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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...

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Gergely Orosz

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79,836 Aufrufe • vor 11 Monaten •via X (Twitter)

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No project has gotten more traction in such a short time than by Peter Steinberger 🦞 But how is he building it? Watch or listen: • YouTube: • Spotify: • Apple: Brought to you by: • Statsig — ⁠The unified platform for flags, analytics, experiments, and more. Join us at The Pragmatic Summit I’m hosting with Statsig, on 11 February: • Sonar – The makers of SonarQube, the industry standard for automated code review. Join me online at the Sonar Summit, on 3rd March: • WorkOS – Everything you need to make your app enterprise ready. If you're in SF on 9 February, stop by at the WorkOS AI Night with The Pragmatic Engineer (free to register):

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How is AI changing software development? Martin Fowler (Martin Fowler) says AI is the biggest shift in the field since high-level languages appeared (ones like Fortran or C, offering a new abstaction level to code at, versus Assembly). We also talked about where GenAI works well, thoughts on refactoring, software architecture and more. I very much recommend checking out the whole episode: • YouTube: • Spotify: • Apple: Brought to you by: •⁠ Statsig ⁠ — ⁠ The unified platform for flags, analytics, experiments, and more. Check it out: •⁠ Linear ⁠ — ⁠ The system for modern product development. Read how Linear does Quality Wednesdays – it’s fascinating:

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Gergely Orosz

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Why does "vibe coding" not lead to anything productive, for the most part? A great person to answer is Addy Osmani: he has been working on Chrome for 10+ years, and has just published his new book: Beyond Vibe Coding. Watch or listen: • YouTube: • Spotify: • Apple: Brought to you by: •⁠ Statsig ⁠ — ⁠ The unified platform for flags, analytics, experiments, and more. •⁠ Linear – The system for modern product development.
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Why does "vibe coding" not lead to anything productive, for the most part? A great person to answer is Addy Osmani: he has been working on Chrome for 10+ years, and has just published his new book: Beyond Vibe Coding. Watch or listen: • YouTube: • Spotify: • Apple: Brought to you by: •⁠ Statsig ⁠ — ⁠ The unified platform for flags, analytics, experiments, and more. •⁠ Linear – The system for modern product development.

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51,789 Aufrufe • vor 8 Monaten

John Ousterhout is a legend in software design - and so a great person to ask: "How will AI coding tools change software engineering and design?" John is the author of A Philosophy of Software Design, taught software design at Stanford, (possibly the first and only software design class for university students) created the Tcl programming language - and currently is busy contributing to the Linux Kernel (!) In this episode of The Pragmatic Engineer, John joins me to talk about why design still matters and how most teams struggle to get it right. We dive into his book A Philosophy of Software Design, unpack the difference between top-down and bottom-up approaches, and explore why some popular advice, like writing short methods or relying heavily on TDD, does not hold up, according to John. Watch or listen: • YouTube: • Spotify: • Apple: Brought to you by our wonderful sponsors: •⁠ CodeRabbit — Cut code review time and bugs in half. Use the code PRAGMATIC to get one month free. •⁠ Modal — The cloud platform for building AI applications. Try it at --- Three of my takeaways from talking with John: 𝟭. 𝗧𝗵𝗲 𝗲𝘅𝗽𝗹𝗼𝘀𝗶𝗼𝗻 𝗼𝗳 𝗔𝗜 𝗰𝗼𝗱𝗶𝗻𝗴 𝗰𝗼𝘂𝗹𝗱 𝗺𝗮𝗸𝗲 𝘀𝗼𝗳𝘁𝘄𝗮𝗿𝗲 𝗱𝗲𝘀𝗶𝗴𝗻 𝗺𝗼𝗿𝗲 𝗶𝗺𝗽𝗼𝗿𝘁𝗮𝗻𝘁 𝘁𝗵𝗮𝗻 𝗯𝗲𝗳𝗼𝗿𝗲. Currently, AI coding tools and agents are akin to “tactical tornadoes” that code fast, fix issues fast… while creating new issues and adding tech debt. John doesn’t see the current tools being able to replace high-level design. And so software design could be more important than before – thanks to more code being written than before! 𝟮. 𝗦𝗼𝗳𝘁𝘄𝗮𝗿𝗲 𝗱𝗲𝘀𝗶𝗴𝗻 𝗶𝘀 𝗮 𝗱𝗲𝗰𝗼𝗺𝗽𝗼𝘀𝗶𝘁𝗶𝗼𝗻 𝗽𝗿𝗼𝗯𝗹𝗲𝗺. How do you take a large system and divide it into smaller units that you can implement relatively independently? John believes that the most important idea for all of computer science is just this – decomposition. If you can break up complicated problems into smaller parts: you can solve so many problems! 𝟯. 𝗧𝗲𝘀𝘁 𝗗𝗿𝗶𝘃𝗲𝗻 𝗗𝗲𝘃𝗲𝗹𝗼𝗽𝗺𝗲𝗻𝘁 (𝗧𝗗𝗗) 𝘄𝗼𝗿𝗸𝘀 𝗮𝗴𝗮𝗶𝗻𝘀𝘁 𝗴𝗼𝗼𝗱 𝘀𝗼𝗳𝘁𝘄𝗮𝗿𝗲 𝗱𝗲𝘀𝗶𝗴𝗻. John firmly believes that TDD is counter-productive because it forces thinking about the small details before thinking about the high-level design. This observation could explain why TDD has not gained much traction in the last decade or so! ... and a plenty more more food for thought in our discussion!
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Gergely Orosz

