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#814
Jun 1, 2018 · 50 min
Scaling Ethereum with Raul Jordan and Preston Van Loon
Cryptocurrency infrastructure is a new form of software. Thousands of developers are submitting transactions to Bitcoin and Ethereum, and this transaction volume tests the scalability of current blockchain implementations. The bottlenecks in scalability lead to slow transaction times and high fees. Over the last twenty years, engineers have learned how to scale databases. We’ve learned how to scale Internet applications like e-commerce stores and online games. It’s easy to forget, but there was a time when those systems didn’t perform well either. Scaling a blockchain is different than scaling a relational database or a microservices infrastructure. Blockchains are peer-to-peer databases with an append only ledger shared by thousands of nodes. With different scalability solutions, there are tradeoffs between decentralization, scalability, and security. As an example, in Bitcoin, the core developers are working towards deployment and adoption of lightning network. Some would argue that this approach favors scalability over decentralization. Today’s show is about scaling Ethereum. Raul Jordan and Preston Van Loon are developers who are part of Prysmatic Labs, a team building a sharding implementation for the Go Ethereum client. In this episode, we discuss Ethereum’s approaches to scaling, including sharding and Plasma. 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.
#813
May 31, 2018 · 58 min
Life Science R&D with Sherwin Yu
Ten years ago, a biology researcher was limited by the software tools available. Most of the electronic record keeping was done using Excel and other general purpose tools. Benchling is a suite of software tools that were designed to simplify the lives of life science researchers. Benchling helps with sample tracking, experiment design, and workflow management. Sherwin Yu is an engineering manager at Benchling, and he joins the show to discuss the workflows of the life scientist–how experiments are designed and managed. Life science researchers in both academia and industry use Benchling, and Sherwin spends time talking to them and understanding what they need from their tools. We also talked about the impact of CRISPR, robotic cloud laboratories, and other future developments. 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.
#812
May 30, 2018 · 51 min
Container Native Development with Ralph Squillace
Containers have improved deployments and resource utilization. Kubernetes created a platform to manage those containers and orchestrate them into distributed applications. In today’s episode, we explore tools that improve the workflow of the application developer who is working with Kubernetes, including Helm, Draft, and Brigade. Helm is a package manager for Kubernetes, which allows users to find, share, and use software that is built for Kubernetes. The unit of installation for Helm users is a Helm Chart. Installing a Helm Chart can simplify the deployment of a database, load balancer, or continuous integration tool. Draft is a tool for simplifying the containerization process. When a developer runs Draft, a Dockerfile is created to containerize the application, and a Helm Chart is created to enable the application to be easily deployed. Brigade is a tool for creating and running Kubernetes workflows. Brigade allows for event-driven scripting on top of Kubernetes. Chatops, continuous integration systems, and complex big data pipelines can all be defined with Brigade. Brigade is exciting, because it is a higher level tool on top of Kubernetes–in some ways similar to the “serverless on Kubernetes” systems we have covered in the past. Ralph Squillace is a principal program manager with Microsoft, where he works on containers, Linux, and cloud products. Ralph joins the show to talk about how developing with containers has changed in the last few years, and how it will continue to evolve in the near future. 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.
#811
May 29, 2018 · 49 min
Pi Hole: Ad Blocker Hardware with Jacob Salmela
Ad blockers in the browser protect us from the most annoying marketing messages that the Internet tries to serve to us. But we still pay a price for these ads. We pay the bandwidth costs of requesting these pages. Our browsers are slowed down by these extra requests. Pi Hole is a hardware based ad blocker. Pi Hole acts as a DNS server for all of the traffic that makes its way onto your network. Pi Hole has a blacklist of all the URLs to block–including tracking systems and ad networks. Pi Hole stops these URLs from communicating with all the devices on your network–including your cell phone. Jacob Salmela is the developer of Pi Hole, which he describes as a black hole for advertiser traffic. In this episode, we explain how traditional ad blocking in the browser works, and how things are improved with a piece of dedicated hardware doing the ad blocking. It was also a useful review of the relationship between URLs, IP addresses, your home network, and the broader Internet. 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.
