AI Engineer Melbourne 2026 — Keynotes (Day 1 Morning)

Opening words from the Victorian Government.

A remote welcome to AI Engineer Melbourne from swyx, who coined the term “AI Engineer”.

An analysis of the leading AI models and the real trade-offs between them across intelligence, speed, price, token usage, and beyond, grounded in Artificial Analysis' independent benchmarking. Includes a forward-looking read on the trends shaping where AI is heading.

Token pricing is a noisy headline. What matters in production is what you pay per task once outputs get longer, retries creep in, and “good enough” models get deprecated.

This talk breaks down the current dynamics of the frontier model market from the perspective of Notion AI product team on embarking this with Notion custom agents model, and shares a practical playbook for keeping leverage: stay genuinely multi-provider, invest in product value that is hard to undercut, and use open weight models as an escape hatch for everyday workloads that do not need frontier reasoning.

Software development as we knew it is dead. The old world of hand-crafting every line, endless hiring cycles, and fragile vertical stacks has been overtaken by AI-driven software factories that turn simple prompts into fully functional software through automated iteration. This keynote traces the journey from the Ralph Wiggum Loop to Loom and beyond, exploring what it means when code becomes a commodity and engineering becomes orchestration.

Modern coding agents can write code, run tests, and solve complex tasks, yet most start every session with no memory of what happened before. In this talk, we’ll explore why today’s agents are fundamentally stateless, how memory changes their behaviour, and what happens when agents can learn, remember, and improve over time. Through real-world experiments building large-scale agent systems, we’ll look at the path from simple LLMs to persistent, memory-driven software systems.

Full-duplex voice agents punish the choices text-only agents forgive. A 50ms GC pause is a perceptible glitch. A blocked event loop is a barged-in user talking over your model. Google’s Python ADK is a beautiful kit for building agents — until audio frames arrive every 20 milliseconds and refuse to wait.

This talk walks through gemini-rs, an open-source Rust SDK that rebuilds Google’s Agent Development Kit and the Gemini Multimodal Live wire protocol from scratch, in three layered crates: a zero-copy WebSocket transport, an agent runtime with phase machines and typed prefix-scoped state, and a fluent builder where a production voice agent fits in twenty lines of declarative Rust.

The architectural keystone is a three-lane processor: a sync fast lane for audio and VAD under one millisecond, an async control lane for tool dispatch and phase transitions, and a debounced telemetry lane built on atomics. We’ll cover what broke in the naive translation, why Vertex AI sends binary frames where Google AI sends text, how to compose tool-using agents without rebuilding LangChain, and where the Python ADK still belongs in the stack.