At Xamarin Developer Summit, Jon talks with Jerome Laban about building applications that run everywhere using the Uno Platform.
(00:20) Jerome explains that the Uno Platform is XAML and C# for iOS, Android and WebAssembly using WinUI XAML. On iOS and Android, it’s running on Xamarin.
(01:25) Jerome tells the history of the platform, and how they’ve been working on the platform for six years. When the team at nventive saw WebAssembly support coming, they ported their existing framework to run there, too.
(02:55) The Uno Platform is free and open source; nventive is a service agency that offers development and support for the platform. Jon asks for more information about nventive. It started as a training and general development company, then did Windows Phone, Windows 8 and Windows 10 applications. When Windows Phone went away, they moved their focus to Xamarin development, predating Xamarin Forms. They believe the strucucture of Uno and UWP is often a better approach for them than Xamarin Forms.
(04:55) Jon asks for the relationship between Uno and Xamarin Forms. Jerome explains that they’re generally at the same layer. However, since Uno implements the UWP contract, anything that targets the UWP contract can run on Uno. Xamarin Forms has a part that implements that contract to run on Windows. So… a Xamarin Forms can run on WebAssembly using Uno. You can run Uno components in a Xamarin app, since Uno components are actually Xamarin classic components. You can also run Xamarin components in an Uno application.
(07:00) Jon asks how the XAML front end is run in the browser. Jerome says that Uno renders the XAML as HTML elements. If you view source, it’s mostly div’s. HTML is treated as a subsystem that’s abstracted away.
(08:37) Jon asks about the Calculator application. The Uno team took the Microsoft open source calculator application, written in C++ and XAML, and got it to run on Uno. Jon was very impressed to bring it up on his phone’s browser. Jerome says that works on Android in the browser, and there are also Android and iOS applications.
(09:35) Jon asks how they ported Calc to Uno. Jerome said that the tricky part was to pinvoke into C and C++ from a WebAssembly module – Jerome had to add that support and submitted the pull request to Mono. There are three parts – a calculation that dates back to 1999 / Windows 3 that is all C and C++ code that they didn’t change; the rest is C++ 11 code which they translated to C# using regular expressions. The XAML and resources are the same.
(12:20) The Calculator is in published in 65 languages, so they are getting bug reports in 65 languages. There’s good accessibility support, so for instance you can enable voiceover in the mobile application.
(13:10) Jon asks how to build an application for Uno Platform. Jerome explains the File / New Project process using the extension.
(13:55) Jon asks about deployment. For iOS and Android, the output is the same as any Xamarin project; it’s just a standard UWP application, and for WebAssembly it currently tags along with the Blazor tooling.
(15:00) Jon asks about other getting started information. Jerome runs through several, and points to the GitHub repo for more links.
(16:20) Jon wraps up with a callout to UnoConf on September 19-20, as well as plans for a 2020 UnoConf in the works.
(03:30) Martin walks us through his career, which includes early adoption of .NET, stepping away for a bit to pursue Node and Java development, and returning to the .NET fold in his current role as a Developer Evangelist focused on .NET for AWS.
(08:00) Martin shares to how other developers are returning to .NET and the freshness in the community. .NET developers are progressive with AWS and large systems.
(10:40) Martin speaks to his evangelism roles with Microsoft, Oracle and now AWS. Spoiler alert. It’s not just standing at a booth, but real-world storytelling of customer use cases and encouraging platform adoption.
(13:15) Jon and Martin talk about the intersection of AWS and .NET development in which AWS is the original cloud hosting option so there are highly skilled, highly progressive .NET shops along with AWS experts that are new to .NET, and .NET developers who are completely new to AWS and cloud computing.
(20:35) Jon asks about AWS Lambda. Martin touches upon other hosting options including Elastic Beanstalk and containers and then digs into serverless.
(22:35) Martin shares how one can spin up a full-blown application which leverages serverless infrastructure using AWS Visual Studio Tools. It’s something along the lines of AWS Visual Studio Tools > File > New > AWS Lambda and Sample Projects > ASP.NET Core Project, which creates an MVC website with Lambda entry point and provisioned API Gateway, which provides complete MVC sample application hosted in AWS.
