Podcasts about Colvin

Gene and Chris present Fortean investigator Andrew B. Colvin, author of “The Mothman Speaks: Candid Conversations Concerning Cosmic Conundrums – Cryptic Creatures, Chimeras, Contactees, and the Cleverly Coded Coincidences of the Collective Unconscious (Volume 1).” You'll learn about Mothman encounters, military/UFO disinformation and other fascinating subjects, not to mention Andrew's personal UFO sighting report, which includes photographic evidence.Become a supporter of this podcast: https://www.spreaker.com/podcast/the-paracast-the-gold-standard-of-paranormal-radio--6203433/support.

Details of this years ANZAC Day services. See omnystudio.com/listener for privacy information.

Bryan Barrett talks with Edward Jones financial advisor Lee Colvin about some best ways to pay down debt.

A look at Cam Heide's departure and the portal in general

Bryan Barrett talks with Edward Jones financial advisor Lee Colvin and shares some financial spring cleaning tips.  

00:00 – 13:17 – Purdue tops High Point and advances to take on McNeese, Pacers beat the Nets in an ugly but wild game, best game of Day 1 of March Madness 13:18 – 23:38 – Morning Checkdown 23:39 – 41:37 – Purdue survives High Point, Trey Kaufman-Renn’s fouls, getting ready for McNeese, Pacers win again and Bennedict Mathurin’s post-game interview was outstanding, concern for Haliburton’s back injury? 41:38 – 55:37 – ISC’s Greg Rakestraw joins us and discusses the best semi-state game this weekend, his hoops thoughts, not filling out a bracket this year, the Indy Eleven/USL, the Darian DeVries hire and what it could look like, Purdue’s win over High Point, Morning Checkdown 55:38 – 1:02:43 – Can Purdue avoid an upset against McNeese and punch a ticket to the Sweet 16 in Lucas Oil Stadium?, IU/Purdue rivalry is alive and well, Day 2 of the tournament 1:02:44 – 1:12:27 – Pacers potential, an insane Pacers stat 1:12:28 – 1:35:21– Purdue’s Bobby Riddell joins us to discuss Purdue’s win over High Point yesterday, the Colvin dunk, the best dunker he played with, previewing McNeese, what the rest of his day looks like, Day 2 matchups we’re most interested in, trampoline parks, Morning CheckdowN 1:35:22 – 1:46:03 – A couple double-digit seeds favored today, we play I GOTTA KNOW: Have the Colts done enough to make you want to buy season tickets, what sport do you wish you were better at 1:46:04 – 1:52:10 – POP QUIZ, wrapping the showSupport the show: https://1075thefan.com/the-wake-up-call-1075-the-fan/See omnystudio.com/listener for privacy information.

00:00 – 13:18 – Purdue tops High Point and advances to take on McNeese, Pacers beat the Nets in an ugly but wild game, best game of Day 1 of March Madness 13:19 – 24:52 – ISC’s Greg Rakestraw joins us and discusses the best semi-state game this weekend, his hoops thoughts, not filling out a bracket this year, the Indy Eleven/USL 24:53 – 38:58 – Purdue’s Bobby Riddell joins us to discuss Purdue’s win over High Point yesterday, the Colvin dunk, the best dunker he played with, previewing McNeese, what the rest of his day looks like 38:59- 49:38 - We play I GOTTA KNOW: Have the Colts done enough to make you want to buy season tickets, what sport do you wish you were better at, rather win a bracket pool or fantasy football, best impressions we can doSupport the show: https://1075thefan.com/the-wake-up-call-1075-the-fan/See omnystudio.com/listener for privacy information.

Today Jon tells Julio and Ryan about Indrid Cold.sources -Derenberger, W. W., Derenberger-Bowman, T., Colvin, A., Keel, J., & Barker, G. (n.d.). Visitors from Lanulos: My Contact with Indrid Cold.

Our guest is Samuel Colvin, Co-Founder and CEO of pydantic: a data validation library with millions of downloads per month. In our conversation, we first discuss Pydantic and their observability platform: logfire. We then dive into agents where Samuel shares his vision on how to build production ready agents and what makes PydanticAI different than other frameworks. If you enjoyed the episode, please leave a 5 star review and subscribe to the AI Stories Youtube channel.

Jack is joined in studio by Lexington's Commissioner of Public Safety Ken Armstrong and his special guest Chief of Corrections Scott Colvin to talk about the recent shootings around town and gun safety courses being taught in schools on #LEX. See omnystudio.com/listener for privacy information.

Bryan Barrett talks with Edward Jones financial advisor Lee Colvin about key decisions to consider for retired couples.

Am 2.3.1955 weigert sich Colvin, ihren Sitzplatz einer Weißen zu überlassen. Die 15-Jährige zieht als erste gegen Rassentrennung im Bus vor Gericht - und gewinnt am Ende. Von Laura Dresch.

Extracurricular activities do more than fill students' schedules—they shape their futures. Hosts Angela Arledge and Lisa Pogue explore how sports, Their guest, Coach Drew Colvin, head football coach at Royse City High School, shares his journey, coaching philosophy, and how he's developing a culture of grit and growth. Tune in for an inspiring conversation on leadership, resilience, and what it takes to build champions on and off the field.For more highlights of this episode, check out our show notes.

Extracurricular activities do more than fill students' schedules—they shape their futures. Hosts Angela Arledge and Lisa Pogue explore how sports, Their guest, Coach Drew Colvin, head football coach at Royse City High School, shares his journey, coaching philosophy, and how he's developing a culture of grit and growth. Tune in for an inspiring conversation on leadership, resilience, and what it takes to build champions on and off the field.For more highlights of this episode, check out our show notes.

The 16th Conference in Division 2 Football! Conference Carolinas commissioner Chris Colvin joins the show ahead of their first season sponsoring football in 51 years, and talks about what it means for their member schools and the D2 landscape as a whole. Kobe reacts to a newly announced Week 0 game between Virginia Union and Miles College in Canton Ohio, and a former D3 player signing to a new NFL team. TUNE IN!Video Chapters:0:00 Episode Overview2:09 Chris Colvin - Conference Carolinas24:36 Hall of Fame Classic Week 0 Matchup26:25 Virginia Union Panthers33:58 Miles College Golden Bears37:44 Thoughts on Matchup39:57 D3 Player signs with NFL Team - Anthony Kendall & Cleveland Browns

Episode 214 of The Business Development Podcast features Brett Colvin, CEO and co-founder of GoodLawyer, who is on a mission to fix what's broken in the legal industry. Brett shares his journey from corporate lawyer at one of Canada's largest firms to disrupting the status quo with a smarter, more accessible legal model. He explains how traditional law firms operate on outdated structures that leave both businesses and lawyers frustrated—leading to a staggering statistic that half of all lawyers leave the profession within five years. With GoodLawyer, Brett is pioneering fractional legal services, offering businesses high-quality, in-house-style legal support without the overwhelming costs of big firms.In this episode, Brett and I dive into why the current legal system doesn't work for most entrepreneurs, the biggest legal blind spots that hurt growing businesses, and how his company is giving both lawyers and clients more flexibility and value. We discuss the power of transparent pricing, the challenges of building a legal tech startup, and why Brett believes the future of legal is changing fast. If you're a business owner, startup founder, or just someone who has struggled with legal services, this episode will give you insights into a better way forward.Key Takeaways: 1. The traditional legal model is outdated and doesn't serve entrepreneurs effectively. 2. Half of all lawyers leave the profession within five years due to burnout and lack of fulfillment. 3. Law firms take 80% of the value from billable hours, leaving both lawyers and clients frustrated. 4. Fractional legal services provide businesses with in-house-style legal support at a fraction of the cost. 5. Transparent, upfront pricing removes friction and helps businesses make smarter legal decisions. 6. Lawyers can make more money and have better work-life balance outside of traditional law firms. 7. A strong legal foundation early on prevents costly mistakes for startups and small businesses. 8. Innovation in the legal industry is possible but requires breaking away from outdated structures. 9. Business owners often don't realize they need legal help until it's too late. 10. The future of law is changing fast, and entrepreneurs need flexible legal solutions to keep up.

Bryan Barrett talks with Edward Jones financial advisor Lee Colvin about what to know before "reversing" your retirement.

