How Block is becoming the most AI-native enterprise in the world | Dhanji R. Prasanna
Lenny Rachitsky: There's a lot of talk about productivity gains through AI.
Lenny Rachitsky：有很多关于通过人工智能提高生产力的讨论。
There's this camp of people that are so overhyped, nothing's working, nobody's actually adopting this at scale.
这个阵营的人过于夸张，没有任何效果，没有人真正大规模采用这一点。
Dhanji R.
丹吉 R.
Prasanna: We see a significant amount of games.
Prasanna：我们看到了大量的比赛。
We find engineering teams that are very, very AI forward are reporting about eight to 10 hours save per week.
我们发现非常非常先进的人工智能工程团队报告称每周节省了大约 8 到 10 个小时。
Whenever I hear a stat like this, I think an important element is this is the worst it will ever be.
每当我听到这样的统计数据时，我认为一个重要的因素是，这是有史以来最糟糕的情况。
This is now the baseline.
这是现在的基线。
The truth is the value is changing every day, so you need to ride that wave along with it.
事实上，价值每天都在变化，所以你需要顺应潮流。
Lenny Rachitsky: There's a story I heard you share on a different podcast where there's an engineer who has Goose watching.
Lenny Rachitsky：我听你在另一个播客上分享了一个故事，其中有一位工程师正在观看 Goose。
Dhanji R.
丹吉 R.
Prasanna: You'll be talking to a colleague on Slack or an email, and they'll be discussing some feature that they think is useful to implement.
Prasanna：您将通过 Slack 或电子邮件与同事交谈，他们将讨论一些他们认为对实现有用的功能。
Now a few hours later, he'll find that Goose has already tried to build that feature and opened a PR for it on Git.
几个小时后，他会发现 Goose 已经尝试构建该功能并在 Git 上为其打开了 PR。
Lenny Rachitsky: What level of engineer is most benefiting from these tools?
Lenny Rachitsky：什么级别的工程师最能从这些工具中受益？
Dhanji R.
丹吉 R.
Prasanna: What's been surprising and really amazing, the non-technical people using AI agents and programming tools to build things, the people that are able to embrace it to optimize for their particular workday and their particular set of tasks are really showing the most impact from these tools.
Prasanna：令人惊讶且真正令人惊叹的是，使用人工智能代理和编程工具来构建事物的非技术人员，能够利用它来优化其特定工作日和特定任务集的人们确实显示出这些工具的最大影响。
Lenny Rachitsky: How do you think things will look in a couple of years in terms of how engineers work that's different from today?
Lenny Rachitsky：您认为几年后工程师的工作方式会与今天有何不同？
Dhanji R.
丹吉 R.
Prasanna: All these LLMs are sitting idle overnight and on weekends, while humans aren't there.
Prasanna：所有这些法学硕士都在夜间和周末闲置，而人类却不在那里。
There's no need for that.
没必要这样。
They should be working all the time.
他们应该一直在工作。
They should be trying to build in anticipation of what we want.
他们应该努力构建我们想要的东西。
Lenny Rachitsky: What's maybe the most counterintuitive lesson you've learned about building products or building teams?
Lenny Rachitsky：您在构建产品或构建团队时学到的最违反直觉的教训可能是什么？
Dhanji R.
丹吉 R.
Prasanna: A lot of engineers think that code quality is important to building a successful product.
Prasanna：许多工程师认为代码质量对于构建成功的产品非常重要。
The two have nothing to do with each other.
两者没有任何关系。
Lenny Rachitsky: Today my guest is Dhanji Prasanna.
Lenny Rachitsky：今天我的客人是 Dhanji Prasanna。
Dhanji is Chief Technology Officer at Block, where he oversees a team of over 3,500 people.
Dhanji 是 Block 的首席技术官，他管理着一支超过 3,500 人的团队。
With Dhanji's leadership, Block has become one of the most AI-native large companies in the world and has basically achieved what many eng and product leaders are trying to achieve within their companies.
在 Dhanji 的领导下，Block 已成为全球最具 AI 本土化的大公司之一，并基本实现了许多工程和产品领导者在公司内试图实现的目标。
Lenny Rachitsky: In our conversation, we chat about their internal open source agent called Goose, that by their measure is saving employees on average eight to 10 hours a week of work time, and that number is going up, how AI specifically making their teams more productive and the teams that are benefiting most.
Lenny Rachitsky：在我们的谈话中，我们谈论了他们名为 Goose 的内部开源代理，按照他们的衡量标准，该代理每周平均为员工节省 8 到 10 个小时的工作时间，而且这个数字还在上升，人工智能如何具体提高他们的团队的生产力以及受益最大的团队。
Interestingly, it's not the engineering team, what it took to shift the culture to be very AI-oriented, the very boring change they made internally that boosted productivity even more than any AI tool.
有趣的是，不是工程团队，而是他们在内部做出的非常无聊的改变，比任何人工智能工具都更能提高生产力。
Lenny Rachitsky: Also, lessons from building Google Wave and Google Plus and Cash app and so much more.
Lenny Rachitsky：另外，还有构建 Google Wave、Google Plus 和 Cash 应用程序等等的经验教训。
This episode is for anyone curious to see what a highly AI-forward technology-driven large company looks like and can act like.
本集适合任何想了解高度人工智能技术驱动的大公司的外观和行为的人。
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。
Lenny Rachitsky: Also, if you become an annual subscriber of my newsletter, you get a year free of 16 incredible products including Devin, Replit, Lovable, Bolt, n8n, Linear, Superhuman, Descript, Wispr Flow, Gamma, Perplexity, Warp, Granola, Magic Patterns, Raycast, ChatPRD and Mobbin.
Lenny Rachitsky：此外，如果您成为我的时事通讯的年度订阅者，您将获得一年免费的 16 种令人难以置信的产品，包括 Devin、Replit、Lovable、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 Product Pass。
With that, I bring you Dhanji Prasanna after a short word from our sponsors.
在我们的赞助商简短发言后，我为您带来了 Dhanji Prasanna。
Lenny Rachitsky: This episode is brought to you by Sinch the Customer Communications Cloud.
Lenny Rachitsky：本集由客户通信云 Sinch 为您带来。
Here's the thing about digital customer communications.
这是关于数字客户沟通的事情。
Whether you're sending marketing campaigns, verification codes, or account alerts, you need them to reach users reliably, that's where Sinch comes in.
无论您是发送营销活动、验证码还是帐户提醒，您都需要它们可靠地联系用户，这就是 Sinch 的用武之地。
Over 150,000 businesses, including eight of the top 10 largest tech companies globally use Sinch's API to build messaging, email and calling into their products.
超过 150,000 家企业（包括全球十大科技公司中的八家）使用 Sinch 的 API 在其产品中构建消息传递、电子邮件和通话功能。
Lenny Rachitsky: And there's something big happening in messaging that product teams need to know about.
Lenny Rachitsky：产品团队需要了解消息传递中正在发生的重大事件。
Rich Communication Services or RCS.
丰富的通信服务或 RCS。
Think of RCS as SMS 2.0.
将 RCS 视为 SMS 2.0。
Instead of getting texts from a random number, your users will see your verified company name and logo without needing to download anything new.
您的用户将看到您经过验证的公司名称和徽标，而不是从随机数字中获取文本，而无需下载任何新内容。
It's a more secure and branded experience, plus you get features like interactive carousels and suggested replies, and here's why this matters.
这是一种更安全、更品牌化的体验，此外，您还可以获得交互式轮播和建议回复等功能，这就是为什么这很重要。
US carriers are starting to adopt RCS.
美国运营商开始采用 RCS。
Lenny Rachitsky: Sinch is already helping major bands send RCS messages around the world and they're helping Lenny's podcast listeners get registered first before the rush hits the US market.
Lenny Rachitsky：Sinch 已经在帮助主要乐队在世界各地发送 RCS 消息，并且他们正在帮助 Lenny 的播客听众在热潮席卷美国市场之前首先注册。
Learn more and get started at sinch.com/lenny.
如需了解更多信息并开始使用，请访问sinch.com/lenny。
That's S-I-N-C-H .com/lenny.
那是 S-I-N-C-H .com/lenny。
This episode is brought to you by Figma, makers of Figma Make.
本集由 Figma Make 的制作者 Figma 为您带来。
When I was a PM at Airbnb, I still remember when Figma came out and how much it improved how we operated as a team.
当我在 Airbnb 担任产品经理时，我仍然记得 Figma 的问世以及它对我们团队运作方式的改善。
Lenny Rachitsky: Suddenly, I could involve my whole team in the design process, give feedback on design concepts really quickly, and it just made the whole product development process so much more fun.
Lenny Rachitsky：突然之间，我可以让我的整个团队参与到设计过程中，快速地对设计概念提供反馈，这让整个产品开发过程变得更加有趣。
But Figma never felt like it was for me.
但 Figma 从来不觉得它适合我。
It was great for giving feedback and designs, but as a builder, I wanted to make stuff.
它非常适合提供反馈和设计，但作为一名构建者，我想要制作东西。
That's why Figma built Figma Make.
这就是 Figma 构建 Figma Make 的原因。
With just a few prompts, you can make any idea or design into a fully functional prototype or app that anyone can iterate on and validate with customers.
只需几个提示，您就可以将任何想法或设计制作成功能齐全的原型或应用程序，任何人都可以与客户进行迭代和验证。
Lenny Rachitsky: Figma Make is a different kind of vibe coding tool, because it's all in Figma.
Lenny Rachitsky：Figma Make 是一种不同类型的氛围编码工具，因为它全部都在 Figma 中。
You can use your team's existing design building blocks, making it easy to create outputs that look good and feel real and are connected to how your team builds.
您可以使用团队现有的设计构建块，从而轻松创建看起来不错、感觉真实并与团队构建方式相关的输出。
Stop spending so much time telling people about your product vision and instead show it to them.
不要再花太多时间告诉人们你的产品愿景，而是向他们展示它。
Make code back prototypes and apps fast with Figma Make.
使用 Figma Make 快速制作代码返回原型和应用程序。
Check it out at figma.com/lenny.
请访问Figma.com/lenny 进行查看。
Dhanji, thank you so much for being here and welcome to the podcast.
Dhanji，非常感谢您来到这里，欢迎来到播客。
Dhanji R.
丹吉 R.
Prasanna: Thank you Lenny.
普拉萨娜：谢谢你，莱尼。
It's a great pleasure to be here.
很高兴来到这里。
Lenny Rachitsky: I want to start with a letter that I hear you wrote to Jack Dorsey to convince him that he and that Block needed to take AI a lot more seriously.
Lenny Rachitsky：我想从一封信开始，我听说你写给 Jack Dorsey，让他相信他和 Block 需要更加认真地对待人工智能。
I think you called it your AI manifesto and it seems like it really worked.
我认为你称其为你的人工智能宣言，而且看起来它确实有效。
We're going to talk a lot about the changes that came as a result of that.
我们将详细讨论由此带来的变化。
So let me just ask, what did you say in this letter and what happened right after you sent that letter to him?
那么我想问一下，你在这封信中说了什么，以及你把这封信寄给他之后发生了什么？
Dhanji R.
丹吉 R.
Prasanna: So about two and a half years ago or so, Jack really felt like things needed to change.
Prasanna：大约两年半前，杰克真的觉得事情需要改变。
I think he had a sense that the industry was going in a different direction.
我认为他有一种感觉，这个行业正在朝着不同的方向发展。
So he got about 40 of the company's top executives into a room on a weekly basis, and they all used to sort of talk everything through that was going on and he added me to that group.
因此，他每周都会邀请大约 40 名公司高管到一个房间，他们都会讨论正在发生的所有事情，然后他将我添加到该小组中。
Dhanji R.
丹吉 R.
Prasanna: So at some point, I observed that we were talking about lots of deep things, lots of relevant things, but no one was really paying attention to AI, and so that's when I wrote that letter.
Prasanna：所以在某个时候，我发现我们正在谈论很多深入的事情，很多相关的事情，但没有人真正关注人工智能，所以就在那时我写了那封信。
And to be honest, it's I think taken on a life of its own, but there wasn't much to the letter other than I think we should do this.
说实话，我认为这是有其自己的生命力的，但除了我认为我们应该这样做之外，这封信中没有太多内容。
I think we should do it centrally and it's important for us to be ahead of the game and be an AI native company because that's where the industry is heading.
我认为我们应该集中精力来做这件事，对我们来说保持领先并成为一家人工智能本土公司很重要，因为这就是行业的发展方向。
Lenny Rachitsky: Let me just say it's important to note you were not CTO at this point.
Lenny Rachitsky：我只想说，值得注意的是，您此时还不是首席技术官。
You were just a senior engineer kind of person?
你只是高级工程师一类的人？
Dhanji R.
丹吉 R.
Prasanna: No, yeah, in fact, I was part-time at the time because I had just had a kid and I was coming back in and I was helping out one of the engineering teams and then Jack came over to Sydney and spent two days with me and both of us like long walks.
Prasanna：不，是的，事实上，我当时是兼职，因为我刚刚生了一个孩子，我要回来帮助其中一个工程团队，然后杰克来到悉尼，和我一起度过了两天，我们俩都喜欢长途散步。
So we walked all around Sydney and talked it through up and down, and then yeah, he offered me the job and I thought it was a great opportunity once in a lifetime, so I took it.
所以我们在悉尼走了一圈，从头到尾地讨论了一下，然后，是的，他给了我这份工作，我认为这是一生一次的绝佳机会，所以我接受了。
Lenny Rachitsky: It's like be careful what you're good at sort of situation.
Lenny Rachitsky：这就像要小心你擅长的情况。
Okay.
好的。
So what were some of the bigger changes that you made after Jack is on board and Block execs are on board of are, "Cool, this is completely right.
那么，在 Jack 加入和 Block 高管加入之后，你们做出的一些更大的改变是：“酷，这是完全正确的。
We need to go much bigger and think much more deeply about how AI is changing, how we build and how we should build." Or some of the bigger changes that you made from a perspective of other companies listening to this, trying to think about what they should be doing?
我们需要走得更远，更深入地思考人工智能如何改变、我们如何构建以及我们应该如何构建。”或者您从其他公司的角度做出的一些更大的改变，聆听这些内容，试图思考他们应该做什么？
Dhanji R.
丹吉 R.
Prasanna: At the start, my main focus was to get block to think like a technology company.
Prasanna：一开始，我的主要关注点是阻止像科技公司一样思考。
And for a long time we had had a little bit of, I'm going to call it identity drift, maybe.
很长一段时间以来，我们都经历了一点，我可能称之为身份漂移。
We were talking about ourselves as a financial services company.
我们谈论的是我们自己作为一家金融服务公司。
Some people called us FinTech, all of this stuff.
有些人称我们为金融科技，诸如此类的东西。
But when I started working at what was then known as Square, we were always thought of as a technology company just like Google or Facebook or any of the others.
但当我开始在当时名为 Square 的公司工作时，我们一直被认为是一家科技公司，就像谷歌、Facebook 或其他公司一样。
Dhanji R.
丹吉 R.
Prasanna: And so I wanted to get us back to that.
Prasanna：所以我想让我们回到那个话题。
And so the first thing I did was to try and institute a number of programs that focused on that.
所以我做的第一件事就是尝试制定一些以此为重点的项目。
So everything from getting the top ICs in the company together to talk to each other, to starting a whole bunch of special projects.
从让公司的顶尖 IC 人员聚集在一起互相交谈，到启动一大堆特殊项目，一切都是如此。
So we got about two to five engineers per project.
所以我们每个项目大约有两到五名工程师。
There were about eight or nine different projects and we had reinstituted, the company-wide hack week.
大约有八九个不同的项目，我们重新建立了全公司范围的黑客周。
Dhanji R.
丹吉 R.
Prasanna: And so all of this just kind of created a little bit of a spark of, "Hey, we're building technology again, we're trying to push the frontier again." And that's how it started, and then there were a whole number of steps after that where we went from a GM structure to a functional org structure, which was I think the key to making our transformation into being more of an AI-native company.
Prasanna：所以所有这一切都产生了一点火花，“嘿，我们再次构建技术，我们再次尝试推动前沿。”这就是它的开始，然后是一系列的步骤，我们从总经理结构转变为职能组织结构，我认为这是让我们转型为一家人工智能本土公司的关键。
Lenny Rachitsky: Okay, talk more about that.
Lenny Rachitsky：好的，多谈谈这个问题。
What does that mean?
这意味着什么？
What does that look like?
那看起来像什么？
Why is that so important?
为什么这如此重要？
Dhanji R.
丹吉 R.
Prasanna: Absolutely.
普拉萨那：当然。
So when we were in our mature phase, so when Square was working quite well, it was a very large business, and then we had started Cash App and that also followed suit.
所以当我们处于成熟阶段时，当 Square 运作得很好时，它是一个非常大的业务，然后我们推出了 Cash App，也纷纷效仿。
We had spun them out almost as what we call a GM structure.
我们几乎将它们拆分成我们所说的通用汽车结构。
So they were effectively run as a portfolio of independent companies and they had their own CEOs who all reported to Jack and it was still one single executive team, but they had separate engineering practices, they had separate design teams.
