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Software Daily: The world through the lens of software.

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#634
Sep 26, 2017 · 57 min
Dashboarding and Query Latency with Tom O’Neill
A dashboard is a data visualization that aggregates metrics in a way that we can quickly understand. In a modern software company, everyone uses dashboards–from salespeople to DevOps to HR. Each dashboard represents a query that must be updated frequently, so that anyone looking at it is getting up-to-date information. The data set being queried might be getting updated quickly in the case of time series or log data. Some queries require joins between disparate data sources. How do you keep dashboards accurate? How do you keep query latency down? Tom O’Neill is the CTO of Periscope Data, a company that makes popular dashboarding tools. In this episode, Tom explains the data engineering that underlies Periscope data. We explore topics such as caching, columnar data, and Redshift. The iOS app is the first project to come out of the Software Engineering Daily Open Source Project. There are more projects on the way, and we are looking for contributors–if you want to help build a better SE Daily experience, check out github.com/softwareengineeringdaily. We are working on an Android app, the iOS app, a recommendation system, and a web frontend. Help us build a new way to consume software engineering content at github.com/softwareengineeringdaily.
#633
Sep 25, 2017 · 54 min
Static Analysis with Paul Anderson
Static analysis is the process of evaluating code for errors, memory leaks, and security vulnerabilities. The “static” part refers to the fact that the code is not running. This differentiates it from unit tests and integration tests, which evaluate the runtime characteristics of code. If you use an IDE or a linter, you are using a basic form of static analysis all the time. More sophisticated static analysis tools can be used to analyze code in sensitive domains like healthcare or automobiles. During static analysis, we can discover problems in the code by evaluating the structure of a program. Buffer overruns can be identified before they turn into a vulnerability like Heartbleed. Null pointer exceptions can be fixed before they cause a segmentation fault. Concurrency issues can be serialized before they result in a problematic race condition. Today’s guest Paul Anderson is the VP of engineering at GrammaTech, where he works on CodeSonar, a static analysis tool. We discussed how static analysis works, why it is useful, and how it fits into a modern software delivery pipeline. Full disclosure: GrammaTech is a sponsor of Software Engineering Daily.
#632
Sep 22, 2017 · 1 hr 27 min
The Coding VC with Leo Polovets
The underlying cause of failure for many startups is that the founders are afraid of discomfort. An environment where everyone is comfortable is unlikely to be an environment where personal growth and value creation is occurring. When you are in a startup, calibrating the right amount of discomfort is often about calibrating risk. What are your risks? Can you quantify them? Can you enumerate them? Multiplying out the probability of surviving each of those risks, then multiplying that number times the sum of the discounted future cash flows of your business will give you the expected value of your business. Under the right circumstances, entrepreneurship has much higher expected value than a stable engineering job. The important difference is variance. Your business needs to be able to withstand the variance that bad luck can provide. And entrepreneurs themselves need to be able to withstand the variance implied by the fact that their business can completely fail and go to zero. Leo Polovets is a partner with Susa Ventures. He worked as an early engineer at Linkedin, Google, and Factual, and he blogs at Coding VC. In this episode we talked about the proper mindset for founding a company–how to think about risk, mistakes, discomfort, and finance. Coding VC blog
#631
Sep 21, 2017 · 56 min
Tinder Growth Engineering with Alex Ross
Tinder is a popular dating app where each user swipes through a sequence of other users in order to find a match. Swiping left means you are not interested. Swiping right means you would like to connect with the person. The simple premise of Tinder has led to massive growth, and the app is now also used to discover new friends and create casual meetings. Every social network knows–if you are not growing, then you are dying. Growth is so important to Tinder, they have a large engineering organization devoted to five facets of growth: new users, activation, retention, dropoff, and anti-spam. These five segments cover the entire Tinder user lifecycle, and there is a sub-team in charge of each of the five areas. No matter what kind of Tinder user you are, there are growth engineers focused on your experience. Alex Ross is the director of engineering for the growth team at Tinder. His job requires a mix of data science, data engineering, psychology, and setting proper KPIs (key performance indicators). Each subteam has KPIs that determine how well they are doing with growth–and if the wrong KPI is set, it can create bad incentives. For example, a growth team that is focused only on getting users to spend more time engaging with Tinder would have an incentive to create so-called “dark patterns” that trigger addiction.
#630
Sep 20, 2017 · 54 min
Brave Browser with Jonathan Sampson
Online advertising enables free content and services of the Internet. One of the free services that is powered by advertising is the browser. 60% of web browsing is done through Chrome, which is owned by Google, which is powered by advertising. The application that most of us use to explore the web is made by a company that relies on ads, so it is unsurprising that the default of that browser is to allow close tracking of user behavior. When you hit a website, a variety of trackers are logging your data for the purpose of serving you better ads. Some people don’t like ads, and they don’t like being tracked–but what is the alternative? How else can we get all the content we want? Since the 90’s, engineers have envisioned an Internet powered by micropayments. A micropayments system in your browser would allow users to pay for content with money instead of adtech. Brave is a web browser built with a modern view of advertising, privacy, and economics. Brave users can pay for content with their money OR by paying attention to ads. This system is formalized through the Basic Attention Token (BAT), a cryptocurrency that can be used to purchase user attention. Jonathan Sampson is a senior developer relations specialist with Brave Software. He joins the show to talk about the problems with the browsing experience and what Brave is doing to stop it.
#629
Sep 19, 2017 · 50 min
Deep Learning Systems with Milena Marinova
The applications that demand deep learning range from self-driving cars to healthcare, but the way that models are developed and trained is similar. A model is trained in the cloud and deployed to a device. The device engages with the real world, gathering more data. That data is sent back to the cloud, where it can improve the model. From the processor level to the software frameworks at the top of the stack, the impact of deep learning is so significant that it is driving changes everywhere. At the hardware level, new chips are being designed to perform the matrix calculations at the heart of a neural net. At the software level, programmers are empowered by new frameworks like Neon and TensorFlow. In between the programmer and the hardware, middleware can transform software models into representations that can execute with better performance. Milena Marinova is the senior director of AI solutions at the Intel AI products group, and joins the show today to talk about modern applications of machine learning and how those translate into Intel’s business strategy around hardware, software, and cloud. Full disclosure: Intel is a sponsor of Software Engineering Daily. Question of the Week: What is your favorite continuous delivery or continuous integration tool? Email jeff@softwareengineeringdaily.com and a winner will be chosen at random to receive a Software Engineering Daily hoodie. Data Skeptic podcast: Generative Adversarial Networks
#628
Sep 18, 2017 · 55 min
Spotify Event Delivery with Igor Maravic
Spotify is a streaming music company with more than 50 million users. Whenever a user listens to a song, Spotify records that event and uses it as input to learn more about the user’s preferences. Listening to a song is one type of event–there are hundreds of others. Opening the Spotify app, skipping a song, sharing a playlist with a friend–all of these are events that provide valuable insights to Spotify. These are not the only types of events that Spotify cares about. There are also events that occur at the infrastructure level–for example a logging server that runs out of disk space. There are events that are relevant to all the users on Spotify–for example a new album release from Taylor Swift. An “event” is an object that needs to be registered within a system. Since there are so many events on a platform like Spotify, delivering and processing them reliably requires significant investment. Modern Internet companies are built by connecting cloud services, databases, and internal tools together. These different systems might respond to different events in different ways. Each system subscribes to the types of events that it wants to hear. Since there are so many events, and they might be received at uneven bursts, a modern architecture has a scalable queueing system to buffer events. To put an event on the queue, the event producer “publishes” that event to the queue. The event is then received by each “subscriber.” That’s why queueing is often known as pub/sub–publish/subscribe. Igor Maravic is an engineer with Spotify. In this episode, he explains why pub/sub is a key element of Spotify’s infrastructure–and he describes the migration that Spotify has made from Apache Kafka to Google Cloud Pubsub. If you like this episode, we have done many other shows about cloud infrastructure. You can check out our back catalog by downloading the Software Engineering Daily app for iOS, where you can listen to all of our old episodes, and easily discover new topics that might interest you. You can upvote the episodes you like and get recommendations based on your listening history. With 600 episodes, it is hard to find the episodes that appeal to you, and we hope the app helps with that.
