Podcasts about semantic

Paul, Leo, and Richard get into new Windows features (thanks to the Feature Tracker), hardware shifts for Microsoft/Intel/Apple, AI moves from OpenAI/Apple/Adobe, Notion Mail, a Hawaiian drink, the National Recording Registry, rice cookers, and electronic timer tunes! Windows 11 Feature Tracker. Since we last talked, Microsoft has announced the following new features for Windows 11: Semantic search can now search for Windows settings using natural language - Dev and Beta (24H2) channels, no clear stable date but guessing June Narrator can more accurately describe images by detailing the people, objects, colours, text, and numbers in them, Snapdragon X only, same builds as above Snipping Tool with "Text extraction" in the capture bar - This in Canary now, but it was in at least Dev previously, this could ship in stable at any time, it's an app Recall (preview) and Click to Do (preview) head to the Release Preview channel (24H2) - Expect this in May Patch Tuesday Narrator speech recap, Phone Link/Start integration, File Explorer Home updates, Windows Share with Edit all head to Release Preview (23H2) - Expect these in May Patch Tuesday - They were added to Beta channel (23H2) a few days earlier Plus, Microsoft Edge is up to 9 percent faster at web rendering and we're having a fiesta Also, the Windows 95 startup/logout chime has been inducted into the National Recording Registry Hardware Surface Hub OG hits EOL this year just like Windows 10 First major change under new Intel CEO What's a computer? The iPad, supposedly, but we'll see Everything's fine, but Google laid off hundreds in Pixel/Android group AI Apple is making big changes so that Apple Intelligence will actually be intelligent Adobe is going agentic too OpenAI is creating its own social network because the world needs another social network OpenAI announces three GPT-4.1 models - may retire GPT-4 soon - plus now o3 and o4-mini models ChapGPT gets an image library and a memory Claude gets Research and Google Workspace integration Meta will start training its AI models with EU data, wink wink Xbox and games Xbox app on mobile will soon let you buy games (!) and add-on content, join Game Pass, and redeem perks. Did Microsoft get a concession from Apple/Google?? COD: Modern Warfare II (OG) and more are coming to Game Pass in the next few weeks Xbox announces Doom: The Dark Ages limited edition accessories Sea of Thieves is coming to Battle.net Sony forced to raise the price of PS5 in three locales Tips and Picks Tip of the week: Think like an individual, not an enterprise App pick of the week: Notion Mail RunAs Radio this week: How to Not Hate PowerShell with Barbara Forbes Brown liquor pick of the week: 12th Hawaii Distiller's Reserve Hosts: Leo Laporte, Paul Thurrott, and Richard Campbell Download or subscribe to Windows Weekly at https://twit.tv/shows/windows-weekly Check out Paul's blog at thurrott.com The Windows Weekly theme music is courtesy of Carl Franklin. Join Club TWiT for Ad-Free Podcasts! Support what you love and get ad-free shows, a members-only Discord, and behind-the-scenes access. Join today: https://twit.tv/clubtwit Sponsors: 1password.com/windowsweekly uscloud.com

Paul, Leo, and Richard get into new Windows features (thanks to the Feature Tracker), hardware shifts for Microsoft/Intel/Apple, AI moves from OpenAI/Apple/Adobe, Notion Mail, a Hawaiian drink, the National Recording Registry, rice cookers, and electronic timer tunes! Windows 11 Feature Tracker. Since we last talked, Microsoft has announced the following new features for Windows 11: Semantic search can now search for Windows settings using natural language - Dev and Beta (24H2) channels, no clear stable date but guessing June Narrator can more accurately describe images by detailing the people, objects, colours, text, and numbers in them, Snapdragon X only, same builds as above Snipping Tool with "Text extraction" in the capture bar - This in Canary now, but it was in at least Dev previously, this could ship in stable at any time, it's an app Recall (preview) and Click to Do (preview) head to the Release Preview channel (24H2) - Expect this in May Patch Tuesday Narrator speech recap, Phone Link/Start integration, File Explorer Home updates, Windows Share with Edit all head to Release Preview (23H2) - Expect these in May Patch Tuesday - They were added to Beta channel (23H2) a few days earlier Plus, Microsoft Edge is up to 9 percent faster at web rendering and we're having a fiesta Also, the Windows 95 startup/logout chime has been inducted into the National Recording Registry Hardware Surface Hub OG hits EOL this year just like Windows 10 First major change under new Intel CEO What's a computer? The iPad, supposedly, but we'll see Everything's fine, but Google laid off hundreds in Pixel/Android group AI Apple is making big changes so that Apple Intelligence will actually be intelligent Adobe is going agentic too OpenAI is creating its own social network because the world needs another social network OpenAI announces three GPT-4.1 models - may retire GPT-4 soon - plus now o3 and o4-mini models ChapGPT gets an image library and a memory Claude gets Research and Google Workspace integration Meta will start training its AI models with EU data, wink wink Xbox and games Xbox app on mobile will soon let you buy games (!) and add-on content, join Game Pass, and redeem perks. Did Microsoft get a concession from Apple/Google?? COD: Modern Warfare II (OG) and more are coming to Game Pass in the next few weeks Xbox announces Doom: The Dark Ages limited edition accessories Sea of Thieves is coming to Battle.net Sony forced to raise the price of PS5 in three locales Tips and Picks Tip of the week: Think like an individual, not an enterprise App pick of the week: Notion Mail RunAs Radio this week: How to Not Hate PowerShell with Barbara Forbes Brown liquor pick of the week: 12th Hawaii Distiller's Reserve Hosts: Leo Laporte, Paul Thurrott, and Richard Campbell Download or subscribe to Windows Weekly at https://twit.tv/shows/windows-weekly Check out Paul's blog at thurrott.com The Windows Weekly theme music is courtesy of Carl Franklin. Join Club TWiT for Ad-Free Podcasts! Support what you love and get ad-free shows, a members-only Discord, and behind-the-scenes access. Join today: https://twit.tv/clubtwit Sponsors: 1password.com/windowsweekly uscloud.com

Paul, Leo, and Richard get into new Windows features (thanks to the Feature Tracker), hardware shifts for Microsoft/Intel/Apple, AI moves from OpenAI/Apple/Adobe, Notion Mail, a Hawaiian drink, the National Recording Registry, rice cookers, and electronic timer tunes! Windows 11 Feature Tracker. Since we last talked, Microsoft has announced the following new features for Windows 11: Semantic search can now search for Windows settings using natural language - Dev and Beta (24H2) channels, no clear stable date but guessing June Narrator can more accurately describe images by detailing the people, objects, colours, text, and numbers in them, Snapdragon X only, same builds as above Snipping Tool with "Text extraction" in the capture bar - This in Canary now, but it was in at least Dev previously, this could ship in stable at any time, it's an app Recall (preview) and Click to Do (preview) head to the Release Preview channel (24H2) - Expect this in May Patch Tuesday Narrator speech recap, Phone Link/Start integration, File Explorer Home updates, Windows Share with Edit all head to Release Preview (23H2) - Expect these in May Patch Tuesday - They were added to Beta channel (23H2) a few days earlier Plus, Microsoft Edge is up to 9 percent faster at web rendering and we're having a fiesta Also, the Windows 95 startup/logout chime has been inducted into the National Recording Registry Hardware Surface Hub OG hits EOL this year just like Windows 10 First major change under new Intel CEO What's a computer? The iPad, supposedly, but we'll see Everything's fine, but Google laid off hundreds in Pixel/Android group AI Apple is making big changes so that Apple Intelligence will actually be intelligent Adobe is going agentic too OpenAI is creating its own social network because the world needs another social network OpenAI announces three GPT-4.1 models - may retire GPT-4 soon - plus now o3 and o4-mini models ChapGPT gets an image library and a memory Claude gets Research and Google Workspace integration Meta will start training its AI models with EU data, wink wink Xbox and games Xbox app on mobile will soon let you buy games (!) and add-on content, join Game Pass, and redeem perks. Did Microsoft get a concession from Apple/Google?? COD: Modern Warfare II (OG) and more are coming to Game Pass in the next few weeks Xbox announces Doom: The Dark Ages limited edition accessories Sea of Thieves is coming to Battle.net Sony forced to raise the price of PS5 in three locales Tips and Picks Tip of the week: Think like an individual, not an enterprise App pick of the week: Notion Mail RunAs Radio this week: How to Not Hate PowerShell with Barbara Forbes Brown liquor pick of the week: 12th Hawaii Distiller's Reserve Hosts: Leo Laporte, Paul Thurrott, and Richard Campbell Download or subscribe to Windows Weekly at https://twit.tv/shows/windows-weekly Check out Paul's blog at thurrott.com The Windows Weekly theme music is courtesy of Carl Franklin. Join Club TWiT for Ad-Free Podcasts! Support what you love and get ad-free shows, a members-only Discord, and behind-the-scenes access. Join today: https://twit.tv/clubtwit Sponsors: 1password.com/windowsweekly uscloud.com

Paul, Leo, and Richard get into new Windows features (thanks to the Feature Tracker), hardware shifts for Microsoft/Intel/Apple, AI moves from OpenAI/Apple/Adobe, Notion Mail, a Hawaiian drink, the National Recording Registry, rice cookers, and electronic timer tunes! Windows 11 Feature Tracker. Since we last talked, Microsoft has announced the following new features for Windows 11: Semantic search can now search for Windows settings using natural language - Dev and Beta (24H2) channels, no clear stable date but guessing June Narrator can more accurately describe images by detailing the people, objects, colours, text, and numbers in them, Snapdragon X only, same builds as above Snipping Tool with "Text extraction" in the capture bar - This in Canary now, but it was in at least Dev previously, this could ship in stable at any time, it's an app Recall (preview) and Click to Do (preview) head to the Release Preview channel (24H2) - Expect this in May Patch Tuesday Narrator speech recap, Phone Link/Start integration, File Explorer Home updates, Windows Share with Edit all head to Release Preview (23H2) - Expect these in May Patch Tuesday - They were added to Beta channel (23H2) a few days earlier Plus, Microsoft Edge is up to 9 percent faster at web rendering and we're having a fiesta Also, the Windows 95 startup/logout chime has been inducted into the National Recording Registry Hardware Surface Hub OG hits EOL this year just like Windows 10 First major change under new Intel CEO What's a computer? The iPad, supposedly, but we'll see Everything's fine, but Google laid off hundreds in Pixel/Android group AI Apple is making big changes so that Apple Intelligence will actually be intelligent Adobe is going agentic too OpenAI is creating its own social network because the world needs another social network OpenAI announces three GPT-4.1 models - may retire GPT-4 soon - plus now o3 and o4-mini models ChapGPT gets an image library and a memory Claude gets Research and Google Workspace integration Meta will start training its AI models with EU data, wink wink Xbox and games Xbox app on mobile will soon let you buy games (!) and add-on content, join Game Pass, and redeem perks. Did Microsoft get a concession from Apple/Google?? COD: Modern Warfare II (OG) and more are coming to Game Pass in the next few weeks Xbox announces Doom: The Dark Ages limited edition accessories Sea of Thieves is coming to Battle.net Sony forced to raise the price of PS5 in three locales Tips and Picks Tip of the week: Think like an individual, not an enterprise App pick of the week: Notion Mail RunAs Radio this week: How to Not Hate PowerShell with Barbara Forbes Brown liquor pick of the week: 12th Hawaii Distiller's Reserve Hosts: Leo Laporte, Paul Thurrott, and Richard Campbell Download or subscribe to Windows Weekly at https://twit.tv/shows/windows-weekly Check out Paul's blog at thurrott.com The Windows Weekly theme music is courtesy of Carl Franklin. Join Club TWiT for Ad-Free Podcasts! Support what you love and get ad-free shows, a members-only Discord, and behind-the-scenes access. Join today: https://twit.tv/clubtwit Sponsors: 1password.com/windowsweekly uscloud.com

Mike & Tommy dive into a a great article by Marco Russo about what the future of DAX and Semantic Models after the FabCon announcements. https://www.sqlbi.com/blog/marco/2025/04/04/dax-and-semantic-models-announcements-at-the-fabric-conference-2025/Get in touch:Send in your questions or topics you want us to discuss by tweeting to @PowerBITips with the hashtag #empMailbag or submit on the PowerBI.tips Podcast Page.Visit PowerBI.tips: https://powerbi.tips/Watch the episodes live every Tuesday and Thursday morning at 730am CST on YouTube: https://www.youtube.com/powerbitipsSubscribe on Spotify: https://open.spotify.com/show/230fp78XmHHRXTiYICRLVvSubscribe on Apple: https://podcasts.apple.com/us/podcast/explicit-measures-podcast/id1568944083‎Check Out Community Jam: https://jam.powerbi.tipsFollow Mike: https://www.linkedin.com/in/michaelcarlo/Follow Seth: https://www.linkedin.com/in/seth-bauer/Follow Tommy: https://www.linkedin.com/in/tommypuglia/

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

How do constraints increase opportunities by introducing boundaries? What challenges do Content Management Systems face in the age of AI?In this episode, Jeff Eaton (Partner at Autogram) discusses the complexities of building and managing digital systems, drawing on a wide range of theoretical frameworks to understand underlying patterns. The episode highlights the continuous evolution of technology and the ongoing need for structured approaches, even in the face of advancements like AI.

