Originally published June 7, 2018 Moore’s Law states that the number of transistors in a dense integrated circuit doubles about every two years. Moore’s Law is less like a “law” and more like an observation or a prediction. Moore’s Law is ending. We can no longer fit an increasing amount of transistors in the same amount of space with a highly predictable rate. Dennard scaling is also coming to an end. Dennard scaling is the observation that as transistors get smaller, the power density stays constant. These changes in hardware trends have downstream effects for software engineers. Most importantly–power consumption becomes much more important. As a software engineer, how does power consumption affect you? It means that inefficient software will either run more slowly or cost more money relative to our expectations in the past. Whereas software engineers writing code 15 years ago could comfortably project that their code would get significantly cheaper to run over time due to hardware advances, the story is more complicated today. Why is Moore’s Law ending? And what kinds of predictable advances in technology can we still expect? John Hennessy is the chairman of Alphabet. In 2017, he won a Turing award (along with David Patterson) for his work on the RISC (Reduced Instruction Set Compiler) architecture. From 2000 to 2016, he was the president of Stanford University. John joins the show to explore the future of computing. While we may not have the predictable benefits of Moore’s Law and Dennard scaling, we now have machine learning. It is hard to plot the advances of machine learning on any one chart (as we explored in a recent episode with OpenAI). But we can say empirically that machine learning is working quite well in production. If machine learning offers us such strong advances in computing, how can we change our hardware design process to make machine learning more efficient? As machine learning training workloads eat up more resources in a data center, engineers are developing domain specific chips which are optimized for those machine learning workloads. The Tensor Processing Unit (TPU) from Google is one such example. John mentioned that chips could become even more specialized within the domain of machine learning. You could imagine a chip that is specifically designed for a LSTM machine learning model. There are other domains where we could see specialized chips–drones, self-driving cars, wearable computers. In this episode, John describes his perspective on the future of computing, and offers some framework for how engineers can adapt to that future. Show Notes   The post Future of Computing with John Hennessy Holiday Repeat appeared first on Software Engineering Daily.

Software bugs cause unexpected problems at every company.  Some problems are small. A website goes down in the middle of the night, and the outage triggers a phone call to an engineer who has to wake up and fix the problem. Other problems can be significantly larger. When a major problem occurs, it can cause millions of dollars in losses and requires hours of work to fix. When software unexpectedly breaks, it is called an incident. To triage these incidents, an engineer uses a combination of tools, including Slack, GitHub, cloud providers, and continuous deployment systems. These different tools emit updates that can be received by an incident response platform, which allow the on-call engineer to have the information they need centralized to more easily work through the incident. On-call rotation means that different people will be responsible for dealing with different incidents that occur. When an incident happens, the current engineer who is on-call may not be aware that a similar incident happened last week. It might be easier for the new engineer to triage the issue if they have insights about how the incident was managed during the first time. Chris Riley is a DevOps advocate with Splunk. He joins the show to discuss the application of machine learning to incident response. We discuss the different data points that are created during an incident, and how that data can be used to build models for different types of incidents, which can generate information to help the engineer respond appropriately to an incident. Full disclosure: Splunk is a sponsor of Software Engineering Daily. Sponsorship inquiries: sponsor@softwareengineeringdaily.com The post Incident Response Machine Learning with Chris Riley appeared first on Software Engineering Daily.

Video surveillance impacts human lives every day.  On most days, we do not feel the impact of video surveillance. But the effects of video surveillance have tremendous potential. It can be used to solve crimes and find missing children. It can be used to intimidate journalists and empower dictators. Like any piece of technology, video surveillance can be used for good or evil. Video recognition lets us make better use of video feeds. A stream of raw video doesn’t provide much utility if we can’t easily model its contents. Without video recognition, we must have a human sitting in front of the video to manually understand what is going on in that video. Veronica Yurchuk and Kosh Shysh are the founders of Traces.ai, a company building video recognition technology focused on safety, anonymity, and positive usage. They join the show to discuss the field of video analysis, and their vision for how video will shape our lives in the future. Sponsorship inquiries: sponsor@softwareengineeringdaily.com Check out our active projects: We are hiring a head of growth. If you like Software Engineering Daily and consider yourself competent in sales, marketing, and strategy, send me an email: jeff@softwareengineeringdaily.com FindCollabs is a place to build open source software. The SEDaily app for iOS and Android includes all 1000 of our old episodes, as well as related links, greatest hits, and topics. Subscribe for ad-free episodes. The post Traces: Video Recognition with Veronica Yurchuk and Kostyantyn Shysh appeared first on Software Engineering Daily.