16,506 Aufrufe • vor 1 Jahr

Chris Lattner (Chris Lattner) is one of the most influential engineers of the past two decades. He created LLVM, Swift, contributed to TensorFlow, and created the Mojo programming language. What was the story about creating Swift - and why did he face resistance inside Apple when wanting to replace Objective C? What did he learn at Tesla, Google and CPU maker SiFive, that led him to working on Mojo at Modular? We cover these and many more in today's episode. Watch or listen: • YouTube: • Spotify: • Apple: Brought to you by: •⁠ Statsig ⁠ — ⁠ The unified platform for flags, analytics, experiments, and more. •⁠ Linear – The system for modern product development. My favorite quote from Chris in this episode: “I believe in the power of programmers. I believe in the human potential of people that want to create things. And that’s fundamentally why I love software is that you can create anything that you can imagine.”
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Chris Lattner (Chris Lattner) is one of the most influential engineers of the past two decades. He created LLVM, Swift, contributed to TensorFlow, and created the Mojo programming language. What was the story about creating Swift - and why did he face resistance inside Apple when wanting to replace Objective C? What did he learn at Tesla, Google and CPU maker SiFive, that led him to working on Mojo at Modular? We cover these and many more in today's episode. Watch or listen: • YouTube: • Spotify: • Apple: Brought to you by: •⁠ Statsig ⁠ — ⁠ The unified platform for flags, analytics, experiments, and more. •⁠ Linear – The system for modern product development. My favorite quote from Chris in this episode: “I believe in the power of programmers. I believe in the human potential of people that want to create things. And that’s fundamentally why I love software is that you can create anything that you can imagine.”

Gergely Orosz

206,271 Aufrufe • vor 7 Monaten

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.
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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

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How have servers and the cloud evolved in the last 30 years, and what might be next? Bryan Cantrill (Bryan Cantrill) has been at the thick of the industry since the Dotcom Boom and Bust, and shares fascinating stories from both this time; on how AWS was born; why Kubernetes was key for GCP to gain ground, and why they're building a completely custom server rack computer at Oxide Computer Company. Bryan is one of my all-time favorite people to talk with - don't miss this one. Teaser - he talked about how Jeff Bezos tricked almost everyone in tech to believe that the cloud was a terrible business, and how Oracle (yes, Oracle!) taught him something extremely valuable he uses to this date: • YouTube: • Spotify: • Apple: Thanks to our wonderful season sponsors: •⁠ Statsig ⁠ — ⁠ The unified platform for flags, analytics, experiments, and more. Check it out: •⁠ Linear ⁠ — ⁠ The system for modern product development. They now support AI agents and you can also build your own - see how:

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Gergely Orosz

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What if we're actually in the middle of the third golden age of software engineering? This is what Grady Booch sees happening. If you are anxious about the state of the industry, you want to watch/listen to Grady's longer-term perspective and stories. Watch the full episode here: 00:00 Intro 01:58 The first golden age of software engineering 18:59 The software crisis 33:01 The second golden age of software engineering 42:21 Y2K and the Dotcom crash 45:47 Early AI 47:34 The third golden age of software engineering 51:48 Why software engineers will very much be needed 58:46 Grady responds to Dario Amodei 1:06:54 New skills engineers will need to succeed 1:10:04 Resources for studying complex systems 1:14:33 How to thrive during periods of change 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. Join me online at the Sonar Summit on March 3rd, where I talk about practical tactics for the AI era. • WorkOS – Everything you need to make your app enterprise-ready.
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What if we're actually in the middle of the third golden age of software engineering? This is what Grady Booch sees happening. If you are anxious about the state of the industry, you want to watch/listen to Grady's longer-term perspective and stories. Watch the full episode here: 00:00 Intro 01:58 The first golden age of software engineering 18:59 The software crisis 33:01 The second golden age of software engineering 42:21 Y2K and the Dotcom crash 45:47 Early AI 47:34 The third golden age of software engineering 51:48 Why software engineers will very much be needed 58:46 Grady responds to Dario Amodei 1:06:54 New skills engineers will need to succeed 1:10:04 Resources for studying complex systems 1:14:33 How to thrive during periods of change 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. Join me online at the Sonar Summit on March 3rd, where I talk about practical tactics for the AI era. • WorkOS – Everything you need to make your app enterprise-ready.

Gergely Orosz

86,896 Aufrufe • vor 4 Monaten

Amazon AI engineer just showed how to take Claude apps from prototype to production on AWS. 19-minutes. Free. By the Amazon AWS team. Claude Code → Bedrock → fully deployed, scalable, secure. Worth more than any $500 vibe-coding AI course.
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Amazon AI engineer just showed how to take Claude apps from prototype to production on AWS. 19-minutes. Free. By the Amazon AWS team. Claude Code → Bedrock → fully deployed, scalable, secure. Worth more than any $500 vibe-coding AI course.

Movez

25,498 Aufrufe • vor 1 Monat

Why did Meta build its own internal developer tooling instead of using industry-standard solutions like GitHub? And what do their systems "Sandcastle," "Landcastle", "Butterflybot" and "Phabricator" do? 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. Watch or listen: • YouTube: • Spotify: • Apple: Brought to you by our wonderful sponsors: • Swarmia — The engineering intelligence platform for modern software organizations. Start a free trial at • Sentry — Error and performance monitoring for developers. Get 3 months of the Team Plan for free at --- Three of my biggest takeaways from our conversation: 𝟭. “𝗦𝘁𝗮𝗰𝗸𝗲𝗱 𝗱𝗶𝗳𝗳𝘀” 𝗺𝗮𝗸𝗲𝘀 𝗮 𝗹𝗼𝘁 𝗼𝗳 𝘀𝗲𝗻𝘀𝗲 𝗶𝗻𝘀𝗶𝗱𝗲 𝗰𝗼𝗺𝗽𝗮𝗻𝗶𝗲𝘀. However, it makes less sense when working on, e.g., open source projects. Perhaps this is a reason that GitHub has not added support for this workflow — even though it’s popular inside companies like Meta or Uber. We previously did a deepdive on Stacked Diffs (and why you should know about them). 𝟮. 𝗧𝗵𝗲𝗿𝗲 𝗰𝗼𝘂𝗹𝗱 𝗯𝗲 𝗮𝗻 𝗶𝗻𝗱𝘂𝘀𝘁𝗿𝘆-𝘄𝗶𝗱𝗲 𝗺𝗼𝘃𝗲𝗺𝗲𝗻𝘁 𝘁𝗼 𝗺𝗼𝗻𝗼𝗿𝗲𝗽𝗼𝘀: at mid-sized and larger scaleups and tech companies. Tomas sees a lot of scaleups they work with the move from polyrepos (several repositories) to monorepos. Moving to a monorepo is still a lot of work and requires custom tooling: and this is why it was limited to the largest tech companies in the past. Interesting to hear about this change! 𝟯. 𝗔𝗜 𝗰𝗼𝗱𝗶𝗻𝗴 𝘁𝗼𝗼𝗹𝘀 𝗶𝗻𝗰𝗿𝗲𝗮𝘀𝗲 𝘁𝗵𝗲 𝗶𝗺𝗽𝗼𝗿𝘁𝗮𝗻𝗰𝗲 𝗼𝗳 𝗾𝘂𝗮𝗹𝗶𝘁𝘆 𝗰𝗼𝗱𝗲 𝗿𝗲𝘃𝗶𝗲𝘄𝘀. We’ll see more code churned out by AI: but an engineer needs to review it before it goes out. It’s a good question how we can stick to thorough code reviews when it is so tempting to just say, “Looks good to me (LGTM)” and have the seemingly correct code merged.
1:13

