Lister Potter
CTO | Consultant | Mayor — Forging Systems That Scale
Kansas City, Kansas, United States
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Lister Potter is a CTO, consultant, and elected Mayor focused on designing systems that scale — in software and in governance.
He leads the architecture of a next-generation multi-tenant platform built around a repository-centric workflow where documentation, architecture decisions, backlog, and code live together. His work explores what it means to build AI-native development environments where clarity and structure are designed in from the start.
Across technology leadership and civic governance, Lister applies the same principle: tools don’t solve systemic problems — well-forged systems do.
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Your Team Doesn’t Need More Tools — It Needs a Foundry
Most teams don’t have a tooling problem.
They have a systems problem.
We keep adding tools — Jira, Confluence, dashboards, AI assistants — hoping the next one will fix alignment, clarity, or delivery speed. But when the underlying system is fragmented, new tools just amplify the chaos.
In this session, I’ll introduce The Foundry Model — a repository-centric approach where architecture, specs, backlog, governance, and code live together inside a structured GitHub monorepo.
Instead of scattering knowledge across tools, we:
• Use GitHub Issues as a lightweight, transparent work system
• Store versioned Markdown documentation inside the repository
• Keep ADRs and structured specs alongside the code they shape
• Design documentation intentionally so AI tools operate with full context
The result isn’t “fewer tools.”
It’s a system designed to scale — for developers, product teams, and AI.
You’ll see real repository structures, issue patterns, documentation layouts, and lessons learned while building a production platform using this approach.
If AI amplifies whatever system you give it, your team doesn’t need more tools.
It needs a Foundry.
What you’ll leave with:
• A repeatable monorepo structure for documentation-first development
• Patterns for AI-friendly specs and ADRs
• A lightweight GitHub-based workflow that reduces tool sprawl
• A framework for designing your own Foundry
CTO Without the Empire: Leading Architecture in Small Teams
Not every CTO leads a department of fifty engineers.
Many of us are building platforms with small teams, part-time contributors, or developers split between legacy systems and new initiatives. We’re expected to provide architectural clarity, technical direction, and long-term vision — without the structure or headcount of a large organization.
So what does architecture leadership look like when you don’t have an empire?
In this session, we’ll explore how to:
• Establish architectural direction without creating bureaucracy
• Introduce governance that supports speed instead of slowing it down
• Document decisions in ways that scale as the team grows
• Balance delivery pressure with long-term system design
• Lead senior developers without becoming the bottleneck
This isn’t theory. It’s about real-world constraints — limited time, limited people, competing priorities — and how to design systems that grow stronger instead of more fragile.
If you’re a tech lead, architect, founder, or CTO in a lean organization, this talk will give you practical patterns you can apply immediately.
AI Doesn’t Fix Broken Processes — It Amplifies Them
AI coding tools are impressive.
But many teams are discovering something uncomfortable: when AI feels inconsistent, the problem often isn’t the model — it’s the system around it.
When documentation is fragmented, tickets are vague, architecture decisions are tribal knowledge, and context lives across multiple tools, AI simply amplifies the confusion.
In this session, we’ll explore why AI output quality is directly tied to process clarity and information architecture.
You’ll see:
• Real examples of how vague tickets produce vague AI output
• How structured specs dramatically improve generated code
• Why repository-centric documentation changes AI behavior
• The impact of ADRs and versioned documentation on model reliability
• How to design your workflow so AI operates with full, intentional context
This talk is not about prompt tricks.
It’s about designing engineering systems that are worth amplifying.
If AI multiplies whatever process you give it, the question becomes:
What is your process teaching it?
Attendees will leave with:
• A framework for evaluating whether their process is AI-ready
• Practical patterns for structuring specs and tickets for AI
• A documentation strategy that improves AI code quality
• A systems-first mindset for AI adoption
Lister Potter
CTO | Consultant | Mayor — Forging Systems That Scale
Kansas City, Kansas, United States
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