Brought to You By: •⁠ Statsig ⁠ — ⁠ The unified platform for flags, analytics, experiments, and more. Statsig built a complete set of data tools that allow engineering teams to measure the impact of their work. This toolkit is SO valuable to so many teams, that OpenAI - who was a huge user of Statsig - decided to acquire the company, the news announced last week. Talk about validation! Check out Statsig. •⁠ Linear – The system for modern product development. Here’s an interesting story: OpenAI switched to Linear as a way to establish a shared vocabulary between teams. Every project now follows the same lifecycle, uses the same labels, and moves through the same states. Try Linear for yourself. — What does it take to do well at a hyper-growth company? In this episode of The Pragmatic Engineer, I sit down with Charles-Axel Dein, one of the first engineers at Uber, who later hired me there. Since then, he’s gone on to work at CloudKitchens. He’s also been maintaining the popular Professional programming reading list GitHub repo for 15 years, where he collects articles that made him a better programmer.  In our conversation, we dig into what it’s really like to work inside companies that grow rapidly in scale and headcount. Charles shares what he’s learned about personal productivity, project management, incidents, interviewing, plus how to build flexible skills that hold up in fast-moving environments.  Jump to interesting parts: • 10:41 – the reality of working inside a hyperscale company • 41:10 – the traits of high-performing engineers • 1:03:31 – Charles’ advice for getting hired in today’s job market We also discuss: • How to spot the signs of hypergrowth (and when it’s slowing down) • What sets high-performing engineers apart beyond shipping • Charles’s personal productivity tips, favorite reads, and how he uses reading to uplevel his skills • Strategic tips for building your resume and interviewing  • How imposter syndrome is normal, and how leaning into it helps you grow • And much more! If you’re at a fast-growing company, considering joining one, or looking to land your next role, you won’t want to miss this practical advice on hiring, interviewing, productivity, leadership, and career growth. — Timestamps (00:00) Intro (04:04) Early days at Uber as engineer #20 (08:12) CloudKitchens’ similarities with Uber (10:41) The reality of working at a hyperscale company (19:05) Tenancies and how Uber deployed new features (22:14) How CloudKitchens handles incidents (26:57) Hiring during fast-growth (34:09) Avoiding burnout (38:55) The popular Professional programming reading list repo (41:10) The traits of high-performing engineers  (53:22) Project management tactics (1:03:31) How to get hired as a software engineer (1:12:26) How AI is changing hiring (1:19:26) Unexpected ways to thrive in fast-paced environments (1:20:45) Dealing with imposter syndrome  (1:22:48) Book recommendations  (1:27:26) The problem with survival bias  (1:32:44) AI’s impact on software development  (1:42:28) Rapid fire round — The Pragmatic Engineer deepdives relevant for this episode: •⁠ Software engineers leading projects •⁠ The Platform and Program split at Uber •⁠ Inside Uber’s move to the Cloud •⁠ How Uber built its observability platform •⁠ From Software Engineer to AI Engineer – with Janvi Kalra — Production and marketing by ⁠⁠⁠⁠⁠⁠⁠⁠https://penname.co/⁠⁠⁠⁠⁠⁠⁠⁠. For inquiries about sponsoring the podcast, email [email protected].