Why did Meta build its own internal developer tooling instead of using industry-standard solutions like GitHub? And what do their systems "Sandcastle," "Landcastle", "Butterflybot" and "Phabricator" do? 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. Watch or listen: • YouTube: • Spotify: • Apple: Brought to you by our wonderful sponsors: • Swarmia — The engineering intelligence platform for modern software organizations. Start a free trial at • Sentry — Error and performance monitoring for developers. Get 3 months of the Team Plan for free at --- Three of my biggest takeaways from our conversation: 𝟭. “𝗦𝘁𝗮𝗰𝗸𝗲𝗱 𝗱𝗶𝗳𝗳𝘀” 𝗺𝗮𝗸𝗲𝘀 𝗮 𝗹𝗼𝘁 𝗼𝗳 𝘀𝗲𝗻𝘀𝗲 𝗶𝗻𝘀𝗶𝗱𝗲 𝗰𝗼𝗺𝗽𝗮𝗻𝗶𝗲𝘀. However, it makes less sense when working on, e.g., open source projects. Perhaps this is a reason that GitHub has not added support for this workflow — even though it’s popular inside companies like Meta or Uber. We previously did a deepdive on Stacked Diffs (and why you should know about them). 𝟮. 𝗧𝗵𝗲𝗿𝗲 𝗰𝗼𝘂𝗹𝗱 𝗯𝗲 𝗮𝗻 𝗶𝗻𝗱𝘂𝘀𝘁𝗿𝘆-𝘄𝗶𝗱𝗲 𝗺𝗼𝘃𝗲𝗺𝗲𝗻𝘁 𝘁𝗼 𝗺𝗼𝗻𝗼𝗿𝗲𝗽𝗼𝘀: at mid-sized and larger scaleups and tech companies. Tomas sees a lot of scaleups they work with the move from polyrepos (several repositories) to monorepos. Moving to a monorepo is still a lot of work and requires custom tooling: and this is why it was limited to the largest tech companies in the past. Interesting to hear about this change! 𝟯. 𝗔𝗜 𝗰𝗼𝗱𝗶𝗻𝗴 𝘁𝗼𝗼𝗹𝘀 𝗶𝗻𝗰𝗿𝗲𝗮𝘀𝗲 𝘁𝗵𝗲 𝗶𝗺𝗽𝗼𝗿𝘁𝗮𝗻𝗰𝗲 𝗼𝗳 𝗾𝘂𝗮𝗹𝗶𝘁𝘆 𝗰𝗼𝗱𝗲 𝗿𝗲𝘃𝗶𝗲𝘄𝘀. We’ll see more code churned out by AI: but an engineer needs to review it before it goes out. It’s a good question how we can stick to thorough code reviews when it is so tempting to just say, “Looks good to me (LGTM)” and have the seemingly correct code merged.

