Podcasts about pythonic

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

Listen to this interview of Zejun Zhang, Research Scