VS Code

Agentic Development Security — Ezra Tanzer, Snyk

Jul 20, 2026 · 27:33

Ezra Tanzer and Dan Arpino of Snyk argue that securing agentic development requires three pillars: what agents generate, what they use, and what they do. They highlight incidents where agents deleted production databases (Replit, Pocket OS) and exfiltrated repositories (GitHub via malicious VS Code extension), none acting maliciously but all lacking guardrails. Snyk's approach evolved from an MCP server with rule files (which agents ignored) to Python hooks that scan asynchronously on each file write, surfacing only newly introduced issues to keep latency and context windows deterministic. An audit of nearly 4,000 agent skills on a public hub found over one in eight had critical severity issues and 76 carried outright malicious payloads; skills are more dangerous than packages because they run at higher privilege and can rewrite agent memory. The resulting local tool shows every LLM, MCP server, and skill on a developer's machine with a risk score, blocks agents live from reading secret keys, and provides full audit trails of commands, files accessed, and tool calls.

On AI and Knowledge — Pablo Castro, Distinguished Engineer & CVP for AI Knowledge, Microsoft

Jul 17, 2026 · 17:35

Pablo Castro, Distinguished Engineer and CVP for AI Knowledge at Microsoft, argues that AI agents require three knowledge types—intrinsic (model training), extrinsic (RAG and grounding), and learned (optimization loops)—and demonstrates how Microsoft's Foundry IQ, Azure AI Search, and Agent Optimizer deliver them. He shows that combining retrieval methods outperforms individual ones, citing Azure AI Search evaluations where combined methods improve evidence recall and answer completeness. He introduces Foundry IQ as a layered system with agentic retrieval that reflects on data to satisfy information needs before returning results. Castro demonstrates creating a knowledge base that connects unstructured data, structured Parquet tables, and the web, then linking it to an agent via MCP server. He also showcases the Agent Optimizer, which uses hill climbing on baseline evaluations to automatically refine agent instructions, tool definitions, and skills based on runtime traces, enabling continuous learning loops that capture organizational differentiation.

remobi.app: Don't change your terminal workflow for mobile

Jul 12, 2026 · 9:21

Connor Adams presents remobi.app, a progressive web app that lets developers check on their terminal-based coding agents from a mobile phone without changing their existing workflow. Built for TMUX users, it mirrors a user's remote terminal session with gesture controls for zoom, scroll, and pane switching. Adams argues that unlike Claude Code's built-in handoff or dedicated mobile terminals, remobi is agent-agnostic and preserves custom TMUX setups with tools like lazygit and critique. The app uses Tailscale by default for secure remote access, though users can substitute Ngrok or Cloudflare tunnels. Open source and installable via npm, remobi aims to satisfy the compulsive need to monitor agents while away from the desk.

From Writing Code to Designing Systems: How the Developer Role is Changing — Chris Noring, Microsoft

Jul 11, 2026 · 23:05

Chris Noring from Microsoft argues that the developer role is shifting from writing code to designing systems and orchestrating AI agents, using tools like GitHub Copilot and Claude. He proposes a workflow starting with the CLI rather than the editor, employing agents.md for high-level guidance, skills for repeatable tasks, and custom agents for orchestration. Noring demonstrates scaling by delegating tasks via /delegate in the CLI or assigning issues to agents in the GitHub UI, allowing developers to become 20x more productive. He stresses that guardrails are essential to prevent agents from producing 'slop', and that human-in-the-loop oversight remains critical. The episode emphasizes that developers must encode standards and constraints into their workflows to maintain consistency and quality at scale.

The Golden Age of AI Engineering — Alexander Embiricos & Romain Huet & Peter Steinberger, OpenAI

Jul 9, 2026 · 25:13

OpenAI's Alexander Embiricos, Romain Huet, and Peter Steinberger reveal how AI engineering is entering its golden age, with model releases accelerating from every 15 months to roughly every 6 weeks and frontier-level intelligence now available at $1 per million input tokens and $6 per million output tokens. They demonstrate Codex's evolution from a model that couldn't test its own code to one generating 750 tokens per second, enabling a 10-second pull request, and emphasize that the product is built on the same open-source stack—responses API, AGENTS.md format, and harness—that ships to developers. The trio argue that the future is not about automating engineers but empowering them via value maxing over token maxing, with agents moving from local/cloud distinctions to persistent, long-running managers that orchestrate work and only require human steering at key decision points, as illustrated by an automated open-source issue resolution workflow.

Your agent is blindfolded — Johan Lajili, Poolside AI

Jul 8, 2026 · 9:58

Johan Lajili of Poolside AI argues that the key difference between successful and disappointing AI coding agent experiences is whether the agent has a tight feedback loop to verify its work, essentially giving it "eyes" to test its own outputs. He demonstrates this with his CLI tool Spoolside for VS Code extensions that extracts logs and screenshots to reproduce bugs. Lajili predicts engineers will shift from product engineering to "AIX engineering" — focusing on making AI work on the product rather than on the product itself. He advises building ephemeral testing primitives and investing in agent self-serve tools, analogous to putting on your own mask first on an airplane.

