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If you’ve ever opened Chrome DevTools, or optimized a page for Core Web Vitals, you’ve used software built by Addy Osmani. Timestamps: 00:00 Intro 02:50 Addy’s current workflow 05:11 Addy’s path into tech 15:04 Addy’s work on jQuery 16:44 TodoMVC 21:44 Getting hired at Google and working on Chrome...

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Gergely Orosz

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396,505 просмотров • 12 дней назад •via X (Twitter)

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Kelsey Hightower has one of the most inspiring stories in tech: he went from a technician installing DSL modems, through self-directed study and very hard work, to one of the very few Distinguished Engineer at Google whom Satya Nadella personally persuaded to join Microsoft. Timestamps: 00:00 Intro 03:34 Kelsey’s first job at McDonald’s 05:04 His non-traditional path into tech 11:45 Landing his first tech job with an A+ certification 15:33 His entrepreneurial years 19:45 Joining Google as a data center technician 27:48 Learning automation at a Rackspace spinoff 33:26 Moving into financial services 50:00 Building a reputation through open source 53:55 From configuration management to containers 1:08:20 The rise of Kubernetes 1:25:05 Why he almost joined NASA instead of Google 1:29:20 Defining DevRel at Google 1:38:20 Demonstrating impact at Google 1:41:20 Microsoft's offer 1:55:20 Learning how to slow down 2:06:39 Advising and investing 2:15:03 A people-first view of GenAI 2:24:27 Using AI with guardrails 2:28:26 Matching AI to the task 2:36:06 Staying relevant in the AI era Brought to you by outstanding teams building products I love: • Antithesis: verify your system’s correctness without human review or traditional integration tests – and avoid bugs or outages. • Sentry: application monitoring software considered “not bad” by millions of developers • Buildkite: CI software built to absorb whatever your coding agents throw at the build queue. OpenAI, Anthropic, Uber and others are customers: Three interesting learnings from Kelsey: 1. Side hustles and doing your own thing teach you business like no IC job can. Before becoming a software engineer at Google, Kelsey was a manager for his comedian friend, operated a computer store, and did IT contracting. These gigs taught him logistics, planning, and about money. All this helped him be far more effective at talking with executives and acting as an executive sponsor inside Google. 2. Can you explain what your startup does without mentioning AI? When Kelsey researches startups seeking his advice, he challenges founders to not say “AI” once. This means that they must explain the actual value their company creates. One unexpected benefit of this is that it often reveals there are easier, cheaper ways to achieve a goal than with AI. 3. It’s very rare to get an extra zero put on your compensation figure – but it happened. Kelsey was a successful, well-paid Google engineer when Microsoft made him an offer that 10x’d his salary (!!). When Kelsey told Google he was planning to take the offer, it matched the offer, proving that his market value had massively increased. It shows that being well paid doesn’t necessarily mean you’re being paid at the correct market rate.
2:52:10

Kelsey Hightower has one of the most inspiring stories in tech: he went from a technician installing DSL modems, through self-directed study and very hard work, to one of the very few Distinguished Engineer at Google whom Satya Nadella personally persuaded to join Microsoft. Timestamps: 00:00 Intro 03:34 Kelsey’s first job at McDonald’s 05:04 His non-traditional path into tech 11:45 Landing his first tech job with an A+ certification 15:33 His entrepreneurial years 19:45 Joining Google as a data center technician 27:48 Learning automation at a Rackspace spinoff 33:26 Moving into financial services 50:00 Building a reputation through open source 53:55 From configuration management to containers 1:08:20 The rise of Kubernetes 1:25:05 Why he almost joined NASA instead of Google 1:29:20 Defining DevRel at Google 1:38:20 Demonstrating impact at Google 1:41:20 Microsoft's offer 1:55:20 Learning how to slow down 2:06:39 Advising and investing 2:15:03 A people-first view of GenAI 2:24:27 Using AI with guardrails 2:28:26 Matching AI to the task 2:36:06 Staying relevant in the AI era Brought to you by outstanding teams building products I love: • Antithesis: verify your system’s correctness without human review or traditional integration tests – and avoid bugs or outages. • Sentry: application monitoring software considered “not bad” by millions of developers • Buildkite: CI software built to absorb whatever your coding agents throw at the build queue. OpenAI, Anthropic, Uber and others are customers: Three interesting learnings from Kelsey: 1. Side hustles and doing your own thing teach you business like no IC job can. Before becoming a software engineer at Google, Kelsey was a manager for his comedian friend, operated a computer store, and did IT contracting. These gigs taught him logistics, planning, and about money. All this helped him be far more effective at talking with executives and acting as an executive sponsor inside Google. 2. Can you explain what your startup does without mentioning AI? When Kelsey researches startups seeking his advice, he challenges founders to not say “AI” once. This means that they must explain the actual value their company creates. One unexpected benefit of this is that it often reveals there are easier, cheaper ways to achieve a goal than with AI. 3. It’s very rare to get an extra zero put on your compensation figure – but it happened. Kelsey was a successful, well-paid Google engineer when Microsoft made him an offer that 10x’d his salary (!!). When Kelsey told Google he was planning to take the offer, it matched the offer, proving that his market value had massively increased. It shows that being well paid doesn’t necessarily mean you’re being paid at the correct market rate.

Gergely Orosz

61,547 просмотров • 2 месяцев назад

Knowing how LLM contexts work and how to work around context limitations – aka “context engineering” – is becoming so important. No better person to explain than dex Timestamps: 00:00 Intro 01:33 Dex’s path into tech 03:34 Early work in platform engineering 05:28 Replicated 11:24 Metalytics 12:36 12-factor agents 18:27 Context engineering 23:38 Harness engineering 26:11 Context overload 30:45 Loop engineering 44:34 Software factories before and after AI 50:33 Automation limits 55:18 Three options for automating 59:00 RPI framework 1:04:16 Intentional compaction 1:11:48 Token harder vs. token smarter 1:16:44 AI slop 1:19:15 HumanLayer 1:29:09 Book recommendation Brought to you by: • Antithesis — with Antithesis, you can use AI agents to work on critical systems without worrying about correctness. Teams like Jane Street, and the etcd community use Antithesis to ship better code, faster. • Buildkite — the CI orchestration platform built for reliable scale. Used by OpenAI, Anthropic, Cursor, Meta, Uber, Ramp, Nvidia, Airbnb and many more. • Sentry — application monitoring software built by developers, for developers. Check out their AI agent, Seer AI, and Sentry MCP. Three interesting learnings from this episode: 1. Lesson learned: Shipping unread code spells disaster within months. Dex experimented with having the model write the code and humans not reviewing anything in July 2025. Four months later, they shut things down and threw the whole system out. Production broke, and no matter how much the team prompted Opus 4.1, the model could not find the root cause. Once fixed, it took three weeks (!!) to re-onboard to a codebase no human had ever read 2. Context engineering 101: figure out where the “dumb zone” begins. As a rule of thumb, the less of the context window that is used, the better the outcomes are. This is because the attention mechanism is quadratic: the more that goes into the context window, the more compute is required to process it all. 3. “You’re completely right!” or “you’re right to push back on that” are phrases that mean it’s time to start a new session. These responses mean the LLM session is trajectory-poisoned, and you’re wasting time and tokens to continue. This is because models are autoregressive.
1:33:10

Knowing how LLM contexts work and how to work around context limitations – aka “context engineering” – is becoming so important. No better person to explain than dex Timestamps: 00:00 Intro 01:33 Dex’s path into tech 03:34 Early work in platform engineering 05:28 Replicated 11:24 Metalytics 12:36 12-factor agents 18:27 Context engineering 23:38 Harness engineering 26:11 Context overload 30:45 Loop engineering 44:34 Software factories before and after AI 50:33 Automation limits 55:18 Three options for automating 59:00 RPI framework 1:04:16 Intentional compaction 1:11:48 Token harder vs. token smarter 1:16:44 AI slop 1:19:15 HumanLayer 1:29:09 Book recommendation Brought to you by: • Antithesis — with Antithesis, you can use AI agents to work on critical systems without worrying about correctness. Teams like Jane Street, and the etcd community use Antithesis to ship better code, faster. • Buildkite — the CI orchestration platform built for reliable scale. Used by OpenAI, Anthropic, Cursor, Meta, Uber, Ramp, Nvidia, Airbnb and many more. • Sentry — application monitoring software built by developers, for developers. Check out their AI agent, Seer AI, and Sentry MCP. Three interesting learnings from this episode: 1. Lesson learned: Shipping unread code spells disaster within months. Dex experimented with having the model write the code and humans not reviewing anything in July 2025. Four months later, they shut things down and threw the whole system out. Production broke, and no matter how much the team prompted Opus 4.1, the model could not find the root cause. Once fixed, it took three weeks (!!) to re-onboard to a codebase no human had ever read 2. Context engineering 101: figure out where the “dumb zone” begins. As a rule of thumb, the less of the context window that is used, the better the outcomes are. This is because the attention mechanism is quadratic: the more that goes into the context window, the more compute is required to process it all. 3. “You’re completely right!” or “you’re right to push back on that” are phrases that mean it’s time to start a new session. These responses mean the LLM session is trajectory-poisoned, and you’re wasting time and tokens to continue. This is because models are autoregressive.

