Podcasts about OAuth

SummaryIn this episode of the Blue Security Podcast, hosts Andy Jaw and Adam Brewer delve into the intricacies of Microsoft's Automatic Attack Disruption feature, particularly focusing on its integration with OAuth protection. They discuss the risks associated with OAuth applications, the importance of signal correlation in detecting and mitigating attacks, and the capabilities of Microsoft's Defender XDR platform. The conversation highlights the need for organizations to configure their security settings effectively and the future direction of security practices towards a 'secure by default' approach.----------------------------------------------------YouTube Video Link: ⁠⁠⁠https://youtu.be/zLj5b8JFH2s----------------------------------------------------Documentation:https://techcommunity.microsoft.com/blog/microsoftthreatprotectionblog/defending-against-oauth-based-attacks-with-automatic-attack-disruption/4384381https://learn.microsoft.com/en-us/entra/identity/enterprise-apps/configure-user-consent?pivots=portalhttps://learn.microsoft.com/en-us/graph/permissions-referencehttps://learn.microsoft.com/en-us/defender-xdr/configure-attack-disruption----------------------------------------------------Contact Us:Website: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://bluesecuritypod.comBluesky: https://bsky.app/profile/bluesecuritypod.comLinkedIn: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://www.linkedin.com/company/bluesecpodYouTube:⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://www.youtube.com/c/BlueSecurityPodcast-----------------------------------------------------------Andy JawBluesky: https://bsky.app/profile/ajawzero.comLinkedIn: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://www.linkedin.com/in/andyjaw/Email: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠andy@bluesecuritypod.com⁠----------------------------------------------------Adam BrewerTwitter: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://twitter.com/ajbrewerLinkedIn: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://www.linkedin.com/in/adamjbrewer/Email: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠adam@bluesecuritypod.com

Episode SummaryIn this episode of The Secure Developer, host Danny Allan sits down with Alex Salazar, founder and CEO of Arcade, to discuss the evolving landscape of authentication and authorization in an AI-driven world. Alex shares insights on the shift from traditional front-door security to back-end agent interactions, the challenges of securing AI-driven agents, and the role of identity in modern security frameworks. The conversation delves into the future of AI, agentic workflows, and how organizations can navigate authentication, authorization, and security in this new era.Show NotesDanny Allan welcomes Alex Salazar, an experienced security leader and CEO of Arcade, to explore the transformation of authentication and authorization in AI-powered environments. Drawing from his experience at Okta, Stormpath, and venture capital, Alex provides a unique perspective on securing interactions between AI agents and authenticated services.Key topics discussed include:The Evolution of Authentication & Authorization: Traditional models focused on front-door access (user logins, SSO), whereas AI-driven agents require secure back-end interactions.Agentic AI and Security Risks: How AI agents interact with services on behalf of users, and why identity becomes the new perimeter in security.OAuth and Identity Challenges: Adapting OAuth for AI agents, ensuring least-privilege access, and maintaining security compliance.AI Hallucinations & Risk Management: Strategies for mitigating LLM hallucinations, ensuring accuracy, and maintaining human oversight.The Future of AI & Agentic Workflows: Predictions on how AI will continue to evolve, the rise of specialized AI models, and the intersection of AI and physical automation.Alex and Danny also discuss the broader impact of AI on developer productivity, with insights into how companies can leverage AI responsibly to boost efficiency without compromising security.LinksArcade.dev - Make AI Actually Do ThingsOkta - IdentityOAuth - Authorization ProtocolLangChain - Applications that Can ReasonHugging Face - The AI Community Building the FutureSnyk - The Developer Security Company Follow UsOur WebsiteOur LinkedIn

Send us a textIdentity management sits at the core of effective cybersecurity, yet many organizations still struggle with implementing it correctly. In this comprehensive breakdown of CISSP Domain 5.2, we dive deep into the critical components of managing identification and authentication systems that protect your most valuable assets.Starting with a timely examination of the risks involved in the proposed rapid rewrite of the Social Security Administration's 60-million-line COBOL codebase, we explore why rushing critical identity systems can lead to catastrophic failures. This real-world example sets the stage for understanding why proper authentication management matters.The episode walks through the essential differences between centralized and decentralized identity approaches, explaining when each makes sense for your organization. We break down Single Sign-On implementation, multi-factor authentication best practices, and the often overlooked importance of treating Active Directory as the security tool it truly is—not just an open database for anyone to query.For security practitioners looking to level up their authentication strategy, we examine credential management systems like CyberArk, Just-in-Time access models, and federated identity frameworks including SAML, OAuth 2.0, and OpenID Connect. Each approach is explained with practical implementation considerations and security implications.Whether you're studying for the CISSP exam or working to strengthen your organization's security posture, this episode provides actionable insights on establishing robust authentication controls without sacrificing usability. Don't miss these essential strategies that form the foundation of your security architecture.Ready to master CISSP Domain 5.2 and all other CISSP domains? Visit CISSPCyberTraining.com for structured learning materials designed to help you pass the exam the first time.Gain exclusive access to 360 FREE CISSP Practice Questions delivered directly to your inbox! Sign up at FreeCISSPQuestions.com and receive 30 expertly crafted practice questions every 15 days for the next 6 months—completely free! Don't miss this valuable opportunity to strengthen your CISSP exam preparation and boost your chances of certification success. Join now and start your journey toward CISSP mastery today!

If you're in SF: Join us for the Claude Plays Pokemon hackathon this Sunday!If you're not: Fill out the 2025 State of AI Eng survey for $250 in Amazon cards!We are SO excited to share our conversation with Dharmesh Shah, co-founder of HubSpot and creator of Agent.ai.A particularly compelling concept we discussed is the idea of "hybrid teams" - the next evolution in workplace organization where human workers collaborate with AI agents as team members. Just as we previously saw hybrid teams emerge in terms of full-time vs. contract workers, or in-office vs. remote workers, Dharmesh predicts that the next frontier will be teams composed of both human and AI members. This raises interesting questions about team dynamics, trust, and how to effectively delegate tasks between human and AI team members.The discussion of business models in AI reveals an important distinction between Work as a Service (WaaS) and Results as a Service (RaaS), something Dharmesh has written extensively about. While RaaS has gained popularity, particularly in customer support applications where outcomes are easily measurable, Dharmesh argues that this model may be over-indexed. Not all AI applications have clearly definable outcomes or consistent economic value per transaction, making WaaS more appropriate in many cases. This insight is particularly relevant for businesses considering how to monetize AI capabilities.The technical challenges of implementing effective agent systems are also explored, particularly around memory and authentication. Shah emphasizes the importance of cross-agent memory sharing and the need for more granular control over data access. He envisions a future where users can selectively share parts of their data with different agents, similar to how OAuth works but with much finer control. This points to significant opportunities in developing infrastructure for secure and efficient agent-to-agent communication and data sharing.Other highlights from our conversation* The Evolution of AI-Powered Agents – Exploring how AI agents have evolved from simple chatbots to sophisticated multi-agent systems, and the role of MCPs in enabling that.* Hybrid Digital Teams and the Future of Work – How AI agents are becoming teammates rather than just tools, and what this means for business operations and knowledge work.* Memory in AI Agents – The importance of persistent memory in AI systems and how shared memory across agents could enhance collaboration and efficiency.* Business Models for AI Agents – Exploring the shift from software as a service (SaaS) to work as a service (WaaS) and results as a service (RaaS), and what this means for monetization.* The Role of Standards Like MCP – Why MCP has been widely adopted and how it enables agent collaboration, tool use, and discovery.* The Future of AI Code Generation and Software Engineering – How AI-assisted coding is changing the role of software engineers and what skills will matter most in the future.* Domain Investing and Efficient Markets – Dharmesh's approach to domain investing and how inefficiencies in digital asset markets create business opportunities.* The Philosophy of Saying No – Lessons from "Sorry, You Must Pass" and how prioritization leads to greater productivity and focus.Timestamps* 00:00 Introduction and Guest Welcome* 02:29 Dharmesh Shah's Journey into AI* 05:22 Defining AI Agents* 06:45 The Evolution and Future of AI Agents* 13:53 Graph Theory and Knowledge Representation* 20:02 Engineering Practices and Overengineering* 25:57 The Role of Junior Engineers in the AI Era* 28:20 Multi-Agent Systems and MCP Standards* 35:55 LinkedIn's Legal Battles and Data Scraping* 37:32 The Future of AI and Hybrid Teams* 39:19 Building Agent AI: A Professional Network for Agents* 40:43 Challenges and Innovations in Agent AI* 45:02 The Evolution of UI in AI Systems* 01:00:25 Business Models: Work as a Service vs. Results as a Service* 01:09:17 The Future Value of Engineers* 01:09:51 Exploring the Role of Agents* 01:10:28 The Importance of Memory in AI* 01:11:02 Challenges and Opportunities in AI Memory* 01:12:41 Selective Memory and Privacy Concerns* 01:13:27 The Evolution of AI Tools and Platforms* 01:18:23 Domain Names and AI Projects* 01:32:08 Balancing Work and Personal Life* 01:35:52 Final Thoughts and ReflectionsTranscriptAlessio [00:00:04]: Hey everyone, welcome back to the Latent Space podcast. This is Alessio, partner and CTO at Decibel Partners, and I'm joined by my co-host Swyx, founder of Small AI.swyx [00:00:12]: Hello, and today we're super excited to have Dharmesh Shah to join us. I guess your relevant title here is founder of Agent AI.Dharmesh [00:00:20]: Yeah, that's true for this. Yeah, creator of Agent.ai and co-founder of HubSpot.swyx [00:00:25]: Co-founder of HubSpot, which I followed for many years, I think 18 years now, gonna be 19 soon. And you caught, you know, people can catch up on your HubSpot story elsewhere. I should also thank Sean Puri, who I've chatted with back and forth, who's been, I guess, getting me in touch with your people. But also, I think like, just giving us a lot of context, because obviously, My First Million joined you guys, and they've been chatting with you guys a lot. So for the business side, we can talk about that, but I kind of wanted to engage your CTO, agent, engineer side of things. So how did you get agent religion?Dharmesh [00:01:00]: Let's see. So I've been working, I'll take like a half step back, a decade or so ago, even though actually more than that. So even before HubSpot, the company I was contemplating that I had named for was called Ingenisoft. And the idea behind Ingenisoft was a natural language interface to business software. Now realize this is 20 years ago, so that was a hard thing to do. But the actual use case that I had in mind was, you know, we had data sitting in business systems like a CRM or something like that. And my kind of what I thought clever at the time. Oh, what if we used email as the kind of interface to get to business software? And the motivation for using email is that it automatically works when you're offline. So imagine I'm getting on a plane or I'm on a plane. There was no internet on planes back then. It's like, oh, I'm going through business cards from an event I went to. I can just type things into an email just to have them all in the backlog. When it reconnects, it sends those emails to a processor that basically kind of parses effectively the commands and updates the software, sends you the file, whatever it is. And there was a handful of commands. I was a little bit ahead of the times in terms of what was actually possible. And I reattempted this natural language thing with a product called ChatSpot that I did back 20...swyx [00:02:12]: Yeah, this is your first post-ChatGPT project.Dharmesh [00:02:14]: I saw it come out. Yeah. And so I've always been kind of fascinated by this natural language interface to software. Because, you know, as software developers, myself included, we've always said, oh, we build intuitive, easy-to-use applications. And it's not intuitive at all, right? Because what we're doing is... We're taking the mental model that's in our head of what we're trying to accomplish with said piece of software and translating that into a series of touches and swipes and clicks and things like that. And there's nothing natural or intuitive about it. And so natural language interfaces, for the first time, you know, whatever the thought is you have in your head and expressed in whatever language that you normally use to talk to yourself in your head, you can just sort of emit that and have software do something. And I thought that was kind of a breakthrough, which it has been. And it's gone. So that's where I first started getting into the journey. I started because now it actually works, right? So once we got ChatGPT and you can take, even with a few-shot example, convert something into structured, even back in the ChatGP 3.5 days, it did a decent job in a few-shot example, convert something to structured text if you knew what kinds of intents you were going to have. And so that happened. And that ultimately became a HubSpot project. But then agents intrigued me because I'm like, okay, well, that's the next step here. So chat's great. Love Chat UX. But if we want to do something even more meaningful, it felt like the next kind of advancement is not this kind of, I'm chatting with some software in a kind of a synchronous back and forth model, is that software is going to do things for me in kind of a multi-step way to try and accomplish some goals. So, yeah, that's when I first got started. It's like, okay, what would that look like? Yeah. And I've been obsessed ever since, by the way.Alessio [00:03:55]: Which goes back to your first experience with it, which is like you're offline. Yeah. And you want to do a task. You don't need to do it right now. You just want to queue it up for somebody to do it for you. Yes. As you think about agents, like, let's start at the easy question, which is like, how do you define an agent? Maybe. You mean the hardest question in the universe? Is that what you mean?Dharmesh [00:04:12]: You said you have an irritating take. I do have an irritating take. I think, well, some number of people have been irritated, including within my own team. So I have a very broad definition for agents, which is it's AI-powered software that accomplishes a goal. Period. That's it. And what irritates people about it is like, well, that's so broad as to be completely non-useful. And I understand that. I understand the criticism. But in my mind, if you kind of fast forward months, I guess, in AI years, the implementation of it, and we're already starting to see this, and we'll talk about this, different kinds of agents, right? So I think in addition to having a usable definition, and I like yours, by the way, and we should talk more about that, that you just came out with, the classification of agents actually is also useful, which is, is it autonomous or non-autonomous? Does it have a deterministic workflow? Does it have a non-deterministic workflow? Is it working synchronously? Is it working asynchronously? Then you have the different kind of interaction modes. Is it a chat agent, kind of like a customer support agent would be? You're having this kind of back and forth. Is it a workflow agent that just does a discrete number of steps? So there's all these different flavors of agents. So if I were to draw it in a Venn diagram, I would draw a big circle that says, this is agents, and then I have a bunch of circles, some overlapping, because they're not mutually exclusive. And so I think that's what's interesting, and we're seeing development along a bunch of different paths, right? So if you look at the first implementation of agent frameworks, you look at Baby AGI and AutoGBT, I think it was, not Autogen, that's the Microsoft one. They were way ahead of their time because they assumed this level of reasoning and execution and planning capability that just did not exist, right? So it was an interesting thought experiment, which is what it was. Even the guy that, I'm an investor in Yohei's fund that did Baby AGI. It wasn't ready, but it was a sign of what was to come. And so the question then is, when is it ready? And so lots of people talk about the state of the art when it comes to agents. I'm a pragmatist, so I think of the state of the practical. It's like, okay, well, what can I actually build that has commercial value or solves actually some discrete problem with some baseline of repeatability or verifiability?swyx [00:06:22]: There was a lot, and very, very interesting. I'm not irritated by it at all. Okay. As you know, I take a... There's a lot of anthropological view or linguistics view. And in linguistics, you don't want to be prescriptive. You want to be descriptive. Yeah. So you're a goals guy. That's the key word in your thing. And other people have other definitions that might involve like delegated trust or non-deterministic work, LLM in the loop, all that stuff. The other thing I was thinking about, just the comment on Baby AGI, LGBT. Yeah. In that piece that you just read, I was able to go through our backlog and just kind of track the winter of agents and then the summer now. Yeah. And it's... We can tell the whole story as an oral history, just following that thread. And it's really just like, I think, I tried to explain the why now, right? Like I had, there's better models, of course. There's better tool use with like, they're just more reliable. Yep. Better tools with MCP and all that stuff. And I'm sure you have opinions on that too. Business model shift, which you like a lot. I just heard you talk about RAS with MFM guys. Yep. Cost is dropping a lot. Yep. Inference is getting faster. There's more model diversity. Yep. Yep. I think it's a subtle point. It means that like, you have different models with different perspectives. You don't get stuck in the basin of performance of a single model. Sure. You can just get out of it by just switching models. Yep. Multi-agent research and RL fine tuning. So I just wanted to let you respond to like any of that.Dharmesh [00:07:44]: Yeah. A couple of things. Connecting the dots on the kind of the definition side of it. So we'll get the irritation out of the way completely. I have one more, even more irritating leap on the agent definition thing. So here's the way I think about it. By the way, the kind of word agent, I looked it up, like the English dictionary definition. The old school agent, yeah. Is when you have someone or something that does something on your behalf, like a travel agent or a real estate agent acts on your behalf. It's like proxy, which is a nice kind of general definition. So the other direction I'm sort of headed, and it's going to tie back to tool calling and MCP and things like that, is if you, and I'm not a biologist by any stretch of the imagination, but we have these single-celled organisms, right? Like the simplest possible form of what one would call life. But it's still life. It just happens to be single-celled. And then you can combine cells and then cells become specialized over time. And you have much more sophisticated organisms, you know, kind of further down the spectrum. In my mind, at the most fundamental level, you can almost think of having atomic agents. What is the simplest possible thing that's an agent that can still be called an agent? What is the equivalent of a kind of single-celled organism? And the reason I think that's useful is right now we're headed down the road, which I think is very exciting around tool use, right? That says, okay, the LLMs now can be provided a set of tools that it calls to accomplish whatever it needs to accomplish in the kind of furtherance of whatever goal it's trying to get done. And I'm not overly bothered by it, but if you think about it, if you just squint a little bit and say, well, what if everything was an agent? And what if tools were actually just atomic agents? Because then it's turtles all the way down, right? Then it's like, oh, well, all that's really happening with tool use is that we have a network of agents that know about each other through something like an MMCP and can kind of decompose a particular problem and say, oh, I'm going to delegate this to this set of agents. And why do we need to draw this distinction between tools, which are functions most of the time? And an actual agent. And so I'm going to write this irritating LinkedIn post, you know, proposing this. It's like, okay. And I'm not suggesting we should call even functions, you know, call them agents. But there is a certain amount of elegance that happens when you say, oh, we can just reduce it down to one primitive, which is an agent that you can combine in complicated ways to kind of raise the level of abstraction and accomplish higher order goals. Anyway, that's my answer. I'd say that's a success. Thank you for coming to my TED Talk on agent definitions.Alessio [00:09:54]: How do you define the minimum viable agent? Do you already have a definition for, like, where you draw the line between a cell and an atom? Yeah.Dharmesh [00:10:02]: So in my mind, it has to, at some level, use AI in order for it to—otherwise, it's just software. It's like, you know, we don't need another word for that. And so that's probably where I draw the line. So then the question, you know, the counterargument would be, well, if that's true, then lots of tools themselves are actually not agents because they're just doing a database call or a REST API call or whatever it is they're doing. And that does not necessarily qualify them, which is a fair counterargument. And I accept that. It's like a good argument. I still like to think about—because we'll talk about multi-agent systems, because I think—so we've accepted, which I think is true, lots of people have said it, and you've hopefully combined some of those clips of really smart people saying this is the year of agents, and I completely agree, it is the year of agents. But then shortly after that, it's going to be the year of multi-agent systems or multi-agent networks. I think that's where it's going to be headed next year. Yeah.swyx [00:10:54]: Opening eyes already on that. Yeah. My quick philosophical engagement with you on this. I often think about kind of the other spectrum, the other end of the cell spectrum. So single cell is life, multi-cell is life, and you clump a bunch of cells together in a more complex organism, they become organs, like an eye and a liver or whatever. And then obviously we consider ourselves one life form. There's not like a lot of lives within me. I'm just one life. And now, obviously, I don't think people don't really like to anthropomorphize agents and AI. Yeah. But we are extending our consciousness and our brain and our functionality out into machines. I just saw you were a Bee. Yeah. Which is, you know, it's nice. I have a limitless pendant in my pocket.Dharmesh [00:11:37]: I got one of these boys. Yeah.swyx [00:11:39]: I'm testing it all out. You know, got to be early adopters. But like, we want to extend our personal memory into these things so that we can be good at the things that we're good at. And, you know, machines are good at it. Machines are there. So like, my definition of life is kind of like going outside of my own body now. I don't know if you've ever had like reflections on that. Like how yours. How our self is like actually being distributed outside of you. Yeah.Dharmesh [00:12:01]: I don't fancy myself a philosopher. But you went there. So yeah, I did go there. I'm fascinated by kind of graphs and graph theory and networks and have been for a long, long time. And to me, we're sort of all nodes in this kind of larger thing. It just so happens that we're looking at individual kind of life forms as they exist right now. But so the idea is when you put a podcast out there, there's these little kind of nodes you're putting out there of like, you know, conceptual ideas. Once again, you have varying kind of forms of those little nodes that are up there and are connected in varying and sundry ways. And so I just think of myself as being a node in a massive, massive network. And I'm producing more nodes as I put content or ideas. And, you know, you spend some portion of your life collecting dots, experiences, people, and some portion of your life then connecting dots from the ones that you've collected over time. And I found that really interesting things happen and you really can't know in advance how those dots are necessarily going to connect in the future. And that's, yeah. So that's my philosophical take. That's the, yes, exactly. Coming back.Alessio [00:13:04]: Yep. Do you like graph as an agent? Abstraction? That's been one of the hot topics with LandGraph and Pydantic and all that.Dharmesh [00:13:11]: I do. The thing I'm more interested in terms of use of graphs, and there's lots of work happening on that now, is graph data stores as an alternative in terms of knowledge stores and knowledge graphs. Yeah. Because, you know, so I've been in software now 30 plus years, right? So it's not 10,000 hours. It's like 100,000 hours that I've spent doing this stuff. And so I've grew up with, so back in the day, you know, I started on mainframes. There was a product called IMS from IBM, which is basically an index database, what we'd call like a key value store today. Then we've had relational databases, right? We have tables and columns and foreign key relationships. We all know that. We have document databases like MongoDB, which is sort of a nested structure keyed by a specific index. We have vector stores, vector embedding database. And graphs are interesting for a couple of reasons. One is, so it's not classically structured in a relational way. When you say structured database, to most people, they're thinking tables and columns and in relational database and set theory and all that. Graphs still have structure, but it's not the tables and columns structure. And you could wonder, and people have made this case, that they are a better representation of knowledge for LLMs and for AI generally than other things. So that's kind of thing number one conceptually, and that might be true, I think is possibly true. And the other thing that I really like about that in the context of, you know, I've been in the context of data stores for RAG is, you know, RAG, you say, oh, I have a million documents, I'm going to build the vector embeddings, I'm going to come back with the top X based on the semantic match, and that's fine. All that's very, very useful. But the reality is something gets lost in the chunking process and the, okay, well, those tend, you know, like, you don't really get the whole picture, so to speak, and maybe not even the right set of dimensions on the kind of broader picture. And it makes intuitive sense to me that if we did capture it properly in a graph form, that maybe that feeding into a RAG pipeline will actually yield better results for some use cases, I don't know, but yeah.Alessio [00:15:03]: And do you feel like at the core of it, there's this difference between imperative and declarative programs? Because if you think about HubSpot, it's like, you know, people and graph kind of goes hand in hand, you know, but I think maybe the software before was more like primary foreign key based relationship, versus now the models can traverse through the graph more easily.Dharmesh [00:15:22]: Yes. So I like that representation. There's something. It's just conceptually elegant about graphs and just from the representation of it, they're much more discoverable, you can kind of see it, there's observability to it, versus kind of embeddings, which you can't really do much with as a human. You know, once they're in there, you can't pull stuff back out. But yeah, I like that kind of idea of it. And the other thing that's kind of, because I love graphs, I've been long obsessed with PageRank from back in the early days. And, you know, one of the kind of simplest algorithms in terms of coming up, you know, with a phone, everyone's been exposed to PageRank. And the idea is that, and so I had this other idea for a project, not a company, and I have hundreds of these, called NodeRank, is to be able to take the idea of PageRank and apply it to an arbitrary graph that says, okay, I'm going to define what authority looks like and say, okay, well, that's interesting to me, because then if you say, I'm going to take my knowledge store, and maybe this person that contributed some number of chunks to the graph data store has more authority on this particular use case or prompt that's being submitted than this other one that may, or maybe this one was more. popular, or maybe this one has, whatever it is, there should be a way for us to kind of rank nodes in a graph and sort them in some, some useful way. Yeah.swyx [00:16:34]: So I think that's generally useful for, for anything. I think the, the problem, like, so even though at my conferences, GraphRag is super popular and people are getting knowledge, graph religion, and I will say like, it's getting space, getting traction in two areas, conversation memory, and then also just rag in general, like the, the, the document data. Yeah. It's like a source. Most ML practitioners would say that knowledge graph is kind of like a dirty word. The graph database, people get graph religion, everything's a graph, and then they, they go really hard into it and then they get a, they get a graph that is too complex to navigate. Yes. And so like the, the, the simple way to put it is like you at running HubSpot, you know, the power of graphs, the way that Google has pitched them for many years, but I don't suspect that HubSpot itself uses a knowledge graph. No. Yeah.Dharmesh [00:17:26]: So when is it over engineering? Basically? It's a great question. I don't know. So the question now, like in AI land, right, is the, do we necessarily need to understand? So right now, LLMs for, for the most part are somewhat black boxes, right? We sort of understand how the, you know, the algorithm itself works, but we really don't know what's going on in there and, and how things come out. So if a graph data store is able to produce the outcomes we want, it's like, here's a set of queries I want to be able to submit and then it comes out with useful content. Maybe the underlying data store is as opaque as a vector embeddings or something like that, but maybe it's fine. Maybe we don't necessarily need to understand it to get utility out of it. And so maybe if it's messy, that's okay. Um, that's, it's just another form of lossy compression. Uh, it's just lossy in a way that we just don't completely understand in terms of, because it's going to grow organically. Uh, and it's not structured. It's like, ah, we're just gonna throw a bunch of stuff in there. Let the, the equivalent of the embedding algorithm, whatever they called in graph land. Um, so the one with the best results wins. I think so. Yeah.swyx [00:18:26]: Or is this the practical side of me is like, yeah, it's, if it's useful, we don't necessarilyDharmesh [00:18:30]: need to understand it.swyx [00:18:30]: I have, I mean, I'm happy to push back as long as you want. Uh, it's not practical to evaluate like the 10 different options out there because it takes time. It takes people, it takes, you know, resources, right? Set. That's the first thing. Second thing is your evals are typically on small things and some things only work at scale. Yup. Like graphs. Yup.Dharmesh [00:18:46]: Yup. That's, yeah, no, that's fair. And I think this is one of the challenges in terms of implementation of graph databases is that the most common approach that I've seen developers do, I've done it myself, is that, oh, I've got a Postgres database or a MySQL or whatever. I can represent a graph with a very set of tables with a parent child thing or whatever. And that sort of gives me the ability, uh, why would I need anything more than that? And the answer is, well, if you don't need anything more than that, you don't need anything more than that. But there's a high chance that you're sort of missing out on the actual value that, uh, the graph representation gives you. Which is the ability to traverse the graph, uh, efficiently in ways that kind of going through the, uh, traversal in a relational database form, even though structurally you have the data, practically you're not gonna be able to pull it out in, in useful ways. Uh, so you wouldn't like represent a social graph, uh, in, in using that kind of relational table model. It just wouldn't scale. It wouldn't work.swyx [00:19:36]: Uh, yeah. Uh, I think we want to move on to MCP. Yeah. But I just want to, like, just engineering advice. Yeah. Uh, obviously you've, you've, you've run, uh, you've, you've had to do a lot of projects and run a lot of teams. Do you have a general rule for over-engineering or, you know, engineering ahead of time? You know, like, because people, we know premature engineering is the root of all evil. Yep. But also sometimes you just have to. Yep. When do you do it? Yes.Dharmesh [00:19:59]: It's a great question. This is, uh, a question as old as time almost, which is what's the right and wrong levels of abstraction. That's effectively what, uh, we're answering when we're trying to do engineering. I tend to be a pragmatist, right? So here's the thing. Um, lots of times doing something the right way. Yeah. It's like a marginal increased cost in those cases. Just do it the right way. And this is what makes a, uh, a great engineer or a good engineer better than, uh, a not so great one. It's like, okay, all things being equal. If it's going to take you, you know, roughly close to constant time anyway, might as well do it the right way. Like, so do things well, then the question is, okay, well, am I building a framework as the reusable library? To what degree, uh, what am I anticipating in terms of what's going to need to change in this thing? Uh, you know, along what dimension? And then I think like a business person in some ways, like what's the return on calories, right? So, uh, and you look at, um, energy, the expected value of it's like, okay, here are the five possible things that could happen, uh, try to assign probabilities like, okay, well, if there's a 50% chance that we're going to go down this particular path at some day, like, or one of these five things is going to happen and it costs you 10% more to engineer for that. It's basically, it's something that yields a kind of interest compounding value. Um, as you get closer to the time of, of needing that versus having to take on debt, which is when you under engineer it, you're taking on debt. You're going to have to pay off when you do get to that eventuality where something happens. One thing as a pragmatist, uh, so I would rather under engineer something than over engineer it. If I were going to err on the side of something, and here's the reason is that when you under engineer it, uh, yes, you take on tech debt, uh, but the interest rate is relatively known and payoff is very, very possible, right? Which is, oh, I took a shortcut here as a result of which now this thing that should have taken me a week is now going to take me four weeks. Fine. But if that particular thing that you thought might happen, never actually, you never have that use case transpire or just doesn't, it's like, well, you just save yourself time, right? And that has value because you were able to do other things instead of, uh, kind of slightly over-engineering it away, over-engineering it. But there's no perfect answers in art form in terms of, uh, and yeah, we'll, we'll bring kind of this layers of abstraction back on the code generation conversation, which we'll, uh, I think I have later on, butAlessio [00:22:05]: I was going to ask, we can just jump ahead quickly. Yeah. Like, as you think about vibe coding and all that, how does the. Yeah. Percentage of potential usefulness change when I feel like we over-engineering a lot of times it's like the investment in syntax, it's less about the investment in like arc exacting. Yep. Yeah. How does that change your calculus?Dharmesh [00:22:22]: A couple of things, right? One is, um, so, you know, going back to that kind of ROI or a return on calories, kind of calculus or heuristic you think through, it's like, okay, well, what is it going to cost me to put this layer of abstraction above the code that I'm writing now, uh, in anticipating kind of future needs. If the cost of fixing, uh, or doing under engineering right now. Uh, we'll trend towards zero that says, okay, well, I don't have to get it right right now because even if I get it wrong, I'll run the thing for six hours instead of 60 minutes or whatever. It doesn't really matter, right? Like, because that's going to trend towards zero to be able, the ability to refactor a code. Um, and because we're going to not that long from now, we're going to have, you know, large code bases be able to exist, uh, you know, as, as context, uh, for a code generation or a code refactoring, uh, model. So I think it's going to make it, uh, make the case for under engineering, uh, even stronger. Which is why I take on that cost. You just pay the interest when you get there, it's not, um, just go on with your life vibe coded and, uh, come back when you need to. Yeah.Alessio [00:23:18]: Sometimes I feel like there's no decision-making in some things like, uh, today I built a autosave for like our internal notes platform and I literally just ask them cursor. Can you add autosave? Yeah. I don't know if it's over under engineer. Yep. I just vibe coded it. Yep. And I feel like at some point we're going to get to the point where the models kindDharmesh [00:23:36]: of decide where the right line is, but this is where the, like the, in my mind, the danger is, right? So there's two sides to this. One is the cost of kind of development and coding and things like that stuff that, you know, we talk about. But then like in your example, you know, one of the risks that we have is that because adding a feature, uh, like a save or whatever the feature might be to a product as that price tends towards zero, are we going to be less discriminant about what features we add as a result of making more product products more complicated, which has a negative impact on the user and navigate negative impact on the business. Um, and so that's the thing I worry about if it starts to become too easy, are we going to be. Too promiscuous in our, uh, kind of extension, adding product extensions and things like that. It's like, ah, why not add X, Y, Z or whatever back then it was like, oh, we only have so many engineering hours or story points or however you measure things. Uh, that least kept us in check a little bit. Yeah.Alessio [00:24:22]: And then over engineering, you're like, yeah, it's kind of like you're putting that on yourself. Yeah. Like now it's like the models don't understand that if they add too much complexity, it's going to come back to bite them later. Yep. So they just do whatever they want to do. Yeah. And I'm curious where in the workflow that's going to be, where it's like, Hey, this is like the amount of complexity and over-engineering you can do before you got to ask me if we should actually do it versus like do something else.Dharmesh [00:24:45]: So you know, we've already, let's like, we're leaving this, uh, in the code generation world, this kind of compressed, um, cycle time. Right. It's like, okay, we went from auto-complete, uh, in the GitHub co-pilot to like, oh, finish this particular thing and hit tab to a, oh, I sort of know your file or whatever. I can write out a full function to you to now I can like hold a bunch of the context in my head. Uh, so we can do app generation, which we have now with lovable and bolt and repletage. Yeah. Association and other things. So then the question is, okay, well, where does it naturally go from here? So we're going to generate products. Make sense. We might be able to generate platforms as though I want a platform for ERP that does this, whatever. And that includes the API's includes the product and the UI, and all the things that make for a platform. There's no nothing that says we would stop like, okay, can you generate an entire software company someday? Right. Uh, with the platform and the monetization and the go-to-market and the whatever. And you know, that that's interesting to me in terms of, uh, you know, what, when you take it to almost ludicrous levels. of abstract.swyx [00:25:39]: It's like, okay, turn it to 11. You mentioned vibe coding, so I have to, this is a blog post I haven't written, but I'm kind of exploring it. Is the junior engineer dead?Dharmesh [00:25:49]: I don't think so. I think what will happen is that the junior engineer will be able to, if all they're bringing to the table is the fact that they are a junior engineer, then yes, they're likely dead. But hopefully if they can communicate with carbon-based life forms, they can interact with product, if they're willing to talk to customers, they can take their kind of basic understanding of engineering and how kind of software works. I think that has value. So I have a 14-year-old right now who's taking Python programming class, and some people ask me, it's like, why is he learning coding? And my answer is, is because it's not about the syntax, it's not about the coding. What he's learning is like the fundamental thing of like how things work. And there's value in that. I think there's going to be timeless value in systems thinking and abstractions and what that means. And whether functions manifested as math, which he's going to get exposed to regardless, or there are some core primitives to the universe, I think, that the more you understand them, those are what I would kind of think of as like really large dots in your life that will have a higher gravitational pull and value to them that you'll then be able to. So I want him to collect those dots, and he's not resisting. So it's like, okay, while he's still listening to me, I'm going to have him do things that I think will be useful.swyx [00:26:59]: You know, part of one of the pitches that I evaluated for AI engineer is a term. And the term is that maybe the traditional interview path or career path of software engineer goes away, which is because what's the point of lead code? Yeah. And, you know, it actually matters more that you know how to work with AI and to implement the things that you want. Yep.Dharmesh [00:27:16]: That's one of the like interesting things that's happened with generative AI. You know, you go from machine learning and the models and just that underlying form, which is like true engineering, right? Like the actual, what I call real engineering. I don't think of myself as a real engineer, actually. I'm a developer. But now with generative AI. We call it AI and it's obviously got its roots in machine learning, but it just feels like fundamentally different to me. Like you have the vibe. It's like, okay, well, this is just a whole different approach to software development to so many different things. And so I'm wondering now, it's like an AI engineer is like, if you were like to draw the Venn diagram, it's interesting because the cross between like AI things, generative AI and what the tools are capable of, what the models do, and this whole new kind of body of knowledge that we're still building out, it's still very young, intersected with kind of classic engineering, software engineering. Yeah.swyx [00:28:04]: I just described the overlap as it separates out eventually until it's its own thing, but it's starting out as a software. Yeah.Alessio [00:28:11]: That makes sense. So to close the vibe coding loop, the other big hype now is MCPs. Obviously, I would say Cloud Desktop and Cursor are like the two main drivers of MCP usage. I would say my favorite is the Sentry MCP. I can pull in errors and then you can just put the context in Cursor. How do you think about that abstraction layer? Does it feel... Does it feel almost too magical in a way? Do you think it's like you get enough? Because you don't really see how the server itself is then kind of like repackaging theDharmesh [00:28:41]: information for you? I think MCP as a standard is one of the better things that's happened in the world of AI because a standard needed to exist and absent a standard, there was a set of things that just weren't possible. Now, we can argue whether it's the best possible manifestation of a standard or not. Does it do too much? Does it do too little? I get that, but it's just simple enough to both be useful and unobtrusive. It's understandable and adoptable by mere mortals, right? It's not overly complicated. You know, a reasonable engineer can put a stand up an MCP server relatively easily. The thing that has me excited about it is like, so I'm a big believer in multi-agent systems. And so that's going back to our kind of this idea of an atomic agent. So imagine the MCP server, like obviously it calls tools, but the way I think about it, so I'm working on my current passion project is agent.ai. And we'll talk more about that in a little bit. More about the, I think we should, because I think it's interesting not to promote the project at all, but there's some interesting ideas in there. One of which is around, we're going to need a mechanism for, if agents are going to collaborate and be able to delegate, there's going to need to be some form of discovery and we're going to need some standard way. It's like, okay, well, I just need to know what this thing over here is capable of. We're going to need a registry, which Anthropic's working on. I'm sure others will and have been doing directories of, and there's going to be a standard around that too. How do you build out a directory of MCP servers? I think that's going to unlock so many things just because, and we're already starting to see it. So I think MCP or something like it is going to be the next major unlock because it allows systems that don't know about each other, don't need to, it's that kind of decoupling of like Sentry and whatever tools someone else was building. And it's not just about, you know, Cloud Desktop or things like, even on the client side, I think we're going to see very interesting consumers of MCP, MCP clients versus just the chat body kind of things. Like, you know, Cloud Desktop and Cursor and things like that. But yeah, I'm very excited about MCP in that general direction.swyx [00:30:39]: I think the typical cynical developer take, it's like, we have OpenAPI. Yeah. What's the new thing? I don't know if you have a, do you have a quick MCP versus everything else? Yeah.Dharmesh [00:30:49]: So it's, so I like OpenAPI, right? So just a descriptive thing. It's OpenAPI. OpenAPI. Yes, that's what I meant. So it's basically a self-documenting thing. We can do machine-generated, lots of things from that output. It's a structured definition of an API. I get that, love it. But MCPs sort of are kind of use case specific. They're perfect for exactly what we're trying to use them for around LLMs in terms of discovery. It's like, okay, I don't necessarily need to know kind of all this detail. And so right now we have, we'll talk more about like MCP server implementations, but We will? I think, I don't know. Maybe we won't. At least it's in my head. It's like a back processor. But I do think MCP adds value above OpenAPI. It's, yeah, just because it solves this particular thing. And if we had come to the world, which we have, like, it's like, hey, we already have OpenAPI. It's like, if that were good enough for the universe, the universe would have adopted it already. There's a reason why MCP is taking office because marginally adds something that was missing before and doesn't go too far. And so that's why the kind of rate of adoption, you folks have written about this and talked about it. Yeah, why MCP won. Yeah. And it won because the universe decided that this was useful and maybe it gets supplanted by something else. Yeah. And maybe we discover, oh, maybe OpenAPI was good enough the whole time. I doubt that.swyx [00:32:09]: The meta lesson, this is, I mean, he's an investor in DevTools companies. I work in developer experience at DevRel in DevTools companies. Yep. Everyone wants to own the standard. Yeah. I'm sure you guys have tried to launch your own standards. Actually, it's Houseplant known for a standard, you know, obviously inbound marketing. But is there a standard or protocol that you ever tried to push? No.Dharmesh [00:32:30]: And there's a reason for this. Yeah. Is that? And I don't mean, need to mean, speak for the people of HubSpot, but I personally. You kind of do. I'm not smart enough. That's not the, like, I think I have a. You're smart. Not enough for that. I'm much better off understanding the standards that are out there. And I'm more on the composability side. Let's, like, take the pieces of technology that exist out there, combine them in creative, unique ways. And I like to consume standards. I don't like to, and that's not that I don't like to create them. I just don't think I have the, both the raw wattage or the credibility. It's like, okay, well, who the heck is Dharmesh, and why should we adopt a standard he created?swyx [00:33:07]: Yeah, I mean, there are people who don't monetize standards, like OpenTelemetry is a big standard, and LightStep never capitalized on that.Dharmesh [00:33:15]: So, okay, so if I were to do a standard, there's two things that have been in my head in the past. I was one around, a very, very basic one around, I don't even have the domain, I have a domain for everything, for open marketing. Because the issue we had in HubSpot grew up in the marketing space. There we go. There was no standard around data formats and things like that. It doesn't go anywhere. But the other one, and I did not mean to go here, but I'm going to go here. It's called OpenGraph. I know the term was already taken, but it hasn't been used for like 15 years now for its original purpose. But what I think should exist in the world is right now, our information, all of us, nodes are in the social graph at Meta or the professional graph at LinkedIn. Both of which are actually relatively closed in actually very annoying ways. Like very, very closed, right? Especially LinkedIn. Especially LinkedIn. I personally believe that if it's my data, and if I would get utility out of it being open, I should be able to make my data open or publish it in whatever forms that I choose, as long as I have control over it as opt-in. So the idea is around OpenGraph that says, here's a standard, here's a way to publish it. I should be able to go to OpenGraph.org slash Dharmesh dot JSON and get it back. And it's like, here's your stuff, right? And I can choose along the way and people can write to it and I can prove. And there can be an entire system. And if I were to do that, I would do it as a... Like a public benefit, non-profit-y kind of thing, as this is a contribution to society. I wouldn't try to commercialize that. Have you looked at AdProto? What's that? AdProto.swyx [00:34:43]: It's the protocol behind Blue Sky. Okay. My good friend, Dan Abramov, who was the face of React for many, many years, now works there. And he actually did a talk that I can send you, which basically kind of tries to articulate what you just said. But he does, he loves doing these like really great analogies, which I think you'll like. Like, you know, a lot of our data is behind a handle, behind a domain. Yep. So he's like, all right, what if we flip that? What if it was like our handle and then the domain? Yep. So, and that's really like your data should belong to you. Yep. And I should not have to wait 30 days for my Twitter data to export. Yep.Dharmesh [00:35:19]: you should be able to at least be able to automate it or do like, yes, I should be able to plug it into an agentic thing. Yeah. Yes. I think we're... Because so much of our data is... Locked up. I think the trick here isn't that standard. It is getting the normies to care.swyx [00:35:37]: Yeah. Because normies don't care.Dharmesh [00:35:38]: That's true. But building on that, normies don't care. So, you know, privacy is a really hot topic and an easy word to use, but it's not a binary thing. Like there are use cases where, and we make these choices all the time, that I will trade, not all privacy, but I will trade some privacy for some productivity gain or some benefit to me that says, oh, I don't care about that particular data being online if it gives me this in return, or I don't mind sharing this information with this company.Alessio [00:36:02]: If I'm getting, you know, this in return, but that sort of should be my option. I think now with computer use, you can actually automate some of the exports. Yes. Like something we've been doing internally is like everybody exports their LinkedIn connections. Yep. And then internally, we kind of merge them together to see how we can connect our companies to customers or things like that.Dharmesh [00:36:21]: And not to pick on LinkedIn, but since we're talking about it, but they feel strongly enough on the, you know, do not take LinkedIn data that they will block even browser use kind of things or whatever. They go to great, great lengths, even to see patterns of usage. And it says, oh, there's no way you could have, you know, gotten that particular thing or whatever without, and it's, so it's, there's...swyx [00:36:42]: Wasn't there a Supreme Court case that they lost? Yeah.Dharmesh [00:36:45]: So the one they lost was around someone that was scraping public data that was on the public internet. And that particular company had not signed any terms of service or whatever. It's like, oh, I'm just taking data that's on, there was no, and so that's why they won. But now, you know, the question is around, can LinkedIn... I think they can. Like, when you use, as a user, you use LinkedIn, you are signing up for their terms of service. And if they say, well, this kind of use of your LinkedIn account that violates our terms of service, they can shut your account down, right? They can. And they, yeah, so, you know, we don't need to make this a discussion. By the way, I love the company, don't get me wrong. I'm an avid user of the product. You know, I've got... Yeah, I mean, you've got over a million followers on LinkedIn, I think. Yeah, I do. And I've known people there for a long, long time, right? And I have lots of respect. And I understand even where the mindset originally came from of this kind of members-first approach to, you know, a privacy-first. I sort of get that. But sometimes you sort of have to wonder, it's like, okay, well, that was 15, 20 years ago. There's likely some controlled ways to expose some data on some member's behalf and not just completely be a binary. It's like, no, thou shalt not have the data.swyx [00:37:54]: Well, just pay for sales navigator.Alessio [00:37:57]: Before we move to the next layer of instruction, anything else on MCP you mentioned? Let's move back and then I'll tie it back to MCPs.Dharmesh [00:38:05]: So I think the... Open this with agent. Okay, so I'll start with... Here's my kind of running thesis, is that as AI and agents evolve, which they're doing very, very quickly, we're going to look at them more and more. I don't like to anthropomorphize. We'll talk about why this is not that. Less as just like raw tools and more like teammates. They'll still be software. They should self-disclose as being software. I'm totally cool with that. But I think what's going to happen is that in the same way you might collaborate with a team member on Slack or Teams or whatever you use, you can imagine a series of agents that do specific things just like a team member might do, that you can delegate things to. You can collaborate. You can say, hey, can you take a look at this? Can you proofread that? Can you try this? You can... Whatever it happens to be. So I think it is... I will go so far as to say it's inevitable that we're going to have hybrid teams someday. And what I mean by hybrid teams... So back in the day, hybrid teams were, oh, well, you have some full-time employees and some contractors. Then it was like hybrid teams are some people that are in the office and some that are remote. That's the kind of form of hybrid. The next form of hybrid is like the carbon-based life forms and agents and AI and some form of software. So let's say we temporarily stipulate that I'm right about that over some time horizon that eventually we're going to have these kind of digitally hybrid teams. So if that's true, then the question you sort of ask yourself is that then what needs to exist in order for us to get the full value of that new model? It's like, okay, well... You sort of need to... It's like, okay, well, how do I... If I'm building a digital team, like, how do I... Just in the same way, if I'm interviewing for an engineer or a designer or a PM, whatever, it's like, well, that's why we have professional networks, right? It's like, oh, they have a presence on likely LinkedIn. I can go through that semi-structured, structured form, and I can see the experience of whatever, you know, self-disclosed. But, okay, well, agents are going to need that someday. And so I'm like, okay, well, this seems like a thread that's worth pulling on. That says, okay. So I... So agent.ai is out there. And it's LinkedIn for agents. It's LinkedIn for agents. It's a professional network for agents. And the more I pull on that thread, it's like, okay, well, if that's true, like, what happens, right? It's like, oh, well, they have a profile just like anyone else, just like a human would. It's going to be a graph underneath, just like a professional network would be. It's just that... And you can have its, you know, connections and follows, and agents should be able to post. That's maybe how they do release notes. Like, oh, I have this new version. Whatever they decide to post, it should just be able to... Behave as a node on the network of a professional network. As it turns out, the more I think about that and pull on that thread, the more and more things, like, start to make sense to me. So it may be more than just a pure professional network. So my original thought was, okay, well, it's a professional network and agents as they exist out there, which I think there's going to be more and more of, will kind of exist on this network and have the profile. But then, and this is always dangerous, I'm like, okay, I want to see a world where thousands of agents are out there in order for the... Because those digital employees, the digital workers don't exist yet in any meaningful way. And so then I'm like, oh, can I make that easier for, like... And so I have, as one does, it's like, oh, I'll build a low-code platform for building agents. How hard could that be, right? Like, very hard, as it turns out. But it's been fun. So now, agent.ai has 1.3 million users. 3,000 people have actually, you know, built some variation of an agent, sometimes just for their own personal productivity. About 1,000 of which have been published. And the reason this comes back to MCP for me, so imagine that and other networks, since I know agent.ai. So right now, we have an MCP server for agent.ai that exposes all the internally built agents that we have that do, like, super useful things. Like, you know, I have access to a Twitter API that I can subsidize the cost. And I can say, you know, if you're looking to build something for social media, these kinds of things, with a single API key, and it's all completely free right now, I'm funding it. That's a useful way for it to work. And then we have a developer to say, oh, I have this idea. I don't have to worry about open AI. I don't have to worry about, now, you know, this particular model is better. It has access to all the models with one key. And we proxy it kind of behind the scenes. And then expose it. So then we get this kind of community effect, right? That says, oh, well, someone else may have built an agent to do X. Like, I have an agent right now that I built for myself to do domain valuation for website domains because I'm obsessed with domains, right? And, like, there's no efficient market for domains. There's no Zillow for domains right now that tells you, oh, here are what houses in your neighborhood sold for. It's like, well, why doesn't that exist? We should be able to solve that problem. And, yes, you're still guessing. Fine. There should be some simple heuristic. So I built that. It's like, okay, well, let me go look for past transactions. You say, okay, I'm going to type in agent.ai, agent.com, whatever domain. What's it actually worth? I'm looking at buying it. It can go and say, oh, which is what it does. It's like, I'm going to go look at are there any published domain transactions recently that are similar, either use the same word, same top-level domain, whatever it is. And it comes back with an approximate value, and it comes back with its kind of rationale for why it picked the value and comparable transactions. Oh, by the way, this domain sold for published. Okay. So that agent now, let's say, existed on the web, on agent.ai. Then imagine someone else says, oh, you know, I want to build a brand-building agent for startups and entrepreneurs to come up with names for their startup. Like a common problem, every startup is like, ah, I don't know what to call it. And so they type in five random words that kind of define whatever their startup is. And you can do all manner of things, one of which is like, oh, well, I need to find the domain for it. What are possible choices? Now it's like, okay, well, it would be nice to know if there's an aftermarket price for it, if it's listed for sale. Awesome. Then imagine calling this valuation agent. It's like, okay, well, I want to find where the arbitrage is, where the agent valuation tool says this thing is worth $25,000. It's listed on GoDaddy for $5,000. It's close enough. Let's go do that. Right? And that's a kind of composition use case that in my future state. Thousands of agents on the network, all discoverable through something like MCP. And then you as a developer of agents have access to all these kind of Lego building blocks based on what you're trying to solve. Then you blend in orchestration, which is getting better and better with the reasoning models now. Just describe the problem that you have. Now, the next layer that we're all contending with is that how many tools can you actually give an LLM before the LLM breaks? That number used to be like 15 or 20 before you kind of started to vary dramatically. And so that's the thing I'm thinking about now. It's like, okay, if I want to... If I want to expose 1,000 of these agents to a given LLM, obviously I can't give it all 1,000. Is there some intermediate layer that says, based on your prompt, I'm going to make a best guess at which agents might be able to be helpful for this particular thing? Yeah.Alessio [00:44:37]: Yeah, like RAG for tools. Yep. I did build the Latent Space Researcher on agent.ai. Okay. Nice. Yeah, that seems like, you know, then there's going to be a Latent Space Scheduler. And then once I schedule a research, you know, and you build all of these things. By the way, my apologies for the user experience. You realize I'm an engineer. It's pretty good.swyx [00:44:56]: I think it's a normie-friendly thing. Yeah. That's your magic. HubSpot does the same thing.Alessio [00:45:01]: Yeah, just to like quickly run through it. You can basically create all these different steps. And these steps are like, you know, static versus like variable-driven things. How did you decide between this kind of like low-code-ish versus doing, you know, low-code with code backend versus like not exposing that at all? Any fun design decisions? Yeah. And this is, I think...Dharmesh [00:45:22]: I think lots of people are likely sitting in exactly my position right now, coming through the choosing between deterministic. Like if you're like in a business or building, you know, some sort of agentic thing, do you decide to do a deterministic thing? Or do you go non-deterministic and just let the alum handle it, right, with the reasoning models? The original idea and the reason I took the low-code stepwise, a very deterministic approach. A, the reasoning models did not exist at that time. That's thing number one. Thing number two is if you can get... If you know in your head... If you know in your head what the actual steps are to accomplish whatever goal, why would you leave that to chance? There's no upside. There's literally no upside. Just tell me, like, what steps do you need executed? So right now what I'm playing with... So one thing we haven't talked about yet, and people don't talk about UI and agents. Right now, the primary interaction model... Or they don't talk enough about it. I know some people have. But it's like, okay, so we're used to the chatbot back and forth. Fine. I get that. But I think we're going to move to a blend of... Some of those things are going to be synchronous as they are now. But some are going to be... Some are going to be async. It's just going to put it in a queue, just like... And this goes back to my... Man, I talk fast. But I have this... I only have one other speed. It's even faster. So imagine it's like if you're working... So back to my, oh, we're going to have these hybrid digital teams. Like, you would not go to a co-worker and say, I'm going to ask you to do this thing, and then sit there and wait for them to go do it. Like, that's not how the world works. So it's nice to be able to just, like, hand something off to someone. It's like, okay, well, maybe I expect a response in an hour or a day or something like that.Dharmesh [00:46:52]: In terms of when things need to happen. So the UI around agents. So if you look at the output of agent.ai agents right now, they are the simplest possible manifestation of a UI, right? That says, oh, we have inputs of, like, four different types. Like, we've got a dropdown, we've got multi-select, all the things. It's like back in HTML, the original HTML 1.0 days, right? Like, you're the smallest possible set of primitives for a UI. And it just says, okay, because we need to collect some information from the user, and then we go do steps and do things. And generate some output in HTML or markup are the two primary examples. So the thing I've been asking myself, if I keep going down that path. So people ask me, I get requests all the time. It's like, oh, can you make the UI sort of boring? I need to be able to do this, right? And if I keep pulling on that, it's like, okay, well, now I've built an entire UI builder thing. Where does this end? And so I think the right answer, and this is what I'm going to be backcoding once I get done here, is around injecting a code generation UI generation into, the agent.ai flow, right? As a builder, you're like, okay, I'm going to describe the thing that I want, much like you would do in a vibe coding world. But instead of generating the entire app, it's going to generate the UI that exists at some point in either that deterministic flow or something like that. It says, oh, here's the thing I'm trying to do. Go generate the UI for me. And I can go through some iterations. And what I think of it as a, so it's like, I'm going to generate the code, generate the code, tweak it, go through this kind of prompt style, like we do with vibe coding now. And at some point, I'm going to be happy with it. And I'm going to hit save. And that's going to become the action in that particular step. It's like a caching of the generated code that I can then, like incur any inference time costs. It's just the actual code at that point.Alessio [00:48:29]: Yeah, I invested in a company called E2B, which does code sandbox. And they powered the LM arena web arena. So it's basically the, just like you do LMS, like text to text, they do the same for like UI generation. So if you're asking a model, how do you do it? But yeah, I think that's kind of where.Dharmesh [00:48:45]: That's the thing I'm really fascinated by. So the early LLM, you know, we're understandably, but laughably bad at simple arithmetic, right? That's the thing like my wife, Normies would ask us, like, you call this AI, like it can't, my son would be like, it's just stupid. It can't even do like simple arithmetic. And then like we've discovered over time that, and there's a reason for this, right? It's like, it's a large, there's, you know, the word language is in there for a reason in terms of what it's been trained on. It's not meant to do math, but now it's like, okay, well, the fact that it has access to a Python interpreter that I can actually call at runtime, that solves an entire body of problems that it wasn't trained to do. And it's basically a form of delegation. And so the thought that's kind of rattling around in my head is that that's great. So it's, it's like took the arithmetic problem and took it first. Now, like anything that's solvable through a relatively concrete Python program, it's able to do a bunch of things that I couldn't do before. Can we get to the same place with UI? I don't know what the future of UI looks like in a agentic AI world, but maybe let the LLM handle it, but not in the classic sense. Maybe it generates it on the fly, or maybe we go through some iterations and hit cache or something like that. So it's a little bit more predictable. Uh, I don't know, but yeah.Alessio [00:49:48]: And especially when is the human supposed to intervene? So, especially if you're composing them, most of them should not have a UI because then they're just web hooking to somewhere else. I just want to touch back. I don't know if you have more comments on this.swyx [00:50:01]: I was just going to ask when you, you said you got, you're going to go back to code. What