The development of self-driving cars is one of the biggest technological changes that is under way. Across the world, thousands of engineers are working on developing self-driving cars. Although it still seems far away, self-driving cars are starting to feel like an inevitability. This is especially true if you spend much time in downtown San Francisco, where you will see a self-driving car being tested every day. Much of the time, that self-driving car will be operated by Cruise. Cruise is a company that is building a self-driving car service. The company has hundreds of engineers working across the stack, from computer vision algorithms to automotive hardware. Cruise’s engineering requires engineers who can work with cloud tools as well as low-latency devices. It also requires product developers and managers to lead these different teams. The field of self-driving is very new. There is not much literature available on how to build a self-driving car. There is even less literature on how to manage a team of engineers that are building, testing, and deploying software and hardware for real cars that are driving around the streets of San Francisco. Mo Elshenawy is VP of engineering at Cruise, and he joins the show to talk about the engineering that is required to develop fully self-driving car technology, as well as how to structure teams to align the roles of product design, software engineering, testing, machine learning, and hardware.  Full disclosure: Cruise is a sponsor of Software Engineering Daily. Sponsorship inquiries: sponsor@softwareengineeringdaily.com Check out our active companies and projects: FindCollabs is a place to build open source software. The SEDaily app for iOS and Android includes all 1000 of our old episodes, as well as related links, greatest hits, and topics. Subscribe for ad-free episodes. The post Cruise: Self-Driving Engineering with Mo Elshenawy appeared first on Software Engineering Daily.

A large sales organization has hundreds of sales people. Each of those sales people manages a set of accounts who they are trying to close sales deals on. Sales people are overseen by managers who ensure that the sales people are performing well. Directors and VPs ensure the scalability and health of the overall sales organization. The sales lifecycle mostly takes place within a piece of software called a CRM: customer relationship management. This tool documents the interactions between sales people and accounts. CRMs have been around for many years, and although CRM software is a useful repository of data, it does not fulfill all the needs of a salesperson. People.ai is a system of machine learning tools built around the sales tooling ecosystem. People.ai helps a sales organization avoid manual data entry, understand areas of potential improvement, and decide on who the highest value sales lead to pursue might be. Andrey Akselrod is the CTO At People.ai and he joins the show to discuss the potential applications of machine learning in the domain of sales, and the engineering work that his company has done. Sponsorship inquiries: sponsor@softwareengineeringdaily.com ANNOUNCEMENTS FindCollabs is a place to find collaborators and build projects. We recently launched GitHub integrations. It’s easier than ever to find collaborators for your open source projects. And if you are looking for some people to start a project with, FindCollabs we have topic rooms that allow you to find other people who are interested in a particular technology, so that you can find people who are curious about React, or cryptocurrencies, or Kubernetes, or whatever you want to build with. Podsheets is an open source podcast hosting platform that we recently launched. We are building Podsheets with the learnings from Software Engineering Daily, and our goal is to be the best place to host and monetize your podcast. If you have been thinking about starting a podcast, check out podsheets.com. New SEDaily app for iOS and for Android. It includes all 1000 of our old episodes, as well as related links, greatest hits, and topics. You can comment on episodes and have discussions with other members of the community. I’ll be commenting on each episode, so if you hear an episode that you have some commentary on, jump onto the app, or on SoftwareDaily.com to share your thoughts. And you can become a paid subscriber for ad free episodes at softwareengineeringdaily.com/subscribe. Altalogy is the company who has been developing much of the software for the newest app, and if you are looking for a company to help you with your mobile and web development, I recommend checking them out.  The post People.ai: Machine Learning for Sales with Andrey Akselrod appeared first on Software Engineering Daily.

