Mike Christensen from Ably critiques the standard HTTP streaming (SSE) approach for AI chat applications, highlighting its fragility and limitations. He introduces the "durable session" pattern, a persistent, shared resource built on pub/sub principles, to create resilient, multi-device AI experiences with live, bidirectional control.
Merve Noyan from Hugging Face discusses how open-source models have achieved parity with closed-source counterparts, highlighting the Hugging Face ecosystem built to support this shift. She covers tools for model selection, local agent deployment, and the transformative "Hugging Face Skills" that allow agents to automate complex ML engineering tasks like fine-tuning models with a single prompt.
Anton Arhipov from JetBrains introduces Koog, a lightweight, Kotlin-native framework for building tool-using LLM agents. This session covers the rationale for using Kotlin in AI, the architecture of Koog agents, and how its graph-based DSL enables the creation of structured, type-safe, and reproducible agent workflows, moving beyond simple prompt-chaining to sophisticated orchestration.
95% of GenAI pilots fail due to feedback integration issues, not deployment challenges. Alessandro Cappelli argues that Reinforcement Learning (RL) provides the only systematic way to incorporate business metrics and production signals to continuously improve models, especially for complex agent-based systems.
Matthias Luebken explains the core principle of building with coding agents: make things easy for them. This talk deconstructs the Pi SDK, showing how a simple loop of an LLM calling CLI tools can lead to emergent capabilities. Luebken presents a real-world B2B sales pipeline built on this principle, where agents handle incoming emails, query CRM/ERP data via simple tools, and generate draft responses, keeping the human in their familiar email client.
This talk explores the journey of building Viktor, an AI employee that lives entirely in Slack. It details the unique challenges of scaling an AI agent from a personal tool to a company-wide coworker, focusing on memory isolation, context management across different Slack interactions (DMs, channels, threads), and the surprising importance of the AI's personality for user adoption.