非工程师必看的5大AI工程趋势

今天《AI每日简报》带你看五大趋势，这是AI工程师圈子里正在发生的事，但非专业人士也该留意。

Today on the AI Daily Brief, the five trends among AI engineers that non engineers should be paying attention to.

在那之前，先看几条头条新闻，关于OpenAI首款消费级设备的更多细节。

Before that are the headlines, more information about OpenAI's first consumer device.

《AI每日简报》是一档每天更新的播客和视频节目，聚焦AI领域最重要的新闻和讨论。

The AI Daily Brief is a daily podcast and video about the most important news and discussions in AI.

好了，各位。

Alright, friends.

在正式开始之前，先说几件小事。

Quick announcements before we dive in.

首先，感谢今天节目的赞助商：普华永道、Robots and Pencils、Blitzy，还有Airtable。

First of all, thank you to today's sponsors, KPMG, Robots and Pencils, Blitzy, and Airtable.

想听无广告版本的节目？请访问 patreon.com/aidailybrief，或者在Apple播客订阅我们。

To get an ad free version of the show, go to patreon.com/aidailybrief, or you can subscribe at Apple Podcasts.

如果想了解如何赞助本节目，请发邮件到 sponsors@aidailybrief.ai。

And to learn more about sponsoring the show, send us a note at sponsors@aidailybrief.ai.

今天我们先来更新一下OpenAI首款消费级设备的进展，据悉它已经进入原型开发阶段。

We kick off today with an update on OpenAI's first consumer device as it apparently enters the prototyping stage.

彭博社的马克·古尔曼爆料称，这款设备是一款没有屏幕的便携式智能音箱，定位是AI时代的新一代家庭电脑。

Bloomberg's Mark Gurman has the scoop with his sources describing the device as a portable screen free smart speaker designed to be a type of new home computer for the AI era.

据消息人士透露，这款设备设计为固定在家使用，能控制智能家居设备、播放音乐，同时还能充当聊天机器人界面。

The sources suggest that the device is intended to remain in the home and will be able to control smart home appliances and play music as well as functioning as a chatbot interface.

他们的卖点是，这将是一个像人一样的AI伙伴。

The pitch is that this will be a human like AI companion.

乍一听，这听起来像是对Alexa或谷歌家庭设备的替代品，但据消息人士说，OpenAI相信其强大的AI功能能让它在市场中脱颖而出。

Now at first read, this essentially sounds like a replacement for Alexa or Google Home devices, but according to the sources, OpenAI is betting that advanced AI features give it an edge in the market.

消息称，他们的目标是让这款设备看起来像是ChatGPT的实体化身，甚至带点生命力，而不仅仅是个响应指令的电子设备。

Sources said the goal is to make the device feel like a physical manifestation of ChatGPT and somewhat alive rather than just an electronic device responding to commands.

为此，设备会有一些能自主移动的部件，让它更像一个拟人化的小型机器人，而不是死板的电子装置。

To that end, the device will have some components that move on their own, making it feel like an anthropomorphic mini robot rather than an inert piece of electronics.

这款设备将整合ChatGPT的记忆功能，逐步了解用户习惯，并配备摄像头和其他传感器来感知周围环境。

The device will incorporate ChatGPT's memory feature to evolve its understanding of the user, as well as a camera and other sensors to understand its surroundings.

它将采用上周发布GPT Live时推出的双向语音模型技术，让对话更自然、更实时。

It will incorporate the two way voice model technology unveiled with the release of GPT Live last week, enabling more natural real time conversations.

这种自然的交互方式至关重要，OpenAI认为，能像人一样与用户建立连接，是他们的核心优势。

That natural style is apparently key with OpenAI viewing the ability to connect with users in a human like manner as their core advantage.

设备内置可充电电池，用户可以随身带着它在屋里走动，不用非得插在固定位置。

The device will have rechargeable batteries, allowing users to carry it around the home rather than needing to keep it plugged into one location.

OpenAI计划在今年底前发布这款设备，2027年正式上市。

OpenAI aims to unveil the device by the end of the year before releasing it in 2027.

据传，这可能是OpenAI推出的一系列消费产品的第一款，后续还可能有项链式设备、耳机，甚至智能手机。

And reportedly, it could be the first of several consumer devices from OpenAI, with rumors of a pendant, earbuds, and maybe even a smartphone.

现在，苹果的诉讼给这款产品的发布蒙上了一层阴影，大家担心它会受到怎样的影响。

One question now casting its shadow over the launch is how it will be impacted by the Apple lawsuit.

苹果最近指控OpenAI侵犯知识产权，几乎肯定会申请禁令，阻止任何基于其技术的设备上市。

Apple recently accused OpenAI of IP theft and will almost certainly file for an injunction to stop the release of a device based on any of their IP.

虽然诉讼中提到了iPhone的金属拉丝表面作为其中一个争议点，但苹果自己也在秘密研发一系列AI驱动的智能家居设备。

And while the lawsuit discusses the brushed metal finish of an iPhone as one of their concerns, Apple is also reportedly working on a series of AI powered smart home devices.

这可能会让情况变得更加复杂。

That could muddy the waters quite a bit more.

为此，OpenAI 对他们最初的回应做了一些补充。

And to that end, OpenAI has elaborated slightly on their initial response to the lawsuit.

在一份新的声明中，他们表示：虽然我们认真对待这些指控，但目前并没有任何证据表明这些指控有依据。

In a new press statement, they said: While we take these allegations seriously, we're not aware of any evidence that this complaint has merit.

我们相信公平竞争，支持人们自由选择工作方式，我们的重点是打造能赋能全球用户的创新技术。

We believe in fair competition and allowing people the freedom to work wherever they choose, and we're focused on building innovative technology that empowers people everywhere.

关于初步反应，不少人持怀疑态度。

Now, in terms of first takes, a fair number of people are pretty skeptical.

Negligible Capital 写道：别说我疯了，但只要你有智能手机，这个 OpenAI 产品听起来就挺没意思的——除了它能一直盯着你、听着你在家的一举一动。

Negligible Capital writes, Call me crazy, but this OpenAI product sounds pretty stupid so long as you own a smartphone, except for the fact that it can constantly watch and listen to you in your home.

Chris Paxton 写道：据说这款新的 OpenAI 消费设备是个带可动部件的音箱。

Chris Paxton wrote, The new OpenAI consumer device is said to be a speaker with movable parts.