#627
Sep 16, 2017 · 44 min
Advertising Analytics with Jonah Goodhart
Moat is one of the most successful advertising technology companies in history. After building a business from measurement of ad impressions, Moat was sold to Oracle for $850 million. Advertising powers the free content on the Internet. Measurement makes it easier for publishers to monetize their content. At Software Engineering Daily, we know this from firsthand experience. The podcast ecosystem has barely any ability to measure success–and that can make it hard to entice advertisers. In podcasting, it is very difficult to understand if an advertising campaign is a success. This illustrates why Moat is important. Improving the analytics on advertising helps publishers, brands, ad agencies, and adtech companies decide how to allocate their capital. Why is it hard to measure advertising success? Why is this a difficult engineering problem? Because there are so many players in the space with conflicting incentives. A brand wants to show ads to people who will buy a product. A publisher wants to display an ad that will maximize revenue. Adtech companies and ad agencies want to take the biggest cut possible from the transactions between brands and publishers. In the midst of all of this, fraudulent traffic providers offer cheap services that drain money from anyone who is not keeping a close eye on their deal flow. In this fog of war, Moat’s goal is to provide transparency where possible. Moat CEO Jonah Goodhart joins this episode to talk about advertising analytics, viewability, and fraud. If you like this episode, we have done many other shows about adtech and advertising fraud. You can check out our back catalog by going to softwareengineeringdaily.com or by downloading the Software Engineering Daily app for iOS, where you can listen to all of our old episodes, and easily discover new topics that might interest you. You can upvote the episodes you like and get recommendations based on your listening history. With 600 episodes, it is hard to find the episodes that appeal to you, and we hope the app helps with that.
#626
Sep 15, 2017 · 50 min
Visual Search with Neel Vadoothker
If I have a picture of a dog, and I want to search the Internet for pictures that look like that dog, how can I do that? I need to make an algorithm to build an index of all the pictures on the Internet. That index can define the different features of my images. I can find mathematical features in each image that describe that image. The mathematical features can be represented by a matrix of numbers. Then I can run the same algorithm on the picture of my dog, which will make another matrix of numbers. I can compare the matrix representing my dog picture to the matrices of all the pictures on the internet. This is what Google and Facebook do–and we covered this topic in our episode about similarity search a few weeks ago. Today, we evaluate a similar problem: searching images within Squarespace. Squarespace is a platform where users can easily build their own website for blogging, e-commerce, or anything else. Neel Vadoothker is a machine learning engineer at Squarespace, and he joins the show to talk about how and why he built a visual similarity search engine. If you like this episode, we have done many other shows about machine learning. You can check out our back catalog by going to softwareengineeringdaily.com or by downloading the Software Engineering Daily app for iOS, where you can listen to all of our old episodes, and easily discover new topics that might interest you. You can upvote the episodes you like and get recommendations based on your listening history. With 600 episodes, it is hard to find the episodes that appeal to you, and we hope the app helps with that.
