Folders, not frameworks: how Taurus makes Claude repeatable for a whole team Giving an AI agent the right context at the right moment is still the hardest part of using coding agents like Claude Code in real, daily work. We've all felt it: the agent is brilliant when it knows where it is, and frustrating when it doesn't. So how do you give it that context — reliably, for more than one person? A small team will work but what happens when you try to on-board 100+ people? The popular answers don't scale. Elaborate memory systems help a single power user, but in an enterprise they become a liability: they're hard to curate, easy to pollute, and brittle the moment you add more people and more projects. And anything built around one person's bespoke setup — their servers, their wiring, their mental model — is expensive to onboard a whole team onto. Honestly, the wheel hasn't been invented yet. Nobody has a clean, proven answer for how context should work at enterprise scale. This is where the Interpreted Context Methodology (ICM) changes the conversation. Its core idea is deceptively simple: folder structure as agent architecture. Instead of orchestration code or a sprawling memory store, the context lives in the folders themselves. A workspace is just numbered folders for each stage, with markdown files (CLAUDE.md, conventions, reference material, working artifacts) that load in layers when an agent starts there. The agent reads downward and stops when it has enough — typically 2–8k tokens instead of 30–50k. You "configure the factory, not the product": set the workspace up once, then every run reuses it with new inputs. Outputs are plain text, editable, reviewable at every step. ICM is elegant because it's filesystem-native and human-readable — a non-developer can reshape a workflow by moving files. But it has one practical dependency that's easy to overlook: When you add more and more folders agents begin to skip information. Guidelines are missed, rules are overlooked. What worked for one person doesn't work for another because the model scans economically and thinks it knows enough. The solution is again simple, the agent has to actually start in the right folder. Start in a central place and the layered context never loads; start in the right place and the agent is instantly grounded. In a team, "just cd to the correct directory" is exactly the kind of invisible, error-prone step that breaks repeatability.

Hi All, this is little different than the normal community post, but I trust the folks who have made it here because you don’t want BS sales pitch but something that works, ie the fundamentals… So my question is related to SEO and web traffic and how AI is changing that (I have zero experience in this area btw). I see a few different approaches to this such as refining content or making pages more ‘custom’ for users. But I also see people talking about having information accessible for the models themselves so that search results are pulling into the chats that people are using to get results rather than actually scrolling google search results pages. And then the wild card in my opinion is how MCP and those visual components can be inserted into chat windows now, continuing the ‘customization’ for the user (my guess is that UI will become even more user specific much like the text results we get back from the models currently). Not sure if all of that makes sense or is as connected as I think it is or will be. So my question: what are yall seeing and where can I start to double down on knowledge, much like Jake is doing here in the community. Thanks!

I've run into what I hope is a no brainer for someone. How do I make a transcript for a video in a website... the right way?

Quick update on the competition schedule so everyone knows what to expect. 📅 NEW CADENCE: TWICE A MONTH We're dropping comps on the 15th and the 30th of every month. Two chances to compete, every month, on a set schedule you can plan around. ✍️ WHY THIS SCHEDULE Spacing them out this way means we can give tailored feedback on every single submission. Not just the winners. Everyone who enters gets notes on what worked, where it's weak, and what to do next. 🎁 WHAT WINNERS GET Along with the prize, every winner gets a 15-minute one-on-one with Jake. Use it to talk through your build, ask questions, or bring whatever else is on your mind. Two comps a month. Feedback on every entry. Direct time with Jake for the winners. Mark your calendar for the 15th and let's get to work!

👋For the last 2 months, I've been heads-down exploring ai image & video generation as my new personal project. I was shock by the capabilities of current ai image/video space. But every ai creators face a common pain point.. this sh*t is hard. Haha. Thats why all youre seeing are ai slops. Then about 1 month ago, I sent the kids to my relative for few weeks and lock myself in a room and went nerd out to build a tool that lets me take an idea and turn it into a profession cinematic video. I recorded it working on a real project and wanted to share what it actually does — and why I'm pouring everything into it. The idea in a nutshell: Instead of guessing what makes a video great, my tool studies one: → I give it an original video as a study case (here's the one I used: [youtube]) → It analyzes that video and pulls out its DNA — the small details in every component that make it work: the shots, the pacing, the sound, the transitions, the feel. → Then it reverse-engineers that DNA into something completely new and my own — and carries it all the way through, even into the editing, mostly automatically. The Unfolded short film bellow received the same DNA treatment, as you will see, the context is similar ..however, it produced a very unique animation perspective using origami. Every user who analyzes a film makes the library richer for the next one. That's a network effect — the rare thing that actually compounds. GitHub for film DNA: fork a look, make it yours, push it back. I have tried to use it to branch off ideas from Game of Thrones series. It is very complex to keep track of all the different kingdoms, characters, props, and story development. But i think i got it figured out. Theres no one size fit all approach for different type of contents. Horror dna cant get mix up with cartoon 😂. But this is the closest thing to a “reusable” end to end video generation. Correct me if im wrong but there isnt a product out there that can solve this yet. This is a foundation to a full content creation pipeline - you re using a proven video as a reference for your own video. Once you figured out your winning format, automate it to create video daily/weekly is the easy part.