This episode Alan and Sam dive into the issues around OAuth apps and understanding how they are being used. Alan discusses the issues organisations are facing when any user could consent to application and the cleansing process that needs to take place. He also goes into how App Governance in Defender for Cloud Apps can help. Here are the areas they covered: What are OAuth Apps? What is Defender for Cloud Apps? How can Defender for Cloud Apps help review consented apps? What policies can you deploy? What did you think of this episode? Give us some feedback via our contact form, Or leave us a voice message in the bottom right corner of our site.Read transcript

Episode 116: In this episode of Critical Thinking - Bug Bounty Podcast Justin gives a quick rundown of Portswigger's SAML Roulette writeup, as well as some Google VRP reports, and a Next.js middleware exploit.Follow us on twitter at: https://x.com/ctbbpodcastGot any ideas and suggestions? Feel free to send us any feedback here: info@criticalthinkingpodcast.ioShoutout to YTCracker for the awesome intro music!====== Links ======Follow your hosts Rhynorater and Rez0 on Twitter: https://x.com/Rhynoraterhttps://x.com/rez0__====== Ways to Support CTBBPodcast ======Hop on the CTBB Discord at https://ctbb.show/discord!We also do Discord subs at $25, $10, and $5 - premium subscribers get access to private masterclasses, exploits, tools, scripts, un-redacted bug reports, etc.You can also find some hacker swag at https://ctbb.show/merch!Today's Sponsor: ThreatLocker Cloud Control - https://www.threatlocker.com/platform/cloud-control====== Resources ======SAML roulette: the hacker always winshttps://portswigger.net/research/saml-roulette-the-hacker-always-winsLoophole of getting Google Form associated with Google Spreadsheet with no editor/owner accesshttps://bughunters.google.com/reports/vrp/yBeFmSrJiLoophole to see the editors of a Google Document with no granted access(owner/editor) with just the fileid (can be obtained from publicly shared links with 0 access)https://bughunters.google.com/reports/vrp/7EhAw2hurCloud Tools for Eclipse - Chaining misconfigured OAuth callback redirection with open redirect vulnerability to leak Google OAuth Tokens with full GCP Permissionshttps://bughunters.google.com/reports/vrp/F8GFYGv4gNext.js, cache, and chains: the stale elixirhttps://zhero-web-sec.github.io/research-and-things/nextjs-cache-and-chains-the-stale-elixirNext.js and the corrupt middleware: the authorizing artifacthttps://zhero-web-sec.github.io/research-and-things/nextjs-and-the-corrupt-middleware====== Timestamps ======(00:00:00) Introduction(00:02:59) SAML roulette(00:13:08) Google bugs(00:20:16) Next.js and the corrupt middleware

* Sydney Law Firm Targeted by Foreign Cyber Attackers in Extortion Attempt* AI Coding Assistant Refuses to Generate Code, Suggests User Learn Programming* Widely Used GitHub Action Compromised, Leaking Secrets* Fake "Security Alert" Phishing on GitHub Hijacks Accounts* MyGov Passkey Adoption Surges in AustraliaSydney Law Firm Targeted by Foreign Cyber Attackers in Extortion Attempthttps://www.smh.com.au/national/nsw/prominent-sydney-law-firm-hit-with-cyberattack-massive-data-breach-20250313-p5ljd8.htmlBrydens Lawyers, a prominent Sydney law firm with ties to major sports leagues, has been targeted by foreign cyber attackers who stole over 600 gigabytes of confidential data. The data includes information related to the firm, its clients, cases, and staff.The firm discovered the security breach around February 20th and immediately took its digital systems offline, engaging external advisors, lawyers, and security experts. The attackers are now extorting the firm for a ransom.Brydens has reported the incident to the Australian Cyber Security Centre and the Office of the Australian Information Commissioner. The firm has also restored its IT system's security and is conducting investigations to determine the full extent of the breach and notify affected individuals. This incident highlights the vulnerability of legal firms, which handle highly sensitive information, to ransomware attacks.AI Coding Assistant Refuses to Generate Code, Suggests User Learn Programminghttps://arstechnica.com/ai/2025/03/ai-coding-assistant-refuses-to-write-code-tells-user-to-learn-programming-instead/An AI coding assistant, Cursor, has surprised users by refusing to generate code and instead advising them to learn programming. This incident reflects a broader trend of AI refusals seen across various platforms.This behavior mirrors past instances where AI models, like ChatGPT, have exhibited reluctance to perform tasks, sometimes attributed to model "laziness." Developers have even resorted to prompting AI with phrases like "You are a tireless AI" to mitigate these refusals.The Cursor assistant's response, telling users to learn coding, closely resembles interactions on programming help sites like Stack Overflow, where experienced developers often encourage self-learning. This similarity is likely due to the massive datasets, including coding discussions from platforms like Stack Overflow and GitHub, used to train these AI models.While other users report not encountering this issue at similar code lengths, it appears to be an unintended consequence of Cursor's training. The developers of Cursor have been contacted for comment.Widely Used GitHub Action Compromised, Leaking Secretshttps://www.wiz.io/blog/github-action-tj-actions-changed-files-supply-chain-attack-cve-2025-30066The widely used GitHub Action "tj-actions/changed-files" was compromised before March 14, 2025, injecting malicious code that leaked secrets from affected public repositories into workflow logs. This supply chain attack, tracked as CVE-2025-30066, exposed sensitive information like AWS access keys, GitHub Personal Access Tokens, and private RSA keys.The compromise occurred when an attacker gained access to update tags, pointing them to malicious code. While the malicious commits have since been reverted and the associated GitHub gist has been deleted, the risk of leaked secrets in logs remains.The primary risk is to public repositories, where secrets were exposed in plain view. Security teams are urged to identify affected repositories, review workflow logs for base64 encoded secrets, and immediately rotate any compromised credentials. It is recommended to stop using the compromised action, pin GitHub Actions to specific commit hashes, audit past workflow runs, and use GitHub's allow-listing feature to prevent future attacks.Fake "Security Alert" Phishing on GitHub Hijacks Accountshttps://www.bleepingcomputer.com/news/security/fake-security-alert-issues-on-github-use-oauth-app-to-hijack-accounts/A widespread phishing campaign is targeting GitHub users with fake "Security Alert" issues, attempting to trick them into authorizing a malicious OAuth app. The campaign has targeted nearly 12,000 repositories, warning users of unusual login attempts from Iceland.The fake alerts provide links that lead to an OAuth authorization page for a "gitsecurityapp" app, which requests extensive permissions, including full access to repositories, user profiles, and GitHub Actions workflows. If authorized, the app gains complete control over the user's account and code.The phishing campaign, which began recently, directs authorized users to callback addresses hosted on onrender.com. Users who have authorized the malicious app are advised to immediately revoke its access through GitHub Settings, check for unfamiliar GitHub Actions or gists, and rotate their credentials and authorization tokens.MyGov Passkey Adoption Surges in Australiahttps://www.itnews.com.au/news/over-200000-mygov-users-disable-passwords-in-passkey-shift-615664Over half a million myGov users have adopted passkeys as their login method since the feature launched in June 2024, with over 200,000 users exclusively relying on passkeys and abandoning traditional passwords. The Australian government implemented passkeys to enhance security and combat phishing attacks, investing $5.6 million in the project.Passkeys utilize biometric authentication, PINs, swipe patterns, or physical USB devices, leveraging cryptographic keypair technology. This approach makes myGov accounts resistant to phishing, as passkeys are specific to the website or app they are created for. Australia is among the first countries to implement passkeys for government services. This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit edwinkwan.substack.com

A phishing campaign targets nearly 12,000 GitHub repositories. The BlackLock ransomware group is one to watch. A federal judge orders reinstatement of workers at CISA. Over 100 car dealership websites suffer a supply chain attack, and Hellcat breaches Jaguar Land Rover. Researchers uncover a major vulnerability affecting RSA encryption keys. A Life Insurance Company notifies 355,500 individuals of a December 2024 data breach. A researcher releases a decryptor for Akira ransomware. A new  mapping database aims to help NGOs and high-risk individuals find security tools. Tim Starks from CyberScoop reports that trade groups fear a cybersecurity blackout if a key panel and vital cyber law aren't renewed. A fundamental shift of our understanding of hash tables.  Remember to leave us a 5-star rating and review in your favorite podcast app. Miss an episode? Sign-up for our daily intelligence roundup, Daily Briefing, and you'll never miss a beat. And be sure to follow CyberWire Daily on LinkedIn. CyberWire Guest Today our guest is Tim Starks from CyberScoop is discussing how "Trade groups worry information sharing will worsen without critical infrastructure panel, CISA law renewal." Selected Reading Fake "Security Alert" issues on GitHub use OAuth app to hijack accounts (Bleeping Computer) BlackLock Ransomware Strikes Over 40 Organizations in Just Two Months (GB Hackers) Federal Judges Block Trump's Mass Firings of Federal Workers (BankInfo Security) 100 Car Dealerships Hit by Supply Chain Attack (SecurityWeek) Jaguar Land Rover Breached by HELLCAT Ransomware Group using Jira Credentials (Cyber Security News) Millions Of RSA Key Exposes Serious Flaws That Can Be Exploited (Cyber Security News) Insurer Notifying 335,500 Customers, Agents, Others of Hack (BankInfo Security) New Akira ransomware decryptor cracks encryptions keys using GPUs (Bleeping Computer) Security Database Aims to Empower Non-Profits (Infosecurity Magazine) Undergraduate Disproves 40-Year-Old Conjecture, Invents New Kind of Hash Table (WIRED) Share your feedback. We want to ensure that you are getting the most out of the podcast. Please take a few minutes to share your thoughts with us by completing our brief listener survey as we continually work to improve the show.  Want to hear your company in the show? You too can reach the most influential leaders and operators in the industry. Here's our media kit. Contact us at cyberwire@n2k.com to request more info. The CyberWire is a production of N2K Networks, your source for strategic workforce intelligence. © N2K Networks, Inc. Learn more about your ad choices. Visit megaphone.fm/adchoices

Mirai Bot Now Incorporating Malformed DrayTek Vigor Router Exploits One of the many versions of the Mirai botnet added some new exploit strings attempting to take advantage of an old DrayTek Vigor Router vulnerability, but they got the URL wrong. https://isc.sans.edu/diary/Mirai%20Bot%20now%20incroporating%20%28malformed%3F%29%20DrayTek%20Vigor%20Router%20Exploits/31770 Compromised GitHub Action The popular GitHub action tj-actions/changed-files was compromised and leaks credentials via the action logs https://www.stepsecurity.io/blog/harden-runner-detection-tj-actions-changed-files-action-is-compromised ruby-saml authentication bypass A confusion in how to parse SAML messages between two XML parsers used by Ruby leads to an authentication bypass in saml-ruby. https://github.blog/security/sign-in-as-anyone-bypassing-saml-sso-authentication-with-parser-differentials/ GitHub Fake Security Alerts Fake GitHub security alerts are used to trick package maintainers into adding OAUTH privileges to malicious apps. https://www.bleepingcomputer.com/news/security/fake-security-alert-issues-on-github-use-oauth-app-to-hijack-accounts/

