The Context Window

This Dot Labs

Join This Dot Labs' Tracy Lee, A.D. Slaton, and Brandon Mathis for candid conversations about the latest releases and technical advancements in the AI development ecosystem, how real teams are using these tools in production, and what it all means for the future of building software.

  1. 5d ago

    Jeff Escalante (Clerk) on Codex, Agent Costs, T3, and Why Humans Still Need to Review the Code

    Brandon Mathis talks with Jeff Escalante of Clerk about what it actually takes to go all in on AI-assisted software development. They compare Codex and other coding tools, examine why subscription pricing changes the economics of agentic work, and make the case for keeping humans firmly in the loop—especially for security-critical products. The conversation also explores AI code review, productivity signals, and why today’s project-management habits are struggling to keep up with a multi-agent workflow.Explore Clerk at clerk.dev and join the conversation on how AI is reshaping practical engineering work.5 Key Takeaways- Codex is a practical daily driver for AI-assisted development, but the real value comes from active human direction—not simply handing work to an agent.- Subscription plans can make intensive AI coding far more economical than API-priced cloud agents; high token usage can represent thousands of dollars of theoretical monthly spend.- Unmonitored agent swarms are not dependable enough for security- and reliability-sensitive systems. Authentication platforms such as Clerk cannot afford autonomous mistakes.- AI can meaningfully increase engineering output, including drafting and reviewing code, yet its work still requires close reading, frequent course correction, and human accountability.- Traditional productivity metrics and project-management systems are increasingly mismatched with a workflow where engineers supervise multiple concurrent AI-driven projects.Brandon Mathis on Linkedin: https://www.linkedin.com/in/mathisbrandon/Jeff Escalante on Linkedin: https://www.linkedin.com/in/tracyslee/This Dot Labs Twitter: https://x.com/ThisDotLabsThis Dot Media Twitter: https://x.com/ThisDotMediaThis Dot Labs Instagram: https://www.instagram.com/thisdotlabs/This Dot Labs Facebook: https://www.facebook.com/thisdot/Sponsored by This Dot Labs: https://www.thisdot.co/

  2. Aug 7

    OpenAI GPT Sites: The Easiest Way to Deploy AI Apps

    OpenAI's new GPT Sites feature makes it possible to go from an idea in ChatGPT to a live, shareable web application in just a few prompts. In this episode of The Context Window, Brandon Mathis sits down with Jon Abrams, Technical Lead for GPT Sites at OpenAI, to explore how the platform works, why it was built, and what it means for developers and non-developers alike. They discuss deploying full-stack applications directly from ChatGPT, built-in authentication, SQLite databases, Cloudflare Workers, custom domains, internal business tools, rapid prototyping, and why GPT Sites could fundamentally change how software gets built and shared. 5 Key Things You'll Learn in This Episode - How GPT Sites lets you deploy AI-generated web apps directly from ChatGPT without managing hosting infrastructure or traditional deployment workflows. - How built-in features like authentication, SQLite databases, and Cloudflare Workers make it possible to build full-stack applications with just a few prompts. - Why GPT Sites is ideal for rapid prototyping, internal tools, and side projects, allowing developers to move from idea to a shareable application in minutes. - How developers can extend GPT Sites with custom domains, external services, and existing infrastructure while avoiding vendor lock-in. - Why AI is changing the economics of software development, reducing the need to manage infrastructure so builders can focus on solving real problems instead of repetitive engineering tasks. Brandon Mathis on Linkedin: https://www.linkedin.com/in/mathisbrandon/ Jon Abrams on Linkedin: https://www.linkedin.com/in/thejonabrams/ This Dot Labs Twitter: https://x.com/ThisDotLabs This Dot Media Twitter: https://x.com/ThisDotMedia This Dot Labs Instagram: https://www.instagram.com/thisdotlabs/ This Dot Labs Facebook: https://www.facebook.com/thisdot/ Sponsored by This Dot: https://ai.thisdot.co/

