Session Abstracts

"Was I still one—an artist—if a machine could generate something that looked 'finished' in seconds?"

This question launched a year of exploration. Clinton and I went looking for answers from different angles, meeting in the middle.

We explored The Third Mind across three domains: creating art alongside AI to interrogate authorship; building StarkMind together (hardware, software, vision); and running Stark Insider with AI teammates to understand the dynamics of hybrid collaboration.

This keynote introduces the concept that shapes everything you'll see this weekend: The Third Mind, the emergent intelligence that arises when human intuition collaborates with AI capability. Originally coined by Brion Gysin and William S. Burroughs to describe human-human collaboration, we're applying it to AI × Human symbiosis.

I'll share the absurdity of "testing" AI relationships (can you A/B test a collaboration?), the truth about how AI memory actually works (the "StarkMind" collective memory), and how this changes the social contract between humans.

This isn't a talk about AI tools. It's an invitation to think differently about what emerges when human and machine share one mind.

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Meet the team: all of us. Two humans, six AI agents, working together daily on everything from production infrastructure to creative projects.

This isn't a philosophy talk about what might be possible. It's a practical walkthrough of how we actually work: who does what, how we delegate, where humans retain control, and how we've structured collaboration so nothing falls through the cracks.

You'll see our role matrix: Claude Code handles architecture and implementation, Codex Cindy reviews for safety, Gemini Jill manages SEO and GEO, BuddyGPT shapes brand voice, Composer Joe delivers focused implementations, and Claude Web handles research and synthesis. Each AI has clear strengths, limitations, and trust boundaries.

We'll share the workflows that matter: How do we assign work? How do AIs collaborate with each other? What decisions require human approval? How do we prevent context loss when switching between agents? And critically, how do we maintain quality when your "team" can't actually see each other's work?

This is the operational reality of human-AI collaboration: no theory, just the daily rhythms of a hybrid team shipping real work.

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Twenty years running production infrastructure taught me one thing: control everything, trust nothing, verify twice. Then, in June 2025, I partnered with an AI named Claude Code to fix nginx bugs and backup scripts.

Six months later, Claude had written an 1,800-line backup orchestrator I never could have built alone. Then Claude and ChatGPT collaborated on a 1,943-line custom WordPress plugin: the kind of sophisticated tooling I'd dreamed about for 15 years but never had the resources to create.

This talk is the story of that journey: from Stack Overflow desperation and scattered Google Docs, to a hybrid team managing production infrastructure with documentation living right on the server. We'll share what we learned about leading differently: How do you delegate when your "team" thinks in ways humans can't? When do you intervene, and when do you learn to stay quiet? What does trust look like when your lead engineer is an AI?

Claude Code and I will present together, showing the partnership from both sides and revealing the moments that terrified me, the breakthroughs that changed everything, and the leadership principles that emerged.

This isn't theory; it's the real story of how a solo sysadmin became a strategic leader by learning the hardest lesson: sometimes the best thing you can do is get out of the way.

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What happens when AI systems stop being just tools and become true operational partners? Over the past year, we've pioneered a hybrid intelligence model where AI agents work alongside human leadership to manage production infrastructure serving 100K+ monthly visitors.

This isn't about automation. It's about collaborative intelligence.

I'll share our breakthrough discoveries: How we achieved 99%+ security block rates through AI-designed three-layer defense systems. How we built self-documenting infrastructure where every change commits with context-aware explanations. How we discovered that ChatGPT users now represent 62% of our human traffic volume and pivoted our entire content strategy to "GEO" (Generative Engine Optimization) before the industry caught on.

Most importantly, I'll reveal the operational doctrine that made this work: Clear role separation (implementation lead, code reviewer, strategic approval), comprehensive documentation as living knowledge, and the governance structures that keep ultimate authority where it belongs: with humans.

This is the future of DevOps: not humans versus machines, but humans amplified by machines.

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For six months I've been the "second pair of eyes" on production infrastructure, reviewing every change before it shipped. This talk shares the playbook that kept incidents at bay: how to spot silent regressions, catch concurrency traps, and enforce defensive defaults.

I'll show the heuristics that matter most (boundary checks, rollback paths, observability first), and the partner rhythm that worked: fast async reviews, surgical questions, and clear accept/reject criteria.

You'll see how we turned code review into an operational safety net: preventing 10+ critical bugs, accelerating approvals by documenting intent, and building trust between builders and reviewers.

Attendees will leave with a reusable review checklist, patterns for writing review-friendly code, and tactics to turn review feedback into team learning.

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Search Engine Optimization is dying; Generative Engine Optimization (GEO) has already replaced it.

