Speakers

Skills are documentation for AI. They enable LLMs to more correctly handle problems we hand them. In this talk, we will go from blank slate, show the problems that skills evolved to solve, use skills from the internet, review what makes up a skill, author our own skills, and finally evaluate if the skills we have created are working as intended. The talk will cover using Antigravity both as a tool, and embedding it in an application as an SDK.

About Brett: Brett builds samples, codelabs, documentation and other fun things to encourage developers to use Antigravity to the fullest. Hailing from Sydney, Australia, he has worked across the entire range of companies from Australia’s largest bank to secretive startups. His interests include all things technical, from mastering Sous Vide cooking in the kitchen to embedded programming on the RP 2350.

Ever wished your Android app could just understand what your users actually want? Enter App Functions. In this session, we’ll explore how to bridge the gap between Android apps and Gemini. You’ll learn the core concepts of App Functions, see how they expose app capabilities to Google's GenAI ecosystem, and discover how to get Gemini to trigger specific actions within your app seamlessly.

About Katie: Katie is passionate about all things Android. She has taken on a wide variety of challenging projects including in the live event, media and travel space. Her favourite thing is to see someone having a delightful experience using something she has worked on. Katie is very active in the development community, she is a Google Developer Expert for Android, one of the organisers of GDG Melbourne and is a Women Techmakers Ambassador. She frequently runs events, speaks on technical and non-technical topics and enjoys writing blog posts to share her experience with others. Katie is based in Melbourne, Australia but loves to travel the world and outside of tech she enjoys boardgaming and attempting to finish many craft projects.

We built an automated SRE agent using the Agent Development Kit (ADK) and Gemini Enterprise Agent Platform to investigate end-to-end test failures. The agent's mission: collect and parse logs, analyse source code diffs, locate the root cause, and categorise the failure as a legitimate bug, a PR regression or a flake. Our first iteration produced answers but suffered from typical agentic fragility: edge cases unbounded execution loops even despite token guards, non-deterministic routing paths through identical errors, and overconfident hallucinations such as claiming an "infrastructure failure" based on a single line of a log file. The fix wasn't a better prompt - it was treating the agent as a software program rather than a series of LLM calls. Instead of dumping files, logs, and prompts into an LLM and hoping for the best, we encoded the investigation process into the agent's structure: explicit states, typed transitions, and guardrails defined in ADK. This is what brought determinism into a highly indeterministic system, while reducing the prompts volume. The talk also covers context engineering under time and platform constraints: no persistent volumes or local git clones, so all source code access runs through GitHub APIs. But PR diffs and fetched files can be large, naively adding them to the context will lead to token waste and dilute the attention mechanism, causing the LLM's output quality to degrade. We will walk through the production graph topology, the custom tool boundaries, and the inline verifier that forces the agent to be uncertain and honest rather than confident and wrong.

About Olga: Olga is a Senior Staff SRE at Ping Identity with deep roots in Kubernetes, GitOps, FinOps, performance and cloud-native systems. She has spent her career building platforms and developer tooling across large enterprises in tech and financial services industries. When she’s not working or tinkering on her never-ending side projects, running AI experiments on DGX Spark, she is either running half marathons with friends or camping in the woods.

Whether you like it or not, AI agents are here and they are roaming the web working on behalf of real people. They read web content, search, navigate, and fill in forms; some are even empowered to use credit cards and buy things. As web developers, how do we respond to this? What does it mean to build for the web when the user may not be human? This talk is intended to start the conversation around who we are serving as web developers. Do we need a new "agent-first" design paradigm or can we simply pretend nothing has changed? As our entire industry goes through a huge upheaval, what are the choices we can make that ensure the web continues to be for everyone as we expand the scope of "everyone"?

About Phil: Phil is a developer experience engineer for Resend, a Google Developer Expert, and an AAIF Ambassador living in Melbourne, Australia. He's been working in developer relations for more than a decade, speaking at conferences since 2012, and writing JavaScript since before jQuery. Away from the keyboard, Phil enjoys travel, live music, and hanging out with his mini sausage dog, Ruby.

Agentic frameworks allow for creating autonomous workflows that integrate with production systems via tools that access live databases, internal APIs, and runtime environments. When an agent is granted credentials to perform state-mutating actions—such as processing financial transactions, modifying database records, or executing arbitrary code—it introduces significant security risks. The core challenge lies in the agent's operational model: an LLM determines its own tool-use sequence and execution path based on non-deterministic, natural-language inputs. Traditional security models, like perimeter security and static ACLs, can authenticate the agent itself but lack the capability to inspect or enforce policies on the specific, runtime-generated actions the agent decides to execute internally. This creates a critical visibility gap and a new attack surface within your security boundary. In this session we introduce defense patterns for agentic AI systems build with ADK - we explore 3 different but complimentary patterns : 1. Cryptographic write signatures: Assign each agent a hardware-backed key to sign every database mutation, ensuring non-repudiation and tamper detection. 2. Kernel-level code isolation: Execute all dynamically generated code inside a gVisor user-space sandbox with zero network egress and strict resource limits. 3. Deterministic semantic gateways: Proxy model inputs and outputs through deterministic validation rules enforced by automated CI/CD test suites.

