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#862
Jul 27, 2018 · 57 min
React Native at Airbnb with Gabriel Peal
React Native allows developers to reuse frontend code between mobile platforms. A user interface component written in React Native can be used in both iOS and Android codebases. Since React Native allows for code reuse, this can save time for developers, in contrast to a model where completely separate teams have to create frontend logic for iOS and Android. React Native was created at Facebook. Facebook itself uses React Native for mobile development, and contributes heavily to the open source React Native repository. In 2016, Airbnb started using React Native in a significant portion of their mobile codebase. Over the next two years, Airbnb saw the advantages and the disadvantages of adopting the cross platform, JavaScript based system. After those two years, the engineering management at Airbnb came to the conclusion to stop using React Native. Gabriel Peal is an engineer at Airbnb who was part of the decision to move off of React Native. Gabriel wrote a blog post giving the backstory for React Native at Airbnb, and he joins the show to give more detail on the decision.
#860
Jul 26, 2018 · 1 hr 3 min
Ghost: Open Source Publishing Platform with John O’Nolan
Blogging is more than 20 years old. Over that period of time, numerous publishing platforms have been created. Squarespace, Blogger, Medium, and Twitter are popular closed source platforms. WordPress has been the most popular open source blogging platform–and much of the Internet (including Software Engineering Daily) runs on WordPress. WordPress is a powerful platform. News companies, ecommerce websites, and many other kinds of businesses use WordPress as their central publishing tool. But WordPress has been around for 15 years–and there are some potential conflicts of interest between WordPress the open source project and WordPress.com (a company started to host WordPress websites). John O’Nolan was working as a WordPress developer when he decided to start a new publishing platform called Ghost. Five years later, the Ghost project is a success–with a thriving open source community, a profitable SaaS business, and companies like Digital Ocean and Mozilla using Ghost to host their blogs. John and I discussed his background with WordPress, what he wanted to do differently with Ghost, and the software architecture of Ghost. We also touched on the Ghost SaaS business and the management of the open source project.
#859
Jul 25, 2018 · 1 hr 4 min
Video Games and Funding Techniques with Howard Marks
Howard Marks ran two video game companies in the 90’s: Activision and Acclaim. While running these companies, he developed a love for entrepreneurship that he maintains today. Howard is the CEO of StartEngine, a company that functions as an accelerator, a crowdfunding platform, and ICO launcher. Howard joins the show to talk about his background as an entrepreneur, as well as some modern alternative funding mechanisms that he’s working on at StartEngine. Hearing Howard’s thoughts on building a video game company in the 90’s was particularly new information to me–it’s an era of software development that we have not covered much at all. As a side note–some listeners have asked recently why we cover subjects such as ICOs, when there have been so many dubious companies that have launched ICOs. There are two reasons why we cover this area. Firstly, cryptocurrencies are a breakthrough computer science construct. It’s important for us to try to understand their implications. The other reason why we cover ICOs is that some technology companies require high upfront capital costs. The amount of capital you have available affects the speed at which your engineering team can move. New funding mechanisms could mean more capital for certain types of software companies–and this could be a good thing and a bad thing, depending on the company.
#858
Jul 24, 2018 · 49 min
Video Machine Learning with Ben Dodson
Video streaming platforms like Netflix offer a convenient way to watch video content. We are now able to watch our favorite TV shows, movies, or content creators on a range of devices. However, buffering while watching videos can be a painful experience on mobile phones and tablets that use 4G or LTE. As streaming becomes available to a wider range of devices with varying bandwidth restrictions, different encodings of the video need to be created for different devices, and different bandwidth situations. To get the best quality viewing possible with the bandwidth available to connections, there needs to be a balance between the resolution of the video, and the bitrate, which defines the data that the video consumes. Mux is a company that builds video hosting and analytics. Ben Dodson is a data scientist at Mux, who built a system for optimizing the bitrate of videos through machine learning. In this episode we discuss video encoding and how Mux solved the problem of serving the highest quality video with the ideal bitrate.