Gergely Orosz

37,722 Aufrufe • vor 1 Jahr

Today on the podcast we're joined by Novak Djokovic to talk about the secret to turning your self-doubt into huge succes and more. Watch or listen today👇 Spotify: Apple: YouTube:
1:05

Today on the podcast we're joined by Novak Djokovic to talk about the secret to turning your self-doubt into huge succes and more. Watch or listen today👇 Spotify: Apple: YouTube:

Jay Shetty

39,112 Aufrufe • vor 10 Monaten

The future of AI is being built now. Introducing new updates to the Windows developer platform that will make it the best for this new era of AI, one where intelligence is deeply integrated across software, silicon & hardware. Read more:
1:28

The future of AI is being built now. Introducing new updates to the Windows developer platform that will make it the best for this new era of AI, one where intelligence is deeply integrated across software, silicon & hardware. Read more:

Windows Developer

24,874 Aufrufe • vor 1 Jahr

Boris Cherny ( Boris Cherny ) created Claude Code, but few know his full career story. Today I'm sharing an interview with him about how he grew as an engineer, we discussed: • Why every engineer needs "side quests" • Why being under leveled is a good thing • The story behind his growth to Principal (IC8) at Meta • Technical book that had the biggest impact on him as an engineer • The most important principle in product engineering • Claude Code stories & competition in AI coding products You can find the full episode here: • YouTube: • Spotify: • Transcript: • Apple:
0:59

Boris Cherny ( Boris Cherny ) created Claude Code, but few know his full career story. Today I'm sharing an interview with him about how he grew as an engineer, we discussed: • Why every engineer needs "side quests" • Why being under leveled is a good thing • The story behind his growth to Principal (IC8) at Meta • Technical book that had the biggest impact on him as an engineer • The most important principle in product engineering • Claude Code stories & competition in AI coding products You can find the full episode here: • YouTube: • Spotify: • Transcript: • Apple:

Ryan Peterman

577,196 Aufrufe • vor 6 Monaten

🚨ZUCK: "Probably in 2025, Meta, as well as other companies, will have an AI that can effectively be a mid-level engineer that you have at your company that can write code. In the beginning that will be very expensive to run, then at some point a lot of the code we have in our apps will be built by AI engineers instead of people engineers. In my opinion that will augment the people working on it. My view is that in the future people will be so much more creative and freed up to do crazy things."
1:02

🚨ZUCK: "Probably in 2025, Meta, as well as other companies, will have an AI that can effectively be a mid-level engineer that you have at your company that can write code. In the beginning that will be very expensive to run, then at some point a lot of the code we have in our apps will be built by AI engineers instead of people engineers. In my opinion that will augment the people working on it. My view is that in the future people will be so much more creative and freed up to do crazy things."

Autism Capital 🧩

1,666,456 Aufrufe • vor 1 Jahr

Some personal hot takes from AI: engineer Miami follows... 1. Software development is a dead-end profession because anyone can be a software developer now. 2. Anyone can use Cursor or any other tool and generate code. Being a coder and being a software engineer are different. 3. Computers used to be gated; now everyone has the power to make computers malleable. Everyone is a software developer now, but that does not mean they are software engineers 4. If you cannot demonstrate how a coding agent works, you are just a consumer and have imposed an artificial glass ceiling on your career as a software engineer. 5. If you are curious, you will have a job. If you have not been curious in the last two years, you are replaceable. 6. SaaS per-seat economics may become unstable as customers need fewer people to achieve results, prompting founders to think about new unit economics 7. Most companies will take two or three years (or more!) to figure out AI transformation. 8. Some companies are already building AI native teams of five to ten people who can build with the grain of AI 9. There will be an explosion in the number of software developers. Software development is now essentially free, and tokens are cheaper than humans 10. Not enough engineers know what it means to be a product engineer 11. JIRA ticket monkeys are cooked 12. If your company has banned AI, you should quit that company 13. AI is more like a musical instrument than just a tool play with it, make discoveries, build intuition learn where AI is good and where it fails
14:55