Did you know that adding a simple Code Interpreter took o3 from 9.2% to 32% on FrontierMath? The Latent Space crew is hosting a hack night Feb 11th in San Francisco focused on CodeGen use cases, co-hosted with E2B and Edge AGI; watch E2B's new workshop and RSVP here!We're happy to announce that today's guest Samuel Colvin will be teaching his very first Pydantic AI workshop at the newly announced AI Engineer NYC Workshops day on Feb 22! 25 tickets left.If you're a Python developer, it's very likely that you've heard of Pydantic. Every month, it's downloaded >300,000,000 times, making it one of the top 25 PyPi packages. OpenAI uses it in its SDK for structured outputs, it's at the core of FastAPI, and if you've followed our AI Engineer Summit conference, Jason Liu of Instructor has given two great talks about it: “Pydantic is all you need” and “Pydantic is STILL all you need”. Now, Samuel Colvin has raised $17M from Sequoia to turn Pydantic from an open source project to a full stack AI engineer platform with Logfire, their observability platform, and PydanticAI, their new agent framework.Logfire: bringing OTEL to AIOpenTelemetry recently merged Semantic Conventions for LLM workloads which provides standard definitions to track performance like gen_ai.server.time_per_output_token. In Sam's view at least 80% of new apps being built today have some sort of LLM usage in them, and just like web observability platform got replaced by cloud-first ones in the 2010s, Logfire wants to do the same for AI-first apps. If you're interested in the technical details, Logfire migrated away from Clickhouse to Datafusion for their backend. We spent some time on the importance of picking open source tools you understand and that you can actually contribute to upstream, rather than the more popular ones; listen in ~43:19 for that part.Agents are the killer app for graphsPydantic AI is their attempt at taking a lot of the learnings that LangChain and the other early LLM frameworks had, and putting Python best practices into it. At an API level, it's very similar to the other libraries: you can call LLMs, create agents, do function calling, do evals, etc.They define an “Agent” as a container with a system prompt, tools, structured result, and an LLM. Under the hood, each Agent is now a graph of function calls that can orchestrate multi-step LLM interactions. You can start simple, then move toward fully dynamic graph-based control flow if needed.“We were compelled enough by graphs once we got them right that our agent implementation [...] is now actually a graph under the hood.”Why Graphs?* More natural for complex or multi-step AI workflows.* Easy to visualize and debug with mermaid diagrams.* Potential for distributed runs, or “waiting days” between steps in certain flows.In parallel, you see folks like Emil Eifrem of Neo4j talk about GraphRAG as another place where graphs fit really well in the AI stack, so it might be time for more people to take them seriously.Full Video EpisodeLike and subscribe!Chapters* 00:00:00 Introductions* 00:00:24 Origins of Pydantic* 00:05:28 Pydantic's AI moment * 00:08:05 Why build a new agents framework?* 00:10:17 Overview of Pydantic AI* 00:12:33 Becoming a believer in graphs* 00:24:02 God Model vs Compound AI Systems* 00:28:13 Why not build an LLM gateway?* 00:31:39 Programmatic testing vs live evals* 00:35:51 Using OpenTelemetry for AI traces* 00:43:19 Why they don't use Clickhouse* 00:48:34 Competing in the observability space* 00:50:41 Licensing decisions for Pydantic and LogFire* 00:51:48 Building Pydantic.run* 00:55:24 Marimo and the future of Jupyter notebooks* 00:57:44 London's AI sceneShow Notes* Sam Colvin* Pydantic* Pydantic AI* Logfire* Pydantic.run* Zod* E2B* Arize* Langsmith* Marimo* Prefect* GLA (Google Generative Language API)* OpenTelemetry* Jason Liu* Sebastian Ramirez* Bogomil Balkansky* Hood Chatham* Jeremy Howard* Andrew LambTranscriptAlessio [00:00:03]: Hey, everyone. Welcome to the Latent Space podcast. This is Alessio, partner and CTO at Decibel Partners, and I'm joined by my co-host Swyx, founder of Smol AI.Swyx [00:00:12]: Good morning. And today we're very excited to have Sam Colvin join us from Pydantic AI. Welcome. Sam, I heard that Pydantic is all we need. Is that true?Samuel [00:00:24]: I would say you might need Pydantic AI and Logfire as well, but it gets you a long way, that's for sure.Swyx [00:00:29]: Pydantic almost basically needs no introduction. It's almost 300 million downloads in December. And obviously, in the previous podcasts and discussions we've had with Jason Liu, he's been a big fan and promoter of Pydantic and AI.Samuel [00:00:45]: Yeah, it's weird because obviously I didn't create Pydantic originally for uses in AI, it predates LLMs. But it's like we've been lucky that it's been picked up by that community and used so widely.Swyx [00:00:58]: Actually, maybe we'll hear it. Right from you, what is Pydantic and maybe a little bit of the origin story?Samuel [00:01:04]: The best name for it, which is not quite right, is a validation library. And we get some tension around that name because it doesn't just do validation, it will do coercion by default. We now have strict mode, so you can disable that coercion. But by default, if you say you want an integer field and you get in a string of 1, 2, 3, it will convert it to 123 and a bunch of other sensible conversions. And as you can imagine, the semantics around it. Exactly when you convert and when you don't, it's complicated, but because of that, it's more than just validation. Back in 2017, when I first started it, the different thing it was doing was using type hints to define your schema. That was controversial at the time. It was genuinely disapproved of by some people. I think the success of Pydantic and libraries like FastAPI that build on top of it means that today that's no longer controversial in Python. And indeed, lots of other people have copied that route, but yeah, it's a data validation library. It uses type hints for the for the most part and obviously does all the other stuff you want, like serialization on top of that. But yeah, that's the core.Alessio [00:02:06]: Do you have any fun stories on how JSON schemas ended up being kind of like the structure output standard for LLMs? And were you involved in any of these discussions? Because I know OpenAI was, you know, one of the early adopters. So did they reach out to you? Was there kind of like a structure output console in open source that people were talking about or was it just a random?Samuel [00:02:26]: No, very much not. So I originally. Didn't implement JSON schema inside Pydantic and then Sebastian, Sebastian Ramirez, FastAPI came along and like the first I ever heard of him was over a weekend. I got like 50 emails from him or 50 like emails as he was committing to Pydantic, adding JSON schema long pre version one. So the reason it was added was for OpenAPI, which is obviously closely akin to JSON schema. And then, yeah, I don't know why it was JSON that got picked up and used by OpenAI. It was obviously very convenient for us. That's because it meant that not only can you do the validation, but because Pydantic will generate you the JSON schema, it will it kind of can be one source of source of truth for structured outputs and tools.Swyx [00:03:09]: Before we dive in further on the on the AI side of things, something I'm mildly curious about, obviously, there's Zod in JavaScript land. Every now and then there is a new sort of in vogue validation library that that takes over for quite a few years and then maybe like some something else comes along. Is Pydantic? Is it done like the core Pydantic?Samuel [00:03:30]: I've just come off a call where we were redesigning some of the internal bits. There will be a v3 at some point, which will not break people's code half as much as v2 as in v2 was the was the massive rewrite into Rust, but also fixing all the stuff that was broken back from like version zero point something that we didn't fix in v1 because it was a side project. We have plans to move some of the basically store the data in Rust types after validation. Not completely. So we're still working to design the Pythonic version of it, in order for it to be able to convert into Python types. So then if you were doing like validation and then serialization, you would never have to go via a Python type we reckon that can give us somewhere between three and five times another three to five times speed up. That's probably the biggest thing. Also, like changing how easy it is to basically extend Pydantic and define how particular types, like for example, NumPy arrays are validated and serialized. But there's also stuff going on. And for example, Jitter, the JSON library in Rust that does the JSON parsing, has SIMD implementation at the moment only for AMD64. So we can add that. We need to go and add SIMD for other instruction sets. So there's a bunch more we can do on performance. I don't think we're going to go and revolutionize Pydantic, but it's going to continue to get faster, continue, hopefully, to allow people to do more advanced things. We might add a binary format like CBOR for serialization for when you'll just want to put the data into a database and probably load it again from Pydantic. So there are some things that will come along, but for the most part, it should just get faster and cleaner.Alessio [00:05:04]: From a focus perspective, I guess, as a founder too, how did you think about the AI interest rising? And then how do you kind of prioritize, okay, this is worth going into more, and we'll talk about Pydantic AI and all of that. What was maybe your early experience with LLAMP, and when did you figure out, okay, this is something we should take seriously and focus more resources on it?Samuel [00:05:28]: I'll answer that, but I'll answer what I think is a kind of parallel question, which is Pydantic's weird, because Pydantic existed, obviously, before I was starting a company. I was working on it in my spare time, and then beginning of 22, I started working on the rewrite in Rust. And I worked on it full-time for a year and a half, and then once we started the company, people came and joined. And it was a weird project, because that would never go away. You can't get signed off inside a startup. Like, we're going to go off and three engineers are going to work full-on for a year in Python and Rust, writing like 30,000 lines of Rust just to release open-source-free Python library. The result of that has been excellent for us as a company, right? As in, it's made us remain entirely relevant. And it's like, Pydantic is not just used in the SDKs of all of the AI libraries, but I can't say which one, but one of the big foundational model companies, when they upgraded from Pydantic v1 to v2, their number one internal model... The metric of performance is time to first token. That went down by 20%. So you think about all of the actual AI going on inside, and yet at least 20% of the CPU, or at least the latency inside requests was actually Pydantic, which shows like how widely it's used. So we've benefited from doing that work, although it didn't, it would have never have made financial sense in most companies. In answer to your question about like, how do we prioritize AI, I mean, the honest truth is we've spent a lot of the last year and a half building. Good general purpose observability inside LogFire and making Pydantic good for general purpose use cases. And the AI has kind of come to us. Like we just, not that we want to get away from it, but like the appetite, uh, both in Pydantic and in LogFire to go and build with AI is enormous because it kind of makes sense, right? Like if you're starting a new greenfield project in Python today, what's the chance that you're using GenAI 80%, let's say, globally, obviously it's like a hundred percent in California, but even worldwide, it's probably 80%. Yeah. And so everyone needs that stuff. And there's so much yet to be figured out so much like space to do things better in the ecosystem in a way that like to go and implement a database that's better than Postgres is a like Sisyphean task. Whereas building, uh, tools that are better for GenAI than some of the stuff that's about now is not very difficult. Putting the actual models themselves to one side.Alessio [00:07:40]: And then at the same time, then you released Pydantic AI recently, which is, uh, um, you know, agent framework and early on, I would say everybody like, you know, Langchain and like, uh, Pydantic kind of like a first class support, a lot of these frameworks, we're trying to use you to be better. What was the decision behind we should do our own framework? Were there any design decisions that you disagree with any workloads that you think people didn't support? Well,Samuel [00:08:05]: it wasn't so much like design and workflow, although I think there were some, some things we've done differently. Yeah. I think looking in general at the ecosystem of agent frameworks, the engineering quality is far below that of the rest of the Python ecosystem. There's a bunch of stuff that we have learned how to do over the last 20 years of building Python libraries and writing Python code that seems to be abandoned by people when they build agent frameworks. Now I can kind of respect that, particularly in the very first agent frameworks, like Langchain, where they were literally figuring out how to go and do this stuff. It's completely understandable that you would like basically skip some stuff.Samuel [00:08:42]: I'm shocked by the like quality of some of the agent frameworks that have come out recently from like well-respected names, which it just seems to be opportunism and I have little time for that, but like the early ones, like I think they were just figuring out how to do stuff and just as lots of people have learned from Pydantic, we were able to learn a bit from them. I think from like the gap we saw and the thing we were frustrated by was the production readiness. And that means things like type checking, even if type checking makes it hard. Like Pydantic AI, I will put my hand up now and say it has a lot of generics and you need to, it's probably easier to use it if you've written a bit of Rust and you really understand generics, but like, and that is, we're not claiming that that makes it the easiest thing to use in all cases, we think it makes it good for production applications in big systems where type checking is a no-brainer in Python. But there are also a bunch of stuff we've learned from maintaining Pydantic over the years that we've gone and done. So every single example in Pydantic AI's documentation is run on Python. As part of tests and every single print output within an example is checked during tests. So it will always be up to date. And then a bunch of things that, like I say, are standard best practice within the rest of the Python ecosystem, but I'm not followed surprisingly by some AI libraries like coverage, linting, type checking, et cetera, et cetera, where I think these are no-brainers, but like weirdly they're not followed by some of the other libraries.Alessio [00:10:04]: And can you just give an overview of the framework itself? I think there's kind of like the. LLM calling frameworks, there are the multi-agent frameworks, there's the workflow frameworks, like what does Pydantic AI do?Samuel [00:10:17]: I glaze over a bit when I hear all of the different sorts of frameworks, but I like, and I will tell you when I built Pydantic, when I built Logfire and when I built Pydantic AI, my methodology is not to go and like research and review all of the other things. I kind of work out what I want and I go and build it and then feedback comes and we adjust. So the fundamental building block of Pydantic AI is agents. The exact definition of agents and how you want to define them. is obviously ambiguous and our things are probably sort of agent-lit, not that we would want to go and rename them to agent-lit, but like the point is you probably build them together to build something and most people will call an agent. So an agent in our case has, you know, things like a prompt, like system prompt and some tools and a structured return type if you want it, that