#625
Sep 14, 2017 · 1 hr 3 min
Doing Anything with George Anders
Software gives us new ways of communicating with each other. Engineers build scalable systems for e-commerce, helpdesk, and video sharing–and these systems do scale, to millions of people. But software alone cannot serve all of the demands of all the users and customers on these platforms. We need customer service representatives to address unexpected demands. We need design specialists to evaluate the interface that made sense to the engineers but not the users. We need sales people to connect our strange software to an impatient prospective customer. Engineers sometimes joke about firing all the non-engineers in the company. As engineers, it is easy to discount all of the work that non-engineers do–it can seem unscalable, or non-quantifiable, or mechanical. But most companies would fall over immediately without support, sales, design, operations, and the multitude of other non-engineering roles. More to the point–people in non-technical roles can drive the success of an en organization. Some of the most influential leaders in tech came from a non-technical background: Stuart Butterfield of Slack; Brian Chesky of Airbnb; Sheryl Sandberg of Google and Facebook. A liberal arts education can foster the perfect set of skills to thrive in a technology company. George Anders is an author whose most recent book is called You Can Do Anything: The Surprising Power of a “Useless” Liberal Arts Education. George is one of my favorite business writers, and some of his past writing includes pieces about Sequoia Capital, Amazon, Linkedin, and a ton of other topics on Quora. If you like this episode, we have done many other shows about business with guests like Seth Godin and Tyler Cowen–indeed many of the shows on Software Engineering Daily are not deeply technical. You can check out our back catalog by downloading the Software Engineering Daily app for iOS, where you can listen to all of our old episodes, and easily discover new topics that might interest you. You can upvote the episodes you like and get recommendations based on your listening history. With 600 episodes, it is hard to find the episodes that appeal to you, and we hope the app helps with that.
#624
Sep 13, 2017 · 57 min
Word2Vec with Adrian Colyer
Machines understand the world through mathematical representations. In order to train a machine learning model, we need to describe everything in terms of numbers. Images, words, and sounds are too abstract for a computer. But a series of numbers is a representation that we can all agree on, whether we are a computer or a human. In recent shows, we have explored how to train machine learning models to understand images and video. Today, we explore words. You might be thinking–”isn’t a word easy to understand? Can’t you just take the dictionary definition?” A dictionary definition does not capture the richness of a word. Dictionaries do not give you a way to measure similarity between one word and all other words in a given language. Word2vec is a system for defining words in terms of the words that appear close to that word. For example, the sentence “Howard is sitting in a Starbucks cafe drinking a cup of coffee” gives an obvious indication that the words “cafe,” “cup,” and “coffee” are all related. With enough sentences like that, we can start to understand the entire language. Adrian Colyer is a venture capitalist with Accel, and blogs about technical topics such as word2vec. We talked about word2vec specifically, and the deep learning space more generally. We also explored how the rapidly improving tools around deep learning are changing the venture investment landscape. If you like this episode, we have done many other shows about machine learning with guests like Matt Zeiler, the founder of Clarif.ai and Francois Chollet, the creator of Keras. You can check out our back catalog by downloading the Software Engineering Daily app for iOS, where you can listen to all of our old episodes, and easily discover new topics that might interest you. You can upvote the episodes you like and get recommendations based on your listening history. With 600 episodes, it is hard to find the episodes that appeal to you, and we hope the app helps with that. Question of the Week: What is your favorite continuous delivery or continuous integration tool? Email jeff@softwareengineeringdaily.com and a winner will be chosen at random to receive a Software Engineering Daily hoodie.
#623
Sep 11, 2017 · 1 hr 8 min
DAO Hack with Matt Leising
The Decentralized Autonomous Organization (DAO) was a digital form of venture capital. It was an ambitious idea–to provide a new decentralized business model for organizing corporations on top of the Ethereum blockchain. Few people in the crypto community were opposed to this premise–but the timeline was short, the code requirements were tremendous, and in retrospect, a vulnerability was inevitable. The DAO launched in May 2016, setting the record for the largest crowdfunding event in history. The following month, the DAO was hacked, millions of dollars of Ether were stolen, and the reverberations of the event were a referendum on how the Ethereum community governs itself. Matt Leising is a reporter for Bloomberg who has chronicled the DAO in his article The Ether Thief. He continues to follow cryptocurrencies closely, as the Internet of money fractals increasingly into the public consciousness. If you like this episode, we have done many other shows about cryptocurrencies and their implications. You can check out our back catalog by downloading the Software Engineering Daily app for iOS, where you can listen to all of our old episodes, and easily discover new topics that might interest you. You can upvote the episodes you like and get recommendations based on your listening history. With 600 episodes, it is hard to find the episodes that appeal to you, and we hope the app helps with that. Errata: Coinbase now supports Bitcoin Cash.