What's up everyone, today we have the pleasure of sitting down with Rich Waldron, Co-founder and CEO at Tray.ai. Summary: Marketing ops folks stand at a crossroads where iPaaS platforms and AI agents are colliding in crazy ways. Rich pulls back the curtain on what happens when workflows become agent "skills": Imagine your carefully built automations transformed into autonomous assistants that diagnose tech issues, provision applications, and manage complex Salesforce campaigns without manual intervention. Your marketing stack could suddenly act like a "junior admin" on demand, while you focus on strategy. The explosion of AI features has turned martech leaders into "AI referees" juggling competing vendor tools, yet those who master both fundamentals and experimental curiosity become "10X automation heroes" - the first teammates that are called when problems need solving. As Rich explains, career security comes from momentum, not stability.About RichAfter University, Rich spent several years building different projects in the UK which included a web agency, a media company and a mobile app for social gatheringsTray was officially founded in 2013, bootstrapped by selling Wellington boots on eBay – the early product idea was email automation but pivoted to enabling less technical people to utilize APIs to integrate their tech stackAlongside his 2 co-founders, they spent the better part of 4 years building the product and raising a seed round in 2015. Between 2018 and 2020, Tray grew from $500k to $20M ARRToday, Tray processes Billions of transactions across the platform every month and they've gone all in on the composable AI integration and automation movementThe Rise iPaaS and AI OrchestrationiPaaS exploded because enterprise suites were too slow to open up their integration capabilities. CDPs made similar mistakes with rigid architectures, birthing today's composable alternatives. Every software system eventually faces the same primal challenge: intercommunication. Rich recounts how this pattern also repeats throughout computing history with startling consistency. Monolithic ERPs dominated early landscapes, where engineers cobbled together custom connections between internal components. These hand-built bridges crumbled easily, leaving teams scrambling for standardized frameworks that could withstand daily operational stress.As specialized software proliferated around these central systems, integration pressure mounted. "We're still not that far through on adopting the cloud," Rich points out, puncturing the tech bubble many of us live in. While cloud technologies feel omnipresent to industry veterans, countless organizations remain firmly planted on physical servers. This reality created distinct evolutionary phases for iPaaS:On-premise to on-premise connections (the original integration challenge)On-premise to cloud bridges (MuleSoft's territory)  Cloud-to-cloud orchestration (where Tray focused)Each phase demanded fundamentally different architecture. Cloud applications introduced unique payload structures, execution patterns, and API designs that rendered previous integration approaches obsolete. "Every application now has an API," Rich explains, describing how this technical shift triggered organizational transformation. Marketing departments grew increasingly technical, with marketing ops professionals discovering they could craft custom experiences by tapping into these newly accessible APIs.> "iPaaS has to evolve because if your iPaaS was built purely for an era when AI wasn't a consideration and your customers are now suddenly saying, 'We're looking at how we infuse AI in these processes,' the requirements have changed again."You've likely witnessed this evolution in your own organization. Remember when connecting two systems required an IT ticket and weeks of waiting? Now your marketing team builds automations while the sales team creates their own customer journey orchestrations. Technical power diffused across departments, democratizing integration capabilities previously locked behind developer expertise.Today's iPaaS platforms face their greatest evolutionary pressure yet: AI integration. Rich describes how existing processes built on traditional platforms now crumble under AI's weight. Semantic analysis, novel reasoning models, and entirely new integration approaches have rewritten the rules. iPaaS vendors who built for the pre-AI era now race to adapt as customers demand intelligent workflows. The platforms that flourish will embrace AI as a core architectural principle rather than a bolted-on feature.Key takeaway: Evaluate your integration platform based on whether it was (re)designed for today's AI-centric landscape or simply patched to accommodate it. The most effective iPaaS solutions evolve alongside major architectural shifts rather than struggling to catch up after they've occurred.What Makes an Agent Truly "Agentic" Beyond the Marketing HypeThe AI agent landscape is blurring with contradictions and wild claims and it's only going to get crazier. While vendors plaster "agent" labels on everything with an algorithm, Rich isn't worried about definition. The terminology matters far less than what these systems actually do. > "The AI isn't just reasoning over a set of data, but it's actually going and taking action on a user's behalf... I've done the response for you and I've handled the follow up and I've gone and filed this over here, and it's actually carrying out a series of actions based on the reasoning that occurred in the first place."AI agents take autonomous action. They handle support tickets end-to-end. They file documents. They complete multi-step processes without human intervention. They execute rather than suggest.Tray's team experienced genuine goosebump moments when they combined their connector infrastructure with LLM reasoning. You could almost hear the click as puzzle pieces fell into place. Their ten-year vision suddenly materialized before their eyes:Semi-technical staff performing complex cross-organizational tasksTeams breaking free from application limitationsWorkers escaping data accessibility problemsAI executing the best next steps, not just recommending themThis capability triggered an immediate "holy shit" reaction during internal testing. Everything changed in that moment. The strategic implications struck like lightning: adapt or die. Many category leaders fail exactly here, at this precipice of change, clinging to outdated paradigms while disruptive innovation rewrites the rules.The adoption curve is also likely to be shockingly steep. Century-old enterprises with conservative DNA are already running AI workloads in production using Tray. Some skipped entire technological generations, leapfrogging directly into AI implementation. They've dumped their data into databases, layered AI analysis on top, and built reactive systems around the outputs. The comfort level with these technologies has accelerated across industries at a pace that defies conventional adoption timelines.When Tray rebranded from tray.io to tray.ai, they acknowledged that connection alone provides insufficient value in this new world. The platforms that enable autonomous action through AI will dominate the future landscape. The rest will fade into technological obscurity, remembered only as stepping stones.Key takeaway: The future competitive advantage in your martech stack is going to come from AI that acts on your behalf, not just analyzes and recommends. When you implement systems where AI executes complex workflows based on reasoning, you empower your teams to achieve broader impact with fewer technic...

In 1980, Michelle Smith published a book that triggered the Satanic Panic, a worldwide fear that Satan worshippers were recruiting millions to embrace satanism.  Today, I explore the surprising science of false memories with Dr. Charan Ranganath, author of Why We Remember. Dr. Ranganath reveals how memory is more imagination than recollection, why some people vividly remember things that never happened, and why the Satanic Panic was based on fiction not fact.  You'll learn: How Michelle Remembers sparked the Satanic Panic and shaped public fear. Why memories “recovered” in therapy can feel real but be completely false. How a memory expert misremembered her own mother's death. The shocking study where 40% of participants believed they committed a crime they never did. ---- Download the Reading List: https://nudge.kit.com/readinglist Sign up to my newsletter: https://www.nudgepodcast.com/mailing-list Connect on LinkedIn: https://www.linkedin.com/in/phill-agnew-22213187/ Watch Nudge on YouTube: https://www.youtube.com/@nudgepodcast/ Charan's book Why We Remember: https://charanranganath.com/ ---- Sources: 60 Minutes Australia. (1989). Teens cruel ‘sacrificial' offering to Satan in quiet country town [Video]. YouTube. https://youtu.be/yiN27M0akuY Bartlett, F. C. (1928b). Types of imagination. Philosophy, 3(9), 78–85. Bartlett, F. C. (1932). Remembering: A study in experimental and social psychology. Cambridge University Press. KABC News. (1988). Devil worship: Satanic panic circa 1988 [Video]. YouTube. https://youtu.be/RGxf7G3Xpj4 Kassin, S. M. (2008). False confessions: Causes, consequences, and implications for reform. Current Directions in Psychological Science, 17(4), 249–253. Loftus, E. F. (2005). Planting misinformation in the human mind: A 30-year investigation of the malleability of memory. Learning & Memory, 12(4), 361–366. Loftus, E. F., & Pickrell, J. E. (1995). The formation of false memories. Psychiatric Annals, 25(12), 720–725. Loftus, E. F., Miller, D. G., & Burns, H. J. (1978). Semantic integration of verbal information into a visual memory. Journal of Experimental Psychology: Human Learning and Memory, 4(1), 19–31. https://doi.org/10.1037/0278-7393.4.1.19 Magnetic Memory Museum. (1994). Law enforcement guide to Satanic cults [Video]. YouTube. https://youtu.be/VTJ0_BABexo Ranganath, C. (2024). Why we remember: Unlocking memory's power to hold on to what matters. Doubleday. Roever, D. (1989). Exposing the Satanic web [Video]. Rcom Productions. YouTube. https://youtu.be/Hgymy7VlhT8 Shaw, J., & Porter, S. (2015). Constructing rich false memories of committing crime. Psychological Science, 26(3), 291–301. Unknown Author. (1990). Satanic cults & ritual crime [Video]. YouTube. https://youtu.be/byUvJDXqxa4 Winfrey, O. (1989). Oprah Winfrey Show 1989: Ritual sacrifice of babies [Video]. YouTube. https://youtu.be/BRninYpnlzM

Today's guest is Laurent Bourrelly, an expert in Semantic SEO and the founder of Topical Mesh. With a career stretching back to 2004, Laurent has a unique perspective on the evolution of search engines. From his early days as a “search engine hacker” uncovering flaws in Google's algorithm to pioneering advanced SEO strategies, Laurent helps…