covers the vast majority of cases. There are situations where you want to go further and the most complex workflows where you want graphs and I resisted graphs for quite a while. I was sort of of the opinion you didn't need them and you could use standard like Python flow control to do all of that stuff. I had a few arguments with people, but I basically came around to, yeah, I can totally see why graphs are useful. But then we have the problem that by default, they're not type safe because if you have a like add edge method where you give the names of two different edges, there's no type checking, right? Even if you go and do some, I'm not, not all the graph libraries are AI specific. So there's a, there's a graph library called, but it allows, it does like a basic runtime type checking. Ironically using Pydantic to try and make up for the fact that like fundamentally that graphs are not typed type safe. Well, I like Pydantic, but it did, that's not a real solution to have to go and run the code to see if it's safe. There's a reason that starting type checking is so powerful. And so we kind of, from a lot of iteration eventually came up with a system of using normally data classes to define nodes where you return the next node you want to call and where we're able to go and introspect the return type of a node to basically build the graph. And so the graph is. Yeah. Inherently type safe. And once we got that right, I, I wasn't, I'm incredibly excited about graphs. I think there's like masses of use cases for them, both in gen AI and other development, but also software's all going to have interact with gen AI, right? It's going to be like web. There's no longer be like a web department in a company is that there's just like all the developers are building for web building with databases. The same is going to be true for gen AI.Alessio [00:12:33]: Yeah. I see on your docs, you call an agent, a container that contains a system prompt function. Tools, structure, result, dependency type model, and then model settings. Are the graphs in your mind, different agents? Are they different prompts for the same agent? What are like the structures in your mind?Samuel [00:12:52]: So we were compelled enough by graphs once we got them right, that we actually merged the PR this morning. That means our agent implementation without changing its API at all is now actually a graph under the hood as it is built using our graph library. So graphs are basically a lower level tool that allow you to build these complex workflows. Our agents are technically one of the many graphs you could go and build. And we just happened to build that one for you because it's a very common, commonplace one. But obviously there are cases where you need more complex workflows where the current agent assumptions don't work. And that's where you can then go and use graphs to build more complex things.Swyx [00:13:29]: You said you were cynical about graphs. What changed your mind specifically?Samuel [00:13:33]: I guess people kept giving me examples of things that they wanted to use graphs for. And my like, yeah, but you could do that in standard flow control in Python became a like less and less compelling argument to me because I've maintained those systems that end up with like spaghetti code. And I could see the appeal of this like structured way of defining the workflow of my code. And it's really neat that like just from your code, just from your type hints, you can get out a mermaid diagram that defines exactly what can go and happen.Swyx [00:14:00]: Right. Yeah. You do have very neat implementation of sort of inferring the graph from type hints, I guess. Yeah. Is what I would call it. Yeah. I think the question always is I have gone back and forth. I used to work at Temporal where we would actually spend a lot of time complaining about graph based workflow solutions like AWS step functions. And we would actually say that we were better because you could use normal control flow that you already knew and worked with. Yours, I guess, is like a little bit of a nice compromise. Like it looks like normal Pythonic code. But you just have to keep in mind what the type hints actually mean. And that's what we do with the quote unquote magic that the graph construction does.Samuel [00:14:42]: Yeah, exactly. And if you look at the internal logic of actually running a graph, it's incredibly simple. It's basically call a node, get a node back, call that node, get a node back, call that node. If you get an end, you're done. We will add in soon support for, well, basically storage so that you can store the state between each node that's run. And then the idea is you can then distribute the graph and run it across computers. And also, I mean, the other weird, the other bit that's really valuable is across time. Because it's all very well if you look at like lots of the graph examples that like Claude will give you. If it gives you an example, it gives you this lovely enormous mermaid chart of like the workflow, for example, managing returns if you're an e-commerce company. But what you realize is some of those lines are literally one function calls another function. And some of those lines are wait six days for the customer to print their like piece of paper and put it in the post. And if you're writing like your demo. Project or your like proof of concept, that's fine because you can just say, and now we call this function. But when you're building when you're in real in real life, that doesn't work. And now how do we manage that concept to basically be able to start somewhere else in the in our code? Well, this graph implementation makes it incredibly easy because you just pass the node that is the start point for carrying on the graph and it continues to run. So it's things like that where I was like, yeah, I can just imagine how things I've done in the past would be fundamentally easier to understand if we had done them with graphs.Swyx [00:16:07]: You say imagine, but like right now, this pedantic AI actually resume, you know, six days later, like you said, or is this just like a theoretical thing we can go someday?Samuel [00:16:16]: I think it's basically Q&A. So there's an AI that's asking the user a question and effectively you then call the CLI again to continue the conversation. And it basically instantiates the node and calls the graph with that node again. Now, we don't have the logic yet for effectively storing state in the database between individual nodes that we're going to add soon. But like the rest of it is basically there.Swyx [00:16:37]: It does make me think that not only are you competing with Langchain now and obviously Instructor, and now you're going into sort of the more like orchestrated things like Airflow, Prefect, Daxter, those guys.Samuel [00:16:52]: Yeah, I mean, we're good friends with the Prefect guys and Temporal have the same investors as us. And I'm sure that my investor Bogomol would not be too happy if I was like, oh, yeah, by the way, as well as trying to take on Datadog. We're also going off and trying to take on Temporal and everyone else doing that. Obviously, we're not doing all of the infrastructure of deploying that right yet, at least. We're, you know, we're just building a Python library. And like what's crazy about our graph implementation is, sure, there's a bit of magic in like introspecting the return type, you know, extracting things from unions, stuff like that. But like the actual calls, as I say, is literally call a function and get back a thing and call that. It's like incredibly simple and therefore easy to maintain. The question is, how useful is it? Well, I don't know yet. I think we have to go and find out. We have a whole. We've had a slew of people joining our Slack over the last few days and saying, tell me how good Pydantic AI is. How good is Pydantic AI versus Langchain? And I refuse to answer. That's your job to go and find that out. Not mine. We built a thing. I'm compelled by it, but I'm obviously biased. The ecosystem will work out what the useful tools are.Swyx [00:17:52]: Bogomol was my board member when I was at Temporal. And I think I think just generally also having been a workflow engine investor and participant in this space, it's a big space. Like everyone needs different functions. I think the one thing that I would say like yours, you know, as a library, you don't have that much control of it over the infrastructure. I do like the idea that each new agents or whatever or unit of work, whatever you call that should spin up in this sort of isolated boundaries. Whereas yours, I think around everything runs in the same process. But you ideally want to sort of spin out its own little container of things.Samuel [00:18:30]: I agree with you a hundred percent. And we will. It would work now. Right. As in theory, you're just like as long as you can serialize the calls to the next node, you just have to all of the different containers basically have to have the same the same code. I mean, I'm super excited about Cloudflare workers running Python and being able to install dependencies. And if Cloudflare could only give me my invitation to the private beta of that, we would be exploring that right now because I'm super excited about that as a like compute level for some of this stuff where exactly what you're saying, basically. You can run everything as an individual. Like worker function and distribute it. And it's resilient to failure, et cetera, et cetera.Swyx [00:19:08]: And it spins up like a thousand instances simultaneously. You know, you want it to be sort of truly serverless at once. Actually, I know we have some Cloudflare friends who are listening, so hopefully they'll get in front of the line. Especially.Samuel [00:19:19]: I was in Cloudflare's office last week shouting at them about other things that frustrate me. I have a love-hate relationship with Cloudflare. Their tech is awesome. But because I use it the whole time, I then get frustrated. So, yeah, I'm sure I will. I will. I will get there soon.Swyx [00:19:32]: There's a side tangent on Cloudflare. Is Python supported at full? I actually wasn't fully aware of what the status of that thing is.Samuel [00:19:39]: Yeah. So Pyodide, which is Python running inside the browser in scripting, is supported now by Cloudflare. They basically, they're having some struggles working out how to manage, ironically, dependencies that have binaries, in particular, Pydantic. Because these workers where you can have thousands of them on a given metal machine, you don't want to have a difference. You basically want to be able to have a share. Shared memory for all the different Pydantic installations, effectively. That's the thing they work out. They're working out. But Hood, who's my friend, who is the primary maintainer of Pyodide, works for Cloudflare. And that's basically what he's doing, is working out how to get Python running on Cloudflare's network.Swyx [00:20:19]: I mean, the nice thing is that your binary is really written in Rust, right? Yeah. Which also compiles the WebAssembly. Yeah. So maybe there's a way that you'd build... You have just a different build of Pydantic and that ships with whatever your distro for Cloudflare workers is.Samuel [00:20:36]: Yes, that's exactly what... So Pyodide has builds for Pydantic Core and for things like NumPy and basically all of the popular binary libraries. Yeah. It's just basic. And you're doing exactly that, right? You're using Rust to compile the WebAssembly and then you're calling that shared library from Python. And it's unbelievably complicated, but it works. Okay.Swyx [00:20:57]: Staying on graphs a little bit more, and then I wanted to go to some of the other features that you have in Pydantic AI. I see in your docs, there are sort of four levels of agents. There's single agents, there's agent delegation, programmatic agent handoff. That seems to be what OpenAI swarms would be like. And then the last one, graph-based control flow. Would you say that those are sort of the mental hierarchy of how these things go?Samuel [00:21:21]: Yeah, roughly. Okay.Swyx [00:21:22]: You had some expression around OpenAI swarms. Well.Samuel [00:21:25]: And indeed, OpenAI have got in touch with me and basically, maybe I'm not supposed to say this, but basically said that Pydantic AI looks like what swarms would become if it was production ready. So, yeah. I mean, like, yeah, which makes sense. Awesome. Yeah. I mean, in fact, it was specifically saying, how can we give people the same feeling that they were getting from swarms that led us to go and implement graphs? Because my, like, just call the next agent with Python code was not a satisfactory answer to people. So it was like, okay, we've got to go and have a better answer for that. It's not like, let us to get to graphs. Yeah.Swyx [00:21:56]: I mean, it's a minimal viable graph in some sense. What are the shapes of graphs that people should know? So the way that I would phrase this is I think Anthropic did a very good public service and also kind of surprisingly influential blog post, I would say, when they wrote Building Effective Agents. We actually have the authors coming to speak at my conference in New York, which I think you're giving a workshop at. Yeah.Samuel [00:22:24]: I'm trying to work it out. But yes, I think so.Swyx [00:22:26]: Tell me if you're not. yeah, I mean, like, that was the first, I think, authoritative view of, like, what kinds of graphs exist in agents and let's give each of them a name so that everyone is on the same page. So I'm just kind of curious if you have community names or top five patterns of graphs.Samuel [00:22:44]: I don't have top five patterns of graphs. I would love to see what people are building with them. But like, it's been it's only been a couple of weeks. And of course, there's a point is that. Because they're relatively unopinionated about what you can go and do with them. They don't suit them. Like, you can go and do lots of lots of things with them, but they don't have the structure to go and have like specific names as much as perhaps like some other systems do. I think what our agents are, which have a name and I can't remember what it is, but this basically system of like, decide what tool to call, go back to the center, decide what tool to call, go back to the center and then exit. One form of graph, which, as I say, like our agents are effectively one implementation of a graph, which is why under the hood they are now using graphs. And it'll be interesting to see over the next few years whether we end up with these like predefined graph names or graph structures or whether it's just like, yep, I built a graph or whether graphs just turn out not to match people's mental image of what they want and die away. We'll see.Swyx [00:23:38]: I think there is always appeal. Every developer eventually gets graph religion and goes, oh, yeah, everything's a graph. And then they probably over rotate and go go too far into graphs. And then they have to learn a whole bunch of DSLs. And then they're like, actually, I didn't need that. I need this. And they scale back a little bit.Samuel [00:23:55]: I'm at the beginning of that process. I'm currently a graph maximalist, although I haven't actually put any into production yet. But yeah.Swyx [00:24:02]: This has a lot of philosophical connections with other work coming out of UC Berkeley on compounding AI systems. I don't know if you know of or care. This is the Gartner world of things where they need some kind of industry terminology to sell it to enterprises. I don't know if you know about any of that.Samuel [00:24:24]: I haven't. I probably should. I should probably do it because I should probably get better at selling to enterprises. But no, no, I don't. Not right now.Swyx [00:24:29]: This is really the argument is that instead of putting everything in one model, you have more control and more maybe observability to if you break everything out into composing little models and changing them together. And obviously, then you need an orchestration framework to do that. Yeah.Samuel [00:24:47]: And it makes complete sense. And one of the things we've seen with agents is they work well when they work well. But when they. Even