Talks, workshops, events, and training for AI Engineers.
Sandipan Bhaumik from Databricks presents a battle-tested five-pillar framework for deploying enterprise AI agents, arguing that starting with model selection leads to inevitable production failures while proper evaluation, observability, and data governance determine success at scale.
Google DeepMind's Gus Martins and Ian Ballantyne introduce Gemma 4, a family of open models (2B to 31B parameters) that deliver frontier-level intelligence with disproportionate efficiency, enabling sovereign AI ownership through local deployment, Apache 2.0 licensing, and on-device capabilities.
Dat Ngo from Arize AI explains how modern AI systems require reimagined observability and evaluation patterns built on OpenTelemetry to manage non-deterministic agents, emphasizing that the future of AI engineering lies in automated experimentation flywheels that eliminate manual dashboard work.
Google DeepMind researcher Brendon Dillon explains text diffusion as a parallel alternative to autoregressive language models that iteratively denoises random tokens rather than generating sequentially, offering significantly lower latency and unique capabilities like self-correction and adaptive computation, though currently limited by high serving costs for large batches.
Jeremy Howard argues that AI coding tools risk trapping developers in addictive 'dark flow' states that diminish psychological well-being, drawing on Self-Determination Theory to advocate for intentional AI use that augments human mastery and autonomy rather than outsourcing complexity.
Joe Reeve from ElevenLabs discusses building a viral AI app that lets users talk to statues via phone calls, exploring how vibe coding with existing APIs enables rapid prototyping, the unique challenges of voice interface design, and the cultural implications of giving physical objects AI-generated voices.
Nick Nisi from WorkOS explains how deleting 95% of his AI agent's skills improved accuracy from 77% to 97%, detailing his 'Case' harness system that uses state machines and cryptographic proof to enforce accountability rather than relying on instructions.
Alex Cheema from EXO Labs argues that AI should function as a local 'exocortex' rather than rented cloud infrastructure, detailing why inference optimization (not training) is the key bottleneck and how exponential improvements in 'intelligence per joule' will make consumer-grade frontier AI feasible within years.
Mardu Swanepoel from Flinn AI analyzes four design patterns shared by top AI agents—focus modes, transparent execution, personalization, and reversibility—to demonstrate how constraining scope, building trust, and reducing downside risk creates more effective human-agent collaboration.
Google DeepMind engineers Ian Ballantyne and KP Sawhney demonstrate their internal "Antigravity" agent platform, revealing how the organization manages massive-scale deployment through strict quota controls, hybrid model architectures, and collaborative multi-agent workflows while grappling with token consumption costs and evaluation complexity.
Rachel Lee Neighbors argues that chat interfaces are merely a transitional phase like the CLI was to GUI, demonstrating how HTTP-based MCP servers and interactive MCP apps can turn agents into an 'infinite canvas' for rich web experiences while eliminating inefficient DOM scraping through emerging Web MCP standards.
Paige from Google DeepMind demonstrates how Gemini 3.1's native multimodal capabilities and AI Studio enable developers to prototype complex media pipelines—from video analysis to code execution—that can be deployed to production with a single click, while advising against building infrastructure that frontier models will soon absorb.