How to measure AI developer productivity in 2025 | Nicole Forsgren

Lenny Rachitsky: A lot of companies are trying to measure productivity for their teams.

Lenny Rachitsky：许多公司都在尝试衡量其团队的生产力。

Nicole Forsgren: Most productivity metrics are a lie.

Nicole Forsgren：大多数生产力指标都是谎言。

If the goal is more lines of code, I can prompt something to write the longest piece of code ever.

如果目标是更多行代码，我可以提示编写有史以来最长的代码。

It's just too easy to gain that system.

获得该系统太容易了。

Lenny Rachitsky: How do I know if my eng team is moving fast enough, if they can move faster, if they're just not performing as well as they can?

Lenny Rachitsky：我如何知道我的工程团队是否进展得足够快，他们是否可以进展得更快，或者他们只是表现得不够好？

Nicole Forsgren: Most teams can move faster.

Nicole Forsgren：大多数团队可以更快地行动。

But faster for what?

但更快是为了什么？

We can ship trash faster every single day.

我们每天都可以更快地运送垃圾。

We need strategy and really smart decisions to know what to ship.

我们需要战略和真正明智的决策来知道要运送什么。

Lenny Rachitsky: One of the biggest issues we're going to probably have with AI is learning how much to trust code that it generates.

Lenny Rachitsky：我们在人工智能方面可能遇到的最大问题之一是了解它生成的代码的信任程度。

Nicole Forsgren: We can't just put in a command and guess something back and accept it.

Nicole Forsgren：我们不能只是输入命令并猜测一些内容并接受它。

We really need to evaluate it.

我们确实需要对其进行评估。

Are we seeing hallucinations?

我们看到幻觉了吗？

What's the reliability?

可靠性如何？

Does it meet the style that we would typically write?

它符合我们通常的写作风格吗？

Lenny Rachitsky: So much of the time is now going to be spent reviewing code versus writing code.

Lenny Rachitsky：现在大部分时间都花在审查代码而不是编写代码上。

Nicole Forsgren: There's some real opportunity there to not just rethink workflows, but rethink how we structure our days and how we structure our work.

Nicole Forsgren：这里有一些真正的机会，不仅可以重新思考工作流程，还可以重新思考我们如何安排我们的生活以及如何安排我们的工作。

Now, we can also make a 45-minute work block useful because getting into the flow is actually kind of handed off, at least, in part to the machine or the machine can help us get back into the flow by, reminding us of context and generating diagrams of the system.

现在，我们还可以让 45 分钟的工作块变得有用，因为进入流程实际上是一种移交，至少部分地交给了机器，或者机器可以帮助我们回到流程，提醒我们上下文并生成系统图表。

Lenny Rachitsky: What's just one thing that you think an eng team, a product team can do this week, next week to get more done?

Lenny Rachitsky：您认为工程团队、产品团队可以在本周、下周做些什么来完成更多工作？

Nicole Forsgren: Honestly, I think the best thing you can do-

Nicole Forsgren：老实说，我认为你能做的最好的事情是——

Lenny Rachitsky: Today, my guest is Nicole Forsgren.

Lenny Rachitsky：今天，我的客人是 Nicole Forsgren。

With so much talk about how AI is increasing developer productivity, more and more people are asking, "How do we measure this productivity gain?

随着人工智能如何提高开发人员生产力的讨论如此之多，越来越多的人在问：“我们如何衡量这种生产力的提高？

And are these AI tools actually helping us or hurting how our developers work?" Nicole has been at the forefront of this space longer than anyone.

这些人工智能工具实际上是在帮助我们还是损害我们开发人员的工作方式？”妮可在这个领域的前沿地位比任何人都长。

She created the most used frameworks for measuring developer experience called DORA and SPACE.

她创建了最常用的衡量开发人员体验的框架，称为 DORA 和 SPACE。

She wrote the most important book in the space called Accelerate and is about to publish her newest book called Frictionless, which gives you a guide to helping your team move faster and do more in this emerging AI world.

她写了该领域最重要的书《Accelerate》，并且即将出版她最新的书《Frictionless》，这本书为您提供了指导，帮助您的团队在这个新兴的人工智能世界中更快地行动并做得更多。

Her core thesis is that AI indeed accelerates coding.

她的核心论点是人工智能确实加速了编码。

But developers aren't speeding up as much as you think because they still have to deal with broken builds and unreliable tools and processes, and a bunch of new bottlenecks that are emerging.

但开发人员的速度并没有您想象的那么快，因为他们仍然必须处理损坏的构建、不可靠的工具和流程，以及一堆正在出现的新瓶颈。

Lenny Rachitsky: In our conversation, we chat about her current, best and very specific advice for how to measure productivity gains from AI, signs that your team could be moving faster, what companies get wrong when trying to measure engineering productivity, how AI tools are both helping and hurting engineers, including getting into flow states, her seven-step process for setting up a developer experience team at your company, how to get buy-in and measure the impact of a team like this and a ton more.

Lenny Rachitsky：在我们的谈话中，我们讨论了她当前关于如何衡量人工智能生产力提升的最佳且非常具体的建议、您的团队可以更快行动的迹象、公司在尝试衡量工程生产力时会犯的错误、人工智能工具如何帮助和伤害工程师，包括进入心流状态、她在公司建立开发人员体验团队的七步流程、如何获得支持并衡量这样的团队的影响等等。

This episode is for anyone looking to improve the performance of their engineering teams.

本集适合任何希望提高工程团队绩效的人。

If you enjoy this podcast, don't forget to subscribe and follow it in your favorite podcasting app or YouTube.

如果您喜欢这个播客，请不要忘记订阅并在您最喜欢的播客应用程序或 YouTube 中关注它。

It helps tremendously.

它有很大帮助。

Also, to become an annual subscriber of my newsletter, you get a year free of 15 incredible products including Lovable, Replit, Bolt, n8n, Linear, Superhuman, Descript, Wispr Flow, Gamma, Perplexity, Warp, Granola, Magic Patterns, Raycast, ChatPRD, and Mobbin.

此外，成为我的时事通讯的年度订阅者，您可以免费获得 15 种令人难以置信的产品，包括 Lovable、Replit、Bolt、n8n、Linear、Super human、Descript、Wispr Flow、Gamma、Perplexity、Warp、Granola、Magic Patterns、Raycast、ChatPRD 和 Mobbin。

Head on over to lennysnewsletter.com and click product pass.

前往 lennysnewsletter.com 并单击产品通行证。

With that, I bring Nicole Forsgren.

接下来，我带来了妮可·福斯格伦。

Lenny Rachitsky: This episode is brought to you by Mercury.

Lenny Rachitsky：本集由 Mercury 为您带来。

I've been banking with Mercury for years.

我多年来一直在 Mercury 从事银行业务。

And honestly, I can't imagine banking any other way at this point.

老实说，我目前无法想象以任何其他方式进行银行业务。

I switched from Chase and, holy moly, what a difference.

我从大通银行转来的，天哪，有什么不同。

Sending wires, tracking spend, giving people on my team access to move money around is so freaking easy.

发送电汇、跟踪支出、让我的团队成员能够转移资金是如此简单。

Where most traditional banking websites and apps are clunky and hard to use, Mercury is meticulously designed to be an intuitive and simple experience.

大多数传统银行网站和应用程序都笨重且难以使用，而 Mercury 则经过精心设计，提供直观、简单的体验。

And Mercury brings all the ways that you use money into a single product, including credit cards, invoicing, bill pay, reimbursements for your teammates, and capital.

Mercury 将您使用金钱的所有方式整合到一个产品中，包括信用卡、发票、账单支付、队友报销和资本。

Whether you're a funded tech startup looking for ways to pay contractors and earn yield on your idle cash or an agency that needs to invoice customers and keep them current, or an e-commerce brand that needs to stay on top of cash flow and access capital, Mercury can be tailored to help your business perform at its highest level.

无论您是一家资金雄厚的科技初创公司，正在寻求向承包商付款并利用闲置现金赚取收益的方式，还是一家需要向客户开具发票并保持其最新状态的机构，或者是一家需要掌控现金流并获取资金的电子商务品牌，Mercury 都可以量身定制，帮助您的企业发挥最高水平。

Lenny Rachitsky: See what over 200,000 entrepreneurs love about Mercury.

Lenny Rachitsky：了解超过 200,000 名企业家对 Mercury 的喜爱。

Visit mercury.com to apply online in 10 minutes.

10 分钟内访问mercury.com 进行在线申请。

Mercury is a FinTech, not a bank.

Mercury 是一家金融科技公司，而不是一家银行。

Banking services are provided through Mercury's FDIC-insured partner banks.

银行服务通过 Mercury 的 FDIC 承保合作银行提供。

For more details, check out the show notes.

欲了解更多详情，请查看演出笔记。

Here's a puzzle for you.

这是给你的一个谜题。

What do OpenAI, Cursor, Perplexity, Vercel, FLAN, and hundreds of other winning companies have in common?

OpenAI、Cursor、Perplexity、Vercel、FLAN 和其他数百家获奖公司有什么共同点？

The answer is they're all powered by today's sponsor, WorkOS.

答案是它们全部由当今的赞助商 WorkOS 提供支持。

If you're building software for enterprises, you've probably felt the pain of integrating single sign-on, skim, RBAC, audited logs, and other features required by big customers.

如果您正在为企业构建软件，您可能已经感受到集成单点登录、浏览、RBAC、审核日志和大客户所需的其他功能的痛苦。

WorkOS turns those deal blockers into drop-in APIs with a modern developer platform built specifically for B2B SaaS.

WorkOS 通过专为 B2B SaaS 构建的现代开发者平台将这些交易阻碍因素转变为嵌入式 API。

Lenny Rachitsky: Whether you're a seed-stage startup trying to land your first enterprise customer or a unicorn expanding globally, WorkOS is the fastest path to becoming enterprise-ready and unlocking growth.

Lenny Rachitsky：无论您是一家试图吸引第一个企业客户的种子期初创公司，还是一家在全球范围内扩张的独角兽，WorkOS 都是实现企业就绪并释放增长的最快途径。

They're essentially Stripe for enterprise features.

它们本质上是用于企业功能的 Stripe。

Visit workos.com to get started or just hit up their Slack support where they have real engineers in there, who answer your questions super fast.

请访问 workos.com 开始使用，或者直接联系他们的 Slack 支持，那里有真正的工程师，他们会快速回答您的问题。

WorkOS allows you to build like the best with delightful APIs, comprehensive docs, and a smooth developer experience.

WorkOS 允许您通过令人愉悦的 API、全面的文档和流畅的开发人员体验进行最佳构建。

Go to workos.com to make your app enterprise-ready today.

立即访问 workos.com 让您的应用程序做好企业准备。

Lenny Rachitsky: Nicole, thank you so much for being here and welcome to the podcast.

Lenny Rachitsky：妮可，非常感谢您来到这里，欢迎来到播客。

Nicole Forsgren: Thank you.

妮可·福斯格伦：谢谢。

It's so good to be here.

来到这里真是太好了。

Lenny Rachitsky: It's so good to have you back.

Lenny Rachitsky：你回来真是太好了。

I was just watching our first episode, which we did two and a half years ago.

我刚刚在看我们两年半前拍摄的第一集。

I was watching it, and I was both shocked and not shocked that we barely talked about AI.

我正在观看它，对于我们几乎没有谈论人工智能，我既感到震惊又不感到惊讶。

The episode was called How to Measure and Improve Developer Productivity, and we got to AI barely like an hour in and we're just like, "Hmm, I wonder what's going to happen with AI and productivity." Does that just blow your mind?

这一集的主题是“如何衡量和提高开发人员的生产力”，我们在不到一个小时的时间里就谈到了人工智能，我们就想，“嗯，我想知道人工智能和生产力会发生什么。”这让你大吃一惊吗？

Nicole Forsgren: Yeah.

妮可·福斯格伦：是的。

Because it was just hitting the scene, it was the topic of so much conversation, and at the same time, so many things don't change.

因为它刚刚出现，所以成为了很多话题的话题，同时，很多事情也没有改变。

So many things are still important, so many things are the same.

很多事情仍然重要，很多事情都是一样的。

Yeah.

是的。

It's also a little wild that it's been two and a half.

已经两年半了，也有点疯狂。

Where does time go?

时间都去哪儿了？

Time is a social construct?

时间是一种社会建构？

Lenny Rachitsky: Yeah.

莱尼·拉奇茨基：是的。

Most of our conversation was just questions like, "Well, how might this impact people?

我们的大部分谈话只是这样的问题：“这会对人们产生什么影响？

How will we change the way we build product?" It was barely a thing back then.

我们将如何改变我们构建产品的方式？”那时候这还不算什么。

Now, it's the only thing that I imagine people want to talk about when they talk about engineering productivity.

现在，我想这是人们在谈论工程生产力时唯一想要谈论的事情。

That's where we're going to be spending a lot of our time focusing on today.

这就是我们今天将花费大量时间关注的地方。

The reason I'm excited about this conversation, it feels like there's been so much money poured into AI tools increasing productivity.

我对这次谈话感到兴奋的原因是，感觉人工智能工具投入了很多资金来提高生产力。

The fastest growing companies in the world are these engineering AI tools.

世界上发展最快的公司就是这些工程人工智能工具。

And now, more and more people are just asking this question of just, "What gains are we getting out of this?

现在，越来越多的人只是问这个问题：“我们从中得到了什么好处？

How much is this actually helping us be more productive?

这实际上对我们提高生产力有多大帮助？

How do we become more productive?"

我们如何变得更有生产力？”

Lenny Rachitsky: You've been at the center of this world for longer than anyone.

Lenny Rachitsky：你处于这个世界中心的时间比任何人都长。

You've invented so many of the frameworks that people rely on now.

您发明了许多人们现在依赖的框架。

So I'm really excited to have you back to talk about this stuff.

所以我很高兴你能回来谈论这些事情。

I want to start with just this term DevEx, it's something that comes up a lot in this whole space, and we're going to hear this term a bunch in this conversation.

我想从 DevEx 这个术语开始，它在整个领域中经常出现，我们将在这次对话中多次听到这个术语。

Can you just explain what is DevEx, this term DevEx?

您能简单解释一下什么是 DevEx，这个术语 DevEx 吗？

Nicole Forsgren: DevEx is developer experience.

Nicole Forsgren：DevEx 是开发者体验。

And when we think about developer experience, we're really talking about what it's like to build software, day to day, for a developer.

当我们考虑开发人员体验时，我们真正谈论的是开发人员每天构建软件的感觉。

So the friction that they face, the workflows that they have to go through, any support that they have.

所以他们面临的摩擦，他们必须经历的工作流程，他们拥有的任何支持。

It's important because when DevEx is poor, everything else just isn't going to help.

这很重要，因为当 DevEx 很差时，其他一切都无济于事。

The best processes, the best tools, the best...