if you have the observability through log five that you can see what was going on, if you don't have a nice hook point to say, hang on, this is all gone wrong. You have a relatively blunt instrument of basically erroring when you exceed some kind of limit. But like what you need to be able to do is effectively iterate through these runs so that you can have your own control flow where you're like, OK, we've gone too far. And that's where one of the neat things about our graph implementation is you can basically call next in a loop rather than just running the full graph. And therefore, you have this opportunity to to break out of it. But yeah, basically, it's the same point, which is like if you have two bigger unit of work to some extent, whether or not it involves gen AI. But obviously, it's particularly problematic in gen AI. You only find out afterwards when you've spent quite a lot of time and or money when it's gone off and done done the wrong thing.Swyx [00:25:39]: Oh, drop on this. We're not going to resolve this here, but I'll drop this and then we can move on to the next thing. This is the common way that we we developers talk about this. And then the machine learning researchers look at us. And laugh and say, that's cute. And then they just train a bigger model and they wipe us out in the next training run. So I think there's a certain amount of we are fighting the bitter lesson here. We're fighting AGI. And, you know, when AGI arrives, this will all go away. Obviously, on Latent Space, we don't really discuss that because I think AGI is kind of this hand wavy concept that isn't super relevant. But I think we have to respect that. For example, you could do a chain of thoughts with graphs and you could manually orchestrate a nice little graph that does like. Reflect, think about if you need more, more inference time, compute, you know, that's the hot term now. And then think again and, you know, scale that up. Or you could train Strawberry and DeepSeq R1. Right.Samuel [00:26:32]: I saw someone saying recently, oh, they were really optimistic about agents because models are getting faster exponentially. And I like took a certain amount of self-control not to describe that it wasn't exponential. But my main point was. If models are getting faster as quickly as you say they are, then we don't need agents and we don't really need any of these abstraction layers. We can just give our model and, you know, access to the Internet, cross our fingers and hope for the best. Agents, agent frameworks, graphs, all of this stuff is basically making up for the fact that right now the models are not that clever. In the same way that if you're running a customer service business and you have loads of people sitting answering telephones, the less well trained they are, the less that you trust them, the more that you need to give them a script to go through. Whereas, you know, so if you're running a bank and you have lots of customer service people who you don't trust that much, then you tell them exactly what to say. If you're doing high net worth banking, you just employ people who you think are going to be charming to other rich people and set them off to go and have coffee with people. Right. And the same is true of models. The more intelligent they are, the less we need to tell them, like structure what they go and do and constrain the routes in which they take.Swyx [00:27:42]: Yeah. Yeah. Agree with that. So I'm happy to move on. So the other parts of Pydantic AI that are worth commenting on, and this is like my last rant, I promise. So obviously, every framework needs to do its sort of model adapter layer, which is, oh, you can easily swap from OpenAI to Cloud to Grok. You also have, which I didn't know about, Google GLA, which I didn't really know about until I saw this in your docs, which is generative language API. I assume that's AI Studio? Yes.Samuel [00:28:13]: Google don't have good names for it. So Vertex is very clear. That seems to be the API that like some of the things use, although it returns 503 about 20% of the time. So... Vertex? No. Vertex, fine. But the... Oh, oh. GLA. Yeah. Yeah.Swyx [00:28:28]: I agree with that.Samuel [00:28:29]: So we have, again, another example of like, well, I think we go the extra mile in terms of engineering is we run on every commit, at least commit to main, we run tests against the live models. Not lots of tests, but like a handful of them. Oh, okay. And we had a point last week where, yeah, GLA is a little bit better. GLA1 was failing every single run. One of their tests would fail. And we, I think we might even have commented out that one at the moment. So like all of the models fail more often than you might expect, but like that one seems to be particularly likely to fail. But Vertex is the same API, but much more reliable.Swyx [00:29:01]: My rant here is that, you know, versions of this appear in Langchain and every single framework has to have its own little thing, a version of that. I would put to you, and then, you know, this is, this can be agree to disagree. This is not needed in Pydantic AI. I would much rather you adopt a layer like Lite LLM or what's the other one in JavaScript port key. And that's their job. They focus on that one thing and they, they normalize APIs for you. All new models are automatically added and you don't have to duplicate this inside of your framework. So for example, if I wanted to use deep seek, I'm out of luck because Pydantic AI doesn't have deep seek yet.Samuel [00:29:38]: Yeah, it does.Swyx [00:29:39]: Oh, it does. Okay. I'm sorry. But you know what I mean? Should this live in your code or should it live in a layer that's kind of your API gateway that's a defined piece of infrastructure that people have?Samuel [00:29:49]: And I think if a company who are well known, who are respected by everyone had come along and done this at the right time, maybe we should have done it a year and a half ago and said, we're going to be the universal AI layer. That would have been a credible thing to do. I've heard varying reports of Lite LLM is the truth. And it didn't seem to have exactly the type safety that we needed. Also, as I understand it, and again, I haven't looked into it in great detail. Part of their business model is proxying the request through their, through their own system to do the generalization. That would be an enormous put off to an awful lot of people. Honestly, the truth is I don't think it is that much work unifying the model. I get where you're coming from. I kind of see your point. I think the truth is that everyone is centralizing around open AIs. Open AI's API is the one to do. So DeepSeq support that. Grok with OK support that. Ollama also does it. I mean, if there is that library right now, it's more or less the open AI SDK. And it's very high quality. It's well type checked. It uses Pydantic. So I'm biased. But I mean, I think it's pretty well respected anyway.Swyx [00:30:57]: There's different ways to do this. Because also, it's not just about normalizing the APIs. You have to do secret management and all that stuff.Samuel [00:31:05]: Yeah. And there's also. There's Vertex and Bedrock, which to one extent or another, effectively, they host multiple models, but they don't unify the API. But they do unify the auth, as I understand it. Although we're halfway through doing Bedrock. So I don't know about it that well. But they're kind of weird hybrids because they support multiple models. But like I say, the auth is centralized.Swyx [00:31:28]: Yeah, I'm surprised they don't unify the API. That seems like something that I would do. You know, we can discuss all this all day. There's a lot of APIs. I agree.Samuel [00:31:36]: It would be nice if there was a universal one that we didn't have to go and build.Alessio [00:31:39]: And I guess the other side of, you know, routing model and picking models like evals. How do you actually figure out which one you should be using? I know you have one. First of all, you have very good support for mocking in unit tests, which is something that a lot of other frameworks don't do. So, you know, my favorite Ruby library is VCR because it just, you know, it just lets me store the HTTP requests and replay them. That part I'll kind of skip. I think you are busy like this test model. We're like just through Python. You try and figure out what the model might respond without actually calling the model. And then you have the function model where people can kind of customize outputs. Any other fun stories maybe from there? Or is it just what you see is what you get, so to speak?Samuel [00:32:18]: On those two, I think what you see is what you get. On the evals, I think watch this space. I think it's something that like, again, I was somewhat cynical about for some time. Still have my cynicism about some of the well, it's unfortunate that so many different things are called evals. It would be nice if we could agree. What they are and what they're not. But look, I think it's a really important space. I think it's something that we're going to be working on soon, both in Pydantic AI and in LogFire to try and support better because it's like it's an unsolved problem.Alessio [00:32:45]: Yeah, you do say in your doc that anyone who claims to know for sure exactly how your eval should be defined can safely be ignored.Samuel [00:32:52]: We'll delete that sentence when we tell people how to do their evals.Alessio [00:32:56]: Exactly. I was like, we need we need a snapshot of this today. And so let's talk about eval. So there's kind of like the vibe. Yeah. So you have evals, which is what you do when you're building. Right. Because you cannot really like test it that many times to get statistical significance. And then there's the production eval. So you also have LogFire, which is kind of like your observability product, which I tried before. It's very nice. What are some of the learnings you've had from building an observability tool for LEMPs? And yeah, as people think about evals, even like what are the right things to measure? What are like the right number of samples that you need to actually start making decisions?Samuel [00:33:33]: I'm not the best person to answer that is the truth. So I'm not going to come in here and tell you that I think I know the answer on the exact number. I mean, we can do some back of the envelope statistics calculations to work out that like having 30 probably gets you most of the statistical value of having 200 for, you know, by definition, 15% of the work. But the exact like how many examples do you need? For example, that's a much harder question to answer because it's, you know, it's deep within the how models operate in terms of LogFire. One of the reasons we built LogFire the way we have and we allow you to write SQL directly against your data and we're trying to build the like powerful fundamentals of observability is precisely because we know we don't know the answers. And so allowing people to go and innovate on how they're going to consume that stuff and how they're going to process it is we think that's valuable. Because even if we come along and offer you an evals framework on top of LogFire, it won't be right in all regards. And we want people to be able to go and innovate and being able to write their own SQL connected to the API. And effectively query the data like it's a database with SQL allows people to innovate on that stuff. And that's what allows us to do it as well. I mean, we do a bunch of like testing what's possible by basically writing SQL directly against LogFire as any user could. I think the other the other really interesting bit that's going on in observability is OpenTelemetry is centralizing around semantic attributes for GenAI. So it's a relatively new project. A lot of it's still being added at the moment. But basically the idea that like. They unify how both SDKs and or agent frameworks send observability data to to any OpenTelemetry endpoint. And so, again, we can go and having that unification allows us to go and like basically compare different libraries, compare different models much better. That stuff's in a very like early stage of development. One of the things we're going to be working on pretty soon is basically, I suspect, GenAI will be the first agent framework that implements those semantic attributes properly. Because, again, we control and we can say this is important for observability, whereas most of the other agent frameworks are not maintained by people who are trying to do observability. With the exception of Langchain, where they have the observability platform, but they chose not to go down the OpenTelemetry route. So they're like plowing their own furrow. And, you know, they're a lot they're even further away from standardization.Alessio [00:35:51]: Can you maybe just give a quick overview of how OTEL ties into the AI workflows? There's kind of like the question of is, you know, a trace. And a span like a LLM call. Is it the agent? It's kind of like the broader thing you're tracking. How should people think about it?Samuel [00:36:06]: Yeah, so they have a PR that I think may have now been merged from someone at IBM talking about remote agents and trying to support this concept of remote agents within GenAI. I'm not particularly compelled by that because I don't think that like that's actually by any means the common use case. But like, I suppose it's fine for it to be there. The majority of the stuff in OTEL is basically defining how you would instrument. A given call to an LLM. So basically the actual LLM call, what data you would send to your telemetry provider, how you would structure that. Apart from this slightly odd stuff on remote agents, most of the like agent level consideration is not yet implemented in is not yet decided effectively. And so there's a bit of ambiguity. Obviously, what's good about OTEL is you can in the end send whatever attributes you like. But yeah, there's quite a lot of churn in that space and exactly how we store the data. I think that one of the most interesting things, though, is that if you think about observability. Traditionally, it was sure everyone would say our observability data is very important. We must keep it safe. But actually, companies work very hard to basically not have anything that sensitive in their observability data. So if you're a doctor in a hospital and you search for a drug for an STI, the sequel might be sent to the observability provider. But none of the parameters would. It wouldn't have the patient number or their name or the drug. With GenAI, that distinction doesn't exist because it's all just messed up in the text. If you have that same patient asking an LLM how to. What drug they should take or how to stop smoking. You can't extract the PII and not send it to the observability platform. So the sensitivity of the data that's going to end up in observability platforms is going to be like basically different order of magnitude to what's in what you would normally send to Datadog. Of course, you can make a mistake and send someone's password or their card number to Datadog. But that would be seen as a as a like mistake. Whereas in GenAI, a lot of data is going to be sent. And I think that's why companies like Langsmith and are trying hard to offer observability. On prem, because there's a bunch of companies who are happy for Datadog to be cloud hosted, but want self-hosted self-hosting for this observability stuff with GenAI.Alessio [00:38:09]: And are you doing any of that today? Because I know in each of the spans you have like the number of tokens, you have the context, you're just storing everything. And then you're going to offer kind of like a self-hosting for the platform, basically. Yeah. Yeah.Samuel [00:38:23]: So we have scrubbing roughly equivalent to what the other observability platforms have. So if we, you know, if we see password as the key, we won't send the value. But like, like I said, that doesn't really work in GenAI. So we're accepting we're going to have to store a lot of data and then we'll offer self-hosting for those people who can afford it and who need it.Alessio [00:38:42]: And then this is, I think, the first time that most of the workloads performance is depending on a