One of the greatest benefits of learning AI isn’t just saving time or getting more done. It’s changing the way you approach challenges. Before, you might encounter something difficult and immediately think: “I don’t know how to do this.” But as you become more comfortable with AI, that thinking begins to shift. “I don’t know how to do this yet, but I can figure out how AI can help me.” Suddenly, unfamiliar tasks feel less intimidating. New ideas seem more achievable. Problems become opportunities to explore rather than obstacles to avoid. AI doesn’t have to replace your skills. It can give you the confidence to develop new ones. And that shift in mindset might be the most valuable return on AI education of all.

I've been almost 100% using Claude, Claude Code, Sonnet 5, Sonnet 5.5, and occasionally Opus. This month I've run into usage limit caps twice and that's two times more than I've run into it in the entire 6 months that I've been deep diving into Claude and ICM (knowledge management, specifically around product management and a software development lifecycle ICM). I really don't feel like I'm doing anything more than I was a few months ago. I'm curious: is this something that everyone's running into? Is this something I need to do some additional context cleanup with my primary Markdown files? I went through an exercise. The first time I hit a session limit and removed a lot of content from primary Markdown files but today within 3 hours I hit my 5-hour limit again.. I'm just wondering if it's just me and my folders and my files or if it's others experiencing similar feelings this month over last month. Thought? .

I'm currently using AI for drafting emails (side hustle) and building infrastructure for my full-time job (VP of Communication and Digital Ops), and right now I'm building structures and trying to implement ICM into all my regular projects. One thing that caught my attention from this is how specific paid-for agents are a waste; and you turn everything into folders and files. That resonates deeply with me, but I haven't been able to articulate it. Love this group and method. I'm excited to learn further and about the ICM AND Animations and see how structured approaches to AI can help me build better systems and infrastructure at Work as well as build side income outside of work.

I've had a couple of my content creator clients reach out using ICM as our default staging architecture and process. Does anybody have an existing working/functional pipeline and which tools are you using? I'd love to know what others are doing before I try to build this myself. Mind you we don't want to be building video content out of nothing. We want to be editing video so that's the clarification. I see a lot of posts and a lot of content around building AI videos and I really don't want to do that necessarily in this use case. I want to manipulate our existing video files. We started looking at connecting to CapCut via a CapCut extension/MCP. That's not really supported by CapCut but is community-supported. Anyway just wondering whatever others are doing before I go headfirst into trying to solve this problem myself. I appreciate any and all feedback, thoughts, and considerations.

the expensive part of using Claude for real work isn't the thinking. it's the doing: hours of running commands, waiting on builds, polling jobs, re-reading output. every one of those steps burns tokens, and that's what pushes you onto the pricier plans. so i split it. Claude Code writes the spec and the plan, and checks the result at the end. a local model does the long, mechanical work in between, on my own machine, for free. i've been doing this with Ornith 9B (8-bit, on my own engine). on my last run it merged 75 commits across two repos and got every test passing. the planning and the final check were a small slice of what doing the whole job with Claude would have cost. the catch: a small local model only works if the plan is written for a small model. so i wrote down everything i learned into a file you hand to Claude along with your task: https://git.teamide.dev/David/TeamIDE-methodology/src/branch/main/Model-Best-Practices/Ornith-q8-best-practices.md what's in it: - what the local model is good at: git work, running and polling long jobs, following written rules - what to expect: its work is more reliable than its reports. it doesn't go looking for context on its own. it doesn't know when it's done. it slows down at long context - how to brief it: exact files and line ranges to read, absolute paths, small edits, every rule written down, every step ending in a command with the exact output it should print, stop rules ("if it's already done, say so and stop") - how to run it: one task per fresh session, keep the context small, make the plan file the memory so any session can pick up where the last one stopped - before anything lands: check its claims against git and the logs, never its summary. on my runs that caught every wrong note it wrote how to use it: give Claude Code the file plus your task, and ask it to write a spec and plan in that style for your local model. run the plan locally, then bring the result back to Claude to verify before you merge.