最好的流程、最好的工具、最好的……

whatever magic you have, if the DevEx is bad, everything kind of takes-

无论你有什么魔力，如果 DevEx 很糟糕，一切都会变得-

Lenny Rachitsky: Within DevEx is productivity, and I think the key insight that you had and other folks in the space of that is not just productivity, but there's also engineering happiness.

Lenny Rachitsky：DevEx 的核心是生产力，我认为您和其他人在该领域的关键见解不仅仅是生产力，还有工程幸福感。

We're going to get into a lot of these parts, but just maybe speak to...

我们将讨论很多这些部分，但也许只是谈谈......

there's productivity and there's broader components to engineers being successful at a company.

工程师在公司取得成功需要生产力和更广泛的要素。

Nicole Forsgren: Yeah.

妮可·福斯格伦：是的。

I love that point because productivity, first of all, is hard to define anyway.

我喜欢这一点，因为生产力首先是很难定义的。

But if you're just looking at output, you can get there in a lot of different ways.

但如果您只关注输出，则可以通过多种不同的方式实现。

But if you're getting there in ways that are high toil or high friction, then at some point, a developer is going to burn out.

但如果你以高辛苦或高摩擦的方式达到目标，那么在某些时候，开发人员就会精疲力尽。

Or if it's super high cognitive load, if it's hard to even think about what you're doing because concentrating on the mechanics of...

或者，如果认知负荷超高，甚至很难思考自己在做什么，因为专注于……的机制。

the plumbing of something, then you don't have the brain space left to come up with really innovative solutions and questions.

某物的管道，那么你就没有足够的大脑空间来提出真正创新的解决方案和问题。

So I love that it's kind of this self-reinforcing loop in terms of, "You do more work, you do better work." And it's better for people, it's better for the systems, it's better for our customers.

所以我喜欢这种自我强化的循环，“你做更多的工作，你就会做得更好。”这对人、对系统、对我们的客户都更好。

Lenny Rachitsky: I was going to get to this later, but I want to actually get to this right now, this idea of flow state for engineers.

Lenny Rachitsky：我本来打算稍后再讲这个，但我现在想真正讲这个，工程师的心流状态的想法。

I was an engineer, actually, early in my career.

事实上，在我职业生涯的早期，我是一名工程师。

I went to a school for computer science.

我去了一所计算机科学学校。

I was an engineer for 10 years.

我当了 10 年工程师。

The best part of the job for me was just this flow state you enter when you're coding and building, and just things feel like so fun.

对我来说，这份工作最棒的部分就是你在编码和构建时进入的流程状态，一切感觉都很有趣。

It feels like AI is making that harder in a lot of ways because there's all these agents you're working with now, there's all this code that's kind of being written for you.

感觉人工智能在很多方面都让这变得更加困难，因为你现在正在使用所有这些代理，所有这些代码都是为你编写的。

Talk about just the importance of flow state to a developer, happiness, developer productivity, and just what you've seen AI impacting.

谈谈心流状态对开发者的重要性、幸福感、开发者生产力，以及你所看到的人工智能的影响。

How you've seen AI impacting that?

您如何看待人工智能对此的影响？

Nicole Forsgren: Well, there are lots of different ways to talk about DevEx.

Nicole Forsgren：嗯，谈论 DevEx 有很多不同的方式。

One way to talk about it is kind of three key things that have components that are important of themselves, and they also kind of reinforce each other.

谈论它的一种方式是三个关键的事情，它们都有各自重要的组成部分，而且它们也互相加强。

Flow state is one of them, cognitive load is another, and then feedback loops are another.

心流状态是其中之一，认知负荷是另一个，然后反馈循环是另一个。

I think when you touch on this...

我想当你触及这个问题时...

Your question about flow state is a really good one, and I'll admit we're just a few years into this.

你关于心流状态的问题是一个非常好的问题，我承认我们才进入这个问题几年。

We're still figuring out what the best flow state and cognitive requirements are for people in this because, to your point, sometimes we're getting interrupted all the time.

我们仍在弄清楚人们在这方面的最佳心流状态和认知要求是什么，因为就你而言，有时我们总是被打断。

You don't just get in the flow and lock down, and write a whole bunch of code and do the typing of a whole bunch of code as much anymore.

您不再只是进入流程并锁定，然后编写一大堆代码并输入一大堆代码。

Instead, you're kind of creating a prompt, getting some code back and reviewing the code, trying to integrate what's happening in the system, and that can really interrupt.

相反，您需要创建一个提示，获取一些代码并检查代码，尝试集成系统中发生的事情，这确实可能会中断。

Nicole Forsgren: At the same time though, it can contribute to flow if...

Nicole Forsgren：但与此同时，如果……它可以促进心流。

I've seen some senior engineers pull together some tool chains that are really incredible, where they figured out how to keep the flow going.

我见过一些高级工程师将一些令人难以置信的工具链组合在一起，他们找到了如何保持流程继续进行。

The fast feedback loops really, really work well for them.

快速反馈循环对他们来说确实非常有效。

They can kind of assign out different pieces to agents.

他们可以将不同的部分分配给代理。

It helps them keep in the flow in terms of...

它可以帮助他们在……方面保持流畅。

Instead of details and line-by-line writing, they're in the flow in terms of, "What's my goal?

他们不再是细节和逐行写作，而是在“我的目标是什么？

What are the pieces that I need to get there?

我需要哪些东西才能到达那里？

How quickly can I get there?

我多快可以到达那里？

So then, I can step back and kind of evaluate everything, and then dive back in and fix some pieces."

那么，我可以退后一步，评估一切，然后再重新投入并修复一些部分。”

Lenny Rachitsky: Is there anything more you could say about this engineer that figured out this really cool workflow, about just what that looks like?

Lenny Rachitsky：关于这位工程师，你还有什么要说的吗？他想出了这个非常酷的工作流程，以及它看起来是什么样子？

Nicole Forsgren: I've spoken with a handful of them, and I've kind of watched them work.

Nicole Forsgren：我和他们中的一些人谈过，我也观察过他们的工作。

I haven't built it myself yet.

我自己还没有搭建过。

It's on my list.

它在我的名单上。

They've been able to set up this really incredible workspace and workflow where...

他们已经能够建立这个真正令人难以置信的工作空间和工作流程，其中......

Right now, a lot of us play around with tools and...

现在，我们很多人都在使用工具......

We'll put in a prompt and we'll get a few lines back or maybe we'll put in a prompt and we'll get whole programs back.

我们将输入一个提示，然后我们会返回几行，或者也许我们会输入一个提示，然后我们会返回整个程序。

Well, what they can do is they can...

好吧，他们能做的是他们能……

Many times I'll see them say, to help prime it, "This is what I want to build.

很多时候，我会看到他们为了帮助启动它而说：“这就是我想要构建的。

It needs to have these basic architectural components.

它需要具有这些基本的架构组件。

It needs to have this kind of a stack.

它需要有这样的堆栈。

It needs to follow this general workflow.

它需要遵循这个一般工作流程。

Help me think that through," and it'll kind of design it for it.

帮我想清楚，”它会为此设计。

And then for each piece, it'll assign an agent to go work on each pace in parallel, and then it'll say and upfront, "These need to be able to work together, make sure it's architected correctly.

然后，对于每个部分，它会指派一个代理并行处理每个步骤，然后它会预先说：“这些需要能够协同工作，确保其架构正确。

Make sure we use appropriate APIs and conventions."

确保我们使用适当的 API 和约定。”

Nicole Forsgren: Then at the end, they can let it run for a few minutes.

Nicole Forsgren：最后，他们可以让它运行几分钟。

They can think through something else that's interesting or they anticipate is going to be hairy, and they come back to something that's probably a little better than vibe coded.

他们可以思考其他有趣的事情，或者他们预计会很棘手的事情，然后他们会回到可能比氛围编码更好一点的事情。

Because they were so systematic about it upfront, they're much closer to something that looks like production code.

因为他们一开始就非常系统化，所以他们更接近于看起来像生产代码的东西。

Lenny Rachitsky: So what I'm hearing is spending a little time upfront planning, what all these AI engineers are doing, versus just powering through and just figuring out as you go.

Lenny Rachitsky：所以我听到的是花一点时间进行前期规划，所有这些人工智能工程师正在做的事情，而不是仅仅通过电源并边走边搞清楚。

Lenny Rachitsky: Okay, cool.

莱尼·拉奇茨基：好的，酷。

Let me get to this quite a core question that I think on is a lot of people's minds.

让我来谈谈这个我认为很多人都关心的核心问题。

A lot of companies are trying to measure productivity for their teams, "Is this improving our productivity?

许多公司正在尝试衡量其团队的生产力，“这是否提高了我们的生产力？

Is this hurting our productivity?" So let me just start with this question, how are people doing this wrong currently when they try to measure their productivity gains with AI?

这会损害我们的生产力吗？”那么，让我从这个问题开始，当人们试图通过人工智能来衡量生产力的提高时，他们目前是如何做错的？

Nicole Forsgren: I'll say most productivity metrics are a lie.

Nicole Forsgren：我想说大多数生产力指标都是谎言。

It's really tricky because, historically...

这真的很棘手，因为从历史上看......

Now, look, lines of code has always been a bad metric, but many folks still use lines of code-

现在，看，代码行数一直是一个不好的指标，但许多人仍然使用代码行数 -

Nicole Forsgren: ...

妮可·福斯格伦：……

yeah, as some proxy as some proxy for output or productivity or complexity or something.

是的，作为产出、生产力、复杂性等的一些代理。

Well, now, for many of the systems, that they would sometimes whisper and not super talk about that uses lines of code, it's just blown out of the water because, "What do you mean by lines of code?" If the goal is more lines of code, I can prompt something to write the longest piece of code ever and add tons of comments.

好吧，现在，对于许多系统来说，他们有时会低声谈论，而不是超级谈论使用代码行，它只是从水中吹出来，因为，“你所说的代码行是什么意思？”如果目标是更多行代码，我可以提示编写有史以来最长的代码并添加大量注释。

We know that agents and LLMs tend to be very verbose by definition, and so it's just too easy to gain that system and then introduce complexity and technical debt into all of the work that you're doing.

我们知道，代理人和法学硕士的定义往往非常冗长，因此很容易获得该系统，然后将复杂性和技术债务引入到您正在做的所有工作中。

I will say there are some things that we can kind of watch and pay attention to because...

我想说有些事情我们可以观察和关注，因为......

So lines of code as a productivity metric isn't great, it's pretty bad.

因此，将代码行数作为生产力指标并不是很好，而是非常糟糕。

But now, it's kind of more relevant if we can tease out which code came from people and which code came from AI because now we can answer downstream questions.

但现在，如果我们能够弄清楚哪些代码来自人类，哪些代码来自人工智能，那就更有意义了，因为现在我们可以回答下游问题。

Nicole Forsgren: "What is the code survivability rate?

Nicole Forsgren：“代码生存率是多少？

What is the quality of our code?

我们的代码质量如何？

Is our code being fed back into trained systems?

我们的代码是否被反馈到训练有素的系统中？

And for that code that's retraining systems later, especially if we're doing fine-tuning and local tuning, how much of that is machine generated?

对于稍后重新训练系统的代码，特别是如果我们正在进行微调和本地调整，其中有多少是机器生成的？

What types of loops is that creating, and what types of patterns or biases might it be inadvertently introducing?" On the one hand, it's not good as a productivity metric, but it can be useful.

它会创建什么类型的循环，以及它可能会无意中引入什么类型的模式或偏差？”一方面，它作为生产力指标并不好，但它可能很有用。

I'll even say the same for DORA.

我什至会对朵拉说同样的话。

I have done DORA metrics, their speed metrics, their stability metrics.

我做了 DORA 指标，他们的速度指标，他们的稳定性指标。

If that's all you're looking at, it's not going to be sufficient anymore because AI has now changed the way we think about feedback loops.

如果这就是您所看到的一切，那么它就不再足够了，因为人工智能现在已经改变了我们对反馈循环的思考方式。

They need to be much faster.

他们需要更快。

Now, what DORA's meant for, kind of assessing the pipeline overall in terms of speed and stability.

现在，DORA 的目的是从速度和稳定性方面评估整个管道。

Still, that works.

不过，这还是有效的。

But we can't just blindly apply the existing metrics we've used before because we'll miss super important phenomenon and changes in the way people work.

但我们不能盲目应用以前使用过的现有指标，因为我们会错过非常重要的现象和人们工作方式的变化。

Lenny Rachitsky: Interesting.

莱尼·拉奇茨基：有趣。

You invented DORA, that was kind of the main framework people used for a long time to measure productivity.

你发明了 DORA，这是人们长期以来用来衡量生产力的主要框架。

And then there's SPACE, there's Core 4, there's probably others.

然后是 SPACE、Core 4，可能还有其他。

So what I'm hearing here is all these are kind of out of date now, where AI is contributing large portions of code.

所以我在这里听到的是，所有这些现在都已经过时了，人工智能正在贡献大部分代码。

Nicole Forsgren: I will say if it is a prescriptive metric, it needs to be used only in the way it was prescribed.

Nicole Forsgren：我会说，如果它是一个规定性指标，则只需按照规定的方式使用它。

Nicole Forsgren: DORA 4, there are four key metrics.

Nicole Forsgren：DORA 4 有四个关键指标。

There's two speed metrics, deployment frequency and lead time.

有两个速度指标：部署频率和交付时间。

So code commit to code deploy.

所以代码提交到代码部署。

There's stability metrics, MTTR and change fail rate.

有稳定性指标、MTTR 和变更失败率。

If those are used to assess the speed of the pipeline and the general performance of the pipeline, that's great.

如果这些用于评估管道的速度和管道的总体性能，那就太好了。

If you're trying to use those to understand...

如果你想用它们来理解......

Because implied in that is feedback loops, right, because you used to kind of get feedback from customers.

因为这隐含着反馈循环，对吧，因为你过去常常从客户那里得到反馈。

But we can't just use that blindly now when we're using AI, as an example, because we have feedback loops much earlier and not even just at the local build and test phase.

但举例来说，当我们现在使用人工智能时，我们不能盲目地使用它，因为我们很早就有了反馈循环，甚至不仅仅是在本地构建和测试阶段。

We have feedback loops throughout, and even sometimes in the middle of some of the pipeline, that we really want to leverage in ways that weren't as useful before.

我们在整个过程中都有反馈循环，甚至有时在某些管道的中间，我们确实希望以以前不那么有用的方式利用反馈循环。

I won't say they weren't possible, but we just didn't really focus there.

我不会说它们不可能，但我们只是没有真正关注这一点。

Nicole Forsgren: So those are prescriptive metrics.

Nicole Forsgren：所以这些都是规定性指标。

When we think about SPACE, SPACE is a framework.

当我们思考空间时，空间是一个框架。

It doesn't tell you what metric to use.

它不会告诉您使用什么指标。

So I'll say, sometimes people get real frustrated because I didn't tell them what to measure.