Some personal hot takes from AI: engineer Miami follows... 1. Software development is a dead-end profession because anyone can be a software developer now. 2. Anyone can use Cursor or any other tool and generate code. Being a coder and being a software engineer are different. 3. Computers used to be gated; now everyone has the power to make computers malleable. Everyone is a software developer now, but that does not mean they are software engineers 4. If you cannot demonstrate how a coding agent works, you are just a consumer and have imposed an artificial glass ceiling on your career as a software engineer. 5. If you are curious, you will have a job. If you have not been curious in the last two years, you are replaceable. 6. SaaS per-seat economics may become unstable as customers need fewer people to achieve results, prompting founders to think about new unit economics 7. Most companies will take two or three years (or more!) to figure out AI transformation. 8. Some companies are already building AI native teams of five to ten people who can build with the grain of AI 9. There will be an explosion in the number of software developers. Software development is now essentially free, and tokens are cheaper than humans 10. Not enough engineers know what it means to be a product engineer 11. JIRA ticket monkeys are cooked 12. If your company has banned AI, you should quit that company 13. AI is more like a musical instrument than just a tool play with it, make discoveries, build intuition learn where AI is good and where it fails

geoff @ ai:worldfair

49,554 Aufrufe • vor 7 Tagen

Great to have Graphite as a sponsor of the podcast: Graphite is the developer productivity platform that brings stacked pull requests (the workflow best-in-class companies like Meta, Google & Uber already use) to every software company at GitHub. It's awesome:
0:59

Great to have Graphite as a sponsor of the podcast: Graphite is the developer productivity platform that brings stacked pull requests (the workflow best-in-class companies like Meta, Google & Uber already use) to every software company at GitHub. It's awesome:

Gergely Orosz

20,919 Aufrufe • vor 1 Jahr

Why is observability so hard to do well - and so expensive, in general? What is "Observability 2.0" and is Open Telemetry any good? In today's episode of The Pragmatic Engineer Podcast, we answer all of these with Charity Majors , co-author of the O'Reilly book "Observability Engineering," former engineer at Parse/Facebook, and cofounder and CTO at Honeycomb Watch it here: • YouTube: • Apple: • Spotify: • Summary and transcript: Brought to you by our wonderful sponsors - check out their offerings: • Sonar — Trust your developers – verify your AI-generated code. • Vanta — Automate compliance and simplify security with Vanta ---- Topics we cover in this episode: • 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! --- My biggest takeaways: 1. The DevOps movement feels like it’s in its final days, having served its purpose. 2. Lots of people get dashboards wrong! Charity doesn’t think that static dashboards are helpful to engineering teams at all. In fact, misusing dashboards is one of the most common observability practices. 3. Observability will be especially important for AI use cases in these ways: a) o11y for LLMs: to get data on how they behave and to be able to debug behaviors. This is relevant for teams building and operating AI models. b) o11y for code generated by AI: the generated code should have the right amount of observability in place. Once the code is deployed to production, developers need to be able to get a sense of how the code is behaving there!
0:46

Why is observability so hard to do well - and so expensive, in general? What is "Observability 2.0" and is Open Telemetry any good? In today's episode of The Pragmatic Engineer Podcast, we answer all of these with Charity Majors , co-author of the O'Reilly book "Observability Engineering," former engineer at Parse/Facebook, and cofounder and CTO at Honeycomb Watch it here: • YouTube: • Apple: • Spotify: • Summary and transcript: Brought to you by our wonderful sponsors - check out their offerings: • Sonar — Trust your developers – verify your AI-generated code. • Vanta — Automate compliance and simplify security with Vanta ---- Topics we cover in this episode: • 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! --- My biggest takeaways: 1. The DevOps movement feels like it’s in its final days, having served its purpose. 2. Lots of people get dashboards wrong! Charity doesn’t think that static dashboards are helpful to engineering teams at all. In fact, misusing dashboards is one of the most common observability practices. 3. Observability will be especially important for AI use cases in these ways: a) o11y for LLMs: to get data on how they behave and to be able to debug behaviors. This is relevant for teams building and operating AI models. b) o11y for code generated by AI: the generated code should have the right amount of observability in place. Once the code is deployed to production, developers need to be able to get a sense of how the code is behaving there!

Gergely Orosz

25,485 Aufrufe • vor 1 Jahr