Supported by Our Partners • Sentry — Error and performance monitoring for developers. • The Software Engineer’s Guidebook: Written by me (Gergely) – now out in audio form as well. — In today’s episode of The Pragmatic Engineer, I am joined by former Uber colleague, Gautam Korlam. Gautam is the Co-Founder of Gitar, an agentic AI startup that automates code maintenance. Gautam was mobile engineer no. 9 at Uber and founding engineer for the mobile platform team – and so he learned a few things about scaling up engineering teams. We talk about: • How Gautam accidentally deleted Uber’s Java monorepo – really! • Uber's unique engineering stack and why custom solutions like SubmitQueue were built in-house • Monorepo: the benefits and downsides of this approach • From Engineer II to Principal Engineer at Uber: Gautam’s career trajectory • Practical strategies for building trust and gaining social capital  • How the platform team at Uber operated with a product-focused mindset • Vibe coding: why it helps with quick prototyping • How AI tools are changing developer experience and productivity • Important skills for devs to pick up to remain valuable as AI tools spread • And more! — Timestamps (00:00) Intro (02:11) How Gautam accidentally deleted Uber’s Java Monorepo (05:40) The impact of Gautam’s mistake (06:35) Uber’s unique engineering stack (10:15) Uber’s SubmitQueue (12:44) Why Uber moved to a monorepo (16:30) The downsides of a monorepo (18:35) Measurement products built in-house  (20:20) Measuring developer productivity and happiness  (22:52) How Devpods improved developer productivity  (27:37) The challenges with cloud development environments (29:10) Gautam’s journey from Eng II to Principal Engineer (32:00) Building trust and gaining social capital  (36:17) An explanation of Principal Engineer at Uber—and the archetypes at Uber  (45:07) The platform and program split at Uber (48:15) How Gautam and his team supported their internal users  (52:50) Gautam’s thoughts on developer productivity  (59:10) How AI enhances productivity, its limitations, and the rise of agentic AI (1:04:00) An explanation of Vibe coding (1:07:34) An overview of Gitar and all it can help developers with  (1:10:44) Top skills to cultivate to add value and stay relevant (1:17:00) Rapid fire round — The Pragmatic Engineer deepdives relevant for this episode: • The Platform and Program split at Uber • How Uber is measuring engineering productivity • Inside Uber’s move to the Cloud • How Uber built its observability platform • Software Architect Archetypes — 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 • WorkOS — The modern identity platform for B2B SaaS. • The Software Engineer’s Guidebook: Written by me (Gergely) – now out in audio form as well • Augment Code — AI coding assistant that pro engineering teams love — Not many people know that I have a brother: Balint Orosz. Balint is also in tech, but in many ways, is the opposite of me. While I prefer working on backend and business logic, he always thrived in designing and building UIs. While I opted to work at more established companies, he struck out on his own and started his startup, Distinction. And yet, our professional paths have crossed several times: at one point in time I accepted an offer to join Skyscanner as a Principal iOS Engineer – and as part of the negotiation, I added a clause to my contrac that I will not report directly or indirectly to the Head of Mobile: who happened to be my brother, thanks to Skyscanner acquiring his startup the same month that Skyscanner made an offer to hire me. Today, Balint is the founder and CEO of Craft, a beloved text editor known for its user-friendly interface and sleek design – an app that Apple awarded the prestigious Mac App of the Year in 2021. In our conversation, we explore how Balint approaches building opinionated software with an intense focus on user experience. We discuss the lessons he learned from his time building Distinction and working at Skyscanner that have shaped his approach to Craft and its development. In this episode, we discuss: • Balint’s first startup, Distinction, and his time working for Skyscanner after they acquired it • A case for a balanced engineering culture with both backend and frontend priorities  • Why Balint doesn’t use iOS Auto Layout • The impact of Craft being personal software on front-end and back-end development • The balance between customization and engineering fear in frontend work • The resurgence of local-first software and its role in modern computing • The value of building a physical prototype  • How Balint uses GenAI to assist with complicated coding projects  • And much more! — Timestamps (00:00) Intro (02:13) What it’s like being a UX-focused founder  (09:00) Why it was hard to gain recognition at Skyscanner  (13:12) Takeaways from Skyscanner that Balint brought to Craft  (16:50) How frameworks work and why they aren’t always a good fit (20:35) An explanation of iOS Auto Layout and its pros and cons  (23:13) Why Balint doesn’t use Auto Layout  (24:23) Why Craft has one code base  (27:46) Craft’s unique toolbar features and a behind the scenes peek at the code  (33:15) Why frontend engineers have fear around customization  (37:11) How Craft’s design system differs from most companies  (42:33) Behaviors and elements Craft uses rather than having a system for everything  (44:12) The back and frontend architecture in building personal software  (48:11) Shifting beliefs in personal computing  (50:15) The challenges faced with operating system updates  (50:48) The resurgence of local-first software (52:31) The value of opinionated software for consumers  (55:30) Why Craft’s focus is on the user’s emotional experience (56:50) The size of Craft’s engineering department and platform teams (59:20) Why Craft moves faster with smaller teams (1:01:26) Balint’s advice for frontend engineers looking to demonstrate value  (1:04:35) Balint’s breakthroughs using GenAI (1:07:50) Why Balint still writes code (1:09:44) Rapid fire round — The Pragmatic Engineer deepdives relevant for this episode: • The AI hackathon at Craft Docs • Engineering career paths at Big Tech and scaleups • Thriving as a Founding Engineer: lessons from the trenches • The past and future of modern backend practices — 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 • WorkOS — The modern identity platform for B2B SaaS. • Graphite — The AI developer productivity platform.  • Formation — Level up your career and compensation with Formation. — In today’s episode of The Pragmatic Engineer, I am joined by a senior software engineer and cartoonist, Manu Cornet. Manu spent over a decade at Google, doing both backend and frontend development. He also spent a year and a half at Twitter before Elon Musk purchased it and rebranded it to X. But what Manu is most known for are his hilarious internet comics about the tech world, including his famous org chart comic from 2011 about Facebook, Apple, Amazon, and Microsoft. In today’s conversation, we explore many of his comics, discuss the meaning behind them, and talk about the following topics:  • The viral org chart comic that captured the structure of Big Tech companies • Why Google is notorious for confusing product names • The comic that ended up on every door at Google • How Google’s 20% time fostered innovation—and what projects came from it • How one of Manu’s comics predicted Google Stadia’s failure—and the reasons behind it • The value of connecting to users directly  • Twitter’s climate before and after Elon Musk’s acquisition and the mass layoffs that followed • And more! — Timestamps (00:00) Intro (02:01) Manu’s org structure comic  (07:10) Manu’s “Who Sues Who” comic (09:15) Google vs. Amazon comic (14:10) Confusing names at Google (20:00) Different approaches to sharing information within companies (22:20) The two ways of doing things at Google (25:15) Manu’s code reviews comic (27:45) The comic that was printed on every single door of Google (30:55) An explanation of 20% at Google (36:00) Gmail Labs and Google Stadia (41:36) Manu’s time at Twitter and the threat of Elon Musk buying (47:07) How Manu helped Gergely with a bug on Twitter (49:05) Musk’s acquirement of Twitter and the resulting layoffs (59:00) Manu’s comic about his disillusionment with Twitter and Google (1:02:37) Rapid fire round — The Pragmatic Engineer deepdives relevant for this episode: • How Manu creates comics • Consolidating technologies • Is Big Tech becoming more cutthroat? — 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 • WorkOS — The modern identity platform for B2B SaaS • CodeRabbit — Cut code review time and bugs in half • Augment Code — AI coding assistant that pro engineering teams love — How do you architect a live streaming system to deal with more load than it’s ever been done before? Today, we hear from an architect of such a system: Ashutosh Agrawal, formerly Chief Architect of JioCinema (and currently Staff Software Engineer at Google DeepMind.) We take a deep dive into video streaming architecture, tackling the complexities of live streaming at scale (at tens of millions of parallel streams) and the challenges engineers face in delivering seamless experiences. We talk about the following topics:  • How large-scale live streaming architectures are designed • Tradeoffs in optimizing performance • Early warning signs of streaming failures and how to detect them • Why capacity planning for streaming is SO difficult • The technical hurdles of streaming in APAC regions • Why Ashutosh hates APMs (Application Performance Management systems) • Ashutosh’s advice for those looking to improve their systems design expertise • And much more! — Timestamps (00:00) Intro (01:28) The world record-breaking live stream and how support works with live events (05:57) An overview of streaming architecture (21:48) The differences