所以我想说，有时人们会感到非常沮丧，因为我没有告诉他们要衡量什么。

But now, I think that's the power of it.

但现在，我认为这就是它的力量。

We're actually seeing that SPACE applies fairly well in these new emerging contexts like AI because we still want to look at...

我们实际上看到 SPACE 在人工智能等新兴环境中应用得相当好，因为我们仍然想看看……

SPACE is an acronym.

空间是一个缩写词。

We still want to look at satisfaction.

我们仍然想看看满意度。

We still want to look at performance, what's the outcome.

我们仍然想看看表现，结果如何。

We still want to look at activity.

我们仍然想看看活动。

Yes, in some ways, lines of code and number of PRs can be useful for something, or number of alerts or number of things, activities or counts.

是的，在某些方面，代码行数和 PR 数量可能对某些事物、警报数量或事物、活动或计数有用。

Seize communication and collaboration, this is also super important and useful because it's how our systems communicate with each other, and also how our people do.

抓住沟通和协作，这也非常重要和有用，因为这是我们的系统相互沟通的方式，也是我们的员工的工作方式。

"What proportion of work is being offloaded to a chat bot versus talking to a senior engineer on the team?" More isn't always better and less isn't always better, it depends.

“与与团队中的高级工程师交谈相比，有多少比例的工作被转移给聊天机器人？”更多并不总是更好，更少并不总是更好，这取决于情况。

Nicole Forsgren: And then efficiency and flow, "Can people get in the flow?

Nicole Forsgren：然后是效率和流程，“人们可以融入流程吗？

How much time does it take to do things?

做事情需要多少时间？

What is the flow like through our system?" Here, I would probably add a couple of dimensions.

我们系统的流程是什么样的？”在这里，我可能会添加几个维度。

So chatting with some of the early authors to say trust.

所以跟一些早期的作者聊天说信任。

Not to say trust wasn't important before, but now it's very, very front of mind.

并不是说信任以前不重要，但现在它非常非常重要。

Right?

正确的？

Before you build your code, if the compile comes back, you're fine.

在构建代码之前，如果编译返回，那就没问题。

And that's the way it is.

事情就是这样。

LLMs are non-deterministic.

LLM 是不确定的。

Right now, we can't just put in a command and guess something back and accept it.

现在，我们不能只是输入命令并猜测一些内容并接受它。

We really need to evaluate it, so, "Are we seeing hallucinations?

我们确实需要对其进行评估，因此，“我们看到的是幻觉吗？

What's the reliability?

可靠性如何？

Does it meet the style that we would typically write?

它符合我们通常的写作风格吗？

And if it doesn't meet, is that fine?" So it depends on...

如果不满足的话，可以吗？”所以这取决于...

Prescriptive.

规定性的。

You got to make sure you're using it fit for purpose.

您必须确保您使用它适合目的。

Right?

正确的？

Lenny Rachitsky: We're going to get to your current thinking on the best way to do this stuff.

Lenny Rachitsky：我们将了解您目前对做这件事的最佳方式的想法。

You have a book coming out that explains how to do this well, so we're going to get to that.

你们出版了一本书，解释了如何做好这件事，所以我们将着手解决这个问题。

One thing I wanted to highlight in our last chat that we had, you highlighted that one of the biggest issues we're going to probably have with AI is trust, understanding and learning how much to trust the code that it generates, and also how much...

我想在我们上次聊天中强调的一件事是，你强调了我们在人工智能方面可能遇到的最大问题之一是信任、理解和学习对它生成的代码的信任程度，以及......

you said this, two and a half years ago, that so much of the time is now going to be spent reviewing code versus writing code.

您在两年半前说过，现在大部分时间都花在审查代码而不是编写代码上。

That's exactly what I'm hearing.

这正是我所听到的。

Nicole Forsgren: I think it'll be interesting to see how that impacts the way we structure work moving forward.

Nicole Forsgren：我认为看看这如何影响我们未来构建工作的方式将会很有趣。

We were talking about flow state and cognitive load.

我们谈论的是心流状态和认知负荷。

Now that our attention has to focus on things at certain times and it's broken up from how we used to do it, I think there's some real opportunity there to, not just rethink workflows, but rethink how we structure our days and how we structure our work.

现在我们的注意力必须在某些时候集中在某些事情上，并且它与我们过去的做法不同，我认为有一些真正的机会，不仅要重新思考工作流程，还要重新思考我们如何安排我们的日子和我们如何安排我们的工作。

Lenny Rachitsky: Can you say more about that?

Lenny Rachitsky：你能多说一点吗？

Just what is that?

那是什么？

What are you thinking will be happening?

你认为会发生什么？

Where do you think things go?

你认为事情会走向何方？

What are you seeing working?

你看到什么工作？

Nicole Forsgren: This is purely speculative.

Nicole Forsgren：这纯粹是推测。

But for example, Gloria Mark has done some really good work on attention and deep work, and humans can get about four hours of good deep work a day.

但例如，格洛丽亚·马克（Gloria Mark）在注意力和深度工作方面做了一些非常好的工作，人类每天可以进行大约四个小时的良好深度工作。

That's about it.

就是这样。

Nicole Forsgren: That's kind of the upper limit-ish for the most part, and I'm sure people are going to be like, "Well, I am superhuman and I can do-

妮可·福斯格伦：这在很大程度上是上限，我相信人们会说，“好吧，我是超人，我可以做到——

Lenny Rachitsky: What if you take 20 grams of creatine?

Lenny Rachitsky：如果您服用 20 克肌酸会怎样？

Nicole Forsgren: Yeah.

妮可·福斯格伦：是的。

So in the context of knowing we have about four hours of good deep work...

因此，在知道我们有大约四个小时的深度工作的情况下......

I'm sure many of us have probably hit this, right?

相信很多人都遇到过这个吧？

We have good periods.

我们有美好的时期。

Maybe it's morning, maybe it's afternoon for folks.

对于人们来说，也许是早上，也许是下午。

And then you hit a time where you're like, "I'm going to clean up my inbox because that is all I can do right now.

然后你会想，“我要清理我的收件箱，因为这是我现在能做的一切。

I can be functional, but I'm not going to come up with my best innovative, problem solving, authoring, code writing work." A lot of times, the way to do that and to get into it is to have these long chunks to get into flow and to get that deep work.

我可以发挥职能，但我不会拿出我最好的创新、解决问题、创作、代码编写工作。”很多时候，做到这一点并进入其中的方法是让这些长块进入流动并进行深入的工作。

Usually, I'm two hours-ish.

通常，我会花两个小时左右。

An hour can be tricky because it could take time to get into that state.

一个小时可能会很棘手，因为进入那种状态可能需要时间。

Okay.

好的。

Well, when we think about what it used to be like, back in the old days, three years ago, three and a half years ago, we could block off four hours of time and we could probably get two or three hours of really good work done.

好吧，当我们想想过去是什么样子，三年前，三年半前，我们可以抽出四个小时的时间，我们可能可以完成两三个小时的真正出色的工作。

Because we were just focused, right?

因为我们只是专注，对吧？

There were no interruptions, minimal interruptions.

没有任何干扰，最小程度的干扰。

Nicole Forsgren: Now, the nature of writing code and systems itself is interrupt driven or full of interruptions, at least, because you start something and then it interjects.

Nicole Forsgren：现在，编写代码和系统本身的本质是中断驱动的或充满中断，至少是因为你启动了一些东西，然后它就会插入。

So how do we think about that?

那么我们如何看待这个问题呢？

Does that mean that a four-hour word block is still useful?

这是否意味着四个小时的单词块仍然有用？

Probably.

可能吧。

But does that mean that now we can also make a 45-minute work block useful?

但这是否意味着现在我们也可以让 45 分钟的工作时间变得有用呢？

Because getting into the flow is actually kind of handed off, at least, in part to the machine or the machine can help us get back into the flow by reminding us of context and generating diagrams of the system and all the things.

因为进入流程实际上是一种移交，至少部分地交给了机器，或者机器可以通过提醒我们上下文并生成系统和所有事物的图表来帮助我们重新回到流程中。

So I think that's a really, really interesting area that's just ripe for questions and opportunity.

所以我认为这是一个非常非常有趣的领域，问题和机会都已经成熟了。

And please, folks, do this research and come back to me because...

各位，请做一下这项研究，然后再回来找我，因为……

It might not make my list, but it's such a great question.

它可能不会出现在我的清单上，但这是一个很好的问题。

Lenny Rachitsky: That is so interesting.

Lenny Rachitsky：这太有趣了。

Essentially, every engineer is turning into an EM, engineering manager, coordinating all of these junior AI engineers.

本质上，每个工程师都在转变为 EM、工程经理，协调所有这些初级人工智能工程师。

So your point is even if you have a 30-hour block, you can get deep into code, but you can unblock all these AI engineers that are running off doing tasks.

所以你的观点是，即使你有 30 小时的阻塞，你也可以深入代码，但你可以解除所有这些正在执行任务的人工智能工程师的阻塞。

Plus, your point is they remind you of just like, "Here's where you left off.

另外，你的观点是它们会提醒你，“这是你离开的地方。

Okay.

好的。

You can just jump into this code, maybe make some tweaks."

你可以直接跳入这段代码，也许可以做一些调整。”

Lenny Rachitsky: So interesting.

莱尼·拉奇茨基：太有趣了。

Let me zoom out a little bit and...

让我把镜头缩小一点然后...

Before we get into your framework for how to approach developer experience, the latest thinking you've got, beyond just obviously engineers doing more is great, what's your best pitch for why companies should really, really focus on developer experience?

在我们讨论如何处理开发人员体验的框架之前，除了显然工程师做得更多是很棒之外，您的最新想法是什么？您对为什么公司应该真正非常关注开发人员体验的最佳宣传是什么？

Nicole Forsgren: I hate to say return of investment, but the business value is...

Nicole Forsgren：我讨厌说投资回报，但商业价值是......

the opportunity here is huge.

这里的机会是巨大的。

In general, we write software for fun and for hobbies, but we also have software because it meets a business need.

一般来说，我们编写软件是为了娱乐和爱好，但我们拥有软件也是因为它满足了业务需求。

It helps us with market share, it helps us attract and retain customers, it helps us do all of these things.

它帮助我们获得市场份额，帮助我们吸引和留住客户，它帮助我们做所有这些事情。

And I think DevEx is important because it enables all of that software creation, it enables all of that problem solving.

我认为 DevEx 很重要，因为它支持所有软件创建，支持所有问题解决。

It enables the super rapid experimentation with customers that...

它使客户能够进行超快速的实验...

Before, you'd need a while for a prototype and maybe a little bit longer to actually flight it through an A/B test on a production system.

以前，您需要一段时间来制作原型，并且可能需要更长的时间才能在生产系统上进行 A/B 测试。

You can do it in hours, right now.

您现在就可以在几个小时内完成。

Lenny Rachitsky: Maybe the opposite end of the spectrum, getting very tactical, before we get into the larger framework, what's just one thing that you think an eng team, a product team can do this week, next week to help their developer experience maybe get more done?

Lenny Rachitsky：也许是另一个极端，变得非常战术化，在我们进入更大的框架之前，您认为工程团队、产品团队可以在本周、下周做一件事来帮助他们的开发人员体验可能完成更多工作？

Nicole Forsgren: Honestly, I think the best thing you can do is go talk to people and listen.

Nicole Forsgren：老实说，我认为你能做的最好的事情就是与人们交谈并倾听。

I love that the audience of this podcast is primarily PMs because they tend to be really good at this.

我喜欢这个播客的观众主要是产品经理，因为他们往往非常擅长这一点。

And I would say start with listening and not with tools and automation.

我想说从倾听开始，而不是从工具和自动化开始。

So many times companies are like, "Well, I'm just going to build this tool," or, "I'm going to build this thing." Often you build a thing that you yourself have had a challenge with or that is easy to do, easy to automate.

很多时候，公司会说，“好吧，我只是要构建这个工具”，或者“我要构建这个东西”。通常，您构建的东西是您自己遇到的挑战，或者很容易做到，易于自动化。

And if you just go talk to people and ask the developers like, "Think of yesterday, what did you do yesterday?

如果你去和人们交谈并询问开发人员，“想想昨天，你昨天做了什么？

Walk me through it.

引导我完成它。

What were the points that were just delightful?

哪些点是令人愉快的？

What were the points that were really difficult?

哪些点是真正困难的？

Where did you get frustrated?

你在哪里感到沮丧？

Where did you get slowed down?

你在哪里变慢了？

Where was there friction?" If you go talk to a handful of people, a lot of times, you can surface a handful of things that are relatively low lift and still have impact or you can identify a process that's unnecessarily complex and slow.

哪里有摩擦？”如果你和几个人交谈，很多时候，你可以发现一些相对较低但仍然有影响的事情，或者你可以识别出一个不必要的复杂和缓慢的过程。

Lenny Rachitsky: So the listening to, I hear, almost is you want to help your teams move faster and be happier eng teams.

Lenny Rachitsky：所以我听到的几乎是你想要帮助你的团队更快地行动并成为更快乐的工程师团队。

Your advice is just, "Before you do anything, just go ask them what is bothering you."

你的建议只是，“在你做任何事情之前，先问问他们是什么困扰着你。”

Nicole Forsgren: Go ask them, yeah.

Nicole Forsgren：去问他们，是的。

And trust me, most developers are going to be more than happy to tell you what's broken and what's bad.

相信我，大多数开发人员都会非常乐意告诉您哪些地方出了问题，哪些地方不好。

I'll say, there was one company that I had worked with.

我会说，我曾与一家公司合作过。

I remember they had a process that was really difficult and it was on an old mainframe system, and they were going to have to replat the whole thing and so they never went to work on it or talk about it.

我记得他们有一个非常困难的流程，而且是在一个旧的大型机系统上，他们将不得不重新设计整个系统，所以他们从来没有去研究它或谈论它。

Everyone hated it because it was this huge delay.

每个人都讨厌它，因为这是一个巨大的延迟。

I mean, all they had to do was change a process.

我的意思是，他们所要做的就是改变一个流程。

Sometimes all you have to do is change a process.

有时您所要做的就是改变流程。

And they changed it so that instead of...

他们改变了它，而不是......

I think someone had to print it out and walk it down three or four flights, and they get approval.

我认为有人必须将其打印出来并在三到四次飞行中进行演示，然后他们才能获得批准。

And then someone else had to walk it back up, and so it was just that interim.

然后其他人必须把它重新拉回来，所以这只是一个过渡。

They didn't replat anything.

他们没有重新安装任何东西。

They didn't redesign anything major.

他们没有重新设计任何重大的东西。

They just sent an email.

他们刚刚发了一封电子邮件。

Lenny Rachitsky: Let me push on that and...

Lenny Rachitsky：让我继续努力……

I'm curious just what are the most common things people do.

我很好奇人们最常做的事情是什么。

If you're just starting on, "Okay, we need to focus on engineering experience," what do you find are the most...