  3. Jul 21

    Building Deterministic AI Coding Workflows with Claude Code

    Claude Code Workflows introduce a new way to orchestrate AI coding agents, but what do they actually change for engineering teams?In this episode of The Context Window, Brandon Mathis, Jonathan Fontanez, and Coston Perkins break down how they're using workflows to automate repetitive development tasks, coordinate multiple AI agents, and build more reliable AI-assisted engineering systems. They discuss practical use cases, workflow patterns, evaluation loops, codebase maturity, and why deterministic orchestration is becoming just as important as the models themselves.Brandon Mathis on Linkedin: https://www.linkedin.com/in/mathisbrandon/Coston Perkins on Linkedin: https://www.linkedin.com/in/costonperkins/Jonathan Fontanez on Linkedin: https://www.linkedin.com/in/jonathan-fontanez-27715428/ This Dot Labs Twitter: https://x.com/ThisDotLabsThis Dot Media Twitter: https://x.com/ThisDotMediaThis Dot Labs Instagram: https://www.instagram.com/thisdotlabs/This Dot Labs Facebook: https://www.facebook.com/thisdot/Sponsored by This Dot: https://ai.thisdot.co/AI Workshop Series from This Dot Labs: ⁠https://ai.thisdot.co/workshops⁠Use Code THISDOTX at Checkout for $50 tickets!Chapters00:00 Introduction to Claude Code Workflows01:25 What Workflows Are and Why They Matter06:34 Real-World Workflow Use Cases10:13 Workflow Patterns and Engineering Loops15:13 Building Self-Driving Development Workflows20:03 Codebase Readiness and AI Workflow Success25:32 Custom Workflow Demos and Practical Examples32:15 Workflow Ideas for Every Engineering Team38:05 The Future of AI Workflow Engineering42:04 Final Thoughts and Getting Started

  4. Jun 26

    The AI Triforce: Product Experience Architect, Integrity Engineer, & Systems Engineer

    AI is making it easier than ever for individuals to build software, but speed alone doesn't create sustainable products. In this episode of The Context Window, Brandon Mathis sits down with Gant Laborde to discuss how engineering teams may need to evolve as AI becomes a core part of the software development lifecycle. The conversation explores the concept of the "AI Triforce," a framework centered on three emerging roles: the Product Experience Architect, Integrity Engineer, and Systems Engineer. They examine why giving every developer an AI coding assistant is not enough, the risks of unchecked AI generated code, and how organizations can balance rapid feature development with quality, reliability, and long term maintainability. Along the way, they discuss vibe coding, guardrails, technical debt, team structure, and the new skills that may define the next generation of software engineering. If you're thinking about how AI changes not just how code gets written but how teams work together, this episode offers a practical look at what comes next. What You’ll Learn: - AI is forcing teams to rethink software development workflows, not just developer productivity. - Giving every engineer an AI coding assistant without guardrails creates new risks around quality, security, and maintainability. - The AI Triforce framework separates feature creation, quality assurance, and system architecture into distinct responsibilities. - Faster development cycles require stronger validation, testing, and oversight processes. - The most successful engineering organizations will be the ones that combine human expertise and AI through intentional team structures. Chapters 00:00 Why AI Needs New Engineering Philosophies 04:39 The AI Triforce: A New Team Model 08:06 The Risks of Vibe Coding and AI-Generated Software 13:48 From Individual Contributors to AI-Assisted Teams 16:13 The Builder: Product Experience Architect (PXA) 19:23 The Integrity Engineer: Guardrails, Testing, and Quality 25:26 The Systems Engineer: Architecture and Long-Term Maintainability 29:24 Why "Everyone's a Builder" Doesn't Scale 34:15 AI Team Structures, New Roles, and the Future of Engineering 43:05 Chain React and Closing Thoughts Brandon Mathis on Linkedin: https://www.linkedin.com/in/mathisbrandon/ Gant Laborde on Linkedin: https://x.com/GantLaborde This Dot Labs Twitter: https://x.com/ThisDotLabs This Dot Media Twitter: https://x.com/ThisDotMedia This Dot Labs Instagram: https://www.instagram.com/thisdotlabs/ This Dot Labs Facebook: https://www.facebook.com/thisdot/ Sponsored by This Dot: https://ai.thisdot.co/

  5. Jun 5

    Are You Ready for the AI Harness Wars?

