Podcasts about Graph theory

Episode: 2467 Graph Theory and the Königsberg Bridge Problem.  Today, the bridges of Königsberg.

Dominique de Werra is an emeritus professor of Operations Research at EPFL (Ecole Polytechnique Federale de Lausanne) in Switzerland. His research fields include Combinatorial Optimization, Graph Theory, Scheduling and Timetabling. After spending a few years as an assistant professor in Management Sciences at the University of Waterloo (Canada) he joined the Math Department of EPFL. He conducted a collection of Operational Research projects (applied as well as theoretical) with a number of industrial partners. He is an associate editor of Discrete Applied Mathematics, Discrete Mathematics, Annals of Operations Research and a member of a dozen of editorial boards of international journals. From 1990 to 2000 Dominique de Werra was the Vice-President of EPFL; he was in charge of the international relations and represented his institution in many academy networks in Europe (like the CLUSTER network of excellence which he chaired). He was also in charge of all education programs of EPFL. He was President of IFORS (the International Federation of Operational Research Societies) from 2010 to 2012. In 1987-1988 he was President of EURO, the European Association of Operational Research Societies. In 1985–1986 he was President of ASRO, the Swiss Operations Research Society. In 1995 he was the laureate of the EURO Gold Medal. He has obtained Honorary Degrees from the University of Paris, the Technical University of Poznan (Poland) and the University of Fribourg (Switzerland). In 2012 he was awarded the EURO Distinguished Service Medal. He published over 200 papers in international scientific journals. He also wrote and edited several books. He was member of many committees in various countries of Europe and America (evaluation of institutions, accreditation, strategic orientation, etc.).

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

Have you wondered about graph theory and how to start exploring it in Python? What resources and Python libraries can you use to experiment and learn more? This week on the show, former co-host David Amos returns to talk about what he's been up to and share his knowledge about graph theory in Python.

In this compelling episode, join us as we sit down with Alex Flom, whose life story weaves through the extremes of human experience. The podcast is divided into three distinct segments, each offering a unique window into Alex's world. The first segment captures Alex's intense experiences in the Navy, where he shares a gripping tale of resilience and bravery, detailing his time with a team of SEALs in Afghanistan under constant enemy fire.   The conversation takes a poignant turn as Alex opens up about a deeply personal loss - the tragic story of his brother, James Flom, and Shannon Norton. This part of our talk reveals the profound impact of personal tragedy and the strength to move forward.   In the final segment, we venture into the realms of graph theory and AI, discussing its implications on future technologies and our cognitive processes. This philosophical and technical discussion explores the potential of AI in transforming our understanding of data and relationships. Alex's insights into graph theory are not just intellectually stimulating; they're a glimpse into the future of technology. So buckle up for an enlightening journey with Alex Flom, where valor meets vision, and intellect intersects innovation.

Paolo Toth is "Professor Emeritus" of “Operations Research” at DEI: (Department of Electrical and Information Engineering “Guglielmo Marconi”, Alma Mater Studiorum University of Bologna, A.D. 1088), where he was Full Professor from 1983 to 2011. His research interests include Operations Research and Mathematical Programming methodologies and, in particular, the design and implementation of effective exact and heuristic algorithms for Combinatorial Optimization and Graph Theory problems, and their application to real-world Transportation, Logistics, Loading, Routing, Crew Management, Railway Optimization problems. He is author of more than 190 papers published in international journals and of the book "Knapsack Problems: Algorithms and Computer Implementations" (coauthor S. Martello; J. Wiley, 1990). He is also Co-editor of the books "The Vehicle Routing Problem" (SIAM Monographs on Discrete Mathematics and Applications, 2002) and "Vehicle Routing: Problems, Methods and Applications” (MOS-SIAM Series on Optimization, 2014). He was President of EURO (Association of the European Operational Research Societies) for the period 1995-1996, and President of IFORS (International Federation of the Operational Research Societies) for the period 2001-2003. He acted as Chair of the Program Committee for the Triennial IFORS Conference in 1999. He received several international awards, among which: the "EURO Gold Medal" (the highest distinction within Operations Research in Europe) in 1998; the "Robert Herman Lifetime Achievement Award in Transportation Science" (from INFORMS) in 2005; the "INFORMS Fellowship" in 2016; the “EURO Distinguished Service Award” in 2019; the "IFORS Fellowship" in 2020. In May 2003, the University of Montreal conferred him a "Doctorate honoris causa" in Operational Research. In October 2012, at the INFORMS Annual Meeting), he delivered the “IFORS Distinguished Plenary Lecture”; in July 2023, at the IFORS Triennial Conference, he delivered the “EURO Plenary Address”. He supervised more than 200 master theses, 25 PhD students from 6 different countries, and 16 Post-Docs.

Dimes and Judas discuss women lying about being master fishermen, Divorced Cocaine Trudeau starting a war with India, and the trial of a Montreal writer for the Daily Stormer convicted by the therapeutic state for laughing at people who didn't die in the holocaust. This leads to an investigation into the importance of secret societies and graph theory, citing the book “The Square and the Tower” by Niall Ferguson. They expound upon the fluid dynamics intellectual and revolutionary movements, and how to employ secret societies in the 21t century. Lastly on this edition of the Copepranos Society, Dimes welcomes back Clossington to discuss his recent article on the wild history of piracy and the harnessing of filibusters to realize manifest destiny in America. Sponsor Link: https://axios-remote-fitness-coaching.ck.page/0af3833d64?via=dimes Timestamps: 0:26 – Building a House With Your Ass 2:36 - Boomers Don't Understand Dry Aged Beef 9:37 – Women Lying About Being Master Fishers 15:52 – Scanning Fake Michelle Obama Pregnancy Photos In a Trench Coat 25:42 – Envisioning a Future where we Weren't Raised Canadian 32:29 – Justin Trudeau Accuses India of Assassination 39:42 – The Air India Terrorist Attack 42:33 – The Liberals Inviting a Nazi Legend to Parliament 47:12 – Trudeau Doing Divorced Cocaine 55:30 – Trudeau Making White Men Back Again 58:03 – New Show for Paywall Subscribers & Merch Update 1:01:43 – Sponsor: Axios Fitness Coaching https://axios-remote-fitness-coaching.ck.page/0af3833d64?via=dimes 1:10:30 – Montreal Neo-Nazi Writer Jailed for Hate by the Therapeutic State 1:15:33 – The Medicalization of Penalization Explored 1:29:42 – “The Square and the Tower” Discussion Begins 1:33:57 – The Importance of Secret Societies and Closed Fraternities 1:37:57 – The Illuminati Were Real and Operated like Spores 1:46:45 – Graph Theory and the Function of Social Networks 1:53:42 – Examples of Secret Societies in Academia 1:57:07 – The Seven Insights on How Networks Function 1:59:16 – Anti-Node Behavior in the Dissident Right 2:06:35 – The Round Table and Hierarchies Wielding Networks for Conquest 2:17:23 – Clossington Interview Begins

Jayme Szwarcfiter is a Professor Emeritus at Universidade Federal do Rio de Janeiro (UFRJ) and Visiting Professor at Universidade Estadual do Rio de Janeiro (UERJ) in Brazil. His research interests are related to Graph Theory, Algorithms, Theory of Computation, and Discrete Mathematics. He has published more than 170 journal papers and several influential textbooks in these areas, and has supervised dozens of masters and doctoral students. Jayme is a Full Member of the Brazilian Academy of Sciences, and he has received numerous national and international awards such as the Grand Cross for Scientific Merit, the Almirante Álvaro Alberto prize, the Scientific Merit Prize awarded by the Brazilian Computer Society, and the Luis Federico Leloir prize, awarded by the Ministry of Science, Technology and Productive Innovation of Argentina. In addition, Jayme was a visiting professor in many countries like the US, England, Scotland, Argentina, Germany, France, Poland, Israel, Czech Republic, and Japan.

Ethan Buchman, co-founder of Cosmos and a crypto-nerd-core-rapper, came to my show to discuss what's going on with Cosmos, what is collaborative finance, and how he wants to apply graph theory to finance.  Ethan Buchman: https://twitter.com/buchmanster ►► OKX Sign up for an OKX Trading Account then deposit & trade to unlock mystery box rewards of up to $10,000! 

Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2023.07.18.549314v1?rss=1 Authors: Metzen, D., Stammen, C., Fraenz, C., Schlüter, C., Johnson, W., Güntürkün, O., DeYoung, C. G. Abstract: Previous research investigating relations between general intelligence and graph-theoretical properties of the brain's intrinsic functional network has yielded contradictory results. A promising approach to tackle such mixed findings is multi-center analysis. For this study, we analyzed data from four independent data sets (total N greater than 2000) to identify robust associations amongst samples between g factor scores and global as well as node-specific graph metrics. On the global level, g showed no significant associations with global efficiency in any sample, but significant positive associations with global clustering coefficient and small-world propensity in two samples. On the node-specific level, elastic-net regressions for nodal efficiency and local clustering yielded no brain areas that exhibited consistent associations amongst data sets. Using the areas identified via elastic-net regression in one sample to predict g in other samples was not successful for nodal efficiency and only led to significant predictions between two data sets for local clustering. Thus, using conventional graph theoretical measures based on resting-state imaging did not result in replicable associations between functional connectivity and general intelligence. Copy rights belong to original authors. Visit the link for more info Podcast created by Paper Player, LLC

Graph theory is the study of connections, as may be seen in the London Underground map with stations linked by rails, or a transportation network with cities linked by roads. Dating back to the 18th century, the subject increasingly took hold in the 20th century, developing rapidly from mainly recreational puzzles to a mainstream area of study with widespread applications and strong links to computer science.This illustrated historical talk will survey this century of development.A lecture by Robin Wilson recorded on 13 June 2023 at Barnard's Inn Hall, LondonThe transcript and downloadable versions of the lecture are available from the Gresham College website: https://www.gresham.ac.uk/watch-now/graph-theoryGresham College has offered free public lectures for over 400 years, thanks to the generosity of our supporters. There are currently over 2,500 lectures free to access. We believe that everyone should have the opportunity to learn from some of the greatest minds. To support Gresham's mission, please consider making a donation: https://gresham.ac.uk/support/Website:  https://gresham.ac.ukTwitter:  https://twitter.com/greshamcollegeFacebook: https://facebook.com/greshamcollegeInstagram: https://instagram.com/greshamcollegeSupport the show

Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2023.04.17.537216v1?rss=1 Authors: Zhu, H., Fitzhugh, M. C., Keator, L. M., Johnson, L., Rorden, C., Bonilha, L., Fridriksson, J., Rogalsky, C. Abstract: The dual-stream model of speech processing has been proposed to represent the cortical networks involved in speech comprehension and production. Although it is arguably the prominent neuroanatomical model of speech processing, it is not yet known if the dual-stream model represents actual intrinsic functional brain networks. Furthermore, it is unclear how disruptions after a stroke to the functional connectivity of the dual-stream model's regions are related to specific types of speech production and comprehension impairments seen in aphasia. To address these questions, in the present study, we examined two independent resting-state fMRI datasets: (1) 28 neurotypical matched controls and (2) 28 chronic left-hemisphere stroke survivors with aphasia collected at another site. Structural MRI, as well as language and cognitive behavioral assessments, were collected. Using standard functional connectivity measures, we successfully identified an intrinsic resting-state network amongst the dual-stream model's regions in the control group. We then used both standard functional connectivity analyses and graph theory approaches to determine how the functional connectivity of the dual-stream network differs in individuals with post-stroke aphasia, and how this connectivity may predict performance on clinical aphasia assessments. Our findings provide strong evidence that the dual-stream model is an intrinsic network as measured via resting-state MRI, and that weaker functional connectivity of the hub nodes of the dual-stream network defined by graph theory methods, but not overall average network connectivity, is weaker in the stroke group than in the control participants. Also, the functional connectivity of the hub nodes predicted specific types of impairments on clinical assessments. In particular, the relative strength of connectivity of the right hemisphere's homologues of the left dorsal stream hubs to the left dorsal hubs versus right ventral stream hubs is a particularly strong predictor of post-stroke aphasia severity and symptomology. Copy rights belong to original authors. Visit the link for more info Podcast created by Paper Player, LLC

Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2023.02.21.529361v1?rss=1 Authors: Akbari, S., Deevband, M. R., Alvar, A. A., Zadeh, E. F., Tabar, H. R., Kelley, P., Tavakoli, M. Abstract: Development of Parkinon's disease causes functional impairment in the brain network of Parkinson's patients. The aim of this study is to analyze brain networks of people with Parkinson's disease based on higher resolution parcellations and newer graphical features. The topological features of brain networks were investigated in Parkinson's patients (19 individuals) compared to healthy individuals (17 individuals) using graph theory. In addition, four different methods were used in graph formation to detect linear and nonlinear relationships between functional magnetic resonance imaging (fMRI) signals. The functional connectivity between the left precuneus and the left amygdala, as well as between the vermis_1_2 and the left temporal lobe was evaluated for the healthy and the patient groups. The difference between the healthy and patient groups was evaluated by non-parametric t-test and U-test. Based on the results, Parkinson's patients showed a significant decrease in centrality criterion compared to healthy subjects. Furtheremore, changes in regional features of brain network were observed. There was also a significant difference between the two groups of healthy subjects and Parkinson's patients in different areas by applying centrality criterion and the correlation coefficients. The results obtained for topological features indicate changes in the functional brain network of Parkinson's patients. Finally, similar areas obtained by all three methods of graph formation in the evaluation of connectivity between paired regions in the brain network of Parkinson's patients increased the reliability of the results. Copy rights belong to original authors. Visit the link for more info Podcast created by Paper Player, LLC

A graph theory-based multi-scale analysis of hierarchical cascade in molecular clouds : Application to the NGC 2264 region by B. Thomasson et al. on Wednesday 14 September The spatial properties of small star-clusters suggest that they may originate from a fragmentation cascade of the cloud for which there might be traces up to a few dozen of kAU. Our goal is to investigate the multi-scale spatial structure of gas clumps, to probe the existence of a hierarchical cascade and to evaluate its possible link with star production in terms of multiplicity. From the Herschel emission maps of NGC 2264, clumps are extracted using getsf software at each of their associated spatial resolution, respectively [8.4, 13.5, 18.2, 24.9, 36.3]". Using the spatial distribution of these clumps and the class 0/I Young Stellar Object (YSO) from Spitzer data, we develop a graph-theoretic analysis to represent the multi-scale structure of the cloud as a connected network. From this network, we derive three classes of multi-scale structure in NGC 2264 depending on the number of nodes produced at the deepest level: hierarchical, linear and isolated. The structure class is strongly correlated with the column density $N_{rm H_2}$ since the hierarchical ones dominate the regions whose N$_{rm H_2} > 6 times 10^{22}$cm$^{-2}$. Although the latter are in minority, they contain half of the class 0/I YSOs proving that they are highly efficient in producing stars. We define a novel statistical metric, the fractality coefficient F that measure the fractal index describing the scale-free process of the cascade. For NGC 2264, we estimate F = 1.45$pm$0.12. However, a single fractal index fails to fully describe a scale-free process since the hierarchical cascade starts at a 13 kAU characteristic spatial scale. Our novel methodology allows us to correlate YSOs with their multi-scale gaseous environment. This hierarchical cascade that drives efficient star formation is suspected to be both hierarchical and rooted by the larger-scale gas environment up to 13 kAU. arXiv: http://arxiv.org/abs/http://arxiv.org/abs/2206.01154v3