#857
Jul 23, 2018 · 55 min
Kubernetes in the Enterprise with Aparna Sinha
Enterprises want to update their technology faster. One way an enterprise can accelerate the adoption of new tools is to move more aggressively towards the cloud. By giving internal developers access to the cloud, it becomes easier to provision new servers–allowing for rapid experimentation, test environments, and scalability. In previous shows we have explored how large enterprises successfully learn to move their technology faster. Much of this process is rooted in being able to experiment quickly–which requires well-defined testing procedures, and the ability to quickly provision and destroy infrastructure. Many enterprises have large on-premise infrastructure deployments. An enterprise’s movement towards the cloud can be made complex by this existing set of servers. In today’s show, Aparna Sinha discusses how Kubernetes is useful for enterprises–and how it can improve development speed, experimentation, and observability. Aparna is the leader of the product team for Kubernetes and Container Engine at Google. Much of her job is centered around understanding what would be useful to enterprises who are choosing a cloud provider. The open source version of Kubernetes is useful on its own, but most enterprises choose a managed provider of Kubernetes–such as Google Kubernetes Engine–to help with support and onboarding . Full disclosure: Google is a sponsor of Software Engineering Daily.
#856
Jul 20, 2018 · 55 min
WebAssembly with Lin Clark
JavaScript has been the exclusive language of the web browser for the last 20 years. Whether you use Chrome, Firefox, Internet Explorer, or Safari, your browser interprets and executes code in a virtual machine–and that virtual machine only runs JavaScript. Unfortunately, JavaScript is not ideal for every task we want to perform in the browser. Think about the use cases where you need to use software outside of the browser: video editing, music production, 3D art, video games. These applications require a high degree of performance that is hard to get from raw JavaScript. WebAssembly was created to get better performance on the web. WebAssembly allows code from other languages to be compiled and run in the browser. With WebAssembly, languages such as C, C++, and Rust can be used to achieve major performance gains. WebAssembly is still under development, and eventually more programming languages will be accessible as well. Lin Clark is an engineer on the Mozilla Developer Relations team, and has been working closely on the WebAssembly project. She is the author of a detailed series of illustrated blog posts that explain how WebAssembly works. In this episode, we discuss how WebAssembly came to be, its advantages over a web driven purely by Javascript, what is possible with WebAssembly, and its engineering implementation.
#855
Jul 19, 2018 · 49 min
Botchain with Rob May
“Bots” are becoming increasingly relevant to our everyday interactions with technology. A bot sometimes mediates the interactions of two people. Examples of bots include automated reply systems, intelligent chat bots, classification systems, and prediction machines. These systems are often powered by machine learning systems that are black boxes to the user. Today’s guest Rob May argues that these systems should be auditable and accountable, and that using a blockchain-based identity system for bots is a viable solution to the machine learning auditability problem. Rob is the CEO of Talla, a knowledge base provider for business teams. The Botchain project was spun out of Talla as a solution to the problem of bot identity. In this episode, we talk about Botchain and the application of blockchain to bot identity, the current state of ICOs, and the viability of utility token ecosystems. Botchain has its own cryptotoken called “Botcoin.”
#853
Jul 18, 2018 · 55 min
Build a Bank: N26 with Pat Kua
Banking has been a part of the economy for 600 years. Banking has always been evolving. The most recent evolution: the financial industry has been going digital. Newer “fintech” companies have created innovative ways of doing everything related to money–from friendly payments to budgeting; from business transactions to insurance. However, the traditional banks themselves have been relatively slow at adjusting to these digital changes, creating the opportunity for native digital banks–often referred to as “challenger banks.” N26 is a digital first bank established in Berlin and is active in 17 European countries with a million users. Pat Kua is the CTO at N26. Digital banks are hosted on the cloud, without a physical branch the user must go to to perform an operation. The user accesses their account through a mobile application, and can complete everything online from opening an account to performing transactions. This system has numerous advantages, like simplicity for the user, and higher scalability in terms of users from the bank’s perspective. In this episode, we discuss the advantages of digital banks, the fintech industry and N26 as a itself. We also explore product development and how Pat manages his time as CTO–which is a useful discussion for anyone who is learning to be a technical leader or manager.