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Supported by Our Partners •⁠ WorkOS — The modern identity platform for B2B SaaS. •⁠ Statsig ⁠ — ⁠ The unified platform for flags, analytics, experiments, and more. • Sonar —  Code quality and code security for ALL code.  — What happens when a company goes all in on AI? At Shopify, engineers are expected to utilize AI tools, and they’ve been doing so for longer than most. Thanks to early access to models from GitHub Copilot, OpenAI, and Anthropic, the company has had a head start in figuring out what works. In this live episode from LDX3 in London, I spoke with Farhan Thawar, VP of Engineering, about how Shopify is building with AI across the entire stack. We cover the company’s internal LLM proxy, its policy of unlimited token usage, and how interns help push the boundaries of what’s possible. In this episode, we cover: • How Shopify works closely with AI labs • The story behind Shopify’s recent Code Red • How non-engineering teams are using Cursor for vibecoding • Tobi Lütke’s viral memo and Shopify’s expectations around AI • A look inside Shopify’s LLM proxy—used for privacy, token tracking, and more • Why Shopify places no limit on AI token spending  • Why AI-first isn’t about reducing headcount—and why Shopify is hiring 1,000 interns • How Shopify’s engineering department operates and what’s changed since adopting AI tooling • Farhan’s advice for integrating AI into your workflow • And much more! — Timestamps (00:00) Intro (02:07) Shopify’s philosophy: “hire smart people and pair with them on problems” (06:22) How Shopify works with top AI labs  (08:50) The recent Code Red at Shopify (10:47) How Shopify became early users of GitHub Copilot and their pivot to trying multiple tools (12:49) The surprising ways non-engineering teams at Shopify are using Cursor (14:53) Why you have to understand code to submit a PR at Shopify (16:42) AI tools' impact on SaaS  (19:50) Tobi Lütke’s AI memo (21:46) Shopify’s LLM proxy and how they protect their privacy (23:00) How Shopify utilizes MCPs (26:59) Why AI tools aren’t the place to pinch pennies (30:02) Farhan’s projects and favorite AI tools (32:50) Why AI-first isn’t about freezing headcount and the value of hiring interns (36:20) How Shopify’s engineering department operates, including internal tools (40:31) Why Shopify added coding interviews for director-level and above hires (43:40) What has changed since Spotify added AI tooling  (44:40) Farhan’s advice for implementing AI tools — The Pragmatic Engineer deepdives relevant for this episode: • How Shopify built its Live Globe for Black Friday • Inside Shopify's leveling split • Real-world engineering challenges: building Cursor • How Anthropic built Artifacts — See the transcript and other references from the episode at ⁠⁠https://newsletter.pragmaticengineer.com/podcast⁠⁠ — Production and marketing by ⁠⁠⁠⁠⁠⁠⁠⁠https://penname.co/⁠⁠⁠⁠⁠⁠⁠⁠. For inquiries about sponsoring the podcast, email [email protected].

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Supported by Our Partners •⁠ Statsig ⁠ — ⁠ The unified platform for flags, analytics, experiments, and more. • Graphite — The AI developer productivity platform.  • Augment Code — AI coding assistant that pro engineering teams love — GitHub recently turned 17 years old—but how did it start, how has it evolved, and what does the future look like as AI reshapes developer workflows? In this episode of The Pragmatic Engineer, I’m joined by Thomas Dohmke, CEO of GitHub. Thomas has been a GitHub user for 16 years and an employee for 7. We talk about GitHub’s early architecture, its remote-first operating model, and how the company is navigating AI—from Copilot to agents. We also discuss why GitHub hires junior engineers, how the company handled product-market fit early on, and why being a beloved tool can make shipping harder at times. Other topics we discuss include: • How GitHub’s architecture evolved beyond its original Rails monolith • How GitHub runs as a remote-first company—and why they rarely use email  • GitHub’s rigorous approach to security • Why GitHub hires junior engineers • GitHub’s acquisition by Microsoft • The launch of Copilot and how it’s reshaping software development • Why GitHub sees AI agents as tools, not a replacement for engineers • And much more! — Timestamps (00:00) Intro (02:25) GitHub’s modern tech stack (08:11) From cloud-first to hybrid: How GitHub handles infrastructure (13:08) How GitHub’s remote-first culture shapes its operations (18:00) Former and current internal tools including Haystack (21:12) GitHub’s approach to security  (24:30) The current size of GitHub, including security and engineering teams (25:03) GitHub’s intern program, and why they are hiring junior engineers (28:27) Why AI isn’t a replacement for junior engineers  (34:40) A mini-history of GitHub  (39:10) Why GitHub hit product market fit so quickly  (43:44) The invention of pull requests (44:50) How GitHub enables offline work (46:21) How monetization has changed at GitHub since the acquisition  (48:00) 2014 desktop application releases  (52:10) The Microsoft acquisition  (1:01:57) Behind the scenes of GitHub’s quiet period  (1:06:42) The release of Copilot and its impact (1:14:14) Why GitHub decided to open-source Copilot extensions (1:20:01) AI agents and the myth of disappearing engineering jobs (1:26:36) Closing — The Pragmatic Engineer deepdives relevant for this episode: • AI Engineering in the real world • The AI Engineering stack •  How Linux is built with Greg Kroah-Hartman •  Stacked Diffs (and why you should know about them) •  50 Years of Microsoft and developer tools — See the transcript and other references from the episode at ⁠⁠https://newsletter.pragmaticengineer.com/podcast⁠⁠ — Production and marketing by ⁠⁠⁠⁠⁠⁠⁠⁠https://penname.co/⁠⁠⁠⁠⁠⁠⁠⁠. For inquiries about sponsoring the podcast, email [email protected].