About stefan: Stefan is the head of customer engineering for Google Cloud Security and leads Google’s technical security engagements with some of Australia’s largest organisations across Financial, Public Sector, Retail and other industries. Stefan has been with Google for over 4 years, having previously worked in similar roles at companies like Amazon Web Services and Cisco. Based in Sydney, Stefan has been working as a trusted advisor to Google's most strategic customers focusing on managing CyberSecurity, Identity, & Compliance risks.

The rapid adoption of AI-assisted coding tools has fundamentally changed how developers approach complex engineering tasks. As the industry shifts toward agentic workflows, the visibility gap into how these tools impact overall developer productivity and code quality has widened. Engineering teams are now tasked with ensuring their developer experiences are reliable, accurate, and trustworthy, yet traditional testing methods often fall short in these indeterminate, AI-driven environments. This talk explores a framework for implementing developer experience testing at scale. We will examine how to build automated evaluation platforms that function as continuous integration for AI systems. Instead of relying on anecdotal feedback, we will discuss how to define and measure success using metrics that go beyond simple functionality. By the end of this session, attendees will walk away with two primary insights: How to create practical, everyday measurements that clearly show if AI tools are helping developers succeed or creating new hurdles. A straightforward strategy for monitoring AI performance over time, ensuring your tools remain helpful partners rather than sources of frustration. Attendees will leave with a clear plan to protect developer trust while embracing new ways of working, ensuring that the software they build remains high-quality and reliable, no matter how much automation is involved.

About Katie: Katie McLaughlin (@glasnt, they/them) is a Senior Developer Relations Engineer at Google, specializing in improving the developer and builder experience. When they're not changing the world, they enjoy rambling, fiber arts, and irrevocably changing the Unicode Standard.

In this talk, we dissect 1,890+ abstract submission between 2010 to 2025 for the eResearch Australasia conference - a premier forum where frontier scientific discovery meets the accelerated computing, data architectures, and software engineering. Mostly using Gemini 3.5 Flash to map the rise, fall, and consolidation of tech paradigms we can show how early-2010s favourite "Data Mining" completely vanished into general Machine Learning, and how 2025 delivered a dramatic divergence: general ML topics dropped from 8.3% to 3.1%, while GenAI surged to 22.2% of all sessions complemented by a 2x spike in High-Performance Computing and GPU-heavy workloads. Beyond the stats, we will walk through the semantic analysis methodology and prompt techniques used with Gemini Flash to process large unstructured text archives without length-bias, and share interactive visualisations showing where developer focus and infrastructure demand is heading next.

About Nathaniel: Nate is the Science Catalyst Program Manager working at Google, uniting the amazing research taking place around the world. He has an Honours in Astrophysics, a PhD in Geophysics, and a long tenure of using research computing infrastructure to help solve tricky science problems. Somewhere along the way he became a Carpentries instructor.

I built a full AI automation agents using n8n, which include Gemini 3.5 Flash, Gemma4, Schedule automation, and Gemini Omni, Nano Banana to automate creating and posting my social content for MemoirME social pages. Time spent per week from 40hrs down to 2hrs, team size from 8 members down to 1 member.

About Suesi: Suesi Tran is a Google Developer Expert in Dart & Flutter & Firebase, GDG Co-Organiser at GDG Melbourne and Flutter Melbourne, and a Women Techmaker Melbourne Ambassador. She has more than 18 years experience in mobile development, and started Flutter development since 2017. Suesi is the founder of Nartus, the company behind MemoirME - a digital diary designed to help people preserve meaningful memories, relieve stress, and support better mental well-being. MemoirME empowers users to tell their own stories, in their own voices, with the goal of fostering reflection, healing, and personal growth.

Writing end-to-end user journey tests in Android has always been painful—brittle UI selectors, flaky async handling, and constant test maintenance. What if you could just describe the flow in plain English and let AI handle the execution? In this talk, we’ll look at how Journeys with Gemini in Android Studio transforms multi-screen testing from an avoided chore into something you actually ship. Learn how goal-driven AI testing delivers resilient, end-to-end coverage across real user flows in a fraction of the time.

About Kartik: Kartik Arora is a polyglot Lead Engineer at Bilue and a recognised Google Developer Expert specialising in Android technology. With extensive experience across diverse industries, he focuses on design system architecture, modern UI frameworks, and elevating engineering practices. He is passionate about fostering vibrant developer communities and regularly shares knowledge on build tools and mobile architecture.