#622
Sep 8, 2017 · 59 min
Software Engineering Daily App with Keith and Craig Holliday
You have probably missed some of the best episodes of Software Engineering Daily. If you listen to just a few episodes a week, it can be difficult to identify the high quality shows. And if you are new to the podcast, you have no idea how to find episodes that might appeal to you. Software Engineering Daily has a discovery problem. We have 600 episodes, and much of the content is evergreen. The shows we did a year ago on Apache Spark, or Ethereum, or ReactJS are still relevant today, and they get plenty of listens. Keith and Craig Holliday built a recommendation system for Software Engineering Daily. Then they built a Software Engineering Daily iOS app to improve the experience of SE Daily listeners. You can use the SE Daily app to find the most popular episodes of this podcast, and to find episode recommendations based on what you have listened to. In this episode, Keith and Craig join the show to explain why they built an app for Software Engineering Daily. You can find all the code for the SE Daily app at github.com/softwareengineeringdaily in case you want to fork it for your own podcast–or if you want to contribute to it. Question of the Week: What is your favorite continuous delivery or continuous integration tool? Email jeff@softwareengineeringdaily.com and a winner will be chosen at random to receive a Software Engineering Daily hoodie.
#621
Sep 7, 2017 · 47 min
Attack Attribution with John Davis
When a cyber attack occurs, how do we identify who committed it? There is no straightforward answer to that question. Even if we know Chinese hackers have infiltrated our power grid with logic bombs, we might not be able to say with certainty whether those hackers were state actors or rogue Chinese hackers looking for an offensive asset to sell to their government. Even if we know someone in Russia launched an attack on the banking system in Ukraine, we might not know whether that attack came from the government or from aggressive non-governmental forces. Accurate cyberattack attribution is key to preventing diplomatic mistakes in the modern battleground of the Internet. Today’s guest John Davis is one of the authors of the report called “Stateless Attribution: Toward International Accountability in Cyberspace”. John is a senior information scientist with RAND Corporation, a non-profit institution that helps improve policy and decisionmaking through research and analysis. This report was commissioned by Microsoft, and it provides a deep assessment of our current ability to attribute a cyberattack to the perpetrator of that attack. If you like this episode, we have done many other shows about security, with guests like Bruce Schneier and Samy Kamkar. You can check out our back catalog by downloading the Software Engineering Daily app for iOS, where you can listen to all of our old episodes, and easily discover new topics that might interest you. You can upvote the episodes you like and get recommendations based on your listening history. With 600 episodes, it is hard to find the episodes that appeal to you, and we hope the app helps with that.
#620
Sep 6, 2017 · 52 min
Car and IoT Security with Chris Craig
Ransomware and DDoS attacks happen all the time. Sometimes they affect large swaths of users. WannaCry ransomware froze the computer systems in hospitals. Mirai botnet DDoS attacks took down a DNS provider, making Netflix and Twitter inaccessible for a short period of time. These are innocent attacks compared to what we could face from a world where cars, heart rate monitors, and other safety critical machinery become connected to the Internet. This is not a new subject–we have covered it in previous episodes about security. But it’s a deep subject, and there is much ground to cover. Chris Craig joins the show for this episode–he is a security researcher at Oak Ridge National Lab. He studies network and cloud security, and in this episode he brings his broad expertise to subjects like IoT security, car security, and the question of standards–what do we need to standardize and certify as the internet becomes connected to physical infrastructure? Thanks to Jared Smith for the introduction. When Safety and Security Become One Standardisation and Certification of the ‘Internet of Things’
#619
Sep 5, 2017 · 52 min
Artificial Intelligence APIs with Simon Chan
Software companies that have been around for a decade have a ton of data. Modern machine learning techniques are able to turn that data into extremely useful models. Salesforce users have been entering petabytes of data into the company’s CRM tool since 1999. With its Einstein suite of products, Salesforce is using that data to build new product features and APIs. Simon Chan is the senior director of product management with Einstein. He oversees the efforts to give longtime Salesforce customers new value, and the efforts to build brand new APIs for image recognition and recommendation systems, which can form the backbone of entirely new businesses. Companies spend billions of dollars on sales and marketing, and I wanted to understand where the best opportunities for Salesforce were. Simon and I spent much of our time exploring higher level applications, but we got to lower level engineering eventually. There are 600 episodes of Software Engineering Daily, and it can be hard to find the shows that will interest you. If you have an iPhone and you listen to a lot of Software Engineering Daily, check out the Software Engineering Daily mobile app in the iOS App Store. Every episode can be accessed through the app, and we give you recommendations based on the ones you have already heard.