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Supported by Our Partners •⁠ Statsig ⁠ — ⁠ The unified platform for flags, analytics, experiments, and more. •⁠ Sinch⁠ — Connect with customers at every step of their journey. •⁠ Modal⁠ — The cloud platform for building AI applications. — How has Microsoft changed since its founding in 1975, especially in how it builds tools for developers? In this episode of The Pragmatic Engineer, I sit down with Scott Guthrie, Executive Vice President of Cloud and AI at Microsoft. Scott has been with the company for 28 years. He built the first prototype of ASP.NET, led the Windows Phone team, led up Azure, and helped shape many of Microsoft’s most important developer platforms. We talk about Microsoft’s journey from building early dev tools to becoming a top cloud provider—and how it actively worked to win back and grow its developer base. In this episode, we cover: • Microsoft’s early years building developer tools  • Why Visual Basic faced resistance from devs back in the day: even though it simplified development at the time • How .NET helped bring a new generation of server-side developers into Microsoft’s ecosystem • Why Windows Phone didn’t succeed  • The 90s Microsoft dev stack: docs, debuggers, and more • How Microsoft Azure went from being the #7 cloud provider to the #2 spot today • Why Microsoft created VS Code • How VS Code and open source led to the acquisition of GitHub • What Scott’s excited about in the future of developer tools and AI • And much more! — Timestamps (00:00) Intro (02:25) Microsoft’s early years building developer tools (06:15) How Microsoft’s developer tools helped Windows succeed (08:00) Microsoft’s first tools were built to allow less technically savvy people to build things (11:00) A case for embracing the technology that’s coming (14:11) Why Microsoft built Visual Studio and .NET (19:54) Steve Ballmer’s speech about .NET (22:04) The origins of C# and Anders Hejlsberg’s impact on Microsoft  (25:29) The 90’s Microsoft stack, including documentation, debuggers, and more (30:17) How productivity has changed over the past 10 years  (32:50) Why Gergely was a fan of Windows Phone—and Scott’s thoughts on why it didn’t last (36:43) Lessons from working on (and fixing)  Azure under Satya Nadella  (42:50) Codeplex and the acquisition of GitHub (48:52) 2014: Three bold projects to win the hearts of developers (55:40) What Scott’s excited about in new developer tools and cloud computing  (59:50) Why Scott thinks AI will enhance productivity but create more engineering jobs — The Pragmatic Engineer deepdives relevant for this episode: • Microsoft is dogfooding AI dev tools’ future • Microsoft’s developer tools roots • Why are Cloud Development Environments spiking in popularity, now? • Engineering career paths at Big Tech and scaleups • How Linux is built with Greg Kroah-Hartman — See the transcript and other references from the episode at ⁠⁠https://newsletter.pragmaticengineer.com/podcast⁠⁠ — Production and marketing by ⁠⁠⁠⁠⁠⁠⁠⁠https://penname.co/⁠⁠⁠⁠⁠⁠⁠⁠. For inquiries about sponsoring the podcast, email [email protected].