Gergely Orosz

63,174 просмотров • 1 месяц назад

What is it like to be a founding engineer at a startup, and how do you become one? Michelle Lim joined Warp as the founding engineer, and she's now a founder at Flint AI where she is hiring founding engineers. We talk through her journey and tactics helpful for anyone wanting to make an impact at a startup. 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. I really liked how Michelle was realistic how, as a founding engineer, you have two jobs at once: #1: Software engineering. Build the stuff you need to do #2: Everything else to help the business succeed, including lots of things that are not to do with software engineering! Do all of these once you did job #1 well. In return, you get to learn a lot on how a business is built - which will help you if and when you become a founder yourself! Thanks for the great convo, Michelle, and keep it up!
0:58

What is it like to be a founding engineer at a startup, and how do you become one? Michelle Lim joined Warp as the founding engineer, and she's now a founder at Flint AI where she is hiring founding engineers. We talk through her journey and tactics helpful for anyone wanting to make an impact at a startup. 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. I really liked how Michelle was realistic how, as a founding engineer, you have two jobs at once: #1: Software engineering. Build the stuff you need to do #2: Everything else to help the business succeed, including lots of things that are not to do with software engineering! Do all of these once you did job #1 well. In return, you get to learn a lot on how a business is built - which will help you if and when you become a founder yourself! Thanks for the great convo, Michelle, and keep it up!

Gergely Orosz

17,126 просмотров • 9 месяцев назад

Few people care more about software performance than Casey Muratori. Give him a few minutes of your attention with this episode, and he'll convince you to learn to read Assembly (no, really, I finally started to read it, it's really not that scary, esp with an AI that can help explain the sequences). Timestamps: 00:00 Intro 05:17 Games at Microsoft 12:52 Building games 16:00 Why performance matters 27:12 Why you should learn to read assembly 30:36 Designing for optimization 42:51 How to get better at writing performant software 49:04 Understanding how the CPU works 55:53 Building games then and now 1:05:56 How game engines changed building games 1:10:48 Why new games compete with old games 1:13:25 GTA 6: why is it taking so long? 1:16:59 Casey's critique of clean code 1:21:48 Casey's take on TDD 1:24:30 What is good code? 1:27:32 What makes a good software engineer? 1:33:56 Why Casey doesn't code with AI 1:39:01 AI's impact on the game industry 1:44:43 AI and burnout 1:50:21 Why you should read papers Brought to you by: • Antithesis – verify your system’s correctness without human review or traditional integration tests – and avoid bugs or outages • Sentry – application monitoring software considered “not bad” by millions of developers • turbopuffer – a vector and full-text search engine built on object storage. It’s fast, cheap, and extremely scalable Three interesting things we talked about: 1. Is performance starting to matter to businesses? Enterprise software buyers care mainly about cost, compliance, and capabilities – but not performance. Even so, there are some products gaining major popularity and market share due to their performance, such as File Pilot (next-gen file explorer) and the Blick video editor. Is the tide turning? 2. Profiler-driven performance optimization is the wrong way to optimize The standard way of optimizing is to profile the application, tweak hotspots, then check if the stats have improved. But this only finds a local minimum; Casey says every engineer he’s worked with who was a great “optimizer” began by establishing what the hardware could theoretically do, and then did not stop until they’d closed the gap to that performance level. 3. Take a grain of salt with conventional wisdom that premature optimization is the “root of all evil” Many devs use it as an excuse to delay performance optimization, but Casey says that not optimizing in time could mean that only performance hotspots can be fixed later, and not the architectural issues that create poor performance. Architect your system to be performant, or you’ll have trouble solving problems without a rewrite!
1:53:58

Few people care more about software performance than Casey Muratori. Give him a few minutes of your attention with this episode, and he'll convince you to learn to read Assembly (no, really, I finally started to read it, it's really not that scary, esp with an AI that can help explain the sequences). Timestamps: 00:00 Intro 05:17 Games at Microsoft 12:52 Building games 16:00 Why performance matters 27:12 Why you should learn to read assembly 30:36 Designing for optimization 42:51 How to get better at writing performant software 49:04 Understanding how the CPU works 55:53 Building games then and now 1:05:56 How game engines changed building games 1:10:48 Why new games compete with old games 1:13:25 GTA 6: why is it taking so long? 1:16:59 Casey's critique of clean code 1:21:48 Casey's take on TDD 1:24:30 What is good code? 1:27:32 What makes a good software engineer? 1:33:56 Why Casey doesn't code with AI 1:39:01 AI's impact on the game industry 1:44:43 AI and burnout 1:50:21 Why you should read papers Brought to you by: • Antithesis – verify your system’s correctness without human review or traditional integration tests – and avoid bugs or outages • Sentry – application monitoring software considered “not bad” by millions of developers • turbopuffer – a vector and full-text search engine built on object storage. It’s fast, cheap, and extremely scalable Three interesting things we talked about: 1. Is performance starting to matter to businesses? Enterprise software buyers care mainly about cost, compliance, and capabilities – but not performance. Even so, there are some products gaining major popularity and market share due to their performance, such as File Pilot (next-gen file explorer) and the Blick video editor. Is the tide turning? 2. Profiler-driven performance optimization is the wrong way to optimize The standard way of optimizing is to profile the application, tweak hotspots, then check if the stats have improved. But this only finds a local minimum; Casey says every engineer he’s worked with who was a great “optimizer” began by establishing what the hardware could theoretically do, and then did not stop until they’d closed the gap to that performance level. 3. Take a grain of salt with conventional wisdom that premature optimization is the “root of all evil” Many devs use it as an excuse to delay performance optimization, but Casey says that not optimizing in time could mean that only performance hotspots can be fixed later, and not the architectural issues that create poor performance. Architect your system to be performant, or you’ll have trouble solving problems without a rewrite!

Gergely Orosz

95,079 просмотров • 5 дней назад

There are few people who have impacted the software engineering industry like Kent Beck 🌻 has. He'd never before told his career story from start to today in one sitting - until now. What a treat. Timestamps: 00:00 Intro 03:47 Human engineers aren’t going away 08:00 Kent's path into tech 13:50 Undergraduate and graduate studies 17:21 Kent’s first programming job 18:54 The rise and fall of Smalltalk 27:04 Working with Ward Cunningham 37:36 Design patterns 44:05 Working at Apple 51:08 CRC Cards 59:29 Testing tools in the language 1:04:22 The C3 project with Martin Fowler 1:09:54 Extreme Programming 1:16:25 Developing TDD 1:25:07 Writing the Agile Manifesto 1:30:00 Agile’s impact 1:32:40 Agile’s downside 1:37:32 The Dotcom Bust 1:44:30 Lessons from working at Facebook 1:59:44 Kent’s ‘Good to Great’ program at Facebook 2:06:07 Soft skills engineers need to learn 2:09:30 AI and the challenges of acceleration 2:15:53 Explore, expand, extract 2:22:33 What Kent is excited about Brought to you by: • Antithesis – verify your system’s correctness without human review or traditional integration tests – and avoid bugs or outages • turbopuffer – a vector and full-text search engine built on object storage. It’s fast, cheap, and extremely scalable • WorkOS – everything you need to make your app enterprise ready Kent shared so many previously untold stories - like how he was fired from Apple (!!), how he and Ward Cunningham used a thesaurus to find the right words, how the Agile Manifesto came together. My favorite reflection from is this though: The human part is the most important one in software engineering. As Kent explained: “This is the biggest cosmic, practical joke ever. As young people, we were promised: “Okay, here’s this computer and once you’ve completely understand this computer, you’ll be fine. That’s all you need to do.” So I set out the first part of my career just to become the best programmer that I could be because that’s what it would take to be successful. And then you realize: sorry, there’s this whole human side. Your ability to affect change in the world is gated by your ability to communicate with, to soothe, to understand other human beings. And those are exactly the skills that I thought I didn’t need to learn! So I was promised: just understand the computer and you’ll be successful. And then someone went “just kidding, understand people!” And now I was in a position of being ten years behind.”
2:28:43

There are few people who have impacted the software engineering industry like Kent Beck 🌻 has. He'd never before told his career story from start to today in one sitting - until now. What a treat. Timestamps: 00:00 Intro 03:47 Human engineers aren’t going away 08:00 Kent's path into tech 13:50 Undergraduate and graduate studies 17:21 Kent’s first programming job 18:54 The rise and fall of Smalltalk 27:04 Working with Ward Cunningham 37:36 Design patterns 44:05 Working at Apple 51:08 CRC Cards 59:29 Testing tools in the language 1:04:22 The C3 project with Martin Fowler 1:09:54 Extreme Programming 1:16:25 Developing TDD 1:25:07 Writing the Agile Manifesto 1:30:00 Agile’s impact 1:32:40 Agile’s downside 1:37:32 The Dotcom Bust 1:44:30 Lessons from working at Facebook 1:59:44 Kent’s ‘Good to Great’ program at Facebook 2:06:07 Soft skills engineers need to learn 2:09:30 AI and the challenges of acceleration 2:15:53 Explore, expand, extract 2:22:33 What Kent is excited about Brought to you by: • Antithesis – verify your system’s correctness without human review or traditional integration tests – and avoid bugs or outages • turbopuffer – a vector and full-text search engine built on object storage. It’s fast, cheap, and extremely scalable • WorkOS – everything you need to make your app enterprise ready Kent shared so many previously untold stories - like how he was fired from Apple (!!), how he and Ward Cunningham used a thesaurus to find the right words, how the Agile Manifesto came together. My favorite reflection from is this though: The human part is the most important one in software engineering. As Kent explained: “This is the biggest cosmic, practical joke ever. As young people, we were promised: “Okay, here’s this computer and once you’ve completely understand this computer, you’ll be fine. That’s all you need to do.” So I set out the first part of my career just to become the best programmer that I could be because that’s what it would take to be successful. And then you realize: sorry, there’s this whole human side. Your ability to affect change in the world is gated by your ability to communicate with, to soothe, to understand other human beings. And those are exactly the skills that I thought I didn’t need to learn! So I was promised: just understand the computer and you’ll be successful. And then someone went “just kidding, understand people!” And now I was in a position of being ten years behind.”