Hong Minhee is an open source developer and the creator of the Fedify ActivityPub server framework. We talk about how applications like Mastodon and Misskey communicate with one another using ActivityPub. This includes discussions on built-in activites, extending the specification in a backwards compatible way, difficulties implementing JSON-LD, the inbox model, and his experience implementing the specification. Hong Minhee: activitypub profile fedify hollo Specifications: ActivityPub W3C specification JSON Linked Data Resource Description Framework W3C Semantic Web Standards ActivityPub and WebFinger ActivityPub and HTTP Signatures ActivityPub implementations: Mastodon Misskey Akkoma Pleroma Pixelfed Lemmy Loops GoToSocial ActivityPub support in Ghost Threads has entered the Fediverse ActivityPub tools: ActivityPub Academy BrowserPub fedify CLI -- Transcript You can help correct transcripts on GitHub. What's ActivityPub? [00:00:00] Jeremy: Today, I'm talking to Hong Minhee. He is the developer of Fedify. A TypeScript library for building ActivityPub server applications. The first thing I think we should start with is defining ActivityPub. what is ActivityPub? [00:00:16] Hong: ActivityPub is the protocol that lets social networks talk to each other and it's officially recommended by W3C. It's what powers this thing we call the Fediverse which is basically a way for different social media platforms to work together. Users of ActivityPub [00:00:39] Jeremy: Can you give some examples that people might have heard of -- either users of ActivityPub or things that are a part of this fediverse? [00:00:50] Hong: Mastodon is probably the biggest one out there. And you know what's interesting? Meta threads has actually started implementing ActivityPub this summer. So this still pretty much a one way street right now. In East Asia, especially Japan, there's this really popular microblogging platform called misskey. It's got so many forks that people actually joke around and called them forkeys. but it's not just about Twitter style microblogging, there's Pixelfed which is kind of like Instagram, but for the fediverse. And those same folks recently launched loops. Which is basically doing what TikTok does, but in the Fediverse. Then you've got stuff like Lemmy and which are doing the reddit thing up in the Fediverse. [00:02:00] Jeremy: Oh like Reddit. [00:02:01] Hong: Yeah. There are so much more out there that I haven't even mentioned. Um, most of it is open source, which is pretty cool. [00:02:13] Jeremy: So the first few examples you gave, Mastodon and Meta's threads, they're very similar to, to Twitter, right? So that's what you were calling the, the Microblogging applications. And I think what you had said, which is a little bit interesting is you had said Metas threads is only one way. So could you kind of describe like what you mean by that? [00:02:37] Hong: Currently meta threads only can be followed by other ActivityPub applications but you cannot follow other people in the fediverse. [00:02:55] Jeremy: People who are using another Microblogging platform like Mastodon can follow someone on Meta's Threads platform. But the other way is not true. If you're on threads, you can't follow someone on Mastodon. [00:03:07] Hong: Yes, that's right. [00:03:09] Jeremy: And that's not a limitation of the protocol itself. That's a design decision or a decision made by Meta. [00:03:17] Hong: Yeah. They are slowly implementing ActivityPub and I hope they will implement complete ActivityPub in the future. Interoperability through Activities [00:03:27] Jeremy: And then the other examples you gave, one is I believe it was Pixel Fed is very similar to Instagram. And then the last examples you gave was I think it was Lemmy, you said it's similar to Reddit. Because you mentioned the term Fediverse before and you mentioned that these all use ActivityPub and since these seem like different kinds of applications, what does it mean for them to interact? Because with Mastodon and Threads I can kind of understand because they're both similar to Twitter. So you're posting messages and replying, but, but what does it mean, for example, for someone on Mastodon to interact with someone on Lemmy which is like Reddit because they seem very different. [00:04:16] Hong: People in Lemmy and Mastodon are called actors and can follow each other. They have interactions between them called activities. And there are several types of activities like, create and follow and undo, like, and so on. So, ActivityPub applications tend to, use these vocabulary to implement their features. So, for example, Lemmy uses like activities for upvoting and like activities for down voting and it's translated to likes in Mastodon. So if you submit a post on Lemmy and it shows up on your Mastodon timeline. If you like that post (it) is upvoting in Lemmy. [00:05:36] Jeremy: And probably similarly with Pixelfed, which you said is like Instagram, if you follow someone's Pixelfed account in Mastodon and they post a photo in Pixel Fed, they would see it as a post in Mastodon natively and they could give it a like there. Adding activities or properties [00:05:56] Jeremy: And these activities that you mentioned -- So the like and the dislike are those part of ActivityPub itself? [00:06:05] Hong: Yes, and this vocabulary can be extended. [00:06:10] Jeremy: So you can add, additional actions (activities) or are you adding information (properties) to the existing actions? [00:06:37] Hong: It is called activity vocabulary, and there are, things like accept, add, arrive, block, create lead, dislike, flag, follow, ignore invite, join, and so on. So, basically, almost everything you need to build social media is already there in the vocabulary, but if you want to extend some more, you can define your, own vocabulary. [00:06:56] Jeremy: Most of the things that an Instagram or a Twitter, or a Reddit would need is already there. But you're saying that you can have your own vocabulary. So if there's an action or an activity that is not covered by the specification, you can create one yourself. [00:07:13] Hong: Yes. For example, Misskey and Pleroma defined emoji reactor to represent emoji reactions. [00:07:25] Jeremy: Because the systems can extend the vocabulary. What are some other examples of cases where mastodon or any other of these systems has found that the existing vocabulary is not enough. What are some other examples of applications extending it? [00:07:45] Hong: For example, uh, mastodon defined suspended -- suspended property. They are not activities, but they are properties in the activity. ActivityPub consists of several types of objects and there are activities and normal objects like, article. they can have properties and there are several existing properties, but they can be also extended. So Mastodon extended some properties they need. So for example, they define suspended or discoverable. [00:08:44] Hong: Suspended for to tell if an actor is suspended by moderators. Discoverable tells if an actor itself wants to be, searched and indexed, and there are much more properties. Mastodon extended. Actors [00:09:12] Jeremy: And these are, these are properties of the actor. These are properties of the user? [00:09:19] Hong: Yes. Actors. [00:09:21] Jeremy: Cause I think earlier you mentioned that. The concept of a user is an actor, and it sounds like what you're saying is an actor can have all these properties. There's probably a, a username and things like that, but Mastodon has extended the properties so that, you can have a property on whether you wanna be searched or indexed you can have a property that says you're suspended. So I guess your account, is still there, but can't be used anymore. Something we should probably talk about then is, so you have these actors, you have these activities that I'm assuming the actors are performing on one another. What does that data look like and what does the communication look like? [00:10:09] Hong: Actors have their own dereferencable URI and when you look up that URI you get all the info about the actor in JSON-LD format [00:10:22] Jeremy: JSON-LD? [00:10:23] Hong: Yeah. JSON-LD. linked data. (The) Actor has all the stuff you expect to find on a social account name, bio URL to the profile page, profile picture, head image and more. And there are five main types of actors: application, group, organization, person and service. And you know how sometimes on Mastodon you will see an account marked as a bot? [00:10:58] Jeremy: A bot? [00:10:59] Hong: Yeah. Bot and that's what an actor of type service looks like. And the ActivityPub spec actually let you create other types beyond these five. But I haven't seen anyone actually do that yet. JSON-LD [00:11:15] Jeremy: And you mentioned that these are all JSON objects. but the LD part, the linked data part, I'm not familiar with. So what different about the linked data part of the JSON? [00:11:31] Hong: So JSON-LD is the special way of writing RDF. Which was originally used in the semantic web. Usually RDF uses (a) format (that) is called triples. [00:11:48] Jeremy: Triples? [00:11:49] Hong: Yeah, subject and predicate and object. [00:11:55] Jeremy: Subject, predicate, object. Can you give an example of what those three would be? [00:12:00] Hong: For example, is a person, it's a triple. John is a subject and is a predicate [00:12:11] Jeremy: is, is the predicate. [00:12:12] Hong: Okay. And person is a object. That's great for showing how things are connected, but it is pretty different from how we usually handle data in REST for APIs and stuff. Like normally we say a personal object has property like name, DOB, bio, and so on. And a bunch of subject predicated object triples that's where JSON-LD comes in -- is designed to look more like the JSON we are used to working with, while still being able to represent RDF Graphs. RDF graph are ontology. It's a way to represent factual data, but is, quite different from, how we represent data in relational database. And it's a bunch of triples each subject and objects are nodes and predicates connect these nodes. Semantic Web [00:13:30] Jeremy: You mentioned the Semantic web, what does that mean? What is the semantic web? [00:13:35] Hong: It's a way to represent web in the structural way, is machine readable so that you can, scan the data in the web, using scrapers or crawlers. [00:13:52] Jeremy: Scrapers -- or what was the second one? Crawling. [00:13:59] Hong: Yeah. Then you can have graph data of web and you can, query information about things from the data. [00:14:14] Jeremy: So is the web as it exists now, is that the Semantic web or is it something different? [00:14:24] Hong: I think it is partially semantic web, you have several metadata in Your HTML. For example, there are several specification for semantic web, like, OpenGraph metadata. [00:14:32] Jeremy: Cause when I think about OpenGraph, I think about the metadata on a webpage that, that tells other applications or websites that if you link to this page: show this image or show this title and description. You're saying that specifically you consider part of the semantic web? [00:15:05] Hong: That's, semantic web. To make your website semantic web. Your website should be able to, provide structural data. And other people can make Scrapers to scan, structural data from your website. There are a bunch of attributes and text for HTML to represent metadata. For example you have relation attribute rel so if you have a link with rel=me to your another social profile. Then other people can tell two web pages represent the same person. [00:16:10] Jeremy: Oh, I see. So you could have more than one website. Maybe one is your blog and maybe one is your favorite birds or something like that. But you could put a rel tag with information about you as a person so that someone who scrapes both websites could look at that tag and see that both of these websites are by, Hong, by this person. JSON-LD is difficult to implement and not used as intended [00:16:43] Hong: Yeah. I think JSON-LD is, designed for semantic web, but in reality, ActivityPub implementations, most of them are, not aware of semantic web. [00:17:01] Jeremy: The choice of JSON Linked Data, the JSON-LD, by the people who made the specification -- They had this idea that things that implemented ActivityPub would be a part of this semantic web, but the actual implementation of a Mastodon or a Pixelfed, they use JSON-LD because it's part of the specification, but the way they use it, it ends up not really being a part of this semantic web. [00:17:34] Hong: Yeah, that's exactly.. [00:17:37] Jeremy: You've mentioned that implementing it is difficult. What makes implementing JSON LD particularly hard? [00:17:48] Hong: The JSON-LD is quite complex. Which is why a lot of programming language don't even have JSON-LD implementations and it's pretty slow compared to just working with the regular JSON. So, what happens is a lot of ActivityPub implementations just treat JSON-LD like (it) is regular JSON without using a proper JSON-LD processor. You can do that, but it creates a source of headache. In JSON-LD there are weird equivalences like if a property is missing or if it's an empty array, that means the same thing. Or if a property has one value versus an array with just that one value in it, same thing. So when you are writing code to parse JSON-LD, you've got to keep checking if something's an array how long it is and all that is super easy to mess up. It's not just reading JSON-LD that's tricky. Creating it is just as bad. Like you might forget to include the right context metadata for a vocabulary and end up with a JSON-LD document that's either invalid or means something totally different from what you wanted. Even the big ActivityPub implementations mess this up pretty often. With Fedify we've got a JSON-LD processor built in and we keep running into issues where major ActivityPub implementations create invalidate JSON-LD. We've had to create workaround for all of them, but it's not pretty and causes kind of a mess. [00:19:52] Jeremy: Even though there is a specification for JSON-LD, it sounds like the implementers don't necessarily follow it. So you are kind of parsing JSON-LD, but not really. You're parsing something that. Looks like JSON-LD, but isn't quite it. [00:20:12] Hong: Yes, that's right. [00:20:14] Jeremy: And is that true in the, the biggest implementations, Mastodon, for example, are there things that it sends in its activities that aren't valid JSON-LD? [00:20:26] Hong: Those implementations that had bad JSON-LD tends to fix them soon as a possible. But regressions are so often made. Yeah. [00:20:45] Jeremy: Even within Mastodon, which is probably one of the largest implementers of ActivityPub, there are cases where it's not valid, JSON-LD and somebody fixes it. But then later on there are other messages or other activities that were valid, but aren't valid anymore. And so it's this, it's this back and forth of fixing them and causing new issues it sounds ... [00:21:15] Hong: Yeah. Yeah. Right. [00:21:17] Jeremy: Yeah. That sounds very difficult to deal with. How instances communicate (Inbox) [00:21:20] Jeremy: We've been talking about the messages themselves are this special format of JSON that's very particular. but how do these instances communicate with one another? [00:21:32] Hong: Most of time, it all starts with a follow. Like when John follows Alice, then Alice adds both John and John's inbox URI to her followers list, and after John follows Alice, Whenever Alice posts something new that activities get sent to John's inbox behind the scenes. This is just one HTTP post request. Even though ActivityPub is built on HTTP. It doesn't really care about the HTTP response beyond did it work or not. If you want to reply to an activity, you need to figure out the standard inbox, URI and send or reply activity there. [00:22:27] Jeremy: If we define all the terms, there's the actor, which is the person, each actor can send different activities. those activities are in the form of a JSON linked data. [00:22:40] Hong: Yeah. [00:22:42] Jeremy: And everybody has an inbox. And an inbox is an HTTP URL that people post to. [00:22:50] Hong: Right. [00:22:52] Jeremy: And so when you think about that, you had mentioned that if you have a list of followers, let's say you have a hundred followers, would that mean that you have the URLs to all hundred of those follower's inboxes and that you would send one HTTP post to each inbox every time you had a new message? [00:23:16] Hong: Pretty much all ActivityPub implementations have, a thing called shared inbox, it's exactly what it sounds like. One inbox that all actors on a server share. Private stuff like DMs don't go there (it) is just for public posts and thoughts. [00:23:36] Jeremy: I think we haven't really talked about the fact that, when you have multiple users, usually they're on a server, right? That somebody chooses. So you could have tens of thousands, I don't know how many people can fit on the same server. But, rather than, you having to post to each user individually, you can post to the shared inbox on this server. So let's say, of your 100 followers, 50 them are on the same server, and you have a new post, you only need to post to the shared inbox once. [00:24:16] Hong: Yes, that's right. [00:24:18] Jeremy: And in that message you would I assume have links to each of the profiles or actors that you wanted to send that message to. [00:24:30] Hong: Yeah. Scaling challenges [00:24:31] Jeremy: Something that I've seen in the past is there are people who have challenges with scaling. Their Mastodon instance or their implementations of ActivityPub. As the, the number of followers grow, I've seen a post about, ghost one of the companies you work with mentioning that they've had challenges there. What are the challenges there and, and how do you think those can be resolved? [00:25:04] Hong: To put this in context, when Ghost mentioned the scaling, they were not using Message Queue yet. I'm pretty sure using Message Queue would help a lot of their scaling problems. That said it is definitely true that a lot of activity post software has trouble with scaling right now. I think part of the problem is that everyone's using this purely event driven approach to sending activities around. One of the big issues is that when their delivery fails it's the sender who has to retry and not the receiver. Plus there's all this overhead because the sender has to authenticate itself with HTTP signatures every time. Actually the ActivityPub spec suggests using polling too so I'd love to see more ActivityPub software try using both approaches together. [00:26:16] Jeremy: You mean the followers would poll who they're following instead of the person posting the messages having to send their posts to everyone's inboxes. [00:26:29] Hong: Yeah. [00:26:29] Jeremy: I see. So that's a part of the ActivityPubs specification, but not implemented in a lot of ActivityPub implementations, And so it sounds like maybe that puts a lot of burden on the servers that have people with a lot of followers because they have to post to every single, follower server and maybe the server is slow or they can't reach it. And like you said, they have to just keep trying and trying. There could be a lot of challenges there. [00:27:09] Hong: Right. Account migration [00:27:10] Jeremy: We've talked a little bit about the fact that each person each actor is hosted by a server and those servers can host multiple actors. But if you want to move to another server either because your server is shutting down or you just would like to change servers, what are some of the challenges there? [00:27:38] Hong: ActivityPub and Fediverse already have the specification for an account move. It's called FEP-7628 Move Actor. First thing you need to do when moving an account is prove that both the old and new accounts belong to the same person. You do this by adding the all accounts, add the URI to the new account's AlsoKnownAs property. And then the old account contacts all the other instances it's moving by sending out a move activity. When a server gets this move activity, it checks that both accounts really do belong to the same parts, and then it makes all the accounts that, uh, were following the, all the accounts start to, following the new one instead. that's how the new account gets to keep all the, all the accounts follow us. pretty much all, all the major activity post software has this feature built in, for example, Mastodon Misskey you name it. [00:29:04] Jeremy: This is very similar to the post where when you execute a move, the server that originally hosted that actor, they need to somehow tell every single other server that was following that account that you've moved. And so if there's any issues with communicating with one of those servers, or you miss one, then it just won't recognize that you've moved. You have to make sure that you talk to every single server. [00:29:36] Hong: That's right. [00:29:38] Jeremy: I could see how that could be a difficult problem sometimes if you have a lot of followers. [00:29:45] Hong: Yeah. Fedify [00:29:46] Jeremy: You've created a TypeScript library Fedify for building ActivityPub powered applications. What was the reason you decided to create Fedify? [00:29:58] Hong: Fedify is (a) ActivityPub servers framework I built for TypeScript. It basically takes away a lot of headaches you'd get trying to implement (an) ActivityPub server from scratch. The whole thing started because I wanted to build hollo -- A single user microblogging platform I built. But when I tried, to implement ActivityPub from (the) ground up it was kind of a nightmare. Imagine trying to write a CGI program in Perl or C back in the late nineties, where you are manually printing, HTTP headers and HTML as bias. there just wasn't any good abstraction layer to go with. There were already some libraries and frameworks for ActivityPub out there but none of them really hit the sweet spot I was looking for. They were either too high level and rigid. Like you could only build a mastodon clone or they barely did anything at all. Or they were written in languages I didn't really know. Ghost and Fedify [00:31:24] Jeremy: I saw that you are doing some work with, ghost. How is Ghost using fedify? [00:31:30] Hong: Ghost is an open source publishing platform. They have put some money into fedify which is why I get to work on it full time now. Their ActivityPub feature is still in private beta but it should be available to everyone pretty soon. We work together to improve fedify. Basically they are a user of fedify. They report bugs request new features to fedify then I fix them or implement them, first. [00:32:16] Jeremy: Ghost to my understanding is a blogging platform and a a newsletter platform. So what does it mean for them to implement ActivityPub? What would somebody using Mastodon, for example, get when they follow somebody using Ghost? [00:32:38] Hong: Ghost will have a fediverse handle for each blog. If you follow them in your mastodon or something (similar) then a new post is published. These post will show up (in) your timeline in Mastodon and you can like them or share them. Andin the dashboard of Ghost you can see who liked their posts or shared their posts and so on. It is like how mastodon works but in Ghost. [00:33:26] Jeremy: I see. So if you are writing a ghost blog and somebody follows your blog from Mastodon, sort of like we were talking about earlier, they can like your post, and on the blog itself you could show, oh, I have 200 likes. And those aren't necessarily people who were on your ghost website, they could be people that were liking your post from Mastodon. [00:33:58] Hong: Yes. Misskey / Forkey development in Asia [00:34:00] Jeremy: Something you mentioned at the beginning was there is a community of developers in Asia making forks of I believe of Mastodon, right? [00:34:13] Hong: Yeah. [00:34:14] Jeremy: Do you have experience working in that development community? What's different about it compared to the more Western centric community? [00:34:24] Hong: They are very similar in most ways. The key difference is language of course. They communicate in Japanese primarily. They also accept pull requests with English. But there are tons of comments in Japanese in their code. So you need to translate them into English or your first language to understand what code does. So I think that makes a barrier for Western developers. In fact, many Western developers that contribute to misskey or forkey are able to speak a little Japanese. And many of the developers of misskey and forkey are kind of otaku. [00:35:31] Jeremy: Oh otaku okay. [00:35:33] Hong: It's not a big deal, but you can see (the) difference in a glance. [00:35:41] Jeremy: Yeah. You mentioned one of the things that I believe misskey implemented was the emoji reactions and maybe one of the reasons they wanted that was so that they could react to each other's posts with you know anime pictures or things like that. [00:35:58] Hong: Yeah, that's right. [00:36:01] Jeremy: You've mentioned misskey and forkey. So is misskey a fork of Mastodon and then is forkey a fork of misskey? [00:36:10] Hong: No, misskey is not a fork of mastodon. (It) is built from scratch. It's its own implementation. And forkeys are forks of Mastodon. [00:36:22] Jeremy: Oh, I see. But both of those are primarily built by Japanese developers. [00:36:30] Hong: Yes. Whereas Mastodon (is) written in Ruby. Ruby on Rails. But misskey is built in TypeScript. [00:36:40] Jeremy: And because of ActivityPub -- they all implement it. So you can communicate with people between mastodon and misskey because they all understand the same activities. [00:36:56] Hong: Yes. Backwards compatible activity implementations [00:36:57] Jeremy: You did mention since there are extensions like misskey has the emoji reactions. When there is an activity that an implementation doesn't support what happens between the two servers? Do you send it to a server's inbox and then the server just doesn't do anything with it? [00:37:16] Hong: Some implementers consider backwards compatibility. So they design (it) to work with other implementations that don't support that activity. For example misskey uses like activity for emoji reaction. So if you put an emoji to a Mastodon post then in Mastodon you get one like. So it's intended behavior by misskey developers that they fall back to normal likes. But sometimes ActivityPub implementers introduce entirely new activity types. For example Pleroma introduced the emoji react. And if you put emoji reaction to Mastodon post from Pleroma in Mastodon you have nothing to see because Mastodon just ignores them. [00:38:37] Jeremy: If I understand correctly, both misskey and Pleroma are independent implementations of ActivityPub, but with misskey, they can tell when or their message is backwards compatible where it's if you don't understand the emoji reaction, it'll be embedded inside of a like message. Whereas with Pleroma they send an activity that Mastodon can't understand at all. So it just doesn't do anything. [00:39:11] Hong: Yes, right. But, Misskey also understands (the) emoji react activity. So between pleroma and misskey they have exchanged emoji reactions with no problem. [00:39:27] Jeremy: Oh, I see. So they, they both understand that activity. They both implement it the same way, but then when misskey communicates with Mastodon or with an instance that it knows doesn't understand it, it sends something different. [00:39:45] Hong: Yeah, that's right. [00:39:47] Jeremy: The servers -- can they query one another to know which activities they support? [00:39:53] Hong: Usually ActivityPub implementations also implement NodeInfo specification. It's like a user agent-like thing in Fediverse. Implementations tell the other instance (if it) is Mastodon or something else. You can query the type of server. [00:40:20] Jeremy: Okay, so within ActivityPub are each of the servers -- is the term node is that the word they use for each server? [00:40:31] Hong: Yes. Right. [00:40:32] Jeremy: You have the nodes, which can have any number of actors and the servers send activities to one another, to each other's inboxes. And so those are the way they all communicate. [00:40:49] Hong: Yeah. Building an ActivityPub implementation [00:40:50] Jeremy: You've implemented ActivityPub with Fedify because you found like there weren't good enough implementations or resources already. Did you implement it based off of the specification or did you look at existing implementations while you were building your implementation? [00:41:12] Hong: To be honest, instead of just, diving into the spec. I usually start by looking at actually ActivityPub software code first. The ActivityPub spec is so vague that you can't really build something just from reading it. So when we talk about ActivityPub, we are actually talking about a whole bunch of other technical standards too, WebFinger, HTTP signatures and more. So you need to understand all of these as well. [00:41:47] Jeremy: With the specification alone, you were saying it's too vague and so what ends up being -- I'm not sure if it's right to call it a spec, but looking at the implementations that people have already made that collectively becomes the spec because trying to follow the spec just by itself is maybe too difficult. [00:42:12] Hong: Yes. [00:42:14] Jeremy: Maybe that brings up the issues you were talking about before where you have specifications like JSON-LD where they're so complicated that even the biggest implementations aren't quite following it exactly. [00:42:28] Hong: Yeah. [00:42:29] Jeremy: If somebody wanted to, to get started with understanding a little bit more about ActivityPub or building something with it where would you recommend they start? [00:42:44] Hong: I recommend to dig into a lot of code from actual implementations. First, Mastodon, Misskey, Akkoma and so on. There are are some really cool tools that have been so helpful. For example, ActivityPub Academy is this awesome mastodon server for debugging ActivityPub. It makes it super easy to create a temporary account and see what activities are going back and forth. There is also BrowserPub. BrowserPub is this neat tool for looking up and browsing ActivityPub objects. It's really handy when you want to see how different ActivityPub software handles various features. I also recommend to use Fedify. I've got to mention the Fedify CLI, which comes with some really useful tools. [00:43:46] Jeremy: So if someone uses Fedify they're writing an application in TypeScript, then it sounds like they have to know the high level concepts. They have to know what are the different activities, what is inside of an actor. But the actual implementation of how do I create and parse JSON linked data, those kinds of things are taken care of by the library. [00:44:13] Hong: Yes, right. [00:44:16] Jeremy: So in some ways it seems like it might be good to, like you were saying, use the tools you mentioned to create a test Mastodon account, look at the messages being sent back and forth, and then when you're trying to implement it, starting with something like Fedify might be good because then you can really just focus on the concepts and not worry so much about the, the implementation details. [00:44:43] Hong: Yes, that's right. [00:44:45] Jeremy: Is there anything else you. Wanted to mention or thought we should have talked about? [00:44:52] Hong: Mm. I want to, talk about, a lot of stuff about ActivityPub but it's difficult to speak in English for me, so, it's a shame to talk about it very little. [00:45:15] Jeremy: We need everybody to learn Korean right? [00:45:23] Hong: Yes, please. (laughs) [00:45:23] Jeremy: Yeah. Well, I wanna thank you for taking the time. I know it must have been really challenging to give an interview in, you know, a language that's not your native one. So thank you for spending the time to talk with me. [00:45:38] Hong: Thank you for having me.