00:01:54 What is Diplomacy?00:07:27 Stalemate Lines00:14:32 Graph Theory00:28:24 Graph Theory & Diplomacy00:31:18 Problem #1: Unit Placement00:38:22 Problem #2: Stalemate Detection00:48:23 Problem #3: Stalemate Construction1:00:16 Q&AParticipate in future Masterclass sessions by joining the vWDC Discord server at https://discord.gg/jbdZtRFMTAVisit the BrotherBored blogSupport Your Bored Brother on PatreonIf you enjoy BrotherBored's Diplomacy Dojo, please subscribe and leave a review letting us know! The Diplomacy Dojo is available on most podcatchers, as well as on the BrotherBored YouTube channel.  ★ Support this podcast on Patreon ★

John Urschel received his bachelors and masters in mathematics from Penn State and then went on to become a professional football player for the Baltimore Ravens in 2014. During his second season, Urschel began his graduate studies in mathematics at MIT alongside his professional football career. Urschel eventually decided to retire from pro football to pursue his real passion, the study of mathematics, and he completed his doctorate in 2021. Urschel is currently a scholar at the Institute for Advanced Study where he is actively engaged in research on graph theory, numerical analysis, and machine learning. In addition, Urschel is the author of Mind and Matter, a New York Times bestseller about his life as an athlete and mathematician, and has been named as one of Forbes 30 under 30 for being an outstanding young scientist. In this episode, John and I discuss a hodgepodge of topics in spectral graph theory. We start off light and discuss the famous Braess's Paradox in which traffic congestion can be increased by opening a road. We then discuss the graph Laplacian to enable us to present Cheeger's Theorem, a beautiful result relating graph bottlenecks to graph eigenvalues. We then discuss various graph embedding and clustering results, and end with a discussion of the PageRank algorithm that powers Google search. Originally published on June 9, 2022 on YouTube: https://youtu.be/O6k0JRpA2mg Corrections: 01:14:24 : The inequalities are reversed here. It is corrected at 01:16:16. Timestamps: 00:00:00 : Introduction 00:04:30 : Being a professional mathematician and academia vs industry 00:09:41 : John's taste in mathematics 00:13:00 : Outline 00:17:23 : Braess's Paradox: "Opening a highway can increase traffic congestion." 00:25:34 : Prisoner's Dilemma. We need social forcing mechanisms to avoid undesirable outcomes (traffic jams). 00:31:20 : What is a graph 00:36:33 : Graph bottlenecks. Practical situations: Task assignment, the economy, organizational management. 00:42:44 : Quantifying bottlenecks: Cheeger's constant 00:46:43 : Cheeger's constant sample computations 00:52:07 : NP Hardness 00:55:48 : Graph Laplacian 00:58:30 : Graph Laplacian: Relation to Laplacian from calculus 01:00:27 : Graph Laplacian: 1-dimensional example 01:01:22 : Graph Laplacian: Analyst's Laplacian vs Geometer's Laplacian (they differ by a minus sign) 01:04:44 : Graph Laplacian: Some history 01:07:35 : Cheeger's Inequality: Statement 01:09:37 : ***Cheeger's Inequality: A great example of beautiful mathematics*** 01:10:46 : Cheeger's Inequality: Computationally tractable approximation of Cheeger's constant 01:14:57 : Rayleigh quotient, discussion of proof of Cheeger's inequality 01:19:16 : Harmonic oscillators: Springs heuristic for lambda_2 and Cheeger's inequality 01:22:20 : Interlude: Tutte's Spring Embedding Theorem (planar embeddings in terms of springs) 01:26:23 : Harmonic oscillators: Resume lambda_2 discussion and spring tension 01:29:45 : Interlude: Graph drawing using eigenfunctions 01:33:17 : Harmonic oscillators: Resume lambda_2 discussion: complete graph example and bottleneck is a perturbation of two disconnected components 01:38:26 : Summary thus far and graph bisection 01:42:44 : Graph bisection: Large eigenvalues for PCA vs low eigenvalues for spectral bisection 01:43:40 : Graph bisection: 1-dimensional intuition 01:44:40 : Graph bisection: Nodal domains 01:46:29 : Graph bisection: Aha, the 1-d example now makes sense. Splitting by level set of second eigenfunction is a good way to partition domain. 01:47:43 : Spectral graph clustering (complementary to graph bisection) 01:51:50 : Ng-Jordan-Weiss paper 01:52:10 : PageRank: Google's algorithm for ranking search results 01:53:44 : PageRank: Markov chain (Markov matrix) 01:57:32 : PageRank: Stationary distribution 02:00:20 : Perron-Frobenius Theorem 02:06:10 : Spectral gap: Analogy between bottlenecks for graphs and bottlenecks for Markov chain mixing 02:07:56 : Conclusion: State of the field, Urschel's recent results 02:10:28 : Joke: Two kinds of mathematicians   Further Reading: A. Ng, M. Jordan, Y. Weiss. "On Spectral Clustering: Analysis and an algorithm" D. Spielman. "Spectral and Algebraic Graph Theory"

How are you ensuring your employees are happy and will stay with your company? Your employees are the most important piece to your business and as such you must learn what they want in their and career and enable it. In today's discussion we focus on empathy as the future of business in tech, why it's important for developers to gain business acumen for greater success in their role, how a new product, YugaHQ, can help companies predict and prepare for employee turnover and much more. Our guest today, Kiran Kanakadandi, has an Electrical Engineering degree from Sreenidhi Institute of Science and Technology in Hyderabad, India, a MS of Computer Science from Clemson and has published a thesis on Graph Theory. He began his career as a kernel and distributed systems developer at NetApp. Our guest is a 2 time startup founder, founding Potatop Technologies and is currently serving as the founder/CEO of YugaHQ, an employee retention SaaS. Kiran believes empathy is the best business model. He is the author of a US Patent, speaks 4 languages, enjoys listening to deep Indian classical music, American classics like Towns Van Zandt, and is a movie trivia buff. Connect with Kiran: Twitter LinkedIn Show notes and helpful resources: Kiran's Career paths for developers article Kiran's On Business Awareness for Engineers article Kiran's Three parts of your job article Kiran's Always Be Rehiring article Paul Graham - Do things that don't scale - Essay Indian Classical Music: Uyyalalugavayya performed by Dr Mangalampalli Balamuralikrishna and composed by Thyagaraja written in Telugu over 200 years ago Indian Classical Music: Bhavayami Gopalabalam performed by Dr M S Subbulakshmi and composed by Annamacharya over 800 years ago in Sanskrit Film music with music video: Chaiya Chaiya by A R Rahman from the movie Dil Se "I'll be here in the morning" by Townes Van Zandt - this is Kiran's favorite version Productivity Hack => Chrome plugins - Marvelous Suspender (tab remover) and Tab Manager Plus Building something cool or solving interesting problems? Want to be on this show? Send me an email at jointhepodcast@unfilteredbuild.com Podcast produced by Unfiltered Build - dream.design.develop.