Gergely Orosz

26,602 просмотров • 2 месяцев назад

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

70,867 просмотров • 2 месяцев назад

The three ways AI, particularly long-horizon agents, are changing the role of PMs: "First, product development is changing because the models and the capabilities are growing so fast that if you try to be very strict and stringent about describing exactly what you're going to build, its not going to work. It's very important for the PMs to be understanding of what these models are capable of and to be hands-on. The only thing PMs do now is articulate what the customer needs are at the highest level. But the actual product is built bottoms up by engineers, researchers, and product managers and designers all working together on the code itself. Second, the product manager and designer role are merging increasingly. When given the choice between an extra designer and extra engineer, they're saying, give us an extra engineer. And the other very important thing that's fundamentally different is non-deterministic software. Today, you could do X, Y happens. But if you do slight variation of X something completely different happens. That means someone has to evaluate whether or not what the software is producing is reasonable or not across various use cases. So the PM's job is to be very clear at a high level of what the user needs are, and then have a very clear sense of whether this product is good to ship or not by evaluating it."
2:58

The three ways AI, particularly long-horizon agents, are changing the role of PMs: "First, product development is changing because the models and the capabilities are growing so fast that if you try to be very strict and stringent about describing exactly what you're going to build, its not going to work. It's very important for the PMs to be understanding of what these models are capable of and to be hands-on. The only thing PMs do now is articulate what the customer needs are at the highest level. But the actual product is built bottoms up by engineers, researchers, and product managers and designers all working together on the code itself. Second, the product manager and designer role are merging increasingly. When given the choice between an extra designer and extra engineer, they're saying, give us an extra engineer. And the other very important thing that's fundamentally different is non-deterministic software. Today, you could do X, Y happens. But if you do slight variation of X something completely different happens. That means someone has to evaluate whether or not what the software is producing is reasonable or not across various use cases. So the PM's job is to be very clear at a high level of what the user needs are, and then have a very clear sense of whether this product is good to ship or not by evaluating it."

Patrick OShaughnessy

41,820 просмотров • 7 месяцев назад

In 2025, it was rational to be skeptical about whether AI would change the future of software development. In 2026, it's not, anymore. With Charity Majors: Timestamps: 00:00 Intro 02:56 How Parse led to Honeycomb 06:00 The limits of individual productivity metrics 09:08 How Charity’s perspective on AI has evolved 13:50 Rewriting code vs. editing code 19:20 Production as a stage of development 22:14 Code reviews 26:56 Non-deterministic systems 31:11 Sensible uses of AI 37:41 The two AI camps 44:40 Why AI works so well for building software 49:42 DevOps 55:13 Modern observability 1:00:40 Handling context overload 1:01:56 What’s new in Observability Engineering’s 2nd edition 1:07:45 What effective leadership looks like 1:10:25 Engineering management: what is changing? 1:16:31 Junior engineers 1:18:01 AI fatigue 1:21:39 Book recommendations Brought to you by: • Antithesis — turbocharge testing of your systems by running your whole system under aggressive fault injection. Teams like Jane Street, and the etcd community rely on Antithesis. • Buildkite — the CI platform trusted by OpenAI, Anthropic, Cursor, Meta, Uber, NVIDIA, Airbnb and many more. Engineered to absorb whatever your coding agents throw at the build queue. • WorkOS — make your app and agents Enterprise Ready, with SSO, SCIM, RBAC, and more. 1. The question engineers need to answer: what would it take for you to be fully comfortable shipping code you have not read? Charity believes it is a “when” and not an “if” that professional software engineers will ship code they never looked at – and thus do not understand – to production. Engineering is building the systems that validate this code, and allow shipping with full confidence. 2. AI could have the software industry go through the “pets” to “cattle” change that compute infra went through in the 2010s. Up to now, writing software from scratch was far more expensive than editing existing software. But now, generating hundreds of variants of a function can be done faster than how long it would take you to hand-write it once. Charity believes that we might be at the beginning of the transition from “pets” to “cattle” that happened at the hardware infrastructure layer. Before the 2010s, configuring and repairing individual servers was commonly done. But with tools like Terraform and Kubernetes, individual servers having issues are no longer fixed up: they are re-created instead. Charity thinks the same might happen with code, sooner rather than later. When there’s an issue with the code, generate new code that solves it, and is verifyably correct.​ 3. Non-deterministic systems require more engineering discipline versus before. With code written by AI, we’re reducing the trust in the code (because we no longer wrote it), so we need to increase trust at the other part of the development process. Specifically, at validation: with things like tests, evals, and conformance testing.
1:26:22

In 2025, it was rational to be skeptical about whether AI would change the future of software development. In 2026, it's not, anymore. With Charity Majors: Timestamps: 00:00 Intro 02:56 How Parse led to Honeycomb 06:00 The limits of individual productivity metrics 09:08 How Charity’s perspective on AI has evolved 13:50 Rewriting code vs. editing code 19:20 Production as a stage of development 22:14 Code reviews 26:56 Non-deterministic systems 31:11 Sensible uses of AI 37:41 The two AI camps 44:40 Why AI works so well for building software 49:42 DevOps 55:13 Modern observability 1:00:40 Handling context overload 1:01:56 What’s new in Observability Engineering’s 2nd edition 1:07:45 What effective leadership looks like 1:10:25 Engineering management: what is changing? 1:16:31 Junior engineers 1:18:01 AI fatigue 1:21:39 Book recommendations Brought to you by: • Antithesis — turbocharge testing of your systems by running your whole system under aggressive fault injection. Teams like Jane Street, and the etcd community rely on Antithesis. • Buildkite — the CI platform trusted by OpenAI, Anthropic, Cursor, Meta, Uber, NVIDIA, Airbnb and many more. Engineered to absorb whatever your coding agents throw at the build queue. • WorkOS — make your app and agents Enterprise Ready, with SSO, SCIM, RBAC, and more. 1. The question engineers need to answer: what would it take for you to be fully comfortable shipping code you have not read? Charity believes it is a “when” and not an “if” that professional software engineers will ship code they never looked at – and thus do not understand – to production. Engineering is building the systems that validate this code, and allow shipping with full confidence. 2. AI could have the software industry go through the “pets” to “cattle” change that compute infra went through in the 2010s. Up to now, writing software from scratch was far more expensive than editing existing software. But now, generating hundreds of variants of a function can be done faster than how long it would take you to hand-write it once. Charity believes that we might be at the beginning of the transition from “pets” to “cattle” that happened at the hardware infrastructure layer. Before the 2010s, configuring and repairing individual servers was commonly done. But with tools like Terraform and Kubernetes, individual servers having issues are no longer fixed up: they are re-created instead. Charity thinks the same might happen with code, sooner rather than later. When there’s an issue with the code, generate new code that solves it, and is verifyably correct.​ 3. Non-deterministic systems require more engineering discipline versus before. With code written by AI, we’re reducing the trust in the code (because we no longer wrote it), so we need to increase trust at the other part of the development process. Specifically, at validation: with things like tests, evals, and conformance testing.