如果您刚刚开始，“好吧，我们需要专注于工程经验”，您会发现什么是最......

two or three most common improvements companies need to make?

公司需要做出两到三个最常见的改进？

Nicole Forsgren: I'll say, I'll kind of echo that process, there's almost always a process that can be improved and that can be improved without a lot of engineering lift or a lot of engineering headcount.

Nicole Forsgren：我会说，我会有点赞同这个过程，几乎总是有一个可以改进的过程，并且可以在不需要大量工程提升或大量工程人员的情况下进行改进。

Most large companies, in particular, have something that is several, several steps.

尤其是大多数大公司，都有几个步骤。

It's the way it is because it's the way it is, but that's no longer the way it is.

事情就是这样，因为事情就是这样，但现在已经不再是这样了。

And even small companies sometimes is just a little too YOLO, and you don't know what it is and you're kind of chasing everyone around.

即使是小公司，有时也有点太 YOLO，你不知道它是什么，你有点追逐周围的每个人。

So if you can create a very lightweight process, that can also be helpful.

因此，如果您可以创建一个非常轻量级的流程，那也会很有帮助。

That can be one of the best places to start, especially if you have limited exposure to the whole rest of the org.

这可能是最好的起点之一，特别是如果您与组织其他部门的接触有限。

Sometimes just a team process can help.

有时只需团队流程即可提供帮助。

Nicole Forsgren: I will say from a business leader's standpoint, a lot of what you can do is provide structure and support for this organizational change.

Nicole Forsgren：我想说，从企业领导者的角度来看，你可以做的很多事情就是为这种组织变革提供结构和支持。

Communicate what you're doing, communicate what the priorities are, communicate why this is important, to celebrate wins.

传达您正在做的事情，传达优先事项，传达为什么这很重要，以庆祝胜利。

Because if folks try to do this, just like a one-off side fully-isolated project, it's really challenging to get some good momentum, to get people to care, and to get them stay involved.

因为如果人们尝试这样做，就像一个一次性的完全孤立的项目一样，那么获得一些良好的动力、让人们关心并让他们继续参与确实是一项挑战。

Because it feels like it's just another internal project that isn't going to matter or that isn't going to get celebrated, but it has these huge upside potential returns for the business.

因为感觉这只是另一个无关紧要或不会受到庆祝的内部项目，但它为企业带来了巨大的潜在回报。

Lenny Rachitsky: It's interesting, what I'm hearing here is nothing about tools or technologies.

Lenny Rachitsky：很有趣，我在这里听到的与工具或技术无关。

It's not like move to this cloud, it's not like install this new deployment system, it's processes and people and org and morale.

这不像迁移到这个云，也不像安装这个新的部署系统，它是流程、人员、组织和士气。

Nicole Forsgren: Yeah.

妮可·福斯格伦：是的。

Now, there will be technical pieces that are very important, especially now with AI, where we're rethinking how build and test systems work.

现在，有些技术部分非常重要，特别是现在有了人工智能，我们正在重新思考构建和测试系统的工作方式。

We're rethinking feedback to users so that it's very, very customized in terms of what is shared and when it is shared.

我们正在重新考虑对用户的反馈，以便在共享内容和共享时间方面进行非常非常定制。

There are a lot of technical pieces that are involved, but that's not the only thing.

涉及到很多技术部分，但这并不是唯一的。

It's necessary but not sufficient, and that doesn't have to be the place that you start.

这是必要的，但还不够，而且这不一定是您开始的地方。

Lenny Rachitsky: I have a hard question I want to ask you that I thought of as you were talking.

Lenny Rachitsky：我有一个很难的问题想问你，在你说话时我想到了这个问题。

I feel like this is the question that most founders and heads think about.

我觉得这是大多数创始人和负责人思考的问题。

And the question is just like, how do I know if my eng team is moving fast enough, if they can move faster, if they're just not performing as well as they can?

问题是，我如何知道我的工程团队是否进展得足够快，他们是否可以进展得更快，或者他们的表现是否不够好？

What are just maybe smells, signs that tell you, "Yeah, my team should be moving faster," versus, "This is just the way it works.

也许只是气味、迹象告诉你，“是的，我的团队应该行动得更快”，而不是“这就是它的运作方式。

This is as fast as they can move"?

这就是他们能移动的最快速度了”？

Nicole Forsgren: Most teams can move faster, right?

Nicole Forsgren：大多数团队都可以更快地行动，对吧？

Also, given what we know about cognitive load, not all speed gains are necessarily good.

此外，根据我们对认知负荷的了解，并非所有速度提升都一定是好的。

Or the upside is going to be kind of limited once you hit kind of a certain point, and most people are not even near that point.

或者，一旦达到某个点，上行空间就会受到限制，而大多数人甚至还没有接近该点。

I don't know a single team, frankly.

坦白说，我一个团队都不认识。

But how do you know?

但你怎么知道呢？

You know if you're always hearing about bills breaking, flaky tests, overly long processes, if you have to request a new system or if you need to provision a new environment, or if it's really, really hard to switch tasks or switch projects.

您知道您是否总是听说账单破裂、不稳定的测试、过长的流程，是否必须请求新系统或是否需要配置新环境，或者是否真的很难切换任务或切换项目。

So if someone has an opportunity to go work in another part of an org and they don't for reasons that are unclear, and not political, and anyone says anything about the system, that's usually a pretty good smell that there's friction somewhere.

因此，如果某人有机会去组织的另一个部门工作，但他们出于不明原因而不去工作，而且与政治无关，并且有人对系统发表任何评论，那么通常会让人感觉到某处存在摩擦。

Nicole Forsgren: Because once you finally figure out your system and you're able to get work done, the switching costs can often be really, really high to go anywhere else.

Nicole Forsgren：因为一旦你最终弄清楚了你的系统并且能够完成工作，那么转到其他地方的转换成本通常会非常非常高。

So sometimes people will do that.

所以有时候人们会这么做。

But I've worked with companies where switching orgs within the company, you had to basically pay the same tax as a new hire because the systems were so different and they were so full of friction, and it was so difficult to do so many things.

但我曾与那些在公司内部进行组织转换的公司合作过，你基本上必须缴纳与新员工相同的税，因为系统是如此不同，而且充满了摩擦，而且做这么多事情是如此困难。

Lenny Rachitsky: I love the first part of your answer especially, which is you can always move faster.

Lenny Rachitsky：我特别喜欢你回答的第一部分，那就是你总是可以走得更快。

I think every founder is going to love hearing that.

我想每个创始人都会喜欢听到这个。

To your point though, there's diminishing returns over time?

但就你的观点而言，随着时间的推移，回报会递减？

Nicole Forsgren: Yeah.

妮可·福斯格伦：是的。

And you don't know about the quality, right?

而且你不知道质量，对吗？

So I think that's the other side is that you can always move faster, but faster for what?

所以我认为另一方面是你总是可以移动得更快，但是更快是为了什么？

Are we making the right business decisions?

我们是否做出了正确的商业决策？

And I think that's especially where PMs come in.

我认为这尤其是产品经理发挥作用的地方。

We can ship trash faster every single day.

我们每天都可以更快地运送垃圾。

We need strategy and really smart decisions to know what to ship, what to experiment with, what features we want to do in what order and what rollout.

我们需要战略和真正明智的决策来知道要发布什么、要试验什么、我们想要以什么顺序执行什么功能以及推出什么。

The strategy is the core piece, and then think about speeding that up.

战略是核心部分，然后考虑加快速度。

If we don't have the other pieces in place, I mean, garbage in, garbage out.

如果我们没有准备好其他部分，我的意思是，垃圾进，垃圾出。

Lenny Rachitsky: I want to follow that thread, but before I do that, just to mirror back what you shared.

Lenny Rachitsky：我想追踪这个线索，但在此之前，只是为了反映你分享的内容。

So signs that your team...

所以这表明你的团队...

There's a lot of low-hanging fruit to improve the productivity of your team as builds are always breaking.

由于构建总是会中断，因此有很多容易实现的目标可以提高团队的生产力。

There's flaky tests are constantly incorrect, false positives.

不稳定的测试总是不正确、误报。

It's hard to context switch between different projects.

不同项目之间的上下文切换很困难。

You just hear people talking about the system, it's just really hard to work with.

你只会听到人们谈论这个系统，它真的很难使用。

Is that roughly right?

大致是这样吗？

Lenny Rachitsky: Cool, okay.

莱尼·拉奇茨基：很酷，好吧。

So going back to the point you just made, there's a sense that AI is making teams so much faster because it's writing all this code for them.

回到你刚才提出的观点，人工智能正在让团队变得更快，因为它正在为他们编写所有这些代码。

You're going to have all these asynchronous agents, engineers working for you.

您将拥有所有这些异步代理、工程师为您工作。

It feels like a core part of your message is that's just a one part of engineering work and there's so much more, including figuring out what to build...

感觉您的信息的核心部分是这只是工程工作的一部分，还有更多的内容，包括弄清楚要构建什么......

an alignment internally.

内部对齐。

Maybe just speak to just...

也许只是说话...

There is a lot of opportunity to improve engineering performance productivity, but there's so many other elements that are not improved through AI?

提高工程性能生产力的机会有很多，但还有很多其他元素没有通过人工智能得到改善？

Nicole Forsgren: Yes.

妮可·福斯格伦：是的。

Or could be in the future, right?

或者可能是在未来，对吗？

Nicole Forsgren: I think there are a lot of ways that we can pull in AI tools to help us refine our strategy, refine our message, think about the experimentation methods or targets of experimentation, or think about our total addressable market, but we need to have that strategy and plan fairly well aligned or at least have two or three alternatives that you want to test.

Nicole Forsgren：我认为我们可以通过多种方式引入人工智能工具来帮助我们完善我们的策略，完善我们的信息，思考实验方法或实验目标，或者思考我们的整个潜在市场，但我们需要使该策略和计划相当一致，或者至少有两个或三个你想要测试的替代方案。

Because now, the engineering can go, or at least the prototyping especially, much, much faster.

因为现在，工程设计，或者至少是原型设计，尤其是，快得多。

We can throw out prototypes.

我们可以扔掉原型。

We can run any tests and experiments that are customer facing, assuming that we have the infrastructure in place, which allows us to learn and progress much faster before.

假设我们拥有适当的基础设施，我们可以运行面向客户的任何测试和实验，这使我们能够更快地学习和进步。

In some places, it used to take months to get something through production to do A/B testing and get feedback.

在某些地方，过去需要几个月的时间才能让某些东西通过生产来进行 A/B 测试并获得反馈。

We can do this in a day or two, definitely under a week.

我们可以在一两天内完成这项工作，绝对不会超过一周。

But we want to make sure that we're building and testing the right things, "Are we partnering with the right...

但我们希望确保我们正在构建和测试正确的东西，“我们是否与正确的伙伴合作......

Do we have the data that we need?"

我们有我们需要的数据吗？”

Nicole Forsgren: And I will say AI can actually be a pretty good partner there if you have a good conversation with it, and then also check with you experts, "What type of data should I be looking at?

Nicole Forsgren：我想说，如果你与人工智能进行良好的对话，那么人工智能实际上可以成为一个非常好的合作伙伴，然后再与你们的专家核实，“我应该查看什么类型的数据？

What type of instrumentation do I need?

我需要什么类型的仪器？

What type of analysis can I do?" Because then, you can also go to your data science team and say, "I'm planning on doing this.

我可以做什么类型的分析？”因为那时，你也可以去找你的数据科学团队说：“我正计划这样做。

I'd like to..." Let's not just YOLO A/B tests because that can be...

我想......”我们不仅仅是 YOLO A/B 测试，因为这可能......

It's a shame to do a large test and end up disrupting users or disrupting customers, or breaking privacy or security protocols and also end up with data that's unusable because you just can't get the signal that you're looking for.

进行大型测试并最终扰乱用户或客户，或者破坏隐私或安全协议，并且最终得到无法使用的数据，这是一种耻辱，因为您无法获得您正在寻找的信号。

But now, I'm also seeing people kind of accelerate that into a few days versus a few weeks.

但现在，我也看到人们将其加速到几天而不是几周。

So they can start those key stakeholder discussions from a much more informed kind of filled out space.

因此，他们可以从更加知情的填充空间开始这些关键利益相关者的讨论。

Lenny Rachitsky: Today's episode is brought to you by Coda.

Lenny Rachitsky：今天的节目由 Coda 为您带来。

I personally use Coda every single day to manage my podcast and also to manage my community.

我个人每天都使用 Coda 来管理我的播客和社区。

It's where I put the questions that I plan to ask every guest that's coming on the podcast, it's where I put my community resources, it's how I manage my workflows.

这是我提出问题的地方，我计划向播客中的每一位来宾提出问题，这是我放置社区资源的地方，这是我管理工作流程的方式。

Here's how Coda can help you.

以下是 Coda 如何帮助您。

Imagine starting a project at work and your vision is clear, you know exactly who's what and where to find the data that you need to do your part.

想象一下，在工作中启动一个项目，您的愿景很清晰，您确切地知道谁是谁，以及在哪里可以找到完成您的工作所需的数据。

In fact, you don't have to waste time searching for anything because everything your team needs from project trackers and OKRs, the documents and spreadsheets lives in one tab all in Coda.

事实上，您不必浪费时间搜索任何内容，因为您的团队所需的项目跟踪器和 OKR、文档和电子表格中的所有内容都位于 Coda 的一个选项卡中。

With Coda's collaborative all-in-one workspace, you get the flexibility of docs, the structure of spreadsheets, the power of applications, and the intelligence of AI all in one easy-to-organize tab.

借助 Coda 的协作式一体化工作区，您可以在一个易于组织的选项卡中获得文档的灵活性、电子表格的结构、应用程序的强大功能以及 AI 的智能。

Lenny Rachitsky: Like I mentioned earlier, I use Coda every single day.

Lenny Rachitsky：就像我之前提到的，我每天都使用 Coda。

And more than 50,000 teams trust Coda to keep them more aligned and focused.

超过 50,000 个团队相信 Coda 可以让他们更加协调和专注。

If you're a startup team looking to increase alignment and agility, Coda can help you move from planning to execution in record time.

如果您是一个希望提高一致性和敏捷性的初创团队，Coda 可以帮助您在创纪录的时间内从计划转向执行。

To try it for yourself, go to coda.io/lenny today and get six months free of the team plan for startups.

要亲自尝试，请立即访问 coda.io/lenny，并获得六个月免费的初创公司团队计划。

That's C-O-D-A-dot-I-O-slash-Lenny to get started for free and get six months of the team plan, coda.io/lenny.

这是 C-O-D-A-dot-I-O-slash-Lenny，免费开始并获得六个月的团队计划，coda.io/lenny。

Lenny Rachitsky: I love that you work with a bunch of different companies and a bunch of different types of businesses.