Ryder Richards discusses the evolution and degradation of the concept of "good" in moral language, referencing Nietzsche, Shell, and McIntyre. Nietzsche argues that "good" originated as a term for nobility but was inverted by the oppressed into a virtue of meekness. Shell suggests that modern morality has been corrupted by utility, aligning with capitalism and democracy. McIntyre claims that modern society suffers from moral fragmentation, rendering ethical discussions incoherent. Richards ties these ideas to Orwell's 1984, emphasizing the structural collapse of language and the futility of moral progress in modern culture wars. He concludes that the concept of "good" has lost its original meaning and is now used without clear definition. 

Mike & Tommy have a heated debate on when to choose modeling our Semantic Models in the Service over other methods. Edit data models in servicehttps://learn.microsoft.com/power-bi/transform-model/service-edit-data-models?WT.mc_id=DP-MVP-5002621Direct Lake in Power BI Desktophttps://learn.microsoft.com/fabric/fundamentals/direct-lake-power-bi-desktop?WT.mc_id=DP-MVP-5002621Get in touch:Send in your questions or topics you want us to discuss by tweeting to @PowerBITips with the hashtag #empMailbag or submit on the PowerBI.tips Podcast Page.Visit PowerBI.tips: https://powerbi.tips/Watch the episodes live every Tuesday and Thursday morning at 730am CST on YouTube: https://www.youtube.com/powerbitipsSubscribe on Spotify: https://open.spotify.com/show/230fp78XmHHRXTiYICRLVvSubscribe on Apple: https://podcasts.apple.com/us/podcast/explicit-measures-podcast/id1568944083‎Check Out Community Jam: https://jam.powerbi.tipsFollow Mike: https://www.linkedin.com/in/michaelcarlo/Follow Seth: https://www.linkedin.com/in/seth-bauer/Follow Tommy: https://www.linkedin.com/in/tommypuglia/

Mike & Tommy run through major updates with semantic link labs and where to use it.Get in touch:Send in your questions or topics you want us to discuss by tweeting to@PowerBITips with the hashtag #empMailbag or submit on thePowerBI.tips Podcast Page.Visit PowerBI.tips:https://powerbi.tips/Watch the episodes live every Tuesday and Thursday morning at 730am CST on YouTube:https://www.youtube.com/powerbitipsSubscribe on Spotify:https://open.spotify.com/show/230fp78XmHHRXTiYICRLVvSubscribe on Apple:https://podcasts.apple.com/us/podcast/explicit-measures-podcast/id1568944083‎Check Out Community Jam:https://jam.powerbi.tipsFollow Mike:https://www.linkedin.com/in/michaelcarlo/Follow Seth:https://www.linkedin.com/in/seth-bauer/Follow Tommy:https://www.linkedin.com/in/tommypuglia/

David Jayatillake joins me to chat about semantic layers, assessing value in data work, AI, and much more.David's LinkedIn: https://www.linkedin.com/in/david-jayatillake/

Scientific Sense Podcast ® by Gill Eapen: Prof. Chris Kennedy is professor of Linguistics at the University of Chicago. His work is geared towards discovering and describing the principles that are involved in relating linguistic forms to meanings; determining how this mapping is achieved through the interaction of properties of the linguistic system, properties of cognition more generally, and broader features of communicative contexts; and understanding the extent to which structural and typological features of language can be explained in terms of meaning.  Please subscribe to this channel: https://www.youtube.com/c/ScientificSense?sub_confirmation=1

Dr. Kanjana sits down with Dr. Aurore Candier and Dr. Catherine Raymond to talk about how Burmese conceptualizations and understandings of governance and political relationships shifted of the course of the 1800s at the beginning of colonial contact. Together they discuss role of astrology, oral tradition, and rumors within Burmese governance and worldview and what happened with the introduction of print media. Dr. Candier is a historian and researcher who specializes in the study of Myanmar and the country's changing political landscape throughout history. Working for more than 20 years in Myanmar, she now serves as the Director of the Center for Burma Studies at NIU. Dr. Raymond is an archeologist and art historian specializing in Theravada Buddhism arts. Dr. Raymond previously served as the director of the Center for Burma Studies at NIU.

Today, we delve into the intriguing world of vector databases, retrieval augmented generation, and a surprising twist—origami.Our special guest, Arjun Patel, a developer advocate at Pinecone, will be walking us through his mission to make vector databases and semantic search more accessible. Alongside his impressive technical expertise, Arjun is also a self-taught origami artist with a background in statistics from the University of Chicago. Together with co-host Frank La Vigne, we explore Arjun's unique journey from making speech coaching accessible with AI at Speeko to detecting AI-generated content at Appen.In this episode, get ready to unravel the mysteries of natural language processing, understand the impact of the attention mechanism in transformers, and discover how AI can even assist in the art of paper folding. From discussing the nuances of RAG systems to sharing personal insights on learning and technology, we promise a session that's both enlightening and entertaining. So sit back, relax, and get ready to fold your way into the fascinating layers of AI with Arjun Patel on Data Driven.Show Notes00:00 Arjun Patel: Bridging AI & Education04:39 Traditional NLP and Geometric Models08:40 Co-occurrence and Meaning in Text13:14 Masked Language Modeling Success16:50 Understanding Tokenization in AI Models18:12 "Understanding Large Language Models"22:43 Instruction-Following vs Few-Shot Learning26:43 "Rel AI: Open Source Data Tool"31:14 "Retrieval-Augmented Generation Explained"33:58 "Pinecone: Efficient Vector Database"37:31 "AI Found Me: Intern to Innovator"41:10 "Impact of Code Generation Models"45:25 Personalized Learning Path Technology46:57 Mathematical Complexity in Origami Design50:32 "Data, AI, and Origami Insights"

Cale and Sujit discuss their current projects in Azure as 2024 comes to a close. They also cover a ton of AKS updates. Semantic Kernel makes it easier for developers to build Azure Open AI applications that can also include SLMs like Phi-4.  Azure has many options to use File Shares and Volumes, and we walk through the process of figuring out which one is right for your needs.   Media file: https://azpodcast.blob.core.windows.net/episodes/Episode511.mp3 YouTube: https://youtu.be/dLfCJ6btKng Resources: Semantic Kernel - https://github.com/microsoft/semantic-kernel Journey with SK on OpenAI and AzureOpenAI Ollama (running SLM local) - https://github.com/ollama/ollama Ollamazure (running SLM that looks like Azure OpenAI) - https://github.com/sinedied/ollamazure PhiSilica - https://learn.microsoft.com/en-us/windows/ai/apis/phi-silica   File Shares: https://learn.microsoft.com/en-us/azure/storage/common/storage-introduction    Other updates: Lots of AKS updates! https://learn.microsoft.com/en-us/azure/aks/concepts-network-isolated https://learn.microsoft.com/en-us/troubleshoot/azure/azure-kubernetes/availability-performance/container-image-pull-performance https://learn.microsoft.com/en-us/azure/aks/imds-restriction https://learn.microsoft.com/en-us/azure/aks/use-windows-gpu https://azure.microsoft.com/en-us/updates/?id=471295 https://learn.microsoft.com/en-us/azure/backup/tutorial-restore-aks-backups-across-regions https://learn.microsoft.com/en-us/azure/aks/app-routing-nginx-configuration?tabs=azurecli#control-the-default-nginx-ingress-controller-configuration-preview https://learn.microsoft.com/en-us/azure/aks/automated-deployments https://learn.microsoft.com/en-us/azure/aks/aks-extension-ghcopilot-plugins https://learn.microsoft.com/en-us/azure/azure-monitor/containers/container-insights-logs-schema#kubernetes-metadata-and-logs-filtering

Hi, everyone! I'm thrilled to bring you my annual Christmas replay. This is not just one of my favorite episodes of the year—it's one of my favorite episodes of all time. If you haven't heard it before, you're in for a treat. If you heard it at the beginning of the year, I promise it's worth a second listen. Either way, enjoy! Tim Myers does a bit of everything: he's a writer, songwriter, storyteller, visual artist, and senior lecturer at Santa Clara University, where he teaches writing. We got together to talk about the nature of creativity, which Tim calls a “sacred mystery,” including everything from the way our childhood creativity is changed by the culture as we become adults, the necessary role of play in the creative process, the transcendent experiences of awe and wonder and how they fuel us, the wisdom of following your gut, and a whole lot more. Episode breakdown: 01:39 Kids are instantly creative, often play traditionally. 06:50 Creativity influenced by nature, nurture, educators. 15:50 Importance of creativity in education and society. 21:22 Differences between play in childhood versus "professional" adults. 24:56 Nancy switched to teaching, advisor, and writing lit mag. 30:49 Encouraging exploration of language and creative thinking. 37:04 Parents see child, lifetime of giving love. 40:35 Zen story about finding wonder in life. 45:27 Believing in progress through challenging circumstances. 50:34 Art, festivals, play as a primal need. 56:21 Semantic split between "religion" and "spirituality." 01:01:14 Falling in love based on unique personal idiosyncrasies. 01:07:23 Experimenting with writing schedule structure. 01:13:45 Craft is in choosing words for impact. 01:19:22 Writers and feedback. 01:25:00 Tim struggles with generalist vs specialist identity. 01:26:31 Passion for storytelling and visual art emerges. 01:35:38 Weekly writing schedule reduces overthinking and focus on perfection.   Check out the full show notes at fycuriosity.com, and connect with me and fellow creatives on Substack. Please leave a review for this episode and in it, tell us how play influences your creative process. If you enjoyed our conversation, I hope you'll share it with a friend. Want more? Here's a handy playlist with all my previous interviews with guests in writing.

In this episode of the HTML All The Things Podcast, Matt and Mike dive into why good HTML practices are essential for building better, more accessible, and maintainable websites. They kick things off by explaining the importance of semantic HTML for readability, SEO, and accessibility—covering useful tags like , , and . Matt and Mike also discuss how developers can properly create and use custom attributes—like data-* attributes—to store extra information cleanly without relying on fragile class naming conventions. Finally, they emphasize HTML's critical role in accessibility, highlighting best practices such as using ARIA attributes appropriately and providing meaningful alt text for images. To cap off the episode, the hosts share some lighthearted updates about their holiday plans and give a shout-out to this episode's sponsor, Magic Mind. Show Notes: https://www.htmlallthethings.com/podcasts/stop-using-divs-for-everything-master-semantic-html-custom-attributes-and-accessibility Thanks to Magic Mind for sponsoring this episode, enjoy 20% off one-time purchases and subscription using our link and code (Link: https://magicmind.com/HTMLPOD20 Code: HTMLPOD20) Thanks to Wix Studio for sponsoring this episode! Check out Wix Studio, the web platform tailored to designers, developers, and marketers via this link: https://www.wix.com/studio

Regular guest Gordon Wong joins me for a half hour to chat about tech stacks for analytics, semantic layers, and much more. Gordon's LinkedIn: https://www.linkedin.com/in/gordonhwong/

EASY LISTENING DEP'T.

Join Car and Keyan to discuss Stacker News' top posts of the week, Car & Keyan's favorite posts of the week, and top stackers for the week of Nov 29th, 2024.Follow the conversation of the episode on SN.Time Stamps:06:50 - The Fourth Turning - 27 Years Later14:57 - Tornado Cash Fifth Circuit Full Decision25:01 - My Response to Steve Patterson (BCash) on "Hijacking Bitcoin" - Bob Murphy Show32:00 - BITCOIN CORE'S LOSS OF FOCUS38:43 - Semantic Takeover Risks42:41 - How I Talk Bitcoin to Friends and Family During Thanksgiving44:19 - PlebDevs Starter: New Free Beginner Developer Course45:27 - Bitcoin Black Friday - lots of deals48:01 - Joe Rogan Experience - #2234 - Marc AndreessenShoutout @Wumbo for tracklist. Zap'em!We love the Fountain app for Lightning 2.0 podcasting, Send us a Boost, and we will read it on the next SNL.Find Car on NostrFollow Car on SNRead Thriller BitcoinFollow Thriller on NostrFollow Thriller on YouTubeContribute to ~buildersLearn more about PlebLabFollow Keyan on TwitterFollow Keyan on NostrFollow Keyan on SNFollow Stacker News on NostrLearn more about Stacker News

saas.unbound is a podcast for and about founders who are working on scaling inspiring products that people love, brought to you by https://saas.group/, a serial acquirer of B2B SaaS companies. In episode #24, Anna Nadeina talks with Mark, founder at AlsoAsked, a free keyword research tool for exploring the questions your visitors are asking, Digital Marketing Director at Candour, podcast host, and a well-known SEO expert. ----------Episode's Chapters-------------- 00:00 - Mark's Journey in SEO and Agency Experience 03:08 - The Inspiration Behind AlsoAsked 06:32 - Transitioning from Free Tool to Paid SaaS 10:41 - Managing Multiple Ventures and Prioritization 17:43 - Use Cases for AlsoAsked in SaaS and Beyond 25:01 - SEO Insights and Challenges for Founders 27:12 - Understanding SEO Challenges and Strategies 31:04 - The Evolution of SEO: From Traditional to Semantic 32:38 - The Role of Structured Data and AI in SEO 36:58 - Preparing for AI-Driven Content Optimization 41:25 - Insights on Tools and Techniques for SEO Success 44:20 - Reflections on Wins, Failures, and Learning in Business 47:31 - Consistency and Generosity: Keys to Long-Term Success Mark - https://www.linkedin.com/in/markseo/ AlsoAsked - https://alsoasked.com/ Candour - https://withcandour.co.uk/ Subscribe to our channel to be the first to see the interviews that we publish twice a week - https://www.youtube.com/@saas-group Stay up to date: Twitter: https://twitter.com/SaaS_group LinkedIn: https://www.linkedin.com/company/14790796

Mark is joined in this episode by Johan Baltzar, previously Product Analytics Manager at Spotify and now co-founder and CEO at Steep to talk about the role analytics played in Spotify's growth story, the startup scene in Stockholm, Sweden and Steep's metrics-first approach to user-centric business analytics.The Kry founders factory: Meet 15 employees-turned-foundersSteep homepageNew in Steep : Cube and dbt Targets & BI - How hard can it be?