Gergely Orosz

30,495 просмотров • 18 дней назад

Why is the creator of OpenCode pretty skeptical about AI productivity gains, and the hype around AI? A very conversation dax (and lots of truth bombs:) Timestamps: 00:00 Intro 07:03 Dax’s path into tech 09:04 Early startup experience 13:16 Getting involved with open source 16:13 OpenCode 23:17 Anthropic banning OpenCode 30:34 From terminal to GUI 32:34 OpenCode’s business model 36:33 Why inference is profitable 39:11 GPU bottlenecks 40:54 AI hype 45:50 AI spending 48:47 Dax’s memo 55:41 Dax’s skepticism of predictions 58:58 Engineering culture at OpenCode 1:02:38 How building works at OpenCode 1:05:36 Taste and quality 1:11:32 Dax’s work setup 1:12:35 The role of engineers and EMs 1:15:50 Advice for engineers 1:18:12 Book recommendation Brought to you by: • Antithesis – verify your system’s correctness without human review or traditional integration tests – and avoid bugs or outages • WorkOS – everything you need to make your app enterprise ready • turbopuffer – a vector and full-text search engine built on object storage. It’s fast, cheap, and extremely scalable Three interesting thoughts from Dax: 1. No AI-native coding agent company is “winning” by being better with AI. Dax says that none of OpenCode’s competitors are crushing them, and that nobody is using AI so well that others cannot compete. 2. Most software engineers profit from AI as time gained, not increased output — unless you change incentives! Dax says the natural way for software engineers to “cash out” their AI tooling gains is with time savings, by doing the same work as before, but faster. Until compensation and motivation structures change, most teams should expect output to stay flat while engineers go home earlier. There’s nothing wrong with this, but AI vendors sell a different outcome to CFOs: increased output. 3. AI code generation mutes the “guilt” of doing the wrong thing, but this builds up tech debt. Pre-AI, writing a hack felt bad, the second time it felt really bad, and by the third time you’d often just refactor in order to fix up the code. Now, the agent hides the hack, which skews devs’ judgment and results in less tech debt being cleaned up.
1:21:01

Why is the creator of OpenCode pretty skeptical about AI productivity gains, and the hype around AI? A very conversation dax (and lots of truth bombs:) Timestamps: 00:00 Intro 07:03 Dax’s path into tech 09:04 Early startup experience 13:16 Getting involved with open source 16:13 OpenCode 23:17 Anthropic banning OpenCode 30:34 From terminal to GUI 32:34 OpenCode’s business model 36:33 Why inference is profitable 39:11 GPU bottlenecks 40:54 AI hype 45:50 AI spending 48:47 Dax’s memo 55:41 Dax’s skepticism of predictions 58:58 Engineering culture at OpenCode 1:02:38 How building works at OpenCode 1:05:36 Taste and quality 1:11:32 Dax’s work setup 1:12:35 The role of engineers and EMs 1:15:50 Advice for engineers 1:18:12 Book recommendation Brought to you by: • Antithesis – verify your system’s correctness without human review or traditional integration tests – and avoid bugs or outages • WorkOS – everything you need to make your app enterprise ready • turbopuffer – a vector and full-text search engine built on object storage. It’s fast, cheap, and extremely scalable Three interesting thoughts from Dax: 1. No AI-native coding agent company is “winning” by being better with AI. Dax says that none of OpenCode’s competitors are crushing them, and that nobody is using AI so well that others cannot compete. 2. Most software engineers profit from AI as time gained, not increased output — unless you change incentives! Dax says the natural way for software engineers to “cash out” their AI tooling gains is with time savings, by doing the same work as before, but faster. Until compensation and motivation structures change, most teams should expect output to stay flat while engineers go home earlier. There’s nothing wrong with this, but AI vendors sell a different outcome to CFOs: increased output. 3. AI code generation mutes the “guilt” of doing the wrong thing, but this builds up tech debt. Pre-AI, writing a hack felt bad, the second time it felt really bad, and by the third time you’d often just refactor in order to fix up the code. Now, the agent hides the hack, which skews devs’ judgment and results in less tech debt being cleaned up.

Gergely Orosz

231,584 просмотров • 3 месяцев назад

Is Traditional Software Engineering Dead? “Does this mean that traditional software engineering is dead? Absolutely not. Software engineers—even the ones who are not necessarily tuning or training AI models—these are now among the most leveraged people on earth. Sure, the guys who are training and tuning models are even more leveraged because they’re building the tool set that software engineers are using. But software engineers still have two massive advantages on you. First, they think in code, so they actually know what’s going on underneath. And all abstractions are leaky. So when you have a computer programming for you—when you have Claude Code or equivalent programming for you—it’s going to make mistakes. It’s going to have bugs. It’s going to have suboptimal architecture. So it’s not going to be quite right. And someone who understands what’s going on underneath will be able to plug the leaks as they occur. So if you want to build a well-architected application, if you want to be able to even specify a well-architected application, if you want to be able to make it run at high performance, if you want it to do its best, if you want to catch the bugs early, then you’re going to want to have a software engineering background. The traditional software engineer is going to be able to use these tools much better. And there are still many kinds of problems in software engineering that are out of scope for these AI programs today. The easiest way to think about those is problems that are outside of their data distribution. For example, if they need to do a binary sort or reverse a linked list, they’ve seen countless examples of that, so they’re extremely good at it. But when you start getting out of their domain—where you have to write very high-performance code, when you’re running on architectures that are novel or brand new, when you’re actually creating new things or solving new problems, then you still need to get in there and hand code it. At least until either there are so many of those examples that new models can be trained on them, or until these models can sufficiently reason at even higher levels of abstraction and crack it on their own… And remember: there is no demand for average. The average app—nobody wants it, at least as long as it’s not filling some niche that is filled by a superior app. The app that is better will win essentially a hundred percent of the market. Maybe there’s some small percentage that will bleed off to the second-best app because it does some little niche feature better than the main app, or it’s cheaper, or something of the sort. But generally speaking, people only want the best of anything. So the bad news is there’s no point in being number two or number three—like in the famous Glengarry Glen Ross scene where Alec Baldwin says, “First place gets a Cadillac Eldorado, second place gets a set of steak knives, and third place you’re fired.” That’s absolutely true in these winner-take-all markets. That’s the bad news: You have to be the best at something if you want to win. However, the set of things you can be best at is infinite. You can always find some niche that is perfect for you, and you can be the best at that thing. This goes back to an old tweet of mine where I said, “Become the best in the world at what you do. Keep redefining what you do until this is true.” And I think that still applies in this age of AI.”
2:56

Is Traditional Software Engineering Dead? “Does this mean that traditional software engineering is dead? Absolutely not. Software engineers—even the ones who are not necessarily tuning or training AI models—these are now among the most leveraged people on earth. Sure, the guys who are training and tuning models are even more leveraged because they’re building the tool set that software engineers are using. But software engineers still have two massive advantages on you. First, they think in code, so they actually know what’s going on underneath. And all abstractions are leaky. So when you have a computer programming for you—when you have Claude Code or equivalent programming for you—it’s going to make mistakes. It’s going to have bugs. It’s going to have suboptimal architecture. So it’s not going to be quite right. And someone who understands what’s going on underneath will be able to plug the leaks as they occur. So if you want to build a well-architected application, if you want to be able to even specify a well-architected application, if you want to be able to make it run at high performance, if you want it to do its best, if you want to catch the bugs early, then you’re going to want to have a software engineering background. The traditional software engineer is going to be able to use these tools much better. And there are still many kinds of problems in software engineering that are out of scope for these AI programs today. The easiest way to think about those is problems that are outside of their data distribution. For example, if they need to do a binary sort or reverse a linked list, they’ve seen countless examples of that, so they’re extremely good at it. But when you start getting out of their domain—where you have to write very high-performance code, when you’re running on architectures that are novel or brand new, when you’re actually creating new things or solving new problems, then you still need to get in there and hand code it. At least until either there are so many of those examples that new models can be trained on them, or until these models can sufficiently reason at even higher levels of abstraction and crack it on their own… And remember: there is no demand for average. The average app—nobody wants it, at least as long as it’s not filling some niche that is filled by a superior app. The app that is better will win essentially a hundred percent of the market. Maybe there’s some small percentage that will bleed off to the second-best app because it does some little niche feature better than the main app, or it’s cheaper, or something of the sort. But generally speaking, people only want the best of anything. So the bad news is there’s no point in being number two or number three—like in the famous Glengarry Glen Ross scene where Alec Baldwin says, “First place gets a Cadillac Eldorado, second place gets a set of steak knives, and third place you’re fired.” That’s absolutely true in these winner-take-all markets. That’s the bad news: You have to be the best at something if you want to win. However, the set of things you can be best at is infinite. You can always find some niche that is perfect for you, and you can be the best at that thing. This goes back to an old tweet of mine where I said, “Become the best in the world at what you do. Keep redefining what you do until this is true.” And I think that still applies in this age of AI.”