(25:40) Martin drives home the message that when AWS Lambda isn’t being used, you aren’t paying anything. You only pay for the compute time you consume. Jon speaks to massive scaling and “micro scaling” in the cloud.
(27:15) Martin talks further about low cost and low scale with functions as well as statically hosting websites which use serverless for auth and lambda processing. Martin walks through the specific use case of Comic Relief, which benefited greatly (93% cost savings) from serverless hosting.
(29:50) Martin speaks to AWS Amplify and there’s more discussion of scalability, burstability, and agility.
(31:00) Martin wraps up the chat sharing how functions allow developers to mix-and-match languages, which promotes using the right tool for the job. For example, audio manipulation using Python, when the rest of the application may be written in C#.
At DevSum Stockholm, Jon talks with Tess Ferrandez about some machine learning applications she’s worked on recently, from sports to shoplifting to cancer detection. Tess talks about the specific ethical considerations that come up when classifying and predicting behavior, and how they worked with them in these real-life examples.
(00:20) Tess has been working on some applied machine learning projects with large customers lately, all focused on computer vision. One project detects soccer goals using computer vision (saving money over hardware based solutions), another detects cancer in microscopy slides, and the third detects shoplifting patterns to minimize
(02:55) Tess has been doing this work in Python rather than .NET. Jon asks if it’s possible to use ML.NET, but Tess says Python is necessary, both because the language is better suited and the community libraries are all in Python.
(04:35) Jon asks Tess about her experiences moving from .NET to Python, and Tess says it’s a struggle since it’s not strongly typed. You can use testing on the parts that handle data, but not on the machine learning parts.
(05:40) Jon asks how much of Tess’ work is done using Jupyter Notebooks. For data exploration, Jypyter works great, but for the actual execution you’ll want to use scripts so it’s testable.
(07:00) Jon asks more about how you can detect shoplifting behavior, since it’s an activity that happens over time. Tess says it’s also difficult because the prediction may be biased against a demographic, e.g. 20-40 year old men.
(07:54) Tess say ethics and machine learning are close to causing the third machine winter, and goes on to describe the previous two machine winters. We now have the machines and the data, but often the data is so unfair that it could lead to severe ripple effects. This can cause bias in predicting behavior racially, biasing against things like medical analysis due to sample source, etc.
(11:30) Jon and Tess discuss the dangers of creating bad feedback loops. Tess talks about an example where Amazon created a system to review CV’s which was biased against women because historically women have had fewer software engineering positions, so this system would have reinforced that by preventing women from getting software engineering positions in the future.
(13:35) There’s also a danger of classifying people based on pictures, since we may assume the computer is unbiased even though the bias may have been introduced due to the sample data. Classifying based in pictures would imply that either people were born criminals or criminality changes their appearance, neither of which are acceptable assumptions.
(16:09) Going back to the shoplifting case, we need to make sure we’re detecting the action of shoplifting rather than classifying the individual’s appearance. For instance, detecting poses, whether the individual was alone. Pre-trained models for things like object and activities help. There are also subtle sources of bias, for instance if all the source videos are from Christmas, the model may be biased against Santa Claus, so you also need to use pre-trained models for background subtraction.
(18:13) Jon asks how important it is to be able to understand how the decisions were made. Tess says it depends based on the impact of the decision, and explains how in the case of cancer detection they determined that color differentiation could be used as a predictor, so the actual application didn’t require machine learning. In the case of football goal detection, there was such a large amount of data (time, video, and sound), it was possible to get very good results.
Kevin and Jon talk with Will Green (@hotgazpacho) about how his small team uses serverless development on the AWS platform to maximize their productivity.
(00:20) Will’s team builds the FireEye Market, which enables you to “discover apps, extensions, and add-ons that integrate with and extend your FireEye experience.”
