Matt Pocock of AI Hero discusses the critical role of classic software engineering principles in the new era of AI development. He explores how concepts like Domain-Driven Design (DDD), deep modules, and intentional architecture are essential for building maintainable systems with AI, and shares his unique teaching philosophy for the rapidly evolving field of AI Engineering.
Samuel Colvin, creator of Pydantic, demonstrates a hands-on workflow for continuously optimizing AI agents in production. The session covers using Logfire for running evaluations, GEPA (Genetic Pareto) for autonomously evolving better prompts, and managed variables to deploy these improvements to live services without redeployment.
A summary of a talk on a new language-agnostic approach using abstract interpretation to find critical vulnerabilities in Zero-Knowledge Proof (ZKP) programs by modeling and detecting mismatches between prover computations and verifier constraints.
A practical guide on improving LLM coding agent performance by giving them direct access to a library's source code. The session demonstrates cloning the Effect repository to extract patterns and guide the agent in building a type-safe application from scratch.
Agent failures are unlike traditional software failures. This workshop provides a practical framework for monitoring production agents, moving beyond evals to real-world observability by using explicit signals (errors, latency) and implicit signals (user frustration, refusals, self-diagnostics) to catch regressions and understand agent behavior.
This presentation by Janak Agarwal from AWS provides a deep dive into scaling serverless applications for mission-critical, high-traffic workloads. It explores AWS Lambda's rapid scaling capabilities for handling extreme traffic bursts and introduces advanced patterns like Provisioned Concurrency for cost optimization during steady-state operations.