Naval

856,756 просмотров • 6 месяцев назад

What are traits of standout software engineers? Here are great observations from Ebi Atawodi - currently Director of Product Management at YouTube Studio, and a former product leader at Netflix and Uber. I worked with Ebi for 4 years at Uber, and in this The Pragmatic Engineer podcast episode we go through how engineers, engineering managers and product managers can work better together. Watch or listen: • YouTube: • Spotify: • Apple: Brought to you by: • WorkOS — The modern identity platform for B2B SaaS • The Software Engineer’s Guidebook: Written by me (Gergely) – now out in audio form as well ---- Some of my takeaways: 𝟭. 𝗣𝗿𝗼𝗱𝘂𝗰𝘁-𝗺𝗶𝗻𝗱𝗲𝗱 𝗲𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝘀 𝗴𝗲𝘁 𝗺𝗼𝗿𝗲 𝘁𝗵𝗶𝗻𝗴𝘀 𝗱𝗼𝗻𝗲: 𝘀𝗼 𝗯𝗲𝗰𝗼𝗺𝗲 𝗼𝗻𝗲 — and if you are a PM or EM, help your engineers become one! One of the biggest gifts from Ebi, myself, and our engineering team was helping all of us become product-minded engineers. Ebi exposed us to how Product worked, made decisions, allocated headcount, and pitched new initiatives to be funded by the business. I wrote more on how to become a product-minded engineer. 𝟮. 𝗚𝗲𝘁 𝘁𝗼 𝗸𝗻𝗼𝘄 𝘁𝗵𝗲 𝗽𝗲𝗿𝘀𝗼𝗻 𝗯𝗲𝗵𝗶𝗻𝗱 𝘁𝗵𝗲 𝗘𝗠/𝗣𝗠/𝗲𝗻𝗴𝗶𝗻𝗲𝗲𝗿/𝗰𝗼𝗹𝗹𝗲𝗮𝗴𝘂𝗲. At work, roles are somewhat artificial. What is not artificial is the person behind the role. It made a massive difference when I got to know the “real Ebi” behind “Ebi, the product manager” — and the other way around, about “Gergely, the engineering manager.” It’s a lot easier to trust a partner when you know more about them than “just” their role at work. 𝟯. 𝗙𝗼𝗰𝘂𝘀 𝗼𝗻 𝗱𝗼𝗶𝗻𝗴 𝗴𝗿𝗲𝗮𝘁 𝘄𝗼𝗿𝗸 𝗮𝘀 𝗮𝗻 𝗲𝗻𝗴𝗶𝗻𝗲𝗲𝗿 — 𝗳𝗶𝗿𝘀𝘁 𝗮𝗻𝗱 𝗳𝗼𝗿𝗲𝗺𝗼𝘀𝘁. As tempting as it is to “game the system” of performance reviews or promotions: both Ebi and I agree that you absolutely can game whatever system your company has in place to fast-track that next promotion, or try to get better performance reviews. But the people who do this, others around them notice — especially engineers! What speeds them up in the short term often slows them down in the long term. This is because more senior positions often need strong referrals: and someone who was visibly focused on their own advancement only can struggle to get these. Do standout work, help others when you can, and be kind to others around you, and years later, other standout colleagues could well tap your shoulder, wanting to recruit you to the companies they are currently at. This is how Ebi often got recruited for increasingly senior roles.
1:00

What are traits of standout software engineers? Here are great observations from Ebi Atawodi - currently Director of Product Management at YouTube Studio, and a former product leader at Netflix and Uber. I worked with Ebi for 4 years at Uber, and in this The Pragmatic Engineer podcast episode we go through how engineers, engineering managers and product managers can work better together. Watch or listen: • YouTube: • Spotify: • Apple: Brought to you by: • WorkOS — The modern identity platform for B2B SaaS • The Software Engineer’s Guidebook: Written by me (Gergely) – now out in audio form as well ---- Some of my takeaways: 𝟭. 𝗣𝗿𝗼𝗱𝘂𝗰𝘁-𝗺𝗶𝗻𝗱𝗲𝗱 𝗲𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝘀 𝗴𝗲𝘁 𝗺𝗼𝗿𝗲 𝘁𝗵𝗶𝗻𝗴𝘀 𝗱𝗼𝗻𝗲: 𝘀𝗼 𝗯𝗲𝗰𝗼𝗺𝗲 𝗼𝗻𝗲 — and if you are a PM or EM, help your engineers become one! One of the biggest gifts from Ebi, myself, and our engineering team was helping all of us become product-minded engineers. Ebi exposed us to how Product worked, made decisions, allocated headcount, and pitched new initiatives to be funded by the business. I wrote more on how to become a product-minded engineer. 𝟮. 𝗚𝗲𝘁 𝘁𝗼 𝗸𝗻𝗼𝘄 𝘁𝗵𝗲 𝗽𝗲𝗿𝘀𝗼𝗻 𝗯𝗲𝗵𝗶𝗻𝗱 𝘁𝗵𝗲 𝗘𝗠/𝗣𝗠/𝗲𝗻𝗴𝗶𝗻𝗲𝗲𝗿/𝗰𝗼𝗹𝗹𝗲𝗮𝗴𝘂𝗲. At work, roles are somewhat artificial. What is not artificial is the person behind the role. It made a massive difference when I got to know the “real Ebi” behind “Ebi, the product manager” — and the other way around, about “Gergely, the engineering manager.” It’s a lot easier to trust a partner when you know more about them than “just” their role at work. 𝟯. 𝗙𝗼𝗰𝘂𝘀 𝗼𝗻 𝗱𝗼𝗶𝗻𝗴 𝗴𝗿𝗲𝗮𝘁 𝘄𝗼𝗿𝗸 𝗮𝘀 𝗮𝗻 𝗲𝗻𝗴𝗶𝗻𝗲𝗲𝗿 — 𝗳𝗶𝗿𝘀𝘁 𝗮𝗻𝗱 𝗳𝗼𝗿𝗲𝗺𝗼𝘀𝘁. As tempting as it is to “game the system” of performance reviews or promotions: both Ebi and I agree that you absolutely can game whatever system your company has in place to fast-track that next promotion, or try to get better performance reviews. But the people who do this, others around them notice — especially engineers! What speeds them up in the short term often slows them down in the long term. This is because more senior positions often need strong referrals: and someone who was visibly focused on their own advancement only can struggle to get these. Do standout work, help others when you can, and be kind to others around you, and years later, other standout colleagues could well tap your shoulder, wanting to recruit you to the companies they are currently at. This is how Ebi often got recruited for increasingly senior roles.

Gergely Orosz

19,552 просмотров • 1 год назад

Google has a Gemini Problem, and Chamath has a plan to fix it 📈 On E225, the besties discussed how Google can cut ChatGPT's lead over Gemini without killing its $200B/year search ads business. David Sacks: "I think the problem that Google has with respect to ChatGPT, is Gemini is not getting the usage, and ChatGPT is just growing like crazy." "If you look at how these models perform according to the benchmarks, Gemini is actually really good, but they have not caught up on the usage side." david friedberg: "Chamath, you're the CEO of Google, you've got a $200B run rate search ad business." "What's the right integration of Gemini such that you don't massively disrupt the search ad business overnight?" "Or do you not care and you're just gonna do it? I think that's the conundrum (Google) is dealing with." Chamath Palihapitiya: " The more difficult question is, what does the integration look like?" "They're already inserting Gemini in all kinds of uncomfortable ways." "So for example, if you use Gmail, or if you use Google Workspace, what happens today is all these random Gemini pop-ups come up all over the place." "That is an implementation that happened at way too junior a level by people that have no product taste." "And if you use the products every day, it would be hard for you to disagree with me." David Sacks: " The Google homepage, would you replace that with an AI chatbot?" Chamath Palihapitiya: " No. Here's what I would do: I would first go to the critical other points that are around, that today do not cannibalize the blue links." "If you look at the traffic patterns, almost as a Sankey diagram, the real thing you should be looking at here is where are the entry points into Google that then result in a clickable link." "And what it would show you is that there are certain places that are highly de-optimized today for revenue generating events." "They happen as a byproduct, but they don't happen as the use case." "So in that example, you would put Gmail as a critical place, the Google one subscription, and there's like five or six other places." "That's where I would put Gemini as the front door and start to habituate 300 to 500 million people a week in using that." "I think then you can figure out over time how much money you can make from all of that, or how it directs derivative revenue, and figure out what to do with Google dot com last." "But my point is, the experience in Gmail should be done today." "The experience in YouTube should be done today." "The experience in Google one should be done today."
2:00

Google has a Gemini Problem, and Chamath has a plan to fix it 📈 On E225, the besties discussed how Google can cut ChatGPT's lead over Gemini without killing its $200B/year search ads business. David Sacks: "I think the problem that Google has with respect to ChatGPT, is Gemini is not getting the usage, and ChatGPT is just growing like crazy." "If you look at how these models perform according to the benchmarks, Gemini is actually really good, but they have not caught up on the usage side." david friedberg: "Chamath, you're the CEO of Google, you've got a $200B run rate search ad business." "What's the right integration of Gemini such that you don't massively disrupt the search ad business overnight?" "Or do you not care and you're just gonna do it? I think that's the conundrum (Google) is dealing with." Chamath Palihapitiya: " The more difficult question is, what does the integration look like?" "They're already inserting Gemini in all kinds of uncomfortable ways." "So for example, if you use Gmail, or if you use Google Workspace, what happens today is all these random Gemini pop-ups come up all over the place." "That is an implementation that happened at way too junior a level by people that have no product taste." "And if you use the products every day, it would be hard for you to disagree with me." David Sacks: " The Google homepage, would you replace that with an AI chatbot?" Chamath Palihapitiya: " No. Here's what I would do: I would first go to the critical other points that are around, that today do not cannibalize the blue links." "If you look at the traffic patterns, almost as a Sankey diagram, the real thing you should be looking at here is where are the entry points into Google that then result in a clickable link." "And what it would show you is that there are certain places that are highly de-optimized today for revenue generating events." "They happen as a byproduct, but they don't happen as the use case." "So in that example, you would put Gmail as a critical place, the Google one subscription, and there's like five or six other places." "That's where I would put Gemini as the front door and start to habituate 300 to 500 million people a week in using that." "I think then you can figure out over time how much money you can make from all of that, or how it directs derivative revenue, and figure out what to do with Google dot com last." "But my point is, the experience in Gmail should be done today." "The experience in YouTube should be done today." "The experience in Google one should be done today."