因此，他们实际上是作为独立公司的组合来运营的，他们有自己的首席执行官，他们都向杰克汇报，而且仍然是一个执行团队，但他们有独立的工程实践，有独立的设计团队。
Dhanji R.
丹吉 R.
Prasanna: They were kind of separate in almost every way except for some shared resources like our foundational resources like legal and some platforms and things like that.
Prasanna：除了一些共享资源（例如我们的基础资源（例如法律和一些平台之类的资源））之外，它们几乎在所有方面都是独立的。
So I think that that was very useful for us for the stage of company that we were in, but when you really want to go deep in technology, when you really want to connect with these things that are industry changing events that are happening, you need a singular focus, and we changed the organization.
所以我认为这对于我们所处的公司阶段非常有用，但是当你真的想深入技术时，当你真的想与正在发生的行业变革事件联系起来时，你需要一个单一的焦点，我们改变了组织。
Dhanji R.
丹吉 R.
Prasanna: So all engineers report into one single team now, all designers report into one single team and there's single head of engineering, single head of design, et cetera.
Prasanna：所以现在所有工程师都向一个团队汇报，所有设计师都向一个团队汇报，并且有一个工程主管、一个设计主管等等。
And so that was the big transformation that we made, and that meant we could really drive forward AI, we could drive forward platform and just technical depth generally.
这就是我们所做的重大转变，这意味着我们可以真正推动人工智能的发展，我们可以推动平台和技术深度的发展。
Lenny Rachitsky: For companies that are struggling with this potentially or trying to figure out how to do this, two things I'm hearing here is start to see yourself as a technology company.
Lenny Rachitsky：对于那些可能正在为此苦苦挣扎或试图弄清楚如何做到这一点的公司，我在这里听到的两件事是开始将自己视为一家技术公司。
It doesn't necessarily apply to every company, but seems like an important element is like we're building technology, we're not a financial company, we're not a real estate company, we're not a technology company.
它不一定适用于每家公司，但似乎一个重要因素是我们正在构建技术，我们不是一家金融公司，我们不是一家房地产公司，我们不是一家科技公司。
And then two is organize the team such that say engineers report up to an engineering leader versus a GM who maybe doesn't understand engineering as well or doesn't take it as seriously as they should.
第二个是组织团队，让工程师向工程领导汇报，而不是向总经理汇报，而总经理可能不太了解工程，或者没有像他们应该的那样认真对待工程。
Dhanji R.
丹吉 R.
Prasanna: Yeah, I think that's pretty much what we did.
Prasanna：是的，我想我们就是这么做的。
And not to lean too heavily on this, but this is what jobs did when he came back to Apple as well.
不要过分依赖这一点，但这也是乔布斯回到苹果后所做的。
He reorganized Apple to be functional, and it wasn't like we were following a playbook.
他对苹果进行了重组，使其能够正常运作，而我们并不是在遵循剧本。
We discovered this as we were investigating what it's going to take to make these teams more tech-focused and to bring our DNA back to our roots, which really was putting engineering and design first, which is what technology first means to me.
我们在调查如何让这些团队更加注重技术并将我们的DNA带回到我们的根源时发现了这一点，这确实是将工程和设计放在第一位，这就是技术对我来说第一的意义。
So yeah, I would say to companies, find your DNA and really try to optimize for what that is in a very simple and clear way.
所以，是的，我想对公司说，找到你的 DNA，并真正尝试以一种非常简单和清晰的方式对其进行优化。
Lenny Rachitsky: Okay, so you made a bunch of changes, you had this manifesto, everyone's on board, you made a bunch of changes.
Lenny Rachitsky：好的，所以你做了很多改变，你有这个宣言，每个人都参与其中，你做了很多改变。
Functional technology first, comparing the way that your say engineering team works today versus two or three years ago, what is most different?
首先是功能技术，比较一下你所说的工程团队现在的工作方式与两三年前的工作方式，最大的不同是什么？
Dhanji R.
丹吉 R.
Prasanna: Not everyone was on board, I'll tell you that.
Prasanna：我告诉你，并不是每个人都参与其中。
It was quite a painful transformation.
这是一个相当痛苦的转变。
I think that one of the things that I learned the most throughout this process is that Conway's Law can be really, really powerful.
我认为在这个过程中我学到最多的一件事是康威定律非常非常强大。
So it's the law that basically says you ship your org structure.
所以法律基本上规定你要交付你的组织结构。
So what you're organized as in terms of teams, in terms of collaborating groups and your operating model matters a lot to what you build.
因此，你的团队、协作小组和运营模式的组织方式对于你构建的内容非常重要。
Dhanji R.
丹吉 R.
Prasanna: And so I think that that was essentially the biggest change is we had a lot of momentum in each of these silos, be it Cash App, be it Afterpay, be it Square or even TIDAL or music streaming service.
Prasanna：所以我认为这本质上是最大的变化是我们在每个孤岛中都有很大的动力，无论是 Cash App，无论是 Afterpay，无论是 Square，甚至是 TIDAL 或音乐流媒体服务。
And no one was really talking to each other, no one was really aligned on technical strategy on what we even wanted to be five years from now as a collective team.
没有人真正互相交谈，没有人真正在技术战略上达成一致，甚至我们希望五年后成为一个集体团队。
And so all those things are different now.
所以现在所有这些事情都不同了。
I'm not saying it's perfect, there's still a long road ahead of us, but we at least speak the same language.
我并不是说它是完美的，我们还有很长的路要走，但我们至少说着同样的语言。
Dhanji R.
丹吉 R.
Prasanna: We're all have access to the same tools, we share the same policies.
Prasanna：我们都可以使用相同的工具，共享相同的政策。
So a certain level of senior engineer means the same thing across the whole company.
因此，一定级别的高级工程师对整个公司来说意味着同样的事情。
People can move from one team to another's into an area of need.
人们可以从一个团队调动到另一个团队，进入有需要的领域。
All of these things are very different.
所有这些事情都非常不同。
But to sum it up, I would say we're technically focused and we're focused on advancing technical excellence as a goal.
但总而言之，我想说我们专注于技术，并且我们致力于以推进技术卓越为目标。
And that just really wasn't that true two to three years ago.
两三年前情况并非如此。
There were other things we were optimizing for then.
当时我们还在优化其他事情。
Lenny Rachitsky: Maybe going one level deeper in terms of how people actually work at a day.
Lenny Rachitsky：也许可以更深入地了解人们一天中的实际工作方式。
So if you're looking at an engineering team, say the average engineering team and maybe also the top most optimal engineering team, how is the way they work today different from a couple of years ago?
因此，如果您正在研究一个工程团队，比如说普通的工程团队，也许还有最优秀的工程团队，他们今天的工作方式与几年前有何不同？
Dhanji R.
丹吉 R.
Prasanna: In the small, certain teams that are very, very AI natives or teams that are building AI first everywhere are working much differently than before because they're using vibe code tools and they're essentially building without writing lines of code by hand, and that just wasn't true through the three years ago.
Prasanna：在一些小型的、非常非常人工智能原生的团队中，或者在各地首先构建人工智能的团队中，他们的工作方式与以前有很大不同，因为他们使用的是 vivi 代码工具，而且他们基本上是在不手工编写代码行的情况下进行构建的，而三年前情况并非如此。
I don't think it was true anywhere in the world.
我认为世界上任何地方都不是这样。
So that's dramatically different in teams that are still working with very heavy legacy code bases.
因此，对于仍在使用非常繁重的遗留代码库的团队来说，这是截然不同的。
Dhanji R.
丹吉 R.
Prasanna: It's less true, but they're also encountering these background AI processes.
Prasanna：这不太正确，但他们也遇到了这些后台人工智能进程。
So we have these tools that run 24/7 or run in the CI pipeline and they're analyzing vulnerabilities.
因此，我们拥有这些 24/7 运行或在 CI 管道中运行的工具，它们正在分析漏洞。
They're looking at even bugs filed on tickets and trying to build patches while engineers are asleep.
他们甚至会查看票证上提交的错误，并尝试在工程师睡觉时构建补丁。
So they come in the next day and look at it.
所以他们第二天就过来看看。
So I would say there are a number of ways in which they're different, but different teams have adapted in different ways depending on how close they are to the tools.
所以我想说它们在很多方面都是不同的，但是不同的团队根据他们与工具的接近程度以不同的方式进行调整。
Lenny Rachitsky: Okay, so let me lean into that AI piece, which is I think where you guys are most ahead of a lot of other companies.
Lenny Rachitsky：好的，让我来谈谈人工智能部分，我认为你们在这方面比许多其他公司最领先。
You guys built your own agent I think is how you describe Goose.
你们建立了自己的代理，我想这就是你们对 Goose 的描述。
So there's a lot of talk about productivity gains through AI.
因此，关于通过人工智能提高生产力的讨论很多。
There's this camp of people are like, you don't understand how much productivity there is to gain from AI.
有这样一群人，你不明白从人工智能中可以获得多少生产力。
It's the future, this is the way it's all going to work.
这就是未来，这就是一切的运作方式。
Lenny Rachitsky: We're all accelerating 10X.
Lenny Rachitsky：我们都在加速 10 倍。
There's also this camp where people are like, I'm so overhyped, nothing's working.
还有一个营地，人们会说，我太夸张了，什么都不起作用。
People talk about it.
人们谈论它。
All these pilots are failing.
所有这些飞行员都失败了。
Nobody's actually adopting this at scale.
没有人真正大规模采用这一点。
I feel like you're probably in that first camp.
我觉得你可能属于第一个阵营。
What sort of gains have you seen practically from AI tools on your teams?
您的团队从人工智能工具中实际看到了哪些收获？
Dhanji R.
丹吉 R.
Prasanna: Our number one priority is through automate Block, which means getting AI and getting AI forms of automation through our entire company.
Prasanna：我们的首要任务是通过自动化 Block，这意味着通过我们整个公司获得人工智能和人工智能形式的自动化。
And we feel that that's just at the beginning of where the utility is with all these large language models, and I think we're going to continue to see that improve.
我们认为这只是所有这些大型语言模型的实用性的开始，我认为我们将继续看到这种改进。
But even now, we find engineering teams that are very, very AI forward that are using Goose every day are reporting about eight to 10 hours saved per week, and this is self-reported.
但即使是现在，我们发现每天使用 Goose 的人工智能非常先进的工程团队报告每周节省了大约 8 到 10 个小时，而且这是自我报告的。
And then we also have a number of check metrics to try and validate that.
然后我们还有一些检查指标来尝试验证这一点。
Dhanji R.
丹吉 R.
Prasanna: So we look at PRs, we look at throughput of features, we look at a whole bunch of things and we have our data scientists come up with a complicated formula that tries to distill it all into something meaningful.
Prasanna：所以我们关注 PR，我们关注功能的吞吐量，我们关注一大堆事情，我们让数据科学家提出一个复杂的公式，试图将其全部提炼成有意义的东西。
And we feel across the whole company, we're probably trending towards 20 to 25% of manual hours saved.
我们感觉整个公司的人工时间可能会节省 20% 到 25%。
And I think that's just the start of all of this.
我认为这只是所有这一切的开始。
I do feel that the more AI-native companies are doing a better job of realizing this.
我确实认为，更多的人工智能本土公司在实现这一点上做得更好。
Dhanji R.
丹吉 R.
Prasanna: So companies that started just with AI startups mostly, but there is some truth to this notion that AI isn't a panacea and it's growing as well in capability.
Prasanna：所以，大多数公司都是从人工智能初创公司起步的，但人工智能并不是万能药，而且它的能力也在不断增长，这一观点是有一定道理的。
So you need to ride that wave along with it.
所以你需要顺势而为。
And I think a lot of the companies aren't realizing this.
我认为很多公司都没有意识到这一点。
They're like, "Well, where's the value?" And the truth is the value is changing every day.
他们会说，“那么，价值在哪里？”事实上，价值每天都在变化。
And so you need to be adaptable and look at what the value is today and plan for what the value will be tomorrow and then slowly expand to the areas where it's most efficacious.
因此，你需要具有适应性，看看今天的价值是什么，并计划明天的价值，然后慢慢扩展到最有效的领域。
Dhanji R.
丹吉 R.
Prasanna: I'll give you an example.
Prasanna：我给你举个例子。
One area in which we find that it's really good is for non-technical teams to be able to build little software tools for themselves.
我们发现它真正有用的一个领域是非技术团队能够为自己构建小型软件工具。
So this has been one of the most surprising and energizing uses of Goose within Block is we'll have our enterprise risk management team build a whole system for self-servicing enterprise risk, and this is compressing weeks of work into hours, or ordinarily, they would be waiting for an internal apps team or something to go and build that and they would put that on their Q2 roadmap and everyone would be twiddling their thumbs until it all clicked into place, but now you can just go and do it.
因此，这是 Goose 在 Block 中最令人惊讶和充满活力的用途之一，我们将让我们的企业风险管理团队构建一个用于自助服务企业风险的整个系统，这会将几周的工作压缩为几个小时，或者通常情况下，他们会等待内部应用程序团队或其他东西来构建它，他们会将其放在第二季度路线图上，每个人都会摆弄拇指，直到一切都按到位，但现在你可以直接去做。
Dhanji R.
丹吉 R.
Prasanna: And so a lot of these kinds of use cases we're seeing an enormous amount of productivity gain in the other area, which I'm really excited about is we have this other tool called Gosling, which is a goose for mobile effectively.
Prasanna：因此，在很多此类用例中，我们看到其他领域的生产力大幅提高，令我感到非常兴奋的是我们有另一个名为 Gosling 的工具，它是一款有效的移动鹅。
So it operates your Android OS at a native level using the accessibility API.
因此，它使用辅助功能 API 在本机级别操作您的 Android 操作系统。
And we use that for automating UI tests.
我们用它来自动化 UI 测试。
Dhanji R.
丹吉 R.
Prasanna: So before, you would have to hire an army of contractors or QAs who would go and click through every screen, but now we can just bake those into automated tests and then give you a report at the end.
Prasanna：所以以前，您必须雇用一群承包商或质量保证人员，他们会点击每个屏幕，但现在我们可以将它们烘焙到自动化测试中，然后在最后给您一份报告。
So we're seeing a lot of advantages in those types of areas, but where you have a lot of depth and a lot of really strong people come together is where AI, I think still underperforms humans.
因此，我们在这些类型的领域看到了很多优势，但我认为，当人工智能有足够的深度和很多真正强大的人聚集在一起时，人工智能仍然表现不佳。
And that's something that's probably going to get better over time, but it's also something where we should lean into as humans.
随着时间的推移，这可能会变得更好，但这也是我们作为人类应该依靠的东西。
Dhanji R.
丹吉 R.
Prasanna: So when you have some very senior engineers and they're thinking about things like architecture and design and race conditions, orchestration, things like this, that's still an area where AI isn't quite there.
Prasanna：所以，当你有一些非常高级的工程师，他们正在考虑诸如架构、设计、竞争条件、编排之类的事情时，这仍然是人工智能还不存在的领域。
And so I think the companies that aren't feeling the success in AI are trying to just throw these tools at their giant code bases and hoping good things will happen, and that's not how it's playing out.
因此，我认为那些没有感受到人工智能成功的公司正在试图将这些工具扔到他们庞大的代码库中，并希望好的事情会发生，但事实并非如此。
Eventually, I do think it'll get there, but right now we're still in the early utility phase.
最终，我确实认为它会实现这一目标，但现在我们仍处于早期实用阶段。
Lenny Rachitsky: Holy moly, there's so much there in what you just shared.
Lenny Rachitsky：天啊，你刚才分享的内容太多了。
There's like five things I want to follow up on.
我想跟进大约五件事。
Okay, so one is this metric you kind of alluded to, which is how you measure the impact of AI in your team.
好的，其中一个就是您提到的这个指标，这就是您衡量人工智能在团队中的影响的方式。
So it was human manual hours saved, is that how you describe it?
所以节省了人工时间，您是这么描述的吗？
Lenny Rachitsky: So it's roughly a fourth of an engineer's time currently is being saved by AI tooling.
Lenny Rachitsky：目前，人工智能工具节省了工程师大约四分之一的时间。
Dhanji R.
丹吉 R.
Prasanna: That metric is across all teams.
Prasanna：这个指标适用于所有团队。
So that would be our support teams, our legal teams, our risk teams, all of them together.
这就是我们的支持团队、法律团队、风险团队，所有这些团队。
Dhanji R.
丹吉 R.
Prasanna: And then on the engineering side, it's very variable because like I said before, it matters how big and how complex the code base is.
Prasanna：然后在工程方面，它是非常可变的，因为就像我之前所说的，代码库的大小和复杂程度很重要。
And so if you're building a totally new Greenfields code base or you're building an app for a new platform, then we're seeing those pretty aggressive gains, but in very complex code bases that already exist, those gains are not quite there yet.
因此，如果您正在构建全新的 Greenfields 代码库，或者正在为新平台构建应用程序，那么我们会看到这些相当激进的收益，但在已经存在的非常复杂的代码库中，这些收益尚未完全实现。
Lenny Rachitsky: That's amazing.
莱尼·拉奇茨基：太棒了。
And whenever I hear a stat like this, I think an important element that people need to think about is this is the worst it will ever be.