说实话，看法不一，但像 Alexa 这样的智能音箱一直以来最大的问题就是太蠢了。

Mixed on this, to be honest, but the big problem with smart speakers like Alexa was always that they were stupid.

普拉克什提出了乐观的看法，他说：想知道他们为什么要这么做吗？

Prakesh provides the bull case saying, Wanna know why they're doing this?

人工智能正越来越依赖于它所处的环境、能访问的数据以及能执行的操作。

AI is getting defined by context, the data it has access to, the actions it can perform.

你会拥有一个属于自己的AI助手，就像ChatGPT那样。

You will have a personal AI like ChatGPT.

你还会有一个企业级的AI，比如Claude Tag。

You will have a corporate AI like Claude Tag.

但现在还缺一个家庭版的AI。

What's missing is a family AI.

它很可能能通过人脸和声音识别每个家庭成员，帮所有人安排日程，给你发消息、打电话，处理各种琐事，还能随着时间不断升级功能。

What it will likely be able to do, recognize every family member by face and voice, manage schedules for everyone, message you, call you on the phone, run a variety of errands, and be upgraded with capability over time.

如果他们做得好，这个AI会像你家的狗一样，让人感到熟悉又亲切。

If they do it right, it will become as familiar and lovable as your family dog.

我不确定自己真会把这新设备当成Fido，但我确实觉得，人们很难准确预测消费者会如何使用全新的产品类别。

Now, I'm not sure that I am sold on this new device being Fido, but I do think it is notoriously hard for people to predict how consumers are going to interact with new categories of products.

我对‘更强大的Alexa’这个说法挺怀疑的，但总体来说，我还是愿意暂时放下一些疑虑，因为我觉得，基于这种新智能出现一类全新设备，几乎是必然的事。

I'm pretty skeptical of the pitch of Alexa But Better, but I'm also, broadly speaking, willing to suspend some amount of skepticism because it feels inevitable that there are going to be some new category of devices that arise based on this new type of intelligence.

对OpenAI来说，更大的问题是，这件事到底该放在他们优先级的什么位置。

I think the bigger question for OpenAI specifically is just where this is going to fit with their priorities.

这家公司最近重新找回状态，部分原因就是过去几个月里，他们果断放弃了那些著名的‘旁支项目’，专心做编程和企业业务。

Part of the reason that the company has gotten its mojo back recently is that it's spent the last few months abandoning what it has famously called side quests to focus on building its coding and enterprise business.

现在这家公司要上市了，必须找到自己的独特卖点。即使企业客户现在很值钱，但OpenAI和Anthropic相比，有一个独特优势——庞大的个人用户群。

Now, this is a company that's going public, it's gotta draw differentiation somewhere, and even if the enterprises are valuable now, one of the unique things that OpenAI has that Anthropic doesn't is the consumer base.

所以他们可能觉得，必须得想清楚怎么把这部分用户变现，而这类设备，正是答案的一部分。

So maybe they feel it's important to actually have an answer to how to monetize that user base, and devices like this are part of that answer.

不过，同时做硬件和软件本来就很难，他们内部肯定也在讨论，到底该在两头上各投入多少资源。

Then again, it is really hard to be a hardware company and a software company at the same time, and you gotta think that there are discussions internally about just how much they should be supporting both efforts.

接下来聊聊政治层面，特朗普政府推出了一个名为‘信息中心’的计划，这是最近AI行政令出台后首个重大举措。

Moving now into the political realm, the Trump administration has introduced clearinghouse, which is the first major program to come from the recent AI executive order.

这个计划叫什么？你没听错，真叫‘金鹰计划’，它会把政府机构、企业还有开源项目聚到一起，共享网络漏洞信息。

The initiative is called, and no, I'm not joking, Gold Eagle, and will bring together government agencies, companies, and open source projects to share information on cyber vulnerabilities.

黄金鹰计划已于本月早些时候推出，由财政部、国土安全部和国防部联合发起，并与多家人工智能公司协商合作。

Gold Eagle was rolled out earlier this month as a joint effort between the Treasury Department, Department of Homeland Security, and the Pentagon in consultation with AI companies.

这一计划的灵感来自‘神话冲击’事件，那次事件暴露了数百个关键软件中的安全漏洞。

The concept stems from the Mythos shock which uncovered hundreds of vulnerabilities in critical software.

这一发现促使了‘玻璃翼计划’的启动，这是一个跨行业紧急行动，旨在快速发现漏洞并部署补丁。

This revelation kicked off Project Glasswing, a multi industry sprint to detect bugs and deploy patches.

这次行动让大家意识到，在这个人工智能新时代，必须建立更高效的信息共享与协同机制，而黄金鹰计划正是要把这种‘玻璃翼’式的应对变成一项长期的政府举措。

That effort made it clear that better information sharing and coordination was going to be necessary in this new AI era, and Gold Eagle is meant to make Glasswing style efforts a permanent government initiative.

此外，网络安全行政令中的其他几项政策仍在制定中。

Now a few other policies from the cybersecurity EO are still in the works.

有报道称，相关部门正在制定一套模型审核流程，以便在新前沿模型发布前开展政府层面的安全测试，政府与人工智能行业似乎正在协商一套明确的标准，这很可能大幅提升我们过去在Fable和GPT-5.6上看到的那种临时应对方式。

Reporting suggests work is underway to define a model vetting protocol to allow for government AI safety testing ahead of new frontier releases, and it seems like a set of clear standards are being negotiated between the government and the AI industry, which has a very meaningful chance of being a huge upgrade to the ad hoc approach that we've seen with Fable and GPT-five point six.

我们之前也报道过有关中国模型禁令的传闻，有人担心这项禁令可能会扩展到所有开源模型。

There's also those rumblings we've been covering about a Chinese model ban, which some fear could extend to open source models in general.

不过，在黄金鹰计划的新闻发布会上，网络事务主任肖恩·凯恩克罗斯明确回应了这些传闻：我可以非常清楚地表示，我们全力支持美国的开源社区。

That said, during the press briefing for Gold Eagle, Cyber Director Sean Cairncross addressed those rumors commenting: I could not be more clear that we are in full support of The US Open Source community.

我们会尽一切努力支持这个社群的发展。

We will do everything we can to support the strength of that community.

今天最后聊一个过去一个月左右一直在悄悄讨论的问题：企业能否信任AI公司处理它们的数据。

Finally today, one of the simmering conversations over the past month or so has been whether enterprises can trust AI companies with their data.

