Talks, workshops, events, and training for AI Engineers.
AI has permeated every stage of the web development lifecycle, from coding agents that use customizable "skills" to browser-native AI debugging tools, fundamentally shifting how developers build, test, and optimize applications.
Joel Becker from METR argues that slowing compute growth would proportionally delay AI capabilities milestones measured by task time horizons, while presenting findings that experienced open-source developers showed minimal productivity gains from AI coding assistants like Cursor, challenging optimistic adoption curves.
Auth0/Okta leaders Patrick Riley and Carlos Galan unveil new AI identity infrastructure including Token Vault for secure credential management and Async OAuth for human approvals, presenting a four-pillar framework to authenticate users and authorize autonomous agent actions across enterprise applications.
Cornelia Davis from Temporal demonstrates how integrating OpenAI's Agents SDK with Temporal's distributed systems platform creates production-ready AI agents that automatically handle crashes, retries, and state persistence without developers writing complex resilience code.
Jeremiah Lowin argues that most MCP servers fail because developers treat them like REST APIs for humans rather than curated interfaces optimized for AI agents' specific constraints around discovery cost, iteration speed, and limited context windows.
Amazon Principal Engineer Al Harris introduces Spec-Driven Development through Kiro, an agentic IDE that replaces unstructured 'vibe coding' with a formal workflow converting prompts into EARS-format requirements and property-based tests, enabling FAANG-scale reliability in AI-assisted development.
Kevin Madura from AlixPartners demonstrates how DSPy shifts AI development from manual prompt engineering to declarative programming, enabling developers to build modular, optimizable Python systems that treat LLMs as first-class citizens while remaining robust to model changes.
Robert Brennan outlines the evolution from single AI coding agents to parallel agent orchestration, demonstrating how breaking massive refactoring tasks into coordinated sub-tasks can deliver 30x productivity improvements on tech debt remediation while maintaining essential human oversight.
SallyAnn DeLucia and Fuad Ali from Arize demonstrate how iterative "prompt learning"—combining automated evaluations with human explanatory feedback—can improve AI agent performance by 15% without fine-tuning, outperforming traditional optimization methods while reducing costs significantly.
Peter Wielander demonstrates how Vercel's open-source Workflow DevKit transforms local AI agents into production-grade systems by wrapping AI SDK code in durable, retryable workflows with minimal refactoring, enabling automatic observability and resumable streams without managing queues or databases.
![Claude Agent SDK [Full Workshop] — Thariq Shihipar, Anthropic](https://i.ytimg.com/vi/TqC1qOfiVcQ/hqdefault.jpg)
Anthropic's Thariq Shihipar introduces the Claude Agent SDK, which packages the Claude Code architecture to build autonomous agents using a 'bash is all you need' philosophy, leveraging Unix primitives and dynamic code generation instead of rigid predefined workflows.