#852
Jul 17, 2018 · 52 min
Git Vulnerability with Edward Thomson
Git is a distributed file system for version control. Git is extremely reliable, fast, and secure, owing to the fact that it is one of the oldest pieces of open source software. But even battle-tested software can have vulnerabilities. In this episode, we explore a subtle git vulnerability that could have potentially led to git users executing malicious scripts when they intended to simply pull a repository. Today’s guest Edward Thomson is a program manager at Microsoft, and a maintainer of libgit2, a C implementation of git. He also writes about git and hosts the podcast All Things Git. He is passionate about git development, which gave me a deeper perspective on something that I just consider a tool. But the only reason that tool is so good–the only reason it fades into the background–is because there are people that are passionate enough to work on it on a regular basis. We also spent some time talking about the vulnerabilities that can spread through shared code environments–particularly in the realm of git, npm, and PHP. And we touched on how deployment workflows around git and Kubernetes are changing. Full disclosure: Microsoft, where Edward works, is a sponsor of Software Engineering Daily.
#851
Jul 16, 2018 · 47 min
Counting People with Andrew Farah
If you operate a restaurant, you want to know how many people are inside your restaurant at any given time. You also want to be able to know your occupancy if you operate a movie theater, coffee shop, or apparel store. Knowing how many people are in your building can answer several business-related questions. Do you need to unlock an additional entrance? Should you open another store? Do you really need a building this big? This might sound like a simple question, but how do you solve the problem of counting people inside of a building? A naive approach to counting people is to use video cameras and count the number of people entering and exiting the building. Machine learning algorithms are good at classifying humans. But the downside of this is that you have to put cameras anywhere you want a people-counter. There are many situations where you would want to count the number of people where a camera is not acceptable. What if you wanted to count people in a privacy preserving way? What if you wanted to obscure any identifiable traits of a person that you were counting? Density is a device for counting people. It sits above a doorway and counts the people who are entering or exiting the building. Andrew Farah is the CEO at Density, and in today’s episode he explains why the problem of counting people is harder than it sounds, and how the Density people counter functions.
#850
Jul 13, 2018 · 56 min
Machine Learning Deployments with Diego Oppenheimer
Machine learning models allow our applications to perform highly accurate inferences. A model can be used to classify a picture as a cat, or to predict what movie I might want to watch. But before a machine learning model can be used to make these inferences, the model must be trained and deployed. In the training process, a machine learning model consumes a data set and learns from it. The training process can consume significant resources. After the training process is over, you have a trained model that you need to get into production. This is known as the “deployment” step. Deployment can be a hard problem. You are taking a program from a training environment to a production environment. A lot can change between these two environments. In production, your model is running on a different machine–which can lead to compatibility issues. If your model serves a high volume of requests, it might need to scale up. In production, you also need caching, and monitoring, and logging. Large companies like Netflix, Uber, and Facebook have built their own internal systems to control the pipeline of getting a model from training into production. Companies who are newer to machine learning can struggle with this deployment process, and these companies usually don’t have the resources to build their own machine learning platform like Netflix. Diego Oppenheiner is the CEO of Algorithmia, a company that has built a system for automating machine learning deployments. This is the second cool product that Algorithmia has built, the first being the algorithm marketplace that we covered in an episode a few years ago. In today’s show, Diego describes the challenges of deploying a machine learning model into production, and how that product was a natural complement to the algorithms marketplace. Full disclosure: Algorithmia is a sponsor of Software Engineering Daily.