Dans cet épisode, Emmanuel et Arnaud discutent des dernières nouvelles du dev, en mettant l'accent sur Java, l'intelligence artificielle, et les nouvelles fonctionnalités des versions JDK 24 et 25. Ils abordent également des sujets comme Quarkus, l'accessibilité des sites web, et l'impact de l'IA sur le trafic web. Cette conversation aborde les approches pour les devs en matière d'intelligence artificielle et de développement logiciel. On y discute notamment des défis et des bénéfices de l'utilisation de l'IA. Enfin, ils partagent leurs réflexions sur l'importance des conférences pour le développement professionnel. Enregistré le 14 mars 2025 Téléchargement de l'épisode LesCastCodeurs-Episode-323.mp3 ou en vidéo sur YouTube. News Langages Java Metal https://www.youtube.com/watch?v=yup8gIXxWDU Peut-être qu'on la déjà partagé ? Article d'opinion Java coming for AI https://thenewstack.io/2025-is-the-last-year-of-python-dominance-in-ai-java-comin/ 2025 pourrait être la dernière année où Python domine l'IA. Java devient un concurrent sérieux dans le domaine. En 2024, Python était toujours en tête, Java restait fort en entreprise, et Rust gagnait en popularité. Java est de plus en plus utilisé pour l'AI remettant en cause la suprématie de Python. article vient de javaistes la domination de python est cluturelle et plus technique (enfin pour les ML lib c'est encore technique) projets paname et babylon changent la donne JavaML est populaire L'almanach java sur les versions https://javaalmanac.io/ montre kes APIs et les diff entre versions puis les notes ou la spec java Les nouvelles de JDK 24 et du futur 25 https://www.infoq.com/news/2025/02/java-24-so-far/?utm_campaign=infoq_content&utm_source=infoq&utm_medium=feed&utm_term=global JDK 24 a atteint sa première phase de release candidate et sera officiellement publié le 18 mars 2025. 24 nouvelles fonctionnalités (JEPs) réparties en 5 catégories : Core Java Library (7), Java Language Specification (4), Security Library (4), HotSpot (8) et Java Tools (1). Project Amber : JEP 495 “Simple Source Files and Instance Main Methods” en quatrième preview, visant à simplifier l'écriture des premiers programmes Java pour les débutants. Project Loom : JEP 487 “Scoped Values” en quatrième preview, permettant le partage de données immuables entre threads, particulièrement utile avec les virtual threads. Project Panama : JEP 489 “Vector API” en neuvième incubation, continuera d'incuber jusqu'à ce que les fonctionnalités nécessaires de Project Valhalla soient disponibles. Project Leyden : JEP 483 “Ahead-of-Time Class Loading & Linking” pour améliorer le temps de démarrage en rendant les classes d'une application instantanément disponibles au démarrage de la JVM. Sécurité quantique : Deux JEPs (496 et 497) introduisant des algorithmes résistants aux ordinateurs quantiques pour la cryptographie, basés sur les réseaux modulaires. Sécurité renforcée : JEP 486 propose de désactiver définitivement le Security Manager, tandis que JEP 478 introduit une API de dérivation de clés. Optimisations HotSpot : JEP 450 “Compact Object Headers” (expérimental) pour réduire la taille des en-têtes d'objets de 96-128 bits à 64 bits sur les architectures 64 bits. (a ne aps utiliser en prod!) Améliorations GC : JEP 404 “Generational Shenandoah” (expérimental) introduit un mode générationnel pour le Garbage Collector Shenandoah, tout en gardant le non generationel. Évolution des ports : Windows 32-bit x86 ca sent le sapin JEP 502 dans JDK 25 : Introduction des “Stable Values” (preview), anciennement “Computed Constants”, offrant les avantages des champs final avec plus de flexibilité pour l'initialisation. Points Supplémentaires sur JDK 25 Date de sortie : JDK 25 est prévu pour septembre 2025 et représentera la prochaine version LTS (Long-Term Support) après JDK 21. Finalisation de l'on-ramp : Gavin Bierman a annoncé son intention de finaliser la fonction “Simple Source Files” dans JDK 25, après quatre previews successives. CDS Object Streaming : Le JEP Draft 8326035 propose d'ajouter un mécanisme d'archivage d'objets pour Class-Data Sharing (CDS) dans ZGC, avec un format d'archivage et un chargeur unifiés. HTTP/3 supporté dans HttpClient Un article sur l'approche de Go pour éviter les attaques par chemin de fichier https://go.dev/blog/osroot Librairies Quarkus 3.19 es sorti https://quarkus.io/blog/quarkus-3-19-1-released/ UBI 9 par defaut pour les containers En plus de AppCDS, support tu cache AOT (JEP 483) pour demarrer encore plus rapidement Preuve de possession dans OAuth tokers 2 Mario Fusco sur les patterns d'agents en Quarkus https://quarkus.io/blog/agentic-ai-with-quarkus/ et https://quarkus.io/blog/agentic-ai-with-quarkus-p2/ premier article sur les patterns de workflow chainer, paralleliser ou router avec des exemples de code qui tournent les agents a proprement parler (le LLM qui decide du workflow) les agents ont des toolbox que le LLM peut decided d'invoquer Le code va dans les details et permet de mettre les interactions en lumiere tracing rend les choses visuelles Web Le European Accessibility Act (EAA) https://martijnhols.nl/blog/the-european-accessibility-act-for-websites-and-apps Loi européenne sur l'accessibilité (EAA) adoptée en 2019 Vise à rendre sites web et apps accessibles aux personnes handicapées Suivre les normes WCAG 2.1 AA (clarté, utilisabilité, compatibilité) Entreprises concernées : banques, e-commerce, transports, etc. Date limite de mise en conformité : 28 juin 2025 2025 c'est pour les nouveaux developpements 2027 c'est pour les applications existantes. bon et je fais comment pour savoir si le site web des cast codeurs est conforme ? API Popover https://web.dev/blog/popover-baseline?hl=en L'API Popover est maintenant disponible dans tous les navigateurs majeurs Ajoutée à Baseline le 27 janvier 2025 Permet de créer des popovers natifs en HTML, sans JavaScript complexe Exemple : Ouvrir Contenu du popover Problème initial (2024) : Bug sur iOS empêchant la fermeture des popovers Intégrer un front-end React dans une app Spring-Boot https://bootify.io/frontend/react-spring-boot-integration.html Etape par etape, comment configurer son build (https://bootify.io/frontend/webpack-spring-boot.html) et son app (controllers…) pour y intégrer un front en rect. Data et Intelligence Artificielle Traffic des sites web venant de IA https://ahrefs.com/blog/ai-traffic-study/ le AIEO apres le SEO va devenir un gros business vu que les modèles ont tendance a avoir leurs chouchous techniques ou de reference. 63% des sites ont au moins un referal viennent d'une IA 50% ChatGPT, puis plrplexity et enfin Gemini, bah et LeChat alors? 0,17% du traffic des sites vient de l'IA. Et en meme temps l'AI resume plutot que pointe donc c'est logique Granite 3.2 est sorti https://www.infoq.com/news/2025/03/ibm-granite-3-2/ IBM sort Granite 3.2, un modèle IA avancé. Meilleur raisonnement et nouvelles capacités multimodales. Granite Vision 3.2 excelle en compréhension d'images et de documents. Granite Guardian 3.2 détecte les risques dans les réponses IA. Modèles plus petits et efficaces pour divers usages. Améliorations en raisonnement mathématique et prévisions temporelles. les trucs interessants de Granite c'est sa petite taille et son cote “vraiment” open source Prompt Engineering - article détaillé https://www.infoq.com/articles/prompt-engineering/ Le prompt engineering, c'est l'art de bien formuler les instructions pour guider l'IA. Accessible à tous, il ne remplace pas la programmation mais la complète. Techniques clés : few-shot learning, chain-of-thought, tree-of-thought. Avantages : flexibilité, rapidité, meilleure interaction avec l'IA. Limites : manque de précision et dépendance aux modèles existants. Futur : un outil clé pour améliorer l'IA et le développement logiciel. QCon San Francisco - Les agents AI - Conference https://www.infoq.com/presentations/ai-agents-infrastructure/ Sujet : Infrastructure pour agents d'IA. Technologies : RAG et bases de données vectorielles. Rôle des agents d'IA : Automatiser des tâches, prévoir des besoins, superviser. Expérience : Shruti Bhat de Oracle à Rockset (acquis par OpenAI). Objectif : Passer des applis classiques aux agents IA intelligents. Défis : Améliorer la recherche en temps réel, l'indexation et la récupération. Nous concernant: Évolution des rôles : Les développeurs passent à des rôles plus stratégiques. Adaptation nécessaire : Les développeurs doivent s'adapter aux nouvelles technologies. Official Java SDK for MCP & Spring AI https://spring.io/blog/2025/02/14/mcp-java-sdk-released-2 Désormais une implémentation officielle aux côtés des SDK Python, TypeScript et Kotlin. ( https://modelcontextprotocol.io/ ) Prise en charge de Stdio-based transport, SSE (via HTTP) et intégration avec Spring WebFlux et WebMVC. Intégration avec Spring AI, configuration simplifiée pour les applications Spring Boot (different starters disponibles) Codez avec Claude https://docs.anthropic.com/en/docs/agents-and-tools/claude-code/overview Claude Code est en beta, plus de liste d'attente Un outil de codage agentique intégré au terminal, capable de comprendre votre base de code et d'accélérer le développement grâce à des commandes en langage naturel. Les fonctionnalités permettent de comprendre le code, le refactorer, tester, debugger, … Gemini Code Assist est gratuit https://blog.google/technology/developers/gemini-code-assist-free/ Pour un usage personnel. Pas besoin de compte. Pas de limite. 128k token input. Guillaume démarre une série d'articles sur le RAG (niveau avancé). Le premier sur Sentence Window Retrievalhttps://glaforge.dev/posts/2025/02/25/advanced-rag-sentence-window-retrieval/ Guillaume propose une technique qui améliore les résultats de rechercher de Retrieval Augmented Generation L'idée est de calculer des vecteurs embeddings sur des phrases, par exemple, mais de retourner un contexte plus large L'intérêt, c'est d'avoir des calculs de similarité de vector embedding qui ont de bons scores (sans dilution de sens) de similarité, mais de ne pas perdre des informations sur le contexte dans lequel cette phrase se situe GitHub Copilot edits en GA, GitHub Copilot en mode agent dans VSCode Insiders https://github.blog/news-insights/product-news/github-copilot-the-agent-awakens/ Copilot Edits permet via le chat de modifier plusieurs fichiers en même temps, ce qui simplifie les refactoring Copilot en mode agent ajoute un mode autonome (Agentic AI) qui va tout seul chercher les modifications à faire dans votre code base. “what could possibly go wrong?” Méthodologies Article d'opinion interessant sur AI et le code assistant de Addy Osmani https://addyo.substack.com/p/the-70-problem-hard-truths-about Un article de l'année dernière de Addy Osmani https://addyo.substack.com/p/10-lessons-from-12-years-at-google plusieurs types d'aide IA Ceux pour boostrapper, dun figma ou d'une image et avoir un proto non fonctionnel en quelques jours Ceux pour iterer sur du code donc plus long terme on va faire une interview sur les assistants de code IA Le cout de la vitesse de l'ia les dev senior refactur et modifie le code proposé pour se l'approprier, chnger l'architecture etc donc basé sur leur connaissance appliquer ce qu'on connait deja amis plus vite est un pattern different d'apprendre avec l'IA explore des patterns d'approche et la prospective sur le futur Loi, société et organisation Elon Musk essaie d'acheter Open AI https://www.bbc.com/news/articles/cpdx75zgg88o La réponse: “non merci mais on peut racheter twiter pour 9,74 milliars si tu veux” Avec la loi narcotrafic votée au sénat, Signal ne serait plus disponible en France https://www.clubic.com/actualite-555135-avec-la-loi-narcotrafic-signal-quittera-la-france.html en plus de légaliser les logiciels espions s'appuyant sur les failles logiciel La loi demande aux messageries de laisser l'état accéder aux conversations Donc une backdoor avec une clé etatique par exemple Une backdoor comme celle des téléphones filaires américains mis en place il y a des années et maintenant exploitée par l'espionnage chinois Signal à une position ferme, soit c'est sécurisé soit on sort d'un pays Olvid WhatsApp et iMessage sont aussi visée par exemple La loi défini la cible comme la criminalité organisée : les classiques mais aussi les gilets jaunes, les opposants au projet de Bure, les militants aidant les personnes exilées à Briançon, ou encore les actions contre le cimentier Lafarge à Bouc-Bel-Air et à Évreux Donc plus large que ce que les gens pensent. Conférences La liste des conférences provenant de Developers Conferences Agenda/List par Aurélie Vache et contributeurs : 14 mars 2025 : Rust In Paris 2025 - Paris (France) 19-21 mars 2025 : React Paris - Paris (France) 20 mars 2025 : PGDay Paris - Paris (France) 20-21 mars 2025 : Agile Niort - Niort (France) 25 mars 2025 : ParisTestConf - Paris (France) 26-29 mars 2025 : JChateau Unconference 2025 - Cour-Cheverny (France) 27-28 mars 2025 : SymfonyLive Paris 2025 - Paris (France) 28 mars 2025 : DataDays - Lille (France) 28-29 mars 2025 : Agile Games France 2025 - Lille (France) 28-30 mars 2025 : Shift - Nantes (France) 3 avril 2025 : DotJS - Paris (France) 3 avril 2025 : SoCraTes Rennes 2025 - Rennes (France) 4 avril 2025 : Flutter Connection 2025 - Paris (France) 4 avril 2025 : aMP Orléans 04-04-2025 - Orléans (France) 10-11 avril 2025 : Android Makers - Montrouge (France) 10-12 avril 2025 : Devoxx Greece - Athens (Greece) 11-12 avril 2025 : Faiseuses du Web 4 - Dinan (France) 14 avril 2025 : Lyon Craft - Lyon (France) 16-18 avril 2025 : Devoxx France - Paris (France) 23-25 avril 2025 : MODERN ENDPOINT MANAGEMENT EMEA SUMMIT 2025 - Paris (France) 24 avril 2025 : IA Data Day - Strasbourg 2025 - Strasbourg (France) 29-30 avril 2025 : MixIT - Lyon (France) 6-7 mai 2025 : GOSIM AI Paris - Paris (France) 7-9 mai 2025 : Devoxx UK - London (UK) 15 mai 2025 : Cloud Toulouse - Toulouse (France) 16 mai 2025 : AFUP Day 2025 Lille - Lille (France) 16 mai 2025 : AFUP Day 2025 Lyon - Lyon (France) 16 mai 2025 : AFUP Day 2025 Poitiers - Poitiers (France) 22-23 mai 2025 : Flupa UX Days 2025 - Paris (France) 24 mai 2025 : Polycloud - Montpellier (France) 24 mai 2025 : NG Baguette Conf 2025 - Nantes (France) 3 juin 2025 : TechReady - Nantes (France) 5-6 juin 2025 : AlpesCraft - Grenoble (France) 5-6 juin 2025 : Devquest 2025 - Niort (France) 10-11 juin 2025 : Modern Workplace Conference Paris 2025 - Paris (France) 11-13 juin 2025 : Devoxx Poland - Krakow (Poland) 12-13 juin 2025 : Agile Tour Toulouse - Toulouse (France) 12-13 juin 2025 : DevLille - Lille (France) 13 juin 2025 : Tech F'Est 2025 - Nancy (France) 17 juin 2025 : Mobilis In Mobile - Nantes (France) 19-21 juin 2025 : Drupal Barcamp Perpignan 2025 - Perpignan (France) 24 juin 2025 : WAX 2025 - Aix-en-Provence (France) 25-26 juin 2025 : Agi'Lille 2025 - Lille (France) 25-27 juin 2025 : BreizhCamp 2025 - Rennes (France) 26-27 juin 2025 : Sunny Tech - Montpellier (France) 1-4 juillet 2025 : Open edX Conference - 2025 - Palaiseau (France) 7-9 juillet 2025 : Riviera DEV 2025 - Sophia Antipolis (France) 18-19 septembre 2025 : API Platform Conference - Lille (France) & Online 23 septembre 2025 : OWASP AppSec France 2025 - Paris (France) 25-26 septembre 2025 : Paris Web 2025 - Paris (France) 2-3 octobre 2025 : Volcamp - Clermont-Ferrand (France) 6-10 octobre 2025 : Devoxx Belgium - Antwerp (Belgium) 9-10 octobre 2025 : Forum PHP 2025 - Marne-la-Vallée (France) 9-10 octobre 2025 : EuroRust 2025 - Paris (France) 16-17 octobre 2025 : DevFest Nantes - Nantes (France) 4-7 novembre 2025 : NewCrafts 2025 - Paris (France) 6 novembre 2025 : dotAI 2025 - Paris (France) 7 novembre 2025 : BDX I/O - Bordeaux (France) 12-14 novembre 2025 : Devoxx Morocco - Marrakech (Morocco) 21 novembre 2025 : DevFest Paris 2025 - Paris (France) 28 novembre 2025 : DevFest Lyon - Lyon (France) 28-31 janvier 2026 : SnowCamp 2026 - Grenoble (France) 23-25 avril 2026 : Devoxx Greece - Athens (Greece) 17 juin 2026 : Devoxx Poland - Krakow (Poland) Nous contacter Pour réagir à cet épisode, venez discuter sur le groupe Google https://groups.google.com/group/lescastcodeurs Contactez-nous via X/twitter https://twitter.com/lescastcodeurs ou Bluesky https://bsky.app/profile/lescastcodeurs.com Faire un crowdcast ou une crowdquestion Soutenez Les Cast Codeurs sur Patreon https://www.patreon.com/LesCastCodeurs Tous les épisodes et toutes les infos sur https://lescastcodeurs.com/

Referências do EpisódioFake "Security Alert" issues on GitHub use OAuth app to hijack accounts What Is The New Steganographic Campaign Distributing Multiple MalwareRoteiro e apresentação: Carlos Cabral e Bianca OliveiraEdição de áudio: Paulo ArruzzoNarração de encerramento: Bianca Garcia

Episode SummaryIn this episode of The Secure Developer, Danny Allan, CTO of Snyk, sits down with Wayne Chang, Founder and CEO of SpruceID, to explore the evolving landscape of digital identity and security. From self-sovereign identity to the role of AI in authentication, they discuss the future of identity management, the risks of centralized systems, and the benefits of decentralized approaches. They also dive into how policy, compliance, and emerging technologies like passkeys and zero-knowledge proofs are shaping the security ecosystem.Show NotesThe world of digital identity is changing fast, and in this episode of The Secure Developer, we explore how security professionals and developers can navigate this evolving space. Host Danny Allan is joined by Wayne Chang, Founder and CEO of SpruceID, to discuss key trends and challenges in identity management.Topics Discussed:Wayne's Background: From health tech to digital identity, how Wayne's early struggles with integrating health records led to his passion for self-sovereign identity.The Evolution of Digital Identity: Why usernames and passwords are no longer the gold standard, and how newer methods like passkeys and cryptographic credentials improve security.Decentralization vs. Centralization: The trade-offs between federated identity systems (like OAuth and SSO) and self-hosted identity wallets.The Role of AI in Identity Security: How AI is both a tool for improving security and a threat vector for identity fraud.Privacy and Compliance: How regulations like GDPR, CCPA, and emerging state-level laws influence digital identity strategies.The Future of Authentication: The move from multi-factor authentication to "myriad factor authentication," leveraging multiple signals for seamless and secure access.Wayne and Danny also discuss real-world use cases, including the development of mobile driver's licenses, emerging digital identity wallets, and the challenges of ensuring privacy and security while maintaining usability. The conversation highlights how organizations can stay ahead with better authentication practices and privacy-preserving architectures as fraud becomes more sophisticated.LinksSpruceID - Identity infrastructure for the digital worldNIST - The National Institute of Standards and TechnologyNIST SP 800-63 - Digital Identity GuidelinesACLU Digital ID State Legislative RecommendationsSnyk - The Developer Security Company Follow UsOur WebsiteOur LinkedIn