Lenny Rachitsky：我喜欢您与许多不同的公司和不同类型的企业合作。

I think very few people get to see inside a lot of different places.

我认为很少有人能够看到很多不同地方的内部。

What kind of gains are you just seeing in terms of increased productivity with AI?

您认为人工智能在提高生产力方面取得了哪些成果？

How big of a gain have you seen?

你看到了多大的收获？

Nicole Forsgren: I'd say it's real, and I would also say we don't have great measures for it yet.

Nicole Forsgren：我想说这是真的，而且我还想说我们还没有采取很好的措施。

We're still trying to figure out what to measure and what that looks like.

我们仍在努力弄清楚要测量什么以及它是什么样子。

One of the best is going to be velocity, all the way through the system, how quickly can you get a feature or a product or something through the system so that you can then experiment a test, either from idea to final end or even kind of a feature and a piece through the system so we can test.

最好的之一就是整个系统的速度，你能多快地通过系统获得一个功能、一个产品或其他东西，这样你就可以进行测试，无论是从想法到最终结束，甚至是一个功能和一个系统的一部分，这样我们就可以进行测试。

That's really good.

那真是太好了。

Now, that's also hard to tie back directly to a particular AI tool in the hands of a particular developer.

现在，这也很难直接与特定开发人员手中的特定人工智能工具联系起来。

But there are some other things that we can look at and we can see, and that I've seen is, again, this kind of rapid prototyping.

但还有一些其他的东西我们可以观察和看到，而我所看到的又是这种快速原型制作。

Nicole Forsgren: I hate lines of code, but I'm going to use the lines of code.

Nicole Forsgren：我讨厌代码行，但我将使用代码行。

We do see...

我们确实看到...

I know I worked with some folks who had kind of a whole set of companies they were looking at, and they found that AI was generating significantly more code for the people who were using it regularly.

我知道我和一些人一起工作过，他们正在研究一整套公司，他们发现人工智能正在为经常使用它的人生成更多的代码。

But then, they also found that for folks who were regular users of AI coding environments, AI ADEs, the tool kind of gave them more code.

但随后，他们还发现，对于 AI 编码环境（AI ADE）的常规用户来说，该工具为他们提供了更多代码。

And then the engineers themselves, the increase was double what the coding agent had given them.

然后是工程师本身，增加的费用是编码代理给他们的两倍。

So one, I'd say, probably it's kind of a secondary or knock on or just a smell is it can unblock you.

所以，我想说，这可能是一种次要的、敲门声或只是一种气味，它可以解锁你。

It can speed up the work that you would already do.

它可以加快您已经要做的工作。

I know sometimes when I work, the first few minutes, it's hard for me to start.

我知道有时当我工作时，最初几分钟，我很难开始。

But once I get started, I'm there.

但一旦我开始，我就在那里。

So they're really good at unblocking and unlocking that.

所以他们真的很擅长解除封锁和解锁。

Lenny Rachitsky: Something I've seen people on Twitter sharing is how good OpenAI Codex, especially, is at finding really gnarly bugs.

Lenny Rachitsky：我在 Twitter 上看到人们分享了 OpenAI Codex 的出色表现，特别是在发现真正棘手的错误方面。

And I think it was Karpathy that shared it.

我认为是卡帕蒂分享了它。

He was so stuck on a bug and, no AI tool could figure it out.

他被一个错误困住了，没有任何人工智能工具可以解决这个问题。

And then the latest version of Codex spent an hour or something, looking into it, and found it for him.

然后最新版本的 Codex 花了一个小时左右的时间，研究了一下，并为他找到了它。

Nicole Forsgren: Yeah.

妮可·福斯格伦：是的。

I'm hearing incredible things like that, right?

我听到过这样令人难以置信的事情，对吗？

Well, and even also writing unit tests and spinning up unit tests, and creating documentation and cleaning up documentation because I know now people are like, "Oh.

好吧，甚至还编写单元测试和旋转单元测试，创建文档和清理文档，因为我知道现在人们会说，“哦。

Well, we have agents.

嗯，我们有代理。

I don't need to read the docs because there's the code there." It turns out, agents rely on good data because it's all about how they've been trained or how they've been grounded.

我不需要阅读文档，因为那里有代码。”事实证明，特工依赖良好的数据，因为这完全取决于他们如何接受培训或如何扎根。

And better data gives you better outcomes, and some of that data includes documentation and comments.

更好的数据可以为您带来更好的结果，其中一些数据包括文档和评论。

The better documentation and the better comments you have, the better performance you're going to get out of your AI tools.

更好的文档和更好的评论，您从人工智能工具中获得的性能就越好。

Lenny Rachitsky: And AI can help you write that documentation.

Lenny Rachitsky：人工智能可以帮助您编写文档。

I've been working with Devin a little bit, and it's really good at that stuff.

我和 Devin 合作过一段时间，他在这方面非常擅长。

Lenny Rachitsky: Okay.

莱尼·拉奇茨基：好的。

Let's talk about this framework, this book.

我们来谈谈这个框架、这本书。

So you're publishing a book called Frictionless, which sounds like a dream, "How do you create a dev team that's frictionless?" It's called Frictionless: 7 Steps to Remove Barriers, Unlock Value, and Outpace Your Competition in the Age of AI.

因此，您正在出版一本名为《Frictionless》的书，这听起来像是一个梦想，“如何创建一个无摩擦的开发团队？”它被称为“无摩擦：在人工智能时代消除障碍、释放价值并超越竞争对手的 7 个步骤”。

There's a seven-step process to this.

这有一个七个步骤的过程。

Walk us through this and maybe give us just context on this book, who it's meant for, what problem it solves, and then the seven steps.

请引导我们完成这个过程，也许只给我们提供这本书的背景信息，它的目标读者，它解决的问题，然后是七个步骤。

Nicole Forsgren: I will say, I also wrote this with Abi Noda who has just...

Nicole Forsgren：我想说，我也和 Abi Noda 一起写过这篇文章，他刚刚......

of DX.

DX。

He has incredible experience in the space.

他在该领域拥有令人难以置信的经验。

He's worked with hundreds of companies and so it was kind of nice bouncing ideas off of him.

他曾与数百家公司合作过，所以从他那里得到想法真是太好了。

Also, thanks to all of the engineering leads and DevEx leads, and CTOs, and engineers that we talked to to make sure that our smells were right.

此外，还要感谢所有与我们交谈以确保我们的嗅觉正确的工程主管、DevEx 主管、CTO 和工程师。

So who is this book for-

那么这本书适合谁——

Lenny Rachitsky: Let me take a tangent on Abi, and DX, since you mentioned him.

Lenny Rachitsky：既然你提到了 Abi 和 DX，那么让我来谈谈他吧。

This is super interesting, and I think it connects so directly with this conversation.

这非常有趣，我认为它与这次谈话直接相关。

Abi started this company called DX, which is such a great name for a company around developer experience.

Abi 创办了这家名为 DX 的公司，对于一家围绕开发人员体验的公司来说，这个名字真是太棒了。

They just sold the company for a billion dollars to Atlassian.

他们刚刚以 10 亿美元的价格将公司卖给了 Atlassian。

It's a very high multiple on their ARR.

这是他们的 ARR 的非常高的倍数。

It, to me, shows exactly why this conversation is so valuable, just how much value companies are putting into improving developer experience.

对我来说，它准确地表明了为什么这次对话如此有价值，以及公司在改善开发人员体验方面投入了多少价值。

Atlassian would spend a billion dollars on this.

Atlassian 将为此花费 10 亿美元。

It's an early stage-ish startup.

这是一家处于早期阶段的初创公司。

It was doing really well and people loved it, but it was like early stage-ish, a billion dollars.

它做得非常好，人们喜欢它，但它还处于早期阶段，价值 10 亿美元。

And the idea is they have all these companies working using Jira and all their products.

他们的想法是让所有这些公司都使用 Jira 及其所有产品。

They're all trying to figure out how do we measure productivity.

他们都在试图弄清楚我们如何衡量生产力。

It's worth a lot of money to them.

这对他们来说值很多钱。

And I know you were an early advisor to them too, so-

我知道你也是他们的早期顾问，所以-

Lenny Rachitsky: ...

莱尼·拉奇茨基：……

it just shows us how important this is.

它只是向我们展示了这是多么重要。

Nicole Forsgren: Yeah.

妮可·福斯格伦：是的。

Well, I think it also shows us how much value you can get out of this.

嗯，我认为这也向我们展示了您可以从中获得多少价值。

There's so much low-hanging fruit, there's so much unlocked potential, and it's hard to know where to start a lot of times even in...

有太多唾手可得的成果，有太多未开发的潜力，即使在……很多时候也很难知道从哪里开始。

I've been at large companies that have a lot of expertise and a lot of really, really smart people.

我曾在拥有大量专业知识和很多非常非常聪明的人的大公司工作过。

But if you haven't kind of been in this space and thinking about it this way, it's hard to know where to start or it's easy to make simple mistakes up front that mean you kind of need to start over later.

但如果你还没有进入这个领域并以这种方式思考它，那么很难知道从哪里开始，或者很容易在前面犯一些简单的错误，这意味着你需要稍后重新开始。

So I guess it also brings us back to, "Who is this book for?" It's for anyone that cares about DevEx, so definitely technology leaders, anyone who's trying to kick off a DevEx program, or is working on a DevEx DevEx improvement program.

所以我想这也让我们回到了“这本书是为谁而写的？”它适用于任何关心 DevEx 的人，当然也包括技术领导者、任何试图启动 DevEx 计划或正在致力于 DevEx DevEx 改进计划的人。

I think it's particularly relevant for PMs because if you're PMing something that involves software building and creating software, improving DevEx will only help your team.

我认为这对于 PM 特别相关，因为如果您正在 PMing 涉及软件构建和创建软件的东西，那么改进 DevEx 只会对您的团队有所帮助。

And also, you have key skills and insights and instincts that are so important to DevEx that many times, I will say, I've seen engineering teams just miss.

而且，你拥有对 DevEx 非常重要的关键技能、洞察力和直觉，我会说，很多时候，我看到工程团队都错过了。

Lenny Rachitsky: Okay.

莱尼·拉奇茨基：好的。

What's the framework?

框架是什么？

What are the steps?

步骤是什么？

Where do people start?

人们从哪里开始？

Nicole Forsgren: The book goes through a seven-step process, and then also kind of provides some key kind of principles at the end.

Nicole Forsgren：这本书经历了七个步骤，最后还提供了一些关键的原则。

Step one is to start the journey.

第一步是开始旅程。

So assuming you're kicking off, you can start the journey.

因此，假设您要开始，您就可以开始旅程。

And this involves what we have already talked about.

这涉及到我们已经讲过的内容。

Go talk to people, have a listening tour, synthesize what you learn, visualize the workflow and tools, get a handle on what the current state is.

与人们交谈，进行聆听之旅，综合您所学到的知识，可视化工作流程和工具，了解当前的状态。

Step two is to get a quick win.

第二步是快速获胜。

So start small, get a quick win, pick the right projects, share out what you've done.

因此，从小事做起，快速获胜，选择正确的项目，分享您所做的事情。

Step three is using data to optimize the work.

第三步是使用数据来优化工作。

So establish some of your data foundation, find the data that's there, start collecting new data, use some surveys for some really fast insights and may include example surveys.

因此，建立一些数据基础，找到现有的数据，开始收集新数据，使用一些调查来获得一些真正快速的见解，并且可能包括示例调查。

Step four then is to decide strategy and priority.

第四步是决定策略和优先级。

Once you have some data, then you need to know of all the things that are potentially broken.

一旦获得了一些数据，您就需要了解所有可能损坏的内容。

And if you've already gotten your quick win of all the things that are left, "What should I do next?" So we walk through some evaluation frameworks there.

如果你已经快速赢得了剩下的所有事情，“接下来我应该做什么？”因此，我们将介绍一些评估框架。

Nicole Forsgren: Step five is to sell your strategy.

Nicole Forsgren：第五步是推销你的策略。

Once you've decided, now you have to kind of convince everyone else.

一旦你决定了，现在你必须说服其他人。

So now you want to get feedback, you want to share why this is the right strategy right now.

所以现在你想获得反馈，你想分享为什么这是目前正确的策略。

Step six is to drive change at your scale.

第六步是推动规模化变革。

So here, we address folks that have local scope of control.

因此，在这里，我们针对的是具有本地控制范围的人。

If you're starting on just a dev team, you want to do it yourself, kind of grassroots effort or global scope of control.

如果您刚开始组建一个开发团队，那么您想自己动手，进行基层努力或进行全球范围的控制。

If you're the VP of developer experience or something, there are some things that you can leverage for a top down, and then how do you drive change when you're kind of somewhere in the middle, because you can leverage both types of strategies.

如果您是开发人员体验副总裁或其他职位，您可以利用一些自上而下的东西，然后当您处于中间位置时如何推动变革，因为您可以利用这两种类型的策略。

And then step seven is to evaluate your progress and show value, and then kind of loop back around.

然后第七步是评估你的进步并展示价值，然后再循环回来。

Nicole Forsgren: I will say that we wrote this so that you could kind of jump into any step wherever you are right now.

Nicole Forsgren：我想说的是，我们写这篇文章的目的是为了让您无论现在身在何处都可以跳入任何步骤。

If you're kicking off a team or an initiative, you'll probably want to start at step one.

如果您要启动一个团队或一项计划，您可能需要从第一步开始。

You should definitely start at step one.

您绝对应该从第一步开始。

If you're joining an existing initiative, you could jump into picking the priority or implementing the changes.

如果您要加入现有计划，您可以直接选择优先级或实施更改。

So those are the seven steps.

这就是七个步骤。

There's a seven steps, there are a few practices that we also recommend.

有七个步骤，我们还推荐一些做法。

So thinking about resourcing it, change management, making technology sustainable, and then also bringing a PM lens to this, "How can we think about developer experience as a product, and how do we think about the metrics that we have as a product?"

因此，要考虑为其提供资源、变革管理、使技术可持续发展，然后引入 PM 视角，“我们如何将开发人员体验视为产品，以及我们如何看待我们作为产品所拥有的指标？”

Lenny Rachitsky: Awesome, okay.

莱尼·拉奇茨基：太棒了，好吧。

I have questions.

我有疑问。

Point people to the book real quick.

快速引导人们阅读这本书。

What's the URL?

网址是什么？

How do they get it?

他们如何得到它？

When does it come out?

什么时候出来？

Nicole Forsgren: Yeah, developerexperiencebook.com.

Nicole Forsgren：是的，developerexperiencebook.com。

Right now, you can sign up for the mailing list.

现在，您可以注册邮件列表。

We'll let you know when it's out on pre-order, and we'll also be sharing pieces of the workbook.

当它开始预订时，我们会通知您，并且我们还将分享工作簿的部分内容。

So we've got almost a hundred page workbook that goes along with the book, and then it should be out by end of year.

