Wireflow is built around a node-based architecture format and interface. This is Lars. For natural distribution of the creative process with respect to image creation, video creation, and editing plus more, it's the tool I lean upon to create all my animations and it is available through API. It's a great tool and one I highly recommend.

I have been living inside ICM for a while now, and it changed how I build. Recently I started pairing it with one more idea, and the combination clicked hard enough that I wanted to share it here and see what you all think. Quick version: ICM 2.0 = ICM (the interpretable filesystem) + Colibri style routing (a router that only wakes the experts a task actually needs). One gives you a system you can read. The other gives you a system that stays light. Together they run local and sovereign (this system build ACTUALLY helped me move the needle, i.e. KPI, creating content, closing deals, etc) --- 🗂️ First, why ICM already wins For anyone newer to it, the core of ICM as I understand it: 1. The filesystem is the operating system. Plain-text steps, one job per folder. No black box. 2. It documents itself. Every folder says where you are and what is next, so you never lose the thread. 3. It is resumable. Close the laptop, come back next week, pick up exactly where you were. 4. It is portable. What runs in your head, in an agent, or on a local model is the same folder of plain steps. The magic is interpretability. You can always open the box and see the reasoning. 🐦 The piece I added: Colibri (the router) The problem with a big system is that it gets heavy. You do not want every folder, every expert, every context loaded for every task. A hummingbird does not flap every muscle to hover, it uses only what the moment needs. Colibri is a mixture-of-experts idea applied to a whole life or business: 1. Only a sliver is ever active. Roughly 5 percent at a time, not the whole library. 2. A router picks the ~8 experts the task needs** and wakes only those folders. 3. Core stays pinned, the rest streams in** on demand and caches while it is hot. 4. It learns to predict which experts you will reach for next, so the right context is already warm. 🧬 Put them together and you get ICM 2.0 1. The ICM filesystem becomes the DNA / schema** — the canonical map of every expert the system can call.

Hey everybody! I hope you are doing well. I know I've been a little quiet the past few months, but I have some interesting news and wanted to share a little 'sauce,' if you will, that I came across while writing and publishing my new AI book on Amazon. By the way, I highly recommend that if you have ever wanted to write a book, you just do it!!! Here's my story: #1. I happened to come up with a new way to use AI (July 2025) and wanted to test whether anyone was even interested in learning it, so I posted my ideas on LinkedIn. #2. After about a month of posting almost every day, I had a good idea of what people were thinking and saying, and how to lay out the book (so I thought LOL), and I started writing. #3. I had the first version of about six chapters done and shared it with 10 people on LinkedIn who expressed interest, and 4 of them actually read it. #4. I took their feedback, revised some chapters, and wrote a couple more. #5. I then gave the book out again (V2) and connected with two people who had read the whole book and were willing to jump on a Zoom call with me. #6. As I watched the first person start using AI the way I thought it should be done, I realized she had built her 'AI Intelligence System' correctly but was still using it like a search engine, and I realized I was MISSING half the book. 🤣 #7. So I condensed the now 8 chapters down to 6 and then wrote the second half of the book, showing people how to use AI not like a search engine or an answer machine. (I call it briefing AI like a CEO.) #8. I gave that version of the book (V3) to about 100 people (A mentor of mine a couple of years ago mentioned that when you do write a book, try to get 100 people to read it before you publish it; that will give you the truth you need about whether it should be published) I received more feedback, made some tweaks, and, after writing the book 4 1/2 times, I went ahead and published it. And believe it or not, I hit best-seller status in two days. The 'secret' is to send your Amazon book link to everyone who read it and ask them to buy it at a low price the day after publishing.

I’ve been building our agency operating system inside Claude Code, and memory quickly became one of the harder architectural problems. Most AI memory systems use a global store with similarity-based retrieval. That can work for a single project, but it becomes risky when the same system handles multiple clients. Relevant context for one client should never be retrieved while working on another. So I built Agency Memory Kit around a different model: Each client folder is its own source of truth. The core mechanics are intentionally simple: - Context loads in a fixed, task-specific order instead of relying on fuzzy retrieval. - New learnings are written back to the relevant client folder. - Ambiguous learnings go into quarantine instead of being assigned automatically. - A weekly process deduplicates memory, recovers useful learnings from missed sessions, and extracts cross-client patterns without carrying over client-specific details. - Recurring tool mistakes can become proposed guardrails, but promotion, archiving, and hard blocks always require human approval. The plugin is the engine. Your data stays in plain Markdown files inside your own folder structure, where it can be reviewed, edited, and versioned with Git. The daily hooks run locally and send no telemetry. The optional weekly consolidation uses your own Anthropic API key. Agency Memory Kit is currently on v0.2.10. It’s open beta, MIT licensed, and tested end to end on macOS and Windows. If you’re building a multi-client or multi-project Claude Code setup, try it! I'm interested in what you would change. Repo here: https://github.com/krsnczky/agency-memory-kit

When Bas first added me to this group, I could barely have told you what an API was. I've been mostly quiet since, because I was heads down building something, and I finally get to show you. But the win I want to share isn't the one I thought I'd be writing about. A few weeks in, I was at my kitchen table testing a feature, and my own app wrote something back to me. A little message, the kind it sends to a stylist. I read it and I cried. Me. The person who built the thing. It had just made me feel seen, as a hairstylist, and I wasn't ready for that. I'm a solo hairstylist. That's my actual job. The app is called Rooty, a companion for stylists who are tired of being told the only way to stay booked is to become a full time content creator. I built it between clients over about seven weeks. Here's the part I think this group will appreciate, and it genuinely surprised me: the code was not the hard part. The boring receipts: - 212 commits, about seven weeks, built between appointments - One Python file, roughly 8,000 lines, zero dependencies, just the standard library - 132 tests - Multi-tenant, real logins, running live right now with actual stylists signing up - Claude Code did the heavy lifting on the code, Anthropic's Haiku model runs the AI features - I directed all of it. Every product decision, every word of the voice, every call about what it should and shouldn't do. I'm not a programmer. I'm the person who knew what it needed to feel like. That last line is the whole thing, so let me stay on it. The code, honestly, the AI and I got through together. What I could not outsource, what no prompt handed me, was knowing when a message sounded like a brand instead of a friend. When it was technically fine and emotionally wrong. I felt those the way I feel a bad section in a haircut, right away, in my gut. And I sent them back. So the "it feels like it gets me" that people react to, that came from real decisions: - The captions and replies learn from my actual writing, not a formula. They sound like a person because they're trained on one. - Every AI feature is its own little room with exactly one job. None of them is a general chatbot. A tool that tries to do everything feels like nothing. A tool that does one thing warmly feels like a friend. - And me. I was the reject button. I read hundreds of generated lines and killed every one that rang false.