每当我听到这样的统计数据时，我认为人们需要考虑的一个重要因素是，这将是有史以来最糟糕的情况。
This is the lowest, this is now the baseline.
这是最低的，这是现在的基线。
And so it may not sound that crazy yet, but it's going to get crazy.
因此，这听起来可能还没有那么疯狂，但它会变得疯狂。
Okay, the other thing that you talked about is Goose, you haven't explained what Goose is.
好吧，你谈到的另一件事是Goose，你还没有解释Goose 是什么。
This is a huge deal.
这是一件大事。
Explain what Goose is and how important this has become to you guys.
解释一下 Goose 是什么以及它对你们来说有多重要。
Dhanji R.
丹吉 R.
Prasanna: So Goose is a general purpose AI agent.
Prasanna：所以 Goose 是一个通用人工智能代理。
So you can think of it as a desktop tool or a program that you can download and install on your computer and then it has a UI.
所以你可以把它想象成一个桌面工具或一个程序，你可以下载并安装在你的计算机上，然后它有一个用户界面。
You can talk to it just like a chatbot and you can say anything from, "Hey Goose, organize my photos by category, and it has the ability to look within your photos and if there are a lot of trees, it'll organize them as nature photos.
你可以像聊天机器人一样与它交谈，你可以说任何内容，“嘿鹅，按类别组织我的照片，它能够查看你的照片，如果有很多树，它会将它们组织为自然照片。
And there are a lot of people, it'll organize them as portraiture." All of this sort of stuff to writing software for you.
而且人很多，它会把他们组织成肖像画。”所有这些都是为了为您编写软件。
Dhanji R.
丹吉 R.
Prasanna: So it can do all of these tasks, and the way we've been able to do this is through something called a model context protocol or the MCP, which a lot of your listeners might've heard.
Prasanna：所以它可以完成所有这些任务，而我们能够做到这一点的方式是通过称为模型上下文协议或 MCP 的东西，很多听众可能都听说过。
And this is something that Anthropic came up with that we were a very early contributor to.
这是 Anthropic 提出的，我们是早期的贡献者。
And the model context protocol is very simply just a set of formalized wrappers around existing tools or existing capabilities.
模型上下文协议非常简单，只是现有工具或现有功能的一组形式化包装器。
So if you have tools that you use in the enterprise, be it Salesforce or be it Snowflake or SQL, any of these things, you can wrap them in the MCP and then it exposes them to your LLM to be able to manipulate.
因此，如果您有在企业中使用的工具，无论是 Salesforce 还是 Snowflake 或 SQL，任何这些东西，您都可以将它们包装在 MCP 中，然后将它们公开给您的 LLM 以便能够操作。
Dhanji R.
丹吉 R.
Prasanna: So until that point, the LLMs were not really able to do much other than chat, but Goose gives these brains arms and legs to go out and act in our digital world, and that's where we find it's had most impact and it's built on this fairly open protocol that anyone can implement.
Prasanna：所以直到那时，法学硕士除了聊天之外并不能做太多事情，但是 Goose 为这些大脑提供了在我们的数字世界中走出去并采取行动的能力，这就是我们发现它影响最大的地方，并且它建立在任何人都可以实施的相当开放的协议之上。
There have been an explosion of MCPs.
MCP 发生了爆炸。
Goose is entirely open source, by the way, so any of you can download it and extend it, write your own MCPs, and that's been our core successes through Goose.
顺便说一句，Goose 是完全开源的，因此任何人都可以下载它并扩展它，编写自己的 MCP，这就是我们通过 Goose 取得的核心成功。
Lenny Rachitsky: Okay.
莱尼·拉奇茨基：好的。
So essentially like Claude code with a UI, desktop app sort of thing built on top of Claude and OpenAI ChatGPT and a bunch of open source models.
所以本质上就像带有 UI 的 Claude 代码，桌面应用程序之类的东西构建在 Claude 和 OpenAI ChatGPT 以及一堆开源模型之上。
Is that right?
是这样吗？
Dhanji R.
丹吉 R.
Prasanna: Yeah, it can use any model.
Prasanna：是的，它可以使用任何模型。
So we have a pluggable provider system and you can either bring your own API keys and use the Claude family models or OpenAI's family models, or you can use open source models and you can download them and use them directly or via Ollama and other, there are several tools that help you do that, but essentially it's taking the capability of these models to generate text and to interpret text and applying them to real world situations.
因此，我们有一个可插入的提供程序系统，您可以携带自己的 API 密钥并使用 Claude 系列模型或 OpenAI 的系列模型，或者您可以使用开源模型，您可以下载它们并直接使用它们或通过 Ollama 等工具，有几种工具可以帮助您做到这一点，但本质上它是利用这些模型的能力来生成文本和解释文本并将它们应用到现实世界的情况。
Dhanji R.
丹吉 R.
Prasanna: So one example that I really like is you can ask Goose to go and build your marketing report and it has MCPs to connect to Snowflake and Tableau and Looker.
Prasanna：我非常喜欢的一个例子是，您可以要求 Goose 去构建您的营销报告，它具有连接到 Snowflake、Tableau 和 Looker 的 MCP。
So it'll write SQL to pull out data from there, it'll do some analysis and a CSV so it can write Python code on your desktop to do all that.
因此，它会编写 SQL 来从那里提取数据，它会进行一些分析和 CSV，以便它可以在桌面上编写 Python 代码来完成所有这些工作。
It will generate some graphs using some JavaScript charting library that it knows about.
它将使用一些它知道的 JavaScript 图表库生成一些图表。
Dhanji R.
丹吉 R.
Prasanna: And then finally, it'll put this all into a PDF or Google Doc or whatever and it can even email it for you or upload it somewhere.
Prasanna：最后，它会将所有内容放入 PDF 或 Google 文档或其他任何文件中，甚至可以通过电子邮件发送给您或将其上传到某个地方。
And it's doing all of this on its own, by the way.
顺便说一下，这一切都是它自己完成的。
No one's sitting here telling it that, you're just saying, "Hey, I want this report, I want this emailed here, I want these pretty charts." And it's orchestrating across all these systems.
没有人坐在这里告诉它，你只是说，“嘿，我想要这份报告，我想要通过电子邮件在这里发送，我想要这些漂亮的图表。”它正在协调所有这些系统。
Lenny Rachitsky: So essentially at Block, instead of using Claude or ChatGPT directly or even Cursor and all these apps, they use Goose?
Lenny Rachitsky：所以基本上在 Block，他们不是直接使用 Claude 或 ChatGPT，甚至是 Cursor 和所有这些应用程序，而是使用 Goose？
Dhanji R.
丹吉 R.
Prasanna: Yeah, we allow our engineers and our general employee population to use any tools that they want.
Prasanna：是的，我们允许我们的工程师和普通员工使用他们想要的任何工具。
Goose is the one that's most well-integrated into all of our systems because it's built on the MCP and it's so easy to create an MCP for an existing system.
Goose 是与我们所有系统集成得最好的一个，因为它是基于 MCP 构建的，并且为现有系统创建 MCP 非常容易。
So for example, if you're using a issue tracking tool and you want some AI automation added to it, before Goose, our teams would have to wait for the vendor to build that AI capability in there, or maybe there's some way in which OpenAI or Anthropic or Google would provide a general purpose capability where we could plug those in.
例如，如果您正在使用问题跟踪工具，并且希望添加一些 AI 自动化功能，那么在 Goose 之前，我们的团队将不得不等待供应商在其中构建 AI 功能，或者 OpenAI 或 Anthropic 或 Google 可能会以某种方式提供通用功能，我们可以将这些功能插入其中。
But with Goose, that's no longer necessary with a few lines of code that an MCP represents.
但有了 Goose，MCP 代表的几行代码就不再需要了。
All these systems are orchestratable with AI basically overnight, and Goose can write its own MCPs.
所有这些系统基本上都可以在一夜之间通过 AI 进行编排，并且 Goose 可以编写自己的 MCP。
So it's pretty bootstrappable as well.
所以它也非常可引导。
Lenny Rachitsky: And this is open source and basically you've spent all this time building this thing, any other company can now implement it and build on all the work you've done?
Lenny Rachitsky：这是开源的，基本上你花了所有时间来构建这个东西，任何其他公司现在都可以实现它并以你所做的所有工作为基础吗？
Dhanji R.
丹吉 R.
Prasanna: Yeah, and we have a lot of companies using Goose pretty actively.
Prasanna：是的，我们有很多公司非常积极地使用 Goose。
I don't want to name too many names, but from our competitors to our close partners, a lot of them are using Goose pretty regularly on their teams.
我不想说出太多名字，但从我们的竞争对手到我们亲密的合作伙伴，他们中的很多人都在他们的团队中经常使用 Goose。
I know Databricks talks about it a lot, but everyone you can think of in this mid-tech tier is using Goose in some form.
我知道 Databricks 对此进行了很多讨论，但您能想到的中级技术层中的每个人都在以某种形式使用 Goose。
Lenny Rachitsky: That's insane.
莱尼·拉奇茨基：这太疯狂了。
This feels like it could've been a massive business of its own, some of the fastest growing companies in the world, basically this is their product and you've built it and given away.
这感觉就像它本身可能是一个庞大的业务，世界上一些增长最快的公司，基本上这是他们的产品，你已经构建了它并赠送了它。
Dhanji R.
丹吉 R.
Prasanna: Yeah, we believe in the power of open source and one of our core missions is to increase openness, and that means contributing to open protocols and contributing to open source.
Prasanna：是的，我们相信开源的力量，我们的核心使命之一是提高开放性，这意味着为开放协议做出贡献并为开源做出贡献。
And as a tech company, we're built on a lot of open source software.
作为一家科技公司，我们建立在许多开源软件的基础上。
I think pretty much every tech company is whether you're talking about Linux or Java or MySQL or any of these essential components, and so we feel like we have a strong imperative to give back.
我认为几乎每家科技公司都是如此，无论你谈论的是 Linux、Java、MySQL 还是任何这些基本组件，因此我们觉得我们有强烈的回馈义务。
Dhanji R.
丹吉 R.
Prasanna: We want to build things that not only are good for us and our customers, but that outlast Block and outgrow Block, that's certainly a core value for us and has been from the beginning even long before this whole AI phase.
Prasanna：我们希望构建的东西不仅对我们和我们的客户有利，而且能够比 Block 更长久、发展得更快，这对我们来说无疑是一个核心价值，而且从一开始甚至早在整个 AI 阶段之前就已经存在了。
So yeah, Goose follows in that proud tradition and yeah, we're very excited that its had the success it's had.
所以，是的，Goose 遵循了这一令人自豪的传统，是的，我们对它所取得的成功感到非常兴奋。
Lenny Rachitsky: What's the story with the name Goose, by the way?
Lenny Rachitsky：顺便问一下，Goose 这个名字有什么故事吗？
Can't help but ask.
忍不住要问一下。
Dhanji R.
丹吉 R.
Prasanna: Goose is a Top Gun reference.
Prasanna：Goose 是《Top Gun》的参考。
So our engineer that came up with it.
所以我们的工程师想出了它。
He also looks exactly like Goose, so it's kind of crazy if you put them side to side, he's going to be really embarrassed with my sharing this, but that's the reason why they call it Goose, and then we lent into the whole bird theme after that.
他看起来也和鹅一模一样，所以如果你把它们并排放在一起，那就有点疯狂了，他会对我分享这个感到非常尴尬，但这就是为什么他们称之为鹅的原因，然后我们就借用了整个鸟的主题。
Lenny Rachitsky: That's incredible.
莱尼·拉奇茨基：这太不可思议了。
There's a story I heard you share on a different podcast where there's an engineer who takes this to the extreme and has Goose watch him.
我听到你在另一个播客上分享了一个故事，其中有一位工程师将这种情况发挥到了极致，并让 Goose 看着他。
Talk about that, share that story.
谈论这个，分享那个故事。
Dhanji R.
丹吉 R.
Prasanna: Yeah, absolutely.
Prasanna：是的，绝对是。
So he is very, very AI-focused and he's trying to extract all these crazy ideas from Goose and Goose can do all of the things that I described through specific interactions with tools, but it can also just watch your screen so it understands how to process images and process the things that it's looking at through screenshots.
所以他非常非常关注人工智能，他试图从 Goose 中提取所有这些疯狂的想法，Goose 可以通过与工具的特定交互来完成我所描述的所有事情，但它也可以只观看你的屏幕，这样它就了解如何处理图像并处理它通过屏幕截图看到的东西。
And so he built this system where it's essentially just watching everything he does all the time and he'll be talking to a colleague on Slack or an email and they'll be discussing some feature that they think is useful to implement.
因此，他构建了这个系统，本质上只是监视他一直所做的一切，他将通过 Slack 或电子邮件与同事交谈，他们将讨论一些他们认为对实现有用的功能。
Dhanji R.
丹吉 R.
Prasanna: And then a few hours later he'll find that Goose has already tried to build that feature and opened a PR for it on Git and all sorts of other wacky things like that.
Prasanna：几个小时后，他会发现 Goose 已经尝试构建该功能，并在 Git 上为它打开了一个 PR 以及诸如此类的其他各种古怪的东西。
So it'll try to nudge him out of a workflow.
因此，它会尝试将他从工作流程中剔除。
If he's running over on a meeting and he's late for something else, it comes up with these creative things that he didn't program or he didn't write prompts for, but that it thinks will help him improve his productivity or improve his work day.
如果他在开会时超时，而他在其他事情上迟到了，它会提出这些他没有编程或没有编写提示的创造性的东西，但它认为这将帮助他提高生产力或改善他的工作日。
So yeah, it's pretty crazy.
是的，这太疯狂了。
You have to have the stomach for it to be that level of tied into your working tools, but it kind of shows you what's possible with tools like this.
您必须有足够的承受能力才能将其与您的工作工具相结合，但它向您展示了这样的工具的可能性。
Lenny Rachitsky: Clearly this is where things are going.
Lenny Rachitsky：显然这就是事情的发展方向。
Once this gets good enough, I love this guy is just trying it.
一旦这变得足够好，我喜欢这个正在尝试的家伙。
So it's basically watching him work and anticipating what he should be doing and does the work for him as a first draft so that he's like, "Oh, the PR is already done on this thing.
所以基本上就是看着他工作，预测他应该做什么，并为他做初稿，这样他就会说，“哦，这件事的公关已经完成了。
We were just talking about it at this meeting." That's incredible.
我们只是在这次会议上讨论了这个问题。”那真是难以置信。
Lenny Rachitsky: How good is it?
莱尼·拉奇茨基：有多好？
Where's it at?
它在哪里？
If you had to go zero to a hundred of like, "Okay, going to, all you have to do is now think and talk and that'll just do your job."
如果你必须从零到一百，“好吧，去吧，你现在所要做的就是思考和交谈，这就完成了你的工作。”
Dhanji R.
丹吉 R.
Prasanna: Yeah, so voice is the other big part of it.
Prasanna：是的，所以声音是其中的另一个重要部分。
It has voice processing capability, so it's always listening to what he's saying as well and trying to interpret that.
它具有语音处理能力，所以它总是在听他说的话并尝试解释它。
I would say that this is mostly an experiment, given that he's on our core Goose team and he contributes to Goose, so he has a day job.
我想说这主要是一个实验，因为他是我们核心 Goose 团队的成员并且他为 Goose 做出了贡献，所以他有一份日常工作。
This is a kind of thing on the side that he was developing.
这是他正在开发的一种侧面的东西。
Dhanji R.
丹吉 R.
Prasanna: So once this evolves into more of a native feature of Goose itself or other tools that we use in the enterprise, I think it can have a lot of legs, but it's already pretty good.
Prasanna：因此，一旦这更多地演变成 Goose 本身或我们在企业中使用的其他工具的原生功能，我认为它可以有很多优势，但它已经相当不错了。
It's probably cutting down enormous amounts of busy work that he has to do.
这可能会减少他必须做的大量繁忙工作。
So for example, one thing he'll do is he'll say, "Oh, I have a meeting conflict.
例如，他会做的一件事是他会说：“哦，我有一个会议冲突。
I can't make it that time, or I have to go pick up my kid."
我那个时间去不了了，不然我得去接我的孩子了。”
Dhanji R.
丹吉 R.
Prasanna: And Goose will automatically reschedule that meeting without him ever sitting in front of his calendar and clicking through 10 times.
Prasanna：Goose 会自动重新安排会议时间，而无需他坐在日历前点击 10 次。
Yeah, so these are things that I think we were waiting for the calendar vendor to build as features into calendar, but we don't need to do that anymore because AI is able to orchestrate this for us.
是的，所以我认为我们正在等待日历供应商将这些内容作为功能构建到日历中，但我们不再需要这样做，因为人工智能能够为我们编排这些内容。
Lenny Rachitsky: This isn't that guy that had four jobs at four different startups that he was able to paralyze all his work and hire people.
Lenny Rachitsky：这不是那个在四家不同的初创公司担任四份工作的人，他能够瘫痪他的所有工作并雇用人员。
Dhanji R.
丹吉 R.
Prasanna: No, it's not.