“Internet of Things” is a term used to describe the increasing connectivity and intelligence of physical objects within our lives.  IoT has manifested within enterprises under the term “Industrial IoT,” as wireless connectivity and machine learning have started to improve devices such as centrifuges, conveyor belts, and factory robotics. In the consumer space, IoT has moved slower than many people expected, and it remains to be seen when we will have widespread computation within consumer devices such as microwaves, washing machines, and lightswitches. IoT computers have different constraints than general purpose computers. Security, reliability, battery life, power consumption, and cost structures are very different in IoT devices than in your laptop or smartphone. One technology that could solve some of the problems within IoT is WebAssembly, a newer binary instruction format for executable programs. Jonathan Beri is a software engineer and the organizer of the San Francisco WebAssembly Meetup. He has significant experience in the IoT industry, and joins the show to discuss the state of WebAssembly, the surrounding technologies, and their impact on IoT. ANNOUNCEMENTS FindCollabs is a place to find collaborators and build projects. We recently launched GitHub integrations. It’s easier than ever to find collaborators for your open source projects. And if you are looking for some people to start a project with, FindCollabs we have topic rooms that allow you to find other people who are interested in a particular technology, so that you can find people who are curious about React, or cryptocurrencies, or Kubernetes, or whatever you want to build with. Podsheets is an open source podcast hosting platform that we recently launched. We are building Podsheets with the learnings from Software Engineering Daily, and our goal is to be the best place to host and monetize your podcast. If you have been thinking about starting a podcast, check out podsheets.com.  New SEDaily app for iOS and for Android. It includes all 1000 of our old episodes, as well as related links, greatest hits, and topics. You can comment on episodes and have discussions with other members of the community. I’ll be commenting on each episode, so if you hear an episode that you have some commentary on, jump onto the app, or on SoftwareDaily.com to share your thoughts. And you can become a paid subscriber for ad free episodes at softwareengineeringdaily.com/subscribe. Altalogy is the company who has been developing much of the software for the newest app, and if you are looking for a company to help you with your mobile and web development, I recommend checking them out.     The post WebAssembly on IoT with Jonathan Beri appeared first on Software Engineering Daily.

A grocery store contains fruit, vegetables, meat, bread, and other items that can expire. In order to keep these items in stock, the store must be aware of how much food has been sold and what has gone bad. When a food item is low in stock, the store needs to order more of that food from a central distribution system. Managing food inventory is not simple. Some kinds of meat might expire faster than others. Avocados do not become ripe at the same rate as apples. In order to keep the shelves stocked, there are manual workflows for checking the inventory and ordering new inventory. Afresh is a company that builds software for grocery stores.  Afresh works with grocery chains that have a central distribution center. These grocery stores already have some software. At the back of the store, inventory management systems maintain records of the items that the store has on the shelves. At the front of the store, checkout systems detect what has been sold and help to update inventory. When the inventory is running low, the store can order more inventory from the central distribution center, so that trucks can deliver more inventory. Afresh improves the operational intelligence of these stores by detecting spoilage among items that are prone to expiration, such as fruit. Volodomyr Kuleshov is the CTO and co-founder of Afresh and he joins the show to discuss the technical challenges of a grocery store, and the software that Afresh is building to make groceries more intelligent.  ANNOUNCEMENTS FindCollabs is a place to find collaborators and build projects. FindCollabs is the company I am building, and we are having an online hackathon with $2500 in prizes. If you are working on a project, or you are looking for other programmers to build a project or start a company with, check out FindCollabs. I’ve been interviewing people from some of these projects on the FindCollabs podcast, so if you want to learn more about the community you can hear that podcast. New Software Daily app for iOS. It includes all 1000 of our old episodes, as well as related links, greatest hits, and topics. You can comment on episodes and have discussions with other members of the community. And you can become a paid subscriber for ad free episodes at softwareengineeringdaily.com/subscribe. Altalogy is the company who has been developing much of the software for the newest app, and if you are looking for a company to help you with your mobile and web development, I recommend checking them out. Upcoming conferences I’m attending: Datadog Dash July 16th and 17th in NYC, Open Core Summit September 19th and 20th in San Francisco. We are hiring two interns for software engineering and business development! If you are interested in either position, send an email with your resume to jeff@softwareengineeringdaily.com with “Internship” in the subject line. The post Afresh: Grocery Store Software with Volodymyr Kuleshov appeared first on Software Engineering Daily.