The All-In Podcast

100,683 просмотров • 1 год назад

Netflix pays AI PMs up to $900K/year. How do you become (and succeed) as one? Marily Nika Marily Nika is an AI PM at Google, and was an AI PM at Meta. In today's episode, she breaks it down: How AI PM Roles Differ at Big Tech Companies – 06:56 How Technical an AI PM Needs to Be – 08:08 Data Handling Responsibilities for AI PMs – 13:10 How PMs Can Improve Collaboration with Researchers – 18:06 Interviewing for AI PM Roles at Big Tech Companies – 22:08 The Role of a PhD in AI Product Management – 26:03 Optimizing LinkedIn for Aspiring AI PMs – 31:55 Roadmap to Becoming an AI PM – 37:05 Transitioning into AI Product Management – 40:52 The Process of Boomeranging Back to a Company – 44:00 AI Tools Every PM Should Know – 48:08 What It’s Like to Be an AI PM at Google – 57:46 How to Develop a Strong Product Strategy – 01:01:21 The Evolution of PM Roles in the Age of AI – 01:05:11 How PMs Can Stay Relevant in an AI-Driven World – 01:08:12 Balancing a Side Business with a Full-Time Job – 01:13:42 The Process of Creating a Course – 01:17:12 LinkedIn Tips for Aspiring AI PM Creators – 01:21:21 Lessons from Product Failures – 01:25:27 Paid promotions included
1:27:33

Netflix pays AI PMs up to $900K/year. How do you become (and succeed) as one? Marily Nika Marily Nika is an AI PM at Google, and was an AI PM at Meta. In today's episode, she breaks it down: How AI PM Roles Differ at Big Tech Companies – 06:56 How Technical an AI PM Needs to Be – 08:08 Data Handling Responsibilities for AI PMs – 13:10 How PMs Can Improve Collaboration with Researchers – 18:06 Interviewing for AI PM Roles at Big Tech Companies – 22:08 The Role of a PhD in AI Product Management – 26:03 Optimizing LinkedIn for Aspiring AI PMs – 31:55 Roadmap to Becoming an AI PM – 37:05 Transitioning into AI Product Management – 40:52 The Process of Boomeranging Back to a Company – 44:00 AI Tools Every PM Should Know – 48:08 What It’s Like to Be an AI PM at Google – 57:46 How to Develop a Strong Product Strategy – 01:01:21 The Evolution of PM Roles in the Age of AI – 01:05:11 How PMs Can Stay Relevant in an AI-Driven World – 01:08:12 Balancing a Side Business with a Full-Time Job – 01:13:42 The Process of Creating a Course – 01:17:12 LinkedIn Tips for Aspiring AI PM Creators – 01:21:21 Lessons from Product Failures – 01:25:27 Paid promotions included

Aakash Gupta

42,853 просмотров • 1 год назад

🎧🍌 New The Peel with Ankur Goyal Ankur is the Founder and CEO of @braintrustdata, the end to end developer platform for building the world's best AI products. Their customers include companies like Instacart, Zapier, Notion, Airtable, Replit, and more. We hit on the importance of LLM evals, advice for building AI products, why the best companies have two AI product roadmaps, how he hates meetings, and all his non-conventional advice for founders. Watch below or links in the replies! Timestamps: 04:04 Why everyone’s now an AI company 06:03 Reasons LLM evals are so important 09:19 Replacing vibe checks with Braintrust 10:37 Making OpenAI’s protocols the standard 11:27 Why the best companies have two AI roadmaps 13:06 Build your product so each LLM release makes it better 14:54 Predicting AGI is impossible 15:54 Why people who work with LLMs aren’t worried about AI safety 16:52 The best developers are all-in on co-pilots 18:11 How AI is changing software development 21:09 Combining IDE, CI/DC, and observability in one product 27:18 Are models more like CPU’s or relational databases? 30:14 How to pick an LLM 33:00 Advice for staying on top of new AI developments 34:30 Why tool calling is so important 38:02 Advice for young software engineers 40:25 Learning to code doing linear algebra homework 42:36 Lack of purpose interning in big tech 44:07 Working at MemSQL learning to be a founder 47:52 How to get a job at a startup 50:43 Building his first startups product on an flight 52:39 Three lessons from his first failed startup 54:46 Don’t delegate what you’re good at 55:46 Why you should be careful listening to VCs advice 57:34 Tactics for successful delegation 59:36 Why Ankur hates meetings 1:02:42 The importance of self-service in unlocking certain customer segments 1:05:14 How Braintrust got started 1:07:45 Advice on picking your target customers 1:10:35 How Braintrust hires with work trials 1:15:21 Balancing security with a modern UI 1:17:49 Why it’s hard to sell non-AI products right now 1:19:21 Advice for selling to large enterprises 1:23:10 Ankur’s favorite AI products
1:27:03

🎧🍌 New The Peel with Ankur Goyal Ankur is the Founder and CEO of @braintrustdata, the end to end developer platform for building the world's best AI products. Their customers include companies like Instacart, Zapier, Notion, Airtable, Replit, and more. We hit on the importance of LLM evals, advice for building AI products, why the best companies have two AI product roadmaps, how he hates meetings, and all his non-conventional advice for founders. Watch below or links in the replies! Timestamps: 04:04 Why everyone’s now an AI company 06:03 Reasons LLM evals are so important 09:19 Replacing vibe checks with Braintrust 10:37 Making OpenAI’s protocols the standard 11:27 Why the best companies have two AI roadmaps 13:06 Build your product so each LLM release makes it better 14:54 Predicting AGI is impossible 15:54 Why people who work with LLMs aren’t worried about AI safety 16:52 The best developers are all-in on co-pilots 18:11 How AI is changing software development 21:09 Combining IDE, CI/DC, and observability in one product 27:18 Are models more like CPU’s or relational databases? 30:14 How to pick an LLM 33:00 Advice for staying on top of new AI developments 34:30 Why tool calling is so important 38:02 Advice for young software engineers 40:25 Learning to code doing linear algebra homework 42:36 Lack of purpose interning in big tech 44:07 Working at MemSQL learning to be a founder 47:52 How to get a job at a startup 50:43 Building his first startups product on an flight 52:39 Three lessons from his first failed startup 54:46 Don’t delegate what you’re good at 55:46 Why you should be careful listening to VCs advice 57:34 Tactics for successful delegation 59:36 Why Ankur hates meetings 1:02:42 The importance of self-service in unlocking certain customer segments 1:05:14 How Braintrust got started 1:07:45 Advice on picking your target customers 1:10:35 How Braintrust hires with work trials 1:15:21 Balancing security with a modern UI 1:17:49 Why it’s hard to sell non-AI products right now 1:19:21 Advice for selling to large enterprises 1:23:10 Ankur’s favorite AI products

Turner Novak 🍌🧢

44,280 просмотров • 2 лет назад

There’s a popular theory that AI will finally make formal verification mainstream because mathematical proof of correctness will be needed when machines write most or all of the code. But will this happen? Hillel Wayne is one of the best people to answer. Timestamps: 00:00 Intro 04:32 The Crossover Project 11:37 What software engineering does better 15:30 What traditional engineering does better 18:17 Formal methods 29:32 TLA+: what it is and demo 36:58 TLA+ at Amazon 38:10 Ways distributed systems break 41:03 Formal methods and systems thinking 46:20 The value of learning math 50:23 What TLA+ is good for and isn’t 52:50 Alloy: a declarative language for software modeling 58:53 Other formal methods tools 1:01:24 Property-based testing 1:05:31 AI and the need for formal verification 1:12:29 Logic for programmers 1:14:35 Hillel’s 2025 prediction on AI’s impact 1:21:30 Book recommendation Brought to you by: • Antithesis – verify your system’s correctness without human review or traditional integration tests – and avoid bugs or outages. • turbopuffer – a vector and full-text search engine built on object storage. It’s fast, cheap, and extremely scalable. • WorkOS – everything you need to make your app enterprise ready. Two things I found especially interesting, talking with Hillel: 1. Amazon used TLA+ to find a bug almost impossible to locate without formal methods. In the paper How AWS uses formal methods, the AWS team shared that they’d found a complicated bug for which the shortest error trace to exhibit was 35 steps (!!). The bug passed unnoticed through extensive design review, code reviews, and testing. AWS concluded they wouldn’t have uncovered it if they’d stuck to conventional testing approaches. 2. Why not use formal verification for everything, then? It’s because specs in the real world are a nightmare to write. Even a simple problem like “find the file in a directory that has the most lines” gets complicated when modeled with formal methods. We would have to answer questions like: ‘do we look at ASCII or UTF-8 new line characters, what about unreadable files, and Symlinks?’ Without formal methods, we can write a simple verification that is right in 99%+ of cases. Formal methods require a lot of extra effort for the less than 1% of exotic use cases!
1:24:52