#618
Sep 4, 2017 · 58 min
Information Theory with Jimmy Soni and Rob Goodman
We write code in a language that looks like English. Whether it is JavaScript, Fortran, or assembly language, that code is an abstraction on top of layers of intermediate languages, binary, transistors, and physics. 100 years ago, this would have seemed like magic. Most of us know about Alan Turing, who described the vision of a multipurpose computer with the concept of the Turing machine. Less well known is the scientist Claude Shannon, who laid the groundwork of information theory. With information theory, we can compress data and communicate it efficiently. Jimmy Soni and Rob Goodman are the authors of “A Mind at Play,” a biography of Claude Shannon. Claude’s unique insights about information were made possible by his willingness to involve himself in lots of different areas–science, art, juggling, warfare. This interview gives insights for how we can think of new ideas by synthesizing disparate subjects. There are 600 episodes of Software Engineering Daily, and it can be hard to find the shows that will interest you. If you have an iPhone and you listen to a lot of Software Engineering Daily, check out the Software Engineering Daily mobile app in the iOS App Store. Every episode can be accessed through the app, and we give you recommendations based on the ones you have already heard.
#617
Sep 1, 2017 · 46 min
Healthcare AI with Cosima Gretton
Automation will make healthcare more efficient and less prone to error. Today, machine learning is already being used to diagnose diabetic retinopathy and improve radiology accuracy. Someday, an AI assistant will assist a doctor in working through a complicated differential diagnosis. Our hospitals look roughly the same today as they did ten years ago, because getting new technology into the hands of doctors and nurses is a slow process–just ask anyone who has tried to sell software in the healthcare space. But technological advancement in healthcare is inevitable. Cosima Gretton is a medical doctor and a product manager with KariusDX, a company that is building diagnostic tools for infectious diseases. She writes about the future of healthcare, exploring the ways that workflows will change and how human biases could impact the diagnostic process–even in the presence of sophisticated AI.
#616
Aug 31, 2017 · 40 min
Lending Machine Learning with Ofer Mendelevitch
Loans give people more financial security. If people know that they can receive a loan, they will be more willing to take intelligent risks. A loan can allow for a short-term investment that pays off enough to justify the interest rate on that loan. For the lender, a loan can be a fantastic return on capital–as long as the lendee does not default. When banks were the rulers of the financial infrastructure, most of them would err on the side of caution when it came to lending. They would adhere strictly to credit scores, and a wanting customer would be out of luck if they did not have a credit score, or if their credit score had gotten lower than acceptable. Newer fintech companies are taking advantage of data sources other than credit scores. They are using machine learning in conjunction with these new data sources to find viable lendees who would be overlooked by traditional institutions. Ofer Mendelevitch is the VP of data science at LendUp. He joins the show to explain why loans are important, how LendUp functions, and the machine learning systems that power an intelligent system of lending.
#615
Aug 30, 2017 · 50 min
Industrial IoT with Jayson Delancey
Sensors are being attached to trains, lightposts, and all kinds of factory equipment. Industrial machinery gives off high volumes of data that can be captured, stored, and processed with machine learning in order to improve workflows and ensure safety. Jayson Delancey works at GE, which is building tools and systems to manage large IoT deployments. The full stack for enterprise IoT involves tools for managing thousands of sensors; databases for storing all the data that is coming off of these devices; authentication and authorization systems for enforcing security. There is a lot to do. In this episode, Jayson surveys some of the technology GE is building with Predix, its industrial IoT platform. He also talked about some of the large scale IoT deployments he has seen.

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