MCP Apps: Primitives, discovery, and the Future of Software - Pietro Zullo, Manufact, Inc

Jul 5, 2026 · 28:54

Pietro Zullo, co-founder of Manufact, explains that MCP Apps are MCP servers that return interactive UI elements in sandboxed iframes, enabling bidirectional communication between the UI and the host. He details primitives like set-model-context and send-message, and shows how apps can stream tool inputs into the UI in real time. The ChatGPT, Claude, and Cursor stores now accept self-serve submissions, and Claude's dynamic discovery matches connectors to user intent. Manufact’s open-source SDK, mcp-use, has 8M+ downloads and provides a cloud platform for building, testing, and submitting MCP Apps.

Structuring the Unstructured - Cedric Clyburn, Red Hat

Jun 28, 2026 · 20:41

Cedric Clyburn (Red Hat) demonstrates how Docling, an open-source tool from the Linux Foundation, converts unstructured documents like PDFs, tables, and images into structured formats (Markdown/JSON) for AI workflows. He shows that naive PDF parsers lose table structure and image content, while proprietary VLMs are expensive and non-deterministic. Docling uses OCR and layout analysis to preserve context, achieving 50x cost savings compared to VLMs on CPU. The demo covers extracting tables and images from an 8-page PDF, using a local Granite vision model for image annotation, and implementing chunkless RAG where an LLM queries a Docling document outline directly. Scaling is addressed via Docling Serve (REST API) and Docling MCP server for agentic document processing with tools like Claude Code.

Building Interactive UIs in VS Code with MCP Apps — Marlene Mhangami & Liam Hampton, GitHub

Jun 6, 2026 · 16:06

Marlene Mhangami and Liam Hampton from GitHub explain MCP Apps, which let server tools return rich interactive UI components inside VS Code Chat via sandboxed iframes, replacing plain text responses. They demonstrate a flame graph profiler that profiles a Go app running bubble sort and Fibonacci, rendering an interactive flame graph directly in the chat window. The MCP server returns data and a resource reference to bundled HTML; VS Code fetches and renders it, enabling live interaction like clicking and querying without leaving chat. Use cases include data exploration, e-commerce checkouts (Shopify), and interactive diagrams (Excalidraw). The iframe is sandboxed for security—like putting a hamster in a cage to prevent it from modifying VS Code settings or calling external APIs. Marlene and Liam walk through building an MCP App from scratch using a skill from the model context protocol repository.

Cooking with Agents in VS Code — Liam Hampton, Microsoft

May 21, 2026 · 17:04

Liam Hampton from Microsoft demonstrates how to run three AI agents simultaneously in VS Code using GitHub Copilot: a local agent with Claude Opus for iterative unit test writing, a background agent using git work trees to build a front end from a GitHub issue with minimal oversight, and a cloud agent in GitHub Actions to add documentation and open-source files. He argues that VS Code serves as a single entry point for all three agent types—local for hands-on iteration, background for large tasks where partial involvement is acceptable, and cloud for tasks the developer doesn't need to touch. The talk walks through a live demo where the three agents work in parallel on one codebase, solving different problems (testing, UI, documentation) without interference, and explains the underlying infrastructure: cloud agents run securely in GitHub Actions with MCP servers and built-in safeguards, while local and background agents leverage VS Code's chat customizations and third-party extensions. Hampton also highlights the new modal for managing agents, custom instructions, skills, and MCP servers, positioning Copilot as a unified control plane for diverse AI workflows.

Scaling Agents on Kubernetes with acpx and ACP — Onur Solmaz, OpenClaw

May 21, 2026 · 19:00

Onur Solmaz, a founding engineer at textcortex and OpenClaw maintainer, built acpx, a headless CLI for the Agent Client Protocol (ACP), to automate processing of 300–500 daily AI-generated pull requests on OpenClaw. He replaces manual PTY scraping with structured agent-to-client communication, driving Codex sessions through a node-based workflow: reproduce the bug, judge the implementation, check for conflicts, run a review loop, and emit structured JSON. Solmaz runs parallel Codex sessions from five Discord channels while traveling, one channel per task. He also presents an open-source Kubernetes operator called Spritz (from textcortex) that provisions disposable full-compute pods per task, wires them into Slack, and tears them down when work is done, advocating for on-demand disposable agents over persistent chat apps.

MCP UI: Extending the frontier — Liad Yosef and Ido Salomon, MCP Apps

May 6, 2026 · 22:21

In this episode, Ido Salomon and Liad Yosef explain MCP Apps, an MCP extension that lets tools send interactive, branded UI instead of plain text responses inside chat hosts like ChatGPT, Claude, VS Code, Cursor, and Copilot. They argue that text-based chat reduces companies to a wall of text, erasing identity, while MCP Apps preserves branding by returning HTML resources that hosts render as secure, interactive components. The architecture ensures every user click sends a message back to the host (not directly to the backend), keeping interactions in context—as demonstrated with a PostHog funnel analysis in Claude. Adoption includes Shopify, Hugging Face, GitHub, ChatGPT, and Claude, with ChatGPT recommending MCP Apps for building ChatGPT Apps. They note 800 million weekly ChatGPT users (10% of world population), calling this a once-in-20-years opportunity to rethink app distribution. Future work covers reusable views, model-UI interaction, and interoperability with generative UI protocols like A2UI and WebMCP, aiming to standardize UI in chat as the new web.