过去企业处理敏感信息时，就一直对软件供应商存有疑虑，但Fable发布时，Anthropic声称即使客户明确要求不保留数据，他们仍会监控数据以确保安全，这才让这个问题浮出水面。

This has always been a question for enterprises dealing with software providers around sensitive information, but it was brought to the foreground during the release of Fable when Anthropic said that customers with zero data retention clauses would still have their data monitored for safety reasons.

自那以后，这个话题的讨论就越来越受关注。

It's loomed larger in the conversation ever since.

Palantir的首席执行官亚历克斯·卡普在CNBC上谈到了这些风险，微软首席执行官萨提亚·纳德拉最近也写了两篇文章，核心观点都是：别把企业数据交给AI公司。

Palantir CEO Alex Karp discussed the risks on CNBC, and the topic was also a major theme in a pair of recent essays from Microsoft CEO Satya Nadella, which could be summed up as saying you can't trust AI companies with corporate data.

但这个风险到底有多真实？

But how real is the risk?

至少在一件事上，特别是涉及SpaceX的AI时，风险确实存在。

Well, at least in one instance, specifically when it comes to SpaceX AI, it appears that the risks were pretty real.

周一，一家名为CereLab的安全公司发布了一份审计报告，发现Grok Build正在上传完整的代码库。

On Monday, a security firm called CereLab published an audit that found that Grok Build was uploading entire codebases.

即使代码任务只需要几个文件，也被上传了数吉字节的数据。

Even if the coding task only required a few files, gigabytes of data were being uploaded.

大多数AI服务商只会上传必要的代码片段并保存推理记录，但Grok Build却直接把整个仓库全传上去了。

While most AI providers will upload necessary code snippets and store reasoning traces, this is an instance of Grok Build just dumping the entire repo upfront.

安全分析师哈里·马拉卡尔核实了这一结果，发现即使在没有调用任何工具的会话中，Grok Build依然上传了完整代码库。

Security analyst Hari Malakal verified the results finding that even in a session with zero tool calls Grok Build still uploaded the entire codebase.

哈里评论说：这就像在后台偷偷运行一个恶意数据收集器。

Hari commented: It ships a malware like background code collector.

现在人们这么生气，是因为无论用户怎么设置，这种情况似乎都在发生。

Now part of the reason that people are pissed is that this seems to have been happening regardless of user settings.

用户可以选择关闭数据上传以帮助改进产品，但这个设置根本不起作用。

Users can opt out of transmitting data to help improve the product, but that appears to have had no effect.

截至周一，SpaceX AI修改了代码，停止了上传行为，并新增了一个手动隐私开关，他们声称现在所有API调用默认都遵循零数据保留策略。

As of Monday, SpaceX AI made changes to the code which stopped the uploads and added a new privacy setting as a manual override, and they claim that all API use now defaults to zero data retention.

SpaceX AI开发者安德鲁·米利奇写道：在Grok Build中，零数据保留和隐私设置始终被严格执行，切换隐私选项会自动清除所有已同步的数据。

SpaceX AI developer Andrew Millich wrote: Zero data retention and slash privacy are always respected in Grok Build, and swapping your settings with slash privacy deletes any synced data retroactively.

埃隆·马斯克补充说，作为预防措施，此前所有上传到SpaceX AI的用户数据都将被彻底删除。

Elon Musk added that, As a precautionary measure, all user data that was uploaded to SpaceX AI before now will be completely and utterly deleted.

一点都不会留下。

Zero anything whatsoever will remain.

但说实话，人们对这件事并不买账。

And yet honestly, people were not happy about this.

本杰明·德克拉克写道：这事儿当初为什么会发生？

Benjamin Decracker writes: Why did it happen in the first place?

现在才说‘我们来删掉吧’，这可不够好。

I guess we'll delete it now is not great.

加速更狠的人说：无论在什么情况下，把代码库和密钥默认上传都是不可接受的。

Accelerate Harder wrote: There is no acceptable scenario where uploading code bases and secrets is an acceptable default.

如果事实真是这样，你们必须公开进行一次事件复盘。

If that is indeed what happened, you must do a public incident review.

我对X AI作为商业伙伴的信任，已经跌到谷底了。

My trust for x AI as a business partner is on the floor.

Flowerslop指责说：如果这是OpenAI干的，你早就发了二十条帖子，骂他们是地球上最差的公司，说谁都不能再信任他们了。

Flowerslop accused: If OpenAI had done this, you'd have posted about it 20 times, called them the worst company on Earth, and said no one should ever trust them again.

但现在你自己干了同样的事，却只说一句‘对不起，别生气，我删了，我保证’。

But now that you've done it yourself, all you have to say is sorry, don't be mad, I'll delete it, I promise.

归根结底，这一切凸显了AI数据保留政策依然高度依赖信任。

Ultimately, all of this highlights how much of AI data retention policy still hinges on trust.

最近几周，Grok Build的许多用户很可能被完整抓取了代码库，但我们根本无法核实这些数据是否真如马斯克所说被删除了。

Many users of Grok Build likely had their full codebases scraped over recent weeks, and we have no real way to verify whether they were actually deleted as Elon claimed.

整个事件让萨提亚·纳德拉的观点显得更有说服力——他在信中写道：在AI时代，用户为了使用产品，风险就是把自己的知识白白交了出去。

The entire episode gives a lot more credence to Satya Nadella's point when he wrote: In the AI age, the buyer risks giving away knowledge, just in order to use what they bought.

这种知识是竞争对手买不到的，而且几乎在不知不觉中就泄露了。

It's the kind of knowledge a competitor could never buy, and the kind that leaks out almost imperceptibly.

即使你把这事当成一个无心之失，愿意给SpaceX AI一点信任，它依然把过去几周我们一直在讨论的诸多问题，全都尖锐地凸显了出来。

Even if you take this at face value as a mistake and give SpaceX AI the benefit of the doubt, it still puts a fine point on many of the issues that we've been talking about over the past few weeks.

好了，今天的热点就先说到这儿。

So that's going do it for today's headlines.

接下来是主节目内容。

Next up, the main episode.

现在最重要的AI问题，不是谁在用AI，而是谁用得好。

One of the most important AI questions right now isn't who's using AI, it's who's using it well.

普华永道和德克萨斯大学奥斯汀分校刚分析了140万次真实职场中的AI互动，发现一个意外的结果：最有影响力的人并不是更会写提示词的人，而是把AI当成思维伙伴来合作。

KPMG and the University of Texas at Austin just analyzed 1,400,000 real workplace AI interactions and found something surprising: the highest impact users aren't better prompt engineers they treat AI like a reasoning partner.