There’s a popular theory that AI will finally make formal verification mainstream because mathematical proof of correctness will be needed when machines write most or all of the code. But will this happen? Hillel Wayne is one of the best people to answer. Timestamps: 00:00 Intro 04:32 The Crossover Project 11:37 What software engineering does better 15:30 What traditional engineering does better 18:17 Formal methods 29:32 TLA+: what it is and demo 36:58 TLA+ at Amazon 38:10 Ways distributed systems break 41:03 Formal methods and systems thinking 46:20 The value of learning math 50:23 What TLA+ is good for and isn’t 52:50 Alloy: a declarative language for software modeling 58:53 Other formal methods tools 1:01:24 Property-based testing 1:05:31 AI and the need for formal verification 1:12:29 Logic for programmers 1:14:35 Hillel’s 2025 prediction on AI’s impact 1:21:30 Book recommendation Brought to you by: • Antithesis – verify your system’s correctness without human review or traditional integration tests – and avoid bugs or outages. • turbopuffer – a vector and full-text search engine built on object storage. It’s fast, cheap, and extremely scalable. • WorkOS – everything you need to make your app enterprise ready. Two things I found especially interesting, talking with Hillel: 1. Amazon used TLA+ to find a bug almost impossible to locate without formal methods. In the paper How AWS uses formal methods, the AWS team shared that they’d found a complicated bug for which the shortest error trace to exhibit was 35 steps (!!). The bug passed unnoticed through extensive design review, code reviews, and testing. AWS concluded they wouldn’t have uncovered it if they’d stuck to conventional testing approaches. 2. Why not use formal verification for everything, then? It’s because specs in the real world are a nightmare to write. Even a simple problem like “find the file in a directory that has the most lines” gets complicated when modeled with formal methods. We would have to answer questions like: ‘do we look at ASCII or UTF-8 new line characters, what about unreadable files, and Symlinks?’ Without formal methods, we can write a simple verification that is right in 99%+ of cases. Formal methods require a lot of extra effort for the less than 1% of exotic use cases!

Gergely Orosz

34,525 просмотров • 1 месяц назад

What does it mean for software engineering when we no longer write the code? Here's the take from Boris Cherny (Boris Cherny), the creator of Claude Code. Timestamps: 00:00 Intro 11:15 Lessons from Meta 19:46 Joining Anthropic 23:08 The origins of Claude Code 32:55 Boris's Claude Code workflow 36:27 Parallel agents 40:25 Code reviews 47:18 Claude Code's architecture 52:38 Permissions and sandboxing 55:05 Engineering culture at Anthropic 1:05:15 Claude Cowork 1:12:48 Observability and privacy 1:14:45 Agent swarms 1:21:16 LLMs and the printing press analogy 1:30:16 Standout engineer archetypes 1:32:12 What skills still matter for engineers 1:35:24 Book recommendations 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. Proactively find and fix issues in real-time with the SonarQube MCP Server: • WorkOS – Everything you need to make your app enterprise ready. Three interesting things from this conversation: 1. Boris automated himself out of code review well before AI. Boris was one of the most prolific code reviewers at Meta company. And he worked hard to minimize time spent on code review. His system::every time he left the same kind of review comment, he logged it in a spreadsheet. Once a pattern hit 3-4 occurrences, he’d write a lint rule to automate it away! 2. PRDs are dead on the Claude Code team: prototypes replaced them. Instead of writing Product Requirement Documents (specs), they build hundreds of working prototypes before shipping a feature. Boris: “There’s just no way we could have shipped this if we started with static mocks and Figma or if we started with a PRD.” 3. This is the year of the generalist (and maybe the year of those with ADHD) Boris’s work has shifted from deep-focus single-threaded coding to managing multiple parallel agents and context-switching rapidly. As Boris put it: “It’s not so much about deep work, it’s about how good I am at context switching and jumping across multiple different contexts very quickly.”
1:37:58

What does it mean for software engineering when we no longer write the code? Here's the take from Boris Cherny (Boris Cherny), the creator of Claude Code. Timestamps: 00:00 Intro 11:15 Lessons from Meta 19:46 Joining Anthropic 23:08 The origins of Claude Code 32:55 Boris's Claude Code workflow 36:27 Parallel agents 40:25 Code reviews 47:18 Claude Code's architecture 52:38 Permissions and sandboxing 55:05 Engineering culture at Anthropic 1:05:15 Claude Cowork 1:12:48 Observability and privacy 1:14:45 Agent swarms 1:21:16 LLMs and the printing press analogy 1:30:16 Standout engineer archetypes 1:32:12 What skills still matter for engineers 1:35:24 Book recommendations 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. Proactively find and fix issues in real-time with the SonarQube MCP Server: • WorkOS – Everything you need to make your app enterprise ready. Three interesting things from this conversation: 1. Boris automated himself out of code review well before AI. Boris was one of the most prolific code reviewers at Meta company. And he worked hard to minimize time spent on code review. His system::every time he left the same kind of review comment, he logged it in a spreadsheet. Once a pattern hit 3-4 occurrences, he’d write a lint rule to automate it away! 2. PRDs are dead on the Claude Code team: prototypes replaced them. Instead of writing Product Requirement Documents (specs), they build hundreds of working prototypes before shipping a feature. Boris: “There’s just no way we could have shipped this if we started with static mocks and Figma or if we started with a PRD.” 3. This is the year of the generalist (and maybe the year of those with ADHD) Boris’s work has shifted from deep-focus single-threaded coding to managing multiple parallel agents and context-switching rapidly. As Boris put it: “It’s not so much about deep work, it’s about how good I am at context switching and jumping across multiple different contexts very quickly.”

Gergely Orosz

490,351 просмотров • 6 месяцев назад

Rahul Vohra on how to measure product/market fit Rahul Vohra is the founder and CEO of Superhuman. He was looking for a metric to measure product/market fit so that he and his team could optimize, and he came across the following methodology from Sean Ellis: Simply ask your users: “How would you feel if you could no longer use the product?” with three options: (1) not disappointed, (2) somewhat disappointed, or (3) very disappointed. It turns out that the benchmark for product/market fit across hundreds of venture-backed startups is 40% of respondents saying “very disappointed”. And as Rahul puts it: “If more than 40% of your users would be very disappointed without your product, then you should focus on growing your company. If less than 40% of your users would be very disappointed without your product, then you’ll probably struggle to grow.” 40% may not sound like a lot, but it’s an incredibly hard benchmark to beat. For example, Slack posed this to 731 customers early in the company’s history, and 51% said they would be very disappointed without Slack. One might expect a terrific product like Slack to have a score of 60-80%, but that wasn’t the case. Rahul’s explanation of why the response options are focused on disappointment rather than happiness is interesting too: “I think the reason behind that is that if you ask people how they feel about a product and you give them positive potential responses, I think it invites more bias. People are more likely to be polite. And it also doesn’t get to the heart of the matter which is: how necessary has your product become in people’s lives? If you’re trying to build a company that’s going to stand the test of time, you really do have to build a product that matters and that people ultimately come to depend on because it’s just so incredible at what it does. And that’s what this question gets to the heart of.”
4:39

Rahul Vohra on how to measure product/market fit Rahul Vohra is the founder and CEO of Superhuman. He was looking for a metric to measure product/market fit so that he and his team could optimize, and he came across the following methodology from Sean Ellis: Simply ask your users: “How would you feel if you could no longer use the product?” with three options: (1) not disappointed, (2) somewhat disappointed, or (3) very disappointed. It turns out that the benchmark for product/market fit across hundreds of venture-backed startups is 40% of respondents saying “very disappointed”. And as Rahul puts it: “If more than 40% of your users would be very disappointed without your product, then you should focus on growing your company. If less than 40% of your users would be very disappointed without your product, then you’ll probably struggle to grow.” 40% may not sound like a lot, but it’s an incredibly hard benchmark to beat. For example, Slack posed this to 731 customers early in the company’s history, and 51% said they would be very disappointed without Slack. One might expect a terrific product like Slack to have a score of 60-80%, but that wasn’t the case. Rahul’s explanation of why the response options are focused on disappointment rather than happiness is interesting too: “I think the reason behind that is that if you ask people how they feel about a product and you give them positive potential responses, I think it invites more bias. People are more likely to be polite. And it also doesn’t get to the heart of the matter which is: how necessary has your product become in people’s lives? If you’re trying to build a company that’s going to stand the test of time, you really do have to build a product that matters and that people ultimately come to depend on because it’s just so incredible at what it does. And that’s what this question gets to the heart of.”