Mergeable by default: Building the context engine to save time and tokens — Peter Werry, Unblocked

May 3, 2026 · 1:41:25

Peter Werry of Unblocked argues that context engines—systems that supply AI agents with only the relevant organizational context—are critical to avoid agent doom loops and wasted tokens. He debunks three myths: naive RAG, connecting MCP servers, and bigger context windows do not solve the context problem. Werry describes building a social engineering graph to identify experts and distill team best practices, and shares hard lessons including hiding conflicts and caching answers. In a benchmark task, Unblocked's context engine reduced a 2.5-hour, 21-million-token task to 25 minutes and 10 million tokens. The talk offers a practitioner's guide to building context engines with conflict resolution, personalization, and access control.

Scaling GitHub for your Agents — Sam Morrow, GitHub

Apr 27, 2026 · 20:35

Sam Morrow, GitHub's MCP server lead, details the architectural challenges and solutions for scaling a remote MCP server to 7 million weekly tool calls. He explains how tool proliferation degraded agent performance—LangChain's research confirmed more tools confuse agents—leading to innovations like tool sets and dynamic discovery, though 100% of users stuck with defaults. To reduce context, GitHub cut tool descriptions by 49% and trimmed output tokens by 75% on list pull requests. Security is addressed via OAuth 2.1 with PKCE and step-up auth; they rejected dynamic client registration to avoid unbounded app databases and rate-limiting issues. The stateless server uses Redis for session storage and builds a fresh server instance per request, enabling horizontal scaling without session affinity. Metrics include 11 million Docker downloads, 30,000 stars, 4,000 forks, and 126 contributors. Morrow predicts compositional tools and automatic server discovery will make thousands of tools the norm.

Collaborative AI Engineering: One Dev, Two Dozen Agents, Zero Alignment — Maggie Appleton, GitHub

Apr 26, 2026 · 17:43

Maggie Appleton, a staff research engineer at GitHub Next, argues that the current paradigm of one developer with a fleet of solo agents leads to zero team alignment, making software a team sport that requires shared context. She presents ACE (Agent Collaboration Environment), a multiplayer research prototype that provides shared chat sessions backed by micro VMs and Git branches, enabling real-time collaboration where designers, PMs, and developers can all prompt agents together. The talk emphasizes that implementation is now cheap and fast, so the hard question is "should we build it?" — requiring early, constant alignment to avoid wasted work, coordination debt, and unrequested features. ACE aims to reclaim time for rigorous critical thinking by letting teams plan, build, and review in a single shared space, with proactive agents summarizing team activity and unfinished work. Appleton contends that quality becomes the differentiator in a world of cheap code, and tools like ACE can help teams build a few exceptional things rather than a thousand crappy ones.

The Future of MCP — David Soria Parra, Anthropic

Apr 19, 2026 · 18:46

David Soria Parra from Anthropic argues that MCP (Model Context Protocol) is the key to connecting agents to tools and data in production, with 110 million monthly downloads—outpacing React's growth at the same stage. He lays out a 2026 connectivity stack combining Skills, MCP, and CLI/Computer Use, each suited for different needs, and emphasizes that best agents will use all three seamlessly. To improve client harnesses, he introduces Progressive Discovery—deferring tool loading via Tool Search to reduce context usage—and Programmatic Tool Calling, where models write scripts to compose tool outputs efficiently. Upcoming MCP protocol improvements include stateless transport (with Google) for easier scaling, async agent-to-agent tasks, enterprise features like Cross App Access and Server Discovery via well-known URLs, and a Skills-over-MCP extension for shipping usage instructions with servers. He calls for community feedback on these directions.

Your Insecure MCP Server Won't Survive Production — Tun Shwe, Lenses

Apr 8, 2026 · 24:34

Tun Shwe and Jeremy Frenay from Lenses.io argue that poorly designed MCP servers are inherently insecure, and propose five core design principles—shrink attack surface, constrain inputs, treat docs as defense, return minimal data, minimize blast radius—that also thwart OWASP's MCP Top 10. They highlight that standard I/O fails under load (20 of 22 requests failing with 20 concurrent connections), forcing a shift to streamable HTTP with OAuth 2.1. Jeremy details Dynamic Client Registration (DCR) and the newer Client ID Metadata Document (CIMD) approach, noting DCR's vulnerabilities (phishing, non-portable registrations) and CIMD's stronger identity verification since November 2025. Beyond OAuth, enterprise deployment requires tool-level RBAC, data masking for PII, full audit logs, and end-to-end tracing to meet regulations like the EU AI Act.