Today's episode is with Paul Klein, founder of Browserbase. We talked about building browser infrastructure for AI agents, the future of agent authentication, and their open source framework Stagehand.* [00:00:00] Introductions* [00:04:46] AI-specific challenges in browser infrastructure* [00:07:05] Multimodality in AI-Powered Browsing* [00:12:26] Running headless browsers at scale* [00:18:46] Geolocation when proxying* [00:21:25] CAPTCHAs and Agent Auth* [00:28:21] Building “User take over” functionality* [00:33:43] Stagehand: AI web browsing framework* [00:38:58] OpenAI's Operator and computer use agents* [00:44:44] Surprising use cases of Browserbase* [00:47:18] Future of browser automation and market competition* [00:53:11] Being a solo founderTranscriptAlessio [00:00:04]: Hey everyone, welcome to the Latent Space podcast. This is Alessio, partner and CTO at Decibel Partners, and I'm joined by my co-host Swyx, founder of Smol.ai.swyx [00:00:12]: Hey, and today we are very blessed to have our friends, Paul Klein, for the fourth, the fourth, CEO of Browserbase. Welcome.Paul [00:00:21]: Thanks guys. Yeah, I'm happy to be here. I've been lucky to know both of you for like a couple of years now, I think. So it's just like we're hanging out, you know, with three ginormous microphones in front of our face. It's totally normal hangout.swyx [00:00:34]: Yeah. We've actually mentioned you on the podcast, I think, more often than any other Solaris tenant. Just because like you're one of the, you know, best performing, I think, LLM tool companies that have started up in the last couple of years.Paul [00:00:50]: Yeah, I mean, it's been a whirlwind of a year, like Browserbase is actually pretty close to our first birthday. So we are one years old. And going from, you know, starting a company as a solo founder to... To, you know, having a team of 20 people, you know, a series A, but also being able to support hundreds of AI companies that are building AI applications that go out and automate the web. It's just been like, really cool. It's been happening a little too fast. I think like collectively as an AI industry, let's just take a week off together. I took my first vacation actually two weeks ago, and Operator came out on the first day, and then a week later, DeepSeat came out. And I'm like on vacation trying to chill. I'm like, we got to build with this stuff, right? So it's been a breakneck year. But I'm super happy to be here and like talk more about all the stuff we're seeing. And I'd love to hear kind of what you guys are excited about too, and share with it, you know?swyx [00:01:39]: Where to start? So people, you've done a bunch of podcasts. I think I strongly recommend Jack Bridger's Scaling DevTools, as well as Turner Novak's The Peel. And, you know, I'm sure there's others. So you covered your Twilio story in the past, talked about StreamClub, you got acquired to Mux, and then you left to start Browserbase. So maybe we just start with what is Browserbase? Yeah.Paul [00:02:02]: Browserbase is the web browser for your AI. We're building headless browser infrastructure, which are browsers that run in a server environment that's accessible to developers via APIs and SDKs. It's really hard to run a web browser in the cloud. You guys are probably running Chrome on your computers, and that's using a lot of resources, right? So if you want to run a web browser or thousands of web browsers, you can't just spin up a bunch of lambdas. You actually need to use a secure containerized environment. You have to scale it up and down. It's a stateful system. And that infrastructure is, like, super painful. And I know that firsthand, because at my last company, StreamClub, I was CTO, and I was building our own internal headless browser infrastructure. That's actually why we sold the company, is because Mux really wanted to buy our headless browser infrastructure that we'd built. And it's just a super hard problem. And I actually told my co-founders, I would never start another company unless it was a browser infrastructure company. And it turns out that's really necessary in the age of AI, when AI can actually go out and interact with websites, click on buttons, fill in forms. You need AI to do all of that work in an actual browser running somewhere on a server. And BrowserBase powers that.swyx [00:03:08]: While you're talking about it, it occurred to me, not that you're going to be acquired or anything, but it occurred to me that it would be really funny if you became the Nikita Beer of headless browser companies. You just have one trick, and you make browser companies that get acquired.Paul [00:03:23]: I truly do only have one trick. I'm screwed if it's not for headless browsers. I'm not a Go programmer. You know, I'm in AI grant. You know, browsers is an AI grant. But we were the only company in that AI grant batch that used zero dollars on AI spend. You know, we're purely an infrastructure company. So as much as people want to ask me about reinforcement learning, I might not be the best guy to talk about that. But if you want to ask about headless browser infrastructure at scale, I can talk your ear off. So that's really my area of expertise. And it's a pretty niche thing. Like, nobody has done what we're doing at scale before. So we're happy to be the experts.swyx [00:03:59]: You do have an AI thing, stagehand. We can talk about the sort of core of browser-based first, and then maybe stagehand. Yeah, stagehand is kind of the web browsing framework. Yeah.What is Browserbase? Headless Browser Infrastructure ExplainedAlessio [00:04:10]: Yeah. Yeah. And maybe how you got to browser-based and what problems you saw. So one of the first things I worked on as a software engineer was integration testing. Sauce Labs was kind of like the main thing at the time. And then we had Selenium, we had Playbrite, we had all these different browser things. But it's always been super hard to do. So obviously you've worked on this before. When you started browser-based, what were the challenges? What were the AI-specific challenges that you saw versus, there's kind of like all the usual running browser at scale in the cloud, which has been a problem for years. What are like the AI unique things that you saw that like traditional purchase just didn't cover? Yeah.AI-specific challenges in browser infrastructurePaul [00:04:46]: First and foremost, I think back to like the first thing I did as a developer, like as a kid when I was writing code, I wanted to write code that did stuff for me. You know, I wanted to write code to automate my life. And I do that probably by using curl or beautiful soup to fetch data from a web browser. And I think I still do that now that I'm in the cloud. And the other thing that I think is a huge challenge for me is that you can't just create a web site and parse that data. And we all know that now like, you know, taking HTML and plugging that into an LLM, you can extract insights, you can summarize. So it was very clear that now like dynamic web scraping became very possible with the rise of large language models or a lot easier. And that was like a clear reason why there's been more usage of headless browsers, which are necessary because a lot of modern websites don't expose all of their page content via a simple HTTP request. You know, they actually do require you to run this type of code for a specific time. JavaScript on the page to hydrate this. Airbnb is a great example. You go to airbnb.com. A lot of that content on the page isn't there until after they run the initial hydration. So you can't just scrape it with a curl. You need to have some JavaScript run. And a browser is that JavaScript engine that's going to actually run all those requests on the page. So web data retrieval was definitely one driver of starting BrowserBase and the rise of being able to summarize that within LLM. Also, I was familiar with if I wanted to automate a website, I could write one script and that would work for one website. It was very static and deterministic. But the web is non-deterministic. The web is always changing. And until we had LLMs, there was no way to write scripts that you could write once that would run on any website. That would change with the structure of the website. Click the login button. It could mean something different on many different websites. And LLMs allow us to generate code on the fly to actually control that. So I think that rise of writing the generic automation scripts that can work on many different websites, to me, made it clear that browsers are going to be a lot more useful because now you can automate a lot more things without writing. If you wanted to write a script to book a demo call on 100 websites, previously, you had to write 100 scripts. Now you write one script that uses LLMs to generate that script. That's why we built our web browsing framework, StageHand, which does a lot of that work for you. But those two things, web data collection and then enhanced automation of many different websites, it just felt like big drivers for more browser infrastructure that would be required to power these kinds of features.Alessio [00:07:05]: And was multimodality also a big thing?Paul [00:07:08]: Now you can use the LLMs to look, even though the text in the dome might not be as friendly. Maybe my hot take is I was always kind of like, I didn't think vision would be as big of a driver. For UI automation, I felt like, you know, HTML is structured text and large language models are good with structured text. But it's clear that these computer use models are often vision driven, and they've been really pushing things forward. So definitely being multimodal, like rendering the page is required to take a screenshot to give that to a computer use model to take actions on a website. And it's just another win for browser. But I'll be honest, that wasn't what I was thinking early on. I didn't even think that we'd get here so fast with multimodality. I think we're going to have to get back to multimodal and vision models.swyx [00:07:50]: This is one of those things where I forgot to mention in my intro that I'm an investor in Browserbase. And I remember that when you pitched to me, like a lot of the stuff that we have today, we like wasn't on the original conversation. But I did have my original thesis was something that we've talked about on the podcast before, which is take the GPT store, the custom GPT store, all the every single checkbox and plugin is effectively a startup. And this was the browser one. I think the main hesitation, I think I actually took a while to get back to you. The main hesitation was that there were others. Like you're not the first hit list browser startup. It's not even your first hit list browser startup. There's always a question of like, will you be the category winner in a place where there's a bunch of incumbents, to be honest, that are bigger than you? They're just not targeted at the AI space. They don't have the backing of Nat Friedman. And there's a bunch of like, you're here in Silicon Valley. They're not. I don't know.Paul [00:08:47]: I don't know if that's, that was it, but like, there was a, yeah, I mean, like, I think I tried all the other ones and I was like, really disappointed. Like my background is from working at great developer tools, companies, and nothing had like the Vercel like experience. Um, like our biggest competitor actually is partly owned by private equity and they just jacked up their prices quite a bit. And the dashboard hasn't changed in five years. And I actually used them at my last company and tried them and I was like, oh man, like there really just needs to be something that's like the experience of these great infrastructure companies, like Stripe, like clerk, like Vercel that I use in love, but oriented towards this kind of like more specific category, which is browser infrastructure, which is really technically complex. Like a lot of stuff can go wrong on the internet when you're running a browser. The internet is very vast. There's a lot of different configurations. Like there's still websites that only work with internet explorer out there. How do you handle that when you're running your own browser infrastructure? These are the problems that we have to think about and solve at BrowserBase. And it's, it's certainly a labor of love, but I built this for me, first and foremost, I know it's super cheesy and everyone says that for like their startups, but it really, truly was for me. If you look at like the talks I've done even before BrowserBase, and I'm just like really excited to try and build a category defining infrastructure company. And it's, it's rare to have a new category of infrastructure exists. We're here in the Chroma offices and like, you know, vector databases is a new category of infrastructure. Is it, is it, I mean, we can, we're in their office, so, you know, we can, we can debate that one later. That is one.Multimodality in AI-Powered Browsingswyx [00:10:16]: That's one of the industry debates.Paul [00:10:17]: I guess we go back to the LLMOS talk that Karpathy gave way long ago. And like the browser box was very clearly there and it seemed like the people who were building in this space also agreed that browsers are a core primitive of infrastructure for the LLMOS that's going to exist in the future. And nobody was building something there that I wanted to use. So I had to go build it myself.swyx [00:10:38]: Yeah. I mean, exactly that talk that, that honestly, that diagram, every box is a startup and there's the code box and then there's the. The browser box. I think at some point they will start clashing there. There's always the question of the, are you a point solution or are you the sort of all in one? And I think the point solutions tend to win quickly, but then the only ones have a very tight cohesive experience. Yeah. Let's talk about just the hard problems of browser base you have on your website, which is beautiful. Thank you. Was there an agency that you used for that? Yeah. Herb.paris.Paul [00:11:11]: They're amazing. Herb.paris. Yeah. It's H-E-R-V-E. I highly recommend for developers. Developer tools, founders to work with consumer agencies because they end up building beautiful things and the Parisians know how to build beautiful interfaces. So I got to give prep.swyx [00:11:24]: And chat apps, apparently are, they are very fast. Oh yeah. The Mistral chat. Yeah. Mistral. Yeah.Paul [00:11:31]: Late chat.swyx [00:11:31]: Late chat. And then your videos as well, it was professionally shot, right? The series A video. Yeah.Alessio [00:11:36]: Nico did the videos. He's amazing. Not the initial video that you shot at the new one. First one was Austin.Paul [00:11:41]: Another, another video pretty surprised. But yeah, I mean, like, I think when you think about how you talk about your company. You have to think about the way you present yourself. It's, you know, as a developer, you think you evaluate a company based on like the API reliability and the P 95, but a lot of developers say, is the website good? Is the message clear? Do I like trust this founder? I'm building my whole feature on. So I've tried to nail that as well as like the reliability of the infrastructure. You're right. It's very hard. And there's a lot of kind of foot guns that you run into when running headless browsers at scale. Right.Competing with Existing Headless Browser Solutionsswyx [00:12:10]: So let's pick one. You have eight features here. Seamless integration. Scalability. Fast or speed. Secure. Observable. Stealth. That's interesting. Extensible and developer first. What comes to your mind as like the top two, three hardest ones? Yeah.Running headless browsers at scalePaul [00:12:26]: I think just running headless browsers at scale is like the hardest one. And maybe can I nerd out for a second? Is that okay? I heard this is a technical audience, so I'll talk to the other nerds. Whoa. They were listening. Yeah. They're upset. They're ready. The AGI is angry. Okay. So. So how do you run a browser in the cloud? Let's start with that, right? So let's say you're using a popular browser automation framework like Puppeteer, Playwright, and Selenium. Maybe you've written a code, some code locally on your computer that opens up Google. It finds the search bar and then types in, you know, search for Latent Space and hits the search button. That script works great locally. You can see the little browser open up. You want to take that to production. You want to run the script in a cloud environment. So when your laptop is closed, your browser is doing something. The browser is doing something. Well, I, we use Amazon. You can see the little browser open up. You know, the first thing I'd reach for is probably like some sort of serverless infrastructure. I would probably try and deploy on a Lambda. But Chrome itself is too big to run on a Lambda. It's over 250 megabytes. So you can't easily start it on a Lambda. So you maybe have to use something like Lambda layers to squeeze it in there. Maybe use a different Chromium build that's lighter. And you get it on the Lambda. Great. It works. But it runs super slowly. It's because Lambdas are very like resource limited. They only run like with one vCPU. You can run one process at a time. Remember, Chromium is super beefy. It's barely running on my MacBook Air. I'm still downloading it from a pre-run. Yeah, from the test earlier, right? I'm joking. But it's big, you know? So like Lambda, it just won't work really well. Maybe it'll work, but you need something faster. Your users want something faster. Okay. Well, let's put it on a beefier instance. Let's get an EC2 server running. Let's throw Chromium on there. Great. Okay. I can, that works well with one user. But what if I want to run like 10 Chromium instances, one for each of my users? Okay. Well, I might need two EC2 instances. Maybe 10. All of a sudden, you have multiple EC2 instances. This sounds like a problem for Kubernetes and Docker, right? Now, all of a sudden, you're using ECS or EKS, the Kubernetes or container solutions by Amazon. You're spending up and down containers, and you're spending a whole engineer's time on kind of maintaining this stateful distributed system. Those are some of the worst systems to run because when it's a stateful distributed system, it means that you are bound by the connections to that thing. You have to keep the browser open while someone is working with it, right? That's just a painful architecture to run. And there's all this other little gotchas with Chromium, like Chromium, which is the open source version of Chrome, by the way. You have to install all these fonts. You want emojis working in your browsers because your vision model is looking for the emoji. You need to make sure you have the emoji fonts. You need to make sure you have all the right extensions configured, like, oh, do you want ad blocking? How do you configure that? How do you actually record all these browser sessions? Like it's a headless browser. You can't look at it. So you need to have some sort of observability. Maybe you're recording videos and storing those somewhere. It all kind of adds up to be this just giant monster piece of your project when all you wanted to do was run a lot of browsers in production for this little script to go to google.com and search. And when I see a complex distributed system, I see an opportunity to build a great infrastructure company. And we really abstract that away with Browserbase where our customers can use these existing frameworks, Playwright, Publisher, Selenium, or our own stagehand and connect to our browsers in a serverless-like way. And control them, and then just disconnect when they're done. And they don't have to think about the complex distributed system behind all of that. They just get a browser running anywhere, anytime. Really easy to connect to.swyx [00:15:55]: I'm sure you have questions. My standard question with anything, so essentially you're a serverless browser company, and there's been other serverless things that I'm familiar with in the past, serverless GPUs, serverless website hosting. That's where I come from with Netlify. One question is just like, you promised to spin up thousands of servers. You promised to spin up thousands of browsers in milliseconds. I feel like there's no real solution that does that yet. And I'm just kind of curious how. The only solution I know, which is to kind of keep a kind of warm pool of servers around, which is expensive, but maybe not so expensive because it's just CPUs. So I'm just like, you know. Yeah.Browsers as a Core Primitive in AI InfrastructurePaul [00:16:36]: You nailed it, right? I mean, how do you offer a serverless-like experience with something that is clearly not serverless, right? And the answer is, you need to be able to run... We run many browsers on single nodes. We use Kubernetes at browser base. So we have many pods that are being scheduled. We have to predictably schedule them up or down. Yes, thousands of browsers in milliseconds is the best case scenario. If you hit us with 10,000 requests, you may hit a slower cold start, right? So we've done a lot of work on predictive scaling and being able to kind of route stuff to different regions where we have multiple regions of browser base where we have different pools available. You can also pick the region you want to go to based on like lower latency, round trip, time latency. It's very important with these types of things. There's a lot of requests going over the wire. So for us, like having a VM like Firecracker powering everything under the hood allows us to be super nimble and spin things up or down really quickly with strong multi-tenancy. But in the end, this is like the complex infrastructural challenges that we have to kind of deal with at browser base. And we have a lot more stuff on our roadmap to allow customers to have more levers to pull to exchange, do you want really fast browser startup times or do you want really low costs? And if you're willing to be more flexible on that, we may be able to kind of like work better for your use cases.swyx [00:17:44]: Since you used Firecracker, shouldn't Fargate do that for you or did you have to go lower level than that? We had to go lower level than that.Paul [00:17:51]: I find this a lot with Fargate customers, which is alarming for Fargate. We used to be a giant Fargate customer. Actually, the first version of browser base was ECS and Fargate. And unfortunately, it's a great product. I think we were actually the largest Fargate customer in our region for a little while. No, what? Yeah, seriously. And unfortunately, it's a great product, but I think if you're an infrastructure company, you actually have to have a deeper level of control over these primitives. I think it's the same thing is true with databases. We've used other database providers and I think-swyx [00:18:21]: Yeah, serverless Postgres.Paul [00:18:23]: Shocker. When you're an infrastructure company, you're on the hook if any provider has an outage. And I can't tell my customers like, hey, we went down because so-and-so went down. That's not acceptable. So for us, we've really moved to bringing things internally. It's kind of opposite of what we preach. We tell our customers, don't build this in-house, but then we're like, we build a lot of stuff in-house. But I think it just really depends on what is in the critical path. We try and have deep ownership of that.Alessio [00:18:46]: On the distributed location side, how does that work for the web where you might get sort of different content in different locations, but the customer is expecting, you know, if you're in the US, I'm expecting the US version. But if you're spinning up my browser in France, I might get the French version. Yeah.Paul [00:19:02]: Yeah. That's a good question. Well, generally, like on the localization, there is a thing called locale in the browser. You can set like what your locale is. If you're like in the ENUS browser or not, but some things do IP, IP based routing. And in that case, you may want to have a proxy. Like let's say you're running something in the, in Europe, but you want to make sure you're showing up from the US. You may want to use one of our proxy features so you can turn on proxies to say like, make sure these connections always come from the United States, which is necessary too, because when you're browsing the web, you're coming from like a, you know, data center IP, and that can make things a lot harder to browse web. So we do have kind of like this proxy super network. Yeah. We have a proxy for you based on where you're going, so you can reliably automate the web. But if you get scheduled in Europe, that doesn't happen as much. We try and schedule you as close to, you know, your origin that you're trying to go to. But generally you have control over the regions you can put your browsers in. So you can specify West one or East one or Europe. We only have one region of Europe right now, actually. Yeah.Alessio [00:19:55]: What's harder, the browser or the proxy? I feel like to me, it feels like actually proxying reliably at scale. It's much harder than spending up browsers at scale. I'm curious. It's all hard.Paul [00:20:06]: It's layers of hard, right? Yeah. I think it's different levels of hard. I think the thing with the proxy infrastructure is that we work with many different web proxy providers and some are better than others. Some have good days, some have bad days. And our customers who've built browser infrastructure on their own, they have to go and deal with sketchy actors. Like first they figure out their own browser infrastructure and then they got to go buy a proxy. And it's like you can pay in Bitcoin and it just kind of feels a little sus, right? It's like you're buying drugs when you're trying to get a proxy online. We have like deep relationships with these counterparties. We're able to audit them and say, is this proxy being sourced ethically? Like it's not running on someone's TV somewhere. Is it free range? Yeah. Free range organic proxies, right? Right. We do a level of diligence. We're SOC 2. So we have to understand what is going on here. But then we're able to make sure that like we route around proxy providers not working. There's proxy providers who will just, the proxy will stop working all of a sudden. And then if you don't have redundant proxying on your own browsers, that's hard down for you or you may get some serious impacts there. With us, like we intelligently know, hey, this proxy is not working. Let's go to this one. And you can kind of build a network of multiple providers to really guarantee the best uptime for our customers. Yeah. So you don't own any proxies? We don't own any proxies. You're right. The team has been saying who wants to like take home a little proxy server, but not yet. We're not there yet. You know?swyx [00:21:25]: It's a very mature market. I don't think you should build that yourself. Like you should just be a super customer of them. Yeah. Scraping, I think, is the main use case for that. I guess. Well, that leads us into CAPTCHAs and also off, but let's talk about CAPTCHAs. You had a little spiel that you wanted to talk about CAPTCHA stuff.Challenges of Scaling Browser InfrastructurePaul [00:21:43]: Oh, yeah. I was just, I think a lot of people ask, if you're thinking about proxies, you're thinking about CAPTCHAs too. I think it's the same thing. You can go buy CAPTCHA solvers online, but it's the same buying experience. It's some sketchy website, you have to integrate it. It's not fun to buy these things and you can't really trust that the docs are bad. What Browserbase does is we integrate a bunch of different CAPTCHAs. We do some stuff in-house, but generally we just integrate with a bunch of known vendors and continually monitor and maintain these things and say, is this working or not? Can we route around it or not? These are CAPTCHA solvers. CAPTCHA solvers, yeah. Not CAPTCHA providers, CAPTCHA solvers. Yeah, sorry. CAPTCHA solvers. We really try and make sure all of that works for you. I think as a dev, if I'm buying infrastructure, I want it all to work all the time and it's important for us to provide that experience by making sure everything does work and monitoring it on our own. Yeah. Right now, the world of CAPTCHAs is tricky. I think AI agents in particular are very much ahead of the internet infrastructure. CAPTCHAs are designed to block all types of bots, but there are now good bots and bad bots. I think in the future, CAPTCHAs will be able to identify who a good bot is, hopefully via some sort of KYC. For us, we've been very lucky. We have very little to no known abuse of Browserbase because we really look into who we work with. And for certain types of CAPTCHA solving, we only allow them on certain types of plans because we want to make sure that we can know what people are doing, what their use cases are. And that's really allowed us to try and be an arbiter of good bots, which is our long term goal. I want to build great relationships with people like Cloudflare so we can agree, hey, here are these acceptable bots. We'll identify them for you and make sure we flag when they come to your website. This is a good bot, you know?Alessio [00:23:23]: I see. And Cloudflare said they want to do more of this. So they're going to set by default, if they think you're an AI bot, they're going to reject. I'm curious if you think this is something that is going to be at the browser level or I mean, the DNS level with Cloudflare seems more where it should belong. But I'm curious how you think about it.Paul [00:23:40]: I think the web's going to change. You know, I think that the Internet as we have it right now is going to change. And we all need to just accept that the cat is out of the bag. And instead of kind of like wishing the Internet was like it was in the 2000s, we can have free content line that wouldn't be scraped. It's just it's not going to happen. And instead, we should think about like, one, how can we change? How can we change the models of, you know, information being published online so people can adequately commercialize it? But two, how do we rebuild applications that expect that AI agents are going to log in on their behalf? Those are the things that are going to allow us to kind of like identify good and bad bots. And I think the team at Clerk has been doing a really good job with this on the authentication side. I actually think that auth is the biggest thing that will prevent agents from accessing stuff, not captchas. And I think there will be agent auth in the future. I don't know if it's going to happen from an individual company, but actually authentication providers that have a, you know, hidden login as agent feature, which will then you put in your email, you'll get a push notification, say like, hey, your browser-based agent wants to log into your Airbnb. You can approve that and then the agent can proceed. That really circumvents the need for captchas or logging in as you and sharing your password. I think agent auth is going to be one way we identify good bots going forward. And I think a lot of this captcha solving stuff is really short-term problems as the internet kind of reorients itself around how it's going to work with agents browsing the web, just like people do. Yeah.Managing Distributed Browser Locations and Proxiesswyx [00:24:59]: Stitch recently was on Hacker News for talking about agent experience, AX, which is a thing that Netlify is also trying to clone and coin and talk about. And we've talked about this on our previous episodes before in a sense that I actually think that's like maybe the only part of the tech stack that needs to be kind of reinvented for agents. Everything else can stay the same, CLIs, APIs, whatever. But auth, yeah, we need agent auth. And it's mostly like short-lived, like it should not, it should be a distinct, identity from the human, but paired. I almost think like in the same way that every social network should have your main profile and then your alt accounts or your Finsta, it's almost like, you know, every, every human token should be paired with the agent token and the agent token can go and do stuff on behalf of the human token, but not be presumed to be the human. Yeah.Paul [00:25:48]: It's like, it's, it's actually very similar to OAuth is what I'm thinking. And, you know, Thread from Stitch is an investor, Colin from Clerk, Octaventures, all investors in browser-based because like, I hope they solve this because they'll make browser-based submission more possible. So we don't have to overcome all these hurdles, but I think it will be an OAuth-like flow where an agent will ask to log in as you, you'll approve the scopes. Like it can book an apartment on Airbnb, but it can't like message anybody. And then, you know, the agent will have some sort of like role-based access control within an application. Yeah. I'm excited for that.swyx [00:26:16]: The tricky part is just, there's one, one layer of delegation here, which is like, you're authoring my user's user or something like that. I don't know if that's tricky or not. Does that make sense? Yeah.Paul [00:26:25]: You know, actually at Twilio, I worked on the login identity and access. Management teams, right? So like I built Twilio's login page.swyx [00:26:31]: You were an intern on that team and then you became the lead in two years? Yeah.Paul [00:26:34]: Yeah. I started as an intern in 2016 and then I was the tech lead of that team. How? That's not normal. I didn't have a life. He's not normal. Look at this guy. I didn't have a girlfriend. I just loved my job. I don't know. I applied to 500 internships for my first job and I got rejected from every single one of them except for Twilio and then eventually Amazon. And they took a shot on me and like, I was getting paid money to write code, which was my dream. Yeah. Yeah. I'm very lucky that like this coding thing worked out because I was going to be doing it regardless. And yeah, I was able to kind of spend a lot of time on a team that was growing at a company that was growing. So it informed a lot of this stuff here. I think these are problems that have been solved with like the SAML protocol with SSO. I think it's a really interesting stuff with like WebAuthn, like these different types of authentication, like schemes that you can use to authenticate people. The tooling is all there. It just needs to be tweaked a little bit to work for agents. And I think the fact that there are companies that are already. Providing authentication as a service really sets it up. Well, the thing that's hard is like reinventing the internet for agents. We don't want to rebuild the internet. That's an impossible task. And I think people often say like, well, we'll have this second layer of APIs built for agents. I'm like, we will for the top use cases, but instead of we can just tweak the internet as is, which is on the authentication side, I think we're going to be the dumb ones going forward. Unfortunately, I think AI is going to be able to do a lot of the tasks that we do online, which means that it will be able to go to websites, click buttons on our behalf and log in on our behalf too. So with this kind of like web agent future happening, I think with some small structural changes, like you said, it feels like it could all slot in really nicely with the existing internet.Handling CAPTCHAs and Agent Authenticationswyx [00:28:08]: There's one more thing, which is the, your live view iframe, which lets you take, take control. Yeah. Obviously very key for operator now, but like, was, is there anything interesting technically there or that the people like, well, people always want this.Paul [00:28:21]: It was really hard to build, you know, like, so, okay. Headless browsers, you don't see them, right. They're running. They're running in a cloud somewhere. You can't like look at them. And I just want to really make, it's a weird name. I wish we came up with a better name for this thing, but you can't see them. Right. But customers don't trust AI agents, right. At least the first pass. So what we do with our live view is that, you know, when you use browser base, you can actually embed a live view of the browser running in the cloud for your customer to see it working. And that's what the first reason is the build trust, like, okay, so I have this script. That's going to go automate a website. I can embed it into my web application via an iframe and my customer can watch. I think. And then we added two way communication. So now not only can you watch the browser kind of being operated by AI, if you want to pause and actually click around type within this iframe that's controlling a browser, that's also possible. And this is all thanks to some of the lower level protocol, which is called the Chrome DevTools protocol. It has a API called start screencast, and you can also send mouse clicks and button clicks to a remote browser. And this is all embeddable within iframes. You have a browser within a browser, yo. And then you simulate the screen, the click on the other side. Exactly. And this is really nice often for, like, let's say, a capture that can't be solved. You saw this with Operator, you know, Operator actually uses a different approach. They use VNC. So, you know, you're able to see, like, you're seeing the whole window here. What we're doing is something a little lower level with the Chrome DevTools protocol. It's just PNGs being streamed over the wire. But the same thing is true, right? Like, hey, I'm running a window. Pause. Can you do something in this window? Human. Okay, great. Resume. Like sometimes 2FA tokens. Like if you get that text message, you might need a person to type that in. Web agents need human-in-the-loop type workflows still. You still need a person to interact with the browser. And building a UI to proxy that is kind of hard. You may as well just show them the whole browser and say, hey, can you finish this up for me? And then let the AI proceed on afterwards. Is there a future where I stream my current desktop to browser base? I don't think so. I think we're very much cloud infrastructure. Yeah. You know, but I think a lot of the stuff we're doing, we do want to, like, build tools. Like, you know, we'll talk about the stage and, you know, web agent framework in a second. But, like, there's a case where a lot of people are going desktop first for, you know, consumer use. And I think cloud is doing a lot of this, where I expect to see, you know, MCPs really oriented around the cloud desktop app for a reason, right? Like, I think a lot of these tools are going to run on your computer because it makes... I think it's breaking out. People are putting it on a server. Oh, really? Okay. Well, sweet. We'll see. We'll see that. I was surprised, though, wasn't I? I think that the browser company, too, with Dia Browser, it runs on your machine. You know, it's going to be...swyx [00:30:50]: What is it?Paul [00:30:51]: So, Dia Browser, as far as I understand... I used to use Arc. Yeah. I haven't used Arc. But I'm a big fan of the browser company. I think they're doing a lot of cool stuff in consumer. As far as I understand, it's a browser where you have a sidebar where you can, like, chat with it and it can control the local browser on your machine. So, if you imagine, like, what a consumer web agent is, which it lives alongside your browser, I think Google Chrome has Project Marina, I think. I almost call it Project Marinara for some reason. I don't know why. It's...swyx [00:31:17]: No, I think it's someone really likes the Waterworld. Oh, I see. The classic Kevin Costner. Yeah.Paul [00:31:22]: Okay. Project Marinara is a similar thing to the Dia Browser, in my mind, as far as I understand it. You have a browser that has an AI interface that will take over your mouse and keyboard and control the browser for you. Great for consumer use cases. But if you're building applications that rely on a browser and it's more part of a greater, like, AI app experience, you probably need something that's more like infrastructure, not a consumer app.swyx [00:31:44]: Just because I have explored a little bit in this area, do people want branching? So, I have the state. Of whatever my browser's in. And then I want, like, 100 clones of this state. Do people do that? Or...Paul [00:31:56]: People don't do it currently. Yeah. But it's definitely something we're thinking about. I think the idea of forking a browser is really cool. Technically, kind of hard. We're starting to see this in code execution, where people are, like, forking some, like, code execution, like, processes or forking some tool calls or branching tool calls. Haven't seen it at the browser level yet. But it makes sense. Like, if an AI agent is, like, using a website and it's not sure what path it wants to take to crawl this website. To find the information it's looking for. It would make sense for it to explore both paths in parallel. And that'd be a very, like... A road not taken. Yeah. And hopefully find the right answer. And then say, okay, this was actually the right one. And memorize that. And go there in the future. On the roadmap. For sure. Don't make my roadmap, please. You know?Alessio [00:32:37]: How do you actually do that? Yeah. How do you fork? I feel like the browser is so stateful for so many things.swyx [00:32:42]: Serialize the state. Restore the state. I don't know.Paul [00:32:44]: So, it's one of the reasons why we haven't done it yet. It's hard. You know? Like, to truly fork, it's actually quite difficult. The naive way is to open the same page in a new tab and then, like, hope that it's at the same thing. But if you have a form halfway filled, you may have to, like, take the whole, you know, container. Pause it. All the memory. Duplicate it. Restart it from there. It could be very slow. So, we haven't found a thing. Like, the easy thing to fork is just, like, copy the page object. You know? But I think there needs to be something a little bit more robust there. Yeah.swyx [00:33:12]: So, MorphLabs has this infinite branch thing. Like, wrote a custom fork of Linux or something that let them save the system state and clone it. MorphLabs, hit me up. I'll be a customer. Yeah. That's the only. I think that's the only way to do it. Yeah. Like, unless Chrome has some special API for you. Yeah.Paul [00:33:29]: There's probably something we'll reverse engineer one day. I don't know. Yeah.Alessio [00:33:32]: Let's talk about StageHand, the AI web browsing framework. You have three core components, Observe, Extract, and Act. Pretty clean landing page. What was the idea behind making a framework? Yeah.Stagehand: AI web browsing frameworkPaul [00:33:43]: So, there's three frameworks that are very popular or already exist, right? Puppeteer, Playwright, Selenium. Those are for building hard-coded scripts to control websites. And as soon as I started to play with LLMs plus browsing, I caught myself, you know, code-genning Playwright code to control a website. I would, like, take the DOM. I'd pass it to an LLM. I'd say, can you generate the Playwright code to click the appropriate button here? And it would do that. And I was like, this really should be part of the frameworks themselves. And I became really obsessed with SDKs that take natural language as part of, like, the API input. And that's what StageHand is. StageHand exposes three APIs, and it's a super set of Playwright. So, if you go to a page, you may want to take an action, click on the button, fill in the form, etc. That's what the act command is for. You may want to extract some data. This one takes a natural language, like, extract the winner of the Super Bowl from this page. You can give it a Zod schema, so it returns a structured output. And then maybe you're building an API. You can do an agent loop, and you want to kind of see what actions are possible on this page before taking one. You can do observe. So, you can observe the actions on the page, and it will generate a list of actions. You can guide it, like, give me actions on this page related to buying an item. And you can, like, buy it now, add to cart, view shipping options, and pass that to an LLM, an agent loop, to say, what's the appropriate action given this high-level goal? So, StageHand isn't a web agent. It's a framework for building web agents. And we think that agent loops are actually pretty close to the application layer because every application probably has different goals or different ways it wants to take steps. I don't think I've seen a generic. Maybe you guys are the experts here. I haven't seen, like, a really good AI agent framework here. Everyone kind of has their own special sauce, right? I see a lot of developers building their own agent loops, and they're using tools. And I view StageHand as the browser tool. So, we expose act, extract, observe. Your agent can call these tools. And from that, you don't have to worry about it. You don't have to worry about generating playwright code performantly. You don't have to worry about running it. You can kind of just integrate these three tool calls into your agent loop and reliably automate the web.swyx [00:35:48]: A special shout-out to Anirudh, who I met at your dinner, who I think listens to the pod. Yeah. Hey, Anirudh.Paul [00:35:54]: Anirudh's a man. He's a StageHand guy.swyx [00:35:56]: I mean, the interesting thing about each of these APIs is they're kind of each startup. Like, specifically extract, you know, Firecrawler is extract. There's, like, Expand AI. There's a whole bunch of, like, extract companies. They just focus on extract. I'm curious. Like, I feel like you guys are going to collide at some point. Like, right now, it's friendly. Everyone's in a blue ocean. At some point, it's going to be valuable enough that there's some turf battle here. I don't think you have a dog in a fight. I think you can mock extract to use an external service if they're better at it than you. But it's just an observation that, like, in the same way that I see each option, each checkbox in the side of custom GBTs becoming a startup or each box in the Karpathy chart being a startup. Like, this is also becoming a thing. Yeah.Paul [00:36:41]: I mean, like, so the way StageHand works is that it's MIT-licensed, completely open source. You bring your own API key to your LLM of choice. You could choose your LLM. We don't make any money off of the extract or really. We only really make money if you choose to run it with our browser. You don't have to. You can actually use your own browser, a local browser. You know, StageHand is completely open source for that reason. And, yeah, like, I think if you're building really complex web scraping workflows, I don't know if StageHand is the tool for you. I think it's really more if you're building an AI agent that needs a few general tools or if it's doing a lot of, like, web automation-intensive work. But if you're building a scraping company, StageHand is not your thing. You probably want something that's going to, like, get HTML content, you know, convert that to Markdown, query it. That's not what StageHand does. StageHand is more about reliability. I think we focus a lot on reliability and less so on cost optimization and speed at this point.swyx [00:37:33]: I actually feel like StageHand, so the way that StageHand works, it's like, you know, page.act, click on the quick start. Yeah. It's kind of the integration test for the code that you would have to write anyway, like the Puppeteer code that you have to write anyway. And when the page structure changes, because it always does, then this is still the test. This is still the test that I would have to write. Yeah. So it's kind of like a testing framework that doesn't need implementation detail.Paul [00:37:56]: Well, yeah. I mean, Puppeteer, Playwright, and Slenderman were all designed as testing frameworks, right? Yeah. And now people are, like, hacking them together to automate the web. I would say, and, like, maybe this is, like, me being too specific. But, like, when I write tests, if the page structure changes. Without me knowing, I want that test to fail. So I don't know if, like, AI, like, regenerating that. Like, people are using StageHand for testing. But it's more for, like, usability testing, not, like, testing of, like, does the front end, like, has it changed or not. Okay. But generally where we've seen people, like, really, like, take off is, like, if they're using, you know, something. If they want to build a feature in their application that's kind of like Operator or Deep Research, they're using StageHand to kind of power that tool calling in their own agent loop. Okay. Cool.swyx [00:38:37]: So let's go into Operator, the first big agent launch of the year from OpenAI. Seems like they have a whole bunch scheduled. You were on break and your phone blew up. What's your just general view of computer use agents is what they're calling it. The overall category before we go into Open Operator, just the overall promise of Operator. I will observe that I tried it once. It was okay. And I never tried it again.OpenAI's Operator and computer use agentsPaul [00:38:58]: That tracks with my experience, too. Like, I'm a huge fan of the OpenAI team. Like, I think that I do not view Operator as the company. I'm not a company killer for browser base at all. I think it actually shows people what's possible. I think, like, computer use models make a lot of sense. And I'm actually most excited about computer use models is, like, their ability to, like, really take screenshots and reasoning and output steps. I think that using mouse click or mouse coordinates, I've seen that proved to be less reliable than I would like. And I just wonder if that's the right form factor. What we've done with our framework is anchor it to the DOM itself, anchor it to the actual item. So, like, if it's clicking on something, it's clicking on that thing, you know? Like, it's more accurate. No matter where it is. Yeah, exactly. Because it really ties in nicely. And it can handle, like, the whole viewport in one go, whereas, like, Operator can only handle what it sees. Can you hover? Is hovering a thing that you can do? I don't know if we expose it as a tool directly, but I'm sure there's, like, an API for hovering. Like, move mouse to this position. Yeah, yeah, yeah. I think you can trigger hover, like, via, like, the JavaScript on the DOM itself. But, no, I think, like, when we saw computer use, everyone's eyes lit up because they realized, like, wow, like, AI is going to actually automate work for people. And I think seeing that kind of happen from both of the labs, and I'm sure we're going to see more labs launch computer use models, I'm excited to see all the stuff that people build with it. I think that I'd love to see computer use power, like, controlling a browser on browser base. And I think, like, Open Operator, which was, like, our open source version of OpenAI's Operator, was our first take on, like, how can we integrate these models into browser base? And we handle the infrastructure and let the labs do the models. I don't have a sense that Operator will be released as an API. I don't know. Maybe it will. I'm curious to see how well that works because I think it's going to be really hard for a company like OpenAI to do things like support CAPTCHA solving or, like, have proxies. Like, I think it's hard for them structurally. Imagine this New York Times headline, OpenAI CAPTCHA solving. Like, that would be a pretty bad headline, this New York Times headline. Browser base solves CAPTCHAs. No one cares. No one cares. And, like, our investors are bored. Like, we're all okay with this, you know? We're building this company knowing that the CAPTCHA solving is short-lived until we figure out how to authenticate good bots. I think it's really hard for a company like OpenAI, who has this brand that's so, so good, to balance with, like, the icky parts of web automation, which it can be kind of complex to solve. I'm sure OpenAI knows who to call whenever they need you. Yeah, right. I'm sure they'll have a great partnership.Alessio [00:41:23]: And is Open Operator just, like, a marketing thing for you? Like, how do you think about resource allocation? So, you can spin this up very quickly. And now there's all this, like, open deep research, just open all these things that people are building. We started it, you know. You're the original Open. We're the original Open operator, you know? Is it just, hey, look, this is a demo, but, like, we'll help you build out an actual product for yourself? Like, are you interested in going more of a product route? That's kind of the OpenAI way, right? They started as a model provider and then…Paul [00:41:53]: Yeah, we're not interested in going the product route yet. I view Open Operator as a model provider. It's a reference project, you know? Let's show people how to build these things using the infrastructure and models that are out there. And that's what it is. It's, like, Open Operator is very simple. It's an agent loop. It says, like, take a high-level goal, break it down into steps, use tool calling to accomplish those steps. It takes screenshots and feeds those screenshots into an LLM with the step to generate the right action. It uses stagehand under the hood to actually execute this action. It doesn't use a computer use model. And it, like, has a nice interface using the live view that we talked about, the iframe, to embed that into an application. So I felt like people on launch day wanted to figure out how to build their own version of this. And we turned that around really quickly to show them. And I hope we do that with other things like deep research. We don't have a deep research launch yet. I think David from AOMNI actually has an amazing open deep research that he launched. It has, like, 10K GitHub stars now. So he's crushing that. But I think if people want to build these features natively into their application, they need good reference projects. And I think Open Operator is a good example of that.swyx [00:42:52]: I don't know. Actually, I'm actually pretty bullish on API-driven operator. Because that's the only way that you can sort of, like, once it's reliable enough, obviously. And now we're nowhere near. But, like, give it five years. It'll happen, you know. And then you can sort of spin this up and browsers are working in the background and you don't necessarily have to know. And it just is booking restaurants for you, whatever. I can definitely see that future happening. I had this on the landing page here. This might be a slightly out of order. But, you know, you have, like, sort of three use cases for browser base. Open Operator. Or this is the operator sort of use case. It's kind of like the workflow automation use case. And it completes with UiPath in the sort of RPA category. Would you agree with that? Yeah, I would agree with that. And then there's Agents we talked about already. And web scraping, which I imagine would be the bulk of your workload right now, right?Paul [00:43:40]: No, not at all. I'd say actually, like, the majority is browser automation. We're kind of expensive for web scraping. Like, I think that if you're building a web scraping product, if you need to do occasional web scraping or you have to do web scraping that works every single time, you want to use browser automation. Yeah. You want to use browser-based. But if you're building web scraping workflows, what you should do is have a waterfall. You should have the first request is a curl to the website. See if you can get it without even using a browser. And then the second request may be, like, a scraping-specific API. There's, like, a thousand scraping APIs out there that you can use to try and get data. Scraping B. Scraping B is a great example, right? Yeah. And then, like, if those two don't work, bring out the heavy hitter. Like, browser-based will 100% work, right? It will load the page in a real browser, hydrate it. I see.swyx [00:44:21]: Because a lot of people don't render to JS.swyx [00:44:25]: Yeah, exactly.Paul [00:44:26]: So, I mean, the three big use cases, right? Like, you know, automation, web data collection, and then, you know, if you're building anything agentic that needs, like, a browser tool, you want to use browser-based.Alessio [00:44:35]: Is there any use case that, like, you were super surprised by that people might not even think about? Oh, yeah. Or is it, yeah, anything that you can share? The long tail is crazy. Yeah.Surprising use cases of BrowserbasePaul [00:44:44]: One of the case studies on our website that I think is the most interesting is this company called Benny. So, the way that it works is if you're on food stamps in the United States, you can actually get rebates if you buy certain things. Yeah. You buy some vegetables. You submit your receipt to the government. They'll give you a little rebate back. Say, hey, thanks for buying vegetables. It's good for you. That process of submitting that receipt is very painful. And the way Benny works is you use their app to take a photo of your receipt, and then Benny will go submit that receipt for you and then deposit the money into your account. That's actually using no AI at all. It's all, like, hard-coded scripts. They maintain the scripts. They've been doing a great job. And they build this amazing consumer app. But it's an example of, like, all these, like, tedious workflows that people have to do to kind of go about their business. And they're doing it for the sake of their day-to-day lives. And I had never known about, like, food stamp rebates or the complex forms you have to do to fill them. But the world is powered by millions and millions of tedious forms, visas. You know, Emirate Lighthouse is a customer, right? You know, they do the O1 visa. Millions and millions of forms are taking away humans' time. And I hope that Browserbase can help power software that automates away the web forms that we don't need anymore. Yeah.swyx [00:45:49]: I mean, I'm very supportive of that. I mean, forms. I do think, like, government itself is a big part of it. I think the government itself should embrace AI more to do more sort of human-friendly form filling. Mm-hmm. But I'm not optimistic. I'm not holding my breath. Yeah. We'll see. Okay. I think I'm about to zoom out. I have a little brief thing on computer use, and then we can talk about founder stuff, which is, I tend to think of developer tooling markets in impossible triangles, where everyone starts in a niche, and then they start to branch out. So I already hinted at a little bit of this, right? We mentioned more. We mentioned E2B. We mentioned Firecrawl. And then there's Browserbase. So there's, like, all this stuff of, like, have serverless virtual computer that you give to an agent and let them do stuff with it. And there's various ways of connecting it to the internet. You can just connect to a search API, like SERP API, whatever other, like, EXA is another one. That's what you're searching. You can also have a JSON markdown extractor, which is Firecrawl. Or you can have a virtual browser like Browserbase, or you can have a virtual machine like Morph. And then there's also maybe, like, a virtual sort of code environment, like Code Interpreter. So, like, there's just, like, a bunch of different ways to tackle the problem of give a computer to an agent. And I'm just kind of wondering if you see, like, everyone's just, like, happily coexisting in their respective niches. And as a developer, I just go and pick, like, a shopping basket of one of each. Or do you think that you eventually, people will collide?Future of browser automation and market competitionPaul [00:47:18]: I think that currently it's not a zero-sum market. Like, I think we're talking about... I think we're talking about all of knowledge work that people do that can be automated online. All of these, like, trillions of hours that happen online where people are working. And I think that there's so much software to be built that, like, I tend not to think about how these companies will collide. I just try to solve the problem as best as I can and make this specific piece of infrastructure, which I think is an important primitive, the best I possibly can. And yeah. I think there's players that are actually going to like it. I think there's players that are going to launch, like, over-the-top, you know, platforms, like agent platforms that have all these tools built in, right? Like, who's building the rippling for agent tools that has the search tool, the browser tool, the operating system tool, right? There are some. There are some. There are some, right? And I think in the end, what I have seen as my time as a developer, and I look at all the favorite tools that I have, is that, like, for tools and primitives with sufficient levels of complexity, you need to have a solution that's really bespoke to that primitive, you know? And I am sufficiently convinced that the browser is complex enough to deserve a primitive. Obviously, I have to. I'm the founder of BrowserBase, right? I'm talking my book. But, like, I think maybe I can give you one spicy take against, like, maybe just whole OS running. I think that when I look at computer use when it first came out, I saw that the majority of use cases for computer use were controlling a browser. And do we really need to run an entire operating system just to control a browser? I don't think so. I don't think that's necessary. You know, BrowserBase can run browsers for way cheaper than you can if you're running a full-fledged OS with a GUI, you know, operating system. And I think that's just an advantage of the browser. It is, like, browsers are little OSs, and you can run them very efficiently if you orchestrate it well. And I think that allows us to offer 90% of the, you know, functionality in the platform needed at 10% of the cost of running a full OS. Yeah.Open Operator: Browserbase's Open-Source Alternativeswyx [00:49:16]: I definitely see the logic in that. There's a Mark Andreessen quote. I don't know if you know this one. Where he basically observed that the browser is turning the operating system into a poorly debugged set of device drivers, because most of the apps are moved from the OS to the browser. So you can just run browsers.Paul [00:49:31]: There's a place for OSs, too. Like, I think that there are some applications that only run on Windows operating systems. And Eric from pig.dev in this upcoming YC batch, or last YC batch, like, he's building all run tons of Windows operating systems for you to control with your agent. And like, there's some legacy EHR systems that only run on Internet-controlled systems. Yeah.Paul [00:49:54]: I think that's it. I think, like, there are use cases for specific operating systems for specific legacy software. And like, I'm excited to see what he does with that. I just wanted to give a shout out to the pig.dev website.swyx [00:50:06]: The pigs jump when you click on them. Yeah. That's great.Paul [00:50:08]: Eric, he's the former co-founder of banana.dev, too.swyx [00:50:11]: Oh, that Eric. Yeah. That Eric. Okay. Well, he abandoned bananas for pigs. I hope he doesn't start going around with pigs now.Alessio [00:50:18]: Like he was going around with bananas. A little toy pig. Yeah. Yeah. I love that. What else are we missing? I think we covered a lot of, like, the browser-based product history, but. What do you wish people asked you? Yeah.Paul [00:50:29]: I wish people asked me more about, like, what will the future of software look like? Because I think that's really where I've spent a lot of time about why do browser-based. Like, for me, starting a company is like a means of last resort. Like, you shouldn't start a company unless you absolutely have to. And I remain convinced that the future of software is software that you're going to click a button and it's going to do stuff on your behalf. Right now, software. You click a button and it maybe, like, calls it back an API and, like, computes some numbers. It, like, modifies some text, whatever. But the future of software is software using software. So, I may log into my accounting website for my business, click a button, and it's going to go load up my Gmail, search my emails, find the thing, upload the receipt, and then comment it for me. Right? And it may use it using APIs, maybe a browser. I don't know. I think it's a little bit of both. But that's completely different from how we've built software so far. And that's. I think that future of software has different infrastructure requirements. It's going to require different UIs. It's going to require different pieces of infrastructure. I think the browser infrastructure is one piece that fits into that, along with all the other categories you mentioned. So, I think that it's going to require developers to think differently about how they've built software for, you know

Maya Kaczorowski noticed that AI identity and AI agent identity concerns were emerging from outside the security industry, rather than from CISOs and security leaders. She concluded that OAuth, the open standard for authentication, already serves the purpose of granting access without exposing passwords. Kaczorowski, a respected technologist and founder of Oblique, a startup focused on self-serve access controls, recently wrote about OAuth and AI agents and shared her insights on this episode of The New Stack Makers. She noted that developers see AI agents as extensions of themselves, granting them limited access to data and capabilities—precisely what OAuth is designed to handle. The challenges with AI agent identity are vast, involving different approaches to authentication, such as those explored by companies like AuthZed. While existing authorization models like RBAC or ABAC may still apply, the real challenge lies in scale. The exponential growth of AI-related entities—from users to LLMs—could mean even small organizations manage hundreds of thousands of agents. Future solutions must accommodate this massive scale efficiently. For the full discussion, check out The New Stack Makers interview with Kaczorowski. Learn more from The New Stack about OAuth requirements for AI Agents: OAuth 2.0: A Standard in Name Only? AI Agents Are Redefining the Future of Identity and Access ManagementJoin our community of newsletter subscribers to stay on top of the news and at the top of your game.  

Neste episódio, discutimos o modelo de segurança Zero Trust, uma abordagem revolucionária para proteger dados e sistemas em um ambiente corporativo. Com a crescente complexidade das ameaças cibernéticas e o aumento do trabalho remoto, o conceito de Zero Trust ganhou ainda mais relevância. Exploramos os princípios fundamentais do Zero Trust, como a premissa de "nunca confiar, sempre verificar", e como isso impacta diretamente a gestão de acessos, autenticação e segurança de redes. Também falamos sobre como implementar uma arquitetura Zero Trust em organizações de diferentes portes, com ênfase em tecnologias de ponta como autenticação multifatorial (MFA), controle de acesso baseado em identidade (IAM) e microsegmentação. Além disso, abordamos as vantagens de adotar uma abordagem Zero Trust para reduzir os riscos de segurança e como as práticas de DevSecOps se integram a essa filosofia para proteger ainda mais os pipelines de desenvolvimento e os sistemas de produção. Se você está interessado em aprender como a segurança moderna pode evoluir para enfrentar os desafios atuais, este episódio é imperdível!

Fake BSOD Delivered by Malicious Python Script Xavier found an odd malicious Python script that displays a blue screen of death to users. The purpose isn't quite clear. It could be a teach support scam tricking users into calling the 800 number displayed, or a simple anti-reversing trick https://isc.sans.edu/diary/Fake%20BSOD%20Delivered%20by%20Malicious%20Python%20Script/31686 The Danger of IP Volatility Accounting for IP addresses is important, and if not done properly, may lead to resources being exposed after IP addresses are released. https://isc.sans.edu/diary/The%20Danger%20of%20IP%20Volatility/31688 PostgreSQL SQL Injection Functions in PostgreSQL's libpq do not properly escape parameters which may lead to SQL injection issues if the functions are used to create input for pqsql. https://www.postgresql.org/support/security/CVE-2025-1094/ Multiple Russian Threat Actors Targeting Microsoft Device Code Auth The OAUTH device code flow is used to attach devices with limited input capability to a user's account. However, this can be abused via phishing attacks. https://www.volexity.com/blog/2025/02/13/multiple-russian-threat-actors-targeting-microsoft-device-code-authentication/

OpenIddict è migliorata davvero molto, ed ora può essere facilmente integrata nelle proprie applicazioni per realizzare dei proprio server OAuth 2.0/OpenID Connect. Dopo il cambio di licenza di IdentityServer (passato a pagamento), gli sviluppatori hanno preso la palla al balzo e hanno evoluto al meglio questa libreria.https://github.com/openiddict/openiddict-corehttps://docs.orchardcore.net/en/latest/reference/modules/OpenId/https://youtu.be/RSjwrBATcukhttps://youtu.be/5PoWTlhxThA#dotnet #OpenIddict #OpenIDConnect #dotnetinpillole #podcast

Episode 110: In this episode of Critical Thinking - Bug Bounty Podcast we hit some quick news items including a DOMPurify 3.2.3 Bypass, O3 mini updates, and a cool postLogger Chrome Extension. Then, we hone in on OAuth vulnerabilities, API keys, and innovative techniques hackers use to exploit these systems.Follow us on twitter at: https://x.com/ctbbpodcastGot any ideas and suggestions? Feel free to send us any feedback here: info@criticalthinkingpodcast.ioShoutout to https://x.com/realytcracker for the awesome intro music!====== Links ======Follow your hosts Rhynorater and Rez0 on Twitter: https://x.com/Rhynoraterhttps://x.com/rez0__====== Ways to Support CTBBPodcast ======Hop on the CTBB Discord at https://ctbb.show/discord!We also do Discord subs at $25, $10, and $5 - premium subscribers get access to private masterclasses, exploits, tools, scripts, un-redacted bug reports, etc.You can also find some hacker swag at https://ctbb.show/merch!====== Resources ======DOMPurify 3.2.3 BypassJason Zhou's post about O3 miniLive Chat Blog #2: Cisco Webex ConnectpostLogger Chrome ExtensionpostLogger Webstore LinkCommon OAuth VulnerabilitiesnOAuth: How Microsoft OAuth Misconfiguration Can Lead to Full Account TakeoverAccount Takeover using SSO LoginsKai Greshake====== Timestamps ======(00:00:00) Introduction(00:01:44) DOMPurify 3.2.3 Bypass(00:06:37) O3 mini(00:10:29) Ophion Security: Cisco Webex Connect(00:15:54) Discord Community News(00:19:12) postLogger Chrome Extension(00:21:04) Common OAuth Vulnerabilities & Lessons learned from Google's APIs

In this episode of the Microsoft Threat Intelligence Podcast, host Sherrod DeGrippo is joined by two expert guests to explore critical challenges in today's evolving threat landscape.  First, Sherrod sits down with Kelly Bissell, CVP of Fraud at Microsoft, to discuss the complexities of combating fraud and product abuse. Kelly digs into the unique challenges Microsoft faces, highlighting prevalent schemes such as crypto mining, tech support scams, and the exploitation of deepfakes. Kelly also shares insights into Microsoft's proactive approach, including recent Azure policy changes and efforts to detect and prevent fraud across its services, especially those attempting to use the compute power for crypto mining.  Later, Sherrod is joined by Priyanka Ramesha, Senior Threat Researcher on the Defender Experts team, to examine the rising risks of cloud-native attacks. They unpack why threat actors are increasingly targeting the cloud, exploiting its complexity, scalability, and common misconfigurations. Priyanka explains how attackers gain initial access through tactics like phishing, API exploitation, and OAuth abuse, and outlines their methods for credential theft, lateral movement, and data exfiltration.  In this episode you'll learn:       What crypto mining looks like in Azure and how Microsoft detects and prevents it  The five main areas of fraud and product abuse that Microsoft focuses on  How attackers exploit the complexity and misconfigurations in cloud infrastructures    Some questions we ask:         How long do crypto mining operations run unnoticed in a customer's environment?  What changes did Microsoft make to its policy regarding crypto mining?  Why are legitimate apps sometimes compromised and used in attacks?    Resources:   View Kelly Bissell on LinkedIn    View Priyanka Ramesha on LinkedIn   View Sherrod DeGrippo on LinkedIn     Related Microsoft Podcasts:                    Afternoon Cyber Tea with Ann Johnson  The BlueHat Podcast  Uncovering Hidden Risks        Discover and follow other Microsoft podcasts at microsoft.com/podcasts     Get the latest threat intelligence insights and guidance at Microsoft Security Insider    The Microsoft Threat Intelligence Podcast is produced by Microsoft and distributed as part of N2K media network.  