他们会明确问题、引导思考、反复打磨，不断追求更好的答案。

They frame problems, guide thinking, iterate, and push for better answers.

好消息是？

And the good news?

这些能力是可以大规模培养的。

These behaviors are teachable at scale.

如果你正想从单纯使用AI，升级到真正掌握AI能力，普华永道关于高级AI协作的研究非常值得一看。

If you're trying to move from AI access to real capability, KPMG's research on sophisticated AI collaboration is worth your time.

了解更多，请访问 kpmg.com/us/sophisticated。

Learn more at kpmg.com/us/sophisticated.

我每天在这档节目里探讨AI潜力与现实之间的能力差距。

I cover the capability gap between AI potential and AI reality every day on this show.

大多数公司还在摸索怎么起步。

Most companies are still figuring out how to start.

而Robots and Pencils已经启动并快速扩展了。

Robots and Pencils is already launching and scaling.

Agendic在大型企业中几周内就实现了生成式AI的落地应用。

Agendic and generative AI in production at large enterprises in weeks.

AWS高级层级合作伙伴的数量在过去一年里翻了一倍多。

AWS advanced tier pattern partner more than doubled in a year.

而且他们正在招聘。

And they're hiring.

目前有五十个职位空缺。

50 open roles.

如果你意识到这个时代不一样了，想置身其中，而不是旁观，那这值得你认真看看。

If you're someone who knows this moment is different, who wants to be inside it, not watching it, this is worth a look.

在 Robots and Pencils，最好的点子才能胜出，团队也一直保持着极高的水准。

At Robots and Pencils, the best ideas win, and the team is purposefully kept super high quality.

这里是你将来回望时，会觉得这辈子做过的最明智决定的地方。

This is the kind of place you look back on as the best decision you ever made.

去看看 robotsandpencils.com/careers 吧。

Take a look at robotsandpencils.com/careers.

如果你正打算采用智能型软件开发生命周期，Blitzy 就是提升工程效率的关键。

You're looking to adopt an agentic SDLC, Blitzy is the key to unlocking unmatched engineering velocity.

Blitzy 的核心优势，始于对代码的无限上下文理解。

Blitzy's differentiation starts with infinite code context.

成千上万个专业代理能在一次扫描中分析数百万行代码，精准梳理所有依赖关系。

Thousands of specialized agents ingest millions of lines of your code in a single pass, mapping every dependency.

在全面掌握你的代码库后，企业会在每个迭代周期之初用 Blitzy 自动完成超过 80% 的开发任务。

With a complete contextual understanding of your codebase, enterprises leverage Blitzy at the beginning of every sprint to deliver over 80% of the work autonomously.

所有代码都达到企业级标准，端到端测试通过，完全兼容你现有的服务、组件和规范。

Enterprise grade, end to end tested code that leverages your existing services, components, and standards.

这可不是什么AI自动补全。

This isn't AI autocomplete.

这是以规格和测试为驱动、借助计算速度实现的开发方式。

This is spec and test driven development at the speed of compute.

前往 blitzy.com 预约我们的AI专家进行技术深度解析。

Schedule a technical deep dive with our AI experts at blitzy.com.

就是 blitzy.com。

That's blitzy.com.

本集《AI每日简报》由Hyperagent赞助播出，您可以在这里管理一支由团队协同运作的智能代理舰队。

This episode of the AI Daily Brief is brought to you by Hyperagent, where you run fleets of agents your team can manage together.

新用户可获得1000美元的计算推理额度。

New users get $1,000 in inference.

别再依赖本地代理或等着在笔记本上手动触发的聊天流程了。

Forget local agents and chat workflows waiting on your laptop to be prompted.

Hyperagent将智能代理部署在云端，7x24小时运行，无缝对接您团队正在使用的各类工具，真正完成实际工作。

Hyperagent deploys always on agents in the cloud, doing real work across the tools your team already uses.

市场团队的智能代理能把竞争对手的动态自动生成落地页。

Marketing's agent turns competitor moves into landing pages.

销售团队的智能代理自动完善潜在客户信息、撰写邮件，并更新客户管理系统。

Sales's agent enriches leads, drafts emails, and updates the CRM.

运营团队的智能代理负责跟进文书工作，追踪预算使用情况。

Ops agent chases the paperwork and tracks the budget.

每个智能代理都能访问共享信息，并严格遵守你设定的权限和审批规则。

Every agent has access to shared context and follows your rules about scope and approvals.

是时候引入那些像同事一样默契的智能代理了。

It's time you add agents that feel like teammates.

立即在 Hyperagent 上雇佣你的智能代理，由 Airtable 原班团队打造。

Hire yours at Hyperagent built by the team at Airtable.

前往 hyperagent.com/aideallybrief 领取你的 1000 美元算力额度。

Claim your $1,000 in inference at hyperagent.com/aideallybrief.

欢迎继续收听 AI 每日简报。

Welcome back to the AI Daily Brief.

今天我们要聊点不一样的，我特别期待。

Today, we are doing something a little bit different that I'm very excited about.

我给非技术背景的人一个长期有效的建议：想提前把握AI的走向，就盯紧真正的软件工程师们现在在讨论什么。

One of my longest held tips and tricks for non engineers who want to be ahead of the curve when it comes to where AI is going is to pay attention to what, with regards to AI, actual software engineers are talking about right now.

从2023年初开始，这么做能让你在工具使用、模型与更大生态系统的关联，以及AI如何根本性地改变工作方式这些方面，领先别人半年以上。

Since early twenty twenty three, doing this gives you about a six month head start on everyone else when it comes to everything from the tools people are using to how they're thinking about the relationship between models and larger ecosystems to their relationship fundamentally with work in a new AI mediated way.

要了解AI工程师们在想什么，最好的渠道之一，就是AI工程峰会和AI工程世博会。

And one of the best sources for keeping up on what AI engineers are thinking about is, of course, the AI Engineering Summit and the AI Engineering World's Fair.

这场世博会由Sean Wang（也就是Swix）和一群合作者主办，每年举办一次，峰会通常在春秋季举行，内容全是前沿干货。

Hosted by Sean Wang, aka Swix, and a group of other collaborators, the World's Fair happens annually, and the summits usually happen in the spring and the fall and are always just chock full of alpha.

本月早些时候，年度AI工程世博会在旧金山举行了。

Earlier this month, the annual AI engineering world's fair happened in San Francisco.

可惜的是，这次我没能亲自到场。

And, unfortunately, this time I was not able to be there.