David Sumpter is a professor of applied mathematics at the University of Uppsala, in Sweden, and the author of a wonderful book called The 10 equations that Rule the World.He talks to Guy Spier about the various applications of mathematics in practical areas of our day-to-day life, such as social media algorithms, graph theory, Bayes' theorem, and even vaccinations.   Full transcript available here: https://aqfd.docsend.com/view/8sw5qidsc44bzkr2 Contents: Pure Mathematics vs. Applied Mathematics (00:00)Graph Theory and its Applications (10:39)The Advertising Equation in Social Media (17:11)Taking Control of the Algorithms that Control Us (20:11)“Fluffy Science” vs. Empirical Studies (25:02)From the Kelly Criterion to Bayes' Theorem (32:22)Mathematics Behind Vaccination Hesitancy (41:55)

David and Randy explore graph theory with Juli. We learn how to use the Graphs.jl Julia package to create graph data structures, and the JuMP.jl package to calculate NP-hard and NP-complete properties of the graphs. This week we also share a bunch of amazing things that our listeners are building in Julia. Thanks to everyone that shared your projects with us! Check out the Twitter thread (https://twitter.com/talkjuliapod/status/1489255938737410051) to see what everyone is working on. Episode links are available in the show notes on our website ➡ https://www.talkjulia.com/6 (https://www.talkjulia/6) ABOUT THE SHOW Talk Julia is a weekly podcast devoted to the Julia programming language. Join hosts David Amos and Randy Davila as we explore Julia news and resources, learn Julia for ourselves, and share our experience and everything that we've learned.

13 March 2007 – 16:00 to 17:00

Dr. Amanda Montejano is a Professor (Titular "A" ) at the Multidisciplinary Unit of Teaching and Research of the Science Faculty (Facultad de Ciencias ) of the National Autonomous University of Mexico in Juriquilla located in the city of Querétaro. She is also Research Fellow at the Mathematical Innovation Center (CINNMA A.C.). Dr. Montejano's main areas of research are Combinatorics and Graph Theory, although she is also interested in some aspects of Combinatorial Number Theory. In a conversation with students from Simon Fraser University, Wassim Khelifi, Connor Marriam, and Manan Sood, Dr. Montejano talks about how she became a mathematician, her research in Ramsey theory, and how it is to be a woman mathematician.

Alex Markham is completing their Postdoc in the Math of Data and AI group at KTH Royal Institute of Technology in Sweden. Their research focuses on developing new algorithms for learning causal models from data. Causal inference is especially appealing to more applied researchers, because it offers an intuitive framework for reasoning about why stuff happens and how we can influence it to happen differently. Alex finds causal inference especially interesting because of the many different fields it draws from, including philosophy, cognitive science, and methodology, as well as computational and mathematical fields, like machine learning, statistics, graph theory, algebraic geometry, and combinatorics. Episode 73's got it all: math, science and philosophy -- join us for a holistic half hour! INTRO Causal Inference Correlation vs. Causality THE BRAIN Neuroimaging & fMRI Statistics Time Variables Complexity Brain-Computer Interface (BCI) Electroencephalography (EEG) Prosthetics The Matrix CAUSALITY Causal Relationships (Direct, Indirect, Mediated) The Limits of Probability & Statistics Extending the Language of Probability The "Do" Operator Symmetry of Correlation "No Causation Without Manipulation" Randomized Controlled Experimentation MATHEMATICS Machine Learning Dependence & Independence (Acyclic) Directed Graphs (DAGs) & Colliders Causal Models Graph Spaces /// CONTACT Alex's Website: causal.dev My Website: rapyourgift.com READINGS Introduction to Causality in Machine Learning by Alexandre Gonfalonieri on Medium: https://towardsdatascience.com/introduction-to-causality-in-machine-learning-4cee9467f06f --- Send in a voice message: https://anchor.fm/abstractcast/message

Episode: 2153 Of cliques and connections: graph theory, society and surveillance.  Today, social cohesion, graph theory, and surveillance.

Graph Theory has been around for a long time. Its use in computing has found a number of applications, most prominently social networks. In this episode I will be talking with Ben Steer and Gabor Szarnyas about their experiences in working with graphs. In particular: how to assess the performance of graphs, their use in science and research, the state of graph query languages and more.Here are a few links you might find useful:- https://www.routledgehandbooks.com/doi/10.1201/b16132-3  Handbook of Graphs, a nice overview. - https://arxiv.org/abs/2012.06171 The Future is Big Graphs! An overview of graph processing systems - for which Gabor is co-author- https://github.com/GraphBLAS/LAGraph LAGraph is a draft library plus a test harness for collecting algorithms that use the GraphBLAS- https://www.tigergraph.com Tiger Graph database- https://neo4j.com Neo4j a popular graph DB- https://opencypher.org Open Cypher - the open source graph query language- http://ldbcouncil.org Linked DB Benchmark Council - https://graphblas.github.io GraphBLAS- https://szarnyasg.github.io/posts/graph-query-languages/ Gabor's post on graph query languages- https://raphtory.github.io Raphtory, a temporal graph tool developed by Ben and othersSupport the Show.Thank you for listening and your ongoing support. It means the world to us! Support the show on Patreon https://www.patreon.com/codeforthought Get in touch: Email mailto:code4thought@proton.me UK RSE Slack (ukrse.slack.com): @code4thought or @piddie US RSE Slack (usrse.slack.com): @Peter Schmidt Mastadon: https://fosstodon.org/@code4thought or @code4thought@fosstodon.org LinkedIn: https://www.linkedin.com/in/pweschmidt/ (personal Profile)LinkedIn: https://www.linkedin.com/company/codeforthought/ (Code for Thought Profile) This podcast is licensed under the Creative Commons Licence: https://creativecommons.org/licenses/by-sa/4.0/

Mathematician and Flutist, Frank Rothe talks about his two life- long loves:  Mathematics and his two works on Number Theory and Modern Algebra and Classics in Graph Theory – both available on Amazon…  and his second but equal love of the flute. He has three CD’s available on Amazon or on CD Universe for flute and […] The post Number Theory and Modern Algebra by Franz Rothe appeared first on WebTalkRadio.net.

Mathematician and Flutist, Frank Rothe talks about his two life- long loves:  Mathematics and his two works on Number Theory and Modern Algebra and Classics in Graph Theory – both available on Amazon…  and his second but equal love of the flute. He has three CD’s available on Amazon or on CD Universe for flute and […] The post Number Theory and Modern Algebra by Franz Rothe appeared first on WebTalkRadio.net.