#849
Jul 12, 2018 · 51 min
Ballerina Language with Tyler Jewell
Modern programming requires lots of integration between APIs. Some of these integrations are trivial–such as using Twilio or Stripe. But there are many more complex integrations. For example, when a large company acquires a smaller company, the acquiring company might want to integrate with that smaller company to leverage the synergies between the two companies. How do you build clean communication patterns between the services of one company and another? Two teams within a single enterprise can also have integration issues. One team might have a different data model than the other team. One team might be using JSON and the other using XML. In these cases, integrations between APIs can take considerable time. Ballerina is a programming language that is designed for writing integrations. Ballerina is made for building services that allow two APIs to communicate easily–in contrast to other patterns of API integration such as those involving an enterprise service bus. Tyler Jewell is the CEO of WSO2, a company that specializes in integrations. WSO2 created the Ballerina language and is investing heavily into it (with ~80 people working on Ballerina today). In this episode we explored integrations–and why this problem required creating a new programming language. BallerinaCon is July 18th in San Francisco and our listeners can attend for free–use code BalCon-SEDaily.
#847
Jul 11, 2018 · 57 min
Flutter in Practice with Randal Schwartz
Flutter allows developers to build cross-platform mobile apps. In our previous show about Flutter, Eric Seidel from Google described the goals of Flutter, why he founded the project, and how Flutter is built. In today’s show, Randal Schwartz talks about Flutter in more detail–including the developer experience of building Flutter apps and why he finds Flutter so exciting. Randal is a longtime software developer who has focused mostly on web applications. He’s also the host of the popular podcast FLOSS Weekly, a show about open source software. Like many developers, Randal has stayed away from mobile development in the past. If you write a mobile application, you have historically had to build iOS and Android apps separately. That is a large up-front cost, and Flutter reduces the cost by allowing users to develop mobile apps for iOS and Android with a single codebase. Randal has been podcasting about open source software for 12 years, so he brings historical depth to this conversation. He has also been working with Dart for several years–Dart is a language developed by Google that is used in Flutter.
#846
Jul 10, 2018 · 55 min
Build a Bank: Nubank with Edward Wible
Nubank was started in 2013 with a credit card that was controlled through a mobile app. At the time, it was the first service in Brazil that allowed customers to do banking without going to a physical bank branch. Since then, Nubank has expanded into additional financial services and currently has 850 employees working in Brazil. Edward Wible is a co-founder and CTO of Nubank and in this episode he discusses his work growing Nubank from a small team of less than 10 people into a company with almost 1000 people. We have covered two other banks in the past few weeks: Monzo and N26. In terms of software engineering and product management, Nubank is similar to Monzo and N26. One characteristic that stood out was Nubank’s use of Clojure, a functional programming language built on the JVM. A question that came up during this show: what is the line between “fintech company” and “bank”? We hope explore this more in future shows about the intersection of money and software. Transcript provided by We Edit Podcasts. Software Engineering Daily listeners can go to weeditpodcasts.com/sed to get 20% off the first two months of audio editing and transcription services. Thanks to We Edit Podcasts for partnering with SE Daily. Please click here to view this show’s transcript.
#845
Jul 9, 2018 · 52 min
Flutter with Eric Seidel
Flutter is a project from Google that is rebuilding user interface engineering from the ground up. Today, most engineering teams have dedicated engineering resources for web, iOS, and Android. These different platforms have their own design constraints, their own toolset, and their own programming languages. But each platform is merely building a user interface. Why should development across these three user surfaces be so different? This was the question that Eric Seidel was asking himself three years ago, when he co-founded the Flutter project. The Flutter project had a few rough starts, as the team tried to figure out exactly what layer of abstraction they were trying to provide. Around that time, ReactJS and React Native were growing in popularity. Seeing the React projects provided some data points, and some inspiration. But Flutter takes a lower level approach to cross platform app development, by presenting a rendering layer and a runtime API that are interfacing with the hardware in the same way that OpenGL does. In today’s episode, Eric joins the show to explain how the Flutter project came to life, and his lessons from starting an ambitious project that took several years to pick up steam. I enjoyed this episode because Flutter could have massive improvements for how quickly we can build apps–and also because Eric is a serious engineer and there are so many insights in this episode about computer science, software engineering, and project management. Transcript provided by We Edit Podcasts. Software Engineering Daily listeners can go to weeditpodcasts.com/sed to get 20% off the first two months of audio editing and transcription services. Thanks to We Edit Podcasts for partnering with SE Daily. Please click here to view this show’s transcript.