因此，我们已经有了与这本书配套的近一百页的工作簿，然后应该会在今年年底出版。

Lenny Rachitsky: Okay.

莱尼·拉奇茨基：好的。

So one piece of this is just this term developer experience feels very intentional in that it's not developer productivity, developer work.

因此，其中一个就是开发人员体验这个术语，感觉非常有意为之，因为它不是开发人员的生产力，而是开发人员的工作。

It's how do we make developer experiences better at our company, which includes they get more done, but also they're happier and things like that.

这就是我们如何让我们公司的开发人员体验更好，包括他们完成更多工作，而且他们更快乐等等。

So I think that's an important element of this, right?

所以我认为这是一个重要因素，对吧？

Nicole Forsgren: Because, again, it's not just about productivity.

Nicole Forsgren：因为，这不仅仅是生产力的问题。

We talked about this from the frame and the lens of, "We need to be building the right thing." And you want to be productive, but you also want to be thinking about...

我们从“我们需要构建正确的东西”的框架和镜头来讨论这一点。你想要提高生产力，但你也想要考虑......

and this is what engineers are also just really incredibly good at, give them a problem and don't tell them how to solve it, and then they can solve it better.

这也是工程师非常擅长的事情，给他们一个问题，但不告诉他们如何解决，然后他们就能更好地解决它。

They have the freedom, they have the innovation, they have the creativity so that they can solve this problem.

他们有自由、有创新、有创造力，这样他们就能解决这个问题。

If it's only about productivity, then it's just lines of code or number PRs or whatever.

如果它只与生产力有关，那么它只是代码行或 PR 数量或其他什么。

But we really want to talk about value and how do we unlock value, and how do we get value faster.

但我们真的想谈谈价值，以及我们如何释放价值，以及如何更快地获得价值。

And that involves, yes, making them more productive and removing friction because then, they have the flow and the cognitive load and the things that we kind of talked about.

是的，这涉及到提高他们的生产力并消除摩擦，因为这样他们就有了流动性、认知负荷以及我们谈论过的事情。

Lenny Rachitsky: Awesome, okay.

莱尼·拉奇茨基：太棒了，好吧。

And then say someone wants to start this team, what does it usually look like.

然后说有人想组建这个团队，通常是什么样子的。

At Airbnb, I remember this team forming.

在 Airbnb，我记得这个团队的组建。

It was just like an engineer or two, getting it started and taking charge.

就像一两​​个工程师一样，启动并负责。

What do you recommend as the pilot team, and then what does it look like as it grows?

作为试点团队，您有什么建议？随着它的发展，它会是什么样子？

Nicole Forsgren: There are a few ways to do this, right?

Nicole Forsgren：有几种方法可以做到这一点，对吗？

So if you're doing it yourself, you could do it with a couple of engineers, maybe a PM or a PGM or a TPM to kind of help communicate.

因此，如果您自己做，您可以与几位工程师一起完成，也许是一名 PM、PGM 或 TPM，以帮助沟通。

Because really, comms plans are just so important here.

因为实际上，通讯计划在这里非常重要。

On a small scale, what we want to do is look for those quick wins, look for things that you can do at small scale.

在小规模上，我们想做的是寻找快速获胜的方法，寻找可以在小规模上做的事情。

Some folks call them things like paper cuts.

有些人称它们为剪纸之类的东西。

There small things that you can do to help people see the value and feel the benefit themselves, "How can a developer's work get better?

您可以做一些小事情来帮助人们看到价值并感受到好处，“开发人员的工作如何变得更好？

How can their day-to-day work get better?

他们的日常工作如何才能变得更好？

Kind of build momentum from there?" If you're working from a top-down structure and you have the remit, you still want some quick wins, but those quick wins can look a little more global in scale because you have the infrastructure or the backing to make different types of changes that aren't only local.

从那里开始建立动力？”如果你在自上而下的结构中工作并且你有职权范围，你仍然想要一些快速的胜利，但这些快速的胜利在规模上看起来可能更加全球化，因为你拥有基础设施或支持来进行不同类型的改变，而不仅仅是本地的。

Nicole Forsgren: So an example of a small local change could be just cleaning up your tests, your test suites.

Nicole Forsgren：所以一个小的本地更改的例子可能只是清理你的测试、你的测试套件。

Any team could do that, any team could do that.

任何团队都可以做到，任何团队都可以做到。

At more global scale, it might be changing organization-wide process that is just overly cumbersome or throwing some resourcing into cleaning up the provisioning environment.

在更大的全球范围内，它可能正在改变过于繁琐的组织范围流程，或者投入一些资源来清理配置环境。

Lenny Rachitsky: Okay.

莱尼·拉奇茨基：好的。

What kind of impact have you seen from teams like this forming, on the engineering teams at their companies?

您认为这样的团队的组建对其公司的工程团队产生了什么样的影响？

Nicole Forsgren: I'll say I've seen a huge impact for smaller companies, hundreds of thousands of dollars for large companies or in the billions.

Nicole Forsgren：我会说我看到了对小公司的巨大影响，对大公司来说有数十万美元或数十亿美元。

Well, also, we need to learn how to communicate that, "What does the math look like?" Many times, we can look at saving time, we can look at saving costs, we can look at a lot of different things.

好吧，我们还需要学习如何传达“数学是什么样的？”很多时候，我们可以考虑节省时间，我们可以考虑节省成本，我们可以考虑很多不同的事情。

We can look at speed to value as speed to market.

我们可以将价值实现速度视为上市速度。

We can look at risk reduction, but the gains really are there.

我们可以着眼于降低风险，但收益确实存在。

I will mention that it tends to follow something like the J-curve.

我会提到它往往遵循 J 曲线之类的东西。

So you'll have a couple of quick wins and it'll look like a big win, and then you'll hit kind of a little divot where suddenly the really obvious projects, the low-hanging fruit are handled.

因此，你将获得一些快速的胜利，看起来像是一场巨大的胜利，然后你会遇到一个小草皮，突然间，真正明显的项目、容易实现的目标得到了处理。

So now, we need to do a little bit of work.

所以现在，我们需要做一些工作。

We might need to build out a little bit more infrastructure.

我们可能需要建造更多的基础设施。

We might need to build out a little more telemetry, so that we can capture the things we want to capture.

我们可能需要构建更多的遥测技术，以便我们能够捕获我们想要捕获的东西。

And then once we get that done, then we start to see those benefits really compound.

一旦我们完成了这一点，我们就会开始看到这些好处确实是复合的。

Lenny Rachitsky: So going back to that measurement number, what do you recommend?

Lenny Rachitsky：那么回到那个测量数字，你有什么建议？

How do people find these numbers?

人们如何找到这些数字？

Because I think that's so much of the power of this is like, "We saved a million dollars doing this." What do you look at to figure that out?

因为我认为这就像“我们这样做节省了一百万美元”。你会看什么来弄清楚这一点？

Nicole Forsgren: I think there are a few different things to keep in mind, like who is our key audience, and we usually have a few key audiences.

Nicole Forsgren：我认为有一些不同的事情需要记住，比如谁是我们的关键受众，我们通常有一些关键受众。

We really want to be able to speak to developers because they're the ones that are going to be using the systems.

我们真的希望能够与开发人员交谈，因为他们将使用这些系统。

They'll be partnering with you on either building them or at least providing feedback about what you're doing.

他们将与您合作构建它们，或者至少提供有关您正在做的事情的反馈。

So for them, we often want to frame this in terms of things they care about.

因此，对于他们来说，我们经常希望根据他们关心的事情来构建这一点。

So time savings.

所以节省时间。

If something gets faster, they can save time.

如果事情变得更快，他们就可以节省时间。

They don't spend time doing setup when they don't need to anymore, related to status reduced toil.

当他们不再需要时，他们不会花时间进行设置，这与状态减少的工作有关。

So compliance and security are super important.

因此合规性和安全性非常重要。

Also, many times it requires several manual steps that...

此外，很多时候它需要几个手动步骤......

I don't say they're not value add.

我并不是说它们没有增值。

They're not value add from an individual human perspective.

从个人的角度来看，它们并没有增加价值。

If we can automate as much as possible, that's great, and improved focus time.

如果我们能够尽可能实现自动化，那就太好了，并且可以缩短专注时间。

Nicole Forsgren: That's from the developer side of you.

Nicole Forsgren：这是来自开发人员方面的意见。

Leadership often cares about...

领导常常关心...

They care about those things, but they often care more about other things.

他们关心这些事情，但他们往往更关心其他事情。

So we could talk about usually costs in dollars, "Can we accelerate revenue?

因此，我们通常可以讨论以美元为单位的成本，“我们可以加快收入吗？

What does our time to value look like?

我们的价值实现时间是什么样的？

What is our velocity?

我们的速度是多少？

How quickly can we get feedback from customers?" And for folks and organizations that are in really competitive environments, that can be really compelling because it's all about speed.

我们多快可以得到客户的反馈？”对于处于真正竞争环境中的个人和组织来说，这确实很有吸引力，因为这一切都与速度有关。

We could talk about saving money.

我们可以谈谈省钱的事。

Here, we can look at maybe quantifying savings.

在这里，我们可以看看量化节省。

One example is test and build.

一个例子是测试和构建。

If we can clean up a test and build suite to a developer, they really want to hear about time saved and more reliable systems.

如果我们可以为开发人员清理测试和构建套件，他们真的很想听到节省时间和更可靠的系统。

There's less toil because they don't have to keep re-running tests or kind of go clean up test suites.

工作量减少了，因为他们不必不断重新运行测试或清理测试套件。

Nicole Forsgren: From the business perspective, cleaning up a test in a build suite can be cloud cost savings because all of those tests are running somewhere on a cloud.

Nicole Forsgren：从业务角度来看，清理构建套件中的测试可以节省云成本，因为所有这些测试都在云上的某个地方运行。

And if they always fail or if it's just kind of a waste of spend, that can be useful, recovering some capacity.

如果它们总是失败或者只是浪费开支，那么这可能很有用，可以恢复一些容量。

We can always talk about time and productivity gains, "How much equivalent developer time are we losing on things that are not necessarily value add?" And then sometimes we can correlate to business outcomes and correlate is usually the best we can do here, but there can be some pretty compelling correlations in terms of speeding up time to value and increase market share, for example.

我们总是可以谈论时间和生产力的提高，“我们在不一定增值的事情上损失了多少相当于开发人员的时间？”有时我们可以将业务成果关联起来，关联通常是我们在这里能做的最好的事情，但例如，在加快价值实现和增加市场份额方面，可能存在一些非常引人注目的关联。

Lenny Rachitsky: Let me follow that thread and come back to this, what I think is the biggest question people have right now with AI and productivity, and I don't think anyone has the answer yet, but I'm curious to get your take of just what should people do today?

Lenny Rachitsky：让我沿着这个线索回到这个话题，我认为人们现在对人工智能和生产力最大的问题是什么，我认为还没有人知道答案，但我很想知道你对人们今天应该做什么的看法？

What's the best approach to understanding what impact AI tools are having on their productivity?

了解人工智能工具对其生产力有何影响的最佳方法是什么？

Because they're spending all this money on there.

因为他们把所有的钱都花在那里了。

I don't know, what are we getting out of this?

我不知道，我们能从中得到什么？

So I guess things are moving faster, but I don't know.

所以我猜事情进展得更快，但我不知道。

So if someone had to just like, "Okay, here's what I should probably try to do," what would be your best advice here for measuring the impact of AI tools on productivity?

因此，如果有人不得不说，“好吧，这就是我应该尝试做的事情”，那么对于衡量人工智能工具对生产力的影响，您的最佳建议是什么？

Nicole Forsgren: I would say it depends.

Nicole Forsgren：我想说这要看情况。

In part, it depends on what your leadership chain really cares about.

在某种程度上，这取决于您的领导链真正关心的是什么。

We are usually pretty good at figuring out what matters to developers and we could communicate that to them.

我们通常很擅长弄清楚什么对开发人员来说很重要，并且我们可以将其传达给他们。

But if we're trying to just identify two or three data points to really kind of focus on, because when we're first starting with data, sometimes it can be challenging, what do they care about?

但是，如果我们试图确定真正需要关注的两个或三个数据点，因为当我们第一次开始使用数据时，有时可能会很有挑战性，那么他们关心什么？

Think about the messaging you've been hearing.

想想你所听到的信息。

Have they been talking about market share?

他们谈论过市场份额吗？

Losing market share or competitiveness in the marketplace, if that's it, focus on speed.

失去市场份额或市场竞争力（如果是这样的话），请关注速度。

Think about ways that you can capture metrics for speed from feature to production or feature to customer or feature to experiment and what that feedback loop looks like if they're talking about profit margin all the time.

考虑如何捕获从功能到生产、功能到客户或功能到实验的速度指标，以及如果他们一直在谈论利润率，反馈循环会是什么样子。

Nicole Forsgren: Now, we always talk about money because this is business.

Nicole Forsgren：现在，我们总是谈论金钱，因为这是生意。

But if that seems to be an overarching narrative, look for ways that you can save money and then translate that into recovered and recouped headcount cost.

但如果这似乎是一个总体叙述，请寻找可以节省资金的方法，然后将其转化为恢复和收回的员工成本。

Or sometimes you'll reinvent, change a process, and then you no longer need as many vendors.

或者有时你会重新发明、改变流程，然后你就不再需要那么多供应商了。

So reductions in vendor spent can also help there.

因此，减少供应商支出也能有所帮助。

I say also it depends because sometimes they'll say something, leadership will say something, and it kind of comes up as a theme.

我说这也要看情况，因为有时他们会说一些话，领导也会说一些话，这会成为一个主题。

If you could solve a problem that they have or it's something that they're focused on, if you can slightly reframe it even, like if they're calling everything developer productivity, go ahead and call it productivity.

如果你可以解决他们遇到的问题或者他们关注的问题，如果你甚至可以稍微重新构建它，就像他们将一切称为开发人员生产力一样，那就继续称之为生产力。

If they're calling it velocity, and velocity is what matters to them, think about how to frame this in terms of velocity.

如果他们称之为速度，而速度对他们来说很重要，请考虑如何用速度来描述它。

If they're talking about transformation or disruption, how does this help with the disruption?

如果他们谈论的是转型或颠覆，这对颠覆有何帮助？

Because then, it will resonate with them.

因为那样的话，就会引起他们的共鸣。

We don't want to make them work to understand what it is that we're doing and the value that we provide.

我们不想让他们了解我们正在做什么以及我们提供的价值。

Lenny Rachitsky: That is such good advice.

Lenny Rachitsky：这是很好的建议。

Just to reflect back, the advice here is if your company's trying to figure out what sort of impact are AI tools having on our company, first, it's just like, what does the company care about most?

回顾一下，这里的建议是，如果你的公司试图弄清楚人工智能工具对我们公司有什么样的影响，首先，就像是，公司最关心什么？

What do leaders care about most?

领导最关心什么？

Could be market share, could be profit margin, could be velocity.

