Why did the normally shy and reticent member of my team do what he did last Thursday? Why did my short-tempered, brutish CEO rise to that occasion with such warmth and largesse? For the last ten months, people have been asking me if I am myself. Who else could I possibly be? Such questions are intriguing and consequential for understanding ourselves and our organizations.

This presentation provides guidance for answering them based on Dr. Little’s work on free traits, personal projects and wellbeing. A central tenet of this perspective is that people often act out of character in order to advance their personal projects. Such behaviour may puzzle us, and we wonder what is going on. What is going on is both complex and deeply human. Audiences come away with some very practical examples of how they drive each other to distraction and with a clearer understanding of why it is essential to their organizations that they continue to do so.

The presentation merges psychological insights with an exceptionally motivating message about respect for differences between individuals. Dr. Little’s presentations have been described by highly diverse groups as ‘utterly hilarious’ and ‘profoundly moving,’ suggesting he is a ‘cross between Robin Williams and Einstein.’

As programmers, we tend to treat data as generic stuff to feed into the algorithms and architectures we love. We don’t really pay attention to the data itself, especially when we have terabytes or petabytes of it.

Huge mistake. And we are trained to make it! It is why it takes a year for a new programmer to be productive at working on the Google ranking algorithm. It held back progress on genome sequencing algorithms. It has cost me more time than I’d care to imagine.

The good news is that you don’t have to look at all of your petabytes of data. Just eyeball a ten record sample when you start, and repeat as you work the data. Even then, eyeballing can be hard work, and done wrong can be worse than doing nothing. But done right, it can be fun, and the data will almost always surprise you. Better yet, you can use your favorite algorithms and architectures to build tools to make eyeballing your data easier and much more effective.

Data-driven systems and machine learning continue to be a significant trend across our industry. However, most attempts at these systems face serious difficulties due the tension between the clean, controlled, lab environments where statisticians apply their skills, and the messy unpredictable, production environments where we want to apply their results at scale.

In this talk, we will provide an overview of the machine learning landscape, with an emphasis on the distinction between machine learning as a scientific practice and the larger concept of machine learning systems. Using this base, we will walk through the challenges of taking machine learning out of the lab and applying it successfully in an industrial setting.

By the conclusion of this talk, the audience should take away a better understanding of machine learning as a practice, together with an idea of what it takes to build and deploy machine-learning systems in an environment that deals with real customers and data at scale.

””It is not necessary to change. Survival is not mandatory.”” – W. Edwards Deming

Work takes time to flow through an organization and ultimately be deployed to production where it captures value. It’s critical to reduce time-to-production. Software – for many organizations and industries – is a competitive advantage.

Organizations break their larger software ambitions into smaller, independently deployable, feature -centric batches of work – microservices. In order to reduce the round-trip between stations of work, organizations collapse or consolidate as much of them as possible and automate the rest; developers and operations beget “devops,” cloud-based services and platforms (like Cloud Foundry) automate operations work and break down the need for ITIL tickets and change management boards.

But velocity, for velocity’s sake, is dangerous. Microservices invite architectural complexity that few are prepared to address. In this talk, we’ll look at how high performance organizations like Ticketmaster, Alibaba, and Netflix make short work of that complexity with Spring Boot and Spring Cloud.

KEYWORDS

Cloud, DevOps, Continuous Delivery, Spring, Cloud Native Java

Distributed Systems are difficult to build and test for two main reasons: partial failure & asynchrony. These two realities of distributed systems must be addressed to create a correct system, and often times the resulting systems have a high degree of complexity. Because of this complexity, testing and verifying these systems is critically important. In this talk we will discuss strategies for proving a system is correct, like formal methods, and less strenuous methods of testing which can help increase our confidence that our systems are doing the right thing

The bitcoin network is the largest distributed computer in existence. It is a solution – suggested in 2008 by Satoshi Nakamoto – to the problem of distributed consensus that many scientists thought impossible.

This talk will be fast-paced and highly technical, and will explain how Bitcoin actually works. The first three times you think you understand Bitcoin you are probably wrong. After this talk you can bump your wrongness down by one.

KEYWORDS

Bitcoin, Distributed Consensus, Blockchain, Cryptocurrency

Personal career management is much like project management, and the same things that trip us up in the technical realm – formulation, resource allocation, estimation, risk assessment – are also too often absent from our thinking about our own career development. How might we benefit from applying our technical thinking to what we normally think of as a soft topic? What lightweight approaches may help us better achieve personal goals?