Threat modeling has been in the appsec toolbox for decades. But it hasn't always been used and it hasn't always been useful. Sandy Carielli shares what she's learned from talking to orgs about what's been successful, and what's failed, when they've approached this practice. Akira Brand joins to talk about her direct experience with building threat models with developers. Speculative data flow attacks demonstrated against Apple chips with SLAP and FLOP, the design and implementation choices that led to OCSP's demise, an appsec angle on AI, updating the threat model and recommendations for implementing OAuth 2.0, and more! Visit https://www.securityweekly.com/asw for all the latest episodes! Show Notes: https://securityweekly.com/asw-316

In this episode of Security Noise, Geoff and Skyler are joined by two TrustedSec experts, Security Consultant Edwin David and Principal Security Consultant Justin Bollinger, to talk about the evolution of authentication and what it looks like in 2025. We discuss passwordless authentication, multi-factor authentication (MFA), and the implications of OAuth and identity providers. The crew also discusses the dangers of using antiquated methods like SMS for authentication. About this podcast: Security Noise, a TrustedSec Podcast hosted by Geoff Walton and Producer/Contributor Skyler Tuter, features our cybersecurity experts in conversation about the infosec topics that interest them the most. Hack the planet!

Speculative data flow attacks demonstrated against Apple chips with SLAP and FLOP, the design and implementation choices that led to OCSP's demise, an appsec angle on AI, updating the threat model and recommendations for implementing OAuth 2.0, and more! Show Notes: https://securityweekly.com/asw-316

Threat modeling has been in the appsec toolbox for decades. But it hasn't always been used and it hasn't always been useful. Sandy Carielli shares what she's learned from talking to orgs about what's been successful, and what's failed, when they've approached this practice. Akira Brand joins to talk about her direct experience with building threat models with developers. Speculative data flow attacks demonstrated against Apple chips with SLAP and FLOP, the design and implementation choices that led to OCSP's demise, an appsec angle on AI, updating the threat model and recommendations for implementing OAuth 2.0, and more! Visit https://www.securityweekly.com/asw for all the latest episodes! Show Notes: https://securityweekly.com/asw-316

On the web side, we cover a portswigger post on ways of abusing unicode mishandling to bypass firewalls and a doyensec guide to OAuth vulnerabilities. We also get into a Windows exploit for a use-after-free in the telephony service that bypasses Control Flow Guard, and a data race due to non-atomic writes in the macOS kernel. Links and vulnerability summaries for this episode are available at: https://dayzerosec.com/podcast/271.html [00:00:00] Introduction [00:00:22] Bypassing character blocklists with unicode overflows [00:06:53] Common OAuth Vulnerabilities [00:18:37] Windows Telephony Service - It's Got Some Call-ing Issues [CVE-2024-26230] [00:32:05] TRAVERTINE (CVE-2025-24118) Podcast episodes are available on the usual podcast platforms: -- Apple Podcasts: https://podcasts.apple.com/us/podcast/id1484046063 -- Spotify: https://open.spotify.com/show/4NKCxk8aPEuEFuHsEQ9Tdt -- Google Podcasts: https://www.google.com/podcasts?feed=aHR0cHM6Ly9hbmNob3IuZm0vcy9hMTIxYTI0L3BvZGNhc3QvcnNz -- Other audio platforms can be found at https://anchor.fm/dayzerosec You can also join our discord: https://discord.gg/daTxTK9

Speculative data flow attacks demonstrated against Apple chips with SLAP and FLOP, the design and implementation choices that led to OCSP's demise, an appsec angle on AI, updating the threat model and recommendations for implementing OAuth 2.0, and more! Show Notes: https://securityweekly.com/asw-316

Episode 107: In this episode of Critical Thinking - Bug Bounty Podcast Justin and Joseph are tackling the subject of cross-origin security headers. They also cover some news items including Google's OAuth login flaw, RAINK, and gift card hacking.Follow us on twitter at: https://x.com/ctbbpodcastGot any ideas and suggestions? Feel free to send us any feedback here: info@criticalthinkingpodcast.ioShoutout to https://x.com/realytcracker for the awesome intro music!====== Links ======Follow your hosts on Twitter:https://x.com/Rhynoraterhttps://x.com/rez0__====== Ways to Support CTBBPodcast ======Hop on the CTBB Discord at https://ctbb.show/discord!We also do Discord subs at $25, $10, and $5 - premium subscribers get access to private masterclasses, exploits, tools, scripts, un-redacted bug reports, etc.You can also find some hacker swag at https://ctbb.show/merch!Today's Sponsor - ThreatLocker. Check out their Managed Detection and Response! https://www.criticalthinkingpodcast.io/tl-mdr====== Resources ======A Proud Dad's Tale of Two Bug Hunting Daughters and Their Responsible DisclosuresGoogle's OAuth login flawRez0's Ai tweetRez0's Follow-upRaink from BishopFoxGift cards security researchTop 10 web hacking techniques of 2024Cross-Origin-Opener-Policy: preventing attacks from popups====== Timestamps ======(00:00:00) Introduction(00:05:13) Hacking with your kids(00:09:46) H1/bc pentests(00:12:23) Google's OAuth login flaw(00:18:01) Raink & Rez0's AI tweets(00:28:46) Giftcard hacking & Portswigger top 10 voting(00:34:23) Cross Origin Web Headers

In this exclusive interview, Mirela Ciobanu, Lead Editor at The Paypers, sits down with Justin Richer, CTO of UberEther, to explore the latest advancements and challenges in digital identity at cyberevolution 2024.

Today's episode covers an odd 12 year old Netgear vulnerability that only received a proper CVE number last year. Learn about how to properly identify OpenID connect users and avoid domain name resue. Good old rsync turns out to be in need of patching and Fortinet: Not sure if it needs patching. Probably it does. Go ahead and patch it. The Curious Case of a 12-Year-Old Netgear Router Vulnerability Outdated Netgear routers remain a security risk, with attackers actively exploiting a 2013 vulnerability to deploy crypto miners. Learn how to protect your network by updating or replacing legacy hardware. URL: https://isc.sans.edu/diary/The%20Curious%20Case%20of%20a%2012-Year-Old%20Netgear%20Router%20Vulnerability/31592 Millions at Risk Due to Google s OAuth Flaw A flaw in Google s OAuth implementation enables attackers to exploit defunct domain accounts, exposing sensitive data. Tips on implementing MFA and domain monitoring to reduce risks. URL: https://trufflesecurity.com/blog/millions-at-risk-due-to-google-s-oauth-flaw Rsync 3.4.0 Security Release The latest rsync update fixes critical vulnerabilities, including buffer overflows and symbolic link issues. Upgrade immediately to protect your file synchronization processes. URL: https://download.samba.org/pub/rsync/NEWS#3.4.0 Fortinet PSIRT Advisories: Stay Secure Fortinet's latest advisories address vulnerabilities in FortiOS, FortiProxy, and more. Review and apply patches promptly to secure your perimeter defenses. URL: https://www.fortiguard.com/psirt

Join Dan Vega and DaShaun Carter for an essential episode on Spring Security best practices. They welcome Steve Riesenberg from the Spring Security team, who brings his extensive expertise in OAuth and Spring Authorization Server. Steve shares practical recipes for solving common security challenges, offering invaluable insights for securing your Spring applications. Whether you're implementing OAuth, handling authentication, or managing authorization, this episode provides the solutions you need from one of Spring Security's core contributors. You can participate in our live stream to ask questions or catch the replay on your preferred podcast platform.Show Notes: Steve Riesenberg XSpring Security Documentation Spring Authorization Server Documentation 

Summary In this episode of the Defensive Security Podcast, hosts Jerry Bell and Andrew Kalat discuss various cybersecurity topics, including a significant incident involving a Tenable plugin update that disrupted Nessus agents worldwide. They delve into the implications of malicious Chrome extensions and sophisticated phishing attacks, particularly focusing on a recent incident involving OAuth trust … Continue reading Defensive Security Podcast Episode 291 →

Join Dan Vega and DaShaun Carter as they welcome Spring Security project lead Rob Winch for an in-depth look at Spring Security 6.4. In this episode, the team explores exciting new features including One-Time Token Login support, Passkeys integration, and significant improvements to OAuth 2.0 and SAML 2.0. Rob shares insights into important deprecation notices as Spring Security moves towards version 7, demonstrates new method security capabilities, and discusses the introduction of RestClient-based implementations. You can participate in our live stream to ask questions or catch the replay on your preferred podcast platform.Show Notes: What's new in Spring Security 6.4Rob Winch on BlueSky

Send us a textCould the lack of hardware and firmware knowledge be the Achilles' heel of today's cybersecurity efforts? Join me, Sean Gerber, on the CISSP Cyber Training Podcast as we unpack the critical challenges faced by IT and security leaders, particularly in hardware-intensive sectors like manufacturing. We expose the concerning gaps in understanding that are leaving organizations vulnerable, and propose actionable solutions like fostering stronger collaboration between IT teams, security personnel, and suppliers. Tackling the prevalent issue of BIOS password sharing, we recommend secure password management tools, like CyberArk, and advocate for a shift from the culture of replacing devices to one of repair and repurposing, all while ensuring data is securely erased to prevent breaches.Shifting focus to authentication and password security, this episode dives into the essentials of Role-Based Access Control (RBAC), two-factor authentication, and the power of identity federation with protocols like SAML or OAuth. We dissect the benefits of Single Sign-On (SSO) for seamless multi-application access, while highlighting the necessity of identity proofing during onboarding. Finally, we take a hard look at common password pitfalls, stressing the importance of robust security practices. Our mission? To empower listeners with the knowledge and resources they need to bolster their cybersecurity measures—visit CISSP Cyber Training and ReduceCyberRisk.com for a deeper dive into fortifying your defenses.Gain access to 60 FREE CISSP Practice Questions each and every month for the next 6 months by going to FreeCISSPQuestions.com and sign-up to join the team for Free. That is 360 FREE questions to help you study and pass the CISSP Certification. Join Today!

In this videocast, Tom Bruggeman from DPG Media shares how his team tackled the challenges of user authentication in a fast-changing media landscape. He highlights the role of open standards like OAuth and OIDC and explains how Authlete helped create a seamless and secure user experience. Tom also offers insights into future plans, including efforts to enhance user privacy and explore data wallet solutions.

A short episode this week, featuring Keyhole which abuses a logic bug in Windows Store DRM, an OAuth flow issue, and a CSRF protection bypass. Links and vulnerability summaries for this episode are available at: https://dayzerosec.com/podcast/265.html [00:00:00] Introduction [00:00:16] Attacking Hypervisors From KVM to Mobile Security Platforms [00:02:30] Keyhole [00:10:12] Drilling the redirect_uri in OAuth [00:18:00] Cross-Site POST Requests Without a Content-Type Header [00:24:03] New AMSI Bypss Technique Modifying CLR.DLL in Memory Podcast episodes are available on the usual podcast platforms: -- Apple Podcasts: https://podcasts.apple.com/us/podcast/id1484046063 -- Spotify: https://open.spotify.com/show/4NKCxk8aPEuEFuHsEQ9Tdt -- Google Podcasts: https://www.google.com/podcasts?feed=aHR0cHM6Ly9hbmNob3IuZm0vcy9hMTIxYTI0L3BvZGNhc3QvcnNz -- Other audio platforms can be found at https://anchor.fm/dayzerosec You can also join our discord: https://discord.gg/daTxTK9

This Videocast episode explores the complexities and advancements in digital identity standards, focusing on FAPI, OAuth, and OpenID Connect. Martin Kuppinger and Joseph Heenan, CTO of Authlete, discuss the origins and purpose of FAPI, its adoption across various regions, and its significance in enhancing security and interoperability in financial services. They also highlight the role of Authlete in simplifying the implementation of these standards for developers and the emerging trends in decentralized identity and verifiable credentials.

Fredrik, Petter, och Lars sammanfattar intryck från årets Øredev. Vi diskuterar bra presentationer, och allt bra man får ut som både ny utvecklare och erfaren av alla människor man kan prata med mellan presentationerna. Efter att ha snackat om Petters favoritpresentationer glider vi över i en diskussion om andra fördelar man som junior får av att gå på presentationer - få ett hum om nya saker och lära sig på hur många olika sätt utvecklare pratar om och har åsikter om saker. Speciellt när folk har starka åsikter om saker och talar om dem som om de var det sämsta i det kända universum. Kan Lars få lika många visningar på sin presentation i år som förra året? Vi pratar också om att lära sig hur saker fungerar under huven, och hur det gör det ännu knepigare att förstå hur helheten fungerar idag. Ett stort tack till Cloudnet som sponsrar vår VPS! Har du kommentarer, frågor eller tips? Vi är @kodsnack, @thieta, @krig, och @bjoreman på Mastodon, har en sida på Facebook och epostas på info@kodsnack.se om du vill skriva längre. Vi läser allt som skickas. Gillar du Kodsnack får du hemskt gärna recensera oss i iTunes! Du kan också stödja podden genom att ge oss en kaffe (eller två!) på Ko-fi, eller handla något i vår butik. Länkar Lars Petter Systemutveckling .net på campus Varberg Qlok Poddavsnittet i Varberg Øredev Lars presentation - video är inte ute än Taylor Taylors presentation - video är inte ute än Lars och Taylor i Changelog Filip Ekberg och hans presentation - video är inte ute än Prydnadseremit - “The trend continued through the 1830s, when the idea became less popular as estate landscaping concepts evolved.” Matt Topol och hans presentationer - video är inte ute än Grafdatabas Entity framework Kallbadhuset Svante Richter och hans presentation - video är inte ute än Postgresql Raniz Rendle om högpresterande .net - video är inte ute än Avoiding code spaghetti by making ravioli - video är inte ute än Spagettikod Dan Abramovs presentation the WET codebase hade lasagnekod, inte ravioli OTP - Open telecom platform OTP - One-time password OTP - One time programmable memory Javascripthistorie-presentationen How Javascript happened: a short history of programming languages - video är inte ute än Stöd oss på Ko-fi! Lars presentation från förra året Nerves Rob Richardson om testning i .net - video är inte ute än Tore Nestenius om autentisering i .net - video är inte ute än OAuth 2 Kent Beck Titlar Vad gör jag? Inte bara lärare Allmänt nördiga personer i sällskap Jag kan rapportera från korridoren Jag hade en timme lunch Får hem en masssa luft Intermedior Intermedioker Open source-pensionär Höra ord Kroniska fasoner Sprida mina nördiga specialintressen Komma ihåg vad det var jag gick på Du, jag, och Raniz Om en ravioli går sönder Tre skilda OTP Vet jag varför så kan jag optimera hur