普拉萨娜：不，不是。
He's someone that I've worked with for a long time and he's been at Block for a long time.
他是我合作了很长时间的人，而且他在 Block 工作了很长时间。
He just loves experimenting and he embodies that culture of experimentation just like our creator of Goose who did the same thing.
他只是喜欢实验，他体现了实验文化，就像我们的 Goose 创始人也做了同样的事情。
Lenny Rachitsky: So let me pull on that thread a little bit.
Lenny Rachitsky：所以让我稍微拉一下这条线。
You're kind of seeing a glimpse of where things are going.
你可以瞥见事情的发展方向。
You're very ahead of the curve in a lot of ways at Block.
在 Block，你在很多方面都非常领先。
How do you think things will look in a couple of years in terms of how engineers work, how product teams work that's different from today?
您认为几年后工程师的工作方式、产品团队的工作方式将会与今天有所不同？
Dhanji R.
丹吉 R.
Prasanna: I think a lot of it is dependent on the improvement of LLM performance, but I can tell you the way I'm trying to change how I work and how I'm trying to change our immediate team's way of working.
Prasanna：我认为这很大程度上取决于法学硕士表现的提高，但我可以告诉你我正在努力改变我的工作方式以及我如何尝试改变我们直接团队的工作方式。
So I think vibe coding has been an interesting, exciting thing, which is you talk to a chatbot essentially and it goes and builds software for you, but I think this is highly limiting.
所以我认为氛围编码是一件有趣、令人兴奋的事情，这就是你本质上与聊天机器人交谈，它会为你构建软件，但我认为这是非常有限的。
Dhanji R.
丹吉 R.
Prasanna: It's very ping pong.
Prasanna：这很像乒乓球。
You do something, you wait for three or four minutes and it comes back with something sort of half-baked and you have to nudge it and guide it and massage it to get where it needs to be.
你做了某件事，等了三四分钟，它就带着半生不熟的东西回来了，你必须轻推它、引导它、按摩它，让它到达它需要的地方。
I think that we're going to see much more autonomy.
我认为我们将会看到更多的自主权。
So where we're working on a couple of experiments with Goose, with the next version of Goose where we're really trying to push it to work not just for two or three or five minutes at a time, our median session length is five minutes and on average, seven, but we're trying to push it to hours.
因此，我们正在使用 Goose 进行一些实验，在 Goose 的下一个版本中，我们确实试图让它不仅仅一次工作两分钟、三分钟或五分钟，我们的中位会话长度是五分钟，平均是七分钟，但我们正在努力将其延长到几个小时。
Dhanji R.
丹吉 R.
Prasanna: We're trying to say, "Hey, all these LLMs are sitting idle overnight and on weekends while humans aren't there, there's no need for that." They should be working all the time.
Prasanna：我们想说的是，“嘿，所有这些法学硕士都在过夜和周末闲置，而没有人在那里，没有必要这样做。”他们应该一直在工作。
They should be trying to build in anticipation of what we want if we go back to the earlier part of the conversation.
如果我们回到谈话的早期部分，他们应该努力预测我们想要什么。
But also I think that they should be able to build in ways that were never possible before.
但我也认为他们应该能够以以前不可能的方式进行建设。
Dhanji R.
丹吉 R.
Prasanna: Before as humans, we had limited resources, limited bandwidth, and a lot of coordination overhead.
Prasanna：作为人类之前，我们的资源、带宽和大量的协调开销都是有限的。
So we would have to choose the best path to try in an experiment, and I don't think we need that anymore.
因此，我们必须选择最佳路径来尝试实验，但我认为我们不再需要这样做了。
We need instead to be able to describe multiple different experiments in a great amount of detail.
相反，我们需要能够详细描述多个不同的实验。
And then maybe we go to sleep and then in the morning, all those experiments are built and we can sort of throw away five or six of them.
然后也许我们去睡觉，然后在早上，所有这些实验都完成了，我们可以扔掉其中的五六个。
Dhanji R.
丹吉 R.
Prasanna: So one of the things that I do regularly, so I write code every day, but one of the things that I do regularly is just throw away huge, huge amounts of code, and it's kind of hard for me because I've never done that before.
Prasanna：所以我经常做的事情之一，所以我每天都编写代码，但我经常做的事情之一就是扔掉大量的代码，这对我来说有点困难，因为我以前从未这样做过。
I mean obviously engineers love deleting code, but this is different.
我的意思是显然工程师喜欢删除代码，但这是不同的。
You build a whole new system or a whole new feature and you're like, "Ah, it doesn't feel exactly right.
你构建了一个全新的系统或一个全新的功能，然后你会想，“啊，感觉不太对劲。
I'm just going to delete and start over."
我只是要删除并重新开始。”
Dhanji R.
丹吉 R.
Prasanna: So I think you're going to see a lot more of that way of working.
Prasanna：所以我认为你会看到更多这样的工作方式。
And I think that you're going to see instead of us, for example, refactoring an app to have a different UI or to evolve into its new version, we're just going to rewrite that app from scratch.
我认为，您将看到，例如，重构应用程序以具有不同的 UI 或演进为新版本，我们将从头开始重写该应用程序，而不是我们。
And one of the things I'm really pushing our teams to think about is what would our world look like if every single release, RM minus RF deleted the entire app and rebuilt it from scratch?
我真正推动我们团队思考的一件事是，如果每个版本（RM 减去 RF）都删除整个应用程序并从头开始重建，我们的世界会是什么样子？
And so we can't really do that today, but I think this shows you some of the direction of what's possible and where these tools are taking us.
所以我们今天还不能真正做到这一点，但我认为这向您展示了一些可能的方向以及这些工具将我们带往何处。
Lenny Rachitsky: What's interesting about that is that there's this common rule in software engineering and just product, don't ever just rewrite.
Lenny Rachitsky：有趣的是，软件工程和产品中有一个共同规则，永远不要重写。
Don't try to rewrite your thing.
不要试图重写你的东西。
You're going to forget all of the small improvements and tweaks and bug fixes people have made over the years, and you think it's going to be the simple straightforward thing.
你会忘记人们多年来所做的所有小改进、调整和错误修复，并且你认为这将是一件简单直接的事情。
It ends up being now it's like a year or more of just getting it back to where it was.
最终，现在需要一年或更长时间才能恢复到原来的状态。
And so interesting that AI now can make that possible, and what you're saying is that's actually maybe the way you should be working.
有趣的是，人工智能现在可以使这成为可能，而你所说的是，这实际上可能是你应该工作的方式。
Dhanji R.
丹吉 R.
Prasanna: I think so.
普拉萨娜：我想是的。
And I think that the trick is getting the AI to respect all of those incremental improvements, yeah, and sort of bake those in as a part of the specification, if you will.
我认为关键是让人工智能尊重所有这些增量改进，是的，如果你愿意的话，可以将它们作为规范的一部分。
Yeah.
是的。
Lenny Rachitsky: Also, the point you made about this agent, just you give it a bunch of ideas that builds them overnight and then you could see, I imagine it goes even further up the stack and comes up with the ideas and then starts building them and then you're like, "Okay, oh, that was a great idea.
Lenny Rachitsky：另外，你对这个代理的观点是，只要你给它一堆想法，在一夜之间构建它们，然后你就可以看到，我想它会更进一步，提出想法，然后开始构建它们，然后你会说，“好吧，哦，这是一个好主意。
Now I can see it immediately in the same workflow."
现在我可以在同一工作流程中立即看到它。”
Dhanji R.
丹吉 R.
Prasanna: Yeah, that's true.
Prasanna：是的，确实如此。
I was actually literally trying what you're saying just last week.
实际上我上周才真正尝试过你所说的。
And so I have this new version of Goose that we're working on and I was asking it to come up with ideas to improve itself and implement it overnight.
因此，我正在开发 Goose 的新版本，我要求它提出改进自身的想法并在一夜之间实施。
And sometimes-
有时——
Dhanji R.
丹吉 R.
Prasanna: ...
普拉萨娜：……
Sometimes it kind of goes off the script entirely and you have to sort of pull it back a bit.
有时它完全脱离了剧本，你必须把它拉回来一点。
So I think we're not quite at that era where it's completely self-improving and completely autonomous, but I do think we're in a transition phase where we can give it that nudge and say, "Hey, here's my wishlist of 10 things that I wish you could do.
所以我认为我们还没有完全进入那个完全自我改进和完全自主的时代，但我确实认为我们正处于一个过渡阶段，我们可以给予它推动并说，“嘿，这是我希望你能做的 10 件事的愿望清单。
Go and figure out the best way to do them." And it's successful I would say on 60% of those things, if the features are well enough described and it struggles on the remaining 40 where you have to kind of intervene and massage it.
去找出最好的方法来完成它们。”我想说，如果功能描述得足够好，它在 60% 的事情上是成功的，而在剩下的 40 件事上它却很困难，你必须进行干预和按摩。
Yeah.
是的。
Lenny Rachitsky: Oh man, I'm just imagining this feature where you give it the goal of drive revenue and growth and then it's just like, "Okay, everyone's fired.
Lenny Rachitsky：天哪，我只是想象这个功能，你给它的目标是推动收入和增长，然后就像，“好吧，每个人都被解雇了。
Here's your paychecks.
这是你的薪水。
I'll take it from here."
我会从这里拿走它。”
Dhanji R.
丹吉 R.
Prasanna: I don't think we're going to be there.
Prasanna：我认为我们不会去那里。
I do think we're going to need a lot of human taste to anchor these AIs so they don't go off script to be honest.
我确实认为我们需要大量的人类品味来锚定这些人工智能，这样他们就不会偏离剧本。
And that's really where our design lead and our design teams are pushing us to think, and that's a differentiator that I think will push us beyond this era of AI slop that everyone's talking about.
这确实是我们的设计主管和设计团队推动我们思考的地方，我认为这是一个差异化因素，它将推动我们超越这个每个人都在谈论的人工智能时代。
So yeah, it's very much anchoring it into a thing that matters to people and the thing that's tasteful and useful and has value.
所以，是的，它在很大程度上将其锚定为对人们重要的东西，以及有品味、有用且有价值的东西。
Lenny Rachitsky: To make that even more concrete, is there an example of something maybe AI was trying to, or a team was trying to pitch where you had to just know this is where humans are going to step in and keep things on track?
Lenny Rachitsky：更具体地说，是否有一个例子可以说明人工智能可能正在尝试做某事，或者一个团队正在尝试推销你必须知道这是人类将介入并使事情步入正轨的地方？
Dhanji R.
丹吉 R.
Prasanna: I'd say it was more around things like process automation or a lot of times I'll get this sort of request where a team will say, "We need to buy this new tool from this vendor because our current tool is entering X, Y and Z." Another team will say, "No, no, no, we can just use Goose to build an app that will do the same thing for us in half the time or less." And then as a human, you're sitting there thinking, "Is any of this necessary?
Prasanna：我想说，这更多是围绕流程自动化之类的事情，或者很多时候我会收到这样的请求，团队会说：“我们需要从这个供应商那里购买这个新工具，因为我们当前的工具正在输入 X、Y 和 Z。”另一个团队会说：“不，不，不，我们可以使用 Goose 构建一个应用程序，它可以在一半或更短的时间内为我们完成同样的事情。”然后作为一个人，你坐在那里思考，“这有必要吗？
If we just change the process, do we even need to think about building tools?" And this is the thing that AI isn't good at, it's not able to have this portfolio judgment or judgment across a global sense of what's important and what matters.
如果我们只是改变流程，我们还需要考虑构建工具吗？”而这正是人工智能不擅长的事情，它无法对什么是重要的、什么是重要的进行全球性的投资组合判断或判断。
Dhanji R.
丹吉 R.
Prasanna: So a lot of times, I tell teams just question the base assumption, particularly our InfoSec teams because they'll twist themselves into knot sometimes trying to secure something and you'll be like, "We'll just ask the team that's building it to do it differently or to not build that at all if it doesn't matter, and then you won't have to increase your surface area of securing it." So I think those are the areas where it's better for a human to use judgment and AI has not done a great job.
Prasanna：所以很多时候，我告诉团队只是质疑基本假设，特别是我们的信息安全团队，因为有时他们会在试图保护某些东西时陷入困境，而你会说，“我们只会要求构建它的团队以不同的方式做它，或者如果不重要的话根本不构建它，然后你就不必增加保护它的表面积。”所以我认为这些领域是人类更适合使用判断力的领域，而人工智能却做得不够好。
Lenny Rachitsky: You make this point about building your own software, your own tools instead of buying stuff.
Lenny Rachitsky：您提出了构建自己的软件、自己的工具而不是购买东西的观点。
This is a big question with AI, is it's going to replace all these SaaS apps to Salesforce over.
这是人工智能的一个大问题，它是否会取代所有这些 SaaS 应用程序到 Salesforce。
Is there a sense of just either how much money you guys have maybe saved building your own stuff, or have you built a new-found respect for the existing SaaS software that everyone's using and pays lots of money for?
是否有一种感觉，你们可能节省了多少钱来构建自己的东西，或者你们是否对每个人都在使用并花费大量金钱的现有 SaaS 软件建立了新的尊重？
Dhanji R.
丹吉 R.
Prasanna: I think there's a trap in getting away from your core purpose as a company.
Prasanna：我认为偏离公司的核心目标存在一个陷阱。
And our core purpose is economic empowerment.
我们的核心目的是经济赋权。
So getting customers or merchants or artists the ability to make a sale or pay their rent or upload their latest creation to TIDAL.
因此，让客户、商人或艺术家能够进行销售、支付租金或将他们的最新创作上传到 TIDAL。
And I think that anything that serves that purpose, we should encourage and we should invest in, but if we're just purely looking at dollars versus dollars, then that's pulling us off that purpose.
我认为任何服务于这个目的的东西，我们都应该鼓励并且我们应该投资，但如果我们只是纯粹地关注美元与美元之间的对比，那么这会让我们偏离这个目标。
Dhanji R.
丹吉 R.
Prasanna: The savings and costs that there might be in replacing a vendor tool by something you build in-house is probably not worth it in the mental bandwidth that you've lost and the amount of the team's technical focus that's being taken away.
Prasanna：用您内部构建的工具替换供应商工具可能节省的成本和成本可能不值得，因为您失去了精神带宽，并且团队的技术重点也被剥夺了。
So yeah, I would say it just keep coming to the thing that matters to you as a company and then the rest will follow from that.
所以，是的，我想说，作为一家公司，它只是继续关注对你来说重要的事情，然后剩下的事情就会随之而来。
Lenny Rachitsky: Yeah, I think people forget just how much maintenance it takes to keep something you've built.
Lenny Rachitsky：是的，我认为人们忘记了维护你所建造的东西需要多少维护。
Like, "Okay, cool, we built it in a weekend and now it's years of endless maintenance and requests and support." And also to your point, it feels like it comes back to the always motto of just focus on your core competencies and then buy everything else.
就像，“好吧，酷，我们在一个周末就构建了它，现在需要多年的无休止的维护、请求和支持。”而且就你的观点而言，感觉就像回到了一贯的座右铭：只关注你的核心竞争力，然后购买其他一切。
Dhanji R.
丹吉 R.
Prasanna: Yeah, it's the classic 80/20 problem, and we have that enough with the apps that we build for our customers.
Prasanna：是的，这是典型的 80/20 问题，我们为客户构建的应用程序已经足够解决这个问题了。
We'll build some great experiments that really resonate, and then we have to spend a lot of time ironing out the long tail of problems.
我们将进行一些真正能引起共鸣的伟大实验，然后我们必须花费大量时间来解决长尾问题。
So in Cash Card, for example, we built the entire functionality of Cash Card, I would say pretty much in a weekend or maybe a week of integration and work.
例如，在现金卡中，我们构建了现金卡的全部功能，我想说几乎在一个周末或一周的集成和工作中。
Dhanji R.
丹吉 R.
Prasanna: And then it took a really long time to iron out all these edge cases where someone would tip twice the value of the bill and then it would completely break something in the back end, or people would use it as a gas station and they have a different way of billing your card.
Prasanna：然后花了很长时间才解决所有这些边缘情况，比如有人会给账单两倍的小费，然后它会完全破坏后端的某些东西，或者人们会把它用作加油站，他们有不同的方式向你的卡计费。
So yeah, it's very much that.
是的，就是这样。
And to your point, I would always come back to what is the reason we're doing this?
就你的观点而言，我总是会回到我们这样做的原因是什么？
Why does it matter to us and to our customers?
为什么它对我们和我们的客户很重要？
And if it doesn't clearly satisfy that, I would just push it off as a not interesting thing.
如果它不能明显满足这一点，我就会把它当作一件无趣的事情来推迟。
Lenny Rachitsky: This episode is brought to you by Persona, the verified identity platform, helping organizations onboard users fight fraud and build trust.
Lenny Rachitsky：本集由经过验证的身份平台 Persona 为您带来，帮助组织的船上用户打击欺诈并建立信任。
We talk a lot on this podcast about the amazing advances in ai, but this can be a double-edged sword.
我们在这个播客上谈论了很多关于人工智能的惊人进步，但这可能是一把双刃剑。
For every wow moment, there are fraudsters using the same tech to wreak havoc, laundering money, taking over employee identities and impersonating businesses.
