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Why did Amazon move from being a monolith, splitting up into services, despite obsessing about response latency? Turns out it was "software physics:" the monolith crossed the 4GB size that could be deployed in one go. Steve Huynh (Steve Huynh) joins me on The Pragmatic Engineer Podcast to talk...

79,836 views • 1 year ago •via X (Twitter)

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. Kent Beck 🌻 is a legend in software engineering: and after coding for 52 years, he's never had more fun than now, he told me. Why? Because AI agents brought back the joy of creating software without the stuff that he's started to hate about coding for so long. Watch or listen: • YouTube: • Spotify: • Apple: Brought to you by: • Sonar — Code quality and code security for ALL code •⁠ Statsig ⁠ — ⁠ The unified platform for flags, analytics, experiments, and more • Augment Code — AI coding assistant that pro engineering teams love Two of my takeaways from this chat with Kent: 𝟭. 𝗞𝗲𝗻𝘁 𝗶𝘀 𝗿𝗲-𝗲𝗻𝗲𝗿𝗴𝗶𝘇𝗲𝗱 𝘁𝗵𝗮𝗻𝗸𝘀 𝘁𝗼 𝘂𝘀𝗶𝗻𝗴 𝗔𝗜 𝗮𝗴𝗲𝗻𝘁𝘀 𝘁𝗼 𝗯𝘂𝗶𝗹𝗱 𝘀𝘁𝘂𝗳𝗳. Kent has been coding for 52 years, and the last decade, he’s gotten a lot more tired of all of it: learning yet another new language or framework, or debugging the issues when using the latest framework. What he loves about these AI agents (and AI coding tools) is how he doesn’t need to know exactly all the details: he can now be a lot more ambitious in his projects. Currently, Kent is building a server in Smalltalk (that he’s been wanting to do for many years) and a Language Server Protocol (LSP) for Smalltalk 𝟮. 𝗙𝗮𝗰𝗲𝗯𝗼𝗼𝗸 𝘄𝗿𝗼𝘁𝗲 𝗻𝗼 𝘂𝗻𝗶𝘁 𝘁𝗲𝘀𝘁𝘀 𝗶𝗻 𝟮𝟬𝟭𝟭, 𝗮𝗻𝗱 𝘁𝗵𝗶𝘀 𝘀𝘁𝘂𝗻𝗻𝗲𝗱 𝗞𝗲𝗻𝘁, 𝗯𝗮𝗰𝗸 𝗶𝗻 𝘁𝗵𝗲 𝗱𝗮𝘆. Kent joined Facebook in 2011, and was taken aback by the lack of testing and how everyone pushed code to production without automated testing. What he came to realize – and appreciate! – was how Facebook had several things balancing this out: • Devs took responsibility for their code very seriously • Nothing at Facebook was “someone else’s problem:” devs would fix bugs when they saw them, regardless of whose commit caused it • Feature flags were heavily used for risky code • Facebook did staged rollouts to smaller markets like New Zealand To this date, Facebook ships code to production in a unique way. We covered more in the deepdive Shipping to Production at

Gergely Orosz

42,514 views • 1 year ago

It's always energizing to do a podcast with Steve Yegge (Steve Yegge, engineer+author, formerly at Amazon+Google, creator of Gas Town). Timestamps: 00:00 Intro 01:43 Steve’s latest projects 02:27 Important blog posts 04:48 Shifts in what engineers need to know 10:46 Steve’s current AI stance 13:23 Steve’s book Vibe Coding 18:25 Layoffs and disruption in tech 31:13 Gas Town 40:10 New ways of working 51:08 The problem of too many people 54:45 Why AI results lag in business 59:57 Gamification and product stickiness 1:04:54 The ‘Bitter Lesson’ explained 1:07:14 The future of software development 1:23:06 Where languages stand 1:24:47 Adapting to change 1:27:32 Steve’s predictions Brought to you by: • Statsig – ⁠ The unified platform for flags, analytics, experiments, and more. • Sonar – The makers of SonarQube, the industry standard for automated code review. • WorkOS – Everything you need to make your app enterprise ready. Three interesting thoughts from Steve that we talked about in this conversation: 1. Reading ability is becoming a blocker for wider AI adoption. Some struggle with walls of text that current AI tools produce, and Steve predicts that in the very near future, most people will program by talking to a visual avatar, not reading terminal output because he observes that five paragraphs is already a lot to read for many devs. 2. What software engineers need to know keeps changing. In the 1990s, any decent software engineer knew Assembly, and today almost no decent developer knows it because Assembly has long been superseded by technical progress. What engineers “need” to know these days is different from the ‘90s and that process continues with AI, changing the parts of the craft that are essential for devs. We grumble about this but that won’t change anything by itself. 3. There’s a “Dracula Effect” where AI-augmented work drains engineers faster than traditional work. This is because AI automates the easy tasks, meaning that engineers are stuck doing high-intensity thinking all day. Steve says you may only get three daily productive hours at max speed, but during that time, you could produce 100x more output than before.

Gergely Orosz

41,987 views • 4 months ago