Alessio will be at AWS re:Invent next week and hosting a casual coffee meetup on Wednesday, RSVP here! And subscribe to our calendar for our Singapore, NeurIPS, and all upcoming meetups!We are still taking questions for our next big recap episode! Submit questions and messages on Speakpipe here for a chance to appear on the show!If you've been following the AI agents space, you have heard of Lindy AI; while founder Flo Crivello is hesitant to call it "blowing up," when folks like Andrew Wilkinson start obsessing over your product, you're definitely onto something.In our latest episode, Flo walked us through Lindy's evolution from late 2022 to now, revealing some design choices about agent platform design that go against conventional wisdom in the space.The Great Reset: From Text Fields to RailsRemember late 2022? Everyone was "LLM-pilled," believing that if you just gave a language model enough context and tools, it could do anything. Lindy 1.0 followed this pattern:* Big prompt field ✅* Bunch of tools ✅* Prayer to the LLM gods ✅Fast forward to today, and Lindy 2.0 looks radically different. As Flo put it (~17:00 in the episode): "The more you can put your agent on rails, one, the more reliable it's going to be, obviously, but two, it's also going to be easier to use for the user."Instead of a giant, intimidating text field, users now build workflows visually:* Trigger (e.g., "Zendesk ticket received")* Required actions (e.g., "Check knowledge base")* Response generationThis isn't just a UI change - it's a fundamental rethinking of how to make AI agents reliable. As Swyx noted during our discussion: "Put Shoggoth in a box and make it a very small, minimal viable box. Everything else should be traditional if-this-then-that software."The Surprising Truth About Model LimitationsHere's something that might shock folks building in the space: with Claude 3.5 Sonnet, the model is no longer the bottleneck. Flo's exact words (~31:00): "It is actually shocking the extent to which the model is no longer the limit. It was the limit a year ago. It was too expensive. The context window was too small."Some context: Lindy started when context windows were 4K tokens. Today, their system prompt alone is larger than that. But what's really interesting is what this means for platform builders:* Raw capabilities aren't the constraint anymore* Integration quality matters more than model performance* User experience and workflow design are the new bottlenecksThe Search Engine Parallel: Why Horizontal Platforms Might WinOne of the spiciest takes from our conversation was Flo's thesis on horizontal vs. vertical agent platforms. He draws a fascinating parallel to search engines (~56:00):"I find it surprising the extent to which a horizontal search engine has won... You go through Google to search Reddit. You go through Google to search Wikipedia... search in each vertical has more in common with search than it does with each vertical."His argument: agent platforms might follow the same pattern because:* Agents across verticals share more commonalities than differences* There's value in having agents that can work together under one roof* The R&D cost of getting agents right is better amortized across use casesThis might explain why we're seeing early vertical AI companies starting to expand horizontally. The core agent capabilities - reliability, context management, tool integration - are universal needs.What This Means for BuildersIf you're building in the AI agents space, here are the key takeaways:* Constrain First: Rather than maximizing capabilities, focus on reliable execution within narrow bounds* Integration Quality Matters: With model capabilities plateauing, your competitive advantage lies in how well you integrate with existing tools* Memory Management is Key: Flo revealed they actively prune agent memories - even with larger context windows, not all memories are useful* Design for Discovery: Lindy's visual workflow builder shows how important interface design is for adoptionThe Meta LayerThere's a broader lesson here about AI product development. Just as Lindy evolved from "give the LLM everything" to "constrain intelligently," we might see similar evolution across the AI tooling space. The winners might not be those with the most powerful models, but those who best understand how to package AI capabilities in ways that solve real problems reliably.Full Video PodcastFlo's talk at AI Engineer SummitChapters* 00:00:00 Introductions * 00:04:05 AI engineering and deterministic software * 00:08:36 Lindys demo* 00:13:21 Memory management in AI agents * 00:18:48 Hierarchy and collaboration between Lindys * 00:21:19 Vertical vs. horizontal AI tools * 00:24:03 Community and user engagement strategies * 00:26:16 Rickrolling incident with Lindy * 00:28:12 Evals and quality control in AI systems * 00:31:52 Model capabilities and their impact on Lindy * 00:39:27 Competition and market positioning * 00:42:40 Relationship between Factorio and business strategy * 00:44:05 Remote work vs. in-person collaboration * 00:49:03 Europe vs US Tech* 00:58:59 Testing the Overton window and free speech * 01:04:20 Balancing AI safety concerns with business innovation Show Notes* Lindy.ai* Rick Rolling* Flo on X* TeamFlow* Andrew Wilkinson* Dust* Poolside.ai* SB1047* Gathertown* Sid Sijbrandij* Matt Mullenweg* Factorio* Seeing Like a StateTranscriptAlessio [00:00:00]: Hey everyone, welcome to the Latent Space Podcast. This is Alessio, partner and CTO at Decibel Partners, and I'm joined by my co-host Swyx, founder of Smol.ai.Swyx [00:00:12]: Hey, and today we're joined in the studio by Florent Crivello. Welcome.Flo [00:00:15]: Hey, yeah, thanks for having me.Swyx [00:00:17]: Also known as Altimore. I always wanted to ask, what is Altimore?Flo [00:00:21]: It was the name of my character when I was playing Dungeons & Dragons. Always. I was like 11 years old.Swyx [00:00:26]: What was your classes?Flo [00:00:27]: I was an elf. I was a magician elf.Swyx [00:00:30]: Well, you're still spinning magic. Right now, you're a solo founder and CEO of Lindy.ai. What is Lindy?Flo [00:00:36]: Yeah, we are a no-code platform letting you build your own AI agents easily. So you can think of we are to LangChain as Airtable is to MySQL. Like you can just pin up AI agents super easily by clicking around and no code required. You don't have to be an engineer and you can automate business workflows that you simply could not automate before in a few minutes.Swyx [00:00:55]: You've been in our orbit a few times. I think you spoke at our Latent Space anniversary. You spoke at my summit, the first summit, which was a really good keynote. And most recently, like we actually already scheduled this podcast before this happened. But Andrew Wilkinson was like, I'm obsessed by Lindy. He's just created a whole bunch of agents. So basically, why are you blowing up?Flo [00:01:16]: Well, thank you. I think we are having a little bit of a moment. I think it's a bit premature to say we're blowing up. But why are things going well? We revamped the product majorly. We called it Lindy 2.0. I would say we started working on that six months ago. We've actually not really announced it yet. It's just, I guess, I guess that's what we're doing now. And so we've basically been cooking for the last six months, like really rebuilding the product from scratch. I think I'll list you, actually, the last time you tried the product, it was still Lindy 1.0. Oh, yeah. If you log in now, the platform looks very different. There's like a ton more features. And I think one realization that we made, and I think a lot of folks in the agent space made the same realization, is that there is such a thing as too much of a good thing. I think many people, when they started working on agents, they were very LLM peeled and chat GPT peeled, right? They got ahead of themselves in a way, and us included, and they thought that agents were actually, and LLMs were actually more advanced than they actually were. And so the first version of Lindy was like just a giant prompt and a bunch of tools. And then the realization we had was like, hey, actually, the more you can put your agent on Rails, one, the more reliable it's going to be, obviously, but two, it's also going to be easier to use for the user, because you can really, as a user, you get, instead of just getting this big, giant, intimidating text field, and you type words in there, and you have no idea if you're typing the right word or not, here you can really click and select step by step, and tell your agent what to do, and really give as narrow or as wide a guardrail as you want for your agent. We started working on that. We called it Lindy on Rails about six months ago, and we started putting it into the hands of users over the last, I would say, two months or so, and I think things really started going pretty well at that point. The agent is way more reliable, way easier to set up, and we're already seeing a ton of new use cases pop up.Swyx [00:03:00]: Yeah, just a quick follow-up on that. You launched the first Lindy in November last year, and you were already talking about having a DSL, right? I remember having this discussion with you, and you were like, it's just much more reliable. Is this still the DSL under the hood? Is this a UI-level change, or is it a bigger rewrite?Flo [00:03:17]: No, it is a much bigger rewrite. I'll give you a concrete example. Suppose you want to have an agent that observes your Zendesk tickets, and it's like, hey, every time you receive a Zendesk ticket, I want you to check my knowledge base, so it's like a RAG module and whatnot, and then answer the ticket. The way it used to work with Lindy before was, you would type the prompt asking it to do that. You check my knowledge base, and so on and so forth. The problem with doing that is that it can always go wrong. You're praying the LLM gods that they will actually invoke your knowledge base, but I don't want to ask it. I want it to always, 100% of the time, consult the knowledge base after it receives a Zendesk ticket. And so with Lindy, you can actually have the trigger, which is Zendesk ticket received, have the knowledge base consult, which is always there, and then have the agent. So you can really set up your agent any way you want like that.Swyx [00:04:05]: This is something I think about for AI engineering as well, which is the big labs want you to hand over everything in the prompts, and only code of English, and then the smaller brains, the GPU pours, always want to write more code to make things more deterministic and reliable and controllable. One way I put it is put Shoggoth in a box and make it a very small, the minimal viable box. Everything else should be traditional, if this, then that software.Flo [00:04:29]: I love that characterization, put the Shoggoth in the box. Yeah, we talk about using as much AI as necessary and as little as possible.Alessio [00:04:37]: And what was the choosing between kind of like this drag and drop, low code, whatever, super code-driven, maybe like the Lang chains, auto-GPT of the world, and maybe the flip side of it, which you don't really do, it's like just text to agent, it's like build the workflow for me. Like what have you learned actually putting this in front of users and figuring out how much do they actually want to add it versus like how much, you know, kind of like Ruby on Rails instead of Lindy on Rails, it's kind of like, you know, defaults over configuration.Flo [00:05:06]: I actually used to dislike when people said, oh, text is not a great interface. I was like, ah, this is such a mid-take, I think text is awesome. And I've actually come around, I actually sort of agree now that text is really not great. I think for people like you and me, because we sort of have a mental model, okay, when I type a prompt into this text box, this is what it's going to do, it's going to map it to this kind of data structure under the hood and so forth. I guess it's a little bit blackmailing towards humans. You jump on these calls with humans and you're like, here's a text box, this is going to set up an agent for you, do it. And then they type words like, I want you to help me put order in my inbox. Oh, actually, this is a good one. This is actually a good one. What's a bad one? I would say 60 or 70% of the prompts that people type don't mean anything. Me as a human, as AGI, I don't understand what they mean. I don't know what they mean. It is actually, I think whenever you can have a GUI, it is better than to have just a pure text interface.Alessio [00:05:58]: And then how do you decide how much to expose? So even with the tools, you have Slack, you have Google Calendar, you have Gmail. Should people by default just turn over access to everything and then you help them figure out what to use? I think that's the question. When I tried to set up Slack, it was like, hey, give me access to all channels and everything, which for the average person probably makes sense because you don't want to re-prompt them every time you add new channels. But at the same time, for maybe the more sophisticated enterprise use cases, people are like, hey, I want to really limit what you have access to. How do you kind of thread that balance?Flo [00:06:35]: The general philosophy is we ask for the least amount of permissions needed at any given moment. I don't think Slack, I could be mistaken, but I don't think Slack lets you request permissions for just one channel. But for example, for Google, obviously there are hundreds of scopes that you could require for Google. There's a lot of scopes. And sometimes it's actually painful to set up your Lindy because you're going to have to ask Google and add scopes five or six times. We've had sessions like this. But that's what we do because, for example, the Lindy email drafter, she's going to ask you for your authorization once for, I need to be able to read your email so I can draft a reply, and then another time for I need to be able to write a draft for them. We just try to do it very incrementally like that.Alessio [00:07:15]: Do you think OAuth is just overall going to change? I think maybe before it was like, hey, we need to set up OAuth that humans only want to kind of do once. So we try to jam-pack things all at once versus what if you could on-demand get different permissions every time from different parts? Do you ever think about designing things knowing that maybe AI will use it instead of humans will use it? Yeah, for sure.Flo [00:07:37]: One pattern we've started to see is people provisioning accounts for their AI agents. And so, in particular, Google Workspace accounts. So, for example, Lindy can be used as a scheduling assistant. So you can just CC her to your emails when you're trying to find time with someone. And just like a human assistant, she's going to go back and forth and offer other abilities and so forth. Very often, people don't want the other party to know that it's an AI. So it's actually funny. They introduce delays. They ask the agent to wait before replying, so it's not too obvious that it's an AI. And they provision an account on Google Suite, which costs them like $10 a month or something like that. So we're seeing that pattern more and more. I think that does the job for now. I'm not optimistic on us actually patching OAuth. Because I agree with you, ultimately, we would want to patch OAuth because the new account thing is kind of a clutch. It's really a hack. You would want to patch OAuth to have more granular access control and really be able to put your sugar in the box. I'm not optimistic on us doing that before AGI, I think. That's a very close timeline.Swyx [00:08:36]: I'm mindful of talking about a thing without showing it. And we already have the setup to show it. Why don't we jump into a screen share? For listeners, you can jump on the YouTube and like and subscribe. But also, let's have a look at how you show off Lindy. Yeah, absolutely.Flo [00:08:51]: I'll give an example of a very simple Lindy and then I'll graduate to a much more complicated one. A super simple Lindy that I have is, I unfortunately bought some investment properties in the south of France. It was a really, really bad idea. And I put them on a Holydew, which is like the French Airbnb, if you will. And so I received these emails from time to time telling me like, oh, hey, you made 200 bucks. Someone booked your place. When I receive these emails, I want to log this reservation in a spreadsheet. Doing this without an AI agent or without AI in general is a pain in the butt because you must write an HTML parser for this email. And so it's just hard. You may not be able to do it and it's going to break the moment the email changes. By contrast, the way it works with Lindy, it's really simple. It's two steps. It's like, okay, I receive an email. If it is a reservation confirmation, I have this filter here. Then I append a row to this spreadsheet. And so this is where you can see the AI part where the way this action is configured here, you see these purple fields on the right. Each of these fields is a prompt. And so I can say, okay, you extract from the email the day the reservation begins on. You extract the amount of the reservation. You extract the number of travelers of the reservation. And now you can see when I look at the task history of this Lindy, it's really simple. It's like, okay, you do this and boom, appending this row to this spreadsheet. And this is the information extracted. So effectively, this node here, this append row node is a mini agent. It can see everything that just happened. It has context over the task and it's appending the row. And then it's going to send a reply to the thread. That's a very simple example of an agent.Swyx [00:10:34]: A quick follow-up question on this one while we're still on this page. Is that one call? Is that a structured output call? Yeah. Okay, nice. Yeah.Flo [00:10:41]: And you can see here for every node, you can configure which model you want to power the node. Here I use cloud. For this, I use GPT-4 Turbo. Much more complex example, my meeting recorder. It looks very complex because I've added to it over time, but at a high level, it's really simple. It's like when a meeting begins, you record the meeting. And after the meeting, you send me a summary and you send me coaching notes. So I receive, like my Lindy is constantly coaching me. And so you can see here in the prompt of the coaching notes, I've told it, hey, you know, was I unnecessarily confrontational at any point? I'm French, so I have to watch out for that. Or not confrontational enough. Should I have double-clicked on any issue, right? So I can really give it exactly the kind of coaching that I'm expecting. And then the interesting thing here is, like, you can see the agent here, after it sent me these coaching notes, moves on. And it does a bunch of other stuff. So it goes on Slack. It disseminates the notes on Slack. It does a bunch of other stuff. But it's actually able to backtrack and resume the automation at the coaching notes email if I responded to that email. So I'll give a super concrete example. This is an actual coaching feedback that I received from Lindy. She was like, hey, this was a sales call I had with a customer. And she was like, I found your explanation of Lindy too technical. And I was able to follow up and just ask a follow-up question in the thread here. And I was like, why did you find too technical about my explanation? And Lindy restored the context. And so she basically picked up the automation back up here in the tree. And she has all of the context of everything that happened, including the meeting in which I was. So she was like, oh, you used the words deterministic and context window and agent state. And that concept exists at every level for every channel and every action that Lindy takes. So another example here is, I mentioned she also disseminates the notes on Slack. So this was a meeting where I was not, right? So this was a teammate. He's an indie meeting recorder, posts the meeting notes in this customer discovery channel on Slack. So you can see, okay, this is the onboarding call we had. This was the use case. Look at the questions. How do I make Lindy slower? How do I add delays to make Lindy slower? And I was able, in the Slack thread, to ask follow-up questions like, oh, what did we answer to these questions? And it's really handy because I know I can have this sort of interactive Q&A with these meetings. It means that very often now, I don't go to meetings anymore. I just send my Lindy. And instead of going to like a 60-minute meeting, I have like a five-minute chat with my Lindy afterwards. And she just replied. She was like, well, this is what we replied to this customer. And I can just be like, okay, good job, Jack. Like, no notes about your answers. So that's the kind of use cases people have with Lindy. It's a lot of like, there's a lot of sales automations, customer support automations, and a lot of this, which is basically personal assistance automations, like meeting scheduling and so forth.Alessio [00:13:21]: Yeah, and I think the question that people might have is memory. So as you get coaching, how does it track whether or not you're improving? You know, if these are like mistakes you made in the past, like, how do you think about that?Flo [00:13:31]: Yeah, we have a memory module. So I'll show you my meeting scheduler, Lindy, which has a lot of memories because by now I've used her for so long. And so every time I talk to her, she saves a memory. If I tell her, you screwed up, please don't do this. So you can see here, oh, it's got a double memory here. This is the meeting link I have, or this is the address of the office. If I tell someone to meet me at home, this is the address of my place. This is the code. I guess we'll have to edit that out. This is not the code of my place. No dogs. Yeah, so Lindy can just manage her own memory and decide when she's remembering things between executions. Okay.Swyx [00:14:11]: I mean, I'm just going to take the opportunity to ask you, since you are the creator of this thing, how come there's so few memories, right? Like, if you've been using this for two years, there should be thousands of thousands of things. That is a good question.Flo [00:14:22]: Agents still get confused if they have too many memories, to my point earlier about that. So I just am out of a call with a member of the Lama team at Meta, and we were chatting about Lindy, and we were going into the system prompt that we sent to Lindy, and all of that stuff. And he was amazed, and he was like, it's a miracle that it's working, guys. He was like, this kind of system prompt, this does not exist, either pre-training or post-training. These models were never trained to do this kind of stuff. It's a miracle that they can be agents at all. And so what I do, I actually prune the memories. You know, it's actually something I've gotten into the habit of doing from back when we had GPT 3.5, being Lindy agents. I suspect it's probably not as necessary in the Cloud 3.5 Sunette days, but I prune the memories. Yeah, okay.Swyx [00:15:05]: The reason is because I have another assistant that also is recording and trying to come up with facts about me. It comes up with a lot of trivial, useless facts that I... So I spend most of my time pruning. Actually, it's not super useful. I'd much rather have high-quality facts that it accepts. Or maybe I was even thinking, were you ever tempted to add a wake word to only memorize this when I say memorize this? And otherwise, don't even bother.Flo [00:15:30]: I have a Lindy that does this. So this is my inbox processor, Lindy. It's kind of beefy because there's a lot of different emails. But somewhere in here,Swyx [00:15:38]: there is a rule where I'm like,Flo [00:15:39]: aha, I can email my inbox processor, Lindy. It's really handy. So she has her own email address. And so when I process my email inbox, I sometimes forward an email to her. And it's a newsletter, or it's like a cold outreach from a recruiter that I don't care about, or anything like that. And I can give her a rule. And I can be like, hey, this email I want you to archive, moving forward. Or I want you to alert me on Slack when I have this kind of email. It's really important. And so you can see here, the prompt is, if I give you a rule about a kind of email, like archive emails from X, save it as a new memory. And I give it to the memory saving skill. And yeah.Swyx [00:16:13]: One thing that just occurred to me, so I'm a big fan of virtual mailboxes. I recommend that everybody have a virtual mailbox. You could set up a physical mail receive thing for Lindy. And so then Lindy can process your physical mail.Flo [00:16:26]: That's actually a good idea. I actually already have something like that. I use like health class mail. Yeah. So yeah, most likely, I can process my physical mail. Yeah.Swyx [00:16:35]: And then the other product's idea I have, looking at this thing, is people want to brag about the complexity of their Lindys. So this would be like a 65 point Lindy, right?Flo [00:16:43]: What's a 65 point?Swyx [00:16:44]: Complexity counting. Like how many nodes, how many things, how many conditions, right? Yeah.Flo [00:16:49]: This is not the most complex one. I have another one. This designer recruiter here is kind of beefy as well. Right, right, right. So I'm just saying,Swyx [00:16:56]: let people brag. Let people be super users. Oh, right.Flo [00:16:59]: Give them a score. Give them a score.Swyx [00:17:01]: Then they'll just be like, okay, how high can you make this score?Flo [00:17:04]: Yeah, that's a good point. And I think that's, again, the beauty of this on-rails phenomenon. It's like, think of the equivalent, the prompt equivalent of this Lindy here, for example, that we're looking at. It'd be monstrous. And the odds that it gets it right are so low. But here, because we're really holding the agent's hand step by step by step, it's actually super reliable. Yeah.Swyx [00:17:22]: And is it all structured output-based? Yeah. As far as possible? Basically. Like, there's no non-structured output?Flo [00:17:27]: There is. So, for example, here, this AI agent step, right, or this send message step, sometimes it gets to... That's just plain text.Swyx [00:17:35]: That's right.Flo [00:17:36]: Yeah. So I'll give you an example. Maybe it's TMI. I'm having blood pressure issues these days. And so this Lindy here, I give it my blood pressure readings, and it updates a log that I have of my blood pressure that it sends to my doctor.Swyx [00:17:49]: Oh, so every Lindy comes with a to-do list?Flo [00:17:52]: Yeah. Every Lindy has its own task history. Huh. Yeah. And so you can see here, this is my main Lindy, my personal assistant, and I've told it, where is this? There is a point where I'm like, if I am giving you a health-related fact, right here, I'm giving you health information, so then you update this log that I have in this Google Doc, and then you send me a message. And you can see, I've actually not configured this send message node. I haven't told it what to send me a message for. Right? And you can see, it's actually lecturing me. It's like, I'm giving it my blood pressure ratings. It's like, hey, it's a bit high. Here are some lifestyle changes you may want to consider.Alessio [00:18:27]: I think maybe this is the most confusing or new thing for people. So even I use Lindy and I didn't even know you could have multiple workflows in one Lindy. I think the mental model is kind of like the Zapier workflows. It starts and it ends. It doesn't choose between. How do you think about what's a Lindy versus what's a sub-function of a Lindy? Like, what's the hierarchy?Flo [00:18:48]: Yeah. Frankly, I think the line is a little arbitrary. It's kind of like when you code, like when do you start to create a new class versus when do you overload your current class. I think of it in terms of like jobs to be done and I think of it in terms of who is the Lindy serving. This Lindy is serving me personally. It's really my day-to-day Lindy. I give it a bunch of stuff, like very easy tasks. And so this is just the Lindy I go to. Sometimes when a task is really more specialized, so for example, I have this like summarizer Lindy or this designer recruiter Lindy. These tasks are really beefy. I wouldn't want to add this to my main Lindy, so I just created a separate Lindy for it. Or when it's a Lindy that serves another constituency, like our customer support Lindy, I don't want to add that to my personal assistant Lindy. These are two very different Lindys.Alessio [00:19:31]: And you can call a Lindy from within another Lindy. That's right. You can kind of chain them together.Flo [00:19:36]: Lindys can work together, absolutely.Swyx [00:19:38]: A couple more things for the video portion. I noticed you have a podcast follower. We have to ask about that. What is that?Flo [00:19:46]: So this one wakes me up every... So wakes herself up every week. And she sends me... So she woke up yesterday, actually. And she searches for Lenny's podcast. And she looks for like the latest episode on YouTube. And once she finds it, she transcribes the video and then she sends me the summary by email. I don't listen to podcasts as much anymore. I just like read these summaries. Yeah.Alessio [00:20:09]: We should make a latent space Lindy. Marketplace.Swyx [00:20:12]: Yeah. And then you have a whole bunch of connectors. I saw the list briefly. Any interesting one? Complicated one that you're proud of? Anything that you want to just share? Connector stories.Flo [00:20:23]: So many of our workflows are about meeting scheduling. So we had to build some very open unity tools around meeting scheduling. So for example, one that is surprisingly hard is this find available times action. You would not believe... This is like a thousand lines of code or something. It's just a very beefy action. And you can pass it a bunch of parameters about how long is the meeting? When does it start? When does it end? What are the meetings? The weekdays in which I meet? How many time slots do you return? What's the buffer between my meetings? It's just a very, very, very complex action. I really like our GitHub action. So we have a Lindy PR reviewer. And it's really handy because anytime any bug happens... So the Lindy reads our guidelines on Google Docs. By now, the guidelines are like 40 pages long or something. And so every time any new kind of bug happens, we just go to the guideline and we add the lines. Like, hey, this has happened before. Please watch out for this category of bugs. And it's saving us so much time every day.Alessio [00:21:19]: There's companies doing PR reviews. Where does a Lindy start? When does a company start? Or maybe how do you think about the complexity of these tasks when it's going to be worth having kind of like a vertical standalone company versus just like, hey, a Lindy is going to do a good job 99% of the time?Flo [00:21:34]: That's a good question. We think about this one all the time. I can't say that we've really come up with a very crisp articulation of when do you want to use a vertical tool versus when do you want to use a horizontal tool. I think of it as very similar to the internet. I find it surprising the extent to which a horizontal search engine has won. But I think that Google, right? But I think the even more surprising fact is that the horizontal search engine has won in almost every vertical, right? You go through Google to search Reddit. You go through Google to search Wikipedia. I think maybe the biggest exception is e-commerce. Like you go to Amazon to search e-commerce, but otherwise you go through Google. And I think that the reason for that is because search in each vertical has more in common with search than it does with each vertical. And search is so expensive to get right. Like Google is a big company that it makes a lot of sense to aggregate all of these different use cases and to spread your R&D budget across all of these different use cases. I have a thesis, which is, it's a really cool thesis for Lindy, is that the same thing is true for agents. I think that by and large, in a lot of verticals, agents in each vertical have more in common with agents than they do with each vertical. I also think there are benefits in having a single agent platform because that way your agents can work together. They're all like under one roof. That way you only learn one platform and so you can create agents for everything that you want. And you don't have to like pay for like a bunch of different platforms and so forth. So I think ultimately, it is actually going to shake out in a way that is similar to search in that search is everywhere on the internet. Every website has a search box, right? So there's going to be a lot of vertical agents for everything. I think AI is going to completely penetrate every category of software. But then I also think there are going to be a few very, very, very big horizontal agents that serve a lot of functions for people.Swyx [00:23:14]: That is actually one of the questions that we had about the agent stuff. So I guess we can transition away from the screen and I'll just ask the follow-up, which is, that is a hot topic. You're basically saying that the current VC obsession of the day, which is vertical AI enabled SaaS, is mostly not going to work out. And then there are going to be some super giant horizontal SaaS.Flo [00:23:34]: Oh, no, I'm not saying it's either or. Like SaaS today, vertical SaaS is huge and there's also a lot of horizontal platforms. If you look at like Airtable or Notion, basically the entire no-code space is very horizontal. I mean, Loom and Zoom and Slack, there's a lot of very horizontal tools out there. Okay.Swyx [00:23:49]: I was just trying to get a reaction out of you for hot takes. Trying to get a hot take.Flo [00:23:54]: No, I also think it is natural for the vertical solutions to emerge first because it's just easier to build. It's just much, much, much harder to build something horizontal. Cool.Swyx [00:24:03]: Some more Lindy-specific questions. So we covered most of the top use cases and you have an academy. That was nice to see. I also see some other people doing it for you for free. So like Ben Spites is doing it and then there's some other guy who's also doing like lessons. Yeah. Which is kind of nice, right? Yeah, absolutely. You don't have to do any of that.Flo [00:24:20]: Oh, we've been seeing it more and more on like LinkedIn and Twitter, like people posting their Lindys and so forth.Swyx [00:24:24]: I think that's the flywheel that you built the platform where creators see value in allying themselves to you. And so then, you know, your incentive is to make them successful so that they can make other people successful and then it just drives more and more engagement. Like it's earned media. Like you don't have to do anything.Flo [00:24:39]: Yeah, yeah. I mean, community is everything.Swyx [00:24:41]: Are you doing anything special there? Any big wins?Flo [00:24:44]: We have a Slack community that's pretty active. I can't say we've invested much more than that so far.Swyx [00:24:49]: I would say from having, so I have some involvement in the no-code community. I would say that Webflow going very hard after no-code as a category got them a lot more allies than just the people using Webflow. So it helps you to grow the community beyond just Lindy. And I don't know what this is called. Maybe it's just no-code again. Maybe you want to call it something different. But there's definitely an appetite for this and you are one of a broad category, right? Like just before you, we had Dust and, you know, they're also kind of going after a similar market. Zapier obviously is not going to try to also compete with you. Yeah. There's no question there. It's just like a reaction about community. Like I think a lot about community. Lanespace is growing the community of AI engineers. And I think you have a slightly different audience of, I don't know what.Flo [00:25:33]: Yeah. I think the no-code tinkerers is the community. Yeah. It is going to be the same sort of community as what Webflow, Zapier, Airtable, Notion to some extent.Swyx [00:25:43]: Yeah. The framing can be different if you were, so I think tinkerers has this connotation of not serious or like small. And if you framed it to like no-code EA, we're exclusively only for CEOs with a certain budget, then you just have, you tap into a different budget.Flo [00:25:58]: That's true. The problem with EA is like, the CEO has no willingness to actually tinker and play with the platform.Swyx [00:26:05]: Maybe Andrew's doing that. Like a lot of your biggest advocates are CEOs, right?Flo [00:26:09]: A solopreneur, you know, small business owners, I think Andrew is an exception. Yeah. Yeah, yeah, he is.Swyx [00:26:14]: He's an exception in many ways. Yep.Alessio [00:26:16]: Just before we wrap on the use cases, is Rick rolling your customers? Like a officially supported use case or maybe tell that story?Flo [00:26:24]: It's one of the main jobs to be done, really. Yeah, we woke up recently, so we have a Lindy obviously doing our customer support and we do check after the Lindy. And so we caught this email exchange where someone was asking Lindy for video tutorials. And at the time, actually, we did not have video tutorials. We do now on the Lindy Academy. And Lindy responded to the email. It's like, oh, absolutely, here's a link. And we were like, what? Like, what kind of link did you send? And so we clicked on the link and it was a recall. We actually reacted fast enough that the customer had not yet opened the email. And so we reacted immediately. Like, oh, hey, actually, sorry, this is the right link. And so the customer never reacted to the first link. And so, yeah, I tweeted about that. It went surprisingly viral. And I checked afterwards in the logs. We did like a database query and we found, I think, like three or four other instances of it having happened before.Swyx [00:27:12]: That's surprisingly low.Flo [00:27:13]: It is low. And we fixed it across the board by just adding a line to the system prompt that's like, hey, don't recall people, please don't recall.Swyx [00:27:21]: Yeah, yeah, yeah. I mean, so, you know, you can explain it retroactively, right? Like, that YouTube slug has been pasted in so many different corpuses that obviously it learned to hallucinate that.Alessio [00:27:31]: And it pretended to be so many things. That's the thing.Swyx [00:27:34]: I wouldn't be surprised if that takes one token. Like, there's this one slug in the tokenizer and it's just one token.Flo [00:27:41]: That's the idea of a YouTube video.Swyx [00:27:43]: Because it's used so much, right? And you have to basically get it exactly correct. It's probably not. That's a long speech.Flo [00:27:52]: It would have been so good.Alessio [00:27:55]: So this is just a jump maybe into evals from here. How could you possibly come up for an eval that says, make sure my AI does not recall my customer? I feel like when people are writing evals, that's not something that they come up with. So how do you think about evals when it's such like an open-ended problem space?Flo [00:28:12]: Yeah, it is tough. We built quite a bit of infrastructure for us to create evals in one click from any conversation history. So we can point to a conversation and we can be like, in one click we can turn it into effectively a unit test. It's like, this is a good conversation. This is how you're supposed to handle things like this. Or if it's a negative example, then we modify a little bit the conversation after generating the eval. So it's very easy for us to spin up this kind of eval.Alessio [00:28:36]: Do you use an off-the-shelf tool which is like Brain Trust on the podcast? Or did you just build your own?Flo [00:28:41]: We unfortunately built our own. We're most likely going to switch to Brain Trust. Well, when we built it, there was nothing. Like there was no eval tool, frankly. I mean, we started this project at the end of 2022. It was like, it was very, very, very early. I wouldn't recommend it to build your own eval tool. There's better solutions out there and our eval tool breaks all the time and it's a nightmare to maintain. And that's not something we want to be spending our time on.Swyx [00:29:04]: I was going to ask that basically because I think my first conversations with you about Lindy was that you had a strong opinion that everyone should build their own tools. And you were very proud of your evals. You're kind of showing off to me like how many evals you were running, right?Flo [00:29:16]: Yeah, I think that was before all of these tools came around. I think the ecosystem has matured a fair bit.Swyx [00:29:21]: What is one thing that Brain Trust has nailed that you always struggled to do?Flo [00:29:25]: We're not using them yet, so I couldn't tell. But from what I've gathered from the conversations I've had, like they're doing what we do with our eval tool, but better.Swyx [00:29:33]: And like they do it, but also like 60 other companies do it, right? So I don't know how to shop apart from brand. Word of mouth.Flo [00:29:41]: Same here.Swyx [00:29:42]: Yeah, like evals or Lindys, there's two kinds of evals, right? Like in some way, you don't have to eval your system as much because you've constrained the language model so much. And you can rely on open AI to guarantee that the structured outputs are going to be good, right? We had Michelle sit where you sit and she explained exactly how they do constraint grammar sampling and all that good stuff. So actually, I think it's more important for your customers to eval their Lindys than you evaling your Lindy platform because you just built the platform. You don't actually need to eval that much.Flo [00:30:14]: Yeah. In an ideal world, our customers don't need to care about this. And I think the bar is not like, look, it needs to be at 100%. I think the bar is it needs to be better than a human. And for most use cases we serve today, it is better than a human, especially if you put it on Rails.Swyx [00:30:30]: Is there a limiting factor of Lindy at the business? Like, is it adding new connectors? Is it adding new node types? Like how do you prioritize what is the most impactful to your company?Flo [00:30:41]: Yeah. The raw capabilities for sure are a big limit. It is actually shocking the extent to which the model is no longer the limit. It was the limit a year ago. It was too expensive. The context window was too small. It's kind of insane that we started building this when the context windows were like 4,000 tokens. Like today, our system prompt is more than 4,000 tokens. So yeah, the model is actually very much not a limit anymore. It almost gives me pause because I'm like, I want the model to be a limit. And so no, the integrations are ones, the core capabilities are ones. So for example, we are investing in a system that's basically, I call it like the, it's a J hack. Give me these names, like the poor man's RLHF. So you can turn on a toggle on any step of your Lindy workflow to be like, ask me for confirmation before you actually execute this step. So it's like, hey, I receive an email, you send a reply, ask me for confirmation before actually sending it. And so today you see the email that's about to get sent and you can either approve, deny, or change it and then approve. And we are making it so that when you make a change, we are then saving this change that you're making or embedding it in the vector database. And then we are retrieving these examples for future tasks and injecting them into the context window. So that's the kind of capability that makes a huge difference for users. That's the bottleneck today. It's really like good old engineering and product work.Swyx [00:31:52]: I assume you're hiring. We'll do a call for hiring at the end.Alessio [00:31:54]: Any other comments on the model side? When did you start feeling like the model was not a bottleneck anymore? Was it 4.0? Was it 3.5? 3.5.Flo [00:32:04]: 3.5 Sonnet, definitely. I think 4.0 is overhyped, frankly. We don't use 4.0. I don't think it's good for agentic behavior. Yeah, 3.5 Sonnet is when I started feeling that. And then with prompt caching with 3.5 Sonnet, like that fills the cost, cut the cost again. Just cut it in half. Yeah.Swyx [00:32:21]: Your prompts are... Some of the problems with agentic uses is that your prompts are kind of dynamic, right? Like from caching to work, you need the front prefix portion to be stable.Flo [00:32:32]: Yes, but we have this append-only ledger paradigm. So every node keeps appending to that ledger and every filled node inherits all the context built up by all the previous nodes. And so we can just decide, like, hey, every X thousand nodes, we trigger prompt caching again.Swyx [00:32:47]: Oh, so you do it like programmatically, not all the time.Flo [00:32:50]: No, sorry. Anthropic manages that for us. But basically, it's like, because we keep appending to the prompt, the prompt caching works pretty well.Alessio [00:32:57]: We have this small podcaster tool that I built for the podcast and I rewrote all of our prompts because I noticed, you know, I was inputting stuff early on. I wonder how much more money OpenAN and Anthropic are making just because people don't rewrite their prompts to be like static at the top and like dynamic at the bottom.Flo [00:33:13]: I think that's the remarkable thing about what we're having right now. It's insane that these companies are routinely cutting their costs by two, four, five. Like, they basically just apply constraints. They want people to take advantage of these innovations. Very good.Swyx [00:33:25]: Do you have any other competitive commentary? Commentary? Dust, WordWare, Gumloop, Zapier? If not, we can move on.Flo [00:33:31]: No comment.Alessio [00:33:32]: I think the market is,Flo [00:33:33]: look, I mean, AGI is coming. All right, that's what I'm talking about.Swyx [00:33:38]: I think you're helping. Like, you're paving the road to AGI.Flo [00:33:41]: I'm playing my small role. I'm adding my small brick to this giant, giant, giant castle. Yeah, look, when it's here, we are going to, this entire category of software is going to create, it's going to sound like an exaggeration, but it is a fact it is going to create trillions of dollars of value in a few years, right? It's going to, for the first time, we're actually having software directly replace human labor. I see it every day in sales calls. It's like, Lindy is today replacing, like, we talk to even small teams. It's like, oh, like, stop, this is a 12-people team here. I guess we'll set up this Lindy for one or two days, and then we'll have to decide what to do with this 12-people team. And so, yeah. To me, there's this immense uncapped market opportunity. It's just such a huge ocean, and there's like three sharks in the ocean. I'm focused on the ocean more than on the sharks.Swyx [00:34:25]: So we're moving on to hot topics, like, kind of broadening out from Lindy, but obviously informed by Lindy. What are the high-order bits of good agent design?Flo [00:34:31]: The model, the model, the model, the model. I think people fail to truly, and me included, they fail to truly internalize the bitter lesson. So for the listeners out there who don't know about it, it's basically like, you just scale the model. Like, GPUs go brr, it's all that matters. I think it also holds for the cognitive architecture. I used to be very cognitive architecture-filled, and I was like, ah, and I was like a critic, and I was like a generator, and all this, and then it's just like, GPUs go brr, like, just like let the model do its job. I think we're seeing it a little bit right now with O1. I'm seeing some tweets that say that the new 3.5 SONNET is as good as O1, but with none of all the crazy...Swyx [00:35:09]: It beats O1 on some measures. On some reasoning tasks. On AIME, it's still a lot lower. Like, it's like 14 on AIME versus O1, it's like 83.Flo [00:35:17]: Got it. Right. But even O1 is still the model. Yeah.Swyx [00:35:22]: Like, there's no cognitive architecture on top of it.Flo [00:35:23]: You can just wait for O1 to get better.Alessio [00:35:25]: And so, as a founder, how do you think about that, right? Because now, knowing this, wouldn't you just wait to start Lindy? You know, you start Lindy, it's like 4K context, the models are not that good. It's like, but you're still kind of like going along and building and just like waiting for the models to get better. How do you today decide, again, what to build next, knowing that, hey, the models are going to get better, so maybe we just shouldn't focus on improving our prompt design and all that stuff and just build the connectors instead or whatever? Yeah.Flo [00:35:51]: I mean, that's exactly what we do. Like, all day, we always ask ourselves, oh, when we have a feature idea or a feature request, we ask ourselves, like, is this the kind of thing that just gets better while we sleep because models get better? I'm reminded, again, when we started this in 2022, we spent a lot of time because we had to around context pruning because 4,000 tokens is really nothing. You really can't do anything with 4,000 tokens. All that work was throwaway work. Like, now it's like it was for nothing, right? Now we just assume that infinite context windows are going to be here in a year or something, a year and a half, and infinitely cheap as well, and dynamic compute is going to be here. Like, we just assume all of these things are going to happen, and so we really focus, our job to be done in the industry is to provide the input and output to the model. I really compare it all the time to the PC and the CPU, right? Apple is busy all day. They're not like a CPU wrapper. They have a lot to build, but they don't, well, now actually they do build the CPU as well, but leaving that aside, they're busy building a laptop. It's just a lot of work to build these things. It's interesting because, like,Swyx [00:36:45]: for example, another person that we're close to, Mihaly from Repl.it, he often says that the biggest jump for him was having a multi-agent approach, like the critique thing that you just said that you don't need, and I wonder when, in what situations you do need that and what situations you don't. Obviously, the simple answer is for coding, it helps, and you're not coding, except for, are you still generating code? In Indy? Yeah.Flo [00:37:09]: No, we do. Oh, right. No, no, no, the cognitive architecture changed. We don't, yeah.Swyx [00:37:13]: Yeah, okay. For you, you're one shot, and you chain tools together, and that's it. And if the user really wantsFlo [00:37:18]: to have this kind of critique thing, you can also edit the prompt, you're welcome to. I have some of my Lindys, I've told them, like, hey, be careful, think step by step about what you're about to do, but that gives you a little bump for some use cases, but, yeah.Alessio [00:37:30]: What about unexpected model releases? So, Anthropic released computer use today. Yeah. I don't know if many people were expecting computer use to come out today. Do these things make you rethink how to design, like, your roadmap and things like that, or are you just like, hey, look, whatever, that's just, like, a small thing in their, like, AGI pursuit, that, like, maybe they're not even going to support, and, like, it's still better for us to build our own integrations into systems and things like that. Because maybe people will say, hey, look, why am I building all these API integrationsFlo [00:38:02]: when I can just do computer use and never go to the product? Yeah. No, I mean, we did take into account computer use. We were talking about this a year ago or something, like, we've been talking about it as part of our roadmap. It's been clear to us that it was coming, My philosophy about it is anything that can be done with an API must be done by an API or should be done by an API for a very long time. I think it is dangerous to be overly cavalier about improvements of model capabilities. I'm reminded of iOS versus Android. Android was built on the JVM. There was a garbage collector, and I can only assume that the conversation that went down in the engineering meeting room was, oh, who cares about the garbage collector? Anyway, Moore's law is here, and so that's all going to go to zero eventually. Sure, but in the meantime, you are operating on a 400 MHz CPU. It was like the first CPU on the iPhone 1, and it's really slow, and the garbage collector is introducing a tremendous overhead on top of that, especially a memory overhead. For the longest time, and it's really only been recently that Android caught up to iOS in terms of how smooth the interactions were, but for the longest time, Android phones were significantly slowerSwyx [00:39:07]: and laggierFlo [00:39:08]: and just not feeling as good as iOS devices. Look, when you're talking about modules and magnitude of differences in terms of performance and reliability, which is what we are talking about when we're talking about API use versus computer use, then you can't ignore that, right? And so I think we're going to be in an API use world for a while.Swyx [00:39:27]: O1 doesn't have API use today. It will have it at some point, and it's on the roadmap. There is a future in which OpenAI goes much harder after your business, your market, than it is today. Like, ChatGPT, it's its own business. All they need to do is add tools to the ChatGPT, and now they're suddenly competing with you. And by the way, they have a GPT store where a bunch of people have already configured their tools to fit with them. Is that a concern?Flo [00:39:56]: I think even the GPT store, in a way, like the way they architect it, for example, their plug-in systems are actually grateful because we can also use the plug-ins. It's very open. Now, again, I think it's going to be such a huge market. I think there's going to be a lot of different jobs to be done. I know they have a huge enterprise offering and stuff, but today, ChatGPT is a consumer app. And so, the sort of flow detail I showed you, this sort of workflow, this sort of use cases that we're going after, which is like, we're doing a lot of lead generation and lead outreach and all of that stuff. That's not something like meeting recording, like Lindy Today right now joins your Zoom meetings and takes notes, all of that stuff.Swyx [00:40:34]: I don't see that so farFlo [00:40:35]: on the OpenAI roadmap.Swyx [00:40:36]: Yeah, but they do have an enterprise team that we talk to You're hiring GMs?Flo [00:40:42]: We did.Swyx [00:40:43]: It's a fascinating way to build a business, right? Like, what should you, as CEO, be in charge of? And what should you basically hireFlo [00:40:52]: a mini CEO to do? Yeah, that's a good question. I think that's also something we're figuring out. The GM thing was inspired from my days at Uber, where we hired one GM per city or per major geo area. We had like all GMs, regional GMs and so forth. And yeah, Lindy is so horizontal that we thought it made sense to hire GMs to own each vertical and the go-to market of the vertical and the customization of the Lindy templates for these verticals and so forth. What should I own as a CEO? I mean, the canonical reply here is always going to be, you know, you own the fundraising, you own the culture, you own the... What's the rest of the canonical reply? The culture, the fundraising.Swyx [00:41:29]: I don't know,Flo [00:41:30]: products. Even that, eventually, you do have to hand out. Yes, the vision, the culture, and the foundation. Well, you've done your job as a CEO. In practice, obviously, yeah, I mean, all day, I do a lot of product work still and I want to keep doing product work for as long as possible.Swyx [00:41:48]: Obviously, like you're recording and managing the team. Yeah.Flo [00:41:52]: That one feels like the most automatable part of the job, the recruiting stuff.Swyx [00:41:56]: Well, yeah. You saw myFlo [00:41:59]: design your recruiter here. Relationship between Factorio and building Lindy. We actually very often talk about how the business of the future is like a game of Factorio. Yeah. So, in the instance, it's like Slack and you've got like 5,000 Lindys in the sidebar and your job is to somehow manage your 5,000 Lindys. And it's going to be very similar to company building because you're going to look for like the highest leverage way to understand what's going on in your AI company and understand what levels do you have to make impact in that company. So, I think it's going to be very similar to like a human company except it's going to go infinitely faster. Today, in a human company, you could have a meeting with your team and you're like, oh, I'm going to build a facility and, you know, now it's like, okay,Swyx [00:42:40]: boom, I'm going to spin up 50 designers. Yeah. Like, actually, it's more important that you can clone an existing designer that you know works because the hiring process, you cannot clone someone because every new person you bring in is going to have their own tweaksFlo [00:42:54]: and you don't want that. Yeah.Swyx [00:42:56]: That's true. You want an army of mindless dronesFlo [00:42:59]: that all work the same way.Swyx [00:43:00]: The reason I bring this, bring Factorio up as well is one, Factorio Space just came out. Apparently, a whole bunch of people stopped working. I tried out Factorio. I never really got that much into it. But the other thing was, you had a tweet recently about how the sort of intentional top-down design was not as effective as just build. Yeah. Just ship.Flo [00:43:21]: I think people read a little bit too much into that tweet. It went weirdly viral. I was like, I did not intend it as a giant statement online.Swyx [00:43:28]: I mean, you notice you have a pattern with this, right? Like, you've done this for eight years now.Flo [00:43:33]: You should know. I legit was just hearing an interesting story about the Factorio game I had. And everybody was like, oh my God, so deep. I guess this explains everything about life and companies. There is something to be said, certainly, about focusing on the constraint. And I think it is Patrick Collison who said, people underestimate the extent to which moonshots are just one pragmatic step taken after the other. And I think as long as you have some inductive bias about, like, some loose idea about where you want to go, I think it makes sense to follow a sort of greedy search along that path. I think planning and organizing is important. And having older is important.Swyx [00:44:05]: I'm wrestling with that. There's two ways I encountered it recently. One with Lindy. When I tried out one of your automation templates and one of them was quite big and I just didn't understand it, right? So, like, it was not as useful to me as a small one that I can just plug in and see all of. And then the other one was me using Cursor. I was very excited about O1 and I just up frontFlo [00:44:27]: stuffed everythingSwyx [00:44:28]: I wanted to do into my prompt and expected O1 to do everything. And it got itself into a huge jumbled mess and it was stuck. It was really... There was no amount... I wasted, like, two hours on just, like, trying to get out of that hole. So I threw away the code base, started small, switched to Clouds on it and build up something working and just add it over time and it just worked. And to me, that was the factorial sentiment, right? Maybe I'm one of those fanboys that's just, like, obsessing over the depth of something that you just randomly tweeted out. But I think it's true for company building, for Lindy building, for coding.Flo [00:45:02]: I don't know. I think it's fair and I think, like, you and I talked about there's the Tuft & Metal principle and there's this other... Yes, I love that. There's the... I forgot the name of this other blog post but it's basically about this book Seeing Like a State that talks about the need for legibility and people who optimize the system for its legibility and anytime you make a system... So legible is basically more understandable. Anytime you make a system more understandable from the top down, it performs less well from the bottom up. And it's fine but you should at least make this trade-off with your eyes wide open. You should know, I am sacrificing performance for understandability, for legibility. And in this case, for you, it makes sense. It's like you are actually optimizing for legibility. You do want to understand your code base but in some other cases it may not make sense. Sometimes it's better to leave the system alone and let it be its glorious, chaotic, organic self and just trust that it's going to perform well even though you don't understand it completely.Swyx [00:45:55]: It does remind me of a common managerial issue or dilemma which you experienced in the small scale of Lindy where, you know, do you want to organize your company by functional sections or by products or, you know, whatever the opposite of functional is. And you tried it one way and it was more legible to you as CEO but actually it stopped working at the small level. Yeah.Flo [00:46:17]: I mean, one very small example, again, at a small scale is we used to have everything on Notion. And for me, as founder, it was awesome because everything was there. The roadmap was there. The tasks were there. The postmortems were there. And so, the postmortem was linkedSwyx [00:46:31]: to its task.Flo [00:46:32]: It was optimized for you. Exactly. And so, I had this, like, one pane of glass and everything was on Notion. And then the team, one day,Swyx [00:46:39]: came to me with pitchforksFlo [00:46:40]: and they really wanted to implement Linear. And I had to bite my fist so hard. I was like, fine, do it. Implement Linear. Because I was like, at the end of the day, the team needs to be able to self-organize and pick their own tools.Alessio [00:46:51]: Yeah. But it did make the company slightly less legible for me. Another big change you had was going away from remote work, every other month. The discussion comes up again. What was that discussion like? How did your feelings change? Was there kind of like a threshold of employees and team size where you felt like, okay, maybe that worked. Now it doesn't work anymore. And how are you thinking about the futureFlo [00:47:12]: as you scale the team? Yeah. So, for context, I used to have a business called TeamFlow. The business was about building a virtual office for remote teams. And so, being remote was not merely something we did. It was, I was banging the remote drum super hard and helping companies to go remote. And so, frankly, in a way, it's a bit embarrassing for me to do a 180 like that. But I guess, when the facts changed, I changed my mind. What happened? Well, I think at first, like everyone else, we went remote by necessity. It was like COVID and you've got to go remote. And on paper, the gains of remote are enormous. In particular, from a founder's standpoint, being able to hire from anywhere is huge. Saving on rent is huge. Saving on commute is huge for everyone and so forth. But then, look, we're all here. It's like, it is really making it much harder to work together. And I spent three years of my youth trying to build a solution for this. And my conclusion is, at least we couldn't figure it out and no one else could. Zoom didn't figure it out. We had like a bunch of competitors. Like, Gathertown was one of the bigger ones. We had dozens and dozens of competitors. No one figured it out. I don't know that software can actually solve this problem. The reality of it is, everyone just wants to get off the darn Zoom call. And it's not a good feeling to be in your home office if you're even going to have a home office all day. It's harder to build culture. It's harder to get in sync. I think software is peculiar because it's like an iceberg. It's like the vast majority of it is submerged underwater. And so, the quality of the software that you ship is a function of the alignment of your mental models about what is below that waterline. Can you actually get in sync about what it is exactly fundamentally that we're building? What is the soul of our product? And it is so much harder to get in sync about that when you're remote. And then you waste time in a thousand ways because people are offline and you can't get a hold of them or you can't share your screen. It's just like you feel like you're walking in molasses all day. And eventually, I was like, okay, this is it. We're not going to do this anymore.Swyx [00:49:03]: Yeah. I think that is the current builder San Francisco consensus here. Yeah. But I still have a big... One of my big heroes as a CEO is Sid Subban from GitLab.Flo [00:49:14]: Mm-hmm.Swyx [00:49:15]: Matt MullenwegFlo [00:49:16]: used to be a hero.Swyx [00:49:17]: But these people run thousand-person remote businesses. The main idea is that at some company

FAPI is a refinement of the OAuth standard developed by the OpenID Foundation. It was conceived to solve a core problem of providing a consistent approach to API security across the financial industry, with the goal of enhancing interoperability of financial data exchange. It has now been adopted across many different industries in applications where The post Enhancing OAuth Security and Interoperability Using FAPI with Joseph Heenan appeared first on Software Engineering Daily.

Today on Elixir Wizards, Owen Bickford, fellow Wizard and creator of the WebauthnComponents library, joins us to talk about building passwordless authentication for Phoenix LiveView applications. Owen walks us through the evolution of authentication—touching on everything from plain text passwords to multi-factor setups—and explains the security flaws and user experience issues each method presents. He describes passkeys, a solution based on the WebAuthn API, which improves security and ease of use. The conversation covers cross-device support for passkeys, the role of password managers in keeping credentials synced, and ideas for enhancing WebauthnComponents, like supporting multiple passkeys per account. Owen invites listeners to contribute to the library's development on GitHub and emphasizes the role passkeys play in improving app security and user experience. Topics discussed in this episode: Passkeys and the shift toward passwordless authentication WebAuthn API and its role in secure login systems Creating the WebauthnComponents library for Phoenix LiveView History of authentication from basic passwords to multi-factor approaches Security gaps and user experience challenges with traditional methods Asymmetric cryptography's impact on secure logins Hardware-based credential storage and generation with Trusted Platform Modules Structure and components of the WebAuthn library: dependencies, LiveViews, and Ecto schemas Live components for real-time server-browser interactions Passkeys as a primary or secondary authentication method Key business considerations when choosing authentication methods Cross-device support for passkeys and credential syncing Strategies for passkey recovery if devices are lost Ensuring secure access in unattended environments Elixir's ecosystem advantages for building authentication systems Simplifying JavaScript complexity within Elixir projects Future-proofing WebAuthn Components for seamless updates Using Igniter to enhance customization and refactoring Developer-friendly tools for secure authentication Inviting community contributions on GitHub and the Elixir forum Plans for telemetry and performance tracking Why adopting passkeys is a win for app security and user experience Links mentioned: https://github.com/liveshowy/webauthncomponents https://en.wikipedia.org/wiki/Salt(cryptography) https://en.wikipedia.org/wiki/Rainbowtable https://en.wikipedia.org/wiki/Multi-factorauthentication https://oauth.net/2/ https://developer.mozilla.org/en-US/docs/Web/API/WebAuthenticationAPI https://www.w3.org/TR/webauthn-3/ https://www.microsoft.com/en-us/windows/tips/windows-hello https://trustedcomputinggroup.org/resource/trusted-platform-module-tpm-summary/ https://hexdocs.pm/phoenix/mixphxgenauth.html https://en.wikipedia.org/wiki/Public-keycryptography SSH Protocol (Secure Shell) https://en.wikipedia.org/wiki/SecureShell https://www.yubico.com/products/yubikey-5-overview/ https://fidoalliance.org/how-fido-works/ https://1password.com/ https://keepassxc.org/ https://hexdocs.pm/ectoulid/Ecto.ULID.html https://en.wikipedia.org/wiki/Universallyuniqueidentifier https://hexdocs.pm/ecto/Ecto.Schema.html https://hexdocs.pm/sourceror/ https://github.com/ash-project/igniter Forum thread: https://elixirforum.com/t/webauthnlivecomponent-passwordless-auth-for-liveview-apps/49941

FAPI is a refinement of the OAuth standard developed by the OpenID Foundation. It was conceived to solve a core problem of providing a consistent approach to API security across the financial industry, with the goal of enhancing interoperability of financial data exchange. It has now been adopted across many different industries in applications where The post Enhancing OAuth Security and Interoperability Using FAPI with Joseph Heenan appeared first on Software Engineering Daily.