KEYWORDS

Professional Development, Personal Career Management, Estimation, Decision Analysis, Planning, Formulation

C++11, C++14, and even C++17 present opportunities for new designs and behaviors. How do these new opportunities actually work out when the project is non-trivial, performance sensitive, and interoperable with other languages? In this session, we will discuss the challenges faced and lessons learned with the use of modern C++ in two recent projects. Aaron is a high performance messaging transport that provides a C++ API that must transparently interoperate with its Java, .NET, and Go APIs. ReactiveSocket is an application protocol providing Reactive Streams semantics over various communication media. Whether you are just curious about modern C++ or are an experienced C/C++ developer, this session will provide some new perspectives.

Teaching a human to understand a language is a hard and complex process. Understanding the meaning expressed through a language is even harder. The industry is ever so excited about accomplishing the mission of making computers understand humans or even act like humans. The mission has been in works for decades. So why this new excitement? What’s new and what is trending in NLP?

This is a talk that takes you through a journey of ””NNLP”“, NLP and recent trends. Hopefully leaving you a bit excited, a bit scared, and a bit more insightful about the NLP space in general.

KEYWORDS

NLP, Natural Language Processing, AI, Human Computer Interaction, Adaptive Interfaces, Context Aware Services, Machine Learning, Big Data, Search, Text Analytics

Artificial Intelligence (AI) in general, and Machine Learning (ML) specifically are indisputably the hottest fields in the industry at the moment and have demonstrably advanced many areas of technology and science alike: web page classification, spelling correction, search ranking, graph building, large-molecule database screening for receptor-protein binding, predictive analytics, etc. etc. However, despite all the aforementioned advances, there are classes of problems (e.g. where “baselining” is required, such as network security) where ML is not a suitable technology to apply to. In fact, there are facets of ML that we simply don’t yet understand. In this session we describe the basics of AI and ML, discuss examples in network security and why ML is not a suitable solution, and finally discuss general shortcomings ML, namely reasoning and debugging.

KEYWORDS

Machine Learning, Deep Learning, Artificial Intelligence, General Intelligence, Machine Cognition, Data Science, Analytics, Security

What can a rogue fighter pilot from the 1960’s teach us about software architecture? Quite a lot, as it turns out. In 1964, John Boyd introduced ””energy-maneuverability”” theory. It showed that the fastest airplane didn’t always win the dogfight. Rather, the one that could accelerate or decelerate fastest would win.

Software architecture today is about gaining and shedding mass rapidly. One must scale up and scale down, and be able to adapt quickly to changing situations. Sadly, enterprise integration destroys maneuverability more often than it helps.

As architects, we can change the way we integrate systems to produce maneuverability, via some different techniques and patterns. Some of these techniques may appear to contradict past notions of sound architecture. Our industry evolves rapidly, however, and last year’s sound practice might just be drag this year.

KEYWORDS

Architecture, Agile, Microservices, Maneuverability, Enterprise, Scale

As the complexity of “client-side” web applications grow, so too does the need for the tools and techniques used in their construction to become more robust. For most of us our focus has primarily been on how we can consume and integrate components that have a level of trust associated with them, and also how we can create our own for consumption within our organisation. More recently though the concept of embracing static typing and a compilation pipeline as part of our application builds has gained traction.

This talk will provide some comparisons between different options for providing type information within your ECMAScript code. We will primarily consider Google’s Closure Compiler, Microsoft’s TypeScript and Facebook’s Flow. This is due to their nature of largely being ””plain old JavaScript”” rather than a whole new language. In addition to the high level comparisons, practical examples of each will be provided plus some personal retrospectives on using Closure Compiler as part of Canva’s development tooling.

KEYWORDS

Closure Compiler, Typescript, Flowtype, JavaScript, ECMAScript, ES6, Type Systems

How fast can we possibly make microservices?

You can have sub-microsecond wire-to-wire response times using off-the-shelf hardware and familiar languages. I’ll show you how to put the technology in place, but that’s the easy part. Why on earth would you want to do this, and how could you possibly stay agile?

An FPGA-powered network adapter can have a response packet on the wire before the server operating system has dispatched the incoming packet, and well before a high-level web stack has even heard about it.

To do this, you need to use hardware design for part of your microservice; you’ll use a programming language that allows intricately parallel expressions. I’ll show you how to do this using Haskell to compile to VHDL, then we’ll tackle the scary parts:

• How do I test and deploy this?
• Can I keep my continuous refactoring and continuous delivery?

We’ll walk through building a toy microservice, and see how to test and update it. We’ll talk about how this fits into the broader architecture of your service, and (crucially) how to convince the people around you to enjoy the challenge.

FGPA, Microservices, Haskell, Hardware Design, Test, Deploy. Continuous Integration

Evolutionary Architecture is rapidly becoming seen as the responsible approach to technology rather than some crazy idea that leads to chaos and despair. This talk follows the evolutionary path of Evolutionary Architecture first. We’ll then try to see where such ideas might lead us in the future.

The results are in: developers LOVE microservices! But are microservices in their current form solving more problems than they create? Many early adopters are finding new limitations as they deploy hundreds or thousands of microservices. Are there perhaps better abstractions than microservices that we could be using instead?