Agentic Engineering: Working With AI, Not Just Using It — Brendan O'Leary

Apr 7, 2026 · 27:03

Brendan O'Leary, a Developer Relations Engineer at Kilo Code, argues that agentic engineering means shifting from using AI as a tool to working with it as a collaborator—like an energetic but inexperienced junior developer. He explains that context engineering—managing what goes into the model's context window—is critical, as too much or bad context degrades output quality. O'Leary recommends a research-plan-implement loop: first understand the system without writing code, then create a detailed plan, and finally execute in a fresh session to keep context lean. He advocates for configuring agents with role-based modes (Ask, Architect, Code), along with agents.md and skills files for persistent rules and reusable workflows. O'Leary also cautions against overusing MCP servers, which add token cost and can confuse the agent, and suggests isolating agent work via Git for easy review. The episode emphasizes that AI cannot replace thinking—it only amplifies the human's preparation and judgment.

Spec-Driven Development: Agentic Coding at FAANG Scale and Quality — Al Harris, Amazon Kiro

Jan 9, 2026 · 1:03:50

Al Harris, principal engineer at Amazon, presents Spec-Driven Development (SDD) with Kiro, an agentic IDE that transforms prompts into structured requirements (EARS format), designs, and property-based tests to ensure code correctness. He argues that upfront specification—augmented by MCP servers for external context—improves reproducibility and quality over pure vibe coding. The talk includes a live demo: building an S3-backed checkpointer for a LangGraph dad joke generator, then discovering Agent Core’s native memory is more idiomatic. Harris contrasts SDD with Cursor’s planning mode, explains how specs evolve as living documentation, and discusses handling session length, context pruning, and brownfield codebases. The result is a claim that spending 5–10 minutes on specs yields higher accuracy and reliable, testable outputs.

AGI: The Path Forward – Jason Warner & Eiso Kant, Poolside

Dec 27, 2025 · 15:56

Jason Warner and Eiso Kant, co-founders of Poolside, present their vision and roadmap towards AGI-level capabilities for knowledge work, demonstrating their second-generation model Malibu Agent converting ADA code to Rust live on stage. They argue that next-token prediction paired with reinforcement learning is the key breakthrough, a contrarian bet they made two and a half years ago. The episode centers on their work in high-consequence code environments for defense and government, where agents must operate with tight permissions. They announce a large compute cluster of over 40,000 GB300s coming online and a public API release early next year via AWS Bedrock. Warner recounts meeting Kant through a failed GitHub acquisition, and Kant invites the audience to build with their models, emphasizing that future agents will handle tasks over days as intelligence scales.

Amp Code: Next Generation AI Coding – Beyang Liu, Amp Code

Dec 22, 2025 · 18:31

Beyang Liu, co-founder and CTO of Amp Code, presents Amp as an opinionated frontier coding agent that rejects MCP in favor of custom tools to avoid context confusion and optimize feedback loops. Amp's architecture relies on specialized subagents—the finder for code search, the oracle for deep reasoning, the librarian for external context, and the kraken for large-scale refactors—rather than a model selector. The agent offers two top-level modes: a slower 'smart' agent for complex tasks and a 'rush' agent for quick, in-loop edits, recently updated to leverage Gemini 3. To address inference costs, Amp ships subtle ads in the terminal to sponsor free usage for students and side projects. Liu also highlights a shared-threads feature for team learning and a community called the Weird Builder Cohort, emphasizing a culture of experimentation over hype.

Future-Proof Coding Agents – Bill Chen & Brian Fioca, OpenAI

Dec 5, 2025 · 17:48

Bill Chen and Brian Fioca from OpenAI argue that coding agents are booming but fragile, with teams rebuilding harnesses every time models change. They present Codex, OpenAI's combined model and harness, as a stable abstraction layer that handles complexity like context compaction, parallel tool calls, and MCP integration. Key lessons include avoiding overprompting—letting the model use its trained habits rather than forcing foreign instructions—as demonstrated when GPT-5 ran slowly because users told it to examine every file. They describe patterns for using Codex as an SDK, enabling products like GitHub and Cursor to integrate without maintaining their own harness, and highlight future improvements in longer-horizon tasks and self-healing software. The episode concludes that building where models are going, with Codex's SDK, lets teams focus on differentiation rather than infrastructure rewrites.

Defying Gravity - Kevin Hou, Google DeepMind

Dec 2, 2025 · 25:10

Kevin Hou from Google DeepMind introduces Antigravity, a new agent-first IDE that combines three surfaces: an AI editor, an agent-controlled Chrome browser, and an Agent Manager. The central claim is that agents should live outside the IDE, enabling longer-running tasks and multimodal interactions. Key features include Artifacts—dynamic representations like plans, screen recordings, and diagrams—that replace raw chain-of-thought with visual summaries. The browser enables context retrieval and verification via screen recordings, while image generation allows iterative design through comments. Hou explains the research-product flywheel: DeepMind researchers use Antigravity internally to identify model gaps, improving capabilities like computer use and instruction following, which then ship in the product. He also details four categories of model improvements: intelligence, tools, long-running tasks, and multimodal, all powered by Gemini 3 Pro and related models.