This interview was recorded at GOTO Copenhagen 2019 for GOTO Unscripted. https://gotopia.techRead the full transcription of this interview here:https://gotopia.tech/articles/continuous-delivery-microservices-serverless-10-minutesNicki Watt - CTO/CEO at OpenCredoKen Mugrage - Principal Technologist - Office of the CTO at ThoughtWorksPreben Thorø - CTO at Trifork SwitzerlandDESCRIPTIONContinuous delivery has been around for more than 15 years, but it's only gained wider adoption in recent years. In this Unscripted interview, Nicki Watt and Ken Mugrage chat with Preben Thorø about the evolution of CD and how it ties in with recent developments in the software architecture space. Find out when you should use CD along with its connection to microservices, serverless, machine learning and graph theory.https://twitter.com/GOTOconhttps://www.linkedin.com/company/goto-https://www.facebook.com/GOTOConferencesRECOMMENDED BOOKSNicki Watt & Aleksa Vukotic • Neo4j in Action • https://amzn.to/3oPmq8oSam Newman • Monolith to Microservices • https://amzn.to/2Nml96ERonnie Mitra & Irakli Nadareishvili • Microservices: Up and Running• https://amzn.to/3c4HmmLRonnie Mitra, Irakli Nadareishvili, Matt McLarty & Mike Amundsen • Microservice Architecture • https://amzn.to/3fVNAb0Ronnie Mitra, Mehdi Medjaoui, Erik Wilde & Mike Amundsen • Continuous API Management • https://amzn.to/3uxdypwJim Webber • Graph Databases • https://amzn.to/3l7k8hjLooking for a unique learning experience?Attend the next GOTO conference near you! Get your ticket at https://gotopia.techSUBSCRIBE TO OUR YOUTUBE CHANNEL - new videos posted almost daily.https://www.youtube.com/user/GotoConferences/?sub_confirmation=1

David Christie, Chairman of Bleckwen, joins Marie and Sam to chat about the amazing world of behaviour detection and how Bleckwen has harnessed machine learning to better detect fraudulent and suspicious behaviour. With a name linked to a "truth serum", Bleckwen has been involved in some "deep truth" investigations dealing with better detection of complex fraud such as PPP fraud, where it is a challenge for banks with larger legacy systems. Credit origination fraud, synthetic ID fraud, and oddities in transaction monitoring patterns, according to David it's all about behaviour.  Marie dives into the use of graph theory - did you know it was used to design London's tube system? - the same approach is used by Bleckwen to identify patterns across banks accounts and parties, how they interact, how often and other similarities.  David also explains how they encourage their team to go further afield to keep their thinking fresh - could Tweets prior to the Catalan unrest in 2018 have helped to detect it earlier?  Sharing their intelligence and some of their algorithms to the wider financial crime prevention community (for free) is an important part of Bleckwen's approach to being a part of the financial crime community. 

Host Samantha Walravens leads an engaging conversation with Liz Maida about how she pivoted her career from civil engineering to internet infrastructure and eventually founded her own cutting-edge cybersecurity company, Uplevel Security. As the founder and CEO of Uplevel Security, she created the industry’s first adaptive system of intelligence that uses graph theory and machine learning to modernize security operations. Learn how Liz is innovating and creating next-generation approaches to enterprise security. Lehigh@NasdaqCenter is an exclusive academic in-residence collaboration between Lehigh University and the Nasdaq Entrepreneurial Center in San Francisco. Our theory of change is to accelerate student transformation and societal impact through inclusive entrepreneurial education, research, and thought leadership.

This interview was recorded for the GOTO Book Club.http://gotopia.tech/bookclubJim Webber - Co-Author of "Graph Databases"Nicki Watt - CTO of OpenCredoDESCRIPTIONDiscover the amazing world of graph databases and how you can leverage graphs to understand your data with Jim Webber, co-author of "Graph Database" and Nicki Watt, CTO at OpenCredo. They'll take you on a journey that starts with the definition of graphs, walks you through case studies and highlights pitfalls.The interview is based on Jim's book "Graph Databases": https://amzn.to/3l7k8hjRead the full transcription of the interview here:https://gotopia.tech/bookclub/episodes/discover-graph-databasesRECOMMENDED BOOKSJim Webber • Graph Databases • https://amzn.to/3l7k8hjFree eBook version at https://graphdatabases.comhttps://twitter.com/GOTOconhttps://www.linkedin.com/company/goto-https://www.facebook.com/GOTOConferencesLooking for a unique learning experience?Attend the next GOTO conference near you! Get your ticket at http://gotopia.techSUBSCRIBE TO OUR YOUTUBE CHANNEL - new videos posted almost daily.https://www.youtube.com/user/GotoConferences/?sub_confirmation=1

In mathematics, nature is a constant driving inspiration; mathematicians are part of nature, so this is natural. A huge part of nature is the idea of things like networks. These are represented by mathematical objects called 'graphs'. Graphs allow us to describe a huge variety of things, such as: the food chain, lineage, plumbing networks, electrical grids, and even friendships. So where did this concept come from? What tools can we use to analyze graphs? And how can you use graph theory to minimize highway tolls? All of this and more on this episode of Breaking Math. Episode distributed under an Creative Commons Attribution-ShareAlike-NonCommercial 4.0 International License. For more information, visit CreativeCommons.org [Featuring: Sofía Baca, Meryl Flaherty] --- This episode is sponsored by · Anchor: The easiest way to make a podcast. https://anchor.fm/app Support this podcast: https://anchor.fm/breakingmathpodcast/support

What's the rumpus, I'm Asaf Shapira and this is NETfrix. In this podcast we will talk about the magical field of Network Science, Graph Theory, Social Network Analysis or SNA and everything in between and that's a lot… Transcripts are available on SNApod.net See you on the other side of NETfrix.

Oxford has gone online for lockdown. So how do our student lectures look? Let Marc Lackenby show you as he looks at paths between vertices in a graph with a view to finding the shortest route between any two vertices. Works for your Satnav for example. We are making these lectures available (there are many more on this YouTube Channel via the Playlist) to give an insight in to the student experience and how we teach Maths in Oxford. All lectures are followed by tutorials where pairs of students spend an hour with their tutor to go through the lectures and accompanying work sheets. An overview of the course and the relevant materials is available here: https://courses.maths.ox.ac.uk/node/44174

Oxford has gone online for lockdown. So how do our student lectures look? Let Marc Lackenby show you as he looks at paths between vertices in a graph with a view to finding the shortest route between any two vertices. Works for your Satnav for example. We are making these lectures available (there are many more on this YouTube Channel via the Playlist) to give an insight in to the student experience and how we teach Maths in Oxford. All lectures are followed by tutorials where pairs of students spend an hour with their tutor to go through the lectures and accompanying work sheets. An overview of the course and the relevant materials is available here: https://courses.maths.ox.ac.uk/node/44174

Denise's choice for STEM and maths was a no brainer, but the rest of the journey wasn't as clear. There were many forks along the way. Denise first told us about her maths studies and how she inadvertedly discovered graphs theory. She described how her aversion for a focus on teaching led her to digging deeper into the graphs field that became the subject of her PhD. We the talked about moving from academia to the industry. Denise told us about her job at the startup PokitDok and how it felt to combine the rigor or data science with the willingness to move fast of the startup world. We brushed over her experience as an evangelist. And finally we talked about DataStax and Denise's role working on OpenSource, with Cassandra, the NoSQL movement and some of the most brilliant minds in the graph theory space. Denise finished by giving us two advices: don't be afraid to fail, and trust yourself!Here are the links of the show:https://www.twitter.com/DeniseKGosnellhttps://github.com/denisekgosnellGraph Technology Makes Teams More Productive https://medium.com/@denisekgosnell/graph-technology-makes-teams-more-productive-ebc549aa45fbGraph Book https://github.com/datastax/graph-bookThe Practitioner's Guide to Graph Data http://shop.oreilly.com/product/0636920205746.doMatthias Broecheler https://www.linkedin.com/in/matthiasbroechelerTed Tanner https://twitter.com/tctjrTeresa Haynes https://en.wikipedia.org/wiki/Teresa_W._HaynesCreditsMusic Aye by Yung Kartz is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.Your hostSoftware Developer‘s Journey is hosted and produced by Timothée (Tim) Bourguignon, a crazy frenchman living in Germany who dedicated his life to helping others learn & grow. More about him at timbourguignon.fr.Want to be next?Do you know anyone who should be on the podcast? Do you want to be next? Drop me a line: info@devjourney.info or via Twitter @timothep.Gift the podcast a ratingPlease do me and your fellow listeners a favor by spreading the good word about this podcast. And please leave a rating (excellent of course) on the major podcasting platforms, this is the best way to increase the visibility of the podcast:Apple PodcastsStitcherGoogle PlayPatreonFinally, if you want to help produce the podcast, support me on Patreon. Every cent you pledge will help pay the hosting bills!Thanks!Support the show (http://bit.ly/2yBfySB)