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Supported by Our Partners • Swarmia — The engineering intelligence platform for modern software organizations. • Sentry — Error and performance monitoring for developers. — Why did Meta build its own internal developer tooling instead of using industry-standard solutions like GitHub? Tomas Reimers, former Meta engineer and co-founder of Graphite, joins the show to talk about Meta's custom developer tools – many of which were years ahead of the industry. From Phababricator to Sandcastle and Butterflybot, Tomas shares examples of Meta’s internal tools that transformed developer productivity at the tech giant. Why did working with stacked diffs and using monorepos become best practices at Meta? How are these practices influencing the broader industry? Why are code reviews and testing looking to become even more critical as AI transforms how we write software? We answer these, and also discuss: • Meta's custom internal developer tools • Why more tech companies are transitioning from polyrepos to monorepos • A case for different engineering constraints within the same organization • How stacked diffs solve the code review bottleneck • Graphite’s origin story and pivot to their current product  • Why code reviews will become a lot more important, the more we use AI coding tools • Tomas’s favorite engineering metric  • And much more! — Timestamps (00:00) Intro (02:00) An introduction to Meta’s in-house tooling  (05:07) How Meta’s integrated tools work and who built the tools (10:20) An overview of the rules engine, Herald  (12:20) The stages of code ownership at Facebook and code ownership at Google and GitHub (14:39) Tomas’s approach to code ownership  (16:15) A case for different constraints within different parts of an organization  (18:42) The problem that stacked diffs solve for  (25:01) How larger companies drive innovation, and who stacking diffs not for  (30:25) Monorepos vs. polyrepos and why Facebook is transitioning to a monorepo (35:31) The advantages of monorepos and why GitHub does not support them  (39:55) AI’s impact on software development  (42:15) The problems that AI creates, and possible solutions (45:25) How testing might change and the testing AI coding tools are already capable of  (48:15) How developer accountability might be a way to solve bugs and bad AI code (53:20) Why stacking hasn’t caught on and Graphite’s work  (57:10) Graphite’s origin story  (1:01:20) Engineering metrics that matter  (1:06:07) Learnings from building a company for developers  (1:08:41) Rapid fire round (1:12:41) Closing — The Pragmatic Engineer deepdives relevant for this episode: • Stacked Diffs (and why you should know about them) • Inside Meta’s engineering culture • Shipping to production • How Uber is measuring engineering productivity — See the transcript and other references from the episode at ⁠⁠https://newsletter.pragmaticengineer.com/podcast⁠⁠ — Production and marketing by ⁠⁠⁠⁠⁠⁠⁠⁠https://penname.co/⁠⁠⁠⁠⁠⁠⁠⁠. For inquiries about sponsoring the podcast, email [email protected].

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Supported by Our Partner DX⁠ → DX is an engineering intelligence platform designed by leading researchers — In today’s episode of The Pragmatic Engineer, I’m joined by Sean Goedecke, Staff Software Engineer at GitHub. Sean is widely known for his viral blog post, “How I ship projects at big tech companies.” In our conversation, he shares how to successfully deliver projects in large tech companies.

Drawing from his experiences at GitHub and Zendesk, Sean reflects on key lessons learned, and we discuss the following topics:  • Why shipping cannot exclude keeping management happy • How to work on stuff the company actually values • Why you should take on extra responsibility to get projects done • Why technical skills are still more important than soft skills • Soft skills you should learn: including learning the “management lingo” • First-hand remote work learnings: advantages, disadvantages, and how to thrive in this setup • … and much more! — Timestamps (00:00) Intro (01:50) An explanation of shipping (05:35) Reasons management may choose to ship something customers don’t love (09:20) A humbling learning from Sean’s time at Zendesk (13:27) The importance of learning which rules need to be broken for good business outcomes (15:28) Common obstacles to shipping (18:13) DRI: Directly responsible individual (23:06) The value of strong technical skills and why moving fast is imperative (28:44) How to leverage your technical skills the right way (32:16) Advice on earning the trust of leadership (36:10) A time Gergely shipped a product for a political reason  (38:30) What GenAI helps software engineers do more easily  (41:08) Sean’s thoughts on GenAI making engineers more ambitious  (43:20) The difficulty of building AI tools (46:10) Advantages of working remotely and strategies for making it work (52:34) Who is best suited to remote work (54:48) How the pandemic provided a remote work trial for Sean (56:45) Rapid fire round — The Pragmatic Engineer deepdives relevant for this episode: • Software Engineers Leading Projects ⁠https://newsletter.pragmaticengineer.com/p/engineers-leading-projects⁠ • Shipping to production ⁠https://newsletter.pragmaticengineer.com/p/shipping-to-production⁠ • Paying down tech debt ⁠https://newsletter.pragmaticengineer.com/p/paying-down-tech-debt⁠ — See the transcript and other references from the episode at ⁠⁠https://newsletter.pragmaticengineer.com/podcast⁠⁠ — Production and marketing by ⁠⁠⁠⁠⁠⁠⁠⁠https://penname.co/⁠⁠⁠⁠⁠⁠⁠⁠. For inquiries about sponsoring the podcast, email [email protected].