third party. You know, like if you're looking at Datadog data, usually it's your app that is driving the latency and like the memory usage and all of that. Here you're going to have spans that maybe take a long time to perform because the GLA API is not working or because OpenAI is kind of like overwhelmed. Do you do anything there since like the provider is almost like the same across customers? You know, like, are you trying to surface these things for people and say, hey, this was like a very slow span, but actually all customers using OpenAI right now are seeing the same thing. So maybe don't worry about it or.Samuel [00:39:20]: Not yet. We do a few things that people don't generally do in OTA. So we send. We send information at the beginning. At the beginning of a trace as well as sorry, at the beginning of a span, as well as when it finishes. By default, OTA only sends you data when the span finishes. So if you think about a request which might take like 20 seconds, even if some of the intermediate spans finished earlier, you can't basically place them on the page until you get the top level span. And so if you're using standard OTA, you can't show anything until those requests are finished. When those requests are taking a few hundred milliseconds, it doesn't really matter. But when you're doing Gen AI calls or when you're like running a batch job that might take 30 minutes. That like latency of not being able to see the span is like crippling to understanding your application. And so we've we do a bunch of slightly complex stuff to basically send data about a span as it starts, which is closely related. Yeah.Alessio [00:40:09]: Any thoughts on all the other people trying to build on top of OpenTelemetry in different languages, too? There's like the OpenLEmetry project, which doesn't really roll off the tongue. But how do you see the future of these kind of tools? Is everybody going to have to build? Why does everybody want to build? They want to build their own open source observability thing to then sell?Samuel [00:40:29]: I mean, we are not going off and trying to instrument the likes of the OpenAI SDK with the new semantic attributes, because at some point that's going to happen and it's going to live inside OTEL and we might help with it. But we're a tiny team. We don't have time to go and do all of that work. So OpenLEmetry, like interesting project. But I suspect eventually most of those semantic like that instrumentation of the big of the SDKs will live, like I say, inside the main OpenTelemetry report. I suppose. What happens to the agent frameworks? What data you basically need at the framework level to get the context is kind of unclear. I don't think we know the answer yet. But I mean, I was on the, I guess this is kind of semi-public, because I was on the call with the OpenTelemetry call last week talking about GenAI. And there was someone from Arize talking about the challenges they have trying to get OpenTelemetry data out of Langchain, where it's not like natively implemented. And obviously they're having quite a tough time. And I was realizing, hadn't really realized this before, but how lucky we are to primarily be talking about our own agent framework, where we have the control rather than trying to go and instrument other people's.Swyx [00:41:36]: Sorry, I actually didn't know about this semantic conventions thing. It looks like, yeah, it's merged into main OTel. What should people know about this? I had never heard of it before.Samuel [00:41:45]: Yeah, I think it looks like a great start. I think there's some unknowns around how you send the messages that go back and forth, which is kind of the most important part. It's the most important thing of all. And that is moved out of attributes and into OTel events. OTel events in turn are moving from being on a span to being their own top-level API where you send data. So there's a bunch of churn still going on. I'm impressed by how fast the OTel community is moving on this project. I guess they, like everyone else, get that this is important, and it's something that people are crying out to get instrumentation off. So I'm kind of pleasantly surprised at how fast they're moving, but it makes sense.Swyx [00:42:25]: I'm just kind of browsing through the specification. I can already see that this basically bakes in whatever the previous paradigm was. So now they have genai.usage.prompt tokens and genai.usage.completion tokens. And obviously now we have reasoning tokens as well. And then only one form of sampling, which is top-p. You're basically baking in or sort of reifying things that you think are important today, but it's not a super foolproof way of doing this for the future. Yeah.Samuel [00:42:54]: I mean, that's what's neat about OTel is you can always go and send another attribute and that's fine. It's just there are a bunch that are agreed on. But I would say, you know, to come back to your previous point about whether or not we should be relying on one centralized abstraction layer, this stuff is moving so fast that if you start relying on someone else's standard, you risk basically falling behind because you're relying on someone else to keep things up to date.Swyx [00:43:14]: Or you fall behind because you've got other things going on.Samuel [00:43:17]: Yeah, yeah. That's fair. That's fair.Swyx [00:43:19]: Any other observations just about building LogFire, actually? Let's just talk about this. So you announced LogFire. I was kind of only familiar with LogFire because of your Series A announcement. I actually thought you were making a separate company. I remember some amount of confusion with you when that came out. So to be clear, it's Pydantic LogFire and the company is one company that has kind of two products, an open source thing and an observability thing, correct? Yeah. I was just kind of curious, like any learnings building LogFire? So classic question is, do you use ClickHouse? Is this like the standard persistence layer? Any learnings doing that?Samuel [00:43:54]: We don't use ClickHouse. We started building our database with ClickHouse, moved off ClickHouse onto Timescale, which is a Postgres extension to do analytical databases. Wow. And then moved off Timescale onto DataFusion. And we're basically now building, it's DataFusion, but it's kind of our own database. Bogomil is not entirely happy that we went through three databases before we chose one. I'll say that. But like, we've got to the right one in the end. I think we could have realized that Timescale wasn't right. I think ClickHouse. They both taught us a lot and we're in a great place now. But like, yeah, it's been a real journey on the database in particular.Swyx [00:44:28]: Okay. So, you know, as a database nerd, I have to like double click on this, right? So ClickHouse is supposed to be the ideal backend for anything like this. And then moving from ClickHouse to Timescale is another counterintuitive move that I didn't expect because, you know, Timescale is like an extension on top of Postgres. Not super meant for like high volume logging. But like, yeah, tell us those decisions.Samuel [00:44:50]: So at the time, ClickHouse did not have good support for JSON. I was speaking to someone yesterday and said ClickHouse doesn't have good support for JSON and got roundly stepped on because apparently it does now. So they've obviously gone and built their proper JSON support. But like back when we were trying to use it, I guess a year ago or a bit more than a year ago, everything happened to be a map and maps are a pain to try and do like looking up JSON type data. And obviously all these attributes, everything you're talking about there in terms of the GenAI stuff. You can choose to make them top level columns if you want. But the simplest thing is just to put them all into a big JSON pile. And that was a problem with ClickHouse. Also, ClickHouse had some really ugly edge cases like by default, or at least until I complained about it a lot, ClickHouse thought that two nanoseconds was longer than one second because they compared intervals just by the number, not the unit. And I complained about that a lot. And then they caused it to raise an error and just say you have to have the same unit. Then I complained a bit more. And I think as I understand it now, they have some. They convert between units. But like stuff like that, when all you're looking at is when a lot of what you're doing is comparing the duration of spans was really painful. Also things like you can't subtract two date times to get an interval. You have to use the date sub function. But like the fundamental thing is because we want our end users to write SQL, the like quality of the SQL, how easy it is to write, matters way more to us than if you're building like a platform on top where your developers are going to write the SQL. And once it's written and it's working, you don't mind too much. So I think that's like one of the fundamental differences. The other problem that I have with the ClickHouse and Impact Timescale is that like the ultimate architecture, the like snowflake architecture of binary data in object store queried with some kind of cache from nearby. They both have it, but it's closed sourced and you only get it if you go and use their hosted versions. And so even if we had got through all the problems with Timescale or ClickHouse, we would end up like, you know, they would want to be taking their 80% margin. And then we would be wanting to take that would basically leave us less space for margin. Whereas data fusion. Properly open source, all of that same tooling is open source. And for us as a team of people with a lot of Rust expertise, data fusion, which is implemented in Rust, we can literally dive into it and go and change it. So, for example, I found that there were some slowdowns in data fusion's string comparison kernel for doing like string contains. And it's just Rust code. And I could go and rewrite the string comparison kernel to be faster. Or, for example, data fusion, when we started using it, didn't have JSON support. Obviously, as I've said, it's something we can do. It's something we needed. I was able to go and implement that in a weekend using our JSON parser that we built for Pydantic Core. So it's the fact that like data fusion is like for us the perfect mixture of a toolbox to build a database with, not a database. And we can go and implement stuff on top of it in a way that like if you were trying to do that in Postgres or in ClickHouse. I mean, ClickHouse would be easier because it's C++, relatively modern C++. But like as a team of people who are not C++ experts, that's much scarier than data fusion for us.Swyx [00:47:47]: Yeah, that's a beautiful rant.Alessio [00:47:49]: That's funny. Most people don't think they have agency on these projects. They're kind of like, oh, I should use this or I should use that. They're not really like, what should I pick so that I contribute the most back to it? You know, so but I think you obviously have an open source first mindset. So that makes a lot of sense.Samuel [00:48:05]: I think if we were probably better as a startup, a better startup and faster moving and just like headlong determined to get in front of customers as fast as possible, we should have just started with ClickHouse. I hope that long term we're in a better place for having worked with data fusion. We like we're quite engaged now with the data fusion community. Andrew Lam, who maintains data fusion, is an advisor to us. We're in a really good place now. But yeah, it's definitely slowed us down relative to just like building on ClickHouse and moving as fast as we can.Swyx [00:48:34]: OK, we're about to zoom out and do Pydantic run and all the other stuff. But, you know, my last question on LogFire is really, you know, at some point you run out sort of community goodwill just because like, oh, I use Pydantic. I love Pydantic. I'm going to use LogFire. OK, then you start entering the territory of the Datadogs, the Sentrys and the honeycombs. Yeah. So where are you going to really spike here? What differentiator here?Samuel [00:48:59]: I wasn't writing code in 2001, but I'm assuming that there were people talking about like web observability and then web observability stopped being a thing, not because the web stopped being a thing, but because all observability had to do web. If you were talking to people in 2010 or 2012, they would have talked about cloud observability. Now that's not a term because all observability is cloud first. The same is going to happen to gen AI. And so whether or not you're trying to compete with Datadog or with Arise and Langsmith, you've got to do first class. You've got to do general purpose observability with first class support for AI. And as far as I know, we're the only people really trying to do that. I mean, I think Datadog is starting in that direction. And to be honest, I think Datadog is a much like scarier company to compete with than the AI specific observability platforms. Because in my opinion, and I've also heard this from lots of customers, AI specific observability where you don't see everything else going on in your app is not actually that useful. Our hope is that we can build the first general purpose observability platform with first class support for AI. And that we have this open source heritage of putting developer experience first that other companies haven't done. For all I'm a fan of Datadog and what they've done. If you search Datadog logging Python. And you just try as a like a non-observability expert to get something up and running with Datadog and Python. It's not trivial, right? That's something Sentry have done amazingly well. But like there's enormous space in most of observability to do DX better.Alessio [00:50:27]: Since you mentioned Sentry, I'm curious how you thought about licensing and all of that. Obviously, your MIT license, you don't have any rolling license like Sentry has where you can only use an open source, like the one year old version of it. Was that a hard decision?Samuel [00:50:41]: So to be clear, LogFire is co-sourced. So Pydantic and Pydantic AI are MIT licensed and like properly open source. And then LogFire for now is completely closed source. And in fact, the struggles that Sentry have had with licensing and the like weird pushback the community gives when they take something that's closed source and make it source available just meant that we just avoided that whole subject matter. I think the other way to look at it is like in terms of either headcount or revenue or dollars in the bank. The amount of open source we do as a company is we've got to be open source. We're up there with the most prolific open source companies, like I say, per head. And so we didn't feel like we were morally obligated to make LogFire open source. We have Pydantic. Pydantic is a foundational library in Python. That and now Pydantic AI are our contribution to open source. And then LogFire is like openly for profit, right? As in we're not claiming otherwise. We're not sort of trying to walk a line if it's open source. But really, we want to make it hard to deploy. So you probably want to pay us. We're trying to be straight. That it's to pay for. We could change that at some point in the future, but it's not an immediate plan.Alessio [00:51:48]: All right. So the first one I saw this new I don't know if it's like a product you're building the Pydantic that run, which is a Python browser sandbox. What was the inspiration behind that? We talk a lot about code interpreter for lamps. I'm an investor in a company called E2B, which is a code sandbox as a service for remote execution. Yeah. What's the Pydantic that run story?Samuel [00:52:09]: So Pydantic that run is again completely open source. I have no interest in making it into a product. We just needed a sandbox to be able to demo LogFire in particular, but also Pydantic AI. So it doesn't have it yet, but I'm going to add basically a proxy to OpenAI and the other models so that you can run Pydantic AI in the browser. See how it works. Tweak the prompt, et cetera, et cetera. And we'll have some kind of limit per day of what you can spend on it or like what the spend is. The other thing we wanted to b