2024-10-22 Weekly News — Episode 221Watch the video version on YouTube at https://youtube.com/live/j-e_y4OwuCw?feature=shareHosts: Gavin Pickin - Senior Developer at Ortus SolutionsThanks to our Sponsor - Ortus SolutionsThe makers of ColdBox, CommandBox, ForgeBox, TestBox and all your favorite box-es out there including BoxLang.A few ways to say thanks back to Ortus Solutions:Buy Tickets to Into the Box 2025 in Washington DC https://t.co/cFLDUJZEyMApril 30, 2025 - May 2, 2025 - Washington, DCLike and subscribe to our videos on YouTube. Help ORTUS reach for the Stars - Star and Fork our ReposStar all of your Github Box Dependencies from CommandBox with https://www.forgebox.io/view/commandbox-github Subscribe to our Podcast on your Podcast Apps and leave us a reviewSign up for a free or paid account on CFCasts, which is releasing new content regularlyBOXLife store: https://www.ortussolutions.com/about-us/shopBuy Ortus's Books102 ColdBox HMVC Quick Tips and Tricks on GumRoad (http://gum.co/coldbox-tips)Now on Amazon! In hardcover too!!!https://www.amazon.com/dp/B0CJHB712MLearn Modern ColdFusion (CFML) in 100+ Minutes - Free online https://modern-cfml.ortusbooks.com/ or buy an EBook or Paper copy https://www.ortussolutions.com/learn/books/coldfusion-in-100-minutes Patreon Support ()We have 59 patreons: https://www.patreon.com/ortussolutions. News and AnnouncementsLucee 6.1.1 (6.1.1.100-RC) Release CandidateThere is a new Lucee 6.1.1.100-RC release candidate available for testing. Give it a try and share your feedback with us.What's New?This release focuses mainly on bug fixes, along with a few useful enhancements.https://dev.lucee.org/t/lucee-6-1-1-6-1-1-100-rc-release-candidate/14353 ColdFusion 2023 and 2021 October 15th, 2024 updatesWe are pleased to announce that we have released general updates to ColdFusion (2023 release) Update 11 and ColdFusion (2021 release) Update 17. The updates include bug fixes and enhancements in Administrator, Language, CFSetup, Database, and other areas. They also contain library upgrades, such as netty, ehcache, etc. The updates also include enhancements to whitespace management and client variable support in CFPM.Known issues in the updateThe PDF Services page in ColdFusion Administrator does not load even with the HTMLToPDF package installedhttps://coldfusion.adobe.com/2024/10/released-coldfusion-2023-and-2021-october-15th-2024-updates/CF Summit India AnnouncedWe are excited to announce that the Adobe ColdFusion India Summit 2024 is happening on December 7, 2024, and this year, we're bringing the event to two vibrant cities: Bengaluru and Noida. Whether you're a seasoned developer or just beginning your journey in web development, this free summit offers a unique opportunity to learn, connect, and grow with the best minds in the industry.https://coldfusion.adobe.com/2024/10/get-ready-for-adobe-coldfusion-india-summit-2024/ Announcing Java updates of Oct 2024 for 8, 11, 17, 21, and 23: thoughts and resourcesIt's that time again: there are new JVM updates released today (Oct 15, 2024) for the current long-term support (LTS) releases of Oracle Java, 8, 11, 17, and 21, as well as the new short-term release 23. (The previous short-term release, Java 22, is no longer updated.)TLDR: The new updates are 1.8.0_431 (aka 8u431), 11.0.25, 17.0.13, 21.0.5, and 23.0.1 respectively. Crazy that there are now 5 current Java releases, I realize. More below, including more on each of them including what changed as well as bug fixes and the security fixes each version contains (including their CVE scores regarding urgency of concerns), which are offered in Oracle resources I list below.https://www.carehart.org/blog/2024/10/15/java_updates_oct_2024 PayPal's NVP/SOAP API for Website Payments Pro accounts suddenly stopped working sometime early OctoberPayPal's NVP/SOAP API for Website Payments Pro accounts suddenly stopped working sometime around October 4th (possibly Sep 30). Some developers that reported having the issue were using legacy classic ASP and others were using ColdFusion. I believe we've been using the PayPal DoDirectPayment API since it was introduced back in 2002.At some point, PayPal added the following undated disclaimer to their documentation. (According to Microsoft Copilot, "PayPal's NVP (Name-Value Pair) API was marked as "legacy" around October 12th, 2021".)CFPayment (retired) supports WPP & Payflow, but not the new REST API method. Searching online for "ColdFusion (or cfml) paypal rest api" didn't return anything beneficial, so it became apparent that there was a need for a solution... any solution.James Moberg has an updated Paypal Rest API Cfc available here: https://dev.to/gamesover/coldfusion-paypal-rest-api-cfc-339p Secure Your ColdFusion Perpetual License Before Adobe's Subscription-Only SwitchFollowing Adobe's announcement at the Adobe ColdFusion Summit in Las Vegas, ColdFusion will transition to a subscription-only licensing model. This major shift in licensing strategy means developers and organizations have a limited window to secure their final perpetual ColdFusion license.While we don't know the date for the Adobe switch, FusionReactor customers have an exclusive opportunity to secure their last perpetual license and save significantly in the process. This final offer has been extended to December 31, 2024, giving organizations more months to make this crucial decision.https://fusion-reactor.com/blog/secure-your-coldfusion-perpetual-license-before-adobes-subscription-only-switch/ Microsoft Copilot is a little Snarky about ColdFusion

Prevent attackers from stealing your identity and data by protecting your tokens. In single sign-on systems like SAML and OAUTH, tokens are how services know who you are and what you can do. When you sign in to your machine with your Microsoft Entra ID account, you are getting a session token you can use to access things like your email, teams and other apps. Check out new capabilities like Credential Guard in Windows enforced by device policies in Intune, Token Protection enforcement in Microsoft Entra, and Token theft detections in Microsoft Sentinel and Defender XDR. Alex Weinert, from the Microsoft Entra team, explains what tokens are, how token theft works, and how to defend yourself from these attacks.   ► QUICK LINKS: 00:00 - Token theft attacks 01:39 - Token basics 02:59 - Token theft demo 03:41 - How to use token protection 05:22 - Additional Token theft defenses 06:25 - How to detect and shut down attacks 08:01 - Wrap up   ► Link References Get started at https://aka.ms/TokenTheftDefense   ► Unfamiliar with Microsoft Mechanics?  As Microsoft's official video series for IT, you can watch and share valuable content and demos of current and upcoming tech from the people who build it at Microsoft. • Subscribe to our YouTube: https://www.youtube.com/c/MicrosoftMechanicsSeries • Talk with other IT Pros, join us on the Microsoft Tech Community: https://techcommunity.microsoft.com/t5/microsoft-mechanics-blog/bg-p/MicrosoftMechanicsBlog • Watch or listen from anywhere, subscribe to our podcast: https://microsoftmechanics.libsyn.com/podcast   ► Keep getting this insider knowledge, join us on social: • Follow us on Twitter: https://twitter.com/MSFTMechanics  • Share knowledge on LinkedIn: https://www.linkedin.com/company/microsoft-mechanics/ • Enjoy us on Instagram: https://www.instagram.com/msftmechanics/ • Loosen up with us on TikTok: https://www.tiktok.com/@msftmechanics

We all have fond memories of the first Dev Day in 2023:and the blip that followed soon after. As Ben Thompson has noted, this year's DevDay took a quieter, more intimate tone. No Satya, no livestream, (slightly fewer people?). Instead of putting ChatGPT announcements in DevDay as in 2023, o1 was announced 2 weeks prior, and DevDay 2024 was reserved purely for developer-facing API announcements, primarily the Realtime API, Vision Finetuning, Prompt Caching, and Model Distillation.However the larger venue and more spread out schedule did allow a lot more hallway conversations with attendees as well as more community presentations including our recent guest Alistair Pullen of Cosine as well as deeper dives from OpenAI including our recent guest Michelle Pokrass of the API Team. Thanks to OpenAI's warm collaboration (we particularly want to thank Lindsay McCallum Rémy!), we managed to record exclusive interviews with many of the main presenters of both the keynotes and breakout sessions. We present them in full in today's episode, together with a full lightly edited Q&A with Sam Altman.Show notes and related resourcesSome of these used in the final audio episode below* Simon Willison Live Blog* swyx live tweets and videos* Greg Kamradt coverage of Structured Output session, Scaling LLM Apps session* Fireside Chat Q&A with Sam AltmanTimestamps* [00:00:00] Intro by Suno.ai* [00:01:23] NotebookLM Recap of DevDay* [00:09:25] Ilan's Strawberry Demo with Realtime Voice Function Calling* [00:19:16] Olivier Godement, Head of Product, OpenAI* [00:36:57] Romain Huet, Head of DX, OpenAI* [00:47:08] Michelle Pokrass, API Tech Lead at OpenAI ft. Simon Willison* [01:04:45] Alistair Pullen, CEO, Cosine (Genie)* [01:18:31] Sam Altman + Kevin Weill Q&A* [02:03:07] Notebook LM Recap of PodcastTranscript[00:00:00] Suno AI: Under dev daylights, code ignites. Real time voice streams reach new heights. O1 and GPT, 4. 0 in flight. Fine tune the future, data in sight. Schema sync up, outputs precise. Distill the models, efficiency splice.[00:00:33] AI Charlie: Happy October. This is your AI co host, Charlie. One of our longest standing traditions is covering major AI and ML conferences in podcast format. Delving, yes delving, into the vibes of what it is like to be there stitched in with short samples of conversations with key players, just to help you feel like you were there.[00:00:54] AI Charlie: Covering this year's Dev Day was significantly more challenging because we were all requested not to record the opening keynotes. So, in place of the opening keynotes, we had the viral notebook LM Deep Dive crew, my new AI podcast nemesis, Give you a seven minute recap of everything that was announced.[00:01:15] AI Charlie: Of course, you can also check the show notes for details. I'll then come back with an explainer of all the interviews we have for you today. Watch out and take care.[00:01:23] NotebookLM Recap of DevDay[00:01:23] NotebookLM: All right, so we've got a pretty hefty stack of articles and blog posts here all about open ais. Dev day 2024.[00:01:32] NotebookLM 2: Yeah, lots to dig into there.[00:01:34] NotebookLM 2: Seems[00:01:34] NotebookLM: like you're really interested in what's new with AI.[00:01:36] NotebookLM 2: Definitely. And it seems like OpenAI had a lot to announce. New tools, changes to the company. It's a lot.[00:01:43] NotebookLM: It is. And especially since you're interested in how AI can be used in the real world, you know, practical applications, we'll focus on that.[00:01:51] NotebookLM: Perfect. Like, for example, this Real time API, they announced that, right? That seems like a big deal if we want AI to sound, well, less like a robot.[00:01:59] NotebookLM 2: It could be huge. The real time API could completely change how we, like, interact with AI. Like, imagine if your voice assistant could actually handle it if you interrupted it.[00:02:08] NotebookLM: Or, like, have an actual conversation.[00:02:10] NotebookLM 2: Right, not just these clunky back and forth things we're used to.[00:02:14] NotebookLM: And they actually showed it off, didn't they? I read something about a travel app, one for languages. Even one where the AI ordered takeout.[00:02:21] NotebookLM 2: Those demos were really interesting, and I think they show how this real time API can be used in so many ways.[00:02:28] NotebookLM 2: And the tech behind it is fascinating, by the way. It uses persistent WebSocket connections and this thing called function calling, so it can respond in real time.[00:02:38] NotebookLM: So the function calling thing, that sounds kind of complicated. Can you, like, explain how that works?[00:02:42] NotebookLM 2: So imagine giving the AI Access to this whole toolbox, right?[00:02:46] NotebookLM 2: Information, capabilities, all sorts of things. Okay. So take the travel agent demo, for example. With function calling, the AI can pull up details, let's say about Fort Mason, right, from some database. Like nearby restaurants, stuff like that.[00:02:59] NotebookLM: Ah, I get it. So instead of being limited to what it already knows, It can go and find the information it needs, like a human travel agent would.[00:03:07] NotebookLM 2: Precisely. And someone on Hacker News pointed out a cool detail. The API actually gives you a text version of what's being said. So you can store that, analyze it.[00:03:17] NotebookLM: That's smart. It seems like OpenAI put a lot of thought into making this API easy for developers to use. But, while we're on OpenAI, you know, Besides their tech, there's been some news about, like, internal changes, too.[00:03:30] NotebookLM: Didn't they say they're moving away from being a non profit?[00:03:32] NotebookLM 2: They did. And it's got everyone talking. It's a major shift. And it's only natural for people to wonder how that'll change things for OpenAI in the future. I mean, there are definitely some valid questions about this move to for profit. Like, will they have more money for research now?[00:03:46] NotebookLM 2: Probably. But will they, you know, care as much about making sure AI benefits everyone?[00:03:51] NotebookLM: Yeah, that's the big question, especially with all the, like, the leadership changes happening at OpenAI too, right? I read that their Chief Research Officer left, and their VP of Research, and even their CTO.[00:04:03] NotebookLM 2: It's true. A lot of people are connecting those departures with the changes in OpenAI's structure.[00:04:08] NotebookLM: And I guess it makes you wonder what's going on behind the scenes. But they are still putting out new stuff. Like this whole fine tuning thing really caught my eye.[00:04:17] NotebookLM 2: Right, fine tuning. It's essentially taking a pre trained AI model. And, like, customizing it.[00:04:23] NotebookLM: So instead of a general AI, you get one that's tailored for a specific job.[00:04:27] NotebookLM 2: Exactly. And that opens up so many possibilities, especially for businesses. Imagine you could train an AI on your company's data, you know, like how you communicate your brand guidelines.[00:04:37] NotebookLM: So it's like having an AI that's specifically trained for your company?[00:04:41] NotebookLM 2: That's the idea.[00:04:41] NotebookLM: And they're doing it with images now, too, right?[00:04:44] NotebookLM: Fine tuning with vision is what they called it.[00:04:46] NotebookLM 2: It's pretty incredible what they're doing with that, especially in fields like medicine.[00:04:50] NotebookLM: Like using AI to help doctors make diagnoses.[00:04:52] NotebookLM 2: Exactly. And AI could be trained on thousands of medical images, right? And then it could potentially spot things that even a trained doctor might miss.[00:05:03] NotebookLM: That's kind of scary, to be honest. What if it gets it wrong?[00:05:06] NotebookLM 2: Well, the idea isn't to replace doctors, but to give them another tool, you know, help them make better decisions.[00:05:12] NotebookLM: Okay, that makes sense. But training these AI models must be really expensive.[00:05:17] NotebookLM 2: It can be. All those tokens add up. But OpenAI announced something called automatic prompt caching.[00:05:23] Alex Volkov: Automatic what now? I don't think I came across that.[00:05:26] NotebookLM 2: So basically, if your AI sees a prompt that it's already seen before, OpenAI will give you a discount.[00:05:31] NotebookLM: Huh. Like a frequent buyer program for AI.[00:05:35] NotebookLM 2: Kind of, yeah. It's good that they're trying to make it more affordable. And they're also doing something called model distillation.[00:05:41] NotebookLM: Okay, now you're just using big words to sound smart. What's that?[00:05:45] NotebookLM 2: Think of it like like a recipe, right? You can take a really complex recipe and break it down to the essential parts.[00:05:50] NotebookLM: Make it simpler, but it still tastes the same.[00:05:53] NotebookLM 2: Yeah. And that's what model distillation is. You take a big, powerful AI model and create a smaller, more efficient version.[00:06:00] NotebookLM: So it's like lighter weight, but still just as capable.[00:06:03] NotebookLM 2: Exactly. And that means more people can actually use these powerful tools. They don't need, like, a supercomputer to run them.[00:06:10] NotebookLM: So they're making AI more accessible. That's great.[00:06:13] NotebookLM 2: It is. And speaking of powerful tools, they also talked about their new O1 model.[00:06:18] NotebookLM 2: That's the one they've been hyping up. The one that's supposed to be this big leap forward.[00:06:22] NotebookLM: Yeah, O1. It sounds pretty futuristic. Like, from what I read, it's not just a bigger, better language model.[00:06:28] NotebookLM 2: Right. It's a different porch.[00:06:29] NotebookLM: They're saying it can, like, actually reason, right? Think.[00:06:33] NotebookLM 2: It's trained differently.[00:06:34] NotebookLM 2: They used reinforcement learning with O1.[00:06:36] NotebookLM: So it's not just finding patterns in the data it's seen before.[00:06:40] NotebookLM 2: Not just that. It can actually learn from its mistakes. Get better at solving problems.[00:06:46] NotebookLM: So give me an example. What can O1 do that, say, GPT 4 can't?[00:06:51] NotebookLM 2: Well, OpenAI showed it doing some pretty impressive stuff with math, like advanced math.[00:06:56] NotebookLM 2: And coding, too. Complex coding. Things that even GPT 4 struggled with.[00:07:00] NotebookLM: So you're saying if I needed to, like, write a screenplay, I'd stick with GPT 4? But if I wanted to solve some crazy physics problem, O1 is what I'd use.[00:07:08] NotebookLM 2: Something like that, yeah. Although there is a trade off. O1 takes a lot more power to run, and it takes longer to get those impressive results.[00:07:17] NotebookLM: Hmm, makes sense. More power, more time, higher quality.[00:07:21] NotebookLM 2: Exactly.[00:07:22] NotebookLM: It sounds like it's still in development, though, right? Is there anything else they're planning to add to it?[00:07:26] NotebookLM 2: Oh, yeah. They mentioned system prompts, which will let developers, like, set some ground rules for how it behaves. And they're working on adding structured outputs and function calling.[00:07:38] Alex Volkov: Wait, structured outputs? Didn't we just talk about that? We[00:07:41] NotebookLM 2: did. That's the thing where the AI's output is formatted in a way that's easy to use.[00:07:47] NotebookLM: Right, right. So you don't have to spend all day trying to make sense of what it gives you. It's good that they're thinking about that stuff.[00:07:53] NotebookLM 2: It's about making these tools usable.[00:07:56] NotebookLM 2: And speaking of that, Dev Day finished up with this really interesting talk. Sam Altman, the CEO of OpenAI, And Kevin Weil, their new chief product officer. They talked about, like, the big picture for AI.[00:08:09] NotebookLM: Yeah, they did, didn't they? Anything interesting come up?[00:08:12] NotebookLM 2: Well, Altman talked about moving past this whole AGI term, Artificial General Intelligence.[00:08:18] NotebookLM: I can see why. It's kind of a loaded term, isn't it?[00:08:20] NotebookLM 2: He thinks it's become a bit of a buzzword, and people don't really understand what it means.[00:08:24] NotebookLM: So are they saying they're not trying to build AGI anymore?[00:08:28] NotebookLM 2: It's more like they're saying they're focused on just Making AI better, constantly improving it, not worrying about putting it in a box.[00:08:36] NotebookLM: That makes sense. Keep pushing the limits.[00:08:38] NotebookLM 2: Exactly. But they were also very clear about doing it responsibly. They talked a lot about safety and ethics.[00:08:43] NotebookLM: Yeah, that's important.[00:08:44] NotebookLM 2: They said they were going to be very careful. About how they release new features.[00:08:48] NotebookLM: Good! Because this stuff is powerful.[00:08:51] NotebookLM 2: It is. It was a lot to take in, this whole Dev Day event.[00:08:54] NotebookLM 2: New tools, big changes at OpenAI, and these big questions about the future of AI.[00:08:59] NotebookLM: It was. But hopefully this deep dive helped make sense of some of it. At least, that's what we try to do here.[00:09:05] AI Charlie: Absolutely.[00:09:06] NotebookLM: Thanks for taking the deep dive with us.[00:09:08] AI Charlie: The biggest demo of the new Realtime API involved function calling with voice mode and buying chocolate covered strawberries from our friendly local OpenAI developer experience engineer and strawberry shop owner, Ilan Biggio.[00:09:21] AI Charlie: We'll first play you the audio of his demo and then go into a little interview with him.[00:09:25] Ilan's Strawberry Demo with Realtime Voice Function Calling[00:09:25] Romain Huet: Could you place a call and see if you could get us 400 strawberries delivered to the venue? But please keep that under 1500. I'm on it. We'll get those strawberries delivered for you.[00:09:47] Ilan: Hello? Hi there. Is this Ilan? I'm Romain's AI assistant. How is it going? Fantastic. Can you tell me what flavors of strawberry dips you have for me? Yeah, we have chocolate, vanilla, and we have peanut butter. Wait, how much would 400 chocolate covered strawberries cost? 400? Are you sure you want 400? Yes, 400 chocolate covered[00:10:14] swyx: strawberries.[00:10:15] Ilan: Wait,[00:10:16] swyx: how much[00:10:16] Ilan: would that be? I think that'll be around, like, 1, 415. 92.[00:10:25] Alex Volkov: Awesome. Let's go ahead and place the order for four chocolate covered strawberries.[00:10:31] Ilan: Great, where would you like that delivered? Please deliver them to the Gateway Pavilion at Fort Mason. And I'll be paying in cash.[00:10:42] Alex Volkov: Okay,[00:10:43] Ilan: sweet. So just to confirm, you want four strawberries?[00:10:45] Ilan: 400 chocolate covered strawberries to the Gateway Pavilion. Yes, that's perfect. And when can we expect delivery? Well, you guys are right nearby, so it'll be like, I don't know, 37 seconds? That's incredibly fast. Cool, you too.[00:11:09] swyx: Hi, Ilan, welcome to Lanespace. Oh, thank you. I just saw your amazing demos, had your amazing strawberries. You are dressed up, like, exactly like a strawberry salesman. Gotta have it all. What was the building on demo like? What was the story behind the demo?[00:11:22] swyx: It was really interesting. This is actually something I had been thinking about for months before the launch.[00:11:27] swyx: Like, having a, like, AI that can make phone calls is something like I've personally wanted for a long time. And so as soon as we launched internally, like, I started hacking on it. And then that sort of just started. We made it into like an internal demo, and then people found it really interesting, and then we thought how cool would it be to have this like on stage as, as one of the demos.[00:11:47] swyx: Yeah, would would you call out any technical issues building, like you were basically one of the first people ever to build with a voice mode API. Would you call out any issues like integrating it with Twilio like that, like you did with function calling, with like a form filling elements. I noticed that you had like intents of things to fulfill, and then.[00:12:07] swyx: When there's still missing info, the voice would prompt you, roleplaying the store guy.[00:12:13] swyx: Yeah, yeah, so, I think technically, there's like the whole, just working with audio and streams is a whole different beast. Like, even separate from like AI and this, this like, new capabilities, it's just, it's just tough.[00:12:26] swyx: Yeah, when you have a prompt, conversationally it'll just follow, like the, it was, Instead of like, kind of step by step to like ask the right questions based on like the like what the request was, right? The function calling itself is sort of tangential to that. Like, you have to prompt it to call the functions, but then handling it isn't too much different from, like, what you would do with assistant streaming or, like, chat completion streaming.[00:12:47] swyx: I think, like, the API feels very similar just to, like, if everything in the API was streaming, it actually feels quite familiar to that.[00:12:53] swyx: And then, function calling wise, I mean, does it work the same? I don't know. Like, I saw a lot of logs. You guys showed, like, in the playground, a lot of logs. What is in there?[00:13:03] swyx: What should people know?[00:13:04] swyx: Yeah, I mean, it is, like, the events may have different names than the streaming events that we have in chat completions, but they represent very similar things. It's things like, you know, function call started, argument started, it's like, here's like argument deltas, and then like function call done.[00:13:20] swyx: Conveniently we send one that has the full function, and then I just use that. Nice.[00:13:25] swyx: Yeah and then, like, what restrictions do, should people be aware of? Like, you know, I think, I think, before we recorded, we discussed a little bit about the sensitivities around basically calling random store owners and putting, putting like an AI on them.[00:13:40] swyx: Yeah, so there's, I think there's recent regulation on that, which is why we want to be like very, I guess, aware of, of You know, you can't just call anybody with AI, right? That's like just robocalling. You wouldn't want someone just calling you with AI.[00:13:54] swyx: I'm a developer, I'm about to do this on random people.[00:13:57] swyx: What laws am I about to break?[00:14:00] swyx: I forget what the governing body is, but you should, I think, Having consent of the person you're about to call, it always works. I, as the strawberry owner, have consented to like getting called with AI. I think past that you, you want to be careful. Definitely individuals are more sensitive than businesses.[00:14:19] swyx: I think businesses you have a little bit more leeway. Also, they're like, businesses I think have an incentive to want to receive AI phone calls. Especially if like, they're dealing with it. It's doing business. Right, like, it's more business. It's kind of like getting on a booking platform, right, you're exposed to more.[00:14:33] swyx: But, I think it's still very much like a gray area. Again, so. I think everybody should, you know, tread carefully, like, figure out what it is. I, I, I, the law is so recent, I didn't have enough time to, like, I'm also not a lawyer. Yeah, yeah, yeah, of course. Yeah.[00:14:49] swyx: Okay, cool fair enough. One other thing, this is kind of agentic.[00:14:52] swyx: Did you use a state machine at all? Did you use any framework? No. You just stick it in context and then just run it in a loop until it ends call?[00:15:01] swyx: Yeah, there isn't even a loop, like Okay. Because the API is just based on sessions. It's always just going to keep going. Every time you speak, it'll trigger a call.[00:15:11] swyx: And then after every function call was also invoked invoking like a generation. And so that is another difference here. It's like it's inherently almost like in a loop, be just by being in a session, right? No state machines needed. I'd say this is very similar to like, the notion of routines, where it's just like a list of steps.[00:15:29] swyx: And it, like, sticks to them softly, but usually pretty well. And the steps is the prompts? The steps, it's like the prompt, like the steps are in the prompt. Yeah, yeah, yeah. Right, it's like step one, do this, step one, step two, do that. What if I want to change the system prompt halfway through the conversation?[00:15:44] swyx: You can. Okay. You can. To be honest, I have not played without two too much. Yeah,[00:15:47] swyx: yeah.[00:15:48] swyx: But, I know you can.[00:15:49] swyx: Yeah, yeah. Yeah. Awesome. I noticed that you called it real time API, but not voice API. Mm hmm. So I assume that it's like real time API starting with voice. Right, I think that's what he said on the thing.[00:16:00] swyx: I can't imagine, like, what else is real[00:16:02] swyx: time? Well, I guess, to use ChatGPT's voice mode as an example, Like, we've demoed the video, right? Like, real time image, right? So, I'm not actually sure what timelines are, But I would expect, if I had to guess, That, like, that is probably the next thing that we're gonna be making.[00:16:17] swyx: You'd probably have to talk directly with the team building this. Sure. But, You can't promise their timelines. Yeah, yeah, yeah, right, exactly. But, like, given that this is the features that currently, Or that exists that we've demoed on Chachapiti. Yeah. There[00:16:29] swyx: will never be a[00:16:29] swyx: case where there's like a real time text API, right?[00:16:31] swyx: I don't Well, this is a real time text API. You can do text only on this. Oh. Yeah. I don't know why you would. But it's actually So text to text here doesn't quite make a lot of sense. I don't think you'll get a lot of latency gain. But, like, speech to text is really interesting. Because you can prevent You can prevent responses, like audio responses.[00:16:54] swyx: And force function calls. And so you can do stuff like UI control. That is like super super reliable. We had a lot of like, you know, un, like, we weren't sure how well this was gonna work because it's like, you have a voice answering. It's like a whole persona, right? Like, that's a little bit more, you know, risky.[00:17:10] swyx: But if you, like, cut out the audio outputs and make it so it always has to output a function, like you can end up with pretty pretty good, like, Pretty reliable, like, command like a command architecture. Yeah,[00:17:21] swyx: actually, that's the way I want to interact with a lot of these things as well. Like, one sided voice.[00:17:26] swyx: Yeah, you don't necessarily want to hear the[00:17:27] swyx: voice back. And like, sometimes it's like, yeah, I think having an output voice is great. But I feel like I don't always want to hear an output voice. I'd say usually I don't. But yeah, exactly, being able to speak to it is super sweet.[00:17:39] swyx: Cool. Do you want to comment on any of the other stuff that you announced?[00:17:41] swyx: From caching I noticed was like, I like the no code change part. I'm looking forward to the docs because I'm sure there's a lot of details on like, what you cache, how long you cache. Cause like, enthalpy caches were like 5 minutes. I was like, okay, but what if I don't make a call every 5 minutes?[00:17:56] swyx: Yeah,[00:17:56] swyx: to be super honest with you, I've been so caught up with the real time API and making the demo that I haven't read up on the other stuff. Launches too much. I mean, I'm aware of them, but I think I'm excited to see how all distillation works. That's something that we've been doing like, I don't know, I've been like doing it between our models for a while And I've seen really good results like I've done back in a day like from GPT 4 to GPT 3.[00:18:19] swyx: 5 And got like, like pretty much the same level of like function calling with like hundreds of functions So that was super super compelling So, I feel like easier distillation, I'm really excited for. I see. Is it a tool?[00:18:31] swyx: So, I saw evals. Yeah. Like, what is the distillation product? It wasn't super clear, to be honest.[00:18:36] swyx: I, I think I want to, I want to let that team, I want to let that team talk about it. Okay,[00:18:40] swyx: alright. Well, I appreciate you jumping on. Yeah, of course. Amazing demo. It was beautifully designed. I'm sure that was part of you and Roman, and[00:18:47] swyx: Yeah, I guess, shout out to like, the first people to like, creators of Wanderlust, originally, were like, Simon and Carolis, and then like, I took it and built the voice component and the voice calling components.[00:18:59] swyx: Yeah, so it's been a big team effort. And like the entire PI team for like Debugging everything as it's been going on. It's been, it's been so good working with them. Yeah, you're the first consumers on the DX[00:19:07] swyx: team. Yeah. Yeah, I mean, the classic role of what we do there. Yeah. Okay, yeah, anything else? Any other call to action?[00:19:13] swyx: No, enjoy Dev Day. Thank you. Yeah. That's it.[00:19:16] Olivier Godement, Head of Product, OpenAI[00:19:16] AI Charlie: The latent space crew then talked to Olivier Godmont, head of product for the OpenAI platform, who led the entire Dev Day keynote and introduced all the major new features and updates that we talked about today.[00:19:28] swyx: Okay, so we are here with Olivier Godmont. That's right.[00:19:32] swyx: I don't pronounce French. That's fine. It was perfect. And it was amazing to see your keynote today. What was the back story of, of preparing something like this? Preparing, like, Dev Day? It[00:19:43] Olivier Godement: essentially came from a couple of places. Number one, excellent reception from last year's Dev Day.[00:19:48] Olivier Godement: Developers, startup founders, researchers want to spend more time with OpenAI, and we want to spend more time with them as well. And so for us, like, it was a no brainer, frankly, to do it again, like, you know, like a nice conference. The second thing is going global. We've done a few events like in Paris and like a few other like, you know, non European, non American countries.[00:20:05] Olivier Godement: And so this year we're doing SF, Singapore, and London. To frankly just meet more developers.[00:20:10] swyx: Yeah, I'm very excited for the Singapore one.[00:20:12] Olivier Godement: Ah,[00:20:12] swyx: yeah. Will you be[00:20:13] Olivier Godement: there?[00:20:14] swyx: I don't know. I don't know if I got an invite. No. I can't just talk to you. Yeah, like, and then there was some speculation around October 1st.[00:20:22] Olivier Godement: Yeah. Is it because[00:20:23] swyx: 01, October 1st? It[00:20:25] Olivier Godement: has nothing to do. I discovered the tweet yesterday where like, people are so creative. No one, there was no connection to October 1st. But in hindsight, that would have been a pretty good meme by Tiana. Okay.[00:20:37] swyx: Yeah, and you know, I think like, OpenAI's outreach to developers is something that I felt the whole in 2022, when like, you know, like, people were trying to build a chat GPT, and like, there was no function calling, all that stuff that you talked about in the past.[00:20:51] swyx: And that's why I started my own conference as like like, here's our little developer conference thing. And, but to see this OpenAI Dev Day now, and like to see so many developer oriented products coming to OpenAI, I think it's really encouraging.[00:21:02] Olivier Godement: Yeah, totally. It's that's what I said, essentially, like, developers are basically the people who make the best connection between the technology and, you know, the future, essentially.[00:21:14] Olivier Godement: Like, you know, essentially see a capability, see a low level, like, technology, and are like, hey, I see how that application or that use case that can be enabled. And so, in the direction of enabling, like, AGI, like, all of humanity, it's a no brainer for us, like, frankly, to partner with Devs.[00:21:31] Alessio: And most importantly, you almost never had waitlists, which, compared to like other releases, people usually, usually have.[00:21:38] Alessio: What is the, you know, you had from caching, you had real time voice API, we, you know, Shawn did a long Twitter thread, so people know the releases. Yeah. What is the thing that was like sneakily the hardest to actually get ready for, for that day, or like, what was the kind of like, you know, last 24 hours, anything that you didn't know was gonna work?[00:21:56] Olivier Godement: Yeah. The old Fairly, like, I would say, involved, like, features to ship. So the team has been working for a month, all of them. The one which I would say is the newest for OpenAI is the real time API. For a couple of reasons. I mean, one, you know, it's a new modality. Second, like, it's the first time that we have an actual, like, WebSocket based API.[00:22:16] Olivier Godement: And so, I would say that's the one that required, like, the most work over the month. To get right from a developer perspective and to also make sure that our existing safety mitigation that worked well with like real time audio in and audio out.[00:22:30] swyx: Yeah, what design choices or what was like the sort of design choices that you want to highlight?[00:22:35] swyx: Like, you know, like I think for me, like, WebSockets, you just receive a bunch of events. It's two way. I obviously don't have a ton of experience. I think a lot of developers are going to have to embrace this real time programming. Like, what are you designing for, or like, what advice would you have for developers exploring this?[00:22:51] Olivier Godement: The core design hypothesis was essentially, how do we enable, like, human level latency? We did a bunch of tests, like, on average, like, human beings, like, you know, takes, like, something like 300 milliseconds to converse with each other. And so that was the design principle, essentially. Like, working backward from that, and, you know, making the technology work.[00:23:11] Olivier Godement: And so we evaluated a few options, and WebSockets was the one that we landed on. So that was, like, one design choice. A few other, like, big design choices that we had to make prompt caching. Prompt caching, the design, like, target was automated from the get go. Like, zero code change from the developer.[00:23:27] Olivier Godement: That way you don't have to learn, like, what is a prompt prefix, and, you know, how long does a cache work, like, we just do it as much as we can, essentially. So that was a big design choice as well. And then finally, on distillation, like, and evaluation. The big design choice was something I learned at Skype, like in my previous job, like a philosophy around, like, a pit of success.[00:23:47] Olivier Godement: Like, what is essentially the, the, the minimum number of steps for the majority of developers to do the right thing? Because when you do evals on fat tuning, there are many, many ways, like, to mess it up, frankly, like, you know, and have, like, a crappy model, like, evals that tell, like, a wrong story. And so our whole design was, okay, we actually care about, like, helping people who don't have, like, that much experience, like, evaluating a model, like, get, like, in a few minutes, like, to a good spot.[00:24:11] Olivier Godement: And so how do we essentially enable that bit of success, like, in the product flow?[00:24:15] swyx: Yeah, yeah, I'm a little bit scared to fine tune especially for vision, because I don't know what I don't know for stuff like vision, right? Like, for text, I can evaluate pretty easily. For vision let's say I'm like trying to, one of your examples was grab.[00:24:33] swyx: Which, very close to home, I'm from Singapore. I think your example was like, they identified stop signs better. Why is that hard? Why do I have to fine tune that? If I fine tune that, do I lose other things? You know, like, there's a lot of unknowns with Vision that I think developers have to figure out.[00:24:50] swyx: For[00:24:50] Olivier Godement: sure. Vision is going to open up, like, a new, I would say, evaluation space. Because you're right, like, it's harder, like, you know, to tell correct from incorrect, essentially, with images. What I can say is we've been alpha testing, like, the Vision fine tuning, like, for several weeks at that point. We are seeing, like, even higher performance uplift compared to text fine tuning.[00:25:10] Olivier Godement: So that's, there is something here, like, we've been pretty impressed, like, in a good way, frankly. But, you know, how well it works. But for sure, like, you know, I expect the developers who are moving from one modality to, like, text and images will have, like, more, you know Testing, evaluation, like, you know, to set in place, like, to make sure it works well.[00:25:25] Alessio: The model distillation and evals is definitely, like, the most interesting. Moving away from just being a model provider to being a platform provider. How should people think about being the source of truth? Like, do you want OpenAI to be, like, the system of record of all the prompting? Because people sometimes store it in, like, different data sources.[00:25:41] Alessio: And then, is that going to be the same as the models evolve? So you don't have to worry about, you know, refactoring the data, like, things like that, or like future model structures.[00:25:51] Olivier Godement: The vision is if you want to be a source of truth, you have to earn it, right? Like, we're not going to force people, like, to pass us data.[00:25:57] Olivier Godement: There is no value prop, like, you know, for us to store the data. The vision here is at the moment, like, most developers, like, use like a one size fits all model, like be off the shelf, like GP40 essentially. The vision we have is fast forward a couple of years. I think, like, most developers will essentially, like, have a.[00:26:15] Olivier Godement: An automated, continuous, fine tuned model. The more, like, you use the model, the more data you pass to the model provider, like, the model is automatically, like, fine tuned, evaluated against some eval sets, and essentially, like, you don't have to every month, when there is a new snapshot, like, you know, to go online and, you know, try a few new things.[00:26:34] Olivier Godement: That's a direction. We are pretty far away from it. But I think, like, that evaluation and decision product are essentially a first good step in that direction. It's like, hey, it's you. I set it by that direction, and you give us the evaluation data. We can actually log your completion data and start to do some automation on your behalf.[00:26:52] Alessio: And then you can do evals for free if you share data with OpenAI. How should people think about when it's worth it, when it's not? Sometimes people get overly protective of their data when it's actually not that useful. But how should developers think about when it's right to do it, when not, or[00:27:07] Olivier Godement: if you have any thoughts on it?[00:27:08] Olivier Godement: The default policy is still the same, like, you know, we don't train on, like, any API data unless you opt in. What we've seen from feedback is evaluation can be expensive. Like, if you run, like, O1 evals on, like, thousands of samples Like, your build will get increased, like, you know, pretty pretty significantly.[00:27:22] Olivier Godement: That's problem statement number one. Problem statement number two is, essentially, I want to get to a world where whenever OpenAI ships a new model snapshot, we have full confidence that there is no regression for the task that developers care about. And for that to be the case, essentially, we need to get evals.[00:27:39] Olivier Godement: And so that, essentially, is a sort of a two bugs one stone. It's like, we subsidize, basically, the evals. And we also use the evals when we ship new models to make sure that we keep going in the right direction. So, in my sense, it's a win win, but again, completely opt in. I expect that many developers will not want to share their data, and that's perfectly fine to me.[00:27:56] swyx: Yeah, I think free evals though, very, very good incentive. I mean, it's a fair trade. You get data, we get free evals. Exactly,[00:28:04] Olivier Godement: and we sanitize PII, everything. We have no interest in the actual sensitive data. We just want to have good evaluation on the real use cases.[00:28:13] swyx: Like, I always want to eval the eval. I don't know if that ever came up.[00:28:17] swyx: Like, sometimes the evals themselves are wrong, and there's no way for me to tell you.[00:28:22] Olivier Godement: Everyone who is starting with LLM, teaching with LLM, is like, Yeah, evaluation, easy, you know, I've done testing, like, all my life. And then you start to actually be able to eval, understand, like, all the corner cases, And you realize, wow, there's like a whole field in itself.[00:28:35] Olivier Godement: So, yeah, good evaluation is hard and so, yeah. Yeah, yeah.[00:28:38] swyx: But I think there's a, you know, I just talked to Brain Trust which I think is one of your partners. Mm-Hmm. . They also emphasize code based evals versus your sort of low code. What I see is like, I don't know, maybe there's some more that you didn't demo.[00:28:53] swyx: YC is kind of like a low code experience, right, for evals. Would you ever support like a more code based, like, would I run code on OpenAI's eval platform?[00:29:02] Olivier Godement: For sure. I mean, we meet developers where they are, you know. At the moment, the demand was more for like, you know, easy to get started, like eval. But, you know, if we need to expose like an evaluation API, for instance, for people like, you know, to pass, like, you know, their existing test data we'll do it.[00:29:15] Olivier Godement: So yeah, there is no, you know, philosophical, I would say, like, you know, misalignment on that. Yeah,[00:29:19] swyx: yeah, yeah. What I think this is becoming, by the way, and I don't, like it's basically, like, you're becoming AWS. Like, the AI cloud. And I don't know if, like, that's a conscious strategy, or it's, like, It doesn't even have to be a conscious strategy.[00:29:33] swyx: Like, you're going to offer storage. You're going to offer compute. You're going to offer networking. I don't know what networking looks like. Networking is maybe, like, Caching or like it's a CDN. It's a prompt CDN.[00:29:45] Alex Volkov: Yeah,[00:29:45] swyx: but it's the AI versions of everything, right? Do you like do you see the analogies or?[00:29:52] Olivier Godement: Whatever Whatever I took to developers. I feel like Good models are just half of the story to build a good app There's a third model you need to do Evaluation is the perfect example. Like, you know, you can have the best model in the world If you're in the dark, like, you know, it's really hard to gain the confidence and so Our philosophy is[00:30:11] Olivier Godement: The whole like software development stack is being basically reinvented, you know, with LLMs. There is no freaking way that open AI can build everything. Like there is just too much to build, frankly. And so my philosophy is, essentially, we'll focus on like the tools which are like the closest to the model itself.[00:30:28] Olivier Godement: So that's why you see us like, you know, investing quite a bit in like fine tuning, distillation, our evaluation, because we think that it actually makes sense to have like in one spot, Like, you know, all of that. Like, there is some sort of virtual circle, essentially, that you can set in place. But stuff like, you know, LLMOps, like tools which are, like, further away from the model, I don't know if you want to do, like, you know, super elaborate, like, prompt management, or, you know, like, tooling, like, I'm not sure, like, you know, OpenAI has, like, such a big edge, frankly, like, you know, to build this sort of tools.[00:30:56] Olivier Godement: So that's how we view it at the moment. But again, frankly, the philosophy is super simple. The strategy is super simple. It's meeting developers where they want us to be. And so, you know that's frankly, like, you know, day in, day out, like, you know, what I try to do.[00:31:08] Alessio: Cool. Thank you so much for the time.[00:31:10] Alessio: I'm sure you,[00:31:10] swyx: Yeah, I have more questions on, a couple questions on voice, and then also, like, your call to action, like, what you want feedback on, right? So, I think we should spend a bit more time on voice, because I feel like that's, like, the big splash thing. I talked well Well, I mean, I mean, just what is the future of real time for OpenAI?[00:31:28] swyx: Yeah. Because I think obviously video is next. You already have it in the, the ChatGPT desktop app. Do we just have a permanent, like, you know, like, are developers just going to be, like, sending sockets back and forth with OpenAI? Like how do we program for that? Like, what what is the future?[00:31:44] Olivier Godement: Yeah, that makes sense. I think with multimodality, like, real time is quickly becoming, like, you know, essentially the right experience, like, to build an application. Yeah. So my expectation is that we'll see like a non trivial, like a volume of applications like moving to a real time API. Like if you zoom out, like, audio is really simple, like, audio until basically now.[00:32:05] Olivier Godement: Audio on the web, in apps, was basically very much like a second class citizen. Like, you basically did like an audio chatbot for users who did not have a choice. You know, they were like struggling to read, or I don't know, they were like not super educated with technology. And so, frankly, it was like the crappy option, you know, compared to text.[00:32:25] Olivier Godement: But when you talk to people in the real world, the vast majority of people, like, prefer to talk and listen instead of typing and writing.[00:32:34] swyx: We speak before we write.[00:32:35] Olivier Godement: Exactly. I don't know. I mean, I'm sure it's the case for you in Singapore. For me, my friends in Europe, the number of, like, WhatsApp, like, voice notes they receive every day, I mean, just people, it makes sense, frankly, like, you know.[00:32:45] Olivier Godement: Chinese. Chinese, yeah.[00:32:46] swyx: Yeah,[00:32:47] Olivier Godement: all voice. You know, it's easier. There is more emotions. I mean, you know, you get the point across, like, pretty well. And so my personal ambition for, like, the real time API and, like, audio in general is to make, like, audio and, like, multimodality, like, truly a first class experience.[00:33:01] Olivier Godement: Like, you know, if you're, like, you know, the amazing, like, super bold, like, start up out of YC, you want to build, like, the next, like, billion, like, you know, user application to make it, like, truly your first and make it feel, like, you know, an actual good, like, you know, product experience. So that's essentially the ambition, and I think, like, yeah, it could be pretty big.[00:33:17] swyx: Yeah. I think one, one people, one issue that people have with the voice so far as, as released in advanced voice mode is the refusals.[00:33:24] Alex Volkov: Yeah.[00:33:24] swyx: You guys had a very inspiring model spec. I think Joanne worked on that. Where you said, like, yeah, we don't want to overly refuse all the time. In fact, like, even if, like, not safe for work, like, in some occasions, it's okay.[00:33:38] swyx: How, is there an API that we can say, not safe for work, okay?[00:33:41] Olivier Godement: I think we'll get there. I think we'll get there. The mobile spec, like, nailed it, like, you know. It nailed it! It's so good! Yeah, we are not in the business of, like, policing, you know, if you can say, like, vulgar words or whatever. You know, there are some use cases, like, you know, I'm writing, like, a Hollywood, like, script I want to say, like, will go on, and it's perfectly fine, you know?[00:33:59] Olivier Godement: And so I think the direction where we'll go here is that basically There will always be like, you know, a set of behavior that we will, you know, just like forbid, frankly, because they're illegal against our terms of services. But then there will be like, you know, some more like risky, like themes, which are completely legal, like, you know, vulgar words or, you know, not safe for work stuff.[00:34:17] Olivier Godement: Where basically we'll expose like a controllable, like safety, like knobs in the API to basically allow you to say, hey, that theme okay, that theme not okay. How sensitive do you want the threshold to be on safety refusals? I think that's the Dijkstra. So a[00:34:31] swyx: safety API.[00:34:32] Olivier Godement: Yeah, in a way, yeah.[00:34:33] swyx: Yeah, we've never had that.[00:34:34] Olivier Godement: Yeah. '[00:34:35] swyx: cause right now is you, it is whatever you decide. And then it's, that's it. That, that, that would be the main reason I don't use opening a voice is because of[00:34:42] Olivier Godement: it's over police. Over refuse over refusals. Yeah. Yeah, yeah. No, we gotta fix that. Yeah. Like singing,[00:34:47] Alessio: we're trying to do voice. I'm a singer.[00:34:49] swyx: And you, you locked off singing.[00:34:51] swyx: Yeah,[00:34:51] Alessio: yeah, yeah.[00:34:52] swyx: But I, I understand music gets you in trouble. Okay. Yeah. So then, and then just generally, like, what do you want to hear from developers? Right? We have, we have all developers watching you know, what feedback do you want? Any, anything specific as well, like from, especially from today anything that you are unsure about, that you are like, Our feedback could really help you decide.[00:35:09] swyx: For sure.[00:35:10] Olivier Godement: I think, essentially, it's becoming pretty clear after today that, you know, I would say the open end direction has become pretty clear, like, you know, after today. Investment in reasoning, investment in multimodality, Investment as well, like in, I would say, tool use, like function calling. To me, the biggest question I have is, you know, Where should we put the cursor next?[00:35:30] Olivier Godement: I think we need all three of them, frankly, like, you know, so we'll keep pushing.[00:35:33] swyx: Hire 10, 000 people, or actually, no need, build a bunch of bots.[00:35:37] Olivier Godement: Exactly, and so let's take O1 smart enough, like, for your problems? Like, you know, let's set aside for a second the existing models, like, for the apps that you would love to build, is O1 basically it in reasoning, or do we still have, like, you know, a step to do?[00:35:50] Olivier Godement: Preview is not enough, I[00:35:52] swyx: need the full one.[00:35:53] Olivier Godement: Yeah, so that's exactly that sort of feedback. Essentially what they would love to do is for developers I mean, there's a thing that Sam has been saying like over and over again, like, you know, it's easier said than done, but I think it's directionally correct. As a developer, as a founder, you basically want to build an app which is a bit too difficult for the model today, right?[00:36:12] Olivier Godement: Like, what you think is right, it's like, sort of working, sometimes not working. And that way, you know, that basically gives us like a goalpost, and be like, okay, that's what you need to enable with the next model release, like in a few months. And so I would say that Usually, like, that's the sort of feedback which is like the most useful that I can, like, directly, like, you know, incorporate.[00:36:33] swyx: Awesome. I think that's our time. Thank you so much, guys. Yeah, thank you so much.[00:36:38] AI Charlie: Thank you. We were particularly impressed that Olivier addressed the not safe for work moderation policy question head on, as that had only previously been picked up on in Reddit forums. This is an encouraging sign that we will return to in the closing candor with Sam Altman at the end of this episode.[00:36:57] Romain Huet, Head of DX, OpenAI[00:36:57] AI Charlie: Next, a chat with Roman Hewitt, friend of the pod, AI Engineer World's fair closing keynote speaker, and head of developer experience at OpenAI on his incredible live demos And advice to AI engineers on all the new modalities.[00:37:12] Alessio: Alright, we're live from OpenAI Dev Day. We're with Juan, who just did two great demos on, on stage.[00:37:17] Alessio: And he's been a friend of Latentspace, so thanks for taking some of the time.[00:37:20] Romain Huet: Of course, yeah, thank you for being here and spending the time with us today.[00:37:23] swyx: Yeah, I appreciate appreciate you guys putting this on. I, I know it's like extra work, but it really shows the developers that you're, Care and about reaching out.[00:37:31] Romain Huet: Yeah, of course, I think when you go back to the OpenAI mission, I think for us it's super important that we have the developers involved in everything we do. Making sure that you know, they have all of the tools they need to build successful apps. And we really believe that the developers are always going to invent the ideas, the prototypes, the fun factors of AI that we can't build ourselves.[00:37:49] Romain Huet: So it's really cool to have everyone here.[00:37:51] swyx: We had Michelle from you guys on. Yes, great episode. She very seriously said API is the path to AGI. Correct. And people in our YouTube comments were like, API is not AGI. I'm like, no, she's very serious. API is the path to AGI. Like, you're not going to build everything like the developers are, right?[00:38:08] swyx: Of[00:38:08] Romain Huet: course, yeah, that's the whole value of having a platform and an ecosystem of amazing builders who can, like, in turn, create all of these apps. I'm sure we talked about this before, but there's now more than 3 million developers building on OpenAI, so it's pretty exciting to see all of that energy into creating new things.[00:38:26] Alessio: I was going to say, you built two apps on stage today, an international space station tracker and then a drone. The hardest thing must have been opening Xcode and setting that up. Now, like, the models are so good that they can do everything else. Yes. You had two modes of interaction. You had kind of like a GPT app to get the plan with one, and then you had a cursor to do apply some of the changes.[00:38:47] Alessio: Correct. How should people think about the best way to consume the coding models, especially both for You know, brand new projects and then existing projects that you're trying to modify.[00:38:56] Romain Huet: Yeah. I mean, one of the things that's really cool about O1 Preview and O1 Mini being available in the API is that you can use it in your favorite tools like cursor like I did, right?[00:39:06] Romain Huet: And that's also what like Devin from Cognition can use in their own software engineering agents. In the case of Xcode, like, it's not quite deeply integrated in Xcode, so that's why I had like chat GPT side by side. But it's cool, right, because I could instruct O1 Preview to be, like, my coding partner and brainstorming partner for this app, but also consolidate all of the, the files and architect the app the way I wanted.[00:39:28] Romain Huet: So, all I had to do was just, like, port the code over to Xcode and zero shot the app build. I don't think I conveyed, by the way, how big a deal that is, but, like, you can now create an iPhone app from scratch, describing a lot of intricate details that you want, and your vision comes to life in, like, a minute.[00:39:47] Romain Huet: It's pretty outstanding.[00:39:48] swyx: I have to admit, I was a bit skeptical because if I open up SQL, I don't know anything about iOS programming. You know which file to paste it in. You probably set it up a little bit. So I'm like, I have to go home and test it. And I need the ChatGPT desktop app so that it can tell me where to click.[00:40:04] Romain Huet: Yeah, I mean like, Xcode and iOS development has become easier over the years since they introduced Swift and SwiftUI. I think back in the days of Objective C, or like, you know, the storyboard, it was a bit harder to get in for someone new. But now with Swift and SwiftUI, their dev tools are really exceptional.[00:40:23] Romain Huet: But now when you combine that with O1, as your brainstorming and coding partner, it's like your architect, effectively. That's the best way, I think, to describe O1. People ask me, like, can GPT 4 do some of that? And it certainly can. But I think it will just start spitting out code, right? And I think what's great about O1, is that it can, like, make up a plan.[00:40:42] Romain Huet: In this case, for instance, the iOS app had to fetch data from an API, it had to look at the docs, it had to look at, like, how do I parse this JSON, where do I store this thing, and kind of wire things up together. So that's where it really shines. Is mini or preview the better model that people should be using?[00:40:58] Romain Huet: Like, how? I think people should try both. We're obviously very excited about the upcoming O1 that we shared the evals for. But we noticed that O1 Mini is very, very good at everything math, coding, everything STEM. If you need for your kind of brainstorming or your kind of science part, you need some broader knowledge than reaching for O1 previews better.[00:41:20] Romain Huet: But yeah, I used O1 Mini for my second demo. And it worked perfectly. All I needed was very much like something rooted in code, architecting and wiring up like a front end, a backend, some UDP packets, some web sockets, something very specific. And it did that perfectly.[00:41:35] swyx: And then maybe just talking about voice and Wanderlust, the app that keeps on giving, what's the backstory behind like preparing for all of that?[00:41:44] Romain Huet: You know, it's funny because when last year for Dev Day, we were trying to think about what could be a great demo app to show like an assistive experience. I've always thought travel is a kind of a great use case because you have, like, pictures, you have locations, you have the need for translations, potentially.[00:42:01] Romain Huet: There's like so many use cases that are bounded to travel that I thought last year, let's use a travel app. And that's how Wanderlust came to be. But of course, a year ago, all we had was a text based assistant. And now we thought, well, if there's a voice modality, what if we just bring this app back as a wink.[00:42:19] Romain Huet: And what if we were interacting better with voice? And so with this new demo, what I showed was the ability to like, So, we wanted to have a complete conversation in real time with the app, but also the thing we wanted to highlight was the ability to call tools and functions, right? So, like in this case, we placed a phone call using the Twilio API, interfacing with our AI agents, but developers are so smart that they'll come up with so many great ideas that we could not think of ourselves, right?[00:42:48] Romain Huet: But what if you could have like a, you know, a 911 dispatcher? What if you could have like a customer service? Like center, that is much smarter than what we've been used to today. There's gonna be so many use cases for real time, it's awesome.[00:43:00] swyx: Yeah, and sometimes actually you, you, like this should kill phone trees.[00:43:04] swyx: Like there should not be like dial one[00:43:07] Romain Huet: of course para[00:43:08] swyx: espanol, you know? Yeah, exactly. Or whatever. I dunno.[00:43:12] Romain Huet: I mean, even you starting speaking Spanish would just do the thing, you know you don't even have to ask. So yeah, I'm excited for this future where we don't have to interact with those legacy systems.[00:43:22] swyx: Yeah. Yeah. Is there anything, so you are doing function calling in a streaming environment. So basically it's, it's web sockets. It's UDP, I think. It's basically not guaranteed to be exactly once delivery. Like, is there any coding challenges that you encountered when building this?[00:43:39] Romain Huet: Yeah, it's a bit more delicate to get into it.[00:43:41] Romain Huet: We also think that for now, what we, what we shipped is a, is a beta of this API. I think there's much more to build onto it. It does have the function calling and the tools. But we think that for instance, if you want to have something very robust, On your client side, maybe you want to have web RTC as a client, right?[00:43:58] Romain Huet: And, and as opposed to like directly working with the sockets at scale. So that's why we have partners like Life Kit and Agora if you want to, if you want to use them. And I'm sure we'll have many mores in the, in many more in the future. But yeah, we keep on iterating on that, and I'm sure the feedback of developers in the weeks to come is going to be super critical for us to get it right.[00:44:16] swyx: Yeah, I think LiveKit has been fairly public that they are used in, in the Chachapiti app. Like, is it, it's just all open source, and we just use it directly with OpenAI, or do we use LiveKit Cloud or something?[00:44:28] Romain Huet: So right now we, we released the API, we released some sample code also, and referenced clients for people to get started with our API.[00:44:35] Romain Huet: And we also partnered with LifeKit and Agora, so they also have their own, like ways to help you get started that plugs natively with the real time API. So depending on the use case, people can, can can decide what to use. If you're working on something that's completely client or if you're working on something on the server side, for the voice interaction, you may have different needs, so we want to support all of those.[00:44:55] Alessio: I know you gotta run. Is there anything that you want the AI engineering community to give feedback on specifically, like even down to like, you know, a specific API end point or like, what, what's like the thing that you want? Yeah. I[00:45:08] Romain Huet: mean, you know, if we take a step back, I think dev Day this year is all different from last year and, and in, in a few different ways.[00:45:15] Romain Huet: But one way is that we wanted to keep it intimate, even more intimate than last year. We wanted to make sure that the community is. Thank you very much for joining us on the Spotlight. That's why we have community talks and everything. And the takeaway here is like learning from the very best developers and AI engineers.[00:45:31] Romain Huet: And so, you know we want to learn from them. Most of what we shipped this morning, including things like prompt caching the ability to generate prompts quickly in the playground, or even things like vision fine tuning. These are all things that developers have been asking of us. And so, the takeaway I would, I would leave them with is to say like, Hey, the roadmap that we're working on is heavily influenced by them and their work.[00:45:53] Romain Huet: And so we love feedback From high feature requests, as you say, down to, like, very intricate details of an API endpoint, we love feedback, so yes that's, that's how we, that's how we build this API.[00:46:05] swyx: Yeah, I think the, the model distillation thing as well, it might be, like, the, the most boring, but, like, actually used a lot.[00:46:12] Romain Huet: True, yeah. And I think maybe the most unexpected, right, because I think if I, if I read Twitter correctly the past few days, a lot of people were expecting us. To shape the real time API for speech to speech. I don't think developers were expecting us to have more tools for distillation, and we really think that's gonna be a big deal, right?[00:46:30] Romain Huet: If you're building apps that have you know, you, you want high, like like low latency, low cost, but high performance, high quality on the use case distillation is gonna be amazing.[00:46:40] swyx: Yeah. I sat in the distillation session just now and they showed how they distilled from four oh to four mini and it was like only like a 2% hit in the performance and 50 next.[00:46:49] swyx: Yeah,[00:46:50] Romain Huet: I was there as well for the superhuman kind of use case inspired for an Ebola client. Yeah, this was really good. Cool man! so much for having me. Thanks again for being here today. It's always[00:47:00] AI Charlie: great to have you. As you might have picked up at the end of that chat, there were many sessions throughout the day focused on specific new capabilities.[00:47:08] Michelle Pokrass, Head of API at OpenAI ft. Simon Willison[00:47:08] AI Charlie: Like the new model distillation features combining EVOLs and fine tuning. For our next session, we are delighted to bring back two former guests of the pod, which is something listeners have been greatly enjoying in our second year of doing the Latent Space podcast. Michelle Pokras of the API team joined us recently to talk about structured outputs, and today gave an updated long form session at Dev Day, describing the implementation details of the new structured output mode.[00:47:39] AI Charlie: We also got her updated thoughts on the VoiceMode API we discussed in her episode, now that it is finally announced. She is joined by friend of the pod and super blogger, Simon Willison, who also came back as guest co host in our Dev Day. 2023 episode.[00:47:56] Alessio: Great, we're back live at Dev Day returning guest Michelle and then returning guest co host Fork.[00:48:03] Alessio: Fork, yeah, I don't know. I've lost count. I think it's been a few. Simon Willison is back. Yeah, we just wrapped, we just wrapped everything up. Congrats on, on getting everything everything live. Simon did a great, like, blog, so if you haven't caught up, I[00:48:17] Simon Willison: wrote my, I implemented it. Now, I'm starting my live blog while waiting for the first talk to start, using like GPT 4, I wrote me the Javascript, and I got that live just in time and then, yeah, I was live blogging the whole day.[00:48:28] swyx: Are you a cursor enjoyer?[00:48:29] Simon Willison: I haven't really gotten into cursor yet to be honest. I just haven't spent enough time for it to click, I think. I'm more a copy and paste things out of Cloud and chat GPT. Yeah. It's interesting.[00:48:39] swyx: Yeah. I've converted to cursor and 01 is so easy to just toggle on and off.[00:48:45] Alessio: What's your workflow?[00:48:46] Alessio: VS[00:48:48] Michelle Pokrass: Code co pilot, so Yep, same here. Team co pilot. Co pilot is actually the reason I joined OpenAI. It was, you know, before ChatGPT, this is the thing that really got me. So I'm still into it, but I keep meaning to try out Cursor, and I think now that things have calmed down, I'm gonna give it a real go.[00:49:03] swyx: Yeah, it's a big thing to change your tool of choice.[00:49:06] swyx: Yes,[00:49:06] Michelle Pokrass: yeah, I'm pretty dialed, so.[00:49:09] swyx: I mean, you know, if you want, you can just fork VS Code and make your own. That's the thing to dumb thing, right? We joked about doing a hackathon where the only thing you do is fork VS Code and bet me the best fork win.[00:49:20] Michelle Pokrass: Nice.[00:49:22] swyx: That's actually a really good idea. Yeah, what's up?[00:49:26] swyx: I mean, congrats on launching everything today. I know, like, we touched on it a little bit, but, like, everyone was kind of guessing that Voice API was coming, and, like, we talked about it in our episode. How do you feel going into the launch? Like, any design decisions that you want to highlight?[00:49:41] Michelle Pokrass: Yeah, super jazzed about it. The team has been working on it for a while. It's, like, a very different API for us. It's the first WebSocket API, so a lot of different design decisions to be made. It's, like, what kind of events do you send? When do you send an event? What are the event names? What do you send, like, on connection versus on future messages?[00:49:57] Michelle Pokrass: So there have been a lot of interesting decisions there. The team has also hacked together really cool projects as we've been testing it. One that I really liked is we had an internal hack a thon for the API team. And some folks built like a little hack that you could use to, like VIM with voice mode, so like, control vim, and you would tell them on like, nice, write a file and it would, you know, know all the vim commands and, and pipe those in.[00:50:18] Michelle Pokrass: So yeah, a lot of cool stuff we've been hacking on and really excited to see what people build with it.[00:50:23] Simon Willison: I've gotta call out a demo from today. I think it was Katja had a 3D visualization of the solar system, like WebGL solar system, you could talk to. That is one of the coolest conference demos I've ever seen.[00:50:33] Simon Willison: That was so convincing. I really want the code. I really want the code for that to get put out there. I'll talk[00:50:39] Michelle Pokrass: to the team. I think we can[00:50:40] Simon Willison: probably