Michael McGuiness

67,106 просмотров • 2 лет назад

.Scott Wu to Joe Lonsdale on why Cognition uses AI in job interviews: "One or two years ago, people had this mentality of 'How do we make sure people aren't using AI while we're interviewing them?' And I think that has totally flipped. Honestly, I think that was wrong... If you're asking the question of like, how do we evaluate people on exactly the thing that AI can already do, that seems kind of the wrong. Our interview process has always been... you can use as much AI as you want to use. We're going to give you a few hours. Just build your whole own product surface. A lot of these are projects that, frankly, is like, if you were trying to do this by hand, you would not be able to get done in a few hours. So you kind of have to use AI for this. But in reality, what we actually want to test is, in addition to how familiar you are with these tools, what we actually want to test is, what do you think is the right thing to build? Or how do you make these product decisions? How do you make these trade-offs? How do you decide or collect information about what you should be doing?"
0:58

.Scott Wu to Joe Lonsdale on why Cognition uses AI in job interviews: "One or two years ago, people had this mentality of 'How do we make sure people aren't using AI while we're interviewing them?' And I think that has totally flipped. Honestly, I think that was wrong... If you're asking the question of like, how do we evaluate people on exactly the thing that AI can already do, that seems kind of the wrong. Our interview process has always been... you can use as much AI as you want to use. We're going to give you a few hours. Just build your whole own product surface. A lot of these are projects that, frankly, is like, if you were trying to do this by hand, you would not be able to get done in a few hours. So you kind of have to use AI for this. But in reality, what we actually want to test is, in addition to how familiar you are with these tools, what we actually want to test is, what do you think is the right thing to build? Or how do you make these product decisions? How do you make these trade-offs? How do you decide or collect information about what you should be doing?"

American Optimist

13,182 просмотров • 5 месяцев назад

.Gokul Rajaram is one of the most prolific product builders and investors of the last 20 years. He helped build the core ads and product businesses at Google, Facebook, Square, and DoorDash, working directly with many of this generation's best founders and CEOs. He's also invested in more than 700 companies giving him an unusually broad view into how products are built and scaled. Gokul has an incredible ability to give precise and prescriptive advice on how to build products, particularly in AI, and he explains his thinking so clearly that you come away knowing exactly how to apply it. We talk about why judgment is the only thing he believes is truly AI-proof, why Zendesk and Slack are more exposed than Salesforce and NetSuite, and what AI-native startups must do to move customers and their data off legacy systems. We cover everything he's learned from building the most important ads businesses, including the only three ways an ad business can make money, and why ChatGPT may be even more powerful than Google or Facebook for highly targeted ads. He also shares inside stories from Larry and Sergey, Zuck, Jack Dorsey, and Tony Xu, about how each of them approaches product, design, and communication. Enjoy! Timestamps: 0:00 Intro 0:35 The Changing Nature of Product Development 4:09 The Merger of Product and Design 4:54 Managing Non-Deterministic Software 9:06 Judgment: The Future-Proof Human Skill 10:41 Building Durable AI Applications 16:43 The Risk to Legacy Software Companies 21:20 Sources of Stickiness in the Age of AI 23:43 Leadership Lessons from Google 27:41 Learning from Mark Zuckerberg 31:16 Jack Dorsey and the Philosophy of Great Design 35:48 The Product Manager as Editor 40:44 Three Pillars of a Successful Ads Business 49:03 Selecting North Star and Check Metrics 56:04 Hiring Functional Experts for the AI Era 1:00:06 Advice for Managing a Career 1:01:33 Evaluating Founder Authenticity 1:05:20 Best Practices for Board Management 1:11:15 The Kindest Thing
1:16:29

.Gokul Rajaram is one of the most prolific product builders and investors of the last 20 years. He helped build the core ads and product businesses at Google, Facebook, Square, and DoorDash, working directly with many of this generation's best founders and CEOs. He's also invested in more than 700 companies giving him an unusually broad view into how products are built and scaled. Gokul has an incredible ability to give precise and prescriptive advice on how to build products, particularly in AI, and he explains his thinking so clearly that you come away knowing exactly how to apply it. We talk about why judgment is the only thing he believes is truly AI-proof, why Zendesk and Slack are more exposed than Salesforce and NetSuite, and what AI-native startups must do to move customers and their data off legacy systems. We cover everything he's learned from building the most important ads businesses, including the only three ways an ad business can make money, and why ChatGPT may be even more powerful than Google or Facebook for highly targeted ads. He also shares inside stories from Larry and Sergey, Zuck, Jack Dorsey, and Tony Xu, about how each of them approaches product, design, and communication. Enjoy! Timestamps: 0:00 Intro 0:35 The Changing Nature of Product Development 4:09 The Merger of Product and Design 4:54 Managing Non-Deterministic Software 9:06 Judgment: The Future-Proof Human Skill 10:41 Building Durable AI Applications 16:43 The Risk to Legacy Software Companies 21:20 Sources of Stickiness in the Age of AI 23:43 Leadership Lessons from Google 27:41 Learning from Mark Zuckerberg 31:16 Jack Dorsey and the Philosophy of Great Design 35:48 The Product Manager as Editor 40:44 Three Pillars of a Successful Ads Business 49:03 Selecting North Star and Check Metrics 56:04 Hiring Functional Experts for the AI Era 1:00:06 Advice for Managing a Career 1:01:33 Evaluating Founder Authenticity 1:05:20 Best Practices for Board Management 1:11:15 The Kindest Thing

Patrick OShaughnessy

1,121,725 просмотров • 7 месяцев назад

Why is Rust different than many/most programming languages? Alice Ryhl works on Google's Android Rust team, is a Rust language team advisor, and is a core maintainer of Tokio (the most widely-used async runtime in Rust) Timestamps: 00:00 Intro 04:09 Tokio: an overview 05:11 What Alice likes about Rust 12:48 Rust for TypeScript engineers 13:51 Moving from C++ to Rust 14:34 Memory safety 18:12 Garbage collection tradeoffs 21:46 Ownership, references, and borrowing 26:59 Unsafe in Rust 31:21 Crates and Cargo 35:55 Language design and RFCs 43:02 Building new features 46:30 Editions vs. versions 49:47 Getting paid to work on Rust 51:27 Contributing to Rust 53:03 Rust in the Linux kernel 55:45 AI use cases for Rust 1:01:35 Learning Rust 1:03:54 Book recommendation Brought to you by: • Antithesis – verify your system’s correctness without human review or traditional integration tests – and avoid bugs or outages. • Sentry – application monitoring software considered “not bad” by millions of developers Three things worth knowing about Rust: 1. Rust was designed to turn implicit failures into compile errors. Where other languages allow you to forget something, Rust makes an omission into a compilation error for things like null checks, uninitialized variables, or error propagation with the ‘?’ character. If you mess something up, it’s almost certain your program will not compile. If it does, at the very least you should see a lint warning. 2. Refactoring in Rust is safe and easy, thanks to the compiler. Alice: “I change a return type or struct field, then just fix the compiler errors until the compiler stops shouting. And then once I’ve done that, I’ve updated every place I need to update.” Rust’s focus on correctness makes refactoring it more straightforward than dynamically-typed languages and Java-style typed ones are to refactor. 3. “Editions” allow Rust to make breaking changes without ‘breaking’ anyone’s code. Rust editions (2015, 2018, 2021, 2024) can be mixed freely across crates. A library on the 2021 edition works seamlessly with a binary on the 2024 edition. This is how Rust evolves syntax (like adding async/await as keywords) without forcing an ecosystem-wide migration. Thanks a lot, Alice for this great discussion! And for your work on Rust.
1:05:52

Why is Rust different than many/most programming languages? Alice Ryhl works on Google's Android Rust team, is a Rust language team advisor, and is a core maintainer of Tokio (the most widely-used async runtime in Rust) Timestamps: 00:00 Intro 04:09 Tokio: an overview 05:11 What Alice likes about Rust 12:48 Rust for TypeScript engineers 13:51 Moving from C++ to Rust 14:34 Memory safety 18:12 Garbage collection tradeoffs 21:46 Ownership, references, and borrowing 26:59 Unsafe in Rust 31:21 Crates and Cargo 35:55 Language design and RFCs 43:02 Building new features 46:30 Editions vs. versions 49:47 Getting paid to work on Rust 51:27 Contributing to Rust 53:03 Rust in the Linux kernel 55:45 AI use cases for Rust 1:01:35 Learning Rust 1:03:54 Book recommendation Brought to you by: • Antithesis – verify your system’s correctness without human review or traditional integration tests – and avoid bugs or outages. • Sentry – application monitoring software considered “not bad” by millions of developers Three things worth knowing about Rust: 1. Rust was designed to turn implicit failures into compile errors. Where other languages allow you to forget something, Rust makes an omission into a compilation error for things like null checks, uninitialized variables, or error propagation with the ‘?’ character. If you mess something up, it’s almost certain your program will not compile. If it does, at the very least you should see a lint warning. 2. Refactoring in Rust is safe and easy, thanks to the compiler. Alice: “I change a return type or struct field, then just fix the compiler errors until the compiler stops shouting. And then once I’ve done that, I’ve updated every place I need to update.” Rust’s focus on correctness makes refactoring it more straightforward than dynamically-typed languages and Java-style typed ones are to refactor. 3. “Editions” allow Rust to make breaking changes without ‘breaking’ anyone’s code. Rust editions (2015, 2018, 2021, 2024) can be mixed freely across crates. A library on the 2021 edition works seamlessly with a binary on the 2024 edition. This is how Rust evolves syntax (like adding async/await as keywords) without forcing an ecosystem-wide migration. Thanks a lot, Alice for this great discussion! And for your work on Rust.

Gergely Orosz

51,961 просмотров • 3 месяцев назад