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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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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