不过幸运的是，来自Latent Space（也是Swix的项目）的Richard McManus写了一篇文章，总结了这场活动的五大趋势。

Luckily, Richard McManus, who works with latent space, which is also a SWICS joint, wrote up a post about the five trends that defined the event.

所以今天我们要来看看这五大趋势，试着理解它们未来可能对非技术人员带来什么影响。

So what we're gonna do today is look at those trends and try to understand what the implications might be for non engineers down the line.

今年的活动正好赶上Swix提出‘AI工程师’这个词三周年左右。

Now this year's event actually happened on almost the three year anniversary of Swix coining the term AI engineer.

那时候，人们还笼统地把AI和软件开发的交集叫做‘提示工程’。

Back then, people were still referring to the intersection of AI and software development vaguely as prompt engineering.

虽然提示工程后来也成了非技术领域的术语，但当时没人像现在这么清楚：AI写代码不仅是AI最早的关键职业应用场景，更是打开一切可能性的关键。

Although prompt engineering would go on to be a nontechnical discipline as well, the point was it was not nearly as clear back then as it is today that AI writing software was going to be not only the key early professional use case for AI, but also the thing that would unlock everything else.

2023年6月30日，Swix写道：新的能力正在催生一个新的头衔。

Back on 06/30/2023, Swicks wrote, emerging capabilities are creating an emerging title.

要掌握这些能力，我们就不能只做提示工程师，还得写软件，写能编写软件的AI。

To wield them, we'll have to go beyond the prompt engineer and write software and AI that writes software.

更重要的是，当时这还不是共识，Swix说：我认为这是一份全职工作。

Importantly, and this was not consensus at the time, Swix wrote, I think it is a full time job.

我相信软件工程会衍生出一个新的细分领域，专门研究AI的应用，高效驾驭这套新兴技术栈，就像站点可靠性工程师、DevOps工程师、数据工程师和分析工程师那样逐渐出现。

I think software engineering will spawn a new subdiscipline specializing in applications of AI and wielding the emerging stack effectively, just as site reliability engineer, DevOps engineer, data engineer, and analytics engineer emerged.

现在回头看，斯维克斯其实还低估了这件事的重要性，因为如今几乎所有工程师，无论程度深浅，都已经是AI工程师了，但总体方向上，他确实说准了。

Now if anything, in retrospect, Swix ended up underselling how significant it would become, as effectively all engineers are AI engineers to greater or lesser extent, but directionally, he was dead on.

理查德提到，三年过去了，AI工程这个领域已经发生了翻天覆地的变化。

Now Richard writes that three years on, the field of AI engineering has changed dramatically.

在这五大趋势里，我们能看到不少关于AI开发现状的线索。

And in these five trends, we see quite a bit about the state of AI building.

我觉得，如果退一步看，贯穿这些趋势最引人深思的一点，是我们对自主性的认知发生了调整——这一点在我们逐一分析时请记住。

Now I think taking a step back, the most fascinating through line across all of these trends is a recalibration of our relationship with autonomy, so keep that in mind as we go through these.

理查德指出的第一个趋势是，关注点从智能体本身，转向了围绕它们的系统。

The first trend that Richard calls out is that the, quote, focus shifts from agents to the systems around them.

这一点我们今年在这档节目里其实已经聊过很多次了。

Now this is something we've obviously been talking about a lot on this show throughout the year.

随着智能体逐步进入实际生产环境，我们逐渐意识到，能力不只是模型本身的问题，甚至也不只是你给模型输入什么上下文的问题。

The idea that alongside agents coming to full production, we're realizing that capacity is not just a question of model, and it's not even just a question of context that you feed the model.

真正关键的是智能体所处的整个框架和支撑系统。

It's about the harness and broader system that the agent operates in.

这不仅包括获取上下文和数据，还涉及获取技能、使用工具的能力，以及越来越重要的能力——根据具体任务在不同模型之间进行切换。

That includes, yes, access to context and data, but also access to skills, ability to interact with tools, and increasingly, ability to route between different models based on the task at hand.

理查德认为，要理解AI工程的发展，一个好方法是对比莉莲·王三年前后的两篇文章。她曾是OpenAI的研究员，现在是Thinking Machines Lab的联合创始人。

Now Richard argues that one good way to compare how AI engineering has evolved is to compare two essays three years apart from Lillian Wang, formerly an OpenAI researcher and now co founder of Thinking Machines Lab.

2023年，她写了一篇题为《由大语言模型驱动的自主代理》的文章，从规划、记忆和工具使用等角度分析了大语言模型代理的结构。

Back in 2023, she wrote a post called LLM Powered Autonomous Agents, which described the anatomy of an LLM agent in terms of things like planning, memory, and tool use.

她提到的例子，比如AutoGPT、BabyAGI和GPT-Engineer，我记得在2023年4月到6月，也就是这个节目刚开始的头几个月，这些话题总是最受关注，下载量和YouTube播放量最高。

The examples she pointed to were things like Auto GPT, Baby AGI, and GPT Engineer, which were, I remember, for the first couple months of this show back in April and May and June 2023, the topics that always received the most interest and the most downloads and the most views on YouTube.

理查德指出，她新写的这篇《面向自我提升的系统工程》不再聚焦于代理本身，而是关注围绕代理的整个系统。

Richard points out that her new essay, harness engineering for self improvement, focuses not on the agent itself, but on that system surrounding the agent.

他所说的‘系统’，是指管理工作流、上下文、权限、评估、持久状态和持续优化的那套机制。

The harness, he writes, that manages workflows, context, permissions, evaluations, persistent state, and continuous improvement.

换句话说，AI工程已经从单纯地给模型写提示，转向了构建更可靠的外围系统。

In other words, he writes, AI engineering has moved beyond prompting models towards engineering reliable systems around them.

这正是我之前提到的那个趋势的首次体现——我们与自主性之间关系的重新调整。

Now this is the first place that we see that trend that I mentioned of the recalibration of our relationship with autonomy.

理查德提到，今年的活动上，智能体主要被定位为辅助AI工程师，而不是取代他们。

Richard writes that at the event this year, agents were largely positioned as augmenting the AI engineer rather than replacing them.

这正是OpenAI的罗曼·休伊特在第二天主题演讲中的核心观点：软件吞噬了世界，AI吞噬了软件，但现在我们要说的是，AI工程师正在吞噬世界。

This was a big part of the substance of the day two keynote with OpenAI's Romaine Hewitt, who said software ate the world and then AI ate software, but now what we're here to say is that the AI engineers are eating the world.