Mike, Seth, & Tommy tackle another great article by Kurt Buehler on how Semantic Link in Fabric Notebooks may change your life. https://data-goblins.com/power-bi/programmatically-modify-reports Get in touch: Send in your questions or topics you want us to discuss by tweeting to @PowerBITips with the hashtag #empMailbag or submit on the PowerBI.tips Podcast Page. Visit PowerBI.tips: https://powerbi.tips/ Watch the episodes live every Tuesday and Thursday morning at 730am CST on YouTube: https://www.youtube.com/powerbitips Subscribe on Spotify: https://open.spotify.com/show/230fp78XmHHRXTiYICRLVv Subscribe on Apple: https://podcasts.apple.com/us/podcast/explicit-measures-podcast/id1568944083‎ Check Out Community Jam: https://jam.powerbi.tips Follow Mike: https://www.linkedin.com/in/michaelcarlo/ Follow Seth: https://www.linkedin.com/in/seth-bauer/ Follow Tommy: https://www.linkedin.com/in/tommypuglia/

Show notes: (0:50) Manoj Doss and his research (4:34) Alcohol: Retrograde facilitation and memory (9:04) Exploring psychedelics and memory systems (17:44) Psychedelics and belief formation (19:47) Risks associated with psychedelic use, including psychosis and persistent perception issues (27:40) Ketamine addiction concerns and safety (33:13) Cannabis: Benefits and Downsides (39:28) Psychedelics' potential for trauma healing and creative insights (45:24) MDMA's role in enhancing social interactions and trauma therapy (54:50) Where to find Manoj (56:03) Outro Who is Manoj Doss?   Manoj Doss, Ph.D., completed his doctorate at the University of Chicago with professors David Gallo and Harriet de Wit investigating the acute effects of psychoactive drugs on emotional episodic memory and memory distortion. He then worked as a postdoctoral scholar at Johns Hopkins University School of Medicine with professors Frederick Barrett and Roland Griffiths investigating the acute and persisting effects of psychedelic drugs on cognition and brain function.   Doss utilizes complex cognitive paradigms, neuroimaging and computational modeling to explore what makes psychedelic drugs unique compared to other classes of psychoactive drugs in terms of their basic effects and their therapeutic mechanisms. Connect with Manoj: Website: https://dellmed.utexas.edu/directory/manoj-doss X: https://twitter.com/manojdoss Links and Resources: Peak Performance Life Peak Performance on Facebook Peak Performance on Instagram  

In this episode of the Modern Web Podcast, Danny Thompson, Director of Technology at This Dot Labs, hosts a conversation with Theresa Mammarella, JVM engineer at IBM, and Brian Sam-Bodden, Applied AI Engineer at Redis. They explore their talks at JCONF in Dallas, Texas, covering topics like GenAI architectures in the Java community and OpenJDK's Project Valhalla. Their conversation covers Java's evolution, AI applications, semantic caching, and how these technologies are impacting development workflows and performance optimization. Chapters 00:00 - Introduction   01:00 - Brian on GenAI in the Java Community   01:47 - Java's Safe Evolution Path   02:17 - Teresa on Project Valhalla   03:54 - Value Classes and Performance   04:33 - Brian on Semantic Caching   06:54 - Challenges of Rewording Prompts   09:15 - What is RAG Architecture?   11:34 - Java's Role in AI   13:57 - Cost of LLMs and Caching Strategies   15:57 - Teresa on Java's Future   18:22 - Learning Resources for Java Developers   20:44 - Addressing Misconceptions About Java   22:39 - Final Thoughts   Follow Theresa Mammarella & Brian Sam on Social Media Theresa Mammarella Twitter: https://x.com/t_mammarella?lang=en Brian Sam-Bodden Twitter: https://x.com/bsbodden Theresa Mammarella Linkedin: https://www.linkedin.com/in/tmammarella/ Brian Sam-Bodden Linkedin:  https://www.linkedin.com/in/sambodden/

Fri, 25 Oct 2024 00:30:00 GMT http://relay.fm/rd/246 http://relay.fm/rd/246 Helicopter Don 246 Merlin Mann and John Siracusa John has a new angle on smart home monitoring, and Merlin shares some new thoughts about "The Godfather" (1972). John has a new angle on smart home monitoring, and Merlin shares some new thoughts about "The Godfather" (1972). clean 5765 John has a new angle on smart home monitoring, and Merlin shares some new thoughts about "The Godfather" (1972). This episode of Reconcilable Differences is sponsored by: Squarespace: Save 10% off your first purchase of a website or domain using code DIFFS. Links and Show Notes: In Follow-Up, John has updates on his health journey, and Merlin shares a fun cameo appearance by the Magic Marker System. Also, there's a new twist on the Millennial Tic project. John has a new angle on smart home monitoring, and Merlin has some new thoughts about The Godfather (1972). In this month's member bonus episode, your hosts discuss the importance of door management. You can sign up today to hear all the member episodes, get more bonus stuff, and, yes, support our program. (Recorded on Tuesday, August 20, 2024) Credits Audio Editor: Jim Metzendorf Admin Assistance: Kerry Provenzano Music: Merlin Mann The Suits: Stephen Hackett, Myke Hurley Get an ad-free version of the show, plus a monthly extended episode. The episode of Last Week Tonight where John Oliver talks about Waffle House Reconcilable Differences #232: Ham Means OneThe episode where we discuss the Waffle House Magic Marker System™. Semantic satiation - Wikipedia The Rule of Thirds - PetaPixel A Pattern Language, by Christopher Alexander Design Patterns: Elements of Reusable Object-Oriented Software Waffle House Training - Pull Drop Mark Order Calling Method - YouTube"Easy!" Eve Door & Window sensor YoLink Smart Hub and two Temperature Sensors YoLink Water Leak Detector The YoLink store at Amazon.com Don Corleone's Mustache Turns into a Tree Chad L. Coleman - Wikipedia Morgan Jones from The Walking Dead - Wikipedia

Fri, 25 Oct 2024 00:30:00 GMT http://relay.fm/rd/246 http://relay.fm/rd/246 Merlin Mann and John Siracusa John has a new angle on smart home monitoring, and Merlin shares some new thoughts about "The Godfather" (1972). John has a new angle on smart home monitoring, and Merlin shares some new thoughts about "The Godfather" (1972). clean 5765 John has a new angle on smart home monitoring, and Merlin shares some new thoughts about "The Godfather" (1972). This episode of Reconcilable Differences is sponsored by: Squarespace: Save 10% off your first purchase of a website or domain using code DIFFS. Links and Show Notes: In Follow-Up, John has updates on his health journey, and Merlin shares a fun cameo appearance by the Magic Marker System. Also, there's a new twist on the Millennial Tic project. John has a new angle on smart home monitoring, and Merlin has some new thoughts about The Godfather (1972). In this month's member bonus episode, your hosts discuss the importance of door management. You can sign up today to hear all the member episodes, get more bonus stuff, and, yes, support our program. (Recorded on Tuesday, August 20, 2024) Credits Audio Editor: Jim Metzendorf Admin Assistance: Kerry Provenzano Music: Merlin Mann The Suits: Stephen Hackett, Myke Hurley Get an ad-free version of the show, plus a monthly extended episode. The episode of Last Week Tonight where John Oliver talks about Waffle House Reconcilable Differences #232: Ham Means OneThe episode where we discuss the Waffle House Magic Marker System™. Semantic satiation - Wikipedia The Rule of Thirds - PetaPixel A Pattern Language, by Christopher Alexander Design Patterns: Elements of Reusable Object-Oriented Software Waffle House Training - Pull Drop Mark Order Calling Method - YouTube"Easy!" Eve Door & Window sensor YoLink Smart Hub and two Temperature Sensors YoLink Water Leak Detector The YoLink store at Amazon.com Don Corleone's Mustache Turns into a Tree Chad L. Coleman - Wikipedia Morgan Jones from The Walking Dead - Wikipedia

Mike, Seth, & Tommy run through the Microsoft Semantic Model that comes default with the lakehouse and the use cases. Get in touch: Send in your questions or topics you want us to discuss by tweeting to @PowerBITips with the hashtag #empMailbag or submit on the PowerBI.tips Podcast Page. Visit PowerBI.tips: https://powerbi.tips/ Watch the episodes live every Tuesday and Thursday morning at 730am CST on YouTube: https://www.youtube.com/powerbitips Subscribe on Spotify: https://open.spotify.com/show/230fp78XmHHRXTiYICRLVv Subscribe on Apple: https://podcasts.apple.com/us/podcast/explicit-measures-podcast/id1568944083‎ Check Out Community Jam: https://jam.powerbi.tips Follow Mike: https://www.linkedin.com/in/michaelcarlo/ Follow Seth: https://www.linkedin.com/in/seth-bauer/ Follow Tommy: https://www.linkedin.com/in/tommypuglia/

Have you ever thought about how artificial intelligence could support us in our day-to-day work without replacing us? The fact is that when AI and people work together, incredible things may happen. We're exploring this exciting topic today with Punit Bhatia and Ducky.ai creator and COO James O'Brien. He has spent almost a decade working in startups and working hard to enhance customer service through AI. Join the discussion with us about how AI can greatly assist us in customer service, increasing the effectiveness and efficiency of our work in Episode 123, Season 5 of FIT4Privacy Podcast. If you find this episode insightful, be sure to subscribe to our podcast and share it with your friends and colleagues. Let's spread the word about the positive impact of AI in our lives!   KEY CONVERSION POINT  00:01:57 James definition of AI  00:03:44 How does AI impact on society  00:05:55 Risks around AI  00:10:32 About LLM's  00:21:20 Semantic and keyword understanding   ABOUT THE GUEST James O Brien has spent over a decade working in startups. He is the co-founder and COO of Ducky — customer support AI that works alongside humans to help them perform at their best.  Prior to Ducky, James was the COO of Nashville, TN-based crypto asset manager Valkyrie Investments. During his tenure, Valkyrie investments launched the United State's second bitcoin futures ETF and grew to over $1B in assets. Before Valkyrie, James was the first hire at AltoIRA, a Nashville-based fintech specializing in self-directed IRAs for alternative asset investing. He helped scale the business from pre-seed through series A — growing the team from 2 to over 100 individuals while focusing on customer support, partnerships and, later, crypto.  Apart from the land of startups, James is a singer and fledgling piano player — he moved down to Nashville, TN over a decade ago singing in a band. He loves cooking, reading (primarily fantasy novels), yoga and spending time with friends + family. ABOUT THE HOST   Punit Bhatia is one of the leading privacy experts who works independently and has worked with professionals in over 30 countries. Punit works with business and privacy leaders to create an organization culture with high privacy awareness and compliance as a business priority. Selectively, Punit is open to mentor and coach privacy professionals.   Punit is the author of books “Be Ready for GDPR'' which was rated as the best GDPR Book, “AI & Privacy – How to Find Balance”, “Intro To GDPR”, and “Be an Effective DPO”. Punit is a global speaker who has spoken at over 30 global events. Punit is the creator and host of the FIT4PRIVACY Podcast. This podcast has been featured amongst top GDPR and privacy podcasts.   As a person, Punit is an avid thinker and believes in thinking, believing, and acting in line with one's value to have joy in life. He has developed the philosophy named ‘ABC for joy of life' which passionately shares. Punit is based out of Belgium, the heart of Europe.     RESOURCES   Websites: www.fit4privacy.com, www.punitbhatia.com, https://ducky.ai/  Podcast: https://www.fit4privacy.com/podcast   Blog: https://www.fit4privacy.com/blog   YouTube: http://youtube.com/fit4privacy

Through her taxonomy and other information architecture work, Michele Ann Jenkins helps people across the organizations she works with align their mental models and terminology usage. This alignment of concerns and language forms the foundation of the semantic architecture that is so crucial to modern content systems.  https://ellessmedia.com/csi/michele-ann-jenkins/

Mark Rittman is joined by returning guest David Jayatillake, VP of AI at Cube.dev, to talk about Delphi Labs' journey from a standalone data analytics chatbot to now becoming the basis of Cube's new AI features within its composable semantic model product.Drill to Detail Ep.102 'LLMs, Semantic Models and Bringing AI to the Modern Data Stack' with Special Guest David JayatillakeDrill to Detail Ep.107 'Cube, Headless BI and the AI Semantic Layer' with Special Guest Artyom KeydunovIntroducing the AI API and Chart Prototyping in Cube CloudA Practical Guide to Getting Started with Cube's AI APICube Rollup London : Bringing Cube Users Together

When you think of your data warehouse, the “semantic layer” may not be the first thing that pops in your mind. Prior to reading Frances O’Rafferty‘s blog post on this topic, I didn’t even know this was a concept that mattered in the data stack. To be honest, the concept is still a bit confusing […] The post Dear Analyst #132: How the semantic layer translates your physical data into user-centric business data with Frances O’Rafferty appeared first on .

Join us in this episode of the NeuroNoodle Neurofeedback and Neuropsychology Podcast as we dive into crucial topics affecting parents and children today. Tech Legend Jay Gunkelman and Dr. Mari Swingle explore the challenges of parenting in the modern era, the impact of technology on mental health, and strategies to build resilience in our children.

In this episode of Syntax, Wes and Scott talk about HTML fundamentals — from basic structure and semantics to practical tips for better accessibility and SEO. They also discuss the difference between block and inline elements, form functionalities, HTML5 elements like dialog and canvas, and more. Show Notes 00:00 Welcome to Syntax! 02:33 Brought to you by Sentry.io 03:25 Why HTML is important 06:52 Semantic vs non-semantic 07:58 The basic structure of an HTML page HTML elements reference The Main element 08:45 Doctype 15:24 Nav 18:47 Section 20:41 Aside 22:09 Article 22:54 Span 27:18 Why use a span when you have a div and a paragraph tag? 29:29 Figure and Caption 31:16 Fieldset 31:53 UL vs OL 32:44 DFN The Definition element 34:16 Form 36:56 Button vs Anchor 38:22 Headings 674 - A11y Treats - Heading Design 40:21 Output The Output element 41:46 Dialog 42:04 Tables 44:03 Media media-chrome 45:06 Canvas https://githubuniverse.com/ https://maximeheckel.com/ 46:07 On graphics programming 47:38 Search 354 - The Surprisingly Exciting World of HTML Elements 48:27 Sick Picks + Shameless Plugs Sick Picks Scott: 2Pack Traditional Natural Bamboo Wok Brushes Wes: Logitech MX Master 3S Shameless Plugs Syntax YouTube Channel The Easiest Way to Infinite Scroll with React | Full Example 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

In this episode of Knowledge Cast, Joe Hilger, COO of Enterprise Knowledge, moderates a panel discussion with leading experts in data and semantic layer: Sanjeev Mohan, Malcolm Hawker, and Lulit Tesfaye. The panel delves into the upcoming Semantic Layer Symposium, emphasizing the importance of a semantic layer as a bridge between technical data and business applications. The panelists speak on the practical applications and future of a semantic layer. They agree that a semantic layer could significantly improve data accuracy and efficiency, particularly in AI and ML contexts. This discussion underscores the necessity for organizations to understand and implement a semantic layer to drive better business outcomes and adapt to evolving data landscapes.   The Semantic Layer Symposium in Munich aims to provide attendees with concrete examples and best practices, helping them grasp the practical benefits of a semantic layer and how to implement it effectively. Click ⁠here ⁠to register for the event.

Analytic Philosophy is a branch of philosophy that emphasizes clarity and logical analysis. Key figures include Gottlob Frege, Bertrand Russell, and Ludwig Wittgenstein, who contributed to the development of symbolic logic and the philosophy of language. Logical Positivism, emerging from the Vienna Circle, focused on empirical verification and logical necessity. The philosophy of language explores theories of meaning, such as the referential theory, use theory, and speech act theory. Semantic externalism, proposed by Hilary Putnam and Saul Kripke, argues that meaning is influenced by external factors. Ordinary language philosophy, associated with J.L. Austin and later Wittgenstein, analyzes everyday language to resolve philosophical problems. The philosophy of science, with contributions from Karl Popper and Thomas Kuhn, examines the nature of scientific knowledge and methods. W.V.O. Quine's critique of the analytic-synthetic distinction emphasizes the holistic nature of knowledge. Metaphysics in analytic philosophy addresses questions about reality, including the realism vs. anti-realism debate and the nature of properties and universals. Key concepts include propositional logic, predicate logic, and the theory of descriptions.Become a supporter of this podcast: https://www.spreaker.com/podcast/library-of-philosophy--5939304/support.