between internet streaming and traditional television.l (22:26) How adaptive bitrate streaming works (25:30) How throttling works on the mobile tower side  (27:46) Leading indicators of streaming problems and the data visualization needed (31:03) How metrics are set  (33:38) Best practices for capacity planning  (35:50) Which resources are planned for in capacity planning  (37:10) How streaming services plan for future live events with vendors (41:01) APAC specific challenges (44:48) Horizontal scaling vs. vertical scaling  (46:10) Why auto-scaling doesn’t work (47:30) Concurrency: the golden metric to scale against (48:17) User journeys that cause problems  (49:59) Recommendations for learning more about video streaming  (51:11) How Ashutosh learned on the job (55:21) Advice for engineers who would like to get better at systems (1:00:10) Rapid fire round — The Pragmatic Engineer deepdives relevant for this episode: • Software architect archetypes https://newsletter.pragmaticengineer.com/p/software-architect-archetypes  • Engineering leadership skill set overlaps https://newsletter.pragmaticengineer.com/p/engineering-leadership-skillset-overlaps  • Software architecture with Grady Booch https://newsletter.pragmaticengineer.com/p/software-architecture-with-grady-booch — 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 • WorkOS — The modern identity platform for B2B SaaS • CodeRabbit — Cut code review time and bugs in half • Augment Code — AI coding assistant that pro engineering teams love — How do you architect a live streaming system to deal with more load than it’s ever been done before? Today, we hear from an architect of such a system: Ashutosh Agrawal, formerly Chief Architect of JioCinema (and currently Staff Software Engineer at Google DeepMind.) We take a deep dive into video streaming architecture, tackling the complexities of live streaming at scale (at tens of millions of parallel streams) and the challenges engineers face in delivering seamless experiences. We talk about the following topics:  • How large-scale live streaming architectures are designed • Tradeoffs in optimizing performance • Early warning signs of streaming failures and how to detect them • Why capacity planning for streaming is SO difficult • The technical hurdles of streaming in APAC regions • Why Ashutosh hates APMs (Application Performance Management systems) • Ashutosh’s advice for those looking to improve their systems design expertise • And much more! — Timestamps (00:00) Intro (01:28) The world record-breaking live stream and how support works with live events (05:57) An overview of streaming architecture (21:48) The differences between internet streaming and traditional television.l (22:26) How adaptive bitrate streaming works (25:30) How throttling works on the mobile tower side  (27:46) Leading indicators of streaming problems and the data visualization needed (31:03) How metrics are set  (33:38) Best practices for capacity planning  (35:50) Which resources are planned for in capacity planning  (37:10) How streaming services plan for future live events with vendors (41:01) APAC specific challenges (44:48) Horizontal scaling vs. vertical scaling  (46:10) Why auto-scaling doesn’t work (47:30) Concurrency: the golden metric to scale against (48:17) User journeys that cause problems  (49:59) Recommendations for learning more about video streaming  (51:11) How Ashutosh learned on the job (55:21) Advice for engineers who would like to get better at systems (1:00:10) Rapid fire round — The Pragmatic Engineer deepdives relevant for this episode: • Software architect archetypes https://newsletter.pragmaticengineer.com/p/software-architect-archetypes  • Engineering leadership skill set overlaps https://newsletter.pragmaticengineer.com/p/engineering-leadership-skillset-overlaps  • Software architecture with Grady Booch https://newsletter.pragmaticengineer.com/p/software-architecture-with-grady-booch — 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. • Graphite — The AI developer productivity platform.  • Vanta — Automate compliance and simplify security with Vanta. — On today’s episode of The Pragmatic Engineer, I’m joined by Chip Huyen, a computer scientist, author of the freshly published O’Reilly book AI Engineering, and an expert in applied machine learning. Chip has worked as a researcher at Netflix, was a core developer at NVIDIA (building NeMo, NVIDIA’s GenAI framework), and co-founded Claypot AI. She also taught Machine Learning at Stanford University. In this conversation, we dive into the evolving field of AI Engineering and explore key insights from Chip’s book, including: • How AI Engineering differs from Machine Learning Engineering  • Why fine-tuning is usually not a tactic you’ll want (or need) to use • The spectrum of solutions to customer support problems – some not even involving AI! • The challenges of LLM evals (evaluations) • Why project-based learning is valuable—but