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Supported by Our Partner DX⁠ → DX is an engineering intelligence platform designed by leading researchers — In today’s exciting episode of The Pragmatic Engineer, I am joined by two members of the Notion mobile apps team, Austin Louden and Karn Saheb. Austin and Karn joined Notion in 2019 when Notion was revamping its mobile apps.  Notion is a versatile productivity and collaboration platform that combines note-taking, task management, and knowledge organization into a single workspace. It is available as a web app, as well as iOS and Android apps for mobile use. In our conversation today, we take a deep dive into how the Notion mobile team operates and discuss the following:  • What the engineering culture is like at Notion  • Why the mobile team focuses so much on app performance • The incremental shift from Cordova to Native  • Notion’s tech stack and frameworks they rely on  • How the mobile team maintains consistency across iOS and Android • Unique features of the development process, including a public beta, using modules, and practices around feature flags • … and much more! — Timestamps (00:00) Intro (02:03) The RFC process at Notion  (06:00) How Notion uses internal channels to share RFCs (07:57) Some of the unique ways the mobile team works (11:07) Why they don’t do sprint planning at Notion—and what they do instead (12:57) An overview of the size of Notion and teams at Notion (13:15) The beginning of mobile at Notion (14:40) A simple explanation of Cordova (15:40) Why Notion decided to revamp mobile in 2019 and shift to Native (18:30) How the mobile team evaluated performance as they made the shift to Native (22:00) Scaling mobile and iterations of moving to Native  (26:04) Why the home tab project was so complex (30:59) Why the mobile team saved the editor for last and other future problems (34:35) How mobile works with other teams  (36:50) How iOS and Android teams work together  (38:28) The tech stack at Notion (39:30) How frameworks are used (41:57) Pros and cons of different frameworks and why Swift was the right choice (45:16) How code reviews work at Notion (48:23) Notion’s mobile team’s testing philosophy (50:18) How the mobile team keeps compile time so fast (52:36) Modules in the iOS app (54:50) Modules in the Android app (56:44) Behind the scenes of an app release and the public beta (1:00:34) Practices around feature flags (1:03:00) The four dev environments at Notion (1:04:48) How development apps work  (1:07:40) How and why you can work offline in Notion mobile  (1:10:24) Austin and Karn’s thoughts on the future of mobile engineering  (1:12:47) Advice for junior engineers (1:16:29) Rapid fire round — The Pragmatic Engineer deepdives relevant for this episode:

— Where to find Austin Louden: • GitHub: https://github.com/austinlouden • LinkedIn: https://www.linkedin.com/in/austinlouden • Website: https://austinlouden.com/ Where to find Karn Saheb: • GitHub: https://github.com/Karn • LinkedIn: https://github.com/Karn • Website: https://karn.io Where to find Gergely: • Newsletter: https://www.pragmaticengineer.com/ • YouTube: https://www.youtube.com/c/mrgergelyorosz • LinkedIn: https://www.linkedin.com/in/gergelyorosz/ • X: https://x.com/GergelyOrosz — References and Transcripts: See the transcript and other references from the episode at https://newsletter.pragmaticengineer.com/podcast — Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email [email protected].

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The first episode of The Pragmatic Engineer Podcast is out. Expect similar episodes every other Wednesday. You can add the podcast in your favorite podcast player, and have future episodes downloaded automatically. Listen now on Apple, Spotify, and YouTube. Brought to you by: • Codeium: ​​Join the 700K+ developers using the IT-approved AI-powered code assistant. • TLDR: Keep up with tech in 5 minutes — On the first episode of the Pragmatic Engineer Podcast, I am joined by Simon Willison. Simon is one of the best-known software engineers experimenting with LLMs to boost his own productivity: he’s been doing this for more than three years, blogging about it in the open. Simon is the creator of Datasette, an open-source tool for exploring and publishing data. He works full-time developing open-source tools for data journalism, centered on Datasette and SQLite. Previously, he was an engineering director at Eventbrite, joining through the acquisition of Lanyrd, a Y Combinator startup he co-founded in 2010. Simon is also a co-creator of the Django Web Framework. He has been blogging