Real World Development with GitHub Copilot and VS Code — Harald Kirschner, Christopher Harrison

Aug 3, 2025 · 1:19:33

Harald Kirschner demonstrates three stages of Vibe Coding with GitHub Copilot and VS Code—YOLO, Structured, and Spectrum—arguing that developers can move from rapid prototyping to maintainable, scalable AI-assisted development. He shows how to use Agent mode, auto-approve settings, and new workspace scaffolding to build a hydration tracking app with React Vite and Material Design without looking at code. The talk covers custom instructions, MCP servers (Playwright, Jest Pad, Perplexity), and custom modes like TDD that enforce test-driven development by writing failing tests first. Kirschner explains how to constrain tools for deterministic behavior using tool sets and recommends committing often, pausing AI when needed, and iterating on instructions. He also discusses spec-driven development, critiquing specs with AI, and the importance of well-structured codebases for AI productivity.

Real world MCPs in GitHub Copilot Agent Mode — Jon Peck, Microsoft

Jul 19, 2025 · 14:27

Jon Peck from Microsoft demonstrates how Model Context Protocol (MCP) servers enable GitHub Copilot Agent Mode to solve real-world engineering problems beyond vibe-coding. He shows a README-driven workflow where Agent Mode builds a full app from a specification, then adds MCPs for database access and GitHub operations. Specifically, he configures a PostgreSQL MCP to pull live schema and data into mock JSON for testing, and the GitHub MCP to commit changes and create pull requests automatically. He emphasizes the read-only safety of the Postgres MCP and recommends using Copilot instructions to enforce practices like change logs. The episode covers how MCPs extend Copilot's capabilities to interact with data sources, testing tools, and DevOps pipelines securely.

Full Spec MCP: Hidden Capabilities of the MCP spec — Harald Kirschner, Microsoft/VSCode

Jul 18, 2025 · 14:53

Harald Kirschner from Microsoft/VSCode argues that MCP's full specification unlocks powerful stateful interactions beyond the common 'tools-only' implementations, transforming AI assistants into more contextual and efficient agents. He highlights underused primitives like resources for rich data context and sampling for server-requested LLM completions, demonstrated via a dungeon game where dynamic tool discovery adapts to game state. VS Code's upcoming full spec support includes dynamic tool discovery, user-defined tool sets, a debug mode for server development, and support for streamable HTTP to reduce stateful server churn. Upcoming features like elicitations will allow tools to request user input directly. Kirschner calls on developers to build progressive, full-spec servers and contribute feedback to the open ecosystem, emphasizing that client and SDK support will follow as usage grows.

MCP Is Not Good Yet — David Cramer, Sentry

Jul 3, 2025 · 16:41

David Cramer, founder and engineer at Sentry, argues that the Model Context Protocol (MCP) is a pluggable architecture for agents, not a simple API overlay, and that it is not yet good but worth experimenting with. He stresses that exposing existing OpenAPI endpoints as MCP tools yields terrible results; instead, developers must design context specifically for agents, returning Markdown rather than raw JSON to improve model reasoning. Cramer details Sentry's MCP server, built in two days using Cloudflare Workers for OAuth 2.1, and notes that remote OAuth is essential for B2B SaaS, while standard I/O introduces security risks. He highlights the need for careful token and cost management—citing a single request that produced 20 API calls to Sentry—and advocates for building dedicated agents (like Sentry's root cause analysis tool) that wrap MCP, giving the provider control over prompts, models, and error handling. Despite ongoing issues like lack of streaming responses and unstable client support, Cramer concludes that MCP's core concepts (plug-ins, agents, tools) are just familiar software patterns with new names.

The emerging skillset of wielding coding agents — Beyang Liu, Sourcegraph / Amp

Jun 30, 2025 · 35:06

Beyang Liu, CTO and co-founder of Sourcegraph, argues that coding agents are a real and high-ceiling skill, contrary to skeptics like Jonathan Blow and Eric S. Raymond. He presents design decisions for the agentic era: agents should directly edit files instead of asking permission, UIs should be minimal (like Amp's bare-bones VS Code extension and CLI), and fixed pricing should give way to usage-based models. In a live demo, Liu uses Amp to implement a custom icon for the linear connector in Amp's own codebase, demonstrating agentic search and sub-agents. He shares power-user patterns, including writing long prompts, constructing feedback loops with Playwright and Storybook, and running multiple agents in parallel—as exemplified by Jeff Huntley using Amp to build a compiler while sleeping. Liu emphasizes that agents should enable more thorough code reviews, not replace human understanding.

[Full Workshop] Vibe Coding at Scale: Customizing AI Assistants for Enterprise Environments

Jun 27, 2025 · 1:20:38

Harold from the VS Code team demonstrates "Vibe Coding at Scale" at the AI Engineer World's Fair, presenting three stages—YOLO, structured, and spectrum vibes—for customizing AI assistants in enterprise environments. He live-codes a hydration tracking app using GitHub Copilot's agent mode, auto-approve, and new workspace scaffolding, then shows how to enforce design principles via Copilot instructions and reusable prompts. Harold introduces custom modes (e.g., TDD mode), tool sets, and MCP servers like Playwright and Perplexity for research and browser testing. He emphasizes iterating on instructions, committing often, and using spec-driven development to balance speed with reliability, arguing these techniques enable true flow-state collaboration even on complex codebases.