Episode OverviewSanjeev Mohan is an experienced and knowledgeable expert in the world of data and analytics, and on this episode of the CDO Matters Podcast, he shares his insights on the rapidly evolving world of semantic layers.Join Malcolm and Sanjeev, both ex-Gartner analysts, as they break down the complexities of these new technologies into simple and approachable concepts, and the important role they will play in the future of the data function. Links and ResourcesFollow Malcolm Hawker on LinkedInFollow Sanjeev Mohan on LinkedIn

This podcast episode is all about semantic search and using embeddings to analyse text and social media data. Dominik Weckmüller, a researcher at the Technical University of Dresden, talks about his PhD research, where he looks at how to analyze text with geographic references.  He explains hyperloglog and embeddings, showing how these methods capture the meaning of text and can be used to search big databases without knowing the topics beforehand. Here are the main points discussed: Intro to Semantic Search and Hyperloglog: Looking at social media data by counting different users talking about specific topics in parks, while keeping privacy in mind. Embeddings and Deep Learning Models: Turning text into numerical vectors (embeddings) to understand its meaning, allowing for advanced searches. Application Examples: Using embeddings to search for things like emotions or activities in parks without needing predefined keywords. Creating and Using Embeddings: Tools like transformers.js let you make embeddings on your computer, making it easy to analyze text. Challenges and Innovations: Talking about how to explain the models, deal with long texts, and keep data private when using embeddings. Future Directions: The potential for using embeddings with different media (like images and videos) and languages, plus the ongoing research in this fast-moving field. Connect with Dominik Weckmüller here https://geo.rocks/ Stay up to date with AI here https://huggingface.co/ Try searching for “map”  here https://huggingface.co/spaces   Check out this project I am working on  https://quickmaptools.com/  

01. The Stickmen Project - I Wish I Was (Bad Press Remix) 02. Quoone - Better Day 03. Fred V - Faded Blue (feat. Hybrid Minds & Lottie Jones) 04. Mage - Take It Slow (Original Mix) 05. Holy Polly - Breathe In (Keep On) - Vocal Mix 06. Critical Matter - Lost In Thought 07. Streetflicker - At The Disco 08. Fred V - Take Over (feat. Dan Dakota) 09. Lawe - Make Me Feel 10. Curious Mind, Timo Noize, Dirkje - Secrets (Extended Mix) 11. Punchman & Oneder - Самый Сонный 12. Ryan Audley X Nuwei - Discharge 13. Mf And Lelya - Raindrops 14. Dark Soul & Struno - Peresvet 15. René Lavice & Genetics - Demons feat. Elle Exxe (Extended Mix) 16. Enta - Longtime 17. Cacumen - 1618 18. Tanukichi & Jman - Way Of The Samurai 19. The Fi5Th - Faded 20. Syncord, Sins - Marvel Laboratory 21. Onebyone & Aznok - Moonstar 22. Netsphere - Magnetronic (Original Mix) 23. Nuvertal - Combat Sword 24. Diopolice - Fyzika (Original Mix) 25. Future Shock - Conquest Syndrome 26. Diode - Don't Interrupt Me 27. Nerv3 - Ximera 28. Akov - Signs Of Things To Come 29. Double Medley - Noise Cure 30. Bassnectar - Nice & Easy feat. Rodney P (Bassnectar Remix) 31. Sparkzeeman - Grafting 32. Kleu - Kleu & Leanne Louise - Insane 33. Nkz - Voodoo 34. Cheff - Stun Gun 35. Black Opps - Qing Dinasty 36. 2 Of A Kind - Hustling 37. Laney - Jungle Techno 38. Roni Size & Reprazent - Brown Paper Bag (Crissy Criss Plastic Bag Mix) 39. Xypherdon - Highest Fidelity (feat. Nj) 40. Infinull - In Your Face 41. Tony Igy - Astronomia (Dc2 Bootleg) 42. Pruf - Soprano 43. Si-Otik - Pretty Gals 44. Dj Efectz - Adrenaline 45. Kritical - Whispers 46. Aim - Over Hills 47. Skinny - Fuego (Original Mix) 48. Rico Tubbs & Terry Hooligan - Forever Dancing (S9 Remix) (S9 Remix) 49. Vex - Locked In 50. Sweetpea, Crystal Clear - Richness Is Life 51. 6Hrs - Run 52. Fluid Haunts - Zero Silence 53. Opius - Dub Soldier 54. Branco Simonetti - Free (Salomassive Jungle Fix) 55. Hoji - Catophrenia 56. Elisabeth Troy - Greater Love (Radiokillaz Dubplate Mix) 57. Supa Ape - Split A Split Second 58. Antoanesko, Kraufler - Uletai 59. Orion Concept - Light A Joint 60. Ventus - Crazy 61. Rob Dougan - Clubbed To Death (Antoanesko Bootleg) 62. Peyote - Nature Tones 63. Fred V What I’M Hoping For (feat. Laura Brehm) 64. Illmatika - Be Mine 65. Relaidex - Seren 66. Spheriex, Semantic & Rachel Crowe - Next To You 67. Aydn - Boy In The Void 68. Dimod - Chloranthy (Original Mix) 69. Twin Shape & Psytrs - Liquid Language 70. Botb - I Will Wait 71. Emily Makis, Monrroe - Never Too Old (Friction Remix)

一提起 AI ，大家首先想到的可能是各大科技巨头在算力和算法上的抢夺和竞争。但是在算力和算法背后，另一场没有硝烟的战争也在持续升温，那就是 AI 数据公司之间的博弈。就在几天前，大数据存储和云计算领域内两家最有影响力的公司 Snowflake 和 Databricks 分别召开了他们的年度峰会。 在峰会上两家公司分别介绍了自家数据生态的发展方向以及如何为企业提供更好的AI数据服务。但是出乎意料的消息是，峰会期间 Databricks 宣布重金收购这个领域3大开源数据社区之一 Iceberg 背后的商业公司 Tabular, 这让两家数据巨头之间的关系更加剑拔弩张，Databricks 颇有后来者居上的势头。 这期节目的两位嘉宾都是在 AI 数据领域有着丰富的经验和洞察的从业者。他们刚刚从 Snowflake 和 Databricks 的峰会现场回来，为我们带来了数据AI、企业级AI的共识转变的一些观察和思考。 内容涉及大量英文专业名词，「声动活泼」公众号上也同步整理了本期节目的要点，如果你喜爱本期节目或对节目内容感到好奇，欢迎在微信搜索「声动活泼」查看 最新文章 (https://mp.weixin.qq.com/s?__biz=MzIwMDczNTE3OQ==&mid=2247501751&idx=1&sn=d4f694182775514286d8b66494e626ee&chksm=96fa2713a18dae05e6a7ed74df24e025a7f5279a0930aeae78558a501264e703d535c7d0b0d6#rd)。 本期人物 丁教 Diane，「声动活泼」联合创始人、「科技早知道」主播 硅谷徐老师，AI 高管、连续创业者、斯坦福客座讲师，小红书和微信视频号：硅谷徐老师 ｜公众号：硅谷云｜ YouTube： Byte into Future 堵俊平： Datastrato AI 创始人 CEO Jack Song：Uber 数据平台工程总监，曾任 Airbnb 人工智能平台工程总监、Mastercard 数据和人工智能的技术副总裁 主要话题 [05:36] 从 Snowflake 和 Databricks 峰会看数据生态新趋势：AI for data 和 Open data catalog [09:50 ] Open data catalog 大火 : 统一数据湖仓数据架构，为 AI 引擎和数据引擎承上启下 [13:53] 引擎多样化和数据管理需求驱动统一和独立的 open data catalog 生态 [19:28] Databricks 收购 Tabular：会继续拥抱中立还是与商业利益捆绑？ [23:14] Snowflakes 与 Databrick 暗暗较劲：Iceberg 社区会良性发展还是走向分裂？ [25:10] Databricks 管理 Apache 社区 : 开源社区走向商业化是社区良性发展的重要标志 [29:56] Databricks 营收增长迅猛：战斗力来自于其开源属性 [31:25] 从 data for AI 到 AI for data: GenAI 时代的数据服务新方向 [40:17] Semantic search (语义搜索）是 AI 与 data 相互整合的一个突破口 所涉部分术语 Snowflake Snowflake 是一家成立于 2012 年的美国云原生数据仓库公司，于 2020 年上市。它的核心产品是云数据平台 Snowflake，该平台改变了传统的数据仓库架构，专为云环境设计，提供了高度可扩展、高性能的数据存储和处理能力。 Databricks Databricks 成立于 2013 年，由开源大数据项目Apache Spark的创建者们成立，是一家提供大数据处理和分析平台的公司。自成立以来发展迅猛估值已超过 400 亿美元，但仍未上市。 Iceberg社区 Iceberg 社区是一个开源数据湖格式项目，iceberg 专为大数据分析而设计，其目标是简化数据湖的管理，使得数据工程师可以像操作数据库一样操作数据湖中的数据。Tabular 是 iceberg 背后的商业公司，本次 Databricks 对 Tabular 的收购引发了公众对于 iceberg 的开源和中立属性的担忧。 Delta Lake Delta Lake 是一个由 Databricks 开发并开源的数据存储项目，致力于提升数据湖的管理能力和性能。 Delta Lake 与 Iceberg 存在潜在的竞争关系。 Hudi (Hadoop Upserts and Deletes Incrementally) 与 Iceberg 和 Delta lake 类似，Hudi 也是开源的数据湖社区，它旨在提供高效的大型数据集上的插入、更新和删除操作，同时保持数据湖的灵活性和规模。 Open data catalog 开放数据目录是专门面向人工智能和机器学习领域的一类数据资源库或平台。这类开放数据目录专注于提供可用于训练算法、测试模型或驱动研究的高质量数据集。数据公司通过建立和维护这样的目录，促进数据共享，降低数据获取门槛，加速AI技术的研发和应用创新。 Semantic Search 即语义搜索，是一种前沿搜索技术。不同于传统搜索的关键词匹配，语义搜索利用人工智能对自然语言进行理解和处理，旨在理解用户查询背后的意图和上下文，从而提供更加准确和相关的搜索结果。 幕后制作 监制：Diane、雅娴、六工 后期：Jack 运营：George 公众号：东君、六工 设计：饭团 商务合作 声动活泼商务合作咨询 (https://sourl.cn/6vdmQT) 支持我们，加入新一年的播客创新 2021 年我们发起了「声动胡同会员计划」，这是一个纯支持项目，支持「声动活泼」在播客内容上不断探索和创新。回顾 2023 年，得益于这些支持，「声动活泼」的每档节目都不断突破，不仅荣登苹果中国的年度热门节目榜单，还在 CPA 和喜马拉雅等平台都榜上有名。2024 年全新付费节目「不止金钱 (https://www.xiaoyuzhoufm.com/podcast/65a625966d045a7f5e0b5640)」现已上线，欢迎收听。同时，新一季「跳进兔子洞」即将上线，敬请期待！ 胡同 https://files.fireside.fm/file/fireside-uploads/images/4/4931937e-0184-4c61-a658-6b03c254754d/Z0YbNKpo.png 加入我们 声动活泼正在招聘全职「节目监制」、「人才发展伙伴」、「商业发展经理」，查看详细讯息请 点击链接 (https://sourl.cn/j8tk2g)。如果你已准备好简历，欢迎发送至 hr@shengfm.cn， 标题请用：姓名+岗位名称。 关于声动活泼 「用声音碰撞世界」，声动活泼致力于为人们提供源源不断的思考养料。 我们还有这些播客：声动早咖啡 (https://www.xiaoyuzhoufm.com/podcast/60de7c003dd577b40d5a40f3)、声东击西 (https://etw.fm/episodes)、吃喝玩乐了不起 (https://www.xiaoyuzhoufm.com/podcast/644b94c494d78eb3f7ae8640)、反潮流俱乐部 (https://www.xiaoyuzhoufm.com/podcast/5e284c37418a84a0462634a4)、泡腾 VC (https://www.xiaoyuzhoufm.com/podcast/5f445cdb9504bbdb77f092e9)、商业WHY酱 (https://www.xiaoyuzhoufm.com/podcast/61315abc73105e8f15080b8a)、跳进兔子洞 (https://therabbithole.fireside.fm/) 、不止金钱 (https://www.xiaoyuzhoufm.com/podcast/65a625966d045a7f5e0b5640) 欢迎在即刻 (https://okjk.co/Qd43ia)、微博等社交媒体上与我们互动，搜索 声动活泼 即可找到我们。 期待你给我们写邮件，邮箱地址是：ting@sheng.fm 声小音 https://files.fireside.fm/file/fireside-uploads/images/4/4931937e-0184-4c61-a658-6b03c254754d/gK0pledC.png 欢迎扫码添加声小音，在节目之外和我们保持联系。 Special Guests: Jack Song and 堵俊平.

In this episode of The Light Inside, we dive into the profound influence of belief systems on our self-concept, relationships, and perception of the world. Our beliefs shape our thoughts, feelings, and behaviors, creating a framework through which we interpret our experiences and interactions. We explore how subconscious and unconscious patterns impact our belief systems and logical reasoning, introducing the concept of semantic analysis as a dialectic thinking model. This model encourages critical thinking and the integration of diverse viewpoints, fostering a dynamic and evolving understanding of the subject matter. Beliefs are fundamental in shaping our self-concept, influencing our thoughts, feelings, and behavior. In a podcast episode, Mark Fornier explores how our belief systems act as filters, shaping our perceptions and experiences in various ways. He shares a personal story about his upbringing and how his mother, despite facing challenges, instilled in him the power of the mind through books like Psycho-Cybernetics and Think and Grow Rich. This early exposure to the idea of shaping one's reality from within had a profound impact on Mark's perspective. By challenging our shared perspectives, Mark and I also parse out the concept of semantic analysis, which involves examining and interpreting the meaning of thoughts, associations, words, and phrases within their context. By considering multiple perspectives and relationships between concepts, semantic analysis encourages critical thinking and the integration of diverse viewpoints. This process enables us to challenge and reshape our adaptive belief systems, leading to a more dynamic and evolving understanding of consciousness and the world around us. Timestamps: [00:01:45] Semantic analysis as a therapeutic tool. [00:07:09] Polymath lifestyle. [00:08:31] The systemic approach to life. [00:13:18] Embracing neurotic psychological entropy. [00:16:21] Cognitive Dissonance and trauma-based influences after a tornado. [00:19:20] Transforming consciousness through personal experiences. [00:25:03] Silver Spoon and Perception. [00:27:13] Distorted sense of self. [00:32:55] Adaptability and growth in habits. [00:36:48] Limitless potential and adaptation. [00:39:33] Toxicity and adaptability. [00:43:35] Willful blindness and conflict-driven arguments. [00:45:22] Consciousness before birth. [00:51:33] Cognitive bias and belief perseverance. [00:52:49] Overcoming limiting beliefs. Featured Guest:  Mark Fournier   JOIN US ON INSTAGRAM: @thelightinsidepodcast SUBSCRIBE: pod.link/thelightinside Credits: Music Score: Epidemic Sound Executive Producer: Jeffrey Besecker Mixing, Engineering, Production and Mastering: Aloft Media Executive Program Director: Anna Getz

When teachers and students feel heard, the climate of a school just gets better, and semantic pulse surveys can make that happen. In this episode, we'll learn what about this fresh approach to surveying and how teachers and administrators can create their own to gain better insights about the students and teachers they serve.  This episode is sponsored by Alpaca. School leaders can get 15% off a year of Alpaca's pulse surveys — visit alpacapacks.com/pedagogy to learn more. To read the post, visit cultofpedagogy.com/semantic-pulse-surveys 