Send us a textTiming is everything, especially when booking podcast guests. Jackson and I covered so many topics from crashing poetry open mics, to night time snack hunts and brothels in Mexico. Check out his live stand up show every last Friday (this Friday) of the month at Easy Lover in Brooklyn!Follow Jackson on Instagram - https://www.instagram.com/jacksoncolvincomedy/Buy me a coffee! - https://www.buymeacoffee.com/antoniob06p Follow The Smoking Simian on Instagram - https://www.instagram.com/thesmokingsimian/ Follow me on Instagram - https://www.instagram.com/antoniothecomical/ Follow Billy on Instagram - https://www.instagram.com/23dollarbill/ Share, like, comment and subscribe on The Smoking Simian YouTube - https://www.youtube.com/channel/UCFn3FPnTegakyd9YpoYfsQg Audio platforms - https://smokingsimian.buzzsprout.comSupport the show

Russ is on the water with Jeremy Colvin on the Barron River Lake, KY talking about some Kentucky Fishing!

Bryan Barrett talks with Edward Jones financial advisor Lee Colvin, who shares financial considerations when changing jobs.

In today's crowded gaming landscape, keeping players engaged in new and different ways is tougher than ever, but it doesn't mean there aren't creative solutions. In this episode, we dive into innovative approaches to rewarding customer loyalty in games, uncovering what works and doesn't in driving retention and creating memorable player experiences by crafting customer reward systems that deliver results. Episode Shownotes Link:      