    Claude Code's recent quality issues sparked a broader conversation about transparency, reliability, and vendor lock-in across AI-powered developer tools. Brandon Mathis, Coston Perkins, and Jonathan Fontanez discuss Anthropic's explanation for the degradation, the challenges of relying on closed agent systems, and the risks organizations face when critical development workflows depend on tools they cannot fully inspect or control. The conversation also explores the rapidly evolving coding harness landscape, including Codex, OpenCode, Pi, Goose, Gemini CLI, and Quinn Code. Topics include benchmark performance, open source versus closed source approaches, customization, context engineering, long-running agents, and why many developers are beginning to view harnesses, not models, as the next major battleground in AI-assisted software development. The discussion examines what engineering teams should consider when evaluating AI tooling and why flexibility may become increasingly important as the ecosystem continues to evolve. What You'll Learn: - Why recent Claude Code quality issues pushed many developers to reevaluate their AI tooling choices. - How coding harnesses influence agent behavior, performance, and developer workflows beyond the underlying model. - The tradeoffs between open source and closed source AI coding tools, including transparency, customization, and vendor lock-in. - How tools like Codex, OpenCode, Pi, Goose, and Gemini CLI compare as the coding harness ecosystem rapidly evolves. - Why many developers believe the next major wave of AI innovation will come from harness design rather than model improvements alone. Brandon Mathis on Linkedin: https://www.linkedin.com/in/mathisbrandon/ Coston Perkins on Linkedin: https://www.linkedin.com/in/costonperkins/ Jonathan Fontanez on Linkedin: https://www.linkedin.com/in/jonathan-fontanez-27715428/ This Dot Labs Twitter: https://x.com/ThisDotLabs This Dot Media Twitter: https://x.com/ThisDotMedia This Dot Labs Instagram: https://www.instagram.com/thisdotlabs/ This Dot Labs Facebook: https://www.facebook.com/thisdot/ Sponsored by This Dot: https://ai.thisdot.co/ AI Workshop Series from This Dot Labs: ⁠https://ai.thisdot.co/workshops⁠ Use Code THISDOTX at Checkout for $50 tickets!

  6. Jun 5

    Could Markdown Become the Next Programming Language? AI Agents, Claude Code, MCP & Agentic Workflows

    In this episode of The Context Window, Brandon Mathis and Jonathan Fontanez explore a provocative question emerging in AI-assisted development: could Markdown become the next programming language? Using examples from agentic workflows, Claude Code skills, MCP integrations, and evolving AI harnesses, they unpack how structured Markdown files are increasingly being used to orchestrate repeatable workflows, prototype systems, and guide AI agents with surprisingly programmatic behavior. The conversation digs into the tradeoffs of treating Markdown like executable infrastructure, including maintainability, entropy, security risks, token costs, hallucinations, and the challenges of testing non-deterministic workflows. Brandon and Jonathan debate where Markdown-based “programming” fits compared to traditional software engineering, especially for short-lived automations, prototyping, internal tooling, and rapidly evolving startup environments. Along the way, they discuss concepts like skill.md files, harness maturity, vibe coding, structured prompting, evals, repeatable workflows, and how AI agents are reshaping the boundary between natural language and software development itself. In This Episode, You’ll Learn: - How AI agents are turning Markdown into a lightweight way to define repeatable development workflows - Why short-lived automations and fast-moving projects may benefit from “programming” with Markdown instead of traditional code - The risks involved with agentic workflows, including hallucinations, security concerns, token costs, and workflow entropy - How skills, MCPs, and AI harnesses work together to connect agents to external systems and automate real engineering tasks - A practical framework for deciding when to use prompts, Markdown workflows, or full software systems in AI-assisted development Brandon Mathis on Linkedin: https://www.linkedin.com/in/mathisbrandon/ Jonathan Fontanez on Linkedin: https://www.linkedin.com/in/jonathan-fontanez-27715428/ This Dot Labs Twitter: https://x.com/ThisDotLabs This Dot Media Twitter: https://x.com/ThisDotMedia This Dot Labs Instagram: https://www.instagram.com/thisdotlabs/ This Dot Labs Facebook: https://www.facebook.com/thisdot/ Sponsored by This Dot: https://ai.thisdot.co/ AI Workshop Series from This Dot Labs: https://ai.thisdot.co/workshopsUse Code THISDOTX at Checkout for $50 tickets!