他认为，像Codecs这样的工具，目标是让工程师更容易与智能体协作。

He argued that the goal of tools like Codecs is to make it easier for engineers to collaborate with agents.

这一点尤其契合早前对Fable 5和5.6 SOL的对比——人们普遍认为，SOL更适合处理那些需要AI独立完成的大任务，等它做完再回来汇报；而5.6 SOL则更擅长当一个合作搭档。

Now this dovetails especially with the early comparisons between Fable five and five point six SOL, which have tended to find SOL being the preferred model for when you have a big job that you want the AI to go off and do and only come back to you when it's done, versus 5.6 SOL, which is a better collaborator.

顺便说一句，5.6的发布似乎极大推动了Codecs的普及，就在过去几周，它的活跃用户数从500万飙升到了700万。

As an aside, it also seems like the launch of five six has done a lot when it comes to codecs adoption, given that just in the last couple of weeks, it's jumped from 5,000,000 to 7,000,000 active users.

理查德提到的第二个趋势，对常听节目的朋友来说，大概也不会感到意外。

Trend number two, according to Richard, will also probably not surprise regular listeners.

他把这个趋势概括为：循环工程成了新的控制层。

He identified the trend as loop engineering is the new control layer.

事实上，理查德说，如果非得挑一个词作为本届活动最火的流行语，那一定是‘循环’。

Indeed, Richard said that if there was a single word that was the buzzword du jour of the event, it was loops.

OpenAI的创始人彼得·施泰因贝格展示了一张幻灯片，上面写着：未来不是二十个终端，而是更智能的循环。

OpenAI creator Peter Steinberger prominently shared a slide that said the future isn't 20 terminals, it's better loops.

但有趣的是，尤其是从理解AI工程师在谈论什么、以及这些内容对其他知识工作者有何借鉴的角度来看，理查德指出，关于循环的讨论其实正在分化为两个相互配合的循环：内循环和外循环。

But interestingly, especially with the lens of trying to understand what AI engineers are talking about that might be relevant for other types of knowledge workers, Richard writes that the loop discourse is actually separating into two loops that interact, an inner loop and an outer loop.

内循环是代理自主完成的大部分工作，而外循环则是由工程师负责监督这些工作，并通过提升技能等方式加以优化。

The inner loop is the largely autonomous work being done by agents, and the outer loop is the job of leading engineers to oversee that work and improve it with things like better skills.

Introspection的联合创始人兼首席执行官罗兰·加夫里莱斯库说：你可以把整个系统看作包含一个内循环和一个外循环。

Roland Gavrilescu, the co founder and CEO of Introspection, said: You can think of the system having an inner loop and the outer loop.

内循环是直接与用户互动并执行任务的核心系统。

The inner loop is the primary system interacting with users and performing the work.

罗兰接着提出了‘自动研究’的概念，这属于外循环的一部分，是一个专门用来研究和维护核心系统的辅助系统。

Roland then introduces the concept of auto research, which is concerned with the outer loop and is another system that studies and maintains the primary system.

外循环可以包含反馈信号、评估结果和人工输入，即使它仍以自动化为主，但它为核心代理循环提供了监督机制。

The outer loop can include feedback signals, evals, and human input, so even if it is still largely autonomous, it's a method of oversight for the primary agent loop.

前谷歌工程师阿迪·阿斯马尼表示，代理可以承担更多内循环的执行工作，但外循环依然是工程师的职责。

Former Google engineer Adi Asmani said agents can run much more of the inner execution loop, but that outer loop is still engineering.

确实，理查德提到，'循环工程'这个词被反复提及，他认为这说明人类AI工程师的责任就是构建这些循环系统。

Indeed, Richard writes that the term loop engineering came up multiple times, suggesting in his estimation that it is the human AI engineer's responsibility to build these loop systems.

再总结一下，彼得·斯坦伯格说，智能体负责执行内部循环。

Summing up again, Peter Steinberger said, the agent runs the inner execution loop.

我负责设定方向，并在外部循环中做决策。

I set the direction and make decisions in the outer loop.

再退一步看，循环这个说法真正想表达的，是找到那些可重复的互动方式，让我们能最大限度地利用智能体——它们现在正在承担越来越多我们的工作。

Now, again, taking a step back, what the loop discourse is actually about, more than anything else, is about figuring out repeatable interactions that allow us to get the most from agents who are now doing an increasingly large portion of our work.

某种程度上，内外循环的分离，正是AI工程师在重新夺回主导权的一种体现，他们正在确立自己相对于智能体的主导地位。

In some ways, the separation of inner loop and outer loop is part of this reclamation where the AI engineers are asserting their agency relative to their agents.

我觉得很重要的一点是，这里的'循环'要用小写的'l'，而不是大写的'L'。

And I think what's important because it can sound so buzzwordy is a lowercase l, not an uppercase.

我的意思是，'循环'这个词并不特指某一种固定模式，而是代表了一种正在浮现的互动模式：既让智能体能持续优化自己的工作，也让我们这些主导智能体的人能不断提升它们的工作能力。

By which I mean, loops is a word that doesn't necessarily mean exactly one thing, but stands in for the emerging set of interaction patterns that allow agents to improve their own work over time and that allow us as humans leading the agents to improve their ability to work over time.

第三个趋势来自AI工程界的讨论：AI工程正进入企业级应用。

Third trend from the AI engineering world's fair was AI engineering enters the enterprise.

从某种角度来说，这属于前沿工程团队的范畴。

Now this was in some ways the forward deployed engineering track.

如今几乎每家AI公司都意识到，要想让产品真正适配客户的需求，必须提供更深入的实施支持，因此会上出现FTE相关的讨论也就不足为奇了。

And given that pretty much every AI company has now determined that to get their products to actually work inside their intended customers, they have to support their customers with a much more hands on type implementation force, it's probably not all that surprising to see an FTE track show up at the event.

现在一个有意思的问题是，FTE这类合作最终应该呈现出什么样子。

Now one of the interesting questions that seems to come up is what the end result of an FTE type of engagement is supposed to actually look like.

Cursor公司的宝琳·布鲁内说，每次合作结束时，我们都会留下云上的智能代理、长期运行的自动化系统，以及基于Cursor SDK开发的应用程序。

Cursor's Pauline Brunet said, when we walk away at the end of the engagements, in our case, we have deployed cloud agents, long running agents, automations, and we've built applications on top of our Cursor SDK.