#810
May 25, 2018 · 55 min
Autonomy with Frank Chen
Self-driving, electric cars will someday outnumber traditional automobiles on the road. As transportation becomes autonomous, it is hard to imagine an industry that will not be affected by the downstream effects of this change. These cars will likely be managed by fleet operators like Lyft and Uber. We will need fewer cars, and the amount of space dedicated to those cars will shrink dramatically. Parking lots, massive roads, and gas stations will be reclaimed or repurposed. City planning departments will have to devise entirely new strategies. As the self-driving cars reach consumer availability, an intricate supply chain for these cars will develop. When smartphones became mass-produced, the costs of GPS devices, accelerometers, and other small components dropped steeply. A consequence of the smartphone supply chain was that other devices like consumer drones became affordable. The self-driving car supply chain will lead to the mass production of building blocks for other new devices. With fewer automotive fatalities, the economics of the car insurance industry might collapse completely. At a minimum, the costs of car insurance will likely shift to the fleet operators, who can purchase that car insurance at prices factoring in their large risk pool. Frank Chen is a deal and research partner with Andreessen Horowitz. In a series of presentations on the Autonomy Ecosystem, Frank explores the effects of our impending shift to self-driving electric cars. His analysis considers changes to energy infrastructure, the competitive landscape of software companies, and a range of other topics. Frank joins the show to discuss autonomous vehicles and the side effects of widespread autonomous deployments. 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.
#809
May 24, 2018 · 58 min
Uber’s Data Platform with Zhenxiao Luo
When a user takes a ride on Uber, the app on the user’s phone is communicating with Uber’s backend infrastructure, which is writing to a database that maintains the state of that user’s activity. This database is known as a transactional database or “OLTP” (online transaction processing). Every active user and driver and UberEATS restaurant is writing data to the transactional data store. Periodically, that data is copied from the transactional data system to a different data storage system, where that data can be queried for large-scale data analysis. For example, if a data scientist at Uber wants to get the average amount of miles that a given user rode in February, that data scientist would issue a query to the analytical data cluster. Uber uses the Hadoop distributed file system (HDFS) to store analytical data. On this file system, Uber has a version history of all of the company’s useful historical data. Trip history, rider activity, driver activity–every data point that is in the transactional database–but in a file format that is easier to query for large scale processing. This file format is known as Parquet. Data scientists, machine learning engineers, and real-time application developers all depend on the massive quantities of data that are stored in these Parquet files on Uber’s HDFS cluster. To simplify the access of that data by many different clients, Uber uses Presto, an analytical query engine originally built at Facebook. Presto translates SQL queries into whatever query language is necessary to access the underlying storage medium–whether that storage system is an ElasticSearch cluster, a set of Parquet files, or a relational database. Presto is useful because it simplifies the relationship between data engineers and the application developers who are building on top of the data engineering infrastructure. In today’s show, Zhenxiao Luo joins to give an end-to-end description of Uber’s data infrastructure–from the ingest point of the OLTP database to the OLAP data storage system on HDFS, to the wide range of data systems and applications that run on top of that OLAP data. 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.
#808
May 23, 2018 · 59 min
Software Law: GDPR, Patents, and Antitrust with Micah Kesselman
The world of software moves faster than the laws that regulate it. When software companies do get regulated, that regulation is often enforced unevenly among different companies. Software continually presents the legal system with new requirements. Consumer data privacy needs to enforced on a granular level. Software developers need a system of protecting their intellectual property. When a company becomes dominant, our legal system needs to scrutinize that company for potential antitrust violations. Micah Kesselman is a lawyer specializing in software IP prosecution. Prior to becoming a lawyer, he studied computer science. He joins the show to discuss a range of issues at the intersection of software and the law–including GDPR, software patents, and self-driving cars. These are topics we will cover in more detail in the future, but it was great to have Micah bring the perspective of a lawyer to the show. Massachusetts Autonomous Vehicles Working Group 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.
#807
May 22, 2018 · 43 min
Container Security with Maya Kaczorowski
Deploying software to a container presents a different security model than deploying an application to a VM. There is a smaller attack surface per container, but the container is colocated on a node with other containers. Containers are meant to have a shorter lifetime than VMs, so there are generally fewer consequences if a container needs to be destroyed and rebuilt due to a potential security vulnerability. Maya Kaczorowski works on container security at Google. In a recent talk at KubeCon, Maya discussed runtime security of containers on Kubernetes. Maya joins the show to discuss container security, and what it means to software developers and operators. Maya also gives guidelines for evaluating the security of your own cluster. We talked about the security benefits of a managed Kubernetes provider, and also explored how some container security vendor software works. 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.