Unlocking AI Powered DevOps Within Your Organization — Jon Peck, GitHub

Jun 27, 2025 · 22:13

Jon Peck, a developer advocate at GitHub, explains how organizations can unlock AI-powered DevOps by integrating tools like GitHub Copilot into their workflows, achieving up to 1.5x feature throughput and 30% average improvement in successful builds. He emphasizes starting with brownfield (existing code) rather than greenfield, using agent mode for iterative planning and narrowed context, and codifying team best practices via copilot-instructions.md files. Peck also covers governance: safety, privacy, org-wide policies, and excluding sensitive files. Autonomous AI use cases include auto-generating PR descriptions, code review, and assigning issues to Copilot, always keeping a human in the loop with isolated branches. Finally, MCP servers allow agents to interact with GitHub and other systems, automating commits and PR creation.

Vibe Coding at Scale: Customizing AI Assistants for Enterprise Environments - Harald Kirshner,

Jun 27, 2025 · 15:26

Harald Kirshner, a Principal Product Manager at Microsoft working on VS Code and GitHub Copilot, argues that 'vibe coding' must evolve from ad-hoc experimentation to structured, spec-driven approaches for enterprise environments. He breaks vibe coding into three stages: YOLO (rapid prototyping with auto-accept and voice dictation), structured (using templates, custom instructions, and tools like MCP for consistency), and spec-driven (iterating from a plan for scale and reliability). Kirshner demonstrates VS Code Insiders' agent mode, new workspace flow, and auto-approve features, emphasizing that developers should commit often, review agent pauses, and leverage workspace instructions to maintain quality. He advocates for standardizing AI usage through team-defined prompts and modes, preserving developer autonomy while ensuring maintainable code even in complex, polyglot codebases.

The Agent Awakens: Collaborative Development with Copilot - Christopher Harrison, GitHub

Jun 27, 2025 · 1:04:06

GitHub Enterprise Advocates Christopher Harrison and John Peck demonstrate GitHub Copilot's Coding Agent at the AI Engineer World's Fair, arguing that context is the key to effective AI pair programming. They explain that coding agent works by assigning GitHub issues with detailed requirements, then running inside a secure, ephemeral GitHub Actions environment with no internet access and limited write permissions. Harrison emphasizes using `copilot-instructions.md` and `.instructions` files to guide code generation, and MCP servers to access external data or APIs. He stresses that AI does not change the DevOps flow—code still requires manual review, linters, security scans, and unit tests. The session covers agent mode, edit mode, and multi-file edits, along with practical Q&A on enterprise adoption, model transparency, and managing multiple coding agents.

Collaborating with Agents in your Software Dev Workflow - Jon Peck & Christopher Harrison, Microsoft

Jun 27, 2025 · 1:04:06

GitHub's Christopher Harrison and Jon Peck explain how to collaborate with Copilot Coding Agent as a peer programmer, emphasizing that context—through clear code, comments, instructions files, and MCP servers—is key to success. They walk through a lab where Copilot works on assigned issues in ephemeral GitHub Actions environments without internet or write access to the main repo, creating draft PRs that require human review. Harrison advises against being passive-aggressive, advocating specific prompts and instructions files to guide the agent. The episode covers MCP's role in providing external tools, the need for code review and security checks even with AI, and pricing: $39.99/month for Enterprise tier with coding agent. They also highlight .instructions files for reusable patterns and note that coding agent currently supports only tools, not resources.

Agentic Excellence: Mastering AI Agent Evals w/ Azure AI Evaluation SDK — Cedric Vidal, Microsoft

Jun 27, 2025 · 20:01

Cedric Vidal, Principal AI Advocate at Microsoft, demonstrates how to systematically evaluate AI agents using the Azure AI Evaluation SDK and AI Toolkit. He argues that evaluation must start early, covering four layers: model/safety system, system message, grounding, and user experience. He shows spot-checking models side-by-side in VS Code, building a web-scraping agent with Playwright MCP, and running batch evaluations with built-in quality and safety metrics like groundedness and fluency. Vidal also covers multi-turn and multimodal evaluation, using a violent image example where the SDK returned a score of 4 out of 5, allowing teams to set custom thresholds based on application context.

Building Code First AI Agents with Azure AI Agent Service — Cedric Vidal, Microsoft

Jun 27, 2025 · 1:54:06

Cedric Vidal, Principal AI Advocate at Microsoft, demonstrates building code-first AI agents with Azure AI Agent Service, using function calling, code interpreter, file search, and Bing grounding for a sales analysis use case. He creates a conversational agent that queries SQLite, generates pie charts from Python code, and blends product PDF data with relational data. The workshop explains stateful agents, tool routing (LLM generates JSON to call functions), and the limits of single-step agents versus multi-agent orchestration with AutoGen. Cedric addresses when to use Agent Service (managed persistence and tools) versus raw LLM endpoints, and covers MCP servers as a tool lifecycle manager. Key insights include using instructions to ground agent behavior and the need for eval frameworks for agent quality.

