someone else's code onto my hardware and watching it run was a weird feeling. Good weird. It's the whole reason I open-sourced this thing, and it happened faster than I thought it would. The porting wasn't perfectly smooth, so I'm writing up the gotchas as a GitHub issue The v2 build guide is real now. The original docs were placeholder, honestly. The new one walks through four stages — parts, PCB, assembly, firmware — and there's a 3D preview on the site that updates per stage. It's here. The cold-boot bug some of you helped debug (pull-up on GPIO0, for anyone else hitting it on AliExpress ESP32-S3) is fully fixed in v2. Why AI is in this workflow. The original section was unclear, so let me lay this out properly. AI shows up in two places: JS for web preview. I sketch patterns in JS first so I can see them in the browser quickly. AI helps translate what I want into that JS. JS → C++ port. Once a JS pattern looks good, AI helps convert it to C++ that runs on the ESP32. The pushback focuses on #1. #2 is more accepted because it's well-defined code-to-code translation. But structurally, they're the same operation. #2 translates JS to C++. #1 translates an English description to JS. Same operation, different input format. If AI translating between two formal languages is fine, the case for AI translating from a looser source into a formal one rests on the same structure. There are two reasons AI is here: One: showing the device's range. The patterns I post daily on Instagram aren't curated artwork. They're mass-produced to demonstrate that this hardware can run a wide variety of generative styles, not just one aesthetic. AI lets me produce enough variations that the breadth becomes visible. Hand-writing every one would mean one pattern a month and nobody understanding what this thing can hold. (Side observation: audience reactions are wild. Ones I think are beautiful flop. Ones I'd discard go to 70k+ views. One did, this week. I clearly don't understand what people see.) Two: keeping the door open to non-coders. My main goal with Patternflow being open source is that someone with no coding background can sit down and make their own pattern. The end artifact is still algorithmic code — noise, weighted random, modular arithmetic, reservoir sampling, exactly the techniques the top comment listed. AI is the bridge that gets a non-coder from "I want it to feel like X" to working code on the board. There's a more general point underneath this. Most of us didn't get into coding because someone handed us a math textbook on day one. We got in because something small worked first — an LED blinked, a hello world printed, a sketch ran — and that "oh, this is mine" moment made the harder learning afterward feel worth doing. Interest came first. Discipline came after. For someone with no coding background today, AI can be what produces that first "oh, this is mine" moment. It doesn't replace learning fundamentals — it gives them a working thing they care about, so that when they later open the code and want to know how it actually does what it does, the learning has something to anchor to. If a non-coder uses Patternflow with AI help, makes a pattern they love, and a month later starts reading the source out of curiosity — that's exactly the path I'm trying to build. For what it's worth, I personally love the fundamentals. I plan to hand-write my own patterns eventually, and I think that's where the real art lives. The AI workflow exists so the system is open to people who don't have that background yet, not because I prefer it for myself. The "gacha" framing in the original was about where #1 still falls short — current LLMs aren't reliable enough yet, maybe 1 in 10 outputs is interesting. So my next step is making that bridge more dependable: define the algorithmic primitives (noise type, color rule, knob mapping, motion function) as constrained options, let the LLM combine them, and always have a human tune the result. If anyone has better workflow ideas in that direction, I'd really like to hear them. One question I want to ask you. From the waitlist survey: about half of respondents said they want to make their own patterns rather than just run preset ones, and a handful asked about MIDI and OSC. I hadn't planned for performance use from the start, but the more I think about it, the more it fits. If you have opinions — USB MIDI, network OSC, both, neither, something else — I'd really like to hear them. I'm at the stage where decisions stick. More context If you want the long, more personal version of all this, I wrote a blog post about it over the weekend — originally in Korean, translated to English. Fair warning, it's long. https://patternflow.work/journal/me-and-patternflow This subreddit has been a big part of why Patternflow kept going. Still is. submitted by /u/GlumPiece7281 [link] [comments]