#844
Jul 6, 2018 · 1 hr 2 min
Future Projection with Tim O’Reilly
Tim O’Reilly’s book What’s the Future? is an overview of business, technology, and society. As the founder of O’Reilly Media, Tim has been steeped in technology trends for the last 40 years. From his vantage point running conferences and publishing technical content, Tim has been able to make informed predictions about what is coming next. In today’s conversation, Tim gave his perspective on how artificial intelligence will impact our world in the coming decades. More importantly–Tim emphasizes the role of human agency. The future is not something that merely happens to us as we sit back and eat popcorn. Today, we make decisions, and those decisions that we make could help lead us to technology utopia or towards the fall of our great technological empire. On the subject of business, Tim gave a radically different perspective than most of the entrepreneurs that come on Software Engineering Daily. In our conversation, he raised the question of why entrepreneurs raise massive amounts of money, get on the treadmill of startup hype, and build a company around negative cash flows. For that model, the only possible outcomes are going public, being acquired, or flaming out completely. O’Reilly Media has been cash flow positive since the beginning, and the company has steadily compounded, growing successively bigger businesses from publishing to conferences to online learning. This episode gave me a lot to think about–just as the O’Reilly Conferences have throughout the years. O’Reilly Media has graciously partnered with SE Daily since we were very small, so I have great admiration for the company and Tim himself. It was a pleasure to get the chance to meet him in person. Transcript provided by We Edit Podcasts. Software Engineering Daily listeners can go to weeditpodcasts.com/sed to get 20% off the first two months of audio editing and transcription services. Thanks to We Edit Podcasts for partnering with SE Daily. Please click here to view this show’s transcript.
#843
Jul 5, 2018 · 53 min
Machine Learning Stroke Identification with David Golan
When a patient comes into the hospital with stroke symptoms, the hospital will give that patient a CAT scan, a 3-dimensional imaging of the patient’s brain. The CAT scan needs to be examined by a radiologist, and the radiologist will decide whether to refer the patient to an interventionist–a surgeon who can perform an operation to lower the risk of long-term damage to the patient’s brain function. After getting the CAT scan, the patient might wait for hours before a radiologist has a chance to look at the scan. In that period of time, the patient’s brain function might be rapidly degrading. To speed up this workflow, a company called Viz.ai built a machine learning model that can recognize whether a patient is at high risk of stroke consequences or not. Many people have predicted that radiologists will be automated away by machine learning in the coming years. This episode presents a much more realistic perspective: first of all, we don’t have nearly enough radiologists, so if we can create automated radiologists that would be a very good thing; second of all, even in this workflow with a cutting-edge machine learning radiologist, you still need the human radiologist in the loop. David Golan is the CTO at Viz.ai, and in today’s show he explains why he is working on a system for automated stroke identification, and the engineering challenges in building that system. Transcript provided by We Edit Podcasts. Software Engineering Daily listeners can go to weeditpodcasts.com/sed to get 20% off the first two months of audio editing and transcription services. Thanks to We Edit Podcasts for partnering with SE Daily. Please click here to view this show’s transcript.
