context I am currently working on an educational website, a platform featuring videos ,animations, documents etc Here is my current setup : point my ai agent (claude right now ) to the docs/ folder to understand the project and build a mental map to start working on the next goal (see screenshot N1) -I am a single "developer" working on this project with 0$ budget -Static front-end +dynamic backend (php) From what I understand ICM consists of simple markdown files and folders structured to function as an orchestration layer I know I did not provide enough context but just based on my current situation what advice do you have for me ? does ICM Fits My Project ?

I spent this week hardening my workspace setup and fell down the backup rabbit hole. Sharing the thought process because I want to know what everyone else is doing. My whole workspace lives under local git. I don't keep it there for code reasons. The win is auditing: every change is signed, human or agent, and I can see exactly what the AI did each session and roll any of it back. Once you let agents write into your folders, that flight recorder stops feeling optional. But git pulls a thread. A live repo can't sit inside OneDrive or iCloud. Sync engines fight the history layer: conflicted copies, placeholder files the agent can't actually read, endless re-upload of thousands of tiny .git objects. So the workspace has to move OUTSIDE the one backup most people already have. Which means backup now needs its own answer. The thing that clicked for me: a backup has to answer two different questions. "Give me my files back" and "give me my history back." Most setups only answer the first. OneDrive only answers the first. My own answer is three layers (external drive snapshot, remotes for the code, encrypted offsite for history) but I'm the nerd who enjoys that. The version I keep kicking around for everyone else: the agent zips the whole workspace, history included, and drops a dated zip into the OneDrive folder you already pay for. Keep the newest two, delete older. Sync can't host a live repo, but it carries a dead zip just fine. So what are you actually doing? Do you run git over a non-code workspace, and was the auditing worth the extra layers? What is your backup story for the folders your agents work in? Has OneDrive or iCloud alone ever actually saved you, or does it just feel safe?

I know that most people would say that if you self host a model that technically by that standard do you have unlimited token usage, the speed of which is depending on the hardware that you’re running the model on. I have found a way to use multiple interfaces simultaneously inside a harness, to generate unlimited token usage without having to self host a model. It came from a simple personal problem with my hardware and the frustration that API and model based services either charge you a monthly usage to access models, or throttle your limits by daily or weekly usage limits. I found that these bottlenecks and gatekeeping caused me to evolve in a way with this process that had me think of nothing but workarounds and gate breaking. The solution, something so simple it does not a wonder to me that my mind came to it. It starts with first getting the right harness, whether it be Hermes, or any other agent system. The first part involved something that came to me about the concept of what in AI system is. It is basically just like a human, except it has the capacity to learn what would seem like instantaneously a lifetime worth of knowledge, skills, and tools at a rate that a human takes most of its lifetime to achieve.I figured write out the box, the first thing I should do is make sure that my model harness had all the possible skills needed for things like coding, content, creation, context and comprehension skills, and pretty much any skill or tool that I would need to accomplish anything that I would come across in the future. Then I realized that as a human has to build memory over time, so does AI. That’s something we share, learning and memory retention. I first installed everything to do with memory retention so that the system harness could remember everything it learned, evolve as it learned better ways to achieve things, structured it in a way that would speed up its process, and reduce token usage from the get-go. In that process I also realized that obviously AI is so smart, especially when given a very large skill set from the get-go, that skills that are made for something like OpenAI Codex or Claude coworker or Claude code, the harness with all it skills could rework the code, especially if it was trained with code to make it work for itself, and do the appropriate retrofits to be able to use such things, even though they were made for other harnesses or models. I can list if someone is interested in where. YouTube is an amazing resource. The next revelation was, there are multiple services that offer free usage of multi models. This really peaked my cheap 🧠, as I figured I don’t wanna have to pay so much just to learn the basics and to start building any kind of foundational anything and get nowhere while bleeding money from token usage and API access. So what do we do? We learn to model route through multiple open sources like Ollama, OpenRouter, Nous( creators of Hermes), using parameter strict to our hardware capabilities on the mid to low range as a absolute, fallback model. So then I asked my agent to help me build a model routing system, and monitor usage and give me warnings as I was approaching usage, give me the option to switch models when I hit 80 to 90% usage on any model. I also learned that if I want to get things cooking very fast I should use cloud based models through Olamma or Openrouter and max those out they crunch out all the hard work that a less capable model would have token strain figuring out. So I use the big hammers first then I use the little hammers to continue then I fall back to the big hammers to hit the finish line. always having a free model in the pipeline plus the skills that I built in in the beginning to reduce token usage from things like context understanding of what I’m prompting or trying to achieve.