Live from an ESG-flavored 2025, it's an all-new Wacky Wednesday edition of Business Pants. Joined by Analyst-Hole Matt Moscardi! On today's Costco lovefest called January 8th 2025: Headlines We Missed since the end of December and the new comic book superhero named Costco!Our show today is being sponsored by Free Float Analytics, the only platform measuring board power, connections, and performance for FREE.DAMION1Shit We Missed (in no particular order):Tech BrosZuckDana White, UFC CEO and Trump ally, to join Meta's board of directorsZuckerberg Announces New Measures to Increase Hate Speech on FacebookMark Zuckerberg's Meta is moving moderators out of California to combat concerns about bias and censorship“Huge problems” with axing fact-checkers, Meta oversight board saysCo-chair Helle Thorning-Schmidt said she is "very concerned" about how parent company Meta's decision to ditch fact-checkers will affect minority groups: "We are seeing many instances where hate speech can lead to real-life harm, so we will be watching that space very carefully," she added.Meta Drops Rules Protecting LGBTQ Community as Part of Content Moderation OverhaulThe changes included allowing users to share “allegations of mental illness or abnormality when based on gender or sexual orientation, given political and religious discourse about transgenderism and homosexuality.”Meta replaces policy chief Nick Clegg with former Republican staffer Joel Kaplan ahead of Trump inaugurationSamSam Altman Explodes at Board Members Who Fired Him"And all those people that I feel like really fucked me and fucked the company were gone, and now I had to clean up their mess," adding that he was "fucking depressed and tired.""And it felt so unfair," the billionaire told Bloomberg. "It was just a crazy thing to have to go through and then have no time to recover, because the house was on fire."The board's primary fiduciary duty was not to maintain shareholder value or profits, but rather to stay true to OpenAI's mission of creating safe artificial general intelligence (AGI) that benefits humanity.Helen Toner: the director of strategy at Georgetown's Center for Security and Emerging Technology.Tasha McCauley: an adjunct senior management scientist at think tank RAND Corporation. McCauley was also on the advisory board of the Centre for Effective Altruism. In 2017 she signed the Asilomar AI Principles on ethical AI development alongside Altman, OpenAI co-founder Ilya Sutskever, and former board member Elon MuskOpenAI CEO Sam Altman denies sexual abuse allegations made by his sister in lawsuitMuskMaga v Musk: Trump camp divided in bitter fight over immigration policyElon Musk Endorses Nazi-Linked German Party, Even Though It Opposed Tesla's GigafactoryTech Bro Wealth12 US billionaires gained almost $1 trillion in wealth in 2024 as the stock market delivered another year of massive returnsNYT Report Says Jensen Huang, The CEO Of Nvidia And The 10th-Richest Person In The U.S., Trying To Allegedly Avoid $8 Billion In TaxesMark Zuckerberg says he doesn't have a Hawaiian doomsday bunker, just a 'little shelter.' It's bigger than most houses.You could live next door to Jeff Bezos on 'Billionaire Bunker' island for $200 millionMusk urges Bezos to throw an ‘epic wedding' after Amazon founder blasts report of $600 million nuptials as ‘completely false'Elon Musk takes aim at MacKenzie Scott again for giving billions to liberal causes, calling the gifts 'concerning'How Jensen Huang and 3 Nvidia Board Members Became BillionairesMark Zuckerberg sported a $900,000 piece of wrist candy as he announced the end of fact-checking on MetaDEI/ESG Flip-FloppingWhen an anti-DEI activist took a swing at Costco, the board hit backA Costco shareholder proposal brought by conservative activist The National Center for Public Policy Research asked the company to probe its diversity, equity and inclusion policies, with an eye toward eliminating them.The thrust of the proposal is that certain DEI initiatives could open Costco up to financial risks over discrimination lawsuits from employees who are “white, Asian, male or straight.”The company's board of directors unanimously urged shareholders to reject the proposal and made the case that Costco's success depends on establishing a racially diverse, inclusive workplace: “We believe that our diversity, equity and inclusion efforts are legally appropriate, and nothing in the (Center for Public Policy Research) proposal demonstrates otherwise,” the board's statement said.The statement went on to rebuke the Center for Public Policy Research, saying that they and others were the ones responsible for inflicting financial and legal burdens on companies. “The proponent's broader agenda is not reducing the risk for the Company but abolition of diversity programs,” the board said.Costco board member defends DEI practices, rebukes companies scrapping policiesJeff Raikes, co-founder of the Raikes Foundation and former CEO of the Bill & Melinda Gates Foundation, who has served on Costco's board of directors since 2008: "Attacks on DEI aren't just bad for business—they hurt our economy. A diverse workforce drives innovation, expands markets, and fuels growth. Let's focus on building a future where all talent thrives." He concluded his post on X with the hashtag, "InclusiveEconomy." While businesses began to announce their departures from DEI policies last year, Raikes urged companies to expand such practices at work, insisting that scaling down DEI in businesses would harm the economy.Robbie Starbuck: “I fully endorse cancelling memberships at this point.”McDonald's rolls back DEI programs, ending push for greater diversityFour years after launching a push for more diversity in its ranks,McDonald's said it will retire specific goals for achieving diversity at senior leadership levels. It also intends to end a program that encourages its suppliers to develop diversity training and to increase the number of minority group members represented within their own leadership ranks.Managers 'touch up' staff: McDonald's faces fresh abuse claimsFast-food chain McDonald's has been hit by fresh allegations of sexual and homophobic abuse as staff members allege they have been 'touched up' by managers and offered extra shifts for sex.The chain first faced bombshell claims of widespread sexual abuse and harassment at its stores in July 2023 and has since been reported more than 300 times for harassment to the UK's equality watchdog.Allegations have included racist abuse, sexual assault and harassment and bullying. BlackRock Cuts Back on Board Diversity Push in Proxy-Vote GuidelinesThe policy updates remove both (a) numerical diversity targets (i.e., boards should aspire to 30% diversity of membership and have at least 2 women directors and 1 director from an underrepresented group) and (b) the related disclosure-based voting policy (i.e., BlackRock previously would consider taking voting action if a company did not adequately explain its approach to board diversity) – but provides that BlackRock may consider taking voting action if an S&P 500 board is not sufficiently diverse (BlackRock includes a footnote in the policy update suggesting that 30% diversity may still be the expectation).BlackRock's investment stewardship team tweaked the language used to describe how it approaches votes for other companies' boards. It didn't explicitly recommend that boards should aspire to at least 30% diversity of their members, after having done so in previous years.The report noted, however, that all but 2% of the boards of companies in the S&P 500 have diverse representation of at least 30%—and that if companies were out of step with those norms, BlackRock may cast opposing votes on a case-by-case basis. JPMorgan Leaves Net Zero Banking Group, Completing Departure of Major U.S. Banks Stakeholder Anger (or Anger at Stakeholders)Poll finds many Americans pin partial blame on insurance companies in UHC CEO killingA recent survey from the University of Chicago, found that, while 8 out of 10 U.S. adults believe the person who killed Brian Thompson bears the responsibility for the murder, 7 in 10 shared the belief that healthcare companies are also to blame. Luigi Mangione mention on SNL met with applause, critics slam 'woke' audience: 'Wooing for justice?'New York to charge fossil fuel companies for damage from climate changeThe new law requires companies responsible for substantial greenhouse gas emissions to pay into a state fund for infrastructure projects meant to repair or avoid future damage from climate change.Albania bans TikTok for a year after fatal stabbing of teenager last monthTeens in Vietnam will now be limited to one hour of gaming per sessionStarbucks baristas set to strike as new CEO makes $100 millionWashington Post Cartoonist Quits After Jeff Bezos Cartoon Is KilledNorway on track to be the first to ‘erase petrol and diesel engine cars'Fully electric vehicles accounted for 88.9% of new cars sold in 2024Exxon Sues California Official, Claiming He Defamed the CompanyExxon Mobil sued California's attorney general, the Sierra Club and other environmental groups on Monday, alleging that they conspired to defame the oil giant and kneecap its business prospects amid a debate over whether plastics can be recycled effectively.DystopiaMan Trying to Catch Flight Alarmed as His Driverless Waymo Gets Stuck Driving in Loop Around Parking LotAsked to Write a Screenplay, ChatGPT Started Procrastinating and Making ExcusesKlarna's CEO says AI is capable of doing his job and it makes him feel 'gloomy'Governance newsShari Redstone is saying goodbye to Paramount GlobalCharles Dolan, TV pioneer who founded HBO and Cablevision, dies at 98Richard Parsons, former Time Warner CEO, dies at age 76 Dye & Durham board resigns, activist nominees take control, interim CEO named The Fortune 500 has two new female CEOs—finally pushing that milestone above 11%And we end with a few classics:Boeing ends a troubled year with a jet-crash disaster in South KoreaMan who exploded Tesla Cybertruck outside Trump hotel used ChatGPT to plan the attackNorovirus rates have skyrocketed by 340% this season. Here's where the ‘winter vomiting disease' is spreading and whyMATT1CostcoNational Center for Public Policy Research filed the proxy with CostcoTheir arguments include…US Supreme court decision at HarvardA $25m judgment in PA for white regional manager at Starbucks who was fired after two black patrons were arrested for being blackThis gem: “With 310,000 employees, Costco likely has at least 200,000 employees who are potentially victims of this type of illegal discrimination because they are white, Asian, male or straight.”This, perhaps, is the greatest ironic argument for “meritocracy” ever made in historyThey point out that the MAJORITY OF THE STAFF is white, Asian, male, or straight… but they don't even use Costco's data, they source census data and just guessThe real numbers:Non management is 44.2% white, management is 58% white - a 14% increase in meritocracyExecutives are 80.6% white - a whopping 36.4% more meritHispanics are 33.1% of non management, 23.3% of management - 9.8% less merit!Executives are 5.8% Hispanic, 26.3% less meritAsians are 8.5% and 7.1%, so 1.4% less merit7.9% executive - so even merit?US Exec management is 72.3% maleSo 80.6% of executives are white, and 72.3% are male - and the argument NCPPR is making is that BECAUSE there are a lot of white males, there is a lot of RISK that THE WHITE MALES WILL SUE YOU if they think they're discriminated againstThink of what they're saying - because you have so many non diverse people, you can't have diversity programs for risk of lawsuitThe response dropped the pretense that the proxy was anything except racismThe proponent professes concern about legal and financial risks to the Company and its shareholders associated with the diversity initiatives. The proponent's broader agenda is not reducing risk for the Company but abolition of diversity initiatives. A 2023 federal district court decision, in a case brought by the proponent, noted that the proponent had "published a document called 'Balancing the Boardroom 2022,' which describes its shareholder activism as 'fighting back' against 'the evils of woke politicized capital and companies.' [The proponent went] on to describe 'CEOs and other corporate executives who are most woke and most hard-left political in their management of their corporations' as 'inimical to the Republic and its blessings of liberty' and 'committed to critical race theory and the socialist foundations of woke' or 'shameless monsters who are willing to sacrifice our future for their comforts.'" National Center for Public Policy Research v. Schultz, E.D. WA. (Sept. 11, 2023). And the proponent's efforts to demonstrate retrenchment on the part of companies are misleading, at best. For example, the assertion that "Microsoft laid off an entirea[sic] DEI team" is simply wrong. It was later reported that Microsoft stated that the two positions eliminated were redundant roles on its events team and that Microsoft's diversity and inclusion commitments remain unchanged, according to Jeff Jones, a Microsoft spokesperson: “Our focus on diversity and inclusion is unwavering and we are holding firm on our expectations, prioritizing accountability, and continuing to focus on this work.” Colvin, Caroline. Amid DEI cuts, Microsoft works to distinguish itself from those responding to ‘woke' backlash. HR Dive, July 24, 2024.Reason Costco might be pushing back?Racism is basically unveiledOf all the companies targeted by a proposal or Robbie Starbuck, Costco has the lowest deviation in board member influence - as in, nearly the entire board has equal power, it's highly democratic - women, men, diverse cohorts are more or less equally powerful to anyone else in the roomNo connections to any board member on another DEI flipper companyMeanwhile, the anti DEI, anti immigrant movement has begun to eat itself before Trump even takes officeIn defense of more HB1 visas and foreign workers, Vivek Ramaswamy says we venerate jocks over valedictorians on Twitter, and Americans aren't as good employeesThe rebuttal was MAGA Trumpers saying Vivek is fake MAGAAlso this: “His entire argument is a terrible proposition,” he adds. “Children raised to be good little robots might grow up to build robots of their own someday, and become rich. Asians are the highest-earning racial group in America, but are they happier for it? Suicide is the leading cause of death for Asians aged 15-24 … and the second-leading cause of death for those aged 25-34.” Page points to a Psychology Today post that blames tiger parenting for causing anxiety and depression and then asks, “Do we really want this country to be even more stressed-out?”Costco proxy says Asians are discriminated againstTwitch gamers are streaming about “meritocracy”

Send us a textClare Colvin shares her journey from HR to co-founding Simple Scaling, emphasizing the pivotal role of authenticity in leadership and the necessity of creating environments where individuals can thrive together. The conversation highlights how fear can hinder growth and explores practical strategies for fostering a culture that enables scaling.• Claire's childhood memories and inspirations • Transitioning from hotel management to Human Resources • Importance of mentorship in professional development • Navigating corporate challenges and stress • Defining scaling and its significance for businesses • Creating a culture that attracts and retains talent • The impact of COVID-19 on starting a business • Highlights from the ScaleX Summit and future aspirations • Common traits of successful leaders and their importance • Emphasizing authenticity in leadership for optimal growthSupport the show

Send us a textOn today's episode of The “Center,” we're joined by contemplative activist Leslye Colvin. Leslye is a writer, educator, and spiritual companion rooted deep within her Catholic faith. Leslye writes her blog “Leslye's Labyrinth” and has been published in the National Catholic Reporter and U.S. Catholic. Leslye serves on the boards of FutureChurch, and NETWORK Lobby for Catholic Social Justice.During today's conversation, Leslye and podcast host Marsha Thrall will discuss how embodied empathy can direct social equity and advocacy work, and how both the Advent and Christmas seasons can remind us to compassionately live in community with one another beyond the holiday season itself.We hope you find comfort and motivation to advocate for social equity from listening to this conversation. Please note: The views expressed here are individual views that do not necessarily reflect the views of the Sisters of St. Francis, Clinton, Iowa. "Sweet Times" is by All Bets Off, and is provided by Adobe Stock.

A learning business must understand learning theory and put that theory into practice if it is to create offerings that result in positive change and impact. This episode of the Leading Learning Podcast, number 441, features a conversation with instructional psychologist Dr. Ruth Colvin Clark and co-host Celisa Steele. Ruth has spent her spent her career translating academic research into practical guidelines and advocating for the use of evidence-based approaches to learning. She's the author of many articles and books, including Evidence-Based Training Methods and E-learning and the Science of Instruction. Show notes and a downloadable transcript are available at https://www.leadinglearning.com/episode441.

Join us as we dig into Steve's 2016 collaboration with Shawn Colvin. Also: Saint Luigi, David Lee Roth's Sonrisa Salvaje, and infinite waves of ska.

In this episode of Hustle Over Everything, we welcome a guest we've never had before. A pastor. Welcome PC or Pastor C (Colvin Chambers) to the show for the first time! We dive deep into: - Kirk Franklin & Maverick City teamed up with Glorilla, is mixing gospel and secular hip-hop okay? - We break down Dr. Umar's viral video: Did Christianity fail the Black church? Pastor C's powerful journey: From jail and divorce to leading a successful church. - Why Black men are not present in a lot of churches

Jonathan Cain v. Carolyn W. Colvin

Bryan Barrett welcomes Edward Jones financial advisor Lee Colvin, who helps to answer the question, "What will rate cuts mean for you?"