在每一个令人惊叹的时刻，都有欺诈者使用相同的技术造成严重破坏，洗钱，接管员工身份并冒充企业。
Lenny Rachitsky: Persona helps combat these threats with automated user business and employee verification.
Lenny Rachitsky：Persona 通过自动化的用户业务和员工验证来帮助应对这些威胁。
Whether you're looking to catch candidate fraud, meet age restrictions or keep your platform safe, Persona helps you verify users in a way that's tailored to your specific needs.
无论您是想发现候选人欺诈、满足年龄限制还是保证平台安全，Persona 都可以帮助您根据您的特定需求定制方式验证用户。
Best of all, Persona makes it easy to know who you're dealing with without adding friction for good users.
最重要的是，Persona 可以让您轻松了解正在与谁打交道，而不会给优秀用户带来麻烦。
This is why leading platforms like Etsy, LinkedIn, Square and Lyft trust Persona to secure their platform persona is also offering my listeners 500 free services per month for one full year, just head to withpersona.com/lenny to get started, that's withpersona.com/lenny.
这就是为什么 Etsy、LinkedIn、Square 和 Lyft 等领先平台信任 Persona 来保护他们的平台角色，并且还为我的听众每月提供 500 项免费服务，为期一年，只需前往 withpersona.com/lenny 即可开始，即 withpersona.com/lenny。
Lenny Rachitsky: Thanks again to Persona for sponsoring this episode.
Lenny Rachitsky：再次感谢 Persona 赞助本集。
One of the biggest parts of the conversation around AI is hiring jobs, things like that.
关于人工智能的讨论中最重要的部分之一是招聘工作，诸如此类的事情。
So I have two kind of this two-part question.
所以我有两种由两部分组成的问题。
One is just how has the rise of all these AI tools, this increased productivity impacted the way you plan head counts and hire?
一是所有这些人工智能工具的兴起，生产力的提高如何影响您规划员工人数和招聘的方式？
And then what do you look for that's different in people you're hiring now that AI is such a big part of the way you guys work?
既然人工智能已经成为你们工作方式的重要组成部分，那么你们在招聘的人员中寻找什么不同之处呢？
Dhanji R.
丹吉 R.
Prasanna: I don't think that things have progressed far enough that it's really impacted in a fundamental way how many people you would need to build an app of the scale of Cash App, for example.
Prasanna：我认为事情还没有取得足够的进展，不足以从根本上影响到构建一个像 Cash App 这样规模的应用程序需要多少人。
I think what's changed for us is much different and it has nothing to do with AI, it's what we talked about earlier is moving from our GM structure to a functional structure.
我认为我们的变化是非常不同的，这与人工智能无关，我们之前谈到的是从我们的GM结构转向职能结构。
And in our GM structure, our incentives were always to think of engineering headcount as a commodity.
在我们的总经理结构中，我们的激励措施始终是将工程人员视为一种商品。
Dhanji R.
丹吉 R.
Prasanna: And so we would just add more engineers if we wanted to build more features and the classic mythical man person month trap or whatever it's called.
Prasanna：因此，如果我们想要构建更多功能和经典神话般的人月陷阱或其他任何名称，我们只需添加更多工程师即可。
And I think that moving to a functional structure completely changes that and you're like, "Well, we can leverage common platforms, common modules, we can bring in experts from across the company to advise us on how better to do this."
我认为转向职能结构完全改变了这一点，你会想，“好吧，我们可以利用通用平台、通用模块，我们可以聘请来自公司各地的专家来为我们提供建议，告诉我们如何更好地做到这一点。”
Dhanji R.
丹吉 R.
Prasanna: And so those kinds of things I think have made it much different and how we hire and we no longer see engineers as a commodity to just add 100 people to go and build the next product in Cash App.
Prasanna：所以我认为这些事情让我们的招聘方式发生了很大的变化，我们不再将工程师视为一种商品，只需添加 100 名员工即可在 Cash App 中构建下一个产品。
But on the AI side, we're very much looking for people that are embracing these tools and that are eager to try and learn from it.
但在人工智能方面，我们非常寻找拥抱这些工具并渴望尝试并从中学习的人。
We're not looking for people who are amazing AI practitioners on the get-go.
我们并不是在寻找一开始就成为出色的人工智能从业者的人。
Dhanji R.
丹吉 R.
Prasanna: I think we have those people and we're interested in those people if they ever want to work with us.
Prasanna：我认为我们有这些人，如果他们想与我们合作，我们会对他们感兴趣。
But I'm much more keen on looking for that college grad who just really is eager to learn about these tools and open to it, or even the veteran who has embraced these tools and figured it out.
但我更热衷于寻找真正渴望了解这些工具并对其持开放态度的大学毕业生，甚至是接受这些工具并弄清楚它的退伍军人。
And that's kind of where we're optimizing for who we look for rather than a specific set of skills.
这就是我们针对我们寻找的人而不是一组特定技能进行优化的地方。
Lenny Rachitsky: So essentially the biggest change is just looking for people that are embracing AI, not being like, "No, I don't need this stuff.
Lenny Rachitsky：所以本质上最大的变化就是寻找拥抱人工智能的人，而不是说“不，我不需要这些东西。
I'm an amazing engineer.
我是一位了不起的工程师。
I don't need to use Cursor or Goose or all these things."
我不需要使用 Cursor 或 Goose 或所有这些东西。”
Dhanji R.
丹吉 R.
Prasanna: Yeah, a learning mindset is how I would put it.
Prasanna：是的，我会说学习心态。
This is something that Jack our CEO talks about a lot is he wants us to be a learning first company.
这是我们的首席执行官杰克经常谈论的事情，他希望我们成为一家学习第一的公司。
So everything we do, every experiment that we ship, what can we learn from it and did we feel that we gave it our best shot?
那么我们所做的一切，我们进行的每一个实验，我们可以从中学到什么，我们是否觉得我们已经尽力了？
And I think that that's more important to him than even sort of coming up with the right business answer every time.
我认为这对他来说比每次都能提出正确的商业答案更重要。
Lenny Rachitsky: What about when you're interviewing?
Lenny Rachitsky：那你面试的时候呢？
Are you encouraging engineers to use AI tools as they're doing exercises?
您是否鼓励工程师在练习时使用人工智能工具？
How did that change over the past year or two?
在过去的一两年里，情况发生了怎样的变化？
Dhanji R.
丹吉 R.
Prasanna: Yeah, we're starting to do that now.
Prasanna：是的，我们现在就开始这样做。
So traditionally we would just use CoderPad or something like that to wipe boards or a problem or even program it in Pseudocode or near Pseudocode.
因此，传统上我们只会使用 CoderPad 或类似的东西来擦除电路板或问题，甚至用伪代码或接近伪代码对其进行编程。
But now we're looking at can you use Vibe code to build something?
但现在我们正在考虑是否可以使用 Vibe 代码来构建某些东西？
How comfortable are you with these tools or how are you thinking about evolving with them as well?
您对这些工具的使用程度如何，或者您如何考虑使用它们进行改进？
Dhanji R.
丹吉 R.
Prasanna: But it's early days yet I would say that it's not clear to me that necessarily how someone knows how to use, be it Goose or Cursor or any of these other tools matters that much to whether they're a good engineer.
Prasanna：但现在还为时过早，我想说的是，我还不清楚一个人如何知道如何使用 Goose、Cursor 或任何其他工具，对于他们是否是一名优秀的工程师来说有那么重要。
I still think that things that we interviewed for in the past, a critical mindset, the ability to really understand deeply the technical nature of a problem is still much more important than whether you're a fully AI native programmer or not.
我仍然认为我们过去面试的东西，批判性的心态，真正深入理解问题的技术本质的能力，仍然比你是否是一个完全的人工智能原生程序员重要得多。
Lenny Rachitsky: Another question that I've always been thinking about a lot of people wonder is what level of engineer is most benefiting from these tools?
Lenny Rachitsky：我一直在思考很多人想知道的另一个问题是，什么级别的工程师最能从这些工具中受益？
You could argue it's the junior engineers now, they could just get all this work done.
你可能会说现在是初级工程师，他们可以完成所有这些工作。
You could argue it's senior engineers because they know so much more about how things work and now they could just orchestrate thousands of agents doing their bidding.
你可能会说这是高级工程师，因为他们对事物的工作原理了解得更多，现在他们可以安排数千名代理执行他们的命令。
What have you seen in terms of which level is benefiting most?
您认为哪个级别受益最大？
Dhanji R.
丹吉 R.
Prasanna: Yeah, so two answers to that.
Prasanna：是的，有两个答案。
One is you're definitely right that the more senior and the more junior they are, the more comfortable or the more eager they are to adopt these AI tools.
一是你绝对是对的，他们越高级和越初级，他们就越舒服或更渴望采用这些人工智能工具。
And I think that's for a variety of reasons, including some of them that you named.
我认为这是出于多种原因，包括您提到的一些原因。
I think the senior people really understand in great depth how everything works.
我认为资深人士确实非常深入地了解一切是如何运作的。
Dhanji R.
丹吉 R.
Prasanna: And so they're almost relieved that this tool exists that can go and do all these things that they've done a million times before and couldn't be bothered.
Prasanna：所以他们几乎松了一口气，因为这个工具的存在可以完成所有这些他们以前已经做过一百万次并且不会被打扰的事情。
And then the junior people are like my niece and nephew on a BlackBerry or something, they're just blitzing through things, not BlackBerry in the early days and iPhones now, they're blitzing through a text message when I'm still seek and destroying through my keyboard, shows you how old I am.
然后初级人员就像我的侄女和侄子一样使用黑莓或其他东西，他们只是快速浏览东西，而不是早期的黑莓和现在的iPhone，当我还在寻找时他们快速浏览短信并通过键盘破坏，告诉你我有多大。
Dhanji R.
丹吉 R.
Prasanna: So I think there's that, but I think the non-technical people using AI agents and programming tools to build things is really what's been surprising and really amazing.
Prasanna：所以我认为是这样的，但我认为非技术人员使用人工智能代理和编程工具来构建东西确实是令人惊讶和令人惊奇的。
And I think that speaks to how these roles are going to evolve in the future.
我认为这说明了这些角色未来将如何演变。
The lines are going to be blurred between whether you're in legal or in risk or in engineering and design even.
无论您处于法律还是风险之中，甚至在工程和设计方面，界限都将变得模糊。
And so I think that the people that are able to embrace it to optimize for their particular work day and their particular set of tasks are really who are showing the most impact from these tools.
因此，我认为那些能够利用它来优化他们的特定工作日和特定任务集的人确实是从这些工具中表现出最大影响的人。
Lenny Rachitsky: It's interesting.
莱尼·拉奇茨基：这很有趣。
No one talks about that element of engineering productivity, which is the reduction of asks from all the other parts of the company to build random one-off things.
没有人谈论工程生产力的这一要素，即减少公司所有其他部门构建随机一次性产品的要求。
That feels like a huge productivity gain for engineers.
对于工程师来说，这感觉像是巨大的生产力提升。
Dhanji R.
丹吉 R.
Prasanna: It is massive, although I think that it's a little bit like the analogy of if you build a bigger highway, you'll just get more cars on the road.
Prasanna：它是巨大的，尽管我认为这有点像如果你建造一条更大的高速公路，你就会在路上看到更多的汽车。
So I think the fact that everyone's building software means that there's more software to be built, more coordination to happen, and everyone's more eager to ship things faster and with greater results.
因此，我认为每个人都在构建软件这一事实意味着有更多的软件需要构建，需要进行更多的协调，并且每个人都更渴望更快地交付产品并取得更好的成果。
And so we're just seeing an overall uptake in velocity and the ask for more features, if that makes sense.
因此，我们只是看到速度的整体提升以及对更多功能的需求（如果这有意义的话）。
Yeah.
是的。
Lenny Rachitsky: Absolutely.
莱尼·拉奇茨基：当然。
And it connects to your point about you're not slowing hiring.
这与你关于你没有放慢招聘的观点有关。
What I'm hearing is just headcount, hiring desires for more engineers, more product people is not slowing at all.
我听到的只是员工人数，招聘更多工程师、更多产品人员的愿望丝毫没有放缓。
You're basically, it's as if AI wasn't really there.
基本上，就好像人工智能并不真正存在一样。
Dhanji R.
丹吉 R.
Prasanna: We're being more thoughtful about it.
Prasanna：我们正在对此进行更加深思熟虑。
So like I said, we were looking at as a commodity in the GM era, and now that we're functional, it's much less about how many engineers we need as a function of the number of features we have in Square or Cash App and in the functional org structure, we think of it much more as what are the areas of optimization?
所以就像我说的，我们在通用汽车时代将其视为一种商品，现在我们已经发挥了作用，我们需要多少工程师就不再是我们需要多少工程师的问题了，而是我们在 Square 或 Cash App 以及功能组织结构中拥有的功能数量的函数，我们更多地认为它是优化的领域是什么？
Where can we build depth and what really accelerates our priorities through things like modularization reuse and going deep into platforms.
我们可以在哪里构建深度，以及通过模块化重用和深入平台等真正加速我们的优先事项。
Lenny Rachitsky: I love this hot take of if you're trying to be more productive, forget AI, just re-org into a functional structure.
Lenny Rachitsky：如果你想提高生产力，忘记人工智能，只需重新组织成一个功能结构，我喜欢这个热门观点。
Dhanji R.
丹吉 R.
Prasanna: It's not wrong in some ways.
Prasanna：从某些方面来说这并没有错。
So here's another really interesting example where we are trying to improve our build times and you were using Goose and a lot of other tools to help us with this too, and they've done remarkable things.
这是另一个非常有趣的例子，我们正在尝试缩短构建时间，而您也使用 Goose 和许多其他工具来帮助我们实现这一点，他们已经做了一些了不起的事情。
So we have this really cool tool that analyzes our test suites and selects the right test to run for changes that were made.
因此，我们拥有这个非常酷的工具，可以分析我们的测试套件并选择正确的测试来运行所做的更改。
Dhanji R.
丹吉 R.
Prasanna: So we cut down basically 50% of test runs this way, which is pretty great, and we're not warming the planet as much with all these unnecessary CPU cycles being wasted on tests.
Prasanna：所以我们通过这种方式基本上减少了 50% 的测试运行，这非常好，而且我们不会因为所有这些不必要的 CPU 周期浪费在测试上而导致地球变暖。
But then things like offloading tests to the cloud or simply just deleting tests that don't make sense anymore, probably save you two to three times that.
但是，像将测试卸载到云端或只是删除不再有意义的测试之类的事情可能会为您节省两到三倍的时间。
Dhanji R.
丹吉 R.
Prasanna: So there is still a portfolio approach that you need to take for lack of a better term.
Prasanna：因此，由于缺乏更好的术语，您仍然需要采取投资组合方法。
It's like that example I told you earlier about should we buy a vendor tool?
就像我之前告诉过你的那个例子，我们应该购买供应商工具吗？
Should we build this in-house?
我们应该在内部构建这个吗？
It's like, "Well, do we even need to do this process at all?" So in some ways, structure matters more than the efficacy of the tools you have.
就像，“好吧，我们是否需要执行这个过程？”因此，在某些方面，结构比所拥有工具的功效更重要。
Lenny Rachitsky: Wise words makes me think about Elon has this whole process for optimize stuff and one of the steps is like, "Do we even need this thing before we start out optimizing and automating it?" Before I zoom out and ask about just general lessons that you've learned over the course of your career, is there anything else that you think might be really valuable or useful to folks that are trying to lean in further into AI or just help their teams think a little bit more forward thinking?
Lenny Rachitsky：明智的话语让我想到 Elon 拥有优化东西的整个流程，其中一个步骤就像是，“在我们开始优化和自动化之前，我们是否需要这个东西？”在我缩小范围并询问您在职业生涯中学到的一般经验之前，您认为对于那些试图进一步深入人工智能或只是帮助他们的团队思考更多前瞻性思维的人来说，还有什么可能真正有价值或有用的东西吗？
Dhanji R.
丹吉 R.
Prasanna: I would say really try and use these tools yourself.
Prasanna：我想说的是，亲自尝试并使用这些工具。
So the way in which I think we've been able to drive most of the adoption is Jack uses Goose, I use Goose, our executive team all have used Goose and use it regularly and use other AI programming tools and assistance as well, and we do it every single day.
因此，我认为我们能够推动大部分采用的方式是 Jack 使用 Goose，我使用 Goose，我们的执行团队都使用 Goose 并定期使用它，还使用其他人工智能编程工具和帮助，我们每天都这样做。
Dhanji R.
丹吉 R.
Prasanna: And so we learn a lot about how our own workflow can change, and that's going to tell you so much more about how are you going to change your organization's workflow than if you're reading a bunch of think pieces on LinkedIn or Harvard Business Review or whatever it is, and then trying to get your teams to follow suit.
Prasanna：所以我们学到了很多关于我们自己的工作流程如何改变的知识，这将告诉你更多关于如何改变你的组织的工作流程，而不是你在 LinkedIn 或哈佛商业评论或其他任何东西上阅读一堆思想文章，然后试图让你的团队效仿。
So I think we do this with everything.