可能是市场份额，可能是利润率，可能是速度。

We need higher velocity or we need to transform, transformation.

我们需要更高的速度，或者我们需要转型、转型。

So your advice there is figure that out based on words and phrases you're hearing.

因此，您的建议是根据您听到的单词和短语来找出答案。

Then figure out ways to measure that, ways to measure market share growing, profit margin increasing.

然后找出衡量这一点的方法，衡量市场份额增长、利润率增长的方法。

I love these examples, like time from feature, idea to production or to experiment, so maybe start tracking that.

我喜欢这些例子，比如从功能、想法到生产或实验的时间，所以也许可以开始跟踪它。

If it's margin, it's money saved by fewer tests, failing or some vendor you don't have to pay for, things like that.

如果是利润的话，那就是通过减少测试、失败或不需要向某些供应商付费等方式节省下来的钱。

And then velocity, I imagine that's where things like DORA come in of just speed of engineering, shipping, or...

然后是速度，我想这就是像 DORA 这样的东西出现的地方，只是工程、运输或……的速度。

What would you think about there for velocity?

对于速度，您有何看法？

Nicole Forsgren: I would say it's actually one of those...

Nicole Forsgren：我想说这实际上是其中之一......

I would pick as broad a swathe as you can.

我会尽可能选择广泛的范围。

So if you can go from idea to customer or idea to experiment, how long does that take?

那么，如果您可以从想法到客户或从想法到实验，需要多长时间？

How long does it typically take, and how long can it take, and does it take now with improved use of AI tooling and reduction in friction?

通常需要多长时间，可以需要多长时间，随着人工智能工具的使用得到改进并减少摩擦，现在是否需要多长时间？

That's where I will say, we talk about this a little bit in the book, how do we deal with attribution challenges?

这就是我要说的，我们在书中稍微讨论了这一点，我们如何应对归因挑战？

What was responsible for this?

造成这种情况的原因是什么？

Was it the DevEx or was it AI?

是 DevEx 还是 AI？

Go ahead and disclose that.

继续透露这一点。

Say, "Yes, we rolled out AI tools.

说：“是的，我们推出了人工智能工具。

We also had this effort in DevEx.

我们在 DevEx 中也做出了这样的努力。

They partnered very closely together." Both of them probably contributed to this, right?

他们合作得非常紧密。”他们两人可能都对此做出了贡献，对吗？

If we had AI tools without the DevEx improvements, we probably would've had some improvements, but not nearly as much.

如果我们拥有没有 DevEx 改进的 AI 工具，我们可能会有一些改进，但不会那么多。

Lenny Rachitsky: If people were starting to do this today, say they're just like, "I want to start measuring developer experience," are there a two or three metrics everybody basically needs they should just start measuring ASAP?

Lenny Rachitsky：如果人们今天开始这样做，说他们就像，“我想开始衡量开发人员体验”，那么是否有两个或三个每个人基本上都需要的指标，他们应该尽快开始衡量？

Nicole Forsgren: If you're just starting today and if you have nothing at all, talk to people, obviously.

Nicole Forsgren：如果你今天才刚刚开始，而且你一无所有，显然，请与人交谈。

After that, I would do surveys because surveys can give you a nice overall view of the landscape quickly so that you know where the big kind of challenges are.

之后，我会进行调查，因为调查可以让你快速全面了解情况，以便你知道最大的挑战在哪里。

I say that because if you're just starting, you might not have instrumentation through your system, all the metrics.

我这么说是因为如果你刚刚开始，你可能没有通过你的系统进行检测，没有所有的指标。

And if you do already, it might not be what you think you want.

如果你已经这样做了，那可能不是你认为你想要的。

Metrics that were designed without purpose, questionable.

毫无目的而设计的指标值得怀疑。

Metrics that were designed for another purpose, they might work for what you want, but they might not, so we can't just assume we have them.

为其他目的而设计的指标，它们可能适合您想要的东西，但也可能不适合，所以我们不能假设我们拥有它们。

That's one reason I like surveys, and we include an example in the book.

这是我喜欢调查的原因之一，我们在书中提供了一个例子。

You can just ask a few questions, "How satisfied are you?

你可以问几个问题：“你满意吗？

What are the biggest barriers to your productivity, or what are the biggest challenges to getting work done?" and let them pick either from a set of tools or maybe a set of processes and then say...

影响你生产力的最大障碍是什么，或者完成工作的最大挑战是什么？”让他们从一组工具或一组流程中进行选择，然后说……

Let them pick three, just three.

让他们选三个，就三个。

Nicole Forsgren: Of those three, how often does this affect you?

Nicole Forsgren：在这三者中，这对你产生影响的频率有多少？

Is this hourly?

这是每小时吗？

Is this daily?

这是日常吗？

Is this weekly?

这是每周吗？

Is this quarterly?

这是季刊吗？

Because sometimes it hits you every single day, and you're just mad about it.

因为有时它每天都会袭击你，而你只是对此感到愤怒。

Sometimes it only hits you once a quarter because it's end of quarter, but it's so onerous, and then kind of open text, like, "Is there anything else we should know?" That can give you incredible signal because by making folks prioritize the top three things...

有时，它每季度只会出现一次，因为已经是季度末了，但这太繁重了，然后是一种开放式的文字，比如“还有什么我们应该知道的吗？”这可以给你带来令人难以置信的信号，因为通过让人们优先考虑最重要的三件事......

Let them pick everything, it makes the data super, super messy.

让他们挑选一切，这会让数据变得非常非常混乱。

But three things and how often, you can just come up with a score or a weighted score if you want, and then go kind of dig into, where should that data be?

但是，如果需要的话，您可以通过三件事以及频率得出一个分数或加权分数，然后深入研究这些数据应该在哪里？

What data do we need?

我们需要什么数据？

But also, then you've got at least some kind of baseline.

而且，这样你至少就有了某种基线。

It'll be a subjective baseline, but now you'll know what the biggest challenges are.

这将是一个主观基线，但现在您会知道最大的挑战是什么。

Lenny Rachitsky: I love how all this just comes back just starting by talking to people and asking them these things, which is very similar to product management and just building great products is, have you talked to your customers?

Lenny Rachitsky：我喜欢这一切只是从与人们交谈并询问他们这些事情开始，这与产品管理非常相似，而只是构建伟大的产品就是，你和你的客户交谈过吗？

Everyone thinks they're doing this, but most people are not doing this enough.

每个人都认为自己正在这样做，但大多数人做得还不够。

Nicole Forsgren: And I will say one thing that's challenging when you think about getting data, so interviews are data and that's important, surveys are a little more quantified because we can turn it into counts, but that's where we also want to be careful.

Nicole Forsgren：我要说的是，当你考虑获取数据时，有一件事是具有挑战性的，所以采访是数据，这一点很重要，调查更加量化，因为我们可以将其转化为计数，但这也是我们要小心的地方。

A lot of folks go to write a survey question and they'll say something like, "Were the build and test system slow or complicated in the last week?" You're asking four different questions there.

很多人去写一个调查问题，他们会说“上周构建和测试系统是否缓慢或复杂？”你在那里问四个不同的问题。

If someone answers yes, was it the build?

如果有人回答是，那是构建吗？

Was it the test?

是考试吗？

Was it slow or was it flaky or complicated or something?

是慢还是不稳定、复杂还是什么？

So it can be really difficult to untangle what the signal is you're actually getting there, and so it is worth the time chatting with someone who's familiar with survey design, having a conversation with Claude or Gemini or ChatGPT around, "Here are the survey questions.

因此，弄清楚你实际到达的信号是什么真的很困难，因此值得花时间与熟悉调查设计的人聊天，与 Claude 或 Gemini 或 ChatGPT 进行对话，“这是调查问题。

Or can you propose some?" And then make sure you take a couple of rounds.

或者你能推荐一些吗？”然后确保你进行了几轮。

Is this a good survey question?

这是一个很好的调查问题吗？

What questions can I answer from the data that I get?

我可以从获得的数据中回答哪些问题？

What problems could I solve?

我能解决什么问题？

If you can't answer a question with data, don't get it.

如果你不能用数据回答问题，就不要理解它。

Lenny Rachitsky: And you have example surveys in your book for folks that want to just copy and paste and not have to think about this much.

Lenny Rachitsky：您在书中为那些只想复制和粘贴而不必考虑太多的人提供了示例调查。

Nicole Forsgren: Yeah, example surveys, a lot of example questions.

Nicole Forsgren：是的，示例调查，很多示例问题。

We even recommend what the format, what the flow should look like, how long it should be, how long it should not be.

我们甚至建议格式、流程应该是什么样子、应该多长、不应该多长。

Lenny Rachitsky: One thing that I was reading is that you don't love happiness surveys specifically, asking engineers how happy they are, is that true?

Lenny Rachitsky：我读到的一件事是，你并不特别喜欢幸福感调查，询问工程师他们有多幸福，这是真的吗？

If so, why is that?

如果是这样，那是为什么呢？

Nicole Forsgren: I don't, no. Well, I'll say I don't love a happiness survey because there are too many things that contribute to happiness.

妮可·福斯格伦：我不，不。好吧，我会说我不喜欢幸福感调查，因为有太多的事情有助于幸福。

Happiness is a lot, right?

幸福很多，对吧？

So happiness is work, happiness is family, happiness is hobbies, happiness is weekends, happiness...

所以幸福就是工作，幸福就是家庭，幸福就是爱好，幸福就是周末，幸福就是……

There are so many things that contribute to happiness.

有很多事情可以带来幸福。

Now, that doesn't mean I don't care about happiness.

现在，这并不意味着我不关心幸福。

I think happiness surveys are not particularly useful here.

我认为幸福感调查在这里并不是特别有用。

What can be helpful is satisfaction and people are like, "That's the same thing." It's not because you can ask, "Are you satisfied with this tool?" and then ask some follow-up questions.

有用的是满意度，人们会说，“那是同一件事。”这并不是因为你可以问“你对这个工具满意吗？”然后问一些后续问题。

Now, those two are related because the more satisfied you are with your job and your tools and the work and your team, it contributes to happiness.

现在，这两者是相关的，因为您对自己的工作、工具、工作和团队越满意，就越有助于幸福。

I used to joke...

我曾经开玩笑...

Remember the golf commercials like, "Happy cows like happy cheese"?

还记得高尔夫广告中的“快乐的奶牛就像快乐的奶酪”吗？

Nicole Forsgren: I had a Calabrian.

Nicole Forsgren：我有一个卡拉布里亚。

That was the best.

那是最好的。

Happy devs make happy code.

快乐的开发人员会编写快乐的代码。

They write better programs, they do better work, they're better team members and collaborators.

他们编写更好的程序，做得更好，他们是更好的团队成员和合作者。

But capturing and trying to directly influence happiness, that's not what we are here for.

但捕捉并试图直接影响幸福，这并不是我们来这里的目的。

It's too challenging, it's too all-encompassing.

这太有挑战性了，太包罗万象了。

Satisfaction can give us some signal.

满意度可以给我们一些信号。

Lenny Rachitsky: In a totally different direction, in terms of just tools you see people using, are there any that just like, "Oh, yeah, this one's really commonly great." For people, this is just a tool people are finding a lot of success with.

Lenny Rachitsky：从一个完全不同的方向来看，就人们使用的工具而言，有没有类似的东西，“哦，是的，这个通常都很棒。”对于人们来说，这只是人们取得巨大成功的一个工具。

There's the common ones, Copilot, Cursor.

有常见的，副驾驶，光标。

I don't know.

我不知道。

Is there anything that stands out that you want to share, just like, "Hey, you should check this tool out.

有什么突出的事情你想分享，就像，“嘿，你应该看看这个工具。

People seem to love it"?

人们似乎很喜欢它”？

Nicole Forsgren: I think they're huge, right?

Nicole Forsgren：我认为它们很大，对吧？

Copilot, Cursor, Gemini.

副驾驶，光标，双子座。

Nicole Forsgren: Yep, Claude Code.

妮可·福斯格伦：是的，克劳德·科德。

I love Claude Code.

我喜欢克劳德·科德。

Lenny Rachitsky: I have a whole post coming on ways to use Claude Code for non-engineering use cases.

Lenny Rachitsky：我有一整篇文章介绍如何将 Claude Code 用于非工程用例。

Lenny Rachitsky: It's so interesting.

莱尼·拉奇茨基：这太有趣了。

For example, Claude Code, "Find ways to clean up storage on my laptop," and it just tells you there's a bunch of files.

例如，Claude Code，“找到清理笔记本电脑存储的方法”，它只是告诉您有一堆文件。

It's just like ChatGPT running on your computer and you could do all kinds of crazy stuff on your computer for you, like a mini God.

它就像在你的计算机上运行的 ChatGPT 一样，你可以在你的计算机上为你做各种疯狂的事情，就像一个迷你上帝。

Nicole Forsgren: I'm going to do that now.

Nicole Forsgren：我现在就这么做。

This is great.

这太棒了。

Lenny Rachitsky: It's so good.

莱尼·拉奇茨基：太棒了。

Yeah, that's why I'm writing this.

是的，这就是我写这篇文章的原因。

I had Dan Shipper was on the podcast and he said Claude Code is the most underrated AI tool out there because people don't realize what it's capable of.

我让 Dan Shipper 在播客上，他说 Claude Code 是最被低估的人工智能工具，因为人们没有意识到它的能力。

It's not just for coding, and that's what I'm trying to explore more and more.

这不仅仅是为了编码，这也是我正在尝试越来越多地探索的。

Okay.

好的。

Is there anything else that you think would be valuable to help people improve their developer experience, help them adapt to this new world of AI and engineering that we haven't covered?

您认为还有什么其他事情对帮助人们改善开发人员体验、帮助他们适应我们尚未涵盖的人工智能和工程新世界有价值吗？

Nicole Forsgren: I think something that's important to think about in general is to bring a product mindset to any type of DevEx improvements that are happening, and also the metrics that we collect and capture.

Nicole Forsgren：我认为总体上需要考虑的重要一点是将产品思维引入正在发生的任何类型的 DevEx 改进以及我们收集和捕获的指标中。

By that, I mean we want to identify a problem, make sure we're solving a problem for a set of users.

我的意思是我们想要确定一个问题，确保我们正在为一组用户解决问题。

We want to think about creating MVPs and experiments and get fast feedback, do some rapid iteration.

我们想要考虑创建 MVP 和实验并获得快速反馈，进行一些快速迭代。

We want to have a strategy.

我们想要制定一个策略。

We want to know who our addressable market is.

我们想知道我们的目标市场是谁。

We want to know what success is.

我们想知道什么是成功。

We want to basically have a go-to-market function.

我们希望基本上具有进入市场的功能。

We need to have comms.

我们需要有通讯。

We need to get continuous feedback from our customers.

我们需要从客户那里获得持续的反馈。

We want to keep improving.

我们希望不断进步。

And, at some point, we want to think about sunsetting something.