In this talk, we’ll cover the limits that some companies have encountered in their large microservices deployments and some non-microservices approaches to those same problems. Since I work at Uber on this very problem, we’ll also talk about the non-microservices systems that Uber is building to maintain developer productivity with a large and growing engineering team.

KEYWORDS

Microservices, Architecture, Scalability

EventStorming enables a team struggling with understanding and exploring a complex business problem to model that problem collaboratively in hours instead of weeks. In this talk you’ll learn how this technique quickly generates shared understanding and insights into how the software can, and should, support the business capability being explored.

In EventStorming, development team members and business people gather in a room and together they create a visual map of the flow of events – important things that happen – through concrete business examples. They use sticky notes to map out a story of how the software system behaves, or should behave, given a particular business problem to solve. This session will teach you the rules of EventStorming and how it can help your team cultivate shared understanding and be more productive.

KEYWORDS

EventStorming, Domain-Driven Design, DDD, Agile

Change in an organization is really hard. This is especially true when a company that was once on the forefront of innovation finds itself having lost that luster through its own growth & success. The past few years there has been a transformation happening at PayPal that is touching every part of the organization to make it innovative again. At the heart of this change is engineering innovation coupled with a new, close partnership between product, design and engineering.

Can your organization be changed? From Bill’s experience at Yahoo!, Netflix, PayPal and consulting with numerous companies he believes there are some core principles you can employ to drive transformation that are all centered around the customer. The question Bill will explore is “How can engineering and design be the catalyst for that change?” While this talk will be inspirational, it will take an honest (and humorous) look at what has worked and what hasn’t worked so well in trying to scale change.

Business applications are demanding ever-greater flexibility, yet responding to evolving requirements is still a challenge for software developers. Programming languages and frameworks are often brittle and inflexible, prone to unpredictable second-order effects which make change not merely difficult but often risky. Can we do better? This talk will explore techniques for designing software systems to be both robust and adaptable.

For examples of patterns which enable flexibility, this talk will focus on Clojure, a dynamically-typed functional language; Datomic, a transactional, distributed database; and clojure.spec, a dynamic testing and validation library.

Modern systems can fail in spectacular ways. Failure isn’t a question of if, but when. Resilient systems can endure and gracefully recover from failures, but ensuring your system has these properties requires thought and some deliberate architectural decisions. Join me in this talk as I attempt to find answers in academic research and industry to build a more robust system. I will also share the lessons learned while applying these patterns at Fastly and key takeaways that you can carry to your own systems.

KEYWORDS

Architecture, Patterns, Resilience, Distributed Systems, Robustness

We no longer live in a world where we build our applications from scratch. We build amazing creations using the blocks provided by libraries, frameworks and components. We trust widely and openly. We share and collaborate.

We design architectures that combine simple single purpose entities into massive complex coupled systems.

Then we try to secure them. What could possibly go wrong?

In this talk we will explore how our approaches to development, architecture and trust change the security of our applications and environments. We will discuss the challenges of securing these systems and some of the practical steps we can take to bring simple security to complex systems.
KEYWORDS

Security, Agile, DevOps, Bootstrapping, Trust, Complexity

What are the characteristics of a good software engineer? It’s a topic many people would argue endlessly about. This is not surprising given we are effectively living in the era of software alchemy.

Some of the best programmers draw on a strong scientific and engineering background. They combine this with craft like coding skills in a virtuous feedback cycle. In this talk we look back at the history of Software Engineering then explore the individual practices and techniques that can help bring out the engineer in you.

KEYWORDS

Software Engineer, Craft, Practices, Techniques, Developer Growth

Connecting with other people, finding a sense of belonging and the need for support are natural human desires. Employees who don’t feel supported at work don’t stay around for long — or if they do, they quickly become unmotivated and unhappy. At a time when organizational structures are flattening and workforces are increasingly fluid, supporting and connecting people is more important than ever. This is where organizational communities of practice come in.

Modern organizations with cross functional teams, have the ability to silo organizations into teams, programmes and functions. They can take people further away from other people that they can learn with. We need a way to bring people with the same concerns back together and this is what communities of practice do.

Communities of practice have many valuable benefits for both individuals and organizations. They include accelerating professional development; breaking down organizational silos; enabling knowledge sharing and management; building better practice; helping to hire and retain staff; and making people happier.

In this session, Emily will pull from experiences of building and growing communities of practice at the Government Digital Service, other government departments and organizations as well as case studies from her ongoing research into this area. You will gain an understanding of why community of practice are so important in modern organizations and practical advice to those who are thinking about setting one up or looking to reinvigorate one that already exists.

KEYWORDS

Continuous Delivery, DevOps, Software Engineering, Quality