所以我认为我们在所有事情上都这样做。
It's feel it, use the product yourself, feel it, understand its strengths and weaknesses and its ergonomics, and then figure out how to apply it to your teams.
就是感觉它，亲自使用产品，感受它，了解它的优点和缺点以及它的人体工程学原理，然后弄清楚如何将它应用到你的团队中。
Lenny Rachitsky: Something I've found helpful in doing that, which I completely agree with, which is stop reading about it, stop listening to us talking about it, just build some stuff.
Lenny Rachitsky：我发现这样做很有帮助，我完全同意，那就是停止阅读它，停止听我们谈论它，只是构建一些东西。
The thing that I found really helpful there is have a specific task or problem you want to solve for yourself because that really motivates you and makes it very real.
我发现真正有帮助的是有一个你想自己解决的特定任务或问题，因为这确实会激励你并使之变得非常现实。
Lenny Rachitsky: For example, just the other day, I was trying to pull images out of a Google Doc.
Lenny Rachitsky：例如，就在前几天，我试图从 Google 文档中提取图像。
Google Doc, it's like I think of it as Hotel California.
谷歌文档，我把它想象成《加州旅馆》。
You put images in there, but there's no way to get them back out unless you do some crazy stuff.
你把图像放在那里，但除非你做一些疯狂的事情，否则没有办法把它们取出来。
So I just went to Lovable and like Bill an app, or I can give you a Google Doc URL and let me download the images real easily and bam.
所以我就去了 Lovable 并给 Bill 点了一个应用程序，或者我可以给你一个 Google 文档 URL，让我轻松下载图像，然后就可以了。
Perfect.
完美的。
Dhanji R.
丹吉 R.
Prasanna: Yeah, great example.
Prasanna：是的，很好的例子。
I did something like this a couple months ago as well, where my son has a whole bunch of therapies, he has additional needs, and so I was trying to gather the receipts for all these therapies and share them with my wife and she will claim it from our insurer, and I was really struggling to do this because they're in various forms.
几个月前我也做了类似的事情，我的儿子接受了一大堆治疗，他有额外的需求，所以我试图收集所有这些治疗的收据并与我的妻子分享，她将从我们的保险公司索赔，我真的很难做到这一点，因为它们有各种形式。
Dhanji R.
丹吉 R.
Prasanna: There are screenshots in some cases or PDFs or whatever.
Prasanna：在某些情况下有屏幕截图或 PDF 等。
So I asked Goose to do this and it was all sitting on my laptop and Goose figured out that it could put all of these receipts into my Apple Notes app into a single note.
因此，我要求 Goose 这样做，所有这些都放在我的笔记本电脑上，Goose 发现它可以将所有这些收据放入我的 Apple Notes 应用程序中，形成一个笔记。
It converted it to HTML so it would sync seamlessly to my phone and then I could email it or share it with her from there.
它将其转换为 HTML，以便它可以无缝同步到我的手机，然后我可以通过电子邮件发送或从那里与她分享。
Dhanji R.
丹吉 R.
Prasanna: And that's just something I just never would've thought of.
Prasanna：这是我从来没有想到过的事情。
And it did this using Apple Script, so it just controlled my computer for me in the background.
它使用 Apple Script 来完成此操作，因此它只是在后台为我控制我的计算机。
Yeah, so these are surprising ways in which these tools help us, and the more you use them to solve real problems to your point, the more you understand what their strengths are and where you can deploy them.
是的，这些工具以令人惊讶的方式帮助我们，你越多地使用它们来解决你的观点的实际问题，你就越了解它们的优势是什么以及可以在哪里部署它们。
Lenny Rachitsky: I love this example.
Lenny Rachitsky：我喜欢这个例子。
So did you just go to Goose and be like, "Here's the problem I have, how would you solve it?"
那么你是否只是去找 Goose 并说：“这是我遇到的问题，你会如何解决它？”
Dhanji R.
丹吉 R.
Prasanna: Yeah, pretty much.
Prasanna：是的，差不多。
I said, "I have all these receipts there in Google Drive, so we have similar origin problem there and I need to get them into a single form and I need to collate the totals and do all this." So it tried a few approaches first.
我说：“我在 Google 云端硬盘上有所有这些收据，所以我们那里有类似的来源问题，我需要将它们放入一个表格中，我需要整理总数并完成所有这些工作。”因此它首先尝试了几种方法。
It tried to download them and it tried to read them using a PDF reader and this and that.
它尝试下载它们并尝试使用 PDF 阅读器阅读它们等等。
And then the thing about Goose that I think a lot of the other AI agents learn from us as well is if it tries a few things and fails, it'll back up and it'll try a different route and it'll just keep going until it makes some progress.
然后，我认为许多其他人工智能代理也向我们学习了关于 Goose 的事情，如果它尝试了一些事情并且失败了，它会备份并尝试不同的路线，并且它会继续前进，直到取得一些进展。
Dhanji R.
丹吉 R.
Prasanna: And that's what it did.
Prasanna：这就是它所做的。
Then it picked Apple Script as a way to do it because it had the MCP extension to control my computer, and this is the same thing that our engineer, we were talking about the other day uses to watch his screen and things like that, but this was a very focused problem and it managed to do that.
然后它选择了 Apple Script 作为实现这一目标的方法，因为它有 MCP 扩展来控制我的计算机，这与我们的工程师，我们前几天谈论的用来观看他的屏幕和类似的东西是一样的，但这是一个非常集中的问题，它成功地做到了这一点。
So yeah, it's surprising what these tools can do and allowing them the flexibility to do that is a big part of learning how to use them.
所以，是的，这些工具的功能令人惊讶，让它们能够灵活地执行这些操作是学习如何使用它们的一个重要部分。
Lenny Rachitsky: That's cool.
莱尼·拉奇茨基：那很酷。
I love the, by the way, can you run Goose as a regular person?
我喜欢这个，顺便问一下，你能像普通人一样经营 Goose 吗？
Can you just download Goose and use that instead of Claude?
您可以直接下载 Goose 并使用它来代替 Claude 吗？
Dhanji R.
丹吉 R.
Prasanna: Yeah, absolutely.
Prasanna：是的，绝对是。
Yeah.
是的。
You can just download it from our URL.
您只需从我们的 URL 下载即可。
We can share it in the show notes for you and yeah, you can install it.
我们可以在演出笔记中为您分享它，是的，您可以安装它。
It comes for Mac and Windows and Linux I believe.
我相信它适用于 Mac、Windows 和 Linux。
It's an electron app, so it'll work on all of them.
这是一个电子应用程序，因此它适用于所有这些应用程序。
It also has a command line, so for people who are more comfortable using that, we have that UI as well.
它还有一个命令行，因此对于更习惯使用命令行的人来说，我们也有该用户界面。
Lenny Rachitsky: Wow, you really are competing with these massive foundational model companies building.
Lenny Rachitsky：哇，你真的在与这些大型基础模型公司竞争。
What's the simplest way to compare Goose to something else?
将 Goose 与其他产品进行比较的最简单方法是什么？
Is it like this Claude code, this simplest comparison or something else?
是像这个克劳德代码，这个最简单的比较还是别的什么？
Dhanji R.
丹吉 R.
Prasanna: I think it's a bit different than Claude Code because at its core, Goose is a platform that implements MCPs.
Prasanna：我认为它与 Claude Code 有点不同，因为从本质上讲，Goose 是一个实现 MCP 的平台。
So MCPs give it this dynamically extensible nature so it can do all of these things for you, whether it's automating things like we were talking about with Google Docs and notes and things like that, or it can do straight up programming tasks for you using other MCPs.
因此，MCP 赋予它动态可扩展的特性，因此它可以为您完成所有这些事情，无论是像我们谈论的 Google 文档和注释之类的自动化事情，还是可以使用其他 MCP 为您直接完成编程任务。
Dhanji R.
丹吉 R.
Prasanna: It can index code and do it that way.
Prasanna：它可以索引代码并以这种方式执行。
So it's really more of an extensible platform.
所以它实际上更像是一个可扩展的平台。
So I would say it sits somewhere between your classic AI assistant where you just ask it, "What's the weather today?
所以我想说它介于经典的人工智能助手之间，你只需问它：“今天天气怎么样？
Can you calculate how many months it's been since this date?" Or whatever it is, to the more focused cursors and Claude codes of the world.
你能算算一下，从今天算起，已经过去了多少个月了吗？”或者无论它是什么，对于世界上更集中的光标和克劳德代码。
Lenny Rachitsky: Basically, it's everything combined wholly and free.
Lenny Rachitsky：基本上，一切都是完全免费的。
You pay for the LM tokens, but yeah.
你需要支付 LM 代币的费用，但是是的。
Dhanji R.
丹吉 R.
Prasanna: Yeah, there's not like an open source models which-
Prasanna：是的，没有像开源模型那样-
Lenny Rachitsky: Oh my God, this is crazy.
莱尼·拉奇茨基：天哪，这太疯狂了。
What a cool team to be on building Goose at Block.
在 Block 上构建 Goose 的团队真是太酷了。
Must having must be having so much fun.
一定有很多乐趣。
Oh man.
噢，伙计。
Okay.
好的。
Let me zoom out a little bit.
让我把镜头缩小一点。
So you've been CTO in LinkedIn right now for just about two years.
您现在在 LinkedIn 担任 CTO 大约两年了。
What's something that you wish you knew before you stepped in this role?
在担任这个角色之前，您希望了解什么？
If you could go back a couple years and just whisper a few tips and tricks or lessons into your ear, what would they be?
如果你可以回到几年前，在你耳边低声说出一些技巧、窍门或教训，它们会是什么？
Dhanji R.
丹吉 R.
Prasanna: I think maybe two different things.
Prasanna：我想也许是两种不同的事情。
One is just the power of Conway's Law, like we talked about before.
一是康威定律的力量，就像我们之前谈到的那样。
It's like how difficult it is to change outcomes without changing the structure of relationships between people in an organization.
这就像在不改变组织中人与人之间的关系结构的情况下改变结果是多么困难。
And I think I always kind of knew that at some level, but really appreciating it in a visceral way is big.
我想我在某种程度上总是知道这一点，但真正发自内心地欣赏它是很重要的。
The other thing that I really learned the hard way maybe is you only hear about it when things are going wrong.
我真正学到的另一件事是，也许只有当事情出错时你才会听到它。
Dhanji R.
丹吉 R.
Prasanna: So when things are going well, you kind of have this eerie silence and you're like, "Well, am I doing the right things here?
Prasanna：所以当事情进展顺利时，你会感到一种令人毛骨悚然的沉默，你会想，“好吧，我在这里做的事情正确吗？
Am I focusing on the right problems?" So having a bit of judgment, having a bit of time to step back and look at things holistically, those are things that you really need to make time for and do on a regular basis, which I wish I had known when I took up the role.
我是否专注于正确的问题？”因此，有一点判断力，有一点时间退后一步，从整体上看待事情，这些都是你真正需要腾出时间并定期做的事情，我希望我在担任这个角色时就知道这一点。
Lenny Rachitsky: Looking back at your time at Block, I keep trying to, I almost say Square because I'm so used to that over the air, but I know Block is the name of the broader company and Square is just one.
Lenny Rachitsky：回顾你在 Block 的时光，我一直在努力，我几乎会说 Square，因为我已经习惯了在广播中这样说，但我知道 Block 是更广泛的公司的名称，而 Square 只是其中之一。
Just so people understand, Square is one business unit, one product within Block.
正如人们所理解的，Square 是 Block 中的一个业务部门、一种产品。
Dhanji R.
丹吉 R.
Prasanna: Correct, yeah, we have Square, Afterpay, Cash App and TIDAL are four major brands, and then we also have Bitkey and Proto that are focused on Bitcoin for us and we chip hardware in those two brands.
Prasanna：正确，是的，我们有 Square、Afterpay、Cash App 和 TIDAL 四个主要品牌，然后我们还有 Bitkey 和 Proto，它们专注于比特币，我们在这两个品牌中使用芯片硬件。
Lenny Rachitsky: Okay, great.
莱尼·拉奇茨基：好的，太好了。
I think that some people are like, what are you guys talking about?
我想有些人会问，你们在说什么？
Okay, cool.
好吧，酷。
So reflecting back on your time at Block, what's maybe the most counterintuitive lesson you've learned about building products or building teams that goes against what most people believe, say common startup wisdom?
因此，回想一下您在 Block 的时光，您在构建产品或构建团队时学到的最违反直觉的教训可能是什么，这些教训与大多数人的看法（即常见的创业智慧）背道而驰？
Dhanji R.
丹吉 R.
Prasanna: I think code quality is one.
Prasanna：我认为代码质量是其中之一。
Being an engineer.
作为一名工程师。
I learned this very early on and it keeps coming true over and over and over again.
我很早就知道了这一点，而且它一次又一次地成为现实。
A lot of engineers think that code quality is important to building a successful product.
许多工程师认为代码质量对于构建成功的产品非常重要。
The two have nothing to do with each other, but my favorite example is YouTube.
这两者没有任何关系，但我最喜欢的例子是 YouTube。
I was working at Google around the time YouTube was acquired and I just remember there was this whole wash of angst about how horrible the YouTube code base is and how terrible their architecture is, and they're storing videos as blobs in MySQL and whatnot.
YouTube 被收购的时候，我正在 Google 工作，我只记得 YouTube 代码库是多么糟糕，他们的架构是多么糟糕，而且他们将视频作为 blob 存储在 MySQL 之类的东西中，人们对此充满了焦虑。
Dhanji R.
丹吉 R.
Prasanna: And you could argue that YouTube is the most successful product at Google by a long way, maybe more successful than many of their others combined.
Prasanna：你可以说 YouTube 是 Google 长期以来最成功的产品，也许比其他许多产品的总和还要成功。
And so it really has very little to do with how well it was architected because the flip side of that Google video, which is product that I don't know if people remember, it existed before YouTube.
所以它实际上与它的架构有多好没有什么关系，因为谷歌视频的另一面，我不知道人们是否还记得，它是在 YouTube 之前就存在的产品。
It supported more formats, it supported higher resolution.
它支持更多格式，支持更高的分辨率。
Dhanji R.
丹吉 R.
Prasanna: You could upload hour long videos, YouTube had none of this.
Prasanna：你可以上传长达一小时的视频，YouTube 没有这些。
It just had the one or two minute quick video thing and it's far and away, blown away its competition.
它只有一两分钟的快速视频，而且距离很远，远远超过了它的竞争对手。
And so I think just keeping that front and center is why are we building these tools or these apps or these products?
因此，我认为保持这一点就是我们构建这些工具、这些应用程序或这些产品的原因？
They're for people to solve a specific problem.
它们是为了人们解决特定问题。
So in our case, it's for a square merchant to make a sale, to sell coffee to you or to sell something they've made.
因此，在我们的例子中，这是一个方形商人进行销售，向您出售咖啡或出售他们制作的东西。
And that's really what's important.
这才是真正重要的。
Dhanji R.
丹吉 R.
Prasanna: It's not really important how well our Android platform performs unless it's serving that need.
Prasanna：除非我们的 Android 平台能够满足这一需求，否则它的性能如何并不重要。
And so I think that's been a really hard one for me over my career.
所以我认为这对我的职业生涯来说是非常艰难的。
And I continually encounter engineers who think we need to refactor, we need to do this in a better way.
我不断遇到认为我们需要重构的工程师，我们需要以更好的方式做到这一点。
And then I'm like, "No, all this code could be thrown away tomorrow.
然后我想，“不，所有这些代码明天都可能被扔掉。
So just focus on what we're trying to build and whom we're trying to build for."
因此，只需专注于我们正在努力构建的内容以及我们正在努力为谁构建。”
Lenny Rachitsky: That is an incredible insight and lesson.
Lenny Rachitsky：这是令人难以置信的见解和教训。
This YouTube story is so fun and such a good example.
YouTube 上的这个故事非常有趣，也是一个很好的例子。
You're saying they were storing the video content in a MySQL set like row and column as a blob data.
你是说他们将视频内容存储在 MySQL 集合中，如行和列作为 blob 数据。
Dhanji R.
丹吉 R.
Prasanna: Yeah, this is what, I didn't actually look the code so I couldn't verify it, but this was the common wisdom.
Prasanna：是的，就是这样，我实际上没有查看代码，所以无法验证它，但这是常识。
And then they had an entirely Python stack that was incredibly slow compared to the state-of-the-art C++ and Java servers that we had hyper-optimized at Google back in those days.
然后他们有一个完全的 Python 堆栈，与当时我们在 Google 进行超优化的最先进的 C++ 和 Java 服务器相比，速度慢得令人难以置信。
Lenny Rachitsky: That is hilarious.
莱尼·拉奇茨基：这太搞笑了。
It makes me think about also companies when you look inside a company, if you work at a company, you're just like, "This is just pure chaos.
当你观察一家公司内部时，这让我想到了公司，如果你在一家公司工作，你就会说，“这纯粹是混乱。
No one knows what's going on.
没有人知道发生了什么事。
This is just about to all fall apart." And that's basically what it's like at every successful hyper-growth company.
这一切即将崩溃。”这基本上就是每家成功的高速增长公司的情况。
Dhanji R.