Tien Chih is an Assistant Professor of Mathematics at Montana State University-Billings. He received his Ph.D. at the University of Montana, Missoula in 2014 under the supervision of George McRae. Tien does research in different fields including Algebraic Combinatorics, Categorical Combinatorics, and Graph Theory. More recently, he has been working on developing homotopy theory in the category of graphs.You can find Tien's website at:https://www.msubillings.edu/mathfaculty/tchih/ https://edfinity.com/products and in particular openintro https://edfinity.co/openintrostats.www.sensemakesmath.comPODCAST: http://sensemakesmath.buzzsprout.com/TWITTER: @SenseMakesMathPATREON: https://www.patreon.com/sensemakesmathFACEBOOK: https://www.facebook.com/SenseMakesMathSTORE: https://sensemakesmath.storenvy.comSupport the show (https://www.patreon.com/sensemakesmath)

The boys sit down with an infinite box of resistors, worry just how diverse their business is and think through the peculiar physics of balloons. Odds and Evenings Twitter - https://twitter.com/OddsAndEvenings Website - http://www.oddsandevenings.com Topics discussed Resistor Mathematics How to Measure Diversity Balloon in a Car Show Notes The XKCD Resistor Problem we Mention (https://xkcd.com/356/) An article I wrote which uses Δ-Y transformations for calculating resistance (http://www.alaricstephen.com/main-featured/2017/10/15/gchq-resistor-puzzle-solution) The Diversity Formula (http://www.countrysideinfo.co.uk/simpsons.htm) An article I wrote about Simpson's Paradox (http://www.alaricstephen.com/main-featured/2016/12/11/simpsons-paradox) The Sprinkler Problem (https://en.wikipedia.org/wiki/Feynman_sprinkler) Credits Hosted By Alaric - http://alaricstephen.com Alex - http://twitter.com/speakmouthwords Editing by Alex Fish schematics by Alaric Theme music by David Russell - https://youtube.com/DavidRussell323

One of the challenges in a (micro-)service landscape is to keep track of all capabilities in the services. Another is to keep track of the consumers of your service and the consumers beyond. Taking our IT landscape as an example: we work with over 120 teams on over 850 different applications varying from services, GUI's, services, data builders and other types of applications. In an environment like ours, it's crucial to understand what applications we have, what functionality they offer and who are working on them.What this episode coversIn the past, the list of applications was maintained on our confluence pages, by hand. Later, engineers integrated properties data to become more accurate and recently a team stood up to make it ‘spot on'. One single source of truth. It's called Software Parade.To keep track of these we use our Software Parade. It is created and maintained with a level of fun and enthusiasm that is contagious. If you listen to this episode, you can feel the level of passion for coding and create great solutions that radiate from those involved in shaping the Software Parade. So we think it is safe to say that this is a fun way to create an overview of hundreds of applications.Software Parade is created on a Graph Database. A Graph Database relies on Graph Theory to store, map and query nodes, edges (also called graphs or relations), and properties to these. Since for the task at hand, the relations or graphs between objects are very important a Graph Database seems a good choice. We choose Neo4j to build the Software Parade.GuestsKristel Nieuwenhuys; Architect with services and applications for our internal organisation in her portfolio. Besides that, she is the product owner of one of our webshop teams.Ronald Willems; Responsible for the IT Architecture within the department Customer Service and Financial Operations at Bol.com. So for example for the Intent recognition, we talked about a few episodes back.Notes

We love to claim that we’re engineers and yet sometimes we have no clue how technology we use really works and what its limitations are… quite often because understanding those limitations would involve diving pretty deep into math (graphs, queuing and system reliability quickly come to mind).Read more ...

We love to claim that we’re engineers and yet sometimes we have no clue how technology we use really works and what its limitations are… quite often because understanding those limitations would involve diving pretty deep into math (graphs, queuing and system reliability quickly come to mind).Read more …

The boys design algorithms to generate puzzles on graphs, spiral out into some polar coordinate calculus and gallop their way through some juggling notation. Odds and Evenings Twitter - https://twitter.com/OddsAndEvenings Website - http://www.oddsandevenings.com Topics discussed Untangling Planar Graphs The Length of Spirals Juggling and Horse Gaits Show Notes A Picture of Untangle (http://www.gameclassification.com/files/games/Untangle.png) The Criteria for Planarity (https://en.wikipedia.org/wiki/Kuratowski%27s_theorem) The Two Spirals in Desmos (a graphing calculator) (https://www.desmos.com/calculator/xqqafvcbf9) Arc Length in Polar Form Derivation (http://sites.millersville.edu/bikenaga/calculus/arc-length-in-polar/arc-length-in-polar.html) Jakob Bernoulli's Gravestone (https://www.flickr.com/photos/deaddogbarking/4545328000) Horse Gaits on Wikipedia (https://en.wikipedia.org/wiki/Horse_gait) Negative Juggling Simulator (http://juggle.wikia.com/wiki/Impossible_Siteswap_Animator) Credits Hosted By Alaric - http://alaricstephen.com Alex - http://twitter.com/speakmouthwords Editing by Alex Rather fitting spiral anecdote by Alaric Theme music by David Russell - https://youtube.com/DavidRussell323

The boys move seemlessly between high philosophy and pirate jokes in this eclectic episode. Odds and Evenings Twitter - https://twitter.com/OddsAndEvenings Website - http://www.oddsandevenings.com Topics discussed Keypad Code Guessing Platonism vs Formalism Pirate Plunder Puzzle Show Notes Pirate Plunder Puzzle (http://www.alaricstephen.com/main-featured/2017/4/10/the-pirate-puzzle) Prisoners in Rainbow Hats (http://www.alaricstephen.com/main-featured/2017/6/13/prisoners-in-rainbow-hats) Z-Order Curves (https://en.wikipedia.org/wiki/Z-order_curve) Credits Hosted By Alaric - http://alaricstephen.com Alex - http://twitter.com/speakmouthwords Editing by Alex Dreams of being a philosopher pirate by Alaric Theme music by David Russell - https://youtube.com/DavidRussell323

Vaidehi loves graphs, and you will too! We end the season with an exploration of what they are, how to define them, and how they're related to discrete mathematics. Based on Vaidehi Joshi's blog post, "A Gentle Introduction to Graph Theory".