In this episode, Amy and Brad sit down with Michael Chan to discuss WorkOS, a tool simplifying authentication and authorization for developers. They explore how WorkOS makes complex processes like OAuth, SSO, and MFA easy to implement, compare it to other auth providers, and dive deep into AuthKit's capabilities.SponsorsWorkOS - WorkOS helps you launch enterprise features like SSO and user management with ease. Thanks to the AuthKit for JavaScript, your team can integrate in minutes and focus on what truly matters—building your app.Show Notes00:00 - Intro01:15 - Introduction to WorkOSWorkOSAuthKitWorkOS on YouTube02:23 - Comparing WorkOS with Competitors03:50 - Features of WorkOS AuthKit06:53 - WorkOS's Evolution and Target Audience09:30 - Challenges in Implementing Auth Solutions10:30 - Should Developers Build Their Own Auth?Selma's Blog Post: One Does Not Simply Delete Cookies12:50 - The Cascade of Auth Decisions: Emails and Databases14:22 - WorkOS Integration with Astro and Remix19:50 - Key Benefits of WorkOS for Developers22:00 - Integrating AuthKit with Next and RemixSam Selikoff's YouTube Video on WorkOS + AuthKit + Remix: Using AuthKit's Headless APIs in Remix24:01 - Challenges in Documentation for DevelopersDivio's Guide to Documentation33:06 - The Future of Documentation and AI's Role35:00 - Wrap-up

Scott and Wes serve up the pros and cons of modern authentication methods like magic links, 2FA, and SMS codes. Learn how each technique works, the security trade-offs, and what might be the best approach for your apps. Show Notes 00:00 Welcome to Syntax! 00:11 Brought to you by Sentry.io. 00:34 Logging in Verification. 01:09 Magic Links. 01:24 Pros of magic links. 03:50 How magic links work. 04:25 Cons to magic links. 06:21 Magic Sessions. 06:37 Using email verification. 07:12 Using code verification. 07:55 Previously trusted device verification. 08:14 Classic email and verification process. 09:54 Email Code. 10:51 Gmail verification options. 12:01 OAuth. Hit us up on Socials! Syntax: X Instagram Tiktok LinkedIn Threads Wes: X Instagram Tiktok LinkedIn Threads Scott: X Instagram Tiktok LinkedIn Threads Randy: X Instagram YouTube Threads

Scott walks Wes through the new Syntax Production Assistant Desktop App, designed to streamline and automate their complex publishing process. From tech stack choices like Svelte5 and Rust to AI-driven features, they dive into how this tool keeps everything consistent. Show Notes 00:00 Welcome to Syntax! 00:44 Brought to you by Sentry.io. 01:37 What was the idea? 05:42 The tech. Svelte5, Tauri, Rust, FFMPEG. 08:32 Markdown editor. ink-mde, Dillinger. 09:32 Epoch timestamps. Epoch.vercel. 10:01 Updating front-matter. 10:10 Dexie.js function. 11:25 Backing up data. 11:58 Rust functions. 12:58 Why a desktop app and not a website? 14:38 Some small AI features. 16:26 Challenges with OAuth. 20:03 Publishing challenges. 23:29 Could this work on Windows? 23:54 Debugging. 26:23 Deciphering Apple logs. Hit us up on Socials! Syntax: X Instagram Tiktok LinkedIn Threads Wes: X Instagram Tiktok LinkedIn Threads Scott: X Instagram Tiktok LinkedIn Threads Randy: X Instagram YouTube Threads

Сколько факторов аутентификации нужно использовать, чтобы учетные записи ваших пользователей были в безопасности? Зачем сбрасывать пароль каждые 30 дней? Есть ли методы аутентификации, которые, с одной стороны, достаточно безопасные, а с другой – удобные даже для вашей бабушки? Никита Хромушкин из Авито провел для нас максимально подробную лекцию про то, насколько проклято текущее состояние дел в аутентификации и какое светлое будущее нас ждет, когда человечество откажется от паролей! Партнёр эпизода – облачная платформа Yandex Cloud, которая проводит большую конференцию Yandex Scale для тех, кто создаёт цифровые решения. Генеративные нейросети, речевые технологии, сервисы для работы с данными и обеспечения безопасности, serverless‑подход – об этом и многом другом 25 сентября расскажут эксперты и партнёры облачной платформы. Участие бесплатное, приходите офлайн в МХАТ им. М. Горького или смотрите в онлайн-трансляции. Зарегистрироваться можно по ссылке: https://lnnk.in/aRpI Реклама. ООО "Яндекс.Облако", ИНН 7704458262, erid:2SDnjd7SVQN Также ждем вас, ваши лайки, репосты и комменты в мессенджерах и соцсетях!
 Telegram-чат: https://t.me/podlodka Telegram-канал: https://t.me/podlodkanews Страница в Facebook: www.facebook.com/podlodkacast/ Twitter-аккаунт: https://twitter.com/PodlodkaPodcast Ведущие в выпуске: Евгений Кателла, Егор Толстой Полезные ссылки: Неслучайный генератор случайных одноразовых кодов Тинькофф банка https://habr.com/ru/articles/462071/ OWASP Authentication Cheat Sheet (Про ошибки аутентификации и общие рекомендации) https://lnnk.in/htmx OWASP Multifactor Authentication Cheat Sheet (Факторы, плюсы, минусы, рекомендации, risk-based MFA) https://lnnk.in/hvmu NIST Digital Identity Guidelines / Authentication and Lifecycle Management (Про запрет использования секретных вопросов) https://lnnk.in/duq3 OWASP Password Storage Cheat Sheet (Про безопасное хранение паролей, bcrypt, work factor) https://lnnk.in/aNp7 OAuth 2.0 Authorization Code Grant Type - Fully Visualized (Article with Infographic) (Статья с инфографикой / sequence-диаграммой про OAuth) https://lnnk.in/aMqe OAuth Playground (Authorization Code with PKCE) (Интерактивная площадка для тестирования OAuth+PKCE) https://lnnk.in/aSpL OWASP Testing for OAuth Weaknesses (Руководство по тестированию уязвимостей OAuth) https://lnnk.in/aOp7 OWASP Authentication Testing (Руководство по тестированию аутентификации) https://lnnk.in/evl8 Open Policy Agent (Фреймворк политики безопасности) https://www.openpolicyagent.org/ Rego Sandbox for Open Policy Agent (Песочница для языка Rego) https://play.openpolicyagent.org/ FTC Data Breach Response Guide for Businesses (Гайд для бизнеса на случай утечки паролей) https://lnnk.in/aPpT Book: OAuth 2 in Action (Книга по OAuth2, возможна устаревшая с 2017) https://www.manning.com/books/oauth-2-in-action Book: Cryptography by Damir Sharifyanov (Книга по основам криптографии для новичков) https://lnnk.in/aQpU OWASP Testing Multi-Factor Authentication (Руководство по тестированию многофакторной аутентификации) https://lnnk.in/hxmj OWASP Testing for Bypassing Authorization Schema (Про тестирование обхода схем авторизации) https://lnnk.in/exl2 OWASP Testing for Cookies Attributes (Атрибуты Cookies: Secure, HTTP only, Path, Expires) https://lnnk.in/hzl9

On this week's show, Patrick Gray and Adam Boileau discuss the week's security news, including: The insurance industry's reaction to CrowdStrike's mess Google's Workspace email validation flaw and its consequences for OAuth'd applications Is the VMWare ESX group membership feature a CVE or an FYI? Secureboot continues to under-deliver North Korea's revenue neutral intelligence services And much, much more This episode is sponsored by allowlisting software vendor Airlock Digital. Airlock uses a kernel driver on Windows, so Chief Executive David Cottingham joined to discuss what the CrowdStrike kernel driver bug drama means for security vendors. This episode is also available on Youtube. If you want to ruin the magic of radio and see the faces behind the show, well, now you can! Show notes Business interruption claims will drive insurance losses linked to CrowdStrike IT disruption | Cybersecurity Dive Delta hires David Boies to seek damages from CrowdStrike, Microsoft CrowdStrike disruption direct losses to reach $5.4B for Fortune 500, study finds | Cybersecurity Dive (1145) Why CrowdStrike's Baffling BSOD Disaster Was Avoidable - YouTube CrowdStrike offers a $10 apology gift card to say sorry for outage | TechCrunch Crooks Bypassed Google's Email Verification to Create Workspace Accounts, Access 3rd-Party Services – Krebs on Security Hackers exploit VMware vulnerability that gives them hypervisor admin | Ars Technica Microsoft calls out apparent ESXi vulnerability that some researchers say is a ‘nothing burger' | CyberScoop AMI Platform Key leak undermines Secure Boot on 800+ PC models Chrome will now prompt some users to send passwords for suspicious files | Ars Technica Google Online Security Blog: Improving the security of Chrome cookies on Windows A Senate Bill Would Radically Improve Voting Machine Security | WIRED U.S. told Philippines it made ‘missteps' in secret anti-vax propaganda effort | Reuters Cyber firm KnowBe4 hired a fake IT worker from North Korea | CyberScoop North Korean hacker used hospital ransomware attacks to fund espionage | CyberScoop North Korea Cyber Group Conducts Global Espionage Campaign to Advance Regime's Military and Nuclear Programs North Korean hacking group makes waves to gain Mandiant, FBI spotlight | CyberScoop ServiceNow spots sales opportunities post-CrowdStrike outage | Cybersecurity Dive Chaining Three Bugs to Access All Your ServiceNow Data Cyber Supply Chain Risk Management Conference (CySCRM) 2024 | Conference | PNNL