NerdOut@Spotify

What happens when the standard tooling for iOS and Android just doesn't cut it anymore? What’s it like to maintain an app when there are literally thousands of commits every week? How do you develop your feature without worrying about everybody else’s feature, when at the end of the day, you’re shipping a single, massive app? In other words, what’s it really like to build apps at Spotify’s scale?

Host Dave Zolotusky talks with Patrick Balestra, a staff engineer on our client platform team, whose job is to make sure every Spotify developer has a great experience building, no matter what Spotify app they’re building for. They talk about adopting Bazel, reducing local build times with remote caching, managing multiple monorepos and zillions of dependencies, doing big tech migrations (Objective-C to Swift, Java to Kotlin) without slowing down, open sourcing our tools, and contributing to the Mobile Native Foundation.

Learn more about how we build mobile apps at Spotify:

Read what else we’re nerding out about on the Spotify Engineering Blog: engineering.atspotify.com

You should follow us on Twitter @SpotifyEng and on LinkedIn!

Whether you’re working at a major financial institution or an audio streaming platform, it turns out engineering teams face a lot of the same challenges to doing their jobs effectively. In this episode, you’ll hear Spotify’s head of technology and platforms, Tyson Singer, discuss developer experience with engineering leaders from Expedia Group, Bank of the West, consulting firm Booz Allen Hamilton, and digital telecom TELUS — all adopters of Backstage, our open platform for building developer portals. This panel discussion took place on December 15, 2022 at the launch event for Spotify Plugins for Backstage, our new developer software bundle.

At the event, Tyson is joined on stage by Guillermo Manzo (Expedia Group), Boyan Vassilev (Bank of the West), Jason Miller (Booz Allen Hamilton), and Nate Axcell (TELUS) to talk about the costs of context switching, breaking down organizational silos, how to get your developers started and how to get them unstuck, open source, inner source, golden paths, and the importance of making sure the best way to do something is the easiest way to do something otherwise it just doesn’t get done.

Learn more about Spotify Plugins for Backstage:

Listen to more episodes about Backstage:

Or check out the links below:

Read what else we’re nerding out about on the Spotify Engineering Blog: engineering.atspotify.com

You should follow us on Twitter @SpotifyEng and on LinkedIn!

There are thousands of developers building great Spotify experiences — who don’t actually work at Spotify. From smart speakers to game consoles to car infotainment systems, how do we support this diverse, creative community building on top of the Spotify platform? Along with providing APIs, SDKs, and other developer tools, it starts with something even more fundamental: listening to and understanding their needs. That’s where our developer advocates come in.

Host Dave Zolotusky talks with Serah Kiburu, a Spotify engineer and senior developer advocate. They discuss how and why Spotify supports third-party developers, the value of being “customer zero” for our own developer tools, our most popular APIs, the importance of listening before you start building, Serah’s journey to Spotify in Stockholm, and how she’s preparing for the robot apocalypse.

Learn more about the resources we provide third-party developers:

Read what else we’re nerding out about on the Spotify Engineering Blog: engineering.atspotify.com

You should follow us on Twitter @SpotifyEng and on LinkedIn!

From Norway to Nebraska, there are individual developers keeping the open source projects that we all depend on going. Last year, Spotify started an annual fund to help support maintainers of free and open source software (FOSS) — the Spotify FOSS Fund. We earmarked 100,000 Euros and then gave it away to nine projects, no strings attached. One of those recipients was Byte Buddy, a project with millions of users, but just one volunteer maintainer — its creator, Rafael Winterhalter. Hear what it takes to sustain something that started as a passion project in 2014 and then grew to become much more.

Rafael talks with our host Dave Zolotusky and Per Ploug, Spotify’s open source tech lead, who helped set up our FOSS fund. They discuss how Spotify decided which projects to fund, why giving away (and accepting) free money is harder than you might think, and what it’s really like maintaining a popular open source project all on your own.

Learn more about the recipients of the the Spotify FOSS Fund and other ways we’re working to create a more sustainable open source ecosystem:

Read what else we’re nerding out about on the Spotify Engineering Blog: engineering.atspotify.com

You should follow us on Twitter @SpotifyEng and on LinkedIn!

Recorded live at midnight EST, December 6, 2022, from Boston, Gothenburg, and Stockholm, via the Spotify Live app. In this special Advent of Code episode, we try to solve the Day 6 puzzle the moment it’s released. Host Dave Zolotusky and Spotify’s chief architect, Niklas Gustavsson (aka, ngn), play against Spotify’s reigning champ, the one and only, unstoppable Jimmy Mårdell. Entering today’s challenge behind ngn in the standings, will Jimmy reclaim his rightful place atop the Spotify leaderboard? Play along with us and find out!

Plus, Spotify engineers share what makes this friendly, low-stakes coding competition so much fun every year, Jimmy answers questions from the live audience (including offering some speed tips), and more.

The Advent of Code season is upon us once again:

Special thanks to Eric Wastl for creating such a fun thing.

Read what else we’re nerding out about on the Spotify Engineering Blog: engineering.atspotify.com

You should follow us on Twitter @SpotifyEng and on LinkedIn!

Instead of performing 10 big software upgrades to our infrastructure every year, what if we did 10,000 small ones, in all our repos, all at once? That’s the idea behind Fleet Management: using automation to take away some of the everyday toil of being a developer, freeing our teams to focus on more interesting problems than migrating to the next version of a low-level software dependency. But for automation at this scale to succeed, our developers would have to be willing to give up some control over their codebases. Could we really get our squad-based culture to adopt a fleet-first mindset?