(02:51) FireEye is a relatively large company, but Will’s team is just four people, and they’re using serverless development to scale and get a lot done quickly. The FireEye Market is a greenfield development project. It’s primarily a single page application that uses GraphQL. When new apps are published, an external provider pings webhooks that kick off background process that cache binaries, notify consumers, etc.
(07:05) Kevin asks about what pushed their team towards serverless technology. Will talks about how serverless lets them maximize the time they devote to delivering business value.
(08:30) Will talks about how they were able to successfully pitch the project internally. While there were some additional costs as they scaled up, they’ve also been able to take advantage of new AWS services that allow them to scale on demand, which has led to savings.
(11:10) Jon asks for more clarification of what Apollo GraphQL‘s role in their architecture.
(12:38) Kevin asks about the learning curve. Will says a lot of it was pretty natural since the team already had a Node background, but learning things like cold start took some work.
(14:25) They used the serverless framework, which helped take care of setting up tedious infrastructure. If they were starting today, they’d seriously look at AWS Amplify, which is a lot more feature rich and includes support for CI/CD.
(15:50) Jon asks how they handle failures, including both code errors and service outages.
(19:49) Kevin asks about concerns with vendor lock-in. Will explains why he prefers to just pick a cloud vendor and learn it.
(20:49) Kevin asks how they manage the complexity of many small services interacting; Will talks about the use of AWS Step Functions to manage state and workflow, and keeping updated diagrams really helps.
(22:40) Kevin asks about the local vs. cloud development experience. Will talks about some local development emulators from the community, but it’s not quite the same as actually hitting the real service.
(24:00) Kevin asks about the testing strategy.
(25:15) Jon asks how things work with version control. Will explains how AWS CodeBuild handles git push build and deploy for them.
(26:00) Jon asks how Will keeps up with all the different AWS services, especially since many aren’t intuitively named. Will defines all the different services they’re using.
(28:48) Will describes his bias against containers: you still have to worry about the underlying operating system, whereas with serverless that’s all abstracted away.
(30:00) Will explains how they designed the system, starting with diagrams on draw.io, continuing to work through requirements, and evolving the system.
(31:52) Will explains what’s different about working with DyanmoDB. There’s a lot, especially access patterns.
(36:03) Jon asks how they handle versioning multiple services and data changes; Will talks about using Step Functions and handling data failures.
(38:25) Jon asks for advice for people who are getting started with serverless on AWS, and Will highly recommends AWS Amplify. There are lots of samples for serverless framework.
(40:39) Kevin asks if it’s possible to migrate an existing application to a serverless architecture. Will says it’s challenging, but you can use CloudFront as a router to start distributing work to serverless services based on URL path segmentation.
At DevSum Stockholm, Jon talks to Matthew Renze (@matthewrenze ) about data science practices to improve both the products they are creating and their software development practices.
(00:20) Matthew explains how he’s been speaking to software developers about applying data science practices to improve both the products they are creating and their software development practices.
(00:40) Data science can add intelligence to applications, machine learning to automate decision-making processes, and deep learning to modify the user interface using anticipatory design.
(03:57) The other side to this is using data science to help build software. The DevOps pipeline provides a lot of objective measures to help improve our software development processes and practices.
(05:51) Software telemetry data can help us prioritize the time we spend on features towards those that are actively used.
(07:12) Jon asks which terms he really needs to understand as a developer. Matthew defines data science, machine learning, deep learning, and reinforcement learning. They discuss how text suggestions and language understanding have progressed, and where generated text can and can’t help.
(13:55) Machine learning can be used for good and for evil – for instance, it’s now possible to forge video in a way that’s really tough to detect. What do we do now? Matthew talks about what we can do as developers to educate those around us and apply ethics to the software we contribute to.
(19:50) How do we handle things like legal liability for machines that are making decisions, like self-driving cars? Matthew puts it in historical context and talks about how we’ll need to adapt our society to accommodate.
(24:12) Jon asks where to get started applying data science today. Matthew gives some pointers on where to get started learning, and how to start with some quick wins like A/B testing and objective software quality metrics.