#842
Jul 3, 2018 · 54 min
Fintech Environment with Michael Walsh
Computer systems consume memory, CPU, battery, data, and network bandwidth as inputs. These systems provide value for the end user by delivering information, virtual objects, and physical products as outputs. Another fundamental resource that is becoming easier to consume as input is money. There are also new outputs–financial constructs that are made possible by cloud computing, machine learning, and cryptocurrencies. This is why so much opportunity exists in fintech. Money has always been a flexible tool for brokerage between humans. But as recently as the early 2000s, the interfaces between money and computers have been clunky and inflexible. Engineers that wanted to build financial systems around money had to work directly with banks and credit card processors. More recently, there has been an explosion in new APIs and completely new financial primitives like cryptocurrencies. In the year 2000, a well-funded team had to struggle to put together a basic ecommerce company. Today, a blue ocean of opportunity has opened up for entrepreneurs building businesses around lending, insurance, underwriting, banking, and every other microcosm of the financial system. Michael Walsh is a general partner and co-founder of Green Visor Capital. In today’s episode, he described his perspective on the modern fintech environment, and what the future holds. Transcript provided by We Edit Podcasts. Software Engineering Daily listeners can go to weeditpodcasts.com/sed to get 20% off the first two months of audio editing and transcription services. Thanks to We Edit Podcasts for partnering with SE Daily. Please click here to view this show’s transcript.
#841
Jul 2, 2018 · 54 min
Kademlia: P2P Distributed Hash Table with Petar Maymounkov
Napster, Kazaa, and Bittorrent are peer-to-peer file sharing systems. In these P2P systems, nodes need to find each other. Users need to be able to search for files that exist across the system. P2P systems are decentralized, so these routing problems must be solved without a centralized service in the middle. Without a centralized service that has all the information in one place, how can you solve these problems of node discovery and file lookup? This is the central question that Petar Maymounkov sought to answer with Kademlia. Kademlia is a peer-to-peer distributed hash table. Kademlia implements the “put” and “get” operations of an efficiently scalable hash table without using any centralized service. Each node in the system maintains its own routing table. When a user queries the system (a “get” operation), that query is serviced by the nodes coordinating with each other to intelligently route the user to their target location. When a file is stored (a “put” operation), that update to the file system can propagate through the network in a decentralized, uncoordinated way. Petar joins the show to give a brief history of P2P networks, why he created Kademlia, and what he is working on today. Transcript provided by We Edit Podcasts. Software Engineering Daily listeners can go to weeditpodcasts.com/sed to get 20% off the first two months of audio editing and transcription services. Thanks to We Edit Podcasts for partnering with SE Daily. Please click here to view this show’s transcript.
#839
Jun 29, 2018 · 58 min
Data Engineering Automation with Mike Kim
Every company has the idea of the “nightly report.” A business analyst comes into the office, sits down in front their inbox, and looks at yesterday’s data. Did sales go up? Did the marketing campaigns bring in the expected number of customers? Was there an increase in helpdesk tickets? The statistics that these reports deliver to human analysts can change the direction of the business. Everyone within a company could use a regular report that documents how the business is changing over time. Outlier.ai is a company that processes the data sets within a business and generates automated reports that are relevant to different people within the organization. If you are an email marketing analyst, your data from MailChimp campaigns will be analyzed. If you manage a customer success team, your Zendesk tickets will be analyzed. If you are a technical support analyst, the crash reports and error messages from your users will be analyzed. In all of these cases, the data gets processed automatically, and a story is sent to you, so that you can have the information in your inbox waiting for you, instead of having to go ask a data scientist to generate it for you. Mike Kim is the CTO of Outlier.ai, and in this show he describes the engineering challenges of integrating with all the different data sets of an organization–and why there is so much value in the idea of the automated “report” or “story” for analysts. In past shows, we have explored how data engineering has progressed over the last twenty years–from database administration to Hadoop cluster management to the emergence of “data breadlines” where analysts wait for a data scientist to process the job they asked for. Outlier represents a step towards a world where the data science reports are delivered to us before we even ask, rather than us having to query the system. Transcript provided by We Edit Podcasts. Software Engineering Daily listeners can go to weeditpodcasts.com/sed to get 20% off the first two months of audio editing and transcription services. Thanks to We Edit Podcasts for partnering with SE Daily. Please click here to view this show’s transcript.

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