丹吉 R.
Prasanna: Yeah, there's some truth to that for sure.
Prasanna：是的，这确实有一定道理。
Yeah.
是的。
Lenny Rachitsky: And so I think again, it's just there's so much more that is more important to the success of a business.
Lenny Rachitsky：所以我再次思考，对于企业的成功来说，还有更多更重要的事情。
And it's what you said is are you solving a real problem for people?
你说的是你在为人们解决真正的问题吗？
Can you get in their hands?
你能到他们手里吗？
Can you continue solving real problems for them?
你能继续为他们解决实际问题吗？
It's not about the quality of the code, it's not how well you operate internally.
这与代码的质量无关，与内部运作的情况无关。
Dhanji R.
丹吉 R.
Prasanna: Absolutely.
普拉萨那：当然。
I think on Cash App we had that as well.
我想在 Cash App 上我们也有这样的功能。
So in the early days of Cash App, I was head of engineering from when we were about 10 engineers to 200 plus and took us to about 10 plus or 20 million users thereabouts.
因此，在 Cash App 的早期，我担任工程主管，当时我们有大约 10 名工程师，现在有 200 多名工程师，并带领我们拥有了大约 10 多名或 2000 万左右的用户。
And there was a very similar thing there.
那里有一个非常相似的事情。
From the outside it looked like everything was really chaotic.
从外面看来，一切真的很混乱。
It's like people would build random experiments and ship them and it just didn't look like we were following strict policies on things like software life cycle and stuff like that, and it was kind of true.
就像人们会随机进行实验并交付它们，但看起来我们并没有在软件生命周期之类的事情上遵循严格的政策，这确实是事实。
Dhanji R.
丹吉 R.
Prasanna: And my philosophy was always, we have all these brilliant engineers and I'm going to do more harm than good by trying to harness them into very strict blinkered areas.
Prasanna：我的理念始终是，我们拥有所有这些才华横溢的工程师，但我试图将他们纳入非常严格的狭隘领域，弊大于利。
If they want to spin their wheels building something that is a complete waste of time for a little bit.
如果他们想继续开发一些东西，那完全是浪费时间。
But at the same time, if they're delivering these amazing things on the flip side, then I'll almost allow that.
但与此同时，如果他们在另一方面提供了这些令人惊奇的东西，那么我几乎会允许这样做。
I'll be okay with that.
我会同意的。
Dhanji R.
丹吉 R.
Prasanna: And it's a fine balance because engineers can really go off and into rabbit holes if you let them.
Prasanna：这是一个很好的平衡，因为如果你允许的话，工程师真的可以掉入兔子洞。
But yeah, there's a certain amount of creativity that chaos breeds and you have to know how to build controlled chaos in some ways.
但是，是的，混乱会产生一定的创造力，你必须知道如何以某种方式构建受控的混乱。
So you have to create a foundation that isn't liable to rupture.
因此，您必须创建一个不易破裂的基础。
You have major liability problems or something like that, or you're going to lose money in our case.
你有重大的责任问题或类似的问题，否则你就会在我们的案例中赔钱。
And so as long as those things are bedded down and you allow your engineers to have the freedom to experiment and iterate and do the things that energizes them, that's the ideal.
因此，只要这些事情都落实下来，并且允许工程师自由地进行实验和迭代，并做那些激发他们活力的事情，那就是理想的。
Lenny Rachitsky: Speaking of controlled chaos, one of your titles during your time at Block, I guess this was while you were actually at Square, was Mad Scientist for four and a half years.
Lenny Rachitsky：说到受控混乱，你在 Block 工作期间的作品之一，我想这是你在 Square 的时候，是四年半的《疯狂科学家》。
Dhanji R.
丹吉 R.
Prasanna: Yeah, that was a time when I was working part-time, mostly because I had very young kids with lots of additional needs and I was a consultant on various different projects and I was trying to help some wacky things get off the ground.
Prasanna：是的，那是我兼职工作的时候，主要是因为我有非常年幼的孩子，他们有很多额外的需求，而我是各种不同项目的顾问，我试图帮助一些古怪的事情取得进展。
And yeah, I'm really grateful to Block that they afforded me the freedom to have that role in my career as well.
是的，我真的很感谢布洛克，他们给了我在职业生涯中扮演这个角色的自由。
Lenny Rachitsky: Maybe one more question before I take us to Fail Corner, which I'll explain.
Lenny Rachitsky：在带我们去失败角之前也许还有一个问题，我会解释一下。
So you've shared a few lessons of things you've learned over the course of your career.
您分享了您在职业生涯中学到的一些经验教训。
Are there any other, just let's say core leadership lessons that you've learned that you think have been important to you being successful at the work that you've done?
还有其他的，比如说您学到的核心领导力课程，您认为这些课程对您在所做的工作中取得成功很重要吗？
Dhanji R.
丹吉 R.
Prasanna: I think start small with everything.
Prasanna：我认为凡事从小事做起。
If you try to boil the ocean to make a cup of tea, I don't know who said that, but it's a really a useful phrase that I keep coming back to.
如果你试图把海洋煮沸来泡一杯茶，我不知道这句话是谁说的，但这是一个非常有用的短语，我不断地想起。
You'll never get there.
你永远也到不了那里。
So if you're making a cup of tea, just make the cup of tea.
所以，如果你要泡一杯茶，就泡一杯茶吧。
You don't need to boil all the water that there is.
您不需要将所有的水都煮沸。
Lenny Rachitsky: That sounds like really not delicious tea.
Lenny Rachitsky：听起来茶确实不好吃。
Ocean water.
海水。
Dhanji R.
丹吉 R.
Prasanna: Yeah, I think there's another one of, I think Carl Sagan said, "If you want to make an apple pie from scratch, you have to first invent the universe." So it's like narrow your scope to the thing that's in front of you and that's achievable.
Prasanna：是的，我认为还有一个，我认为卡尔·萨根说过，“如果你想从头开始制作苹果派，你必须首先发明宇宙。”所以这就像将你的范围缩小到你面前的、可以实现的事情上。
And so that I think is really important and that's one of our core tenets and always has been even when we were just Square in the early days, start small.
所以我认为这非常重要，这是我们的核心原则之一，即使在我们早期还只是 Square 的时候，也是如此，从小事做起。
Lenny Rachitsky: Is there an example that maybe worked really well or maybe didn't work?
Lenny Rachitsky：是否有一个可能非常有效或可能不起作用的例子？
Dhanji R.
丹吉 R.
Prasanna: Yeah, Goose started small.
Prasanna：是的，Goose 开始规模很小。
It was just an engineer working on their own time trying to build something that was useful and that satisfied a thesis that they had.
这只是一名工程师在自己的时间里努力构建一些有用的东西并且满足他们的论文。
So Brad, our creator of Goose, believed very early on, I think long before we heard the buzzword going around that agents would be how we unlock value from LLMs.
因此，我们 Goose 的创造者 Brad 很早就相信，我认为早在我们听到“代理”这句流行语之前，我们就可以从法学硕士中释放价值。
And he built a proof concept and he shared it with a bunch of people.
他建立了一个证明概念，并与一群人分享。
He shared it with Databricks and Anthropic, got them excited and learned a lot from them.
他与 Databricks 和 Anthropic 分享了这些内容，让他们兴奋不已，并从他们身上学到了很多东西。
Dhanji R.
丹吉 R.
Prasanna: And so it just sort of built momentum from there.
Prasanna：所以它只是从那里建立了动力。
And even internally, it was quite a similar thing.
即使在内部，情况也非常相似。
Cash App itself was like that and Cash App started more or less as a hack week sort of idea and grew into a bigger and bigger and bigger thing.
Cash App 本身就是这样，Cash App 一开始或多或少是作为黑客周的想法，后来发展成为一个越来越大的东西。
So a lot of our projects start with these small experiments that we try to then build on top of.
因此，我们的许多项目都是从这些小实验开始的，然后我们尝试在此基础上进行构建。
We became the very first company that was a public company to launch a Bitcoin product.
我们成为第一家推出比特币产品的上市公司。
And that was again a hack week idea that actually Jack and me and another engineer worked on.
这又是一个黑客周的想法，实际上杰克和我以及另一位工程师正在研究这个想法。
Lenny Rachitsky: That was the hackathon team?
Lenny Rachitsky：那是黑客马拉松团队？
You and Jack Dorsey and an engineer?
你和杰克·多尔西还有一名工程师？
Dhanji R.
丹吉 R.
Prasanna: Yeah, and it was great.
Prasanna：是的，非常棒。
We went and bought a cup of coffee, a blue bottle, and it was bought using Bitcoin over cash card.
我们去买了一杯咖啡、一瓶蓝瓶咖啡，是用比特币通过现金卡购买的。
And I'll tell you those in hindsight, probably the most expensive cup of coffee.
事后我会告诉你，这可能是最昂贵的一杯咖啡。
Dhanji R.
丹吉 R.
Prasanna: I think it was 6,000 or 7,000 back then.
Prasanna：我想当时是 6,000 或 7,000。
I don't know.
我不知道。
Dhanji R.
丹吉 R.
Prasanna: But yeah, it's an example of how you get to a working useful product to people if you focus on a small thing first in a build.
Prasanna：但是，是的，这是一个例子，说明如果您首先关注构建中的小事情，那么如何获得对人们有用的产品。
Lenny Rachitsky: And just to double down on this counter too.
Lenny Rachitsky：也只是为了在这个柜台上加倍努力。
"Okay, we have a big idea, we're just going to put a bunch of resources on it and go big immediately."
“好吧，我们有一个伟大的想法，我们将投入大量资源并立即将其付诸实施。”
Dhanji R.
丹吉 R.
Prasanna: Yeah, absolutely.
Prasanna：是的，绝对是。
And I've been part of teams like that too.
我也曾是这样的团队的一员。
So in my career, I worked at Google on this product called Google Wave, which was trying to be everything to everyone and we were 70, 80 engineers building this thing before it even really had any users outside Google.
因此，在我的职业生涯中，我在 Google 开发了这款名为 Google Wave 的产品，该产品试图成为每个人的一切，我们有 70、80 名工程师在构建这个产品，甚至在它真正拥有 Google 之外的任何用户之前。
And so I think that's an example of something that started big, tried to go big on day one and probably lacked some of that meeting the earth where reality lies and adapting accordingly.
所以我认为这是一个例子，它开始很大，在第一天就试图做大，但可能缺乏一些与现实所在的地球相接触并相应地适应的东西。
Lenny Rachitsky: I remember Google Wave.
Lenny Rachitsky：我记得 Google Wave。
Absolutely.
绝对地。
It was beautiful.
很漂亮。
A lot of hype.
很多炒作。
I don't remember what it was for specifically, but it looked really nice.
我不记得它具体是做什么的，但它看起来非常好。
Dhanji R.
丹吉 R.
Prasanna: Yeah, a lot of learnings from that one for me.
Prasanna：是的，我从那件事中学到了很多东西。
Yeah.
是的。
Lenny Rachitsky: What else?
莱尼·拉奇茨基：还有什么？
Any other big lessons?
还有其他重大教训吗？
Dhanji R.
丹吉 R.
Prasanna: Those two are the big ones, but I would also say question base assumptions on everything.
Prasanna：这两个是最重要的，但我也会说对一切的假设都存在疑问。
Sometimes we get into traps where we are as professionals, hyper focused on what we're building that day, that week, that month.
有时，我们会陷入陷阱，因为我们是专业人士，过度专注于那天、那周、那个月我们正在构建的东西。
And we don't stop to think should we even build this at all?
我们根本不会停下来思考我们是否应该建造这个？
Or what's the purpose of building this?
或者说建造这个的目的是什么？
Dhanji R.
丹吉 R.
Prasanna: Could we build something completely different that would matter more to our core reason for being?
Prasanna：我们能否构建一些完全不同的东西，对我们存在的核心理由更重要？
So I would say, yeah, question the sort of base assumptions.
所以我想说，是的，质疑这种基本假设。
It's somewhat of a cliche, but you really need to remind yourself to apply it over and over and over again.
这有点陈词滥调，但你确实需要提醒自己一遍又一遍地应用它。
Lenny Rachitsky: I had a colleague of yours on the podcast back in the day, IO, who worked with you on Cash App.
Lenny Rachitsky：我当时在播客上有一位同事 IO，他和你一起开发了 Cash App。
Lenny Rachitsky: He's a friend of mine, he's amazing.
Lenny Rachitsky：他是我的朋友，他很棒。
He had a quote along those lines of just like, I forget exactly what it was, but it was just get to the bare metal of the thing that you're working on, just touch the thing that you're building and go to the base of it to really understand what's going on.
他引用了一句话，就像，我忘记了它到底是什么，但它只是触及你正在开发的东西的裸露金属，只需触摸你正在构建的东西并深入到它的底部，以真正了解正在发生的事情。
And I imagine that was really important with Building Cash App and Cash Card.
我认为这对于构建现金应用程序和现金卡非常重要。
Dhanji R.
丹吉 R.
Prasanna: Yeah, IO is one of the best product people I've ever worked with and one of my closest friends actually.
Prasanna：是的，IO 是我合作过的最好的产品人员之一，实际上也是我最亲密的朋友之一。
So absolutely with him, and you on that one, yeah.
所以绝对和他在一起，还有你，是的。
Lenny Rachitsky: Okay.
莱尼·拉奇茨基：好的。
I'm going to take us to a recurring segment on the podcast I call Fail Corner.
我将带我们进入播客中我称之为“失败角”的重复片段。
You already shared one example of a product that failed that you worked on.
您已经分享了您开发的一款失败产品的示例。
I'm curious if there's another, and the question is just what's the product you worked on that did not work out?
我很好奇是否还有另一个问题，问题是你开发的产品有哪些没有成功？
Because people listening to this hear all these amazing successful people come on the podcast, share all these stories of success, endless success, but they don't hear the stories when things don't work out.
因为听这个的人听到所有这些令人惊叹的成功人士来到播客，分享所有这些成功的故事，无尽的成功，但他们听不到事情不顺利时的故事。
And so this question is just, "What's a product you worked on that didn't work out and what did that teach you?"
所以这个问题只是，“你开发过的一款产品没有成功，它教会了你什么？”
Dhanji R.
丹吉 R.
Prasanna: It's a very valuable point.
Prasanna：这是非常有价值的一点。
My career has basically been a string of failed product on top of failed product.
我的职业生涯基本上就是一系列失败产品的失败。
And I think that, "Yeah, the Google wave example's there." I worked for Hot Minute on Google+, which was another epic failure.
我认为，“是的，Google Wave 的例子就在那里。”我在 Google+ 上的 Hot Minute 工作，这又是一次史诗般的失败。
Dhanji R.
丹吉 R.
Prasanna: I worked at this social networking startup called Secret, which burned hot for a bright minute and then blew up.
Prasanna：我曾在一家名为 Secret 的社交网络初创公司工作，该公司曾一度炙手可热，然后就爆炸了。
And then there was an email startup that we did, and that was, again, very promising, and then that fizzled.
然后我们创办了一家电子邮件初创公司，这再次是非常有前途的，然后就失败了。
So the co-founder of Canva and I worked on that one.
因此，我和 Canva 的联合创始人致力于这一项目。
So there's been a whole string of failures, but at each point, I think I learned something and I learned that I need to never make that class of failures or errors again.
因此，有一连串的失败，但在每一次失败中，我想我都学到了一些东西，并且我知道我永远不会再犯这类失败或错误。
Dhanji R.
丹吉 R.
Prasanna: And so Cash App was probably the big success for me that a product that I worked on that was very early on and grew to be this giant business and product that people love.
Prasanna：所以 Cash App 对我来说可能是一个巨大的成功，我很早就开发了一个产品，并逐渐发展成为人们喜爱的巨大业务和产品。
So yeah, been my career is essentially taking the learnings from all these failures, getting some humility out of it in the process too, coming into things, willing to listen to other people's points of view, critical points of view, and not just thinking that I have all the answers, yeah.
所以，是的，我的职业生涯本质上就是从所有这些失败中吸取教训，在这个过程中也从中获得一些谦逊，进入事物，愿意倾听其他人的观点，批判性的观点，而不仅仅是认为我拥有所有答案，是的。
Lenny Rachitsky: And I bet all these products that failed had really beautiful code.
Lenny Rachitsky：我敢打赌所有这些失败的产品都有非常漂亮的代码。
A lot of really good architecture decisions were made.
做出了许多非常好的架构决策。
Some of them, some of them were awful in every way.
其中有些人，有些人在各方面都很糟糕。
So many reasons for it to fail.
导致它失败的原因有很多。
Incredible.
极好的。
Dhanji, is there anything else that you wanted to share or I don't know, double down on before we get to our very exciting lightning round?
Dhanji，在我们开始激动人心的闪电回合之前，您还有什么想分享的或者我不知道的吗？
Dhanji R.
丹吉 R.
Prasanna: I would say I think that we're in this era of a lot of change and people are scared or reticent or uncertain about where things are going.
Prasanna：我想说，我认为我们正处于一个充满变化的时代，人们对事情的发展感到害怕、沉默或不确定。
And I think that look at the things that matter to you.
我认为看看对你来说重要的事情。
For us, it's open source, open protocols, improving access for everyone.