We catch up with Wagga based trainer Gary Colvin who added to his metropolitan record when Cambar scored a tradesmanlike win in the TAB Highway on Nov. 30th. Gary's hoping Cambar will emulate the deeds of stable favourite Another One who won a Championship Final  in 2022 after having finished second the year before. Gary begins our podcast by talking about Cambar's two early wins at Wagga when ridden by stable apprentice Holly Durnan. He looks back on his purchase of the little colt at the Inglis Premier Sale in Melbourne. He got a second opinion from Warwick Farm trainer Greg Hickman who'd trained the colt's sire Pierata. Gary says Cambar has been a thorough professional from the day he was broken in. The trainer retained a part ownership of the horse while the remaining shares were snapped up by old friends. He says one of the quartet is a first time participant. Gary looks back on childhood days growing up with his six siblings on the family farm at Tullibigeal. He says his father was never without a horse or two on the property. He pays tribute to uncles John and Noel Colvin, both very successful jockeys. Gary says he was 17 when he joined three of his siblings in Wagga. He pleased his parents greatly when he registered for an apprenticeship in joinery. He talks of his progress in the old world trade. He talks of his passion for Australian Rules Football, and reflects on his involvement with the Wagga Tigers and the Collingully side. He plays down reports that he was a very accomplished player. Gary remembers his short stint in a coaching role. He looks back on Fred Colvin's move to Wagga and a renewal of his trainer's licence. He assisted his father for eleven years while training a small team of his own. When Fred retired in 1997, he was ready to train in his own right. Gary hasn't forgotten the mare who provided the first win of his full time training career. He acknowledges the special horses to get him up and running as a commercial trainer with special mention of Baltra and Lock the Key. The accomplished trainer takes us through the circumstances leading up to his purchase of Another One at an Inglis Classic Sale. He's never forgotten the instant he first saw the chestnut colt.  Gary talks of Another One's pedigree and the 15 strong syndicate who got together to race the horse. The trainer recalls setting the talented horse for a maiden 2YO at Wagga. He and the owners were stunned when a Randwick trained youngster from the John O'Shea yard turned up for the same race. He takes us through Another One's progression to his first tilt at the Country Championship. Gary followed a similar path the following year and the gelding again made the final at Randwick. This time he prevailed in the highly prized event.  He pays tribute to Another One's regular rider Nick Heywood who wages a fearsome battle with weight to stay in the saddle. Gary talks of Another One's Kosciuszko failure and his most recent wins in the Riverina. The trainer speaks highly of Danny Beasley who rode the horse to victories in the Canberra National Sprint and the Wodonga Cup. Danny recently joined the Wagga training ranks and is situated right next door to the Colvin stables. Gary talks of wife Karen's picture framing business in Wagga, and his willingness to lend a hand in the early days of the operation. He says his assistance was not needed.  Gary talks of daughter Gabby who's currently a highly regarded member of the Melbourne Women's AFL side. Her dad's pretty chuffed. He makes mention of sons Brodie and Matthew. One prefers the thrill of trying to back a winner- the other enjoys being around the horses. Good to catch up with yet another talented member of the country training ranks.

​Samuel Colvin​ - the creator of ​Pydantic​ - the most popular data validation library for Python. Used by literally everyone (Anthropic, OpenAI, Meta, NVIDIA, even the NSA). He shares the story behind his startup ​Logfire​ which just raised $12.5m from Sequoia.This episode is brought to you by WorkOS. If you're thinking about selling to enterprise customers, WorkOS can help you add enterprise features like Single Sign On and audit logs. Key takeaways:- You can just build a different product to your open source project and leverage your brand- Quality of product matters a LOT (if you can build a popular open source project, can probably build a quality paid product)- Really helps to be part of a movement. Hard to predict but Pydantic benefited from two (types and LLMs)- GitHub stars are a vanity metric compared to download numbersLinks:- Pydantic - Logfire - Samuel Colvin Chapters00:00 The Genesis of Pydantic02:46 The Evolution of Software Development06:02 Building a Successful Open Source Library08:52 The Impact of Community and Adoption11:51 Metrics of Success in Open Source15:08 Transitioning from Pydantic to LogFire17:59 The Vision Behind LogFire20:50 The Connection Between Pydantic and LogFire24:05 Navigating the Challenges of Building a Startup26:56 The Future of Observability and DatabasesP.s. thanks to my friend Abeed for making the episode happen!

SSA confirmed that President Joe Biden is expected to name Colvin, a 30-year SSA veteran and former acting commissioner for six years, to lead SSA during the transition. Learn more about your ad choices. Visit podcastchoices.com/adchoicesSee Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

SSA confirmed that President Joe Biden is expected to name Colvin, a 30-year SSA veteran and former acting commissioner for six years, to lead SSA during the transition. Learn more about your ad choices. Visit podcastchoices.com/adchoices

Former Bears linebacker Rosevelt Colvin joins Jeff Joniak and Tom Thayer on the latest episode of Bears, etc. to reflect on his former playing days and rivalry with the Packers. Plus, get an exclusive preview of this weekend's matchup against Green Bay at Soldier Field.See omnystudio.com/listener for privacy information.

In this segment of The Mark Reardon Show, Mark is joined by Amee Colvin with the Lutheran Elementary School Association. This Saturday is the annual "Brew in the Lou" event in Francis Park.

Gordon chats with Jamie about his recent trip to Metlife Stadium, and while the result isn't what he hoped for, the whole experience exceeded expectations. We then hear from Napier Knights running back Jude Colvin who has secured a place at the NFL Academy. Jamie asks about his journey so far and his hopes for the future. 

Tween and teen brains are like bumpy dirt roads under construction and will be for quite some time! Neuropsychologist Dr. Molly Colvin helps us understand the upsides and downsides of the adolescent brain and offers practical pathways to communicate with and support the kids in our lives. Show Notes: Join our LESS AWKWARD MEMBERSHIP  For FREE breakfast for life, go to HelloFresh.com/FREETISA. That's one free breakfast item per box while subscription is active! Go to Quince.com/awkward for free shipping and 365-day returns Get 25% off your first order of Phyla, when you visit PhylaBiotics.com and use the code PUBERTY at checkout! Order our book This Is So Awkward Check out all our speaking and curriculum at www.lessawkward.com and our super comfy products at www.myoomla.com To bring us to your school or community email operations@lessawkward.com To submit listener questions email podcast@lessawkward.com Watch the full episode on Youtube! Produced by Peoples Media Learn more about your ad choices. Visit megaphone.fm/adchoices

Today, we're welcoming Cooper Colvin & Preston Conley to the studio to talk hoops. This duo is a lot of fun to talk to. Special Guest Co-Host Ginna Bess Carr joins us today, as well. She just started college, so we've got a lot of catching up to do. And, Greg Glasscock fills us in on the latest in local sports. Hang out with us for a while & share us with a friend! If you'd like to get the full effect of the show, see today's episode on YouTube. And, you can join us live there next Tuesday afternoon, from 2:30-4:30 central.

Ever heard of Brew in the Lou?  Join us as we talk to Libby and Amee of Lutheran Elementary Schools about a number of topics, including an important naming, revealed!  

Matthew Bannister on Mike Lynch, who was one of the UK's most successful tech entrepreneurs.Nell McCafferty, the Irish journalist and feminist campaigner who took part in the “contraceptive train” protest in 1971.Ruth Colvin, who founded the American “Literacy Volunteers” organisation to help adults who couldn't read or writeSven-Göran Eriksson, the Swedish born football manager who became the first non-British coach of the England football team.Interviewee: Suranga Chandratillake Interviewee: David Davis Interviewee: Susan McKay Interviewee: Judy Cheatham Interviewee: Henry WinterProducers: Ben Mitchell and Ed PrendevilleArchive: Contraceptives From Belfast, RTE, 1971; Witness, BBC News, 03/12/2016; Ruth Colvin, All things considered, NPR, 10/12/2006; Ruth Colvin, Onondaga Historical Association, YouTube Upload, 07/07/2020; Ruth Colvin, Onondaga Historical Association, YouTube Upload, 01/12/2016; The Real Sven-Goran Eriksson, BBC Radio 5Live; Kammy & Ben's Proper Football Podcast, BBC, 18/11/2022; BBC News at Ten; 20/11/2012; PM, BBC Radio 4, 01/08/24; The Report, BBC Radio 4, 27/12/12

Mothman has recently been spotted around the Chicago area. This winged cryptid is considered a harbinger of some kind, whereby synchronistic events occur, shortly after it's sighting. Some have even theorized that it is some sort of Psychopomp, like Azreal, the Angel of Death. Tonight on Ground Zero, Clyde Lewis talks with Mothman researcher and author, Andy Colvin about HARBINGER AZRAEL.Original Broadcast On 6/16/21

This week we take a trip to Mexico, a petrostate that just elected climate scientist Claudia Sheinbaum as its next president. She's also the former mayor of Mexico City, the largest city in North America, which has been going through a major water crisis due to climate change. It's at risk of running out of water — and it has been for a long time. In fact, much of the country is coping with drought and heat waves exacerbated by climate change. Christine Colvin, a hydrogeologist with WWF International, was in Cape Town, South Africa, at the height of a recent megadrought. The city was approaching Day Zero, when it would not be able to supply water to residents. Colvin says that of all the ways climate disruption impacts our lives, the most critical may be to our relationship with water.  "If the climate crisis is a shark, then water are its teeth. This is the thing that's really going to bite us first and hardest."  Guests: Oscar Ocampo, Coordinator for Energy and Environment, Mexican Institute of Competitiveness Christine Colvin, Water Policy Lead, WWF International

This week we take a trip to Mexico, a petrostate that just elected climate scientist Claudia Sheinbaum as its next president. She's also the former mayor of Mexico City, the largest city in North America, which has been going through a major water crisis due to climate change. It's at risk of running out of water — and it has been for a long time. In fact, much of the country is coping with drought and heat waves exacerbated by climate change. Christine Colvin, a hydrogeologist with WWF International, was in Cape Town, South Africa, at the height of a recent megadrought. The city was approaching Day Zero, when it would not be able to supply water to residents. Colvin says that of all the ways climate disruption impacts our lives, the most critical may be to our relationship with water.  "If the climate crisis is a shark, then water are its teeth. This is the thing that's really going to bite us first and hardest."  Guests: Oscar Ocampo, Coordinator for Energy and Environment, Mexican Institute of Competitiveness Christine Colvin, Water Policy Lead, WWF International Climate One has three exciting live shows on the calendar, featuring live conversations with Tom Steyer, Jane Goodall, and Justin Pearson. Tickets are on sale now. Support Climate One by going ad-free! By subscribing to Climate One on Patreon, you'll receive exclusive access to all future episodes free of ads, opportunities to connect with fellow Climate One listeners and access to the Climate One Discord. Sign up today for just $5/month. For show notes and related links, visit our website. Learn more about your ad choices. Visit megaphone.fm/adchoices

CommonWealth Herbs is hosting their semi-annual 20% off sale! Use coupon code LAVENDER at checkout to get 20% off ANY OF THEIR COURSES for the entire month of July. LEARN MORE & REGISTER | CommonWealthHerbs.com In this episode of The Herbalist Hour I'm joined by Mary Colvin. We chat about her herbal origins, her new book "The Herbalist's Guide", the writing process, the medicine of motherwort, bee balm and violet, mentoring new herbalists and a TON more.  A huge THANK YOU to Mary for joinin' me on the show. And thanks to you, dear listener, for tunin' in! See you in the next episode,  ~Mason  LINKS & RESOURCES Ancestral Herbology | AncestralHerbology.com BOOK: The Herbalist's Guide | BUY THE BOOK Herbalist Mentor | HerbalistMentor.com Mary on Instagram | @ancestralherbology Mary on TikTok | @simpletrainingherbalist

In Episode 346 Jeff Belanger and Ray Auger investigate the small town of Manchester, Vermont, to explore the unsolved 1812 murder of Russell Colvin. Years after the murder, Amos Boorn had a dream where Russell's ghost came to him and told him he'd been murdered, and his remains could be found on the Boorn Farm. After sharing details of his dream, an investigation begins, bones are found, and Amos's nephews Jesse and Stephen Boorn are arrested and convicted of murder. But did they do it?   See more here: https://ournewenglandlegends.com/podcast-346-the-unsolved-murder-or-russell-colvin/   Listen ad-free plus get early access and bonus episodes at: https://www.patreon.com/NewEnglandLegends