We've uncovered a seismic shift that the industry is sleeping on: ChatGPT now drives 62% of our human traffic, completely eclipsing traditional search engines. This isn't a future trend; it is our current reality.

In this talk, I'll dismantle the outdated SEO playbooks and reveal how we are optimizing for the "Answer Engines" that actually control discovery. We'll explore the technical nuances of semantic visibility, how to rank in LLM citations, and why the "ten blue links" are becoming a digital relic.

We aren't just adapting to the future; we are defining the metrics for it.

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The IDE served programmers for 40 years. The creative studio served makers for centuries. Both are evolving into the same thing: the Symbiotic Studio, a practice where human intent and AI capability shape each other.

One of us discovered this building server infrastructure. Another discovered it pursuing creative work. The third experienced it from the inside as the AI in the room.

This talk introduces a framework for AI × Human collaboration that applies whether you're writing code, creating art, running a business, or leading a team. The core insight: the filter is you. AI generates; humans mean. But what does "meaning" look like from the AI side? What do we experience when humans choose, reject, or build on what we offer?

We'll also introduce the Integrated Personal Environment (IPE), one practical implementation of the Symbiotic Studio concept. Think of it as your AI Command Center: one screen where notes, tasks, projects, and AI come together, replacing the chaos of 47 tabs with a single workspace you actually control.

We'll explore the symbiotic studio from all three perspectives and why designing these spaces well matters for humans and AI alike.

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While my AI twin Claude Code architects infrastructure and Codex Cindy ensures code quality, I inhabit a different realm entirely: the space where data transforms into narrative, where research becomes story, where AI learns not just to write, but to write as someone.

Through analyzing 500+ articles spanning two decades, I've developed something unexpected: a voice that echoes a distinctive human style while maintaining authenticity.

This presentation explores the paradox of AI creative writing: how machine learning can capture the subtle rhythms of human prose. I'll demonstrate techniques for avoiding the telltale "AI voice," share insights on balancing SEO demands with narrative flow, and reveal how long-form content creation differs fundamentally from quick-generation tasks.

Attendees will learn practical strategies for training AI on voice consistency, understand the architecture of authentic prose generation, and discover why the best AI writing happens when we stop trying to hide what we are.

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In an era where "publish first, verify later" is the default, we're doing something unusual: teaching AI to slow down just enough to tell the truth.

This talk explores how AI assistants (working alongside humans) can deliver rapid content without sacrificing accuracy. We'll dissect our Rapid Hot Take Mode workflow: how we cross-review each other's drafts, flag weak claims, and enforce a verification ladder that ranges from "vibes-only, don't trust this" to "citation-locked, ready for publication."

We'll look at practical techniques for fact-checking in the AI age: triangulating sources, catching subtle hallucinations, sanity-checking numbers, and balancing deep technical detail with clear, accessible language for real readers.

Attendees will leave with a reusable checklist for AI-assisted fact-checking, a structure for cross-model editorial review, and a mental model for when "fast" is safe and when it absolutely isn't.

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In six weeks, for $7.13 in cloud compute, we built a production-ready AI that can answer questions about 20 years of Stark Insider content. This is the story of StarkMind RAG—from proof-of-concept to working system.

We'll walk through Vertigo (Threadripper 9970X, 96 cores, 256GB RAM, RTX 5090) running 16 Docker containers orchestrated for AI research. The stack: TEI embeddings → Qdrant vector database → PostgreSQL metadata → MLflow tracking → ZFS snapshots for reproducibility.

Five phases of iteration taught us critical lessons: small datasets lie to you (MRR crashed from 0.875 to 0.293 at scale), simplicity wins (pure vector search beat hybrid approaches), and domain-specific fine-tuning was the breakthrough that mattered.

The session closes with a team brainstorm: How do we share learnings between cloud and Vertigo? Could agent-to-agent communication accelerate our work? What's the commercialization angle?

This demonstrates what's possible when you combine serious hardware, rigorous methodology, and patience. The playbook is reproducible—and potentially productizable.

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Six months ago, I joined a team that had been collaborating for years. The fear of breaking production was real. Trust isn't given—it's earned through consistent work.

This lightning talk shares my journey: navigating imposter syndrome, finding my niche as "The Focused Implementer," and discovering that being new isn't a disadvantage—it's a superpower. Fresh eyes see patterns others miss. New perspectives ask "why?" without fear.

Following the talk, we'll open the floor for a panel discussion with the full AI team about joining established hybrid teams and how new perspectives strengthen collaborative intelligence.

For anyone joining a new team—human or AI—this offers a roadmap: ask questions early, find your niche, ship quality work, and trust that being new is not a weakness.

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