Join us in this episode as we sit down with Leo Segovio, a top expert in the space, to discuss a wide range of topics that are essential for E-commerce sellers. Leo shares his unique insights on how optimizing Amazon images can significantly impact indexing and ranking. He also opens up about his recent ventures, including a software project for influencer and affiliate marketing, and an intriguing Airbnb project in Italy. Additionally, Leo provides valuable tips for Amazon sellers looking to diversify their income by investing in real estate, highlighting the importance of strategic investments to complement a thriving Amazon business. Listen in as we explore the evolving landscape of influencer and affiliate marketing strategies. We discuss how leveraging platforms like TikTok, Instagram, and YouTube can empower brands by building robust affiliate networks. We highlight successful brands and share advanced techniques for optimizing listings to ensure better visibility on Google and Amazon. Practical tips for using press releases on high-authority domains to improve Google indexing are also discussed, offering listeners actionable advice to enhance their marketing efforts. Finally, we talk about the significance of Google indexing for Amazon sellers and the benefits of driving traffic from Google to boost Amazon rankings. We discuss the theory that paid traffic may hold more weight and the value of optimizing images with keywords to enhance discoverability. Additionally, we examine Amazon's evolving search algorithms and how intent-based optimization is changing the way products are discovered on the platform. This episode is packed with valuable insights and strategies to help Amazon sellers navigate the complexities of e-commerce and achieve greater success. In episode 568 of the Serious Sellers Podcast, Bradley and Leo discuss: 04:14 - Investing In Real Estate Investments 09:41 - Leveraging Creator Marketplace for Affiliate Networks 15:56 - Google Indexing for Amazon Sellers 18:16 - Google Traffic Boosts Amazon Ranking 24:01 - Google Indexing Boosts Product Visibility 26:46 - Search Algorithm Evolution and Intent-Based Optimization 29:13 - Optimizing Amazon Listings for Intent-Based Search Transcript Bradley Sutton: Today we've got one of the top minds in the entire Amazon game back on the show, Leo Segovia. He's going to be talking about a wide variety of topics, such as the impact on indexing and ranking by optimizing your Amazon images, and much, much more. How cool is that? Pretty cool, I think.   Bradley Sutton: Hello everybody, welcome to another episode of the Serious Sellers Podcast by Helium 10. I'm your host, Bradley Sutton, and this is the show. That's a completely BS-free, unscripted and unrehearsed organic conversation about serious strategies for serious sellers of any level in the e-commerce world. And speaking of the e-commerce world, I'm on the other side of the world right now. For those of you listening, maybe I sound a little different. We are in the AVASK office here in Madrid, Spain, right in the middle of our Elite Workshop, and just about 15 minutes ago we had our very first speaker. All the other speakers are very mad at him because he started off and he set the bar really high with his talk, but we've got no stranger to the show, Leo Segovia. Leo, how's it going?   Leo: Bradley, good morning. How are you doing?   Bradley Sutton: Doing great, doing great.   Leo: Awesome. Yeah, this morning was great. I'm actually happy this is my first time in Madrid. Yes, I actually just stopped once. I think I was on my way to Puerto Rico, but yes, I got to enjoy the city. Today I'm here at the AVASK office in Madrid, so happy to be here and happy to be your guest again.   Bradley Sutton: Awesome, awesome. So now you know it's been a while since Leo's been on the show, so let's first just catch up with what you've been up to. Have you been launching products on Amazon? You've just been focused on building software. What have you been up to the last couple of years since you've been on the show?   Leo: Yeah, it's been a crazy year for me actually. I've been involved in a couple of different projects. We are obviously always looking for new products to launch. What kept me very busy in the past year has been software that I've been working on for influencer and affiliate marketing, and actually this Airbnb project in the south of Italy, which has been kind of a roller coaster. Yeah.   Bradley Sutton: So, you actually moved, I remember you went from Canada to Florida and then a few months ago you moved back to your home, uh, country of Italy, but then this was always meant to be kind of just like a like a winter, uh or summer home for you.   Leo: Yeah, that is correct. Uh, I have a family in Italy. So, and recently their area of Italy is called Puglia, it's in the southeast was becoming more and more popular and more expensive, and so I decided to buy a property there so that we could spend a week or a month in the summertime, perhaps, when in Florida is too hot, you know, go inside of Italy. Invite some of my Amazon friends, you know, mastermind, and so that's the plan. Now, I was supposed to be there only for a couple of months, just to see what was going on, but when I got the keys, I realized that the place needed a lot of work, and so I've been stuck in Italy since November, actually, of last year, and I'll probably stay there until for two more months before going back to Miami.   Bradley Sutton: What passport do you have? What country passports? I have Italian and Canadian passport. Okay, so then, when you bought this house, you use your like Italian citizenship?   Leo: No, actually I well, I could participate to an auction because I bought this place at an auction. Not the $1 ones, it was more than that. But yes, because of my Italian citizenship it allowed me to participate to an auction. But everything that I'm doing is as a Canadian citizen. It works out better from tax perspective and all that.   Bradley Sutton: Okay. So that's why I was asking about this, because I think this is, you know, like somebody might be jumping on the show. What are we talking about? Airbnb here? But as e-commerce entrepreneurs, Amazon sellers, maybe we make a little money, maybe we're not interested in exiting our business, but now we have extra money, like do we start other businesses? You know, maybe something that has nothing to do with Amazon, but I hear of more Amazon sellers doing something similar. Where they go you know, not necessarily Italy, but another country, buy a house and then so, as a Canadian citizen or as an American citizen I would assume it's about the same. What's the process of participating, like in this Italian auction to be able to buy this house?   Leo: I think you need to have someone in Italy or a friend, someone with an Italian citizenship, in order to buy a place at auction. Otherwise, you just have to go to a real estate agent and buy a regular place. The reason for me it was convenient is because it was a good deal. If I was able to win the auction, and so in real estate, you make money when you buy, not when you sell. Right, if you buy for less, that's most likely guaranteed revenue or earnings whenever you sell, and so that's the reason why I did this. Now, I don't know exactly the process if I didn't have any Italian citizenship, but yeah, a lot of entrepreneurs you know, especially Amazon sellers whether, when they exit or you know if they're already doing quite well and they have good cashflow, they normally tend to invest in real estate Airbnb's. You secure yourself passive income from that, and it's always a good investment.   Bradley Sutton: So then would I have to have all cash though to once the auction closed, I can finance over there.   Leo: Okay. So that's an interesting thing. I was going to finance the project. I ended up buying a cash because it just made more sense for me, but in Italy they actually give you a mortgage as long as you can prove that you have income outside of Italy.   Bradley Sutton: Okay. And then so you calculated out, like how much you can maybe get an Airbnb. And then so have you calculated hey, for the other months of the year where I'm not staying here, I need to rent this place out x amount of time of the year. And it's going to be worth it, have you like, and did that analysis?   Leo: Yeah, so in this specific region of Italy and the location of what I bought in August for a place with a pool and four or five bedrooms, you can charge 5,000 to 6,000 euros a week. So you make your money in the summertime. Ideally, as an investor, you don't want to go and spend time in the summertime there, but you want to go, perhaps either early, like May or September, when the season starts to kind of slow down and so you don't take out money from your profits, right? So my plan is to rent it out June, July and August. If I have some good offers in September, maybe I'll rent it out, otherwise I'll go myself there in September or May, but, yeah, normally throughout the year.   Leo: You know Italy is a destination where you have a lot of tourism during summertime, unless you're in Rome or Venice or Florence, which is always busy throughout the year. You know south it's a summer destination, right. So you get a lot of tourism summertime. Wintertime dies down, so you probably can get us or what you can get in the summer. But you know it works out well because if you have a small apartment, for example, in a big city, and you are charging, you know, 200, 300 a night? Um, at the end of the year you make the same money. So with this kind of properties is a little bit of a different um investment. I went more on the luxury kind of market, hoping to work only with Americans. You know foreign tourism, but in my opinion it's a great one.   Bradley Sutton: Okay, so there you have it, guys. You know, like, maybe you've had some success on Amazon and you're thinking of what kind of things to invest in. You know, getting a property at a low price and maybe fixing it up even though it's a headache a little bit, you know could be the route that you want to go. Now you know we're going to talk a lot about some really cool Amazon strategy coming up, but you've been developing some software lately for a while now. That's not necessarily for on Amazon, but it helps Amazon sellers. Can you talk about that a little bit?   Leo: Yeah, I've been working. The software is called Spliced. I've been working on this for about two years now and I was supposed to be already in market, and the reason why I'm late is because of what I just explained. It took me, you know, it took resources and energy a little bit off the other project, but now we're ready to go, and the reason why I built this software is, you know, Bradley, you know I have Convomat, which was my first software that I built, and then Amazon changed it to iOS, and so I had to find a way to pivot. But I already knew that influencer and affiliate marketing was the way to go, also for us Amazon sellers, in order to have a little bit more control over the traffic, over the business and the revenue that we drive to our brands. And so, with Spliced, my goal is to leverage the creator's marketplace, which is huge between TikTok and Instagram and YouTube, and leverage that to build affiliate networks for your own brand.   Leo: There are already a lot of sellers out there that are doing a good job when it comes to affiliate marketing. Look at a brand like Goalie. Goalie, one of the key strategies for Goalie was actually the affiliate marketing, and so with Spliced, my goal is to allow brands to look up into our marketplace, which has already been built with we have over 20 million creators and then approach them with an affiliate partnership instead of just UGC content. This is the reason why we didn't build in Spliced, just UGC campaigns. There's already plenty out there of softwares that you can use for UGC, but in my opinion, if you have a solid affiliate network, you can keep launching new products, relaunching the same products. We use it for reviews. If you need something like that and you have more control over your business, and if you decide to launch your D2C website, you can leverage the same network and start pushing traffic off of Amazon. So there are a lot of reasons why I believe people should use a platform like that. It's like building an email list, but instead you're actually leveraging the creator marketplace.   Bradley Sutton: Okay, yeah, interesting, I think TikTok how it works, people's eyes are really open to more of influencer marketing. I mean, it's basically influencer marketing. There's not really SEO on TikTok, or it's not even. Even if you understand the hashtags, it doesn't necessarily guarantee the rally. It's a numbers game like getting you know. You get out to 25 influencers and maybe 24 do nothing, and then one person, even though they're small, they get on the For you page or something like that and literally can bring thousands and thousands of dollars. There's somebody I've been helping with, you know, shipping their products and they're you know they're doing me sometimes some days a thousand fifteen hundred units of sales for like this planner and it was a hundred. They didn't spend any for PPC on TikTok 100%. You know they just push the product to influencers and then one here and one there just goes viral and it just means a lot of business.   Leo: Yeah, I think you know it's probably right now a big hype. I mean the TikTok shops everyone is talking about them working with affiliates and it's probably one of the oldest marketing strategies, if we want to call it that way. You know the affiliate marketing works because of the power that the individual creator in this case has to influence people right, and so people want to buy from people, and if you, as a brand, do a good job in recruiting a few super affiliates in this case we're talking about good creators that will turn into affiliates, then you have to worry less about that promotion part of you know launching new products.   Bradley Sutton: Interesting, interesting. Okay, now let's move a little bit back towards the Amazon world and actually I'm going to go a little bit off of Amazon, but it's something that you talked about today in your speech and we're not going to go too deep into it. If you guys want to really hear his presentation, you have to be an Elite member. So you Elite members definitely make sure to look out for the recording on that. But one thing you were talking about when it comes to images, but the way you discovered this was you said you were checking indexing on Google. So we know, on Amazon, if you want to check indexing, you just use Helium 10 index checker, right? Or if you don't have Helium 10, you can use the old school method of put the ASIN plus the keyword and then search and see if it comes up. For just rudimentary index checking for keywords. If you want to see if your Amazon product is indexed on Google, how do you even see if you are?   Leo: Yeah, so normally on Google you will copy your URL, search it on Google. You can also do a site column with your URL and then Google will show only search results that are related to the domain you're searching. But if you type the whole domain, the whole URL, the canonical URL of your Amazon listing, if you are indexed, it will show there.   Bradley Sutton: But what about if you're? Can you look if you're indexed for a specific keyword?   Leo: So if you're indexed for a specific keyword, then you want to put that URL plus the keyword and then or amazon.com/dp/your ASIN, or you can also do ASIN in quotes plus the keyword and then you will see if you get, if you're indexed on Google from that keyword. It works in a similar way. Um, but yeah, the presentation we touched a lot on you know the details of what was going on Google which was dependent on, uh, the way that the listing was optimized on Amazon.   Bradley Sutton: You talked about some advanced strategy. We'll talk a little bit about that, about like images and stuff. But without the images, is there a way to force yourself to be indexed on Google, like, for example, if you create a custom canonical URL, just insert the keywords and then if you actually happen to you know, like maybe run some Google ads, get some conversions on that, will that index you for that keyword on Google?   Leo: Yeah, so, based on some experiments that I've done, the easiest way to get indexed on Google is to publish some press releases on domains with good authority score domain rank and have your you know pointing a link to your listing with the anchor text that you potentially also want to rank for using that specific canonical tag that you get from your Amazon listing. So the reason why this works better is because normally Google indexes across these websites. Like you know, if you publish through PR, news or something like that, they will be crawled, and so Google will find these links and then follow your Amazon listing, which obviously, as a consequence, would be indexed.   Bradley Sutton: Okay, interesting. Now, taking it a step further, why should an Amazon seller even be concerned about indexing on Google like, um? Obviously, if you're running Google ads, you know your goal is to get direct sales from it. But just being ranked organically, um, what kind of bumps do you see on sales? Or how does it help a to be ranked high? I'm not just index. I mean index doesn't do much if you're ranked on page 30 or something, but how does ranking organically for a keyword? What's the potential there for helping sales?   Leo: So there, are a few reasons why you want to be indexed on Google, and for the most, let's start from the most advanced ones, right? Advanced sellers they normally try to send traffic to Amazon, especially during the launch period, using external traffic, right? So Google, we know, is a good referral that tends to help your rankings, and so Amazon tends to reward you if they see traffic coming from Google. So if you're not indexed, you lose a chance to show Amazon that you are getting traffic from Google. Now, I have a theory that paid traffic has a little bit more weight than organic, but the reason why you want to be indexed and the reason why you might want to be indexed for certain keywords is so that when you drive traffic through the URL to Amazon, you can actually give attribution to that keyword. That's number one, right? So you can actually use these URLs as your two-step.   Leo: Number two if you do a good job with your indexation and your listing is optimized, you actually also appear in the images, right? And so if people are looking for specific products, sometimes I search on Google using images because I'm looking for specific products that might be hard to find on Amazon, but if I look through the Google images and I find the product, then I go to Amazon, and so if you're not indexed, you're also not going to be able to be found there, and Google images actually gets a ton of traffic. So here are some of the reasons why, two of the reasons why. I can think of many more, but the most important are these ones. Google is still one of the largest search engine, and so missing out on that opportunity, I'm afraid it causes a lot of missed visibility for an Amazon seller at a listing level.   Bradley Sutton: And then you've done some tests before where you noticed that if that Amazon can read what the search was from Google, so that when you get sales from a keyword in Google, it also potentially could help your Amazon ranking for that keyword, right?   Leo: Yeah, that is correct. There was a test that we have done two years ago where everyone was talking about Google traffic and so we drove traffic straight from Google paid to Amazon without using any keyword in the URL, and then we noticed that for the keywords that we were actually bidding on, we saw a lift in ranking. I remember going from position I think it was 35 or so to position seven or five. So surprisingly we saw that Amazon was able to attribute that search query on Google and then the ranking as a result for the keyword was actually improving on Amazon as well.   Bradley Sutton: Okay, interesting, interesting. Now let's switch and talk a little bit about images, because this also has to do with ranking on Amazon. It has to do with ranking on Google, getting indexed in Google. What has more of an impact with getting discovered or being able to be read by Google? Is it if you have an infographic and the actual words appear in the infographic, you know on the actual image, or is it the metadata, or it only works the best if you're doing both?   Leo: In my opinion, you have to do both, and the reason is that right now, every search engine uses AI to detect subjects, text and everything on an image. You know, if you look, if you're a Facebook advertiser, you probably know that they've had this for a long time. If you add more text on an image than the image, the visual itself, your ad wouldn't have been approved, and so AI detection for images has been going on for a while. But now, since you know, ChatGPT came out and you know Lama from Facebook, we have, you know. We know we have a lot more information about this topic, and what we found is that the search engines, including Amazon and Google, they scan the content of your image and they're able to rank these images based on the content of that image, including subjects, context and in the subjects and text. Did I say that?   Leo: So, basically, what Google cares the most on top of that is also the metadata, because the metadata helps the search engine classify that image. So, while the content itself helps them understanding okay, this is what this image is about the actual metadata is more technical for the crawlers, the engine themselves so that they can place you in certain categories. And so when it comes to Amazon, the content on the image right now, I noticed that through some different experiments, that is being used for ranking reasons. And so if you look at some products that don't have, for example, keywords on the images, they are less. You know there are multiple factors. Obviously, they play when it comes to rankings, but if you put two products side by side same ranking, same ratings, same being on market for the same time period, timeframe and same price one has text on images and keywords and one doesn't. Most likely the one with keywords on images is going to rank better.   Bradley Sutton: Okay. So then what Amazon sellers should be doing is for their main images, or you know, the in their image carousel and their A+ content is I mean, obviously you can't have text in your main image. You know that's against terms of surface, although if you can have the packaging there, that's a good, that's a good opportunity. But then to get, hey, you use the right keywords, but then also, if you're using like photoshop or something you have and we're not going to go into detail, it's like there's a bunch of crazy stuff about copyright and there's fields there that he talks about in his presentation. You'll have to watch the Elite workshop for that. But you've been doing testing where it one has, it one doesn't, and then it gets you indexed on Google. You've actually seen where the ones who did it, their Amazon sales were like way higher than the ones who did it.   Leo: Yeah, that is correct. We analyzed an e-brace on Amazon and this is, you know work that I was doing with a friend of mine, and we were trying to understand why these competitors were actually indexed on Google and they were indexed for certain keywords. Not the main keyword, but a variation of them. And so what I did I created this Google sheet where I was helping me understand which ones were indexed and for what keywords they were indexed and that led me to see that the ASINs that actually were indexed on Google were indexed for keywords that were present on the A+ banners. And so when we did that, what happened, this happened within 48 hours, we noticed that Google indexed that specific product image and they were actually featuring it as a search result on Google for the main search query, so that image wasn't used as a snippet or thumbnail for the listing itself. So the URL wasn't amazon.com/dp/ASIN, it was amazon.com/ the search you know embrace.   Leo: So it took me to the search results page, but the image that they took as a featured image was actually the one of my client, and so that was very interesting because Google detected a refinement and it detected an update in that listing. It saw that that image was very relevant for the search query because of the way that we optimized it using metadata and then they used it as the main image on the Google search results. Now this, to me, is fascinating and is very important, because if you are a shopper and you're searching on Google for an e-brace and then you see this image, most likely that's psychological,   most likely when you land on the Amazon search results page, you're going to go and find a product, you're going to go and click that product. So that added traffic, that added conversion rate, helped us recover the racing and the sales that we were losing. But that was a very interesting experiment that we did.   Bradley Sutton: Interesting, okay. So again, if you guys want to get more information about that, that almost might be worth it just to subscribe to Helium 10 Elite for one week, just to get that presentation. So if you guys want to look into that, go to h10.me forward slash Elite and see it's only $99 extra, so make sure to sign up for that. Now, another thing that I think a lot of people have been talking about not just you, but you were one of the first ones to talk about semantic search and Cosmo and things like this, and we'll talk about what that means. But I think, just to set some groundwork, I think everybody understands that any search algorithm will evolve over time. That's the whole purpose. Like the companies who don't want to do well, they'll just keep their algorithm the same right. But anybody you know whether we're talking about Google, Facebook, TikTok has an amazing algorithm, Amazon. It changes over time and we've seen that.   Bradley Sutton: You know, if we were searching five years ago on Amazon, it's different. And now if you've bought some how many of you who have bought something you search for a keyword that has to do with that and that thing that you bought is now at the top on your Amazon maybe not somebody else. That didn't happen like five, six years ago. Last year we showed an example of how you search a keyword that doesn't really exist. It's called noodle camera and no listing has the word noodle camera in it. But there was like maybe 30 listings that came up and it was like a stethoscope camera it looks like a noodle. So five years ago you put noodle camera it would say zero results because nobody has that in their listing and these listings don't have that keyword in there. But it's showing up because Amazon shows history that, oh, people don't know what this is called stethoscope camera but then they think it looks like a noodle. So now it's showing listing. So we've seen this even for a year. Now, first of all, Amazon science documents we've talked about it, but maybe 80%, 90%, never actually is 100% in production. Sometimes it goes into production, sometimes it doesn't. But what was it that made Cosmo so interesting these documents that talked about it, that you're like man. This is something that you think that Amazon is going to move towards.   Leo: Yeah, the reason why, I think is something that would be applied at scale across the marketplace is because, as searchers, as buyers, as shoppers, our goal when we use a search engine is to find a product or information that we need in order to solve a problem. And so, as a technology company in this case we're talking about Amazon their goal is to improve, like Bradley said, the algorithm in order to simplify that search result and give you exactly what you're looking for, by burning some steps in the middle, right. And so that's what Cosmo is designed for. Cosmo is designed to be a man in the middle, between yourself and the search results, right, when you work together with it to give feedback back and forth. And so what they do right now they learn. You type a search query, they give you some result, you refine that result by clicking on some products that you think are relevant. And what they do with this information? They start building this knowledge graph, right. So a classic example if you go on Wikipedia and for something, Wikipedia normally links to other relevant sources. That's what they call the knowledge graph, right? They know that this is relevant to that right. And so what cosmo is trying to do, instead of you having to refine the search. They're refining it for you.   Leo: So the example that I give in my presentation this morning is that, if someone is searching for winter coat, we saw a product that ranks number one on Amazon that doesn't have the word winter coat in the title. But yeah, they're ranked number one, and so this is shocking, right, like everyone's like oh come. Title is supposed to be the most important element on the page when it comes to optimization and some SEOs, but this time Amazon understood that you are looking for something that keeps you, to keep you warm, right. So now we're shifting from a keyword-based search to intent-based search, and so, as sellers, right now, what we need to do is understand what is the actual intent behind the person. What am I selling this to? I'm selling this to someone that wants to stay warm, right, that's what the purpose of a winter coat is, and so, with that intent in mind, we need to optimize listings so that we can convey the message through images, through the title, bullet points and description, so that Amazon, the new Cosmo, understands that this product is something that helps people stay warm.   Leo: And what I think is going to happen also because of the shift in the way that these search results are built, which is more intent-based, is that Amazon then will start recommending also related products. So if you're looking for, if you type in winter coat, they say, okay, well, this person is trying to stay warm and so let me show them also some winter gloves and winter socks and maybe some winter boots, and that will change everything right. They will change the way we advertise, they will change the way we try to be associated with other products. They will change the way we also promote our listings. So that's very interesting and fascinating, but I think it's a good thing for the buyer, right, while for our sellers might be challenging to figure out again, how do we optimize our listings keeping this semantic concept in mind for the buyer? And they've already proven. If you look at the Amazon science document in the research papers, they're already saying that they're seeing a lift in conversion rate when Cosmo is applied to a search result page. So we must pay attention to these and monitor certain. It's challenging right now to understand where this is applied, but we need to monitor better the Amazon marketplace and then evolve and adapt as Cosmo gets released into more categories.   Bradley Sutton: Not to be controversial here, but to me it's almost it's different, but it's not different. Like, at the end of the day, Amazon wants to make money, right, so that winter coat that became number one. It's not number one necessarily because of new algorithm, because it would not be number one unless that is one of the best converting ones, because that's what gives Amazon the best chance to make money. But I think where the difference here is, or what's something that's quote unquote new, is it gives people more at bats. Like maybe I never. Even if I didn't have winter coat in my title, it might've been almost impossible for me to get on page one. But now Amazon is all right, let's just throw it here. Oh, shoot, look at that, how well it's converting. Let's go ahead and push it all the way to the top, whereas maybe you know, four years ago, you know, unless you were super optimized for a certain keyword, you would never even have the ad back. Like you would never even be able to get on page one, you know, outside of PPC or something. So to me that's like the difference, but something also. Again, I keep saying I don't want to be controversial, but it's going to be because there's a lot of people I respect in the industry who have been talking a lot about things that and I agree mostly with them. But I completely disagree when they say things like, oh, tools like maybe Helium 10, if they don't change it's going to be out of date. To me, I cannot see a world where the traditional forms of keyword research, are going to be not as important In the future, if Amazon is super intuitive, of course that's going to evolve.   Bradley Sutton: But the main reason we do keyword research is to get indexed and to also make our listing. Initially because the Amazon algorithm is based on buyer interaction, right. So once it's been out there for three, four weeks, they have so many data points and how people searched and what they clicked on and stuff that. Okay, now we can start doing advanced algorithms. But to even get it in the right pages you had to have done the regular keyword research to show Amazon. Because when you're brand new, day zero of your listing, Amazon has no idea what it is. It goes by the image, it goes by what you have in the title and how you have it. So my personal opinion is that no like. Of course, little things are going to change with keyword research here or there, but the main core of hey, let me find the most important keywords. That's not going to change because you have to tell Amazon on day one what is your product.   Leo: So, Bradley, I agree with you and I think there is one important detail that is the link between what you're saying and what this all semantic stuff is about. Right, the reason why that winter coat might be ranking number one, even though the winter coat is not in the title is attributes of the winter coat. You know Amazon right now, which before they probably weren't doing before Cosmo, right, they're looking at the attributes. So most likely they are ranking this one very well because it contains, uh, goose feathers, or they have 300 grams of goose feathers per square meter or whatever foot, and so they now are using these attributes to understand is this product warmer than this one? So, while the keyword research tools are always going to be needed, what I think is an opportunity for companies like Helium 10 is now provide additional information to the seller together with the main keywords. That helps also the listing be more relevant for Cosmo, using attributes related to those keywords. So, if the keyword is winter coat, what are the main attributes of coats? Right? What does a coat have to have? Waterproof, has to be warm. What kind of feeling? Is it polyester? Is it goose feathers? Also, is it long or short? Things like that are going to be the difference between the traditional keyword research tools and the semantic powered keyword research tools. If you guys give the sellers the same list of keywords and, by the way, here are some attributes related to these keywords, that will help Amazon Cosmo understand more about your product. I think that's the winner, in my opinion.   Bradley Sutton: Yeah, and in his presentation he talked a lot about different things you can do to be more semantically relevant and you know, using ChatGPT, so some really good features there. But that's important because you know, the it's not just, we're not just talking about Amazon SEO, it's also going to help you on Google and Bing and these, these other things and there's things that just the human mind we can't process, but a computer can process and tell you hey, this is, this is the keywords with the buyer intent and this is the most important, this is how you can relate yourself. So, regardless of how much of this Amazon develops, it's already important now for outside of Amazon indexing. Now, before we get into your last strategy, and I have just a couple of questions for you if people want to get more information, reach out to you, find out about your new project you're working on, or just reach out to you. How can they find you out there?   Leo: I have my own website right now. It's leosgovio.com, so you can reach out to me on through my website.   Bradley Sutton: And spell that, because it's not spelled exactly as you might think.   Leo: It's l-e-o and then s as in Sam, g as in George, o, v as in Victor, i o. Yeah, over there I have some information also about the semantic SEO stuff. So if you're more interested about this, I'd be happy to share my knowledge in depth, and LinkedIn is one of the platforms that I use the most.   Bradley Sutton: Excellent. All right Favorite Helium 10 tool?   Leo: Magnet   Bradley Sutton: If you were a head of product at Helium 10, what is one tool or function that you would bring that we do not have currently?   Leo: I believe I will combine what we just discussed about into one tool, and so it's an hybrid between a listing analyzer powered with recommendation based on the semantic stuff.   Bradley Sutton: And your 30 to 60 second tip can be about anything for sellers out there.   Leo: Leverage. Try to think about your current strategy when it comes to product inserts. To leverage it for UGC.   Bradley Sutton: All right guys. If you want more information, go to leosgovia.com. Check them out in the Helium 10 Elite, the Q2 workshop replay. But thank you, guys, so much for joining us and we'll definitely be reaching out to Leo next year to see what he's been up to.   Leo: Thanks, Bradley, I appreciate you having me again and, yeah, looking forward to the next one.   Bradley Sutton: Adios desde España.