about web development since the early 2000s. In today’s conversation, we dive deep into the realm of Gen AI and talk about the following:  • Simon’s initial experiments with LLMs and coding tools • Why fine-tuning is generally a waste of time—and when it’s not • RAG: an overview • Interacting with GPTs voice mode • Simon’s day-to-day LLM stack • Common misconceptions about LLMs and ethical gray areas  • How Simon’s productivity has increased and his generally optimistic view on these tools • Tips, tricks, and hacks for interacting with GenAI tools • And more! I hope you enjoy this episode. — In this episode, we cover: (02:15) Welcome (05:28) Simon’s ‘scary’ experience with ChatGPT (10:58) Simon’s initial experiments with LLMs and coding tools (12:21) The languages that LLMs excel at (14:50) To start LLMs by understanding the theory, or by playing around? (16:35) Fine-tuning: what it is, and why it’s mostly a waste of time (18:03) Where fine-tuning works (18:31) RAG: an explanation (21:34) The expense of running testing on AI (23:15) Simon’s current AI stack  (29:55) Common misconceptions about using LLM tools (30:09) Simon’s stack – continued  (32:51) Learnings from running local models (33:56) The impact of Firebug and the introduction of open-source  (39:42) How Simon’s productivity has increased using LLM tools (41:55) Why most people should limit themselves to 3-4 programming languages (45:18) Addressing ethical issues and resistance to using generative AI (49:11) Are LLMs are plateauing? Is AGI overhyped? (55:45) Coding vs. professional coding, looking ahead (57:27) The importance of systems thinking for software engineers  (1:01:00) Simon’s advice for experienced engineers (1:06:29) Rapid-fire questions — Where to find Simon Willison: • X: https://x.com/simonw • LinkedIn: https://www.linkedin.com/in/simonwillison/ • Website: https://simonwillison.net/ • Mastodon: https://fedi.simonwillison.net/@simon — Referenced: • Simon’s LLM project: https://github.com/simonw/llm • Jeremy Howard’s Fast Ai: https://www.fast.ai/ • jq programming language: https://en.wikipedia.org/wiki/Jq_(programming_language) • Datasette: https://datasette.io/ • GPT Code Interpreter: https://platform.openai.com/docs/assistants/tools/code-interpreter • Open Ai Playground: https://platform.openai.com/playground/chat • Advent of Code: https://adventofcode.com/ • Rust programming language: https://www.rust-lang.org/ • Applied AI Software Engineering: RAG: https://newsletter.pragmaticengineer.com/p/rag • Claude: https://claude.ai/ • Claude 3.5 sonnet: https://www.anthropic.com/news/claude-3-5-sonnet • ChatGPT can now see, hear, and speak: https://openai.com/index/chatgpt-can-now-see-hear-and-speak/ • GitHub Copilot: https://github.com/features/copilot • What are Artifacts and how do I use them?: https://support.anthropic.com/en/articles/9487310-what-are-artifacts-and-how-do-i-use-them • Large Language Models on the command line: https://simonwillison.net/2024/Jun/17/cli-language-models/ • Llama: https://www.llama.com/ • MLC chat on the app store: https://apps.apple.com/us/app/mlc-chat/id6448482937 • Firebug: https://en.wikipedia.org/wiki/Firebug_(software)# • NPM: https://www.npmjs.com/ • Django: https://www.djangoproject.com/ • Sourceforge: https://sourceforge.net/ • CPAN: https://www.cpan.org/ • OOP: https://en.wikipedia.org/wiki/Object-oriented_programming • Prolog: https://en.wikipedia.org/wiki/Prolog • SML: https://en.wikipedia.org/wiki/Standard_ML • Stabile Diffusion: https://stability.ai/ • Chain of thought prompting: https://www.promptingguide.ai/techniques/cot • Cognition AI: https://www.cognition.ai/ • In the Race to Artificial General Intelligence, Where’s the Finish Line?: https://www.scientificamerican.com/article/what-does-artificial-general-intelligence-actually-mean/ • Black swan theory: https://en.wikipedia.org/wiki/Black_swan_theory • Copilot workspace: https://githubnext.com/projects/copilot-workspace • Designing Data-Intensive Applications: The Big Ideas Behind Reliable, Scalable, and Maintainable Systems: https://www.amazon.com/Designing-Data-Intensive-Applications-Reliable-Maintainable/dp/1449373321 • Bluesky Global: https://www.blueskyglobal.org/ • The Atrocity Archives (Laundry Files #1): https://www.amazon.com/Atrocity-Archives-Laundry-Files/dp/0441013651 • Rivers of London: https://www.amazon.com/Rivers-London-Ben-Aaronovitch/dp/1625676158/ • Vanilla JavaScript: http://vanilla-js.com/ • jQuery: https://jquery.com/ • Fly.io: https://fly.io/ — Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email [email protected].

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