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Andrew Harvey

Coder & Tinkerer



Andrew is the CTO in Residence for Microsoft for Startups in Sydney. He works with startups to help them scale their technology and their companies. Previously he was CTO at CancerAid & Zova, winning an Apple Design Award for Zova in 2016.

Talks at YOW!

Your Team As A Distributed System - YOW! Perth 2019

As we level up in technical roles, often we find ourselves thrust into team leadership and management. This sneaks up on us and we can be left without the skills to adequately understand, engage with and lead our teams. This inevitably has a negative effect on our teams and this effect is multiplied as you scale.

What if we could reach into our toolbox that we use to understand technical problems – software architecture and distributed systems theory – to help us understand our teams? Could we learn to better manage people through this metaphor?

We will explore the dynamics of teams and how they map to our understanding of distributed systems. Using this understanding we can apply distributed systems theory to help unpick some of the dynamics of our teams and how to optimise them for scale.

From communication to culture, we will break down the components of our distributed system and see what makes it tick using things like CAP Theorem and the 8 Fallacies of Distributed Systems. You will walk away with some tools to help understand your team, and set yourself up for successful scaling.

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They're Good Dogs: A Gentle Introduction to Machine Learning with CoreML and Vision - yow-connected-2017

At WWDC this year, Apple announced their CoreML and Vision frameworks. These allow you do easily implement machine learning models inside your app, running on the GPU on an iPhone. Suddenly a world of machine learning is possible in ways that it wasn't before.

In this talk, you'll see a real world example written in just hours which implements a freely available object classification model to find out who is a good dog. Silly as this sounds, it shows the power now at our disposal.

We’ll look at what you need to do to build a simple app, and then dig into some of the innards of machine learning to see what we need to do to create something really useful.

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