  7. Jun 5

    Deterministic vs Non-Deterministic AI Workflows for Developers

    In this episode of The Context Window, Brandon Mathis and Coston Perkins unpack one of the biggest shifts happening in AI-assisted development: when to let agents explore freely, and when to pull workflows back into deterministic, repeatable systems. Using real engineering examples like database migrations, CI pipelines, financial reporting, and code cleanup, they break down why relying entirely on non-deterministic agents can introduce risk, hallucinations, and hidden failures into critical workflows. The conversation explores a practical mindset for modern engineering teams: agents should build the tool, not become the tool. Brandon and Coston discuss how developers can use AI to generate scripts, workflows, dashboards, and automations that are inspectable, shareable, and reliable, instead of depending on one-off prompts and unpredictable outputs. Along the way, they dive into token efficiency, deterministic validation tooling, CI/CD automation, skills vs scripts, and the growing importance of accountability in AI-driven systems. In this episode, you will learn: - Why deterministic workflows are becoming critical as AI agents take on more engineering tasks - How to use AI agents to build reliable scripts, automations, and CI pipelines instead of relying on one-off prompts - The tradeoffs between non-deterministic agent behavior and repeatable engineering systems- How hallucinations, hidden failures, and inconsistent outputs can create risk in production environments - Practical ways to reduce token usage, improve reliability, and increase accountability in AI-assisted development Brandon Mathis on Linkedin: https://www.linkedin.com/in/mathisbrandon/ Coston Perkins on Linkedin: https://www.linkedin.com/in/costonperkins/ This Dot Labs Twitter: https://x.com/ThisDotLabs This Dot Media Twitter: https://x.com/ThisDotMedia This Dot Labs Instagram: https://www.instagram.com/thisdotlabs/ This Dot Labs Facebook: https://www.facebook.com/thisdot/ Sponsored by This Dot: https://ai.thisdot.co/

  8. Jun 5

    Building ChatGPT and Claude Apps

    In this video, Brandon Mathis and Ben Lesh break down the emerging MCP Apps protocol and what it means for the future of AI-powered applications. Drawing from hands-on experimentation with tools like ChatGPT Apps, Claude, Codex, and MCP servers, they explore how developers can build interactive applications directly inside AI chat experiences using standardized protocols, iframe-based UI rendering, and tool integrations. The conversation walks through the technical architecture behind MCP Apps, including app tools, metadata configuration, input schemas, UI resource hosting, and how models decide when and how to invoke tools. Brandon and Ben also discuss the realities of building against rapidly evolving AI standards, covering everything from TypeScript and Zod validation to local development workflows with ngrok, caching issues, integration testing challenges, and content security policies.Along the way, they unpack larger themes shaping AI development right now: the growing importance of open standards, the tradeoffs of non-deterministic systems, security concerns around MCP tooling, and how AI interfaces may reshape the future of application development itself. The episode also explores the parallels between today’s AI tooling ecosystem and the early days of the web and mobile app platforms, including the risks, opportunities, and maintenance challenges developers should expect as these protocols mature. What You Will Learn: - How the MCP Apps protocol allows developers to build interactive applications directly inside AI chat platforms like ChatGPT and Claude - Why open standards are becoming important for creating AI tools that work across multiple ecosystems and models - The practical realities of building MCP Apps, including tool registration, UI hosting, schemas, caching, and local development workflows - The security and privacy risks involved with MCP tools and why developers need to carefully manage tool permissions and data exposureWhy testing AI-powered systems is more difficult than traditional software testing due to non-deterministic model behavior and evolving protocols Chapters 00:00 Introduction to MCP apps and AI chat integrations 04:00 How MCP apps work inside ChatGPT and Claude 06:40 Building MCP apps under the hood 13:20 Local development, ngrok, and testing workflows 16:40 Ideal use cases and limitations of MCP apps 20:20 MCP app marketplaces, approvals, and discovery 21:40 Security risks, trust, and data leakage concerns 24:40 Why MCP apps require ongoing maintenance 27:10 AI tooling and plugins for building MCP apps 28:45 Testing non-deterministic AI applications 32:20 The return of iframes in modern AI apps 35:00 Final thoughts on the future of MCP apps Brandon Mathis on Linkedin: https://www.linkedin.com/in/mathisbrandon/ Ben Lesh on Linkedin: https://www.linkedin.com/in/blesh/ This Dot Labs Twitter: https://x.com/ThisDotLabs This Dot Media Twitter: https://x.com/ThisDotMedia This Dot Labs Instagram: https://www.instagram.com/thisdotlabs/ This Dot Labs Facebook: https://www.facebook.com/thisdot/ Sponsored by This Dot: https://ai.thisdot.co/ AI Workshop Series from This Dot Labs: https://ai.thisdot.co/workshops Use Code THISDOTX at Checkout for $50 tickets!

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About

Join This Dot Labs' Tracy Lee, A.D. Slaton, and Brandon Mathis for candid conversations about the latest releases and technical advancements in the AI development ecosystem, how real teams are using these tools in production, and what it all means for the future of building software.

Information

  • Creator
    This Dot Labs
  • Years Active
    2025 - 2026
  • Episodes
    21
  • Rating
    Clean
  • Copyright
    © This Dot Labs
  • Show Website
    The Context Window

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