Talks, workshops, events, and training for AI Engineers.
Leonie Monigatti from Elastic argues that context engineering is fundamentally 80% agentic search, evolving from rigid RAG pipelines to dynamic agent-driven retrieval that must navigate diverse context sources through carefully curated, specialized search tools.
Samuel Colvin demonstrates optimizing AI agent prompts in production using Jepper, a genetic algorithm library that breeds high-performing prompt variations, combined with Logfire's managed variables for structured configuration and deterministic evaluation against golden datasets.
Michael Arnaldi demonstrates "vibe engineering" by building a TypeScript project with AI agents, revealing that cloning library repositories directly into your codebase—rather than using npm packages—enables AI to learn patterns from source code, while strict TypeScript and custom lint rules act as essential guardrails.
As AI agents grow more complex and autonomous, traditional pre-deployment testing fails to catch the infinite edge cases of production behavior. The video outlines a new observability paradigm combining explicit system metrics with implicit semantic signals and self-diagnostics to monitor agents in real-time.
Nick Nisi and Zack Proser from WorkOS demonstrate how 'skills'—portable, markdown-based context units—solve the 'cold start' problem of AI coding agents by encoding constraints and deterministic scripts that can be shared across teams and projects, eliminating repetitive context reloading.
Raj Navakoti from IKEA reveals why enterprise AI agents fail to move Jira tickets despite strong coding abilities—they lack institutional knowledge. He proposes a 'demand-driven context' method where agents pull knowledge by failing on tasks and demanding specific missing context, transforming undocumented tribal knowledge into curated, reusable blocks.
Angelos Perivolaropoulos from ElevenLabs demonstrates how to train a GPT-2 style language model from scratch using only PyTorch and minimal dependencies, revealing that modern LLM development relies 80% on training methodology and optimization rather than architectural novelty.
Pedro Rodrigues from Supabase details how structured 'skills'—markdown-based instruction sets with progressive disclosure—dramatically improve AI agent performance with complex products, distinguishing them from MCP tools and establishing an evaluation-driven development framework for systematic testing.
Chris Parsons introduces 'Ralph Loops'—a minimalist automation approach where repeatedly prompting an AI agent with the same task outperforms complex orchestration workflows, leveraging the model's self-correction to ship better code with less maintenance.
Cormac Brick from Google AI Edge introduces Tiny LLMs (TLMs) and on-device agent capabilities powered by LiteRT-LM and the new Gemma 4 models, demonstrating how fine-tuned small models (100M-4B parameters) can now deliver sophisticated AI experiences—including multimodal reasoning and tool use—directly on mobile phones, laptops, and even Raspberry Pis without cloud dependency.
Peter Werry argues that as AI agents move toward autonomous 'YOLO mode' execution, simple RAG and MCP connections fail to provide adequate organizational context, creating bottlenecks and 'satisfaction of search' failures where agents stop at superficial answers instead of understanding the historical 'why' behind code decisions.
Liam McGarrigle demonstrates building AI agents in n8n using visual workflows, emphasizing transparent orchestration over black-box automation through configurable memory, chat triggers, and tool integration for practical business applications.