How is the AI OS series that unlocks at Level 3? Has anyone been able to implement the ideas in their own system yet? I’ve been working on a “second brain” for LLMs since December, and I keep hitting the same wall: efficiency. I’ve tried file-based approaches, RAG, and even a semi-custom knowledge graph. They all get part of the way there, but none of them feel quite right once you start dealing with real-world files and services. The big issue for me is ingestion. I want to connect local files, Dropbox, Slack, Teams, SharePoint, etc. without copying everything into another system or creating a mess of duplicates. It feels like we still need better local-first infrastructure before this can be properly optimized. Has anyone found a setup that actually works well? Does the AI OS course address any of that?

What a shit system . 4.8 and 4.7 are not working again. Burning tokens in a speed which is not possible . anybody else any problems as well??? They are funded with to much money but taken care of there users they dont care it's really unbelievable every week I have problems and there is no customer service at all. sending emails they dont answer

Claude Opus 4.8 added dynamic workflows to Claude Code, and they look a lot like skills, subagents, agent teams, and the /goal feature, so it's easy to get confused about when you'd actually reach for one. In this video I break down what dynamic workflows really are, how they stack up against everything else on the complexity ladder, why one prompt burned through half my $200 monthly plan, and the simple question I ask before deciding if a job is even worth a workflow. I also cover the hidden stuff like ultracode mode and /deep-research so you don't accidentally torch your session limit.

Hello everyone, this is my first post ever here. Am I the only one who feels that AI is becoming both more powerful and more overwhelming at the same time? Every day there are new models, new tools, new workflows, new frameworks, new prompting techniques, new automation platforms, and hundreds of people sharing different opinions on what you should learn next. The strange thing is that I don’t feel like I lack knowledge. I’ve spent a lot of time learning AI, automation, tools, and workflows. In many ways, I feel more capable and informed than ever before. Yet sometimes I still find myself struggling with something unexpected: clarity. Not because the information isn’t available, but because it’s everywhere. A tutorial here.A LinkedIn post there.A YouTube video.A newsletter.A community discussion.A new tool launch. Sometimes it feels like the biggest challenge isn’t learning AI anymore—it’s organizing all the information into a clear system that can actually be put into practice. I’m curious: How do you deal with information overload in AI? Do you have a framework, system, or way of filtering what deserves your attention and what doesn’t? Or do you sometimes feel the same tension between knowing a lot and having complete clarity on where to focus next?

In this video I show you how I turned Claude Opus 4.8 into my full AI operating system that runs my businesses, holds all my context, and replaces the constant tab switching between apps. I walk through the Four C's I use to build it (context, connections, capabilities, cadence), the mindset shift of working out of Claude Code by default, how I organize files and skills, and the bike method for safely giving agents more autonomy. By the end you'll know exactly how to set up your own AI OS and the trap to avoid when you start handing it real keys. GITHUB REPO