#806
May 21, 2018 · 59 min
Voice with Rita Singh
A sample of the human voice is a rich piece of unstructured data. Voice recordings can be turned into visualizations called spectrograms. Machine learning models can be trained to identify features of these spectrograms. Using this kind of analytic strategy, breakthroughs in voice analysis are happening at an amazing pace. Rita Singh researches voice at Carnegie Mellon University. Her work studies the high volume of latent data that is available in the human voice. As she explains, just a small fragment of a human voice can be used to identify who a speaker is. Your voice is as distinctive as your fingerprint. Your voice can also reveal medical conditions. Features of the human voice can be strongly correlated with psychiatric symptom severity, and potentially heart disease, cancer, and other illnesses. The human voice can even suggest a person’s physique–your height, weight, and facial features. In this episode, Rita explains the machine learning techniques that she uses to uncover the hidden richness of the human voice. 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.
#805
May 19, 2018 · 1 hr 12 min
Machine Learning with Data Skeptic and Second Spectrum at Telesign
Data Skeptic is a podcast about machine learning, data science, and how software affects our lives. The first guest on today’s episode is Kyle Polich, the host of Data Skeptic. Kyle is one of the best explainers of machine learning concepts I have met, and for this episode, he presented some material that is perfect for this audience: machine learning for software engineers. Second Spectrum is a company that analyzes data from professional sports, turning that data into visualizations, reports, and futuristic sports viewing experiences. We had a previous show about Second Spectrum where we went into the company in detail–it was an excellent show, so I wanted to have Kevin Squire, an engineer from Spectrum, come on the show to talk about how the company builds machine learning tools to analyze sports data. If you have not seen any of the visualizations from Second Spectrum, stop what you are doing and watch a video on it! This year we have had three Software Engineering Daily Meetups: in New York, Boston, and Los Angeles. At each of these Meetups, listeners from the SE Daily community got to meet each other and talk about software–what they are building and what they are excited about. I was happy to be in attendance at each of these, and I am posting the talks given by our presenters. The audio quality is not perfect on these, but there are also no ads. Thanks to Telesign for graciously providing a space and some delicious food for our Meetup. Telesign has beautiful offices in Los Angeles, and they make SMS, voice, and data solutions. If you are looking for secure and reliable communications APIs, check them out. We’d love to have you as part of our community. We will have more Meetups eventually, and you can be notified of these by signing up for our newsletter. Come to SoftwareDaily.com and get involved with the discussion of episodes and software projects. You can also check out our open source projects–the mobile apps, and our website.
#804
May 18, 2018 · 47 min
Alexa Voice Design with Paul Cutsinger
Voice interfaces are a newer form of communicating with computers. Alexa is a voice interface platform from Amazon. Alexa powers the Amazon Echo, as well as Alexa-enabled cars, refrigerators, and dishwashers. Any developer can build a device with a voice interface using a Raspberry Pi. Paul Cutsinger works on Echo and Alexa at Amazon. He’s focused on growing the market of developers who are building voice interfaces. In this episode, Paul describes how to design and implement a voice application for the Amazon Alexa platform. The market for voice powered apps is so new, and there has yet to be a “killer app.” If you like to tinker on new platforms, you will like this episode–and I was surprised by how easy it sounds to build a voice app. Personally I use voice interfaces all the time–to set timers, to find out how to tell if a cucumber has gone bad, to ask what temperature to cook a potato at. Sometimes, when I am lying in bed trying to get to sleep, I will ask my nearest device to read me a Wikipedia article. These are great use cases, but I’m sure we will see something much more groundbreaking in the future. 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.
#803
May 17, 2018 · 57 min
Pulsar Messaging with Lewis Kaneshiro
Message broker systems decouple the consumers and producers of a message channel. In previous shows, we have explored ZeroMQ, PubNub, Apache Kafka, and NATS. In this episode, we talk about another message broker: Apache Pulsar. Pulsar is an open source distributed pub-sub message system originally created at Yahoo. It was used to scale products with high volumes of users–such as Yahoo Mail. There are three components of a Pulsar deployment: the Pulsar broker (which handles the message brokering), Apache Bookkeeper (which handles the durable storage of the messages), and Apache Zookeeper, which manages the distributed coordination. Lewis Kaneshiro joins the show to describe how Apache Pulsar works, and how it compares to other messaging systems like Apache Kafka. Lewis is the CEO of Streamlio, a company that builds messaging and stream processing systems for enterprises, and uses Pulsar in its core product. 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.