even better when paired with structured learning • Exciting potential use cases for AI in education and entertainment • And more! — Timestamps (00:00) Intro  (01:31) A quick overview of AI Engineering (05:00) How Chip ensured her book stays current amidst the rapid advancements in AI (09:50) A definition of AI Engineering and how it differs from Machine Learning Engineering  (16:30) Simple first steps in building AI applications (22:53) An explanation of BM25 (retrieval system)  (23:43) The problems associated with fine-tuning  (27:55) Simple customer support solutions for rolling out AI thoughtfully  (33:44) Chip’s thoughts on staying focused on the problem  (35:19) The challenge in evaluating AI systems (38:18) Use cases in evaluating AI  (41:24) The importance of prioritizing users’ needs and experience  (46:24) Common mistakes made with Gen AI (52:12) A case for systematic problem solving  (53:13) Project-based learning vs. structured learning (58:32) Why AI is not the end of engineering (1:03:11) How AI is helping education and the future use cases we might see (1:07:13) Rapid fire round — The Pragmatic Engineer deepdives relevant for this episode: • Applied AI Software Engineering: RAG https://newsletter.pragmaticengineer.com/p/rag  • How do AI software engineering agents work? https://newsletter.pragmaticengineer.com/p/ai-coding-agents  • AI Tooling for Software Engineers in 2024: Reality Check https://newsletter.pragmaticengineer.com/p/ai-tooling-2024  • IDEs with GenAI features that Software Engineers love https://newsletter.pragmaticengineer.com/p/ide-that-software-engineers-love — 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 • Formation — Level up your career and compensation with Formation.  • WorkOS — The modern identity platform for B2B SaaS • Vanta — Automate compliance and simplify security with Vanta. — In today’s episode of The Pragmatic Engineer, I’m joined by Jonas Tyroller, one of the developers behind Thronefall, a minimalist indie strategy game that blends tower defense and kingdom-building, now available on Steam. Jonas takes us through the journey of creating Thronefall from start to finish, offering insights into the world of indie game development. We explore: • Why indie developers often skip traditional testing and how they find bugs • The developer workflow using Unity, C# and Blender • The two types of prototypes game developers build  • Why Jonas spent months building game prototypes in 1-2 days • How Jonas uses ChatGPT to build games • Jonas’s tips on making games that sell • And more! — Timestamps (00:00) Intro (02:07) Building in Unity (04:05) What the shader tool is used for  (08:44) How a Unity build is structured (11:01) How game developers write and debug code  (16:21) Jonas’s Unity workflow (18:13) Importing assets from Blender (21:06) The size of Thronefall and how it can be so small (24:04) Jonas’s thoughts on code review (26:42) Why practices like code review and source control might not be relevant for all contexts (30:40) How Jonas and Paul ensure the game is fun  (32:25) How Jonas and Paul used beta testing feedback to improve their game (35:14) The mini-games in Thronefall and why they are so difficult (38:14) The struggle to find the right level of difficulty for the game (41:43) Porting to Nintendo Switch (45:11) The prototypes Jonas and Paul made to get to Thronefall (46:59) The challenge of finding something you want to build that will sell (47:20) Jonas’s ideation process and how they figure out what to build  (49:35) How Thronefall evolved from a mini-game prototype (51:50) How long you spend on prototyping  (52:30) A lesson in failing fast (53:50) The gameplay prototype vs. the art prototype (55:53) How Jonas and Paul distribute work  (57:35) Next steps after having the play prototype and art prototype (59:36) How a launch on Steam works  (1:01:18) Why pathfinding was the most challenging part of building Thronefall (1:08:40) Gen AI tools for building indie games  (1:09:50) How Jonas uses ChatGPT for editing code and as a translator  (1:13:25) The pros and cons of being an indie developer  (1:15:32) Jonas’s advice for software engineers looking to get into indie game development (1:19:32) What to look for in a game design school (1:22:46) How luck figures into success and Jonas’s tips for building a game that sells (1:26:32) Rapid fire round — The Pragmatic Engineer deepdives relevant for this episode: • Game development basics https://newsletter.pragmaticengineer.com/p/game-development-basics  • Building a simple game using Unity https://newsletter.pragmaticengineer.com/p/building-a-simple-game — 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 • Sonar —  Trust your developers – verify your AI-generated code. • Vanta —Automate compliance and simplify security with Vanta. — In today's episode of The Pragmatic Engineer, I'm joined by Charity Majors, a well-known observability expert – as well as someone