Introduction to Graph Theory Dover Books on Mathematics by Richard J. Trudeau Amazon: https://www.amazon.com/dp/0486678709 Kindle: https://www.amazon.com/dp/B00BX1DX9U Dover Publications: http://store.doverpublications.com/0486678709.html Readers: Brian Cobb Amy Unger Clint Shryock Justin Campbell Discussions: Preface, Chapter 1: Pure Mathematics Chapter 2: Graphs Chapter 3: Planar Graphs Chapter 4: Euler's Formula Chapter 5: Platonic Graphs Chapter 6: Coloring Chapter 7: The Genus of a Graph Chapter 8: Euler Walks and Hamilton Walks CS Book Club: Computer Science for Everyone Listen to these and more at http://www.csbookclub.com/ Music by http://bensound.com

Добрый день уважаемые слушатели. Представляем новый выпуск подкаста RWpod. В этом выпуске: Ruby Rails 5.1 adds support for limit in batch processing, Rails 5.1 adds delegate_missing_to и Taking screenshots of webpages using Ruby Practical Graph Theory in Ruby, 4 Ways to Secure Your Authentication System и Everything you should know about Ruby Splats Phoenix is better but Rails is more popular, DataTables (video) и RailsConf 2017 (videos) JavaScript Node v8.0.0, Npm v5.0.0 и The state of JavaScript modules REST 2.0 Is Here and Its Name Is GraphQL, Switching From React To Vue.js и How to Migrate from AngularJS to Vue React Native and the biggest pitfalls you will face using it, KharkivCSS #2, MoscowCSS #3 и MinskJS Meetup #2 Conferences Elixir Club Evening 1

Where did graphs come from? (Graph Theory History) In its simplest form, Graph Theory defines a graph as a construct made up of vertices, nodes, or points which are connected by edges, arcs, or lines.1 The connections may be directed, indicating a direction from one node to another, or undirected. Properties are attributes associated with […]

Introduction to Graph Theory Dover Books on Mathematics by Richard J. Trudeau Amazon: https://www.amazon.com/dp/0486678709 Kindle: https://www.amazon.com/dp/B00BX1DX9U Dover Publications: http://store.doverpublications.com/0486678709.html Readers: Brian Cobb Amy Unger Clint Shryock Justin Campbell Discussions: Preface, Chapter 1: Pure Mathematics Chapter 2: Graphs Chapter 3: Planar Graphs Chapter 4: Euler's Formula Chapter 5: Platonic Graphs Chapter 6: Coloring Chapter 7: The Genus of a Graph Chapter 8: Euler Walks and Hamilton Walks CS Book Club: Computer Science for Everyone Listen to these and more at http://www.csbookclub.com/ Music by http://bensound.com

Introduction to Graph Theory Dover Books on Mathematics by Richard J. Trudeau Amazon: https://www.amazon.com/dp/0486678709 Kindle: https://www.amazon.com/dp/B00BX1DX9U Dover Publications: http://store.doverpublications.com/0486678709.html Readers: Brian Cobb Amy Unger Clint Shryock Justin Campbell Discussions: Preface, Chapter 1: Pure Mathematics Chapter 2: Graphs Chapter 3: Planar Graphs Chapter 4: Euler's Formula Chapter 5: Platonic Graphs Chapter 6: Coloring Chapter 7: The Genus of a Graph Chapter 8: Euler Walks and Hamilton Walks CS Book Club: Computer Science for Everyone Listen to these and more at http://www.csbookclub.com/ Music by http://bensound.com

Introduction to Graph Theory Dover Books on Mathematics by Richard J. Trudeau Amazon: https://www.amazon.com/dp/0486678709 Kindle: https://www.amazon.com/dp/B00BX1DX9U Dover Publications: http://store.doverpublications.com/0486678709.html Readers: Brian Cobb Amy Unger Clint Shryock Justin Campbell Discussions: Preface, Chapter 1: Pure Mathematics Chapter 2: Graphs Chapter 3: Planar Graphs Chapter 4: Euler's Formula Chapter 5: Platonic Graphs Chapter 6: Coloring Chapter 7: The Genus of a Graph Chapter 8: Euler Walks and Hamilton Walks CS Book Club: Computer Science for Everyone Listen to these and more at http://www.csbookclub.com/ Music by http://bensound.com

Introduction to Graph Theory Dover Books on Mathematics by Richard J. Trudeau Amazon: https://www.amazon.com/dp/0486678709 Kindle: https://www.amazon.com/dp/B00BX1DX9U Dover Publications: http://store.doverpublications.com/0486678709.html Readers: Brian Cobb Amy Unger Clint Shryock Justin Campbell Discussions: Preface, Chapter 1: Pure Mathematics Chapter 2: Graphs Chapter 3: Planar Graphs Chapter 4: Euler's Formula Chapter 5: Platonic Graphs Chapter 6: Coloring Chapter 7: The Genus of a Graph Chapter 8: Euler Walks and Hamilton Walks CS Book Club: Computer Science for Everyone Listen to these and more at http://www.csbookclub.com/ Music by http://bensound.com

Introduction to Graph Theory Dover Books on Mathematics by Richard J. Trudeau Amazon: https://www.amazon.com/dp/0486678709 Kindle: https://www.amazon.com/dp/B00BX1DX9U Dover Publications: http://store.doverpublications.com/0486678709.html Readers: Brian Cobb Amy Unger Clint Shryock Justin Campbell Discussions: Preface, Chapter 1: Pure Mathematics Chapter 2: Graphs Chapter 3: Planar Graphs Chapter 4: Euler's Formula Chapter 5: Platonic Graphs Chapter 6: Coloring Chapter 7: The Genus of a Graph Chapter 8: Euler Walks and Hamilton Walks CS Book Club: Computer Science for Everyone Listen to these and more at http://www.csbookclub.com/ Music by http://bensound.com

Introduction to Graph Theory Dover Books on Mathematics by Richard J. Trudeau Amazon: https://www.amazon.com/dp/0486678709 Kindle: https://www.amazon.com/dp/B00BX1DX9U Dover Publications: http://store.doverpublications.com/0486678709.html Readers: Brian Cobb Amy Unger Clint Shryock Justin Campbell Discussions: Preface, Chapter 1: Pure Mathematics Chapter 2: Graphs Chapter 3: Planar Graphs Chapter 4: Euler's Formula Chapter 5: Platonic Graphs Chapter 6: Coloring Chapter 7: The Genus of a Graph Chapter 8: Euler Walks and Hamilton Walks CS Book Club: Computer Science for Everyone Listen to these and more at http://www.csbookclub.com/ Music by http://bensound.com

Introduction to Graph Theory Dover Books on Mathematics by Richard J. Trudeau Amazon: https://www.amazon.com/dp/0486678709 Kindle: https://www.amazon.com/dp/B00BX1DX9U Dover Publications: http://store.doverpublications.com/0486678709.html Readers: Brian Cobb Amy Unger Clint Shryock Justin Campbell Discussions: Preface, Chapter 1: Pure Mathematics Chapter 2: Graphs Chapter 3: Planar Graphs Chapter 4: Euler's Formula Chapter 5: Platonic Graphs Chapter 6: Coloring Chapter 7: The Genus of a Graph Chapter 8: Euler Walks and Hamilton Walks CS Book Club: Computer Science for Everyone Listen to these and more at http://www.csbookclub.com/ Music by http://bensound.com

It was early in the morning of New Years Day and Kelly had just bought a purse-load of psychedelic mushrooms from Laramie Wyoming's local "druggist." Kelly handed them out to the assembled company and took some himself. He felt a bit apathetic about the world. But when he went outside to look at the stars, he realized what he wanted more than anything else in the world...a book on combinatorics.