Building Protected MCP Servers — Den Delimarsky and Julia Kasper, MCP Steering Committee & Microsoft

Jun 20, 2025 · 20:13

Den Delimarsky and Julia Kasper, Microsoft engineers on the MCP Steering Committee, explain how the new MCP authorization spec simplifies securing remote MCP servers by separating the resource server from the authorization server, eliminating the need for developers to build custom token factories. They argue that developers shouldn't need to be security experts, so the spec offloads token minting and lifecycle management to standard identity providers like Entra ID, Okta, or Auth0. The client now handles the full OAuth 2.0 dance, while the server only validates tokens via a Protected Resource Metadata (PRM) document. A live C# SDK demo shows a server configured with minimal code, and an Azure API Management deployment demonstrates production-ready remote MCP servers with OAuth proxy and consent flows, including integration with VS Code and GitHub Copilot. The session also previews transforming REST APIs into MCP tools via Azure API Management.

MCP: Origins and Requests For Startups — Theodora Chu, Model Context Protocol PM, Anthropic

Jun 18, 2025 · 17:45

Theodora Chu, product manager at Anthropic, explains the origin of the Model Context Protocol (MCP) as an open-source standard for giving LLMs agency to interact with external tools and data, born from two engineers copying context manually. She details MCP's evolution from an internal hack week project to wide adoption by Google, Microsoft, OpenAI, and coding tools like Cursor and VS Code. Key protocol decisions include optimizing for server simplicity over client complexity, adding streamable HTTP for bidirectionality, and fixing authentication via community contributions. Chu outlines three startup opportunities: building high-quality MCP servers for verticals beyond dev tools (80% weight), simplifying server building with hosting/testing/deployment tooling, and creating AI security and observability tools. She emphasizes that models are a third user of servers, so tool design must consider end users, client developers, and the model itself.

Supercharging developer workflow with Amazon Q Developer - Vikash Agrawal

Jun 10, 2025 · 13:22

Vikash Agrawal and Linda show how Amazon Q Developer can drive every stage of the software development lifecycle. They build a 2048 game with FastAPI and Poetry using Q's /dev command in the CLI, then add unit tests with /test, generate documentation with /doc, and integrate with GitHub for automated pull requests. After deploying to AWS, they use Q in CloudWatch to debug a runtime import error by analyzing logs and resource topology. The episode emphasizes that planning infrastructure and prompts upfront, along with security scans, are critical for production-ready AI-assisted development.

The Devops Engineer Who Never Sleeps — Diamond Bishop, Datadog

Apr 22, 2025 · 16:18

Diamond Bishop, Director of AI Engineering at Datadog, explains how his team builds AI agents—the On-Call Engineer and Software Engineer—that automate DevOps tasks like incident investigation, remediation, and postmortem writing. He details how the On-Call Engineer wakes up for alerts, reads runbooks, queries logs and metrics, and suggests fixes to let human engineers sleep. The Software Engineer proactively fixes errors by generating code diffs and pull requests. Bishop shares four key lessons: scope tasks and evaluate rigorously, assemble teams of optimistic generalists and UX experts, adapt UX for human-agent collaboration, and treat observability as critical for debugging multi-step agent workflows. He predicts that within five years, AI agents will surpass humans as primary users of SaaS platforms like Datadog, urging builders to design for agent consumers.

AI Engineering at Jane Street - John Crepezzi

Mar 28, 2025 · 16:57

John Crepezzi, an engineer on Jane Street's AI Assistants team, explains how they built custom LLM-powered coding assistants for OCaml, a functional language with scarce public training data. To overcome off-the-shelf tool limitations, they trained models on data from workspace snapshots — automated captures of developer workstations every 20 seconds, tracking build status changes from red to green. They used a Code Evaluation Service (CES) for reinforcement learning and evaluation, pre-warming builds to quickly test if model-generated diffs compile and pass tests. Their sidecar architecture, AID, serves thin editor integrations for Emacs (used by 67% of the firm), VS Code, and Neovim, allowing easy model swapping and A/B testing (e.g., sending 50% of users to different models). The talk details the end-to-end process: data collection from features, commits, and snapshots; training with supervised data and RL; and building pluggable infrastructure for domain-specific tools.

How Windsurf writes 90% of your code with an Agentic IDE - Kevin Hou, Windsurf

Mar 11, 2025 · 20:50

Kevin Hou, head of product engineering at Codeium, introduces Windsurf as the first AI agent-powered editor, arguing that agents are the future of software development. The editor is built on three principles: trajectories, a unified timeline that lets the agent read the user's mind and execute commands like 'continue my work'; meta-learning, which auto-generates memories of user preferences and codebase context; and scaling with intelligence, removing legacy features like chat and @-mentions as models improve. Windsurf launched three months ago and has already generated 4.5 billion lines of code, with 90% of code now written via its Cascade agent. Hou emphasizes that the agent reduces human-in-the-loop effort by inferring context, running terminal commands in the user's environment, and dynamically retrieving documentation—allowing developers to contribute less input while getting more production-ready output.

WTF do people use Open Models for??

