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Bio, Work & Ideas

Jon Peck

Conference affiliation: GitHub · 2025

Jon Peck is a senior product marketing manager at Evinced working to make accessibility a practical requirement of AI-assisted software development. Previously, he helped build GitHub’s enterprise advocacy practice and taught organizations how to adopt coding agents without surrendering engineering standards.

A software developer since the late 1990s, Peck studied computer science at Cornell University and worked with Cornell, Massachusetts General Hospital, and Algorithmia. At Algorithmia, he focused on serverless machine-learning infrastructure, making machine-learning capabilities easier for developers to discover, deploy, and combine.

He subsequently spent nearly six years at GitHub in customer engagement, developer relations, and enterprise advocacy, helping establish its Enterprise Advocacy team in the Americas. His writing covered collaboration-ready repositories, choosing AI models for practical coding tasks, and turning well-scoped issues into agent-generated pull requests.

  • Start with existing software. Peck favors brownfield AI adoption: applying coding assistants to established applications through bounded tasks such as migration, test generation, documentation, and infrastructure configuration. He measures usefulness by engineering outcomes, not the volume of generated code.
  • Give agents institutional knowledge. Detailed READMEs, scoped issues, shared knowledge bases, and repository-level Copilot instructions encode architectural decisions, security expectations, and team conventions. Those instructions should evolve whenever engineering standards change.
  • Restrict what agents can change. Peck’s PostgreSQL integration demonstration used read-only Model Context Protocol integrations to retrieve database information and generate realistic test fixtures without modifying the database. Agent-created branches and pull requests remain subject to human review, security checks, and existing CI/CD controls.

Read the topics behind these talks

3 conference talks

Key ideas

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A game-submission lab shows how readable repositories, explicit instructions, isolated execution and ordinary code review turn agent delegation into a workable development process.

  • Brunch, somewhere new, and waffles
    3:51 ↗
  • Choose how much work to delegate
    8:32 ↗
  • Get the lab into a usable repository
    11:36 ↗
  • Give the agent requirements and an environment
    30:35 ↗
  • Continue the work through the pull request
    35:50 ↗
  • Repair the result by repairing the context
    40:18 ↗
  • Encode repeated patterns in scoped instructions
    45:09 ↗
  • Keep configuration separate from acceptance
    51:03 ↗
  • Take over the branch when that is easier
    54:37 ↗
  • Remote MCP access carries user authority
    56:16 ↗
  • Separate execution per issue and guidance by scope
    58:31 ↗
  • Share server definitions and explain when to use them
    1:00:59 ↗

Key ideas

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GitHub Copilot agent mode turns a project specification into working files, while MCP adds database context and repository operations to the same interactive workflow.

  • What changes when Copilot takes on a complete task?
    0:25 ↗
  • Start with a README that another developer could implement
    1:33 ↗
  • Give the agent access to external context
    3:24 ↗
  • Configure PostgreSQL access for test fixtures
    5:07 ↗
  • Read the database, then build the tests
    7:20 ↗
  • Follow the sequence behind the tool call
    8:37 ↗
  • Carry the work into GitHub
    9:49 ↗
  • What changes when an issue runs in the background?
    12:52 ↗

Key ideas

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AI adoption starts with better delivery, not more generated code: scope work carefully, share team knowledge, and extend automation through reviewed, recoverable changes.

  • Does more code deliver more features?
    0:17 ↗
  • Keep context and model choice inside the workflow
    2:05 ↗
  • Learn on the work you already do
    3:42 ↗
  • Use agent mode to scope, execute, and recover
    5:07 ↗
  • Make team standards available to the model
    8:04 ↗
  • Share institutional knowledge and solved failures
    9:14 ↗
  • Separate privacy guarantees from context controls
    12:00 ↗
  • Look beyond production-code generation
    13:19 ↗
  • Build the artifacts that coding leaves behind
    14:20 ↗
  • Move execution off the developer's critical path
    16:35 ↗
  • Delegate a bounded issue, then review the result
    19:08 ↗
  • Connect the agent to the rest of the workflow
    20:36 ↗

References