对我们来说，它是开源的、开放的协议，可以改善每个人的访问。
I've been very lucky in my career to only work on products that are either free or almost free to anyone or they have a free tier and then you pay for some premium services and that are usable by everyone.
在我的职业生涯中，我非常幸运，只开发对任何人免费或几乎免费的产品，或者它们有免费套餐，然后你需要支付一些每个人都可以使用的高级服务。
So anyone can become a Square seller.
所以任何人都可以成为 Square 卖家。
Dhanji R.
丹吉 R.
Prasanna: I remember even in the early days, people used it to pay each other as a peer-to-peer money transfer system and that's why we built Cash App and that was really successful on the back of that.
Prasanna：我记得即使在早期，人们也使用它作为点对点汇款系统来相互付款，这就是我们构建 Cash App 的原因，并且在此基础上取得了真正的成功。
So I think it's really look at the things that are important to you and optimize for them.
所以我认为真正应该关注对你来说重要的事情并针对它们进行优化。
It's not really that important that the technology trends are growing in a certain way because technology is here to serve us, and if we have an important reason for being and an important purpose, then we can make that technology serve us.
技术趋势以某种方式增长并不那么重要，因为技术是为我们服务的，如果我们有重要的存在理由和重要目的，那么我们就可以让技术为我们服务。
And that's much more important than being deep with the technology or being at the forefront of every trend.
这比深入了解技术或走在每种趋势的最前沿更重要。
Lenny Rachitsky: Such great advice when there's so much to pay attention to and so much happening.
Lenny Rachitsky：当有如此多的事情需要关注、有如此多的事情发生时，这是非常好的建议。
So stressful to feel like I'm just not aware of all the things.
压力很大，感觉我只是不知道所有的事情。
I'm not as good as all these people I'm seeing on social media, but what's happening with AI, I'm just so behind.
我不如我在社交媒体上看到的所有这些人，但人工智能正在发生什么，我只是太落后了。
What I'm hearing from you is just like what is actually important to you?
我从你那里听到的就像对你来说真正重要的一样？
And just do that.
就这么做吧。
Don't feel like you need to be the best at everything that's happening on top of all the latest AI news.
除了所有最新的人工智能新闻之外，不要觉得自己需要在所有发生的事情上都是最好的。
Dhanji R.
丹吉 R.
Prasanna: Yeah, exactly.
Prasanna：是的，完全正确。
And if it's not meaningful and fun, then you shouldn't be doing it probably.
如果它没有意义和乐趣，那么你可能就不应该这样做。
Lenny Rachitsky: With that Dhanji, we've reached our very exciting lightning round.
Lenny Rachitsky：有了 Dhanji，我们就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: I see so many books behind you.
Lenny Rachitsky：我看到你身后有很多书。
So I love this first question.
所以我喜欢第一个问题。
I'm excited to see what you pick.
我很高兴看到你选择什么。
What are two or three books that you find yourself recommending most to other people?
您发现自己最向其他人推荐的两三本书是什么？
Dhanji R.
丹吉 R.
Prasanna: Yeah, so I very much of the opinion that you shouldn't read books that are about your daily work or your professional life.
Prasanna：是的，所以我非常认为你不应该阅读有关日常工作或职业生活的书籍。
I read fiction, I read the classics, I read poetry, philosophy, history.
我读小说、读经典、读诗歌、哲学、历史。
These are the books I really enjoy.
这些是我真正喜欢的书。
And I think it expands your mind and gives you creative ideas and helps you question things about the human condition.
我认为它可以拓展你的思维，给你创造性的想法，并帮助你质疑有关人类状况的问题。
Dhanji R.
丹吉 R.
Prasanna: And that's much more valuable than some self-help book or some get good at being an engineering manager book.
Prasanna：这比一些自助书籍或一些擅长成为工程经理的书更有价值。
So yeah, having said that, the Master in Margarita by Mikhail Bulgakov is one that I really love.
所以，是的，话虽如此，米哈伊尔·布尔加科夫的《玛格丽塔大师》是我真正喜欢的一部。
It's a masterpiece of Russian literature.
这是俄罗斯文学的杰作。
And then I've always been drawn to Tennyson's poetry and I find that in the times when I'm most uncertain or grieving, Tennyson's poetry has always resonated with me and helped me find a center.
然后我一直被丁尼生的诗歌所吸引，我发现在我最不确定或最悲伤的时候，丁尼生的诗歌总是能引起我的共鸣并帮助我找到一个中心。
Lenny Rachitsky: Wow.
莱尼·拉奇茨基：哇。
Never heard these recommendations before.
以前从未听说过这些建议。
I'm really excited to check these out.
我真的很高兴能检查这些。
Very cool for a CTO of a big tech company.
对于一家大型科技公司的首席技术官来说非常酷。
What is a favorite recent movie or TV show you've really enjoyed?
您最近最喜欢的电影或电视节目是什么？
Dhanji R.
丹吉 R.
Prasanna: Alien Earth I think is pretty awesome.
Prasanna：我觉得《异形地球》非常棒。
It's by Noah Hawley who did the Fargo TV series.
这是诺亚·霍利 (Noah Hawley) 创作的《冰血暴》电视剧。
And so it's someone with all of these incredible skills in high art filmmaking who's doing a pulp sci-fi show, and it just looks stunning and it feels stunning and it captures all of that essential alien pulpiness that makes it so interesting and fun.
因此，一个在高艺术电影制作方面拥有所有这些令人难以置信的技能的人正在制作一部低俗科幻节目，它看起来令人惊叹，感觉令人惊叹，它捕捉到了所有重要的外星低俗内容，使其变得如此有趣和有趣。
So I really like that, and I'm also watching Slow Horses, which I think is one of the better shows on TV.
所以我真的很喜欢这个，而且我也在看《慢马》，我认为这是电视上最好的节目之一。
Lenny Rachitsky: Love Slow Horses.
莱尼·拉奇茨基：喜欢慢马。
The new season's Out, think the fifth episode just dropped the day we're recording this.
新一季已经出炉了，想想我们录制这个的那天第五集就已经结束了。
So I love that show.
所以我喜欢那个节目。
Alien Earth also just watched it, so creepy and just like all these slimy, gooey little creatures just crawling around.
外星地球也只是看着它，太令人毛骨悚然了，就像所有这些粘糊糊的小生物一样在周围爬行。
Dhanji R.
丹吉 R.
Prasanna: Yeah, I just love the aesthetic and they captured something essential about the original Alien and yeah, they do it, but every scene in Alien Earth feels like you're watching a painting or something or someone's reading a novel to you.
Prasanna：是的，我就是喜欢这种美学，他们捕捉到了原版《异形》中的一些基本要素，是的，他们做到了，但是《异形地球》中的每个场景都感觉就像你正在看一幅画或其他东西，或者有人在给你读一本小说。
It's really unfolds very thoughtfully.
它确实是经过深思熟虑地展开的。
Lenny Rachitsky: I've never watched any alien content in my life and I really enjoyed Alien Earth.
Lenny Rachitsky：我一生中从未看过任何外星内容，而且我真的很喜欢《外星地球》。
I will say the ending, I was just like, it felt like it kind of slowed down a bit.
我会说结局，我只是觉得，感觉有点慢下来了。
I'm just like, "All right, I guess I see where it's going now." But it was really fun to watch.
我只是想，“好吧，我想我知道现在会发生什么了。”但观看起来真的很有趣。
Okay, next question.
好的，下一个问题。
Do you have a favorite product you really enjoyed?
您有真正喜欢的最喜欢的产品吗？
Sorry, a favorite product you've recently discovered that you really enjoy?
抱歉，您最近发现了您真正喜欢的最喜欢的产品？
It could be an app, could be an gadget, could be some kitchen thing.
它可以是一个应用程序，可以是一个小工具，也可以是厨房里的一些东西。
Dhanji R.
丹吉 R.
Prasanna: Well, I'm a gamer.
Prasanna：嗯，我是一名游戏玩家。
I love playing games.
我喜欢玩游戏。
So for me it's the Steam Deck, the Steam Deck OLED, which is their latest version.
所以对我来说，这是 Steam Deck，Steam Deck OLED，这是他们的最新版本。
It's like this gorgeous piece of hardware that lets you play the best games out there, but it's totally extensible and customizable.
它就像这款华丽的硬件，可以让您玩最好的游戏，但它是完全可扩展和可定制的。
And in this era where we're constantly told by big tech companies that we need to lock everything down, we need to lock down the user experience and customizability in order to have things work for people.
在这个时代，大型科技公司不断告诉我们，我们需要锁定一切，我们需要锁定用户体验和可定制性，以便让事情为人们服务。
Dhanji R.
丹吉 R.
Prasanna: I think Valve showed that's totally unnecessary and totally wrong, and you can build the Steam Deck.
Prasanna：我认为 Valve 表明这是完全没有必要的，也是完全错误的，而且你可以构建 Steam Deck。
You can install competing app stores, you can install Windows on it.
你可以安装竞争的应用商店，你可以在上面安装Windows。
You can treat it like a computer, write programs, which I have done to run on it.
你可以把它当作一台计算机，编写程序，我已经这样做了，可以在它上面运行。
So yeah, I think it's an incredible thing and it looks beautiful and it works great.
所以，是的，我认为这是一件令人难以置信的事情，它看起来很漂亮，而且效果很好。
So yeah, big fan.
所以是的，大粉丝。
Lenny Rachitsky: Do you have a favorite life motto that you find yourself coming back to often in work or in life?
Lenny Rachitsky：您有没有最喜欢的人生格言，并且在工作或生活中经常出现？
Dhanji R.
丹吉 R.
Prasanna: If you're not waking up in the morning feeling energized about what you're going to do that day in your professional life, then change something, quit if that's what it comes down to, or find a new way of doing what you're doing.
Prasanna：如果你早上醒来时没有对自己职业生涯中当天要做的事情感到精力充沛，那么就改变一些事情，如果确实如此的话就退出，或者找到一种新的方式来做你正在做的事情。
Just don't accept what's meted out to you.
只是不要接受给予你的东西。
So that's how I've tried to do things, and sometimes it works, sometimes not, but yeah, it's a good thing to ask yourself.
这就是我尝试做事的方式，有时有效，有时无效，但是，是的，问问自己是件好事。
Lenny Rachitsky: I really love this advice.
Lenny Rachitsky：我真的很喜欢这个建议。
It's really hard to do that for a lot of people.
对于很多人来说，确实很难做到这一点。
Is there anything that has helped you get over that fear of just like, "Oh man, I'm going to quit this thing.
有没有什么可以帮助你克服那种恐惧，就像“天哪，我要放弃这件事了。
I don't know where I'm going to go next."
我不知道接下来我要去哪里。”
Dhanji R.
丹吉 R.
Prasanna: The main thing is telling yourself that a year from now, you're going to look back on what looks like a monumental problem, a life-changing thing, and you're going to be like, "Oh, that was so trivial." A lot of times we get into these traps where we're overthinking something or really nervous about making a change, but in hindsight, those don't seem that big.
Prasanna：最重要的是告诉自己，一年后，当你回顾这个看似重大的问题、改变生活的事情时，你会说，“哦，那真是微不足道。”很多时候，我们会陷入这些陷阱，我们会过度思考某些事情，或者对做出改变感到非常紧张，但事后看来，这些似乎并没有那么大。
Dhanji R.
丹吉 R.
Prasanna: And all the time that's passed since and all the events that have happened teach you that there's more to the world and it's never too late to do something useful or never too late to do something that's for yourself and improving yourself.
Prasanna：从那以后发生的所有时间和发生的所有事件都告诉你，世界上还有更多，做一些有用的事情永远不会太晚，或者做一些对自己有利并提高自己的事情永远不会太晚。
So yeah, I think just kind of remembering that things are not as big or bleak or decisive as they seem in the moment is always important.
所以，是的，我认为记住事情并不像此刻看起来那么大、黯淡或决定性总是很重要的。
Lenny Rachitsky: Final question.
莱尼·拉奇茨基：最后一个问题。
So you were a mad scientist at Square for many years.
所以你多年来一直是 Square 的一名疯狂科学家。
Do you have another favorite mad scientist from pop culture or real life?
您在流行文化或现实生活中还有另一位最喜欢的疯狂科学家吗？
Dhanji R.
丹吉 R.
Prasanna: That's an interesting one.
Prasanna：这很有趣。
I think the image that always comes to my mind is Doc Brown from Back to the Future.
我认为我脑海中经常出现的形象是《回到未来》中的布朗博士。
I feel like he's the canonical mad scientist of my generation anyway, but there've been a lot in video games and stuff too, but he was the one that was like, "I'm just going to do this crazy thing because I almost have this burning desire, need to do it, and whether I want to or not, I must build this time machine." And he spends the entire movie trying to fix the problems that it creates.
无论如何，我觉得他是我们这一代典型的疯狂科学家，但在电子游戏和其他东西中也有很多，但他是那种“我要做这件疯狂的事情，因为我几乎有这种强烈的欲望，需要这样做，无论我愿意与否，我都必须建造这台时间机器。”他花了整部电影试图解决它所造成的问题。
But yeah, he has always been a really fun character for me.
但是，是的，他对我来说一直是一个非常有趣的角色。
Lenny Rachitsky: You know what?
莱尼·拉奇茨基：你知道吗？
I think about Pinky from Pinky in the Brain.
我想起《Pinky in the Brain》中的 Pinky。
Dhanji R.
丹吉 R.
Prasanna: Oh yeah, that's a good one too.
Prasanna：哦，是的，这也是一件好事。
Yeah.
是的。
Lenny Rachitsky: Oh man.
莱尼·拉奇茨基：天啊。
Dhanji, this was awesome.
丹吉，这太棒了。
You were wonderful.
你太棒了。
Thank you so much for being here.
非常感谢您来到这里。
Two final questions before we actually wrap up.
在我们真正结束之前还有两个最后问题。
Where can folks find you online if they want to reach out, learn more about say Goose or anything else going on at Block?
如果人们想要联系、了解更多关于 Goose 或 Block 发生的其他事情，他们可以在哪里找到您？
And how can listeners be useful to you?
听众如何对你有用？
Dhanji R.
丹吉 R.
Prasanna: Check out our GitHub pages for Goose and all of the other open source projects we have at Block.
Prasanna：查看我们的 Goose GitHub 页面以及我们在 Block 上拥有的所有其他开源项目。
So there's a lot that's useful there.
所以那里有很多有用的东西。
We do a lot on Android open source as well, so check that stuff out.
我们也在 Android 开源方面做了很多工作，所以请查看这些内容。
You can always find me on LinkedIn, so feel free to connect.
您随时可以在 LinkedIn 上找到我，因此请随时联系。
I'm very happy to be contacted.
我很高兴能与您联系。
Dhanji R.
丹吉 R.
Prasanna: And I would say the way people can be useful is, again, going back to this era we're in of a lot of change and uncertainty, I think people that demand more of their companies, of their employers, of their teams, demand something better.
Prasanna：我想说，人们的有用之道是，再次回到我们所处的这个充满变化和不确定性的时代，我认为人们对他们的公司、他们的雇主、他们的团队要求更多，要求更好的东西。
At Block, we always ask, "Can we default to making this open source?
在 Block，我们总是问：“我们可以默认将其开源吗？
Can we build this for people that are not just us or our customers?
我们可以为不仅仅是我们或我们的客户的人构建这个吗？
Can everyone benefit?"
每个人都能受益吗？”
Dhanji R.
丹吉 R.
Prasanna: And I think that's particularly important in this era of AI where everyone's locking themselves in walled gardens and trying to capture parts of the platform that are emerging.
Prasanna：我认为这在这个人工智能时代尤为重要，每个人都将自己锁在围墙花园中，并试图捕捉正在出现的平台的一部分。
So yeah, just demand more of people.
所以，是的，只是要求更多的人。
The internet was created as a promise for open sharing of information to the benefit of all, and I think that AI should realize that for us.
互联网的创建是为了开放共享信息以造福所有人的承诺，我认为人工智能应该为我们实现这一点。
And so yeah, just demand that of people.
所以，是的，只是要求人们这样做。
Lenny Rachitsky: A really beautiful way to end it.
Lenny Rachitsky：一个非常美妙的结束方式。
Dhanji, thank you so much for being here.
丹吉，非常感谢你来到这里。
Dhanji R.
丹吉 R.
Prasanna: Thank you, Lenny.
普拉萨娜：谢谢你，莱尼。
I really appreciated it.
我真的很感激。
Thank you.
谢谢。
Lenny Rachitsky: Bye everyone.
莱尼·拉奇茨基：大家再见。
Thank you so much for listening.
非常感谢您的聆听。
If you found this valuable, you can subscribe to the show on Apple Podcasts, Spotify, or your favorite podcast app.
如果您发现这很有价值，您可以在 Apple Podcasts、Spotify 或您最喜欢的播客应用程序上订阅该节目。
Also, please consider giving us a rating or leaving a review as that really helps other listeners find the podcast.
另外，请考虑给我们评分或留下评论，因为这确实有助于其他听众找到播客。
You can find all past episodes or learn more about the show at lennyspodcast.com.
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。