#802
May 16, 2018 · 53 min
Gloo: Function Gateway with Idit Levine
Gloo is a function gateway built on top of the popular open source project Envoy. The goal of Gloo is to decouple client-facing APIs from upstream APIs. Gloo is similar to an API gateway, which is a tool that software companies can use to collect all their APIs and one place and impose security, monitoring, and other standards around those APIs. The goal of Gloo is to provide all the tools necessary to glue together traditional and cloud-native applications. Idit Levine is the CEO of Solo.io, a company that is building Gloo and several other projects. 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.
#801
May 15, 2018 · 56 min
Vitess: Scaling MySQL with Sugu Sougoumarane
YouTube runs a large MySQL database to hold the metadata about its videos. As YouTube scaled, the database was sharded, and applications within YouTube had to write queries that were aware of the sharding layout of that database. This is problematic, because it pushes complexity to the application developer. An application developer shouldn’t have to be aware of how a database is laid out among different nodes. The developer should be able to issue a query, and have the cluster simply return the data. Vitess is an open source system for scaling large MySQL databases. Sugu Sougoumarane co-created Vitess at YouTube. Since YouTube is owned by Google, Vitess was able to leverage the Borg cluster manager developed at Google. Once Kubernetes came to market, it became more viable to make Vitess accessible to open source developers. Sugu joins the show to talk about the scalability problems that YouTube’s database infrastructure encountered and the motivations for building Vitess. 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.
#800
May 14, 2018 · 50 min
Cloud Native Computing Foundation with Chris Aniszczyk and Dan Kohn
The Kubernetes ecosystem consists of enterprises, vendors, open source projects, and individual engineers. The Cloud Native Computing Foundation was created to balance the interests of all the different groups within the cloud native community. CNCF has similarities to the Linux Foundation and the Apache Foundation. CNCF helps to guide open source projects in the Kubernetes ecosystem–including Prometheus, Fluentd, and Envoy. With the help of the CNCF, these projects can find common ground where possible. KubeCon is a conference organized by the Cloud Native Computing Foundation. I attended the most recent KubeCon in Copenhagen. KubeCon was a remarkably well-run conference–and the attendees were excited and optimistic. As much traction as Kubernetes has, it is still very early days and it was fun to talk to people and forecast what the future might bring. At KubeCon, I sat down with Chris Aniszczyk and Dan Kohn, who are the COO and director of the CNCF. I was curious about how to scale an organization like the CNCF. In some ways, it is like scaling a government. Kubernetes is growing faster than Linux grew, and the applications of Kubernetes are as numerous as those of Linux. Different constituencies want different things out of Kubernetes–and as those constituencies rapidly grow in number, how do you maintain diplomacy among competing interests? It’s not an easy task, and that diplomacy has been established by keeping in mind lessons from previous open source projects. 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.
#799
May 11, 2018 · 1 hr 3 min
Cluster Schedulers with Ben Hindman
Mesos is a system for managing distributed systems. The goal of Mesos is to help engineers orchestrate resources among multi-node applications like Spark. Mesos can also manage lower level schedulers like Kubernetes. A common misconception is that Mesos aims to solve the same problem as Kubernetes, but Mesos is a higher level abstraction. Ben Hindman co-founded Mesosphere to bring the Mesos project to market. Large enterprises like Uber, Netflix, and Yelp use Mesosphere for resource management. Before he started the company, Ben worked in the Berkeley AMP Lab, a research program where the Spark and Tachyon projects were also born. At this point, he has spent significant time in both academia and industry. This conversation spans distributed systems theory, history, and practice. Ben and I spoke at KubeCon 2018 in Copenhagen–which was an amazing conference. We were both amazed at how big the audience for Kubernetes has gotten, and the pace at which the technology is advancing. Today, Kubernetes is mostly used for scheduling containerized applications that engineers have built themselves. But there will be higher level tools that use Kubernetes as a building block. Much like Zookeeper was used as a building block for Hadoop, Kubernetes will be used to build serverless applications and distributed databases. Once you are using a distributed database built on Kubernetes, you don’t want to think about the container orchestration–you want to think about the raw storage and CPU requirements for that database. This is one reason why Mesos is so compelling. Since Kubernetes creates an increased cardinality of distributed systems, it’s good to know that there is a framework built to manage those higher level applications. 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.