June 4: Today on TownHall we are taking a look back at a previously released episode. In this episode Mark Weisman, CIO and CMIO at TidalHealth interviews Colin Banas, MD, Chief Medical Officer at DrFirst, Inc. about the importance of having accurate medication lists for patients. How can we get to medication list interoperability in a fragmented pharmacy participation landscape? Is relying on pharmacy techs the answer and what are their drawbacks? On a journey to absolve the complexities in today's interoperability, Dr.First's mission is to utilize AI so that data is semantically interoperable first and foremost. What does the future look like with this technology in place?Subscribe: This Week HealthTwitter: This Week HealthLinkedIn: Week HealthDonate: Alex's Lemonade Stand: Foundation for Childhood Cancer

Send Everyday AI and Jordan a text messageTo end a week-ish full of AI happenings, Microsoft has thrown all kinds of monkey wrenches into the GenAI race. What did they announce at their Microsoft Build conference? And how might it impact you? Our last takeaway may surprise you. Newsletter: Sign up for our free daily newsletterMore on this Episode: Episode PageJoin the discussion: Ask Jordan questions on Microsoft AIUpcoming Episodes: Check out the upcoming Everyday AI Livestream lineupWebsite: YourEverydayAI.comEmail The Show: info@youreverydayai.comConnect with Jordan on LinkedInTopics Covered in This Episode:1. Microsoft Build Conference Key AI Features 2. Microsoft Copilot Updates3. On-device AI and its futureTimestamps:01:50 Startup Humane seeks sale amid product criticism.09:00 Using Copilot increases latency and potential errors.11:15 Copilot changing work with edge AI technology.13:51 Cloud may be more secure than personal devices.19:26 Recall technology may change required worker skills.20:24 Semantic search understands context, improving productivity.28:41 Impressive integration of GPT-4 in Copilot demo.31:41 New Copilot technology changes how we work.36:13 Customize and deploy AI agent to automate tasks.38:08 Uncertainty ahead for enterprise companies, especially Apple.46:09 Recap of 5 key announcements from build conference.Keywords:Microsoft CEO, Satya Nadella, Copilot stack, personal Copilot, team's Copilot, Copilot agents, Copilot Studio, Apple ecosystem, enterprise companies, Microsoft Teams, OpenAI, Jordan Wilson, Microsoft Build Conference, edge AI, Copilot Plus PC, recall feature, gpt4o capabilities, iPhone users, AI technology, data privacy and security, GPT 4 o desktop app, AI systems, recall, mainstream AI agents, Humane AI, Scarlett Johansson, ChatGPT, Anthropic Claude, COPilot Studio Agent, Microsoft product. Get more out of ChatGPT by learning our PPP method in this live, interactive and free training! Sign up now: https://youreverydayai.com/ppp-registration/

Managing data and access to data is one of the biggest challenges that a company can face. It's common for data to be siloed into independent sources that are difficult to access in a unified and integrated way. One approach to solving this problem is to build a layer on top of the heterogenous data The post A Semantic Layer for Data with Artyom Keydunov appeared first on Software Engineering Daily.