with strong and grounded opinions. Charity is the co-author of "Observability Engineering" and brings extensive experience as an operations and database engineer and an engineering manager. She is the cofounder and CTO of observability scaleup Honeycomb. Our conversation explores the ever-changing world of observability, covering these topics: • What is observability? Charity’s take • What is “Observability 2.0?” • Why Charity is a fan of platform teams • Why DevOps is an overloaded term: and probably no longer relevant • What is cardinality? And why does it impact the cost of observability so much? • How OpenTelemetry solves for vendor lock-in  • Why Honeycomb wrote its own database • Why having good observability should be a prerequisite to adding AI code or using AI agents • And more! — Timestamps (00:00) Intro  (04:20) Charity’s inspiration for writing Observability Engineering (08:20) An overview of Scuba at Facebook (09:16) A software engineer’s definition of observability  (13:15) Observability basics (15:10) The three pillars model (17:09) Observability 2.0 and the shift to unified storage (22:50) Who owns observability and the advantage of platform teams  (25:05) Why DevOps is becoming unnecessary  (27:01) The difficulty of observability  (29:01) Why observability is so expensive  (30:49) An explanation of cardinality and its impact on cost (34:26) How to manage cost with tools that use structured data  (38:35) The common worry of vendor lock-in (40:01) An explanation of OpenTelemetry (43:45) What developers get wrong about observability  (45:40) A case for using SLOs and how they help you avoid micromanagement  (48:25) Why Honeycomb had to write their database  (51:56) Companies who have thrived despite ignoring conventional wisdom (53:35) Observability and AI  (59:20) Vendors vs. open source (1:00:45) What metrics are good for  (1:02:31) RUM (Real User Monitoring)  (1:03:40) The challenges of mobile observability  (1:05:51) When to implement observability at your startup  (1:07:49) Rapid fire round — The Pragmatic Engineer deepdives relevant for this episode: • How Uber Built its Observability Platform https://newsletter.pragmaticengineer.com/p/how-uber-built-its-observability-platform  • Building an Observability Startup https://newsletter.pragmaticengineer.com/p/chronosphere  • How to debug large distributed systems https://newsletter.pragmaticengineer.com/p/antithesis  • Shipping to production https://newsletter.pragmaticengineer.com/p/shipping-to-production  — 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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Brought to you by: • WorkOS — The modern identity platform for B2B SaaS. • Sevalla — Deploy anything from preview environments to Docker images. • Chronosphere — The observability platform built for control. — Welcome to The Pragmatic Engineer! Today, I’m thrilled to be joined by Grady Booch, a true legend in software development. Grady is the Chief Scientist for Software Engineering at IBM, where he leads groundbreaking research in embodied cognition. He’s the mind behind several object-oriented design concepts, a co-author of the Unified Modeling Language, and a founding member of the Agile Alliance and the Hillside Group. Grady has authored six books, hundreds of articles, and holds prestigious titles as an IBM, ACM, and IEEE Fellow, as well as a recipient of the Lovelace Medal (an award for those with outstanding contributions to the advancement of computing). In this episode, we discuss: • What it means to be an IBM Fellow • The evolution of the field of software development • How UML was created, what its goals were, and why Grady disagrees with the direction of later versions of UML • Pivotal moments in software development history • How the software architect role changed over the last 50 years • Why Grady declined to be the Chief Architect of Microsoft – saying no to Bill Gates! • Grady’s take on large language models (LLMs) • Advice to less experienced software engineers • … and much more! — Timestamps (00:00) Intro (01:56) What it means to be a Fellow at IBM (03:27) Grady’s work with legacy systems (09:25) Some examples of domains Grady has contributed to (11:27) The evolution of the field of software development (16:23) An overview of the Booch method (20:00) Software development prior to the Booch method (22:40) Forming Rational Machines with Paul and Mike (25:35) Grady’s work with Bjarne Stroustrup (26:41) ROSE and working with the commercial sector (30:19) How Grady built UML with Ibar Jacobson and James Rumbaugh (36:08) An explanation of UML and why it was a mistake to turn it into a programming language (40:25) The IBM acquisition and why Grady declined Bill Gates’s job offer  (43:38) Why UML is no longer used in industry  (52:04) Grady’s thoughts on formal methods (53:33) How the software architect role changed over time (1:01:46) Disruptive changes and major leaps in software