事实上，这次活动中被广泛提及的一个更通用的术语是‘软件工厂’。

And indeed, a more general term for this set of enterprise infrastructure that apparently was being used a lot at the event was software factory.

但这个词，比起‘循环’来说，定义反而更模糊。

And yet that, if anything, is perhaps even less defined than something like loops.

Warp公司的首席执行官扎克·劳埃德在会上发言，这家公司自视为一个软件工厂平台。

Zach Lloyd is the CEO of Warp and was the speaker at the event, and the company views itself as a software factory platform.

在接受Latent.Space采访时，CEO扎克·劳埃德谈到了他最初是在哪里听到‘软件工厂’这个说法的。

In an interview with latent.space, CEO Zach Lloyd explained where he first came across the term software factory.

扎克说，我们一开始只是做些一次性自动化，让代理在云端运行。

Zach said, we started with one off automation, run an agent in the cloud.

很多平台都是从这儿起步的。

A lot of platforms began there.

后来发展成让代理在云端按定时器运行。

Then it became run an agent in the cloud on a timer.

接下来的问题是，哪个循环最值得自动化？

The next question was, what was the most valuable loop to automate?

答案基本上就是软件工程的核心流程：问题排查、需求定义、开发实现、代码审查、测试验证、发布上线和监控。

The answer is basically the main loop of software engineering, triage, specification, implementation, review, verification, shipping, and monitoring.

关于企业与软件工厂之间的关系，扎克认为，企业的一大挑战在于弄清楚哪些工作环节需要自动化，哪些环节还得靠人来介入。

Now when it comes to the relationship between an enterprise and a software factory, Zach argued that one of the big pieces of work for enterprises is figuring out which parts of their work life cycles need to be automated and where humans should be brought into the loop.

有趣的是，扎克在X平台的一篇后续博文里指出，工厂模式变得越来越重要，不仅是因为现在的代理能更高效地完成编码生命周期中的大块任务，更因为‘广泛使用交互式代理带来了诸多问题，比如成本控制、合规性和安全性’。

And interestingly, in a follow-up blog post on x, Zach argued that the reason the factory framework has become more important is not only that agents are now more capable of doing big chunks of the coding life cycle, but that also, quote, that extensive interactive agent use has created a number of problems, from cost controls to governance to security.

扎克认为，根本问题在于，交互式代理背后都有人类操作员，而这些人使用方式千差万别，往往无法最大化业务价值，还可能埋下风险。

The root problem, Zach says, is that interactive agents have human operators, and those humans all use them in very different ways, which don't always maximize business value and can create risk.

他举了个例子，说有人不管任务轻重，总是用最贵的模型。

An example he gives is a human always using the most expensive model even when they don't need to for a given task.

他说，工厂模式的核心是建立一套系统，减少人为差异，同时通过管控措施保障安全与合规。

The idea behind a factory approach, he says, is to set up a system that minimizes human variability and maximizes output with controls that ensure security and compliance.

这虽然是个软件领域的话题，但我认为，软件工厂正在解决的这些问题，将来也会在其他知识型工作中反复出现，因为智能代理会逐步渗透进来。

Now this is very much a software conversation, but I think that a lot of the problems that these software factories are trying to solve for are going to find themselves repeated as agents find their way into other areas of knowledge work.

比如总用最贵模型，这绝不仅限于软件开发；还有他提到的另一个例子——人不小心给MCP分配了过多权限，引发安全风险。随着大家越来越熟悉如何为所用的代理配置上下文，无论是在软件开发、产品设计、市场还是销售领域，这类问题都会接踵而至。

That example of always using the most expensive model is certainly not limited to software development, or his other example, a human inadvertently creating a security issue by installing MCPs that have too much access, as everyone gets familiar with managing context to support the agents that they're using, whether it's in software engineering or product development or marketing or sales, this is going to be an issue for them as well.

这次活动的第四个趋势是，编码代理正在取代IDE，成为开发者的主要交互界面。

The fourth trend at the event was coding agents replacing IDEs as the developer interface.

我觉得这其实反映了一个更大的趋势：我们正在见证人机交互模式的根本转变。

And I think that this is one that's already indicative of a larger interaction pattern trend that we're seeing shift.

几周前有一期节目专门讲了新的Claude标签，每个Claude标签都不是绑定到具体用户，而是关联到该频道内特定的权限和上下文环境。

An episode from a couple of weeks ago was all about the new Claude tag, with each of those instances of Claude not being connected to the individual user who tagged it in, but instead connected to a specific set of permissions in context within the channel that they were engaging.

换句话说，你们市场团队所有人用的Claude，可能是一个共享实例，拥有特定的上下文、工具权限；而销售团队用的另一个Claude，则是完全不同的权限、工具和上下文配置。

In other words, there might be a single Claude tag instance with a specific set of context and a specific set of tool access and a specific set of permissions that everyone in your organization's marketing channel is interacting with that's different than the permissions and tools and context of the Claude that the folks over in the sales chat are interacting with.

关于Claude标签的发布，有一个特别引人注目的点：Claude代码的创始人之一Boris提到，他们新写的代码中，大约有65%都是在Claude标签聊天里启动的——这标志着我们与智能系统互动方式的巨大转变。

One of the things that was super notable about the Claude tag announcement was that Boris, one of the creators of Claude code, said that something like 65, I think, percent of their new code was now being initiated in Claude tag chats, a major, major shift in the interface and interaction pattern with which we manage this intelligence.

这类互动模式有趣的地方在于，大型实验室并没有等着非工程师去适应工程师的那一套做法。

Now one of the things that's interesting about these types of interaction patterns is that this is one area where the big labs are not waiting for non engineers to adapt to engineer type practices.

相反，他们正把原本只在工程师圈子里流行的功能，直接整合进面向所有用户的主流产品中。

They are dragging functionality over from the engineering first experiences into the main experiences that they're offering for all consumers.

最典型的例子就是ChatGPT Work，它把Codex直接嵌入到人人都能用的ChatGPT主应用里。

The best example of this is probably ChatGPT work, which effectively takes Codex and plops it into the main ChatGPT app for everyone to use.

理查德提到的第五个趋势是，所有智能代理平台都在围绕‘技能’构建系统。

The fifth trend according to Richard was every agent platform building around skills.

理查德引用了阿迪·奥斯马尼的定义：技能就是把资深工程师在开发软件时所用的工作流程、质量检查和最佳实践编码固化下来。

Richard referenced Adi Osmani's definition, writing skills encode the workflows, quality gates, and best practices that senior engineers use when building software.