Materials Available here:https://media.defcon.org/DEF%20CON%2023/DEF%20CON%2023%20presentations/DEFCON-23-Atlas-Fun-With-Symboliks.pdf Fun with Symboliks atlas dude at Grimm Asking the hard questions... and getting answer! Oh binary, where art thine vulns? Symbolic analysis has been a "thing" for 20 years, and yet it's still left largely to the obscure and the academic researchers (and NASA). several years ago, Invisigoth incorporated the Symboliks subsystem into the Vivisect binary analysis framework. due to that inclusion, the very nature of binary analysis has been broken down, rethought, and arisen out of the ashes. this talk will give an introduction into Symboliks, Graph Theory, and the path forward for reverse engineering and vulnerability research, all from an interactive Python session or scripts. A four time winner of DEF CON capture the flag and retired captain of the team "1@stplace", over the past decade atlas has proved expertise in programmatic reverse-engineering, automated vulnerability discovery and exploitation, and braking into or out of nearly every type of computer system/subsystem. areas of specialty include exmpedded/IoT exploitation, power systems and industrial control systems exploitation, automotive exploitation, and client/server/application exploitation. Twitter: @at1as

@SamuraiT01さんとソーシャルネットワークやグラフについて話をしました。 オープンソースで学ぶ社会ネットワーク分析 2章グラフ理論スピード入門 グラフ理論(Graph Theory) ２０１１年 加古川東高等学校 理数科特別講座 ダイクストラ法（最短経路問題） 六次の隔たり

@SamuraiT01さんとソーシャルネットワークやグラフについて話をしました。

Interview with Keshav Pingali, professor of computer science, Institute for Computational Engineering and Sciences, University of Texas at Austin

This week Ashton and Christian kick off the new year by discussing plans to launch the first major cybersecurity research project on the development sandbox: mapping the internet with traceroute, distributed Nmap, and Hadoop. We discuss strategies and design challenges that will be present early on in development and potential solutions that may be employed. Furthermore, we tie back the project to the latest developments in the North Korea cyber fiasco with Sony Corporation. Cyber Frontiers is all about Exploring Cyber security, Big Data, and the Technologies Shaping the Future Through an Academic Perspective!   Christian Johnson, a student at the

This week Ashton and Christian kick off the new year by discussing plans to launch the first major cybersecurity research project on the development sandbox: mapping the internet with traceroute, distributed Nmap, and Hadoop. We discuss strategies and design challenges that will be present early on in development and potential solutions that may be employed. Furthermore, we tie back the project to the latest developments in the North Korea cyber fiasco with Sony Corporation. Cyber Frontiers is all about Exploring Cyber security, Big Data, and the Technologies Shaping the Future Through an Academic Perspective!   Christian Johnson, a student at the

This week Ashton and Christian kick off the new year by discussing plans to launch the first major cybersecurity research project on the development sandbox: mapping the internet with traceroute, distributed Nmap, and Hadoop. We discuss strategies and design challenges that will be present early on in development and potential solutions that may be employed. Furthermore, we tie back the project to the latest developments in the North Korea cyber fiasco with Sony Corporation. Cyber Frontiers is all about Exploring Cyber security, Big Data, and the Technologies Shaping the Future Through an Academic Perspective!   Christian Johnson, a student at the

In this episode, we talk about cellular automata - including the Game of Life - and graph theory, and interviewed Jonathan Crofts from Nottingham Trent University about his research on complex networks in neuroscience. Show notes and more episodes via www.furthermaths.org.uk/podcasts

Robin Wilson is an Emeritus Professor of Pure Mathematics at the Open University, Emeritus Professor of Geometry at Gresham College, London, and a former Fellow of Keble College, Oxford. He is currently President of the British Society for the History of Mathematics. He has written and edited many books on graph theory, including Introduction to Graph Theory and Four Colours Suffice, and on the history of mathematics, including Lewis Carroll in Numberland. He is involved with the popularization and communication of mathematics and its history, and has been awarded prizes by the Mathematical Association of America for ‘outstanding expository writing’.

Westminster College's Faires Faculty Forum presentations will take place every Wednesday at 11:45 a.m. in Mueller Theater of McKelvey Campus Center (MCC).

This classic GameTek was originally broadcast during a Dice Tower episode on Train Games. Geoff explores the connections (get it - connections?) between creating rail networks and the branch of mathematics called Graph Theory. 5' 35"

Sheely, Stokes, and Wrather discuss Friday Night Lights, Nirvana, Graph Theory, and Christian Hardcore Punk. Episode 57: Freude originally appeared on Overthinking It, the site subjecting the popular culture to a level of scrutiny it probably doesn't deserve. [Latest Posts | Podcast (iTunes Link)]

Professor Robert Aldred, Department of Mathematics and Statistics, Division of Science. Inaugural Professorial Lecture, given on April 7, 2011. Held November 24, 2010.

Professor Robert Aldred, Department of Mathematics and Statistics, Division of Science. Inaugural Professorial Lecture, given on April 7, 2011. Held November 24, 2010.

Professor Robert Aldred, Department of Mathematics and Statistics, Division of Science. Inaugural Professorial Lecture, given on April 7, 2011. Held November 24, 2010.

Professor Robert Aldred, Department of Mathematics and Statistics, Division of Science. Inaugural Professorial Lecture, given on April 7, 2011. Held November 24, 2010.

The global information space provided by the World Wide Web has changed dramatically the way knowledge is shared all over the world. To make this unbelievable huge information space accessible, search engines index the uploaded contents and provide efficient algorithmic machinery for ranking the importance of documents with respect to an input query. All major search engines such as Google, Yahoo or Bing are keyword-based, which is indisputable a very powerful tool for accessing information needs centered around documents. However, this unstructured, document-oriented paradigm of the World Wide Web has serious drawbacks, when searching for specific knowledge about real-world entities. When asking for advanced facts about entities, today's search engines are not very good in providing accurate answers. Hand-built knowledge bases such as Wikipedia or its structured counterpart DBpedia are excellent sources that provide common facts. However, these knowledge bases are far from being complete and most of the knowledge lies still buried in unstructured documents. Statistical machine learning methods have the great potential to help to bridge the gap between text and knowledge by (semi-)automatically transforming the unstructured representation of the today's World Wide Web to a more structured representation. This thesis is devoted to reduce this gap with Probabilistic Graphical Models. Probabilistic Graphical Models play a crucial role in modern pattern recognition as they merge two important fields of applied mathematics: Graph Theory and Probability Theory. The first part of the thesis will present a novel system called Text2SemRel that is able to (semi-)automatically construct knowledge bases from textual document collections. The resulting knowledge base consists of facts centered around entities and their relations. Essential part of the system is a novel algorithm for extracting relations between entity mentions that is based on Conditional Random Fields, which are Undirected Probabilistic Graphical Models. In the second part of the thesis, we will use the power of Directed Probabilistic Graphical Models to solve important knowledge discovery tasks in semantically annotated large document collections. In particular, we present extensions of the Latent Dirichlet Allocation framework that are able to learn in an unsupervised way the statistical semantic dependencies between unstructured representations such as documents and their semantic annotations. Semantic annotations of documents might refer to concepts originating from a thesaurus or ontology but also to user-generated informal tags in social tagging systems. These forms of annotations represent a first step towards the conversion to a more structured form of the World Wide Web. In the last part of the thesis, we prove the large-scale applicability of the proposed fact extraction system Text2SemRel. In particular, we extract semantic relations between genes and diseases from a large biomedical textual repository. The resulting knowledge base contains far more potential disease genes exceeding the number of disease genes that are currently stored in curated databases. Thus, the proposed system is able to unlock knowledge currently buried in the literature. The literature-derived human gene-disease network is subject of further analysis with respect to existing curated state of the art databases. We analyze the derived knowledge base quantitatively by comparing it with several curated databases with regard to size of the databases and properties of known disease genes among other things. Our experimental analysis shows that the facts extracted from the literature are of high quality.