#798
May 10, 2018 · 56 min
Deep Learning Topologies with Yinyin Liu
Algorithms for building neural networks have existed for decades. For a long time, neural networks were not widely used. Recent changes to the cost of compute and the size of our data have made neural networks extremely useful. Our smart phones generate terabytes of useful data. Lower storage costs make it economical to keep that data. Cloud computing democratized the ability to do large scale machine learning across GPUs. Over the last few years, these trends have been driving widespread use of deep learning, in which neural nets with a large series of layers are used to create powerful results in various fields of classification and prediction. Neural networks are a tool for making sense of unstructured data–text, images, sound waves, and videos. “Unstructured” data is data with high volume or high dimensionality. For example, an image has a huge collection of pixels, and each pixel has a color value. One way to think about image classification is that you are finding correlations between those pixels. A certain cluster of pixels might represent an edge. After doing edge detection on pixels, you have a collection of edges. Then you can find correlations between those edges, and build up higher levels of abstraction. Yinyin Liu is a principal engineer and head of data science at the Intel AI products group. She studies techniques for building neural networks. Each different configuration of a neural network for a given problem is called a “topology.” Engineers are always looking at new topologies for solving a deep learning application–such as natural language processing. In this episode, Yinyin describes what a deep learning topology is and describes topologies for natural language processing. We also talk about the opportunities and the bottlenecks in deep learning–including why the tools are so immature, and what it will take to make the tooling better. 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.
#797
May 9, 2018 · 54 min
Data Engineering Podcast with Tobias Macey
Cloud computing lowered the cost and improved accessibility to tools for storing large volumes of data. In the early 2000s, Hadoop caused a revolution in large scale batch processing. Since then, companies have been building ways to store and access their data faster and more efficiently. At the same time, the sheer volume of data has increased and machine learning has given rise to methods of extracting signal from seemingly inconsequential data points. This confluence of factors gave rise to the role of the data engineer. A data engineer defines the data pipeline and supports data scientists and machine learning engineers. Tobias Macey hosts the “Data Engineering Podcast,” where he covers the fast moving world of data engineering–including databases, cloud providers, and open source tools. Tobias and I covered a range of topics in the data engineering space and also spent significant time discussing the world of software engineering podcasting. 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.
#796
May 8, 2018 · 52 min
Stripe Atlas with Patrick McKenzie
Starting an Internet business is harder than it should be. You need to incorporate, create an operating agreement, set up a system to accept payments, and many other straightforward tasks. In the 1990s, this was how it felt to set up anything on the Internet. You always had to stand up a web server on your own infrastructure, before you could get to the interesting part–which was building an actual product. With the popularization of cloud computing, it became massively easier to stand up a server. Because of that lower activation energy, millions of applications and thousands of software businesses got started. But the activation energy required to start a business remains higher than necessary. It feels like standing up a web server in the 90s–lots of tedium and reinventing the wheel that has been done by people before you. This is the motivation behind Stripe Atlas, a project to simplify the process of starting an Internet business. Patrick McKenzie works on Atlas at Stripe. He was previously on the show to discuss his experience leaving a large corporation to work on his own small software companies. And his name has become synonymous with the modern phenomenon of the small software company–he has been writing about this topic for over a decade at Kalzumeus.com. It was great to talk to Patrick once again about Internet businesses, and I’m excited to see Stripe Atlas become something huge. 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.
#795
May 7, 2018 · 55 min
Affirm Engineering with Libor Michalek
When I buy a mattress online, I pay for it with my credit card. Behind the scenes, a complex series of transactions occur between a payment gateway, the credit card company, and a few banks. There are problems with this process–it is slow, complex, and involves the synchronization of several different parties. Some consumers will not want to purchase the mattress because they do not have cash, and the lending rates they get offered are higher than they are willing to spend. If these consumers were presented with more intelligent loan rates, the lender could still make money, the mattress company could still make money, and the consumer would get a new mattress. It’s a missed opportunity all around. Affirm is a consumer financial services company. Their first product offers loans to consumers making purchases. In today’s episode, Affirm CTO Libor Michalek explains how Affirm decided to build this product, and what they have done to scale it. The conversation took me by surprise. Affirm was started by Max Levchin, who was a co-founder of PayPal. I assumed that when Affirm was created, they already knew exactly what they were going to build–because Affirm is a payments company and Max has had knowledge of the payments industry going back two decades. In reality, Affirm started out with more vague ideas around what they were building. They spent some time running small experiments as they looked for product/market fit–just like a bootstrapped startup would have. It was inspiring to know that even an experienced team is willing to go through the humble process of searching for a product within a space they are deeply familiar with. We didn’t get to all the questions I was planning to explore, but I hope to do another show about Affirm in the future. 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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