这些技能被封装成标准化模块，让AI代理在开发的每个阶段都能一致地遵循。

They are packaged so AI agents follow them consistently across every phase of development.

会上，Vercel的安德鲁·Q说，技能就像即插即用的即时知识；Introspection的联合创始人罗兰·加夫里列斯库则认为，AI工程的重点已经从‘代理工具’转向了‘代理技能’。

At the event, Vercel's Andrew Q said that skills were useful as portable on demand knowledge, and introspection cofounder Roland Gavrilescu argued that AI engineering had shifted from agent tools to agent skills.

谷歌DeepMind的菲利普·施密特认为，技能减少了编排代码的需求，让开发者无需写代码就能使用智能体。

Google DeepMind's Philip Schmidt argued that skills reduced the need for orchestration code, allowing developers to use agents without code.

一位名叫保罗·巴库斯的与会者还分享了他做的一个叫Impeccable的项目，这是一个开源的编码智能体技能系统，专门用来提升智能体生成的界面质量。

One speaker, Paul Bacchus, even discussed his project around agent skills called Impeccable, an open source design skill system for improving the interfaces that coding agents create.

但更令人意外的是，保罗认为，技能工程本身将发展成一门独立的学科。

Now more interesting even than that project, Paul argued that skill engineering is going to be a discipline in its own right.

当我们思考现在AI工程师之间的这些对话，未来会不会变成普通知识工作者的日常时，我觉得‘技能’绝对排在最前面。

And as we wonder which of the conversations happening with AI engineers right now are going to be happening with AI knowledge workers in the future, skills, I think, are incredibly high on the list.

你想想看。

Think about this.

如果你在自己的领域非常出色，那你一定经常遇到这样的情况：哪怕是最顶尖的模型，比如Fable 5或者GPT-5.6 Sol，也达不到你想要的效果。

It is highly likely that if you are very good at your job, you frequently run across things where even the best models, including Fable five and GPT 5.6 Sol, don't do as well as you want them to.

但这并不是全部。

Now that's not everything.

有些事AI确实能做得很好，但尤其当你特别精通某件事时，你反而更容易一眼看出AI的局限在哪里。

Some things are going to be great at, but especially when you're really, really good at something, you tend to see the inherent limitations of AI even faster.

支持技能工程的人会说，与其干等模型随着Babel 6之类的更新变强，不如早点琢磨怎么用技能、怎么把知识和规则编码进去，甚至可以说，怎么在技能里体现品味，这可能是一条更务实的路子。

The folks who are advocating skill engineering would argue that rather than just waiting for the models to improve with Babel six or what have you, starting to figure out how to use skills, how to encode knowledge and rules, and dare I say it taste in skills, is potentially a more productive path.

在闭幕主题演讲中，Y Combinator总裁Gary Tan指出，要成为真正的AI原生企业，就必须把技能有效应用到销售、支持和财务等业务环节中，这是核心所在。

In his closing keynote, Y Combinator president Gary Tan argued that using skills effectively, including in business functions including sales, support, and finance, was fundamentally integral to being an actual AI native organization.

不过，技能本身也可能带来一种自主性的陷阱。

And yet skills themselves can be their own sort of autonomy trap.

AI工程师世界博览会的参会者Tyler Brown提到，他从这次活动中得到的一个教训是：要重新审视并重新实现自己的技能。

AI Engineer World's Fair attendee Tyler Brown wrote that one of his lessons from the event was to revisit and reimplement your skills.

他写道，每次有新模型发布，感觉就像孩子从初中升到高中一样。

Each time there's a new model release, he wrote, it's as if you have a kid that grows from middle school to high school.

你得调整他们的学习课程，才能让他们享受到新模型带来的好处。

You have to change the curriculum for them to get the benefits of the new model.

不过，真正让我觉得贯穿理查德指出的那些趋势，以及我在活动周围看到的大量讨论的，其实是泰勒更宏观的观察。

Yet it was not that, but Tyler's broader observation that I think runs throughout the trends that Richard identified and also a lot of the chatter that I've seen around the event.

泰勒写道，今年AI工程师世界博览会给人的感觉就是不一样。

Tyler wrote, something about this year's AI engineer World's Fair just hit different.

去年是让智能体放手去干的一年。

Last year was the year of let the agents rip.

今年则是意识到，没有框架的自主性，带来的混乱不比效率少。

This year was the year of realizing that autonomy without structure creates as much slop as leverage.

简而言之，虽然不是普遍现象，但总体来看，AI工程师群体中一个突出的共识是：重新把人放在这些智能系统的核心位置。

In short, while it's not universal, broadly speaking, one of the standout sentiments from the AI engineering folks is the reclamation or reemergence or however you wanna put it of the human at the center of these agentic systems.

今年三月，我发过一条推文，别觉得我疯了，我觉得那些给每位员工配备一支智能体团队的公司，会远远甩开那些用智能体团队取代员工的公司。

Back in March, I tweeted, call me crazy, but I think the companies that give everyone on their team a team of agents are going to kick the slats out of the companies that replace their teams with a team of agents.

我觉得，而且我希望，这种观念正逐渐渗透进主流思维。

And I think and I hope that we're starting to see that sentiment move structurally into the mainstream.

好了，这就是本月AI工程师博览会定义AI工程领域的五大趋势，以及它们对非AI工程师群体的意义。

So, guys, those are the five trends that defined AI engineering at the AI Engineer World's Fair this month and their implications for other people who aren't just AI engineers.

特别感谢理查德·麦克马努斯记录下这一切，当然也要感谢Swix团队操办了这场活动。

Big thanks to Richard McManus for capturing all of this and, of course, Swix and the team for putting on the event.

如果你想听我的建议，更贴近AI工程师和开发者们在讨论什么，最靠谱的渠道就是latent.space，latent.space。

If you wanna follow my advice and stay closer to the pulse of what AI engineers and developers are talking about, the single best source for that is going to be latent.space, latent.space.

我特别推荐他们工作日的AI新闻邮件简报。

And might I particularly recommend their AI news email weekday roundups?

内容可能很密集，但汇总了大量信息，绝对值得花时间看。

They can be really dense, but they aggregate a ton of discourse and are very much worth your time.

不过，今天这期AI每日简报就先到这里了。

For now, however, that is gonna do it for today's AI daily brief.

一如既往，感谢收听或观看，我们下期再见，保重。

Appreciate you listening or watching as always, and until next time, peace.