Here's what we actually built — and why it matters for you— and **please join this SKOOL group!** If you came here from the second brain post, welcome. Let me show you the actual work. The "second brain that works across AIs, agents, and claws" isn't a concept. It's live infrastructure. Here's what we've built and what you can use right now: openbrainsystem.com — The deep dive on what an open brain system actually is, how to architect one, and why the tools most people are using (Obsidian, Notion, Tiago Forte's PARA) aren't built for the agent-first world we're operating in now. secondbrain.us.com — The technical layer. pgvector, MCP integration, end-to-end build guides. If you want to build rather than just read about it, start here. aiknowledgestack.com — The commercial angle. How AI knowledge infrastructure maps to real business leverage — content operations, client work, agency scale. All of it points toward the same thing: novcog.dev — the actual NovCog Brain implementation. A vectorized, cross-agent memory system that runs across Claude, local models, OpenClaw agents, and anything else in the stack. Not a product pitch. A working system you can replicate. ++++++++++++++++++++++++++++++++++++++++++++++++ In other words, you can build this in an afternoon. This afternoon. The hardened, battle-tested brain that Triston Goodwin has been selling to clients. ++++++++++++++++++++++++++++++++++++++++++++++++ This is what we do here. We build the thing, document it, and hand you the blueprint. The resources above are free. But if you want to be in the room where this gets built in real time — where the sessions, office hours, peer accountability, and live agent builds happen — that's the Hidden State Drift Mastermind. $105/month. Small group by design. Practitioners only. 👉 Upgrade to HSD Mastermind in the membership tab above. If you're not ready for that yet, you're still in the right place. Drop a post and tell us what you're building.

Our mission is to create a true learning community where an exploration of AI, tools, agents and use cases can merge with thoughtful conversations about implications and fundamental ideas. To get a deeper overview of this Skool, click on the Classroom tab above, and enter the Welcome Classroom If you are joining, please consider engaging, not just lurking.Tell us about yourself and where you are in life journey and how tech and AI intersect it. for updates on research, models, and use cases, click on the Classrooms tab and then find the Bleeding Edge Classroom

I built an automated content generation system that runs 24/7 on a Mac Mini in my house. No n8n. No Make. No Docker. No external orchestration dependencies. Pure Python, stdlib, launchd. It publishes across 18 sites daily. Every article is quality-scored against AP Style rubrics before it goes live. Here's the part most automation builders skip: the scoring model had a bias problem. GPT-4.1-mini's safety training bleeds into quality scoring. Political content — elections, protests, international conflict — gets reflexively penalized 3-4 out of 10 regardless of actual writing quality. The fix was chain-of-thought scoring: force the model to reason about specific criteria (headline accuracy, factual coherence, structure, tone) before outputting a score. That eliminated the topic-sensitivity reflex entirely. The quality gate rejects anything below 5.0/10. What passes gets a hero image generated via Fal.ai, publishes through WordPress REST API, and distributes to Bluesky, Telegram, and Tumblr — all with viral scoring that tiers articles into boost, standard, or skip. Cost: $0.92/day. Budget-capped at $2/day, $10/week. But the content generation is only half the system. Every article embeds a 1x1 tracking pixel from a Cloudflare Worker. That pixel tells me exactly which AI crawlers are ingesting the content and when. Within hours of publishing, I can see GPTBot, ClaudeBot, ByteSpider, Meta's external agent — all hitting the content. Not guessing. Measuring. Last week we deployed a 10-article interlinked content series across the network. 500 pixel hits in the first window. Breakdown: 14% GPTBot, 10% Meta, 4% ByteSpider, 2% ClaudeBot, 42% human readers. The content entered at least four major AI training pipelines within hours of publishing. The system improves daily without intervention. Quality scores trend upward because the rubric catches what the model misses. Publishing cadence stays natural with randomized 13-23 minute intervals — no fixed pattern for crawlers to fingerprint. Every run logs to a SQLite database. A daily email report hits my inbox at 7:03am with per-site metrics, quality trends, cost tracking, and pixel data.