Niantic is the company behind Pokemon Go, an augmented reality game where users walk around in the real world and catch Pokemon which appear on their screen. The idea for augmented reality has existed for a long time. But the technology to bring augmented reality to the mass market has appeared only recently. Improved mobile technology makes it possible for a smartphone to display rendered 3-D images over a video stream without running out of battery. Ingress was the first game to come out of Niantic, followed by Pokemon Go, but there are other games on the way. Niantic is also working on the Niantic Real World platform, a “planet-scale” AR platform that will allow independent developers to build multiplayer augmented reality experiences that are as dynamic and entertaining as Pokemon Go. Paul Franceus is an engineer at Niantic, and he joins the show to describe his experience building and launching Pokemon Go, as well as abstracting the technology from Pokemon Go and opening up the Niantic Real World platform to developers. SHOW NOTES Niantic developer contest finalists ANNOUNCEMENTS FindCollabs is a place to find collaborators and build projects. FindCollabs is the company I am building, and we are having an online hackathon with $2500 in prizes. If you are working on a project, or you are looking for other programmers to build a project or start a company with, check out FindCollabs. I’ve been interviewing people from some of these projects on the FindCollabs podcast, so if you want to learn more about the community you can hear that podcast. New Software Daily app for iOS. It includes all 1000 of our old episodes, as well as related links, greatest hits, and topics. You can comment on episodes and have discussions with other members of the community. And you can become a paid subscriber for ad free episodes at softwareengineeringdaily.com/subscribe Upcoming conferences I’m attending: Datadog Dash July 16th and 17th in NYC, Open Core Summit September 19th and 20th in San Francisco We are hiring two interns for software engineering and business development! If you are interested in either position, send an email with your resume to jeff@softwareengineeringdaily.com with “Internship” in the subject line.   The post Niantic Real World with Paul Franceus appeared first on Software Engineering Daily.

Machine learning allows software to improve as that software consumes more data. Machine learning is a tool that every software engineer wants to be able to use. Because machine learning is so broadly applicable, software companies want to make the tools more accessible to the developers across the organization. There are many steps that an engineer must go through to use machine learning, and each additional step inhibits the chances that the engineer will actually get their model into production. An engineer who wants to build machine learning into their application needs access to data sets. They need to join those data sets, and load them into a machine (or multiple machines) where their model can be trained. Once the model is trained, the model needs to test on additional data to ensure quality. If the initial model quality is insufficient, the engineer might need to tweak the training parameters. Once a model is accurate enough, the engineer needs to deploy that model. After deployment, the model might need to be updated with new data later on. If the model is processing sensitive or financially relevant data, a provenance process might be necessary to allow for an audit trail of decisions that have been made by the model. Rob Story and Kelley Rivoire are engineers working on machine learning infrastructure at Stripe. After recognizing the difficulties that engineers faced in creating and deploying machine learning models, Stripe engineers built out Railyard, an API for machine learning workloads within the company. Rob and Kelley join the show to discuss data engineering and machine learning at Stripe, and their work on Railyard. ANNOUNCEMENTS FindCollabs is a place to find collaborators and build projects. FindCollabs is the company I am building, and we are having an online hackathon with $2500 in prizes. If you are working on a project, or you are looking for other programmers to build a project or start a company with, check out FindCollabs. I’ve been interviewing people from some of these projects on the FindCollabs podcast, so if you want to learn more about the community you can hear that podcast. New Software Daily app for iOS. It includes all 1000 of our old episodes, as well as related links, greatest hits, and topics. You can comment on episodes and have discussions with other members of the community. And you can become a paid subscriber for ad free episodes at softwareengineeringdaily.com/subscribe Upcoming conferences I’m attending: Datadog Dash July 16th and 17th in NYC, Open Core Summit September 19th and 20th in San Francisco We are hiring two interns for software engineering and business development! If you are interested in either position, send an email with your resume to jeff@softwareengineeringdaily.com with “Internship” in the subject line. The post Stripe Machine Learning Infrastructure with Rob Story and Kelley Rivoire appeared first on Software Engineering Daily.

Augmented reality applications can be used on smartphones and dedicated AR headsets. On smartphones, ARCore (Google) and ARKit (Apple) allow developers to build for the camera on a user’s smartphone. AR headsets such as Microsoft HoloLens and Magic Leap allow for a futuristic augmented reality headset experience. The most prominent use of augmented reality today is gaming, with a notable example being Niantic’s Pokemon Go. Tony Godar is a software engineer who works on augmented and virtual reality applications. He joins the show to talk about his day job working on virtual reality experiences, and an AR game he built called ARhythm. Tony was the winner of the FindCollabs Hackathon and we also discussed his experience working on the project through FindCollabs. RECENT UPDATES: The FindCollabs Open has started. It is our second FindCollabs hackathon, and we are giving away $2500 in prizes. The prizes will be awarded in categories such as machine learning, business plan, music, visual art, and JavaScript. If one of those areas sounds interesting to you, check out findcollabs.com/open! The FindCollabs Podcast is out! We are booking sponsorships for Q3, find more details at https://softwareengineeringdaily.com/sponsor/ The post Augmented Reality Gaming with Tony Godar appeared first on Software Engineering Daily.