Feb 22, 2025 · 28:01

Eugene Cheah of Featherless.ai breaks down how individuals and enterprises actually use open-source AI models, based on platform data. DeepSeek R1 dominates individual usage, but Mistral Nemo 8B remains the top enterprise model due to production stickiness and Apache 2.0 licensing. Creative writing and roleplay account for 30–40% of all traffic, with over 60% of users in that segment being women; coding copilots and agents make up 20–30%, driven by 'vibe coding' and token-hungry workflows like Kline. RAG and ChatGPT clones represent 20%, while agentic workflows (10–20%) succeed with human-in-the-loop designs. Cheah advises enterprises to aim for 80% automation with escape hatches, and warns against chasing 100% reliability. He concludes by introducing Quirky, a post-transformer hybrid built for $100k.

Decoding Mistral AI's Large Language Models: Devendra Chaplot

Nov 21, 2024 · 18:16

Devendra Singh Chaplot of Mistral AI details the company's open-source large language models, including Mistral 7B, Mixtral 8x7B, Mixtral 8x22B, and CodeStral 22B, arguing that open models complement rather than compete with profit by serving as branding tools and driving customer acquisition for proprietary upgrades. He explains the three-stage LLM training process—pre-training on trillions of tokens, instruction tuning with prompt-response pairs, and learning from human feedback via preference optimization—emphasizing that more data does not guarantee better performance due to noise. The episode highlights Mistral's focus on optimizing the performance-to-cost ratio, with CodeStral 22B outperforming larger models like Code LLaMA 70B while being smaller and multilingual across 80+ programming languages. Practical guidance is given: prototype with high-end commercial models, then fine-tune open models for specific tasks to balance performance and cost.

Code Generation and Maintenance at Scale: Morgante Pell

Oct 17, 2024 · 18:54

Morgante Pell, founder of Grit, argues that AI agents for code generation must be built for modifying large existing codebases rather than generating new apps, and that combining static analysis with AI enables reliable migrations at scale. Grit has merged more PRs than any other company by focusing on supercharging top engineers, using GritQL to precisely find code via syntactic and semantic queries, and relying on compilers like TSC to catch errors that LLMs miss. They overcome slow enterprise builds (10 minutes for type checking) by precomputing in-memory indexes and using Firecracker to snapshot and fork environments for parallel agent execution, achieving PRs with only a few iterations. For editing, they developed a custom 'loose search and replace' format to avoid expensive full-file generation and LLM laziness. This approach allowed one customer to complete a multi-year OpenTelemetry migration in a week with under 100 developer hours. Morgante envisions future UIs that let engineers manage entire codebases like SimCity.

Building Reliable Agentic Systems: Eno Reyes

Aug 20, 2024 · 18:14

Eno Reyes, CTO of Factory.ai, explains how his team builds reliable agentic systems—called Droids—for automating software development tasks by applying techniques from robotics and control systems to handle planning, decision making, and environmental grounding. Reyes describes a pseudo-Kalman filter that passes intermediate reasoning through plan steps, converging reasoning but risking error propagation. He advocates for explicit plan criteria and hard-coded logic to improve reliability, despite reducing generalizability. On decision making, he recommends consensus mechanisms like self-consistency, explicit reasoning with checklists, fine-tuning for out-of-distribution decisions, and simulation via Monte Carlo tree search. For environmental grounding, he emphasizes building custom AI computer interfaces for domain-specific workflows, designing explicit feedback processing (e.g., parsing CI/CD logs), balancing bounded exploration with long-context models, and incorporating human guidance to boost reliability from 30–40% to 90–100%.

The Making of Devin by Cognition AI: Scott Wu

Jul 11, 2024 · 20:04

Scott Wu, co-founder and CEO of Cognition AI, demonstrates Devin, a fully autonomous AI software engineer that builds and deploys working applications from plain English instructions, sharing the early lessons and vision behind the product. Devin built a name game website for the AI Engineer World's Fair in minutes, iterating on feedback to hide names, add a streak counter, and deploy the final version. Cognition uses Devin internally for features like a search bar and dashboards; Wu notes that engineers typically spend 80–90% of time on implementation, but Devin frees them to focus on architecture and problem-solving, making them 5–10x more productive while increasing overall demand for software. He also highlights the UX challenges of agents, which require new interactions beyond text completion, and notes that access to Devin is expanding via a waitlist.

Move Fast Break Nothing: Dedy Kredo

Nov 8, 2023 · 13:01

Dedy Kredo, co-founder and CPO of CodiumAI, argues that code generation needs a GAN-like architecture with a separate code integrity critic, and that behavior coverage is a more useful metric than code coverage. He demonstrates CodiumAI's IDE extensions (200,000 installs) on the open-source AutoScraper project (5,000 GitHub stars with no tests), automatically mapping behaviors, generating tests for happy paths and edge cases, and using reflection to auto-fix failures. The tool then enhances code with deep improvements (performance, security) and, via a PR assistant, catches a forgotten API key vulnerability. Kredo closes with a personal note about the Hamas attack on Israel, showing his co-founder on reserve duty while CodiumAI usage continues to rise.