而且，在某些时候，我们想要考虑停止某些事情。

Is it in maintenance mode?

是否处于维护模式？

Is it sun setting?

是夕阳西下吗？

Nicole Forsgren: And I think that's important in general, but I think it's extra important now because when we have AI tools, we're using AI tools, we're embedding AI into our products, things are changing so rapidly that it can be really important to take half a beat and say, "Okay, what's the problem I'm trying to solve right here?

Nicole Forsgren：我认为这总体上很重要，但我认为现在尤为重要，因为当我们拥有人工智能工具时，我们正在使用人工智能工具，我们将人工智能嵌入到我们的产品中，事情变化如此之快，以至于停下来说：“好吧，我在这里试图解决的问题是什么？

Is this metric that we've had for the last 10 years still important or should this be sunset because it's not really important anymore?

我们过去 10 年使用的这个指标是否仍然重要，还是应该被淘汰，因为它不再那么重要了？

It's not driving the types of decisions and actions that I need."

它并没有推动我需要的决策和行动类型。”

Lenny Rachitsky: Before we get to our exciting lightning round, I want to take us to AI Corner, which is a recurring segment on this podcast.

Lenny Rachitsky：在我们开始激动人心的闪电回合之前，我想带我们先了解一下 AI Corner，这是该播客中反复出现的部分。

Is there some way that you've found a use for an AI tool in your life, in your work that you think might be fun to share, that you think might be useful to other people?

您是否在生活和工作中发现了人工智能工具的用途，您认为分享起来可能很有趣，或者对其他人有用？

Nicole Forsgren: I have been working on some home design and redecorating rooms and stuff.

Nicole Forsgren：我一直在从事一些家居设计和重新装修房间之类的工作。

I'm working with a designer because I know what I like, but I don't know how to get there, I'm not good at this.

我正在与一位设计师合作，因为我知道自己喜欢什么，但我不知道如何到达那里，我不擅长这个。

But I've really been loving ChatGPT and Gemini especially to render pictures for me, so I can give it the floor plan, I can give it one shot of the room that's definitely not what it's supposed to look like, and then I can give it pictures of a couple different things, and then I can just tell it change the walls or change the furniture layout or change something.

但我真的很喜欢 ChatGPT 和 Gemini，特别是为我渲染图片，所以我可以给它平面图，我可以给它一张绝对不是它应该看起来的样子的房间的照片，然后我可以给它一些不同东西的图片，然后我可以告诉它改变墙壁或改变家具布局或改变一些东西。

It helps me and it's relatively quick.

它对我有帮助，而且速度相对较快。

It helps me kind of visualize the things...

它可以帮助我想象这些事情......

Again, I know what I like, but I don't know how to get there, so I know if I like it or not, which is probably a very random use, but it's fun for now.

再说一次，我知道我喜欢什么，但我不知道如何到达那里，所以我知道我是否喜欢它，这可能是一个非常随机的使用，但现在很有趣。

Lenny Rachitsky: My wife does exactly the same thing.

Lenny Rachitsky：我妻子也做同样的事情。

She's sending me constantly, "Here's what this rug will look like in our living room.

她不断地给我发信息，“这就是我们客厅里这张地毯的样子。

Here's this water feature." It's so good and it keeps getting better.

这就是这个水景。”它是如此的好，并且一直在变得更好。

It's just like, "Wow, that's exactly our house with this new rug," and all you do is just upload these two photos and just like, "Cool.

就像，“哇，这正是我们的房子，铺着这张新地毯”，你所要做的就是上传这两张照片，然后就像，“酷。

How would this look in our room?"

这在我们的房间里会是什么样子？”

Nicole Forsgren: Yeah, I've been impressed a couple times.

Nicole Forsgren：是的，有几次给我留下了深刻的印象。

Definitely the machines are listening to us.

毫无疑问，机器正在倾听我们的声音。

It's given me a mock-up of a room or something and then it throws in a dog bed, because I have dogs.

它给了我一个房间或其他东西的模型，然后扔进狗床上，因为我有狗。

I'm like, "I did not tell you to do that, but yeah, that's probably the color and style of dog bed that I should have in this room."

我想，“我没有告诉你这样做，但是，是的，这可能就是我在这个房间里应该有的狗床的颜色和风格。”

Lenny Rachitsky: Speaking of that, have you tried this use case, ask ChatGPT, "Generate an image of what you think my house looks like based on everything you know about me."

Lenny Rachitsky：说到这一点，您是否尝试过这个用例，询问 ChatGPT，“根据您对我的了解，生成您认为我的房子是什么样子的图像。”

Lenny Rachitsky: Because it has memory and it remembers everything you've talked about, and it's hilarious.

Lenny Rachitsky：因为它有记忆，它会记住你谈论过的一切，这很搞笑。

You got to do it.

你必须这么做。

Nicole Forsgren: Okay, that's on my to-do list.

Nicole Forsgren：好的，这在我的待办事项清单上。

Lenny Rachitsky: There we go.

莱尼·拉奇茨基：我们开始吧。

Bonus use case.

奖励用例。

Nicole, with that, we've reached our very exciting lightning round.

妮可，至此，我们进入了非常激动人心的闪电回合。

I've got five questions for you.

我有五个问题要问你。

Are you ready?

你准备好了吗？

Lenny Rachitsky: What are two or three books that you find yourself recommending most to other people?

Lenny Rachitsky：您最常向其他人推荐的两三本书是什么？

Nicole Forsgren: Outlive by Peter Attia is fantastic.

彼得·阿提亚（Peter Attia）的《妮可·福斯格伦：逃亡》（Nicole Forsgren：Outlive）非常棒。

Another one that's I guess maybe related, I hurt my back so it's not great, Back Mechanic by Stuart McGill is incredible.

另一个我想可能相关的，我伤了我的背，所以这不是很好，斯图尔特·麦吉尔的《背部机械师》令人难以置信。

Shout out to anyone who has hurt lower back.

向任何腰部受伤的人大声喊叫。

It's for a lay person to read through and figure out how to fix lower back problems.

它适合非专业人士通读并找出如何解决腰部问题的方法。

It's kind of a random one.

这是一种随机的。

I will say I love How Big Things Get Done.

我会说我喜欢《How Big Things Get Done》。

I can't pronounce the names.

我无法发音这些名字。

I think one's...

我认为一个人...

There's Scandinavian, one is.

有一种是斯堪的纳维亚风格。

It kind of dissects really large projects through recent-ish history and where they failed and why.

它通过近代历史剖析了真正的大型项目，以及它们失败的地方和原因。

And I think it's really interesting for us to think about, especially now in this AI moment where basically all of our at least software systems are going to be changing.

我认为这对我们来说真的很有趣，特别是在现在这个人工智能时代，基本上我们所有的软件系统都将发生变化。

So how do we think about approaching what is essentially going to be a very large project?

那么我们如何看待这个本质上是一个非常大的项目呢？

And then, sorry, I'm going to throw in a bonus one, The Undoing Project by Michael Lewis.

然后，抱歉，我要加一个奖励，迈克尔·刘易斯的《毁灭计划》。

Matt Velloso recommended it to me, and it's so good.

Matt Velloso 向我推荐了它，它非常好。

Nicole Forsgren: I audibly gasped at the last sentence.

Nicole Forsgren：我听到最后一句话时倒吸一口冷气。

Nicole Forsgren: I was .

妮可·福斯格伦：我是。

Yeah, I was not expecting it.

是的，我没想到。

Lenny Rachitsky: I read that and I do not remember that last sentence.

Lenny Rachitsky：我读过，但我不记得最后一句话。

Oh, man.

哦，伙计。

Okay, cool.

好吧，酷。

Next question.

下一个问题。

Do you have a favorite movie or TV show you recently watched and enjoyed?

您最近观看并喜欢最喜欢的电影或电视节目吗？

Nicole Forsgren: I'll say I watch Love Is Blind.

妮可·福斯格伦：我会说我看《爱情是盲目的》。

If I got to shut down at the end of the day, Love Is Blind is fun.

如果我在一天结束时要关机，《爱是盲目的》会很有趣。

Nicole Forsgren: Yeah, very excited...

Nicole Forsgren：是的，非常兴奋......

and Shrinking.

和收缩。

Have you seen Shrinking?

你看过《缩小》吗？

Lenny Rachitsky: No. I think I started The Therapist and yeah, I gave it a shot.

Lenny Rachitsky：不。我想我开始了《治疗师》，是的，我尝试过。

Nicole Forsgren: Strongly recommend it.

妮可·福斯格伦：强烈推荐。

It's cute.

很可爱。

Lenny Rachitsky: Sweet.

莱尼·拉奇茨基：太好了。

Is there a product you've recently discovered that you really love?

您最近发现有一款您真正喜欢的产品吗？

Could be an app, could be some kitchen gadgets, some clothing.

可能是一个应用程序，可能是一些厨房用具，一些衣服。

Nicole Forsgren: I don't know.

妮可·福斯格伦：我不知道。

I may have.

我可能有。

I don't think so.

我不这么认为。

Lenny Rachitsky: Somebody said this and I still remember it.

Lenny Rachitsky：有人说过这句话，我仍然记得。

It's like-

这就像——

Lenny Rachitsky: ...

莱尼·拉奇茨基：……

you make ice cream and stuff with it, right?

你用它做冰淇淋和其他东西，对吧？

Nicole Forsgren: Yeah, and you can basically freeze a protein shake and then it turns it into ice cream-

妮可·福斯格伦：是的，你基本上可以冷冻蛋白质奶昔，然后把它变成冰淇淋——

Nicole Forsgren: ...

妮可·福斯格伦：……

which is delicious.

这很好吃。

Another one is a Jura coffee maker.

另一款是 Jura 咖啡机。

I'd love good coffee and I'm not great at making it, so I can just push the button and it'll give me anything I want, including lattes, cappuccinos or anything.

我喜欢好咖啡，但我不擅长煮咖啡，所以我只需按一下按钮，它就会给我我想要的任何东西，包括拿铁咖啡、卡布奇诺咖啡或任何东西。

So that's kind of fun.

这很有趣。

Lenny Rachitsky: Sweet, okay.

莱尼·拉奇茨基：很好，好吧。

Do you have a favorite-

有没有你最喜欢的——

Nicole Forsgren: Just sugar and caffeine.

Nicole Forsgren：只有糖和咖啡因。

I just need a power through the day.

我只需要一整天的力量。

Lenny Rachitsky: There's the engineering productivity 101.

Lenny Rachitsky：工程生产力 101。

Lenny Rachitsky: Oh, man.

莱尼·拉奇茨基：哦，伙计。

Okay, two more questions.

好吧，还有两个问题。

Do you have a favorite life motto that you often find useful in work or life and come back to in various ways?

您是否有一条最喜欢的人生格言，您经常发现它在工作或生活中很有用，并以各种方式回归？

Nicole Forsgren: Yeah, I think one that's come up a couple times, it's not a verbatim thing, I think it's more the vibe, hindsight is 2020, but it's also really dumb.

Nicole Forsgren：是的，我认为这个问题已经出现过几次了，这不是逐字逐句的事情，我认为这更多是一种氛围，事后看来是 2020 年，但它也非常愚蠢。

I think if we made the best decision we could at the time with the information that we had available, then it is what it is.

我认为，如果我们根据现有信息做出了当时最好的决定，那么事情就是这样。

If you make a bad decision because you made a bad decision and you knew better, you had the information, not great.

如果你做出了一个错误的决定，因为你做了一个错误的决定，而且你知道得更清楚，你掌握了信息，但这并不是很好。

I don't think we give ourselves or other people enough grace because we always end up finding more information out later.

我认为我们没有给自己或其他人足够的恩典，因为我们总是会在以后发现更多信息。

Lenny Rachitsky: Hear, hear.

莱尼·拉奇茨基：听着，听着。

Final question.

最后一个问题。

I was going to ask you something else, but as we are preparing for this, you shared that you have a new role at Google.

我本来想问你一些其他问题，但当我们正在为此做准备时，你透露你在 Google 担任了一个新角色。

Maybe just talk about that, what you're up to there, why you joined Google, anything folks should know.

也许只是谈论一下，你在做什么，为什么加入谷歌，以及人们应该知道的任何事情。

Nicole Forsgren: Sure.

妮可·福斯格伦：当然。

I am senior director of developer intelligence and core developer.

我是开发者情报和核心开发者的高级总监。

It's super exciting and super fun because of all of these things we've been talking about.

由于我们一直在谈论的所有这些事情，这非常令人兴奋和有趣。

It's focused on Google and all their properties and their underlying infrastructure, how can we improve developer experience, developer productivity, velocity, all of these things we've been talking about and, because kind of the numbers person, how do we want to think about measuring it, how does measurement change, how do feedback loops change, how can we improve the experience throughout and then kind of drive that change through an organization in ways that are meaningful and impactful and faster than they've been before.

它专注于谷歌及其所有资产和底层基础设施，我们如何改善开发人员体验，开发人员生产力，速度，所有这些我们一直在谈论的事情，因为数字的人，我们如何考虑衡量它，衡量如何变化，反馈循环如何变化，我们如何改善整个体验，然后以比以前更有意义、有影响力和更快的方式推动整个组织的变革。

Lenny Rachitsky: Nice job, Google, getting Nicole.

Lenny Rachitsky：干得好，Google，找到了 Nicole。

What a win.

真是一场胜利啊。

I need to get some more Google stock ASAP.

我需要尽快购买更多谷歌股票。

Okay, two follow-up questions.

好的，有两个后续问题。

Where can folks find you online and find your book online if they want to dig deeper?

如果人们想深入挖掘，可以在哪里找到您并在线找到您的书？

And how can listeners be useful to you?

听众如何对你有用？

Nicole Forsgren: Online, you can find the book at developerexperiencebook.com, I'm at nicolefv.com, and LinkedIn occasionally.

Nicole Forsgren：在网上，你可以在developerexperiencebook.com 上找到这本书，我在nicolefv.com 上，偶尔也会在LinkedIn 上找到这本书。

Sometimes it's a mess.

有时会很混乱。

I try to wade through all of the noise.

我试图穿过所有的噪音。

I get there to be useful, sign up for the book and the workbooks.

我到那里是为了有用，报名参加这本书和练习册。

The workbooks are free.

练习册是免费的。

I'd love to get any kind of feedback on what works, what doesn't.

我很想得到关于什么有效、什么无效的任何反馈。

I always love hearing those kind of stories.

我总是喜欢听这类故事。

Lenny Rachitsky: Nicole, thank you so much for being here.

Lenny Rachitsky：妮可，非常感谢你来到这里。

My pleasure.

我的荣幸。

Thanks, again.

再次感谢。

Bye, everyone.: Thank you so much for listening.

再见，大家：非常感谢您的聆听。

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您可以在 lennyspodcast.com 上找到所有过去的剧集或了解有关该节目的更多信息。

See you in the next episode.

下一集见。