development (1:07:26) Grady’s early work in AI (1:12:47) Grady’s work with Johnson Space Center (1:16:41) Grady’s thoughts on LLMs  (1:19:47) Why Grady thinks we are a long way off from sentient AI  (1:25:18) Grady’s advice to less experienced software engineers (1:27:20) What’s next for Grady (1:29:39) Rapid fire round — The Pragmatic Engineer deepdives relevant for this episode: • The Past and Future of Modern Backend Practices https://newsletter.pragmaticengineer.com/p/the-past-and-future-of-backend-practices  • What Changed in 50 Years of Computing https://newsletter.pragmaticengineer.com/p/what-changed-in-50-years-of-computing  • AI Tooling for Software Engineers: Reality Check https://newsletter.pragmaticengineer.com/p/ai-tooling-2024 — Where to find Grady Booch: • X: https://x.com/grady_booch • LinkedIn: https://www.linkedin.com/in/gradybooch • Website: https://computingthehumanexperience.com 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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Brought to you by: • WorkOS — The modern identity platform for B2B SaaS. • Sonar —  Trust your developers – verify your AI-generated code. — In today’s episode of The Pragmatic Engineer, I’m joined by Irina Stanescu, a seasoned engineer with over 14 years in software engineering and engineering leadership roles at tech companies like Google and Uber. Now an engineering leadership coach, Irina helps tech professionals build impactful careers, teaches a course on influence, and shares insights through her newsletter, The Caring Techie. In our conversation today, Irina shares her journey of rising through the ranks at Google and Uber. We dive into the following topics:  • An inside look at Google’s unique working processes • How to build credibility as a new engineer • Tactical tips for getting promoted  • The importance of having a career plan and guidance in designing one • Having influence vs. influencing—and how to become more influential  • Essential leadership skills to develop • And so much more — In this episode, we cover: (01:34) Irina’s time at Google (03:10) An overview of ‘design docs’ at Google (08:27) The readiness review at Google (10:40) Why Irina uses spreadsheets (11:44) Irina’s favorite tools and how she uses them (13:46) How Google certifies readability (15:40) Google’s meme generator  (17:36) Advice for engineers thinking about working for an organization like Google (20:14) How promotions work at Google (23:15) How Irina worked towards getting promoted  (27:50) How Irina got her first mentor  (30:44) Organizational shifts at Uber while Irina and Gergely were there (35:50) Why you should prioritize growth over promotion (36:50) What a career plan is and how to build one (40:40) Irina’s current role coaching engineers  (42:23) A simple explanation of influence and influencing  (51:54) Why saying no is necessary at times (54:30) The importance of building leadership skills — The Pragmatic Engineer deepdives relevant for this episode: • Preparing for promotions ahead of time: https://newsletter.pragmaticengineer.com/p/preparing-for-promotions  • Engineering career paths at Big Tech and scaleups: https://newsletter.pragmaticengineer.com/p/engineering-career-paths  • Getting an Engineering Executive Job: https://newsletter.pragmaticengineer.com/p/getting-an-engineering-executive  • The Seniority Rollercoaster: https://newsletter.pragmaticengineer.com/p/the-seniority-rollercoaster — Where to find Irina Stanescu: • X: https://x.com/thecaringtechie • LinkedIn: https://www.linkedin.com/in/irinastanescu/ • Website: https://www.thecaringtechie.com/ • Maven course: Impact through Influence in Engineering Teams: https://maven.com/irina-stanescu/influence-swe 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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Brought to you by: • The Enterprise Ready Conference on October 30th — For B2B leaders building enterprise SaaS. • DX — DX is an engineering intelligence platform designed by leading researchers.  • ByteByteGo — Ace your next system design interview. — You may not be familiar with Bending Spoons, but I guarantee you’ve encountered some of their well-known products, like Evernote and Meetup. In today’s episode of The Pragmatic Engineer, we sit down with three key figures from the Italy-based startup: cofounder and CEO Luca Ferrari, CTO Francesco Mancone, and Evernote product lead Federico Simionato. Bending Spoons has been profitable from day one, and there's plenty we can learn from their unique culture, organizational structure, engineering processes, and hiring practices. In today’s conversation, we cover the following topics: • The controversial acquisitions approach of Bending Spoons • How Bending Spoons spent more than $1 billion in buying tech companies • How the Evernote acquisition happened • How Bending Spoons operates and how it organizes product and platform teams • Why engineering processes are