I’m working on building this alongside my AIOS, but I’m more interested in audience language, positioning, hooks, and voice than just pumping out posts. For context, I have experience with branding and voice from my previous health coaching business, but I’m shifting into a new audience and don’t want to just drag my old messaging into a new market. I’m thinking through a system that can help with: Audience research VOC and language banks Better hooks and topics Content that still sounds like me Messaging that makes the audience feel understood Has anyone built something like this successfully inside their AIOS? What would you start with first: audience research, voice profiling, or content generation? Also, any solid resources, templates, or trainings you’d recommend?

Stanford's STORM research method runs a topic through five different expert perspectives instead of a single prompt, so the blind spots one angle misses get caught by another. I turned it into a free Claude skill that spins up a practitioner, academic, skeptic, economist, and historian, maps where they disagree, then verifies every source before handing you a clean HTML briefing. I also put it head to head against Claude Code's built-in Deep Research, and walk through exactly how to install it and tweak the lenses for your own work.

Hey everyone, new here and going all-in on building my AI OS second brain. Would really appreciate any direction from people further down the road. I've got three goals I'm trying to run in parallel: 1. A trading bot that's basically built (It has self healing loops, mcp connection to trading view, and skills saved. Still in the data-collection phase to confirm it actually has an edge before I trust it. (I copied my trading strategy that has been profitable over the last two years). 2. Grad school (UX Research starting this Spring). I want to use the second brain executive assistant, skills/agents to study, organize research, and get feedback on my writing more efficiently (keeping the actual graded work my own, obviously). 3. Eventually teaching all of this in my own community. How I built the bot and passed grad school with a high GPA, running the back end with AI agents. I've mapped Nate's framework onto it: get the second brain populated (context + connections) → build skills (study set first, then bot-tracking) → automate last once things are battle-tested. Where I'd love input: - For those who've built your AIOS, what do you wish you'd done first vs. wasted time on early? - Any skills that turned out way more useful than expected? - For anyone using this for school or research, what worked without it doing your thinking for you?

How are you securing them, and where are you hosting them? I’m concerned about the risks of connecting an LLM directly to my external tools. Specifically: - Deployment: Are you running the MCP servers locally on your machine, inside Docker containers, or as a managed web service? - Isolation: How are you sandboxing them to prevent prompt injection from triggering dangerous actions? - Access: Do you use strict scope limits on API keys? I want to make sure I’m not opening myself up to data leaks or unauthorized access before I scale this up.

Unpopular take: most people confuse "AI" with "automation" and it's costing them time. AI = pattern recognition. It takes input, finds patterns, gives output. ChatGPT answering your question = AI. An AI AGENT is different — it takes ACTION. "Send me my top customers" → it reads your data, analyzes it, writes a report, sends the email. No human needed in the loop. Once you separate these two ideas, you start seeing automation opportunities everywhere — every repetitive task someone complains about is a potential agent. Curious if others here are experimenting with agent's vs just using chatbots? what's your favourite tool to work with?