Spotify’s chief architect, Niklas Gustavsson (aka, ngn), returns to the podcast to talk to host Dave Zolotusky about how we can safely make changes to thousands of repos at once (without bothering the repos’ owners), why adopting Fleet Management at Spotify was a cultural shift as much as a technological one, how having this level of automation in place helped us mitigate the log4j security incident within a few hours, the intricacies of software dependencies, the benefits of tech standards, and more. Welcome to the Fleet.

Learn more:

Read what else we’re nerding out about on the Spotify Engineering Blog: engineering.atspotify.com

You should follow us on Twitter @SpotifyEng and on LinkedIn!

If you look at any of our open source projects, you’re bound to see the phrase “Made with 💚 at Spotify.” That’s because many open source projects start off as passion projects. But is passion enough? Now that open source code is so essential to how we all build, it’s important for us to treat that code more seriously. To be sustainable, open source can’t rely on passion alone. It needs a little tough love, too. For that, we got an OSPO — an open source program office — to help our developers (and Spotify) get the most out of their open source experience.

Host Dave Zolotusky talks with Per Ploug, our open source tech lead and head of Spotify’s OSPO. You’ll hear about what developers can learn from open sourcing their projects, why we shouldn’t treat open source like a side gig, how to measure the success of an open source project (by the number of stars on GitHub, of course), what open source at Spotify looked like in the past (the good, the bad, the so-so), and what we hope it looks like tomorrow.

Read more about open source at Spotify on the Spotify Engineering blog:

Listen to more episodes about open source at Spotify:

Read what else we’re nerding out about on the Spotify Engineering Blog: engineering.atspotify.com

You should follow us on Twitter @SpotifyEng and on LinkedIn!

What if you could create a guitar solo just by humming it? That’s Basic Pitch, a new open source project from Spotify’s Audio Intelligence Lab. Basic Pitch is a neural network that can analyze the recording of almost any instrument (including your voice) and then transcribe the notes that it detects into MIDI, the standard file format used for musical notation. It’s like speech-to-text, except it’s turning musical performances — whatever you hum, strum, pluck, peck, or tinkle — into a digital score you can edit on your computer.

Hear host Dave Zolotusky talk with Spotify researcher Rachel Bittner about what makes detecting musical notes an interesting machine learning problem. You’ll learn about how musicians use audio-to-MIDI converters to make music, the subtleties of pitch tracking, and why you want your model to capture the main pitch events in the audio as well as all the “wiggly stuff”. Plus, a live demo of the model in action and all the “Hot Cross Buns” you can handle.

Listen to the end of the episode to hear "Virgo", the single that artist-producer Bad Snacks composed using Basic Pitch.🎻🎻🎻🎻🎻🎻

Basic Pitch is open source software that musicians can use online for free and that ML developers and researchers can tinker with and contribute to.

Want more open source goodness from Spotify’s Audio Intelligence Lab? Listen to Ep.06: Bits of Math at Scale to learn about Pedalboard, a Python library for applying studio-quality audio effects at scale.

Read what else we’re nerding out about on the Spotify Engineering Blog: engineering.atspotify.com

You should follow us on Twitter @SpotifyEng and on LinkedIn!

In technology, as in life, bad experiences often start with bad data. This is as true with a misguided aunt setting you up on a blind date as it is with machine learning recommendations about which podcast you might want to listen to next. High quality data is essential to making sure every Spotify listener has a rewarding experience made just for them. But with a half trillion events happening on the platform every day, how do you even begin to sort all that data out?

Enter Laura Lake, senior director of Spotify’s Personalization Insights team. When she first arrived here, even simple questions were difficult to answer without teams having to “knit together 50 different data sources”. In this episode, she talks with host Dave Zolotusky about a critical point in Spotify’s growth and the yearslong journey that resulted in improving and ensuring the quality of the data that all our developers rely on.

Hear about the technological and cultural changes that led to both better quality data and better collaboration between our teams — and how we use the data to build the knowledge models that lead to Discover Weekly, Daily Mix, and a more personalized experience for every one of Spotify’s hundreds of millions of listeners. How do we know if our ML models are doing what we want them to? Did our listeners actually discover something new? Let’s dig into the data.

This is the second episode in our miniseries about machine learning and personalization at Spotify.

Read what else we’re nerding out about on the Spotify Engineering Blog: engineering.atspotify.com

You should follow us on Twitter @SpotifyEng and on LinkedIn!

Get ready: we’re diving into machine learning. Hear how we’re improving personalization with reinforcement learning (RL), what makes ML engineering so different from other kinds of software engineering, and why machine learning at Spotify is really about humans on one side of an algorithm trying to better understand the humans on the other side of it.

Spotify’s director of research, Mounia Lalmas-Roelleke, talks with host Dave Zolotusky about how we’re using RL to optimize recommendations for future rewards, how listening to more diverse content relates to long-term satisfaction, how to teach machines about the difference between p-funk and g-funk, and the upsides of taking the stairs.

Then Dave goes deep into the everyday life of an ML engineer. He talks with senior staff engineer Joe Cauteruccio about what it takes to turn ML theory into code, the value of T-shapedness, the difference between inference errors and bugs, using proxy targets and developing your ML intuition, and why in machine learning something’s probably wrong if everything looks right.

Plus, an ML glossary: our guests educate us on the definitions for cold starts, bandits, and more. This episode is the first in a series about machine learning and personalization at Spotify.

Learn more about ML and personalization:

Recent publications from Spotify Research:

Read what else we’re nerding out about on the Spotify Engineering Blog: engineering.atspotify.com

You should follow us on Twitter @SpotifyEng and on LinkedIn!