different across different products • How ‘radical simplicity’ is baked into everything from engineering processes to pay structure. • And much more! — The Pragmatic Engineer deepdives relevant for this episode: • Good attrition, bad attrition for software engineers: https://newsletter.pragmaticengineer.com/p/attrition  • Healthy oncall practices: https://newsletter.pragmaticengineer.com/p/healthy-oncall-practices • Shipping to production: https://newsletter.pragmaticengineer.com/p/shipping-to-production • QA across the tech industry: https://newsletter.pragmaticengineer.com/p/qa-across-tech — In this episode, we cover: (2:09) Welcome, Luca, Francesco, and Federico from Bending Spoons (03:15) An overview of the well-known apps and products owned by Bending Spoons (06:38) The elephant in the room: how Bending Spoons really acquires companies (09:46) Layoffs: Bending Spoons’ philosophy on this (14:10) Controversial principles (17:16) Revenue, team size, and products (19:35) How Bending Spoons runs AI products and allocates GPUs (23:05) History of the company (27:04) The Evernote acquisition (29:50) Modernizing Evernote’s infrastructure (32:44) “Radical simplicity” and why they try for zero on calls (36:13) More on changes made to the Evernote systems (41:13) How Bending Spoons prioritizes and ships fast  (49:40) What’s new and what’s coming for Bending Spoons (51:08) Organizational structure at the company (54:07) Engineering practices (57:03) Testing approaches (58:53) Platform teams (1:01:52) Bending Spoons tech stack and popular frameworks (1:05:55) Why Bending Spoons hires new grads and less experienced engineers (1:08:09) The structure of careers and titles at Bending Spoons (1:09:50) Traits they look for when hiring  (1:12:50) Why there aren’t many companies doing what Bending Spoons does  — Where to find Luca Ferrari: • X: https://x.com/luke10ferrari • LinkedIn: https://www.linkedin.com/in/luca-ferrari-12418318 Where to find  Francesco Mancone: • LinkedIn: https://www.linkedin.com/in/francesco-mancone Where to find Federico Simionato: • X: https://x.com/fedesimio • LinkedIn: https://www.linkedin.com/in/federicosimionato 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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Brought to you by: • Paragon: ​​Build native, customer-facing SaaS integrations 7x faster. • WorkOS: For B2B leaders building enterprise SaaS — On today’s episode of The Pragmatic Engineer, I’m joined by Quinn Slack, CEO and co-founder of Sourcegraph, a leading code search and intelligence platform. Quinn holds a degree in Computer Science from Stanford and is deeply passionate about coding: to the point that he still codes every day! He also serves on the board of Hack Club, a national nonprofit dedicated to bringing coding clubs to high schools nationwide. In this insightful conversation, we discuss:             • How Sourcegraph's operations have evolved since 2021 • Why more software engineers should focus on delivering business value • Why Quinn continues to code every day, even as a CEO • Practical AI and LLM use cases and a phased approach to their adoption • The story behind Job Fairs at Sourcegraph and why it’s no longer in use • Quinn’s leadership style and his focus on customers and product excellence • The shift from location-independent pay to zone-based pay at Sourcegraph • And much more! — Where to find Quinn Slack: • X: https://x.com/sqs • LinkedIn: https://www.linkedin.com/in/quinnslack/ • Website: https://slack.org/ 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 — In this episode, we cover: (01:35) How Sourcegraph started and how it has evolved over the past 11 years (04:14) How scale-ups have changed  (08:27) Learnings from 2021 and how Sourcegraph’s operations have streamlined (15:22) Why Quinn is for gradual increases in automation and other thoughts on AI (18:10) The importance of changelogs (19:14) Keeping AI accountable and possible future use cases  (22:29) Current limitations of AI (25:08) Why early adopters of AI coding tools have an advantage  (27:38) Why AI is not yet capable of understanding existing codebases  (31:53) Changes at Sourcegraph since the deep dive on The Pragmatic Engineer blog (40:14) The importance of transparency and understanding the different forms of compensation (40:22) Why Sourcegraph shifted to zone-based pay (47:15) The journey from engineer to CEO (53:28) A comparison of a typical week 11 years ago vs. now (59:20) Rapid fire round The Pragmatic Engineer deepdives relevant for this episode: • Inside Sourcegraph’s engineering culture: Part 1 https://newsletter.pragmaticengineer.com/p/inside-sourcegraphs-engineering-culture• Inside Sourcegraph’s engineering culture: Part 2 https://newsletter.pragmaticengineer.com/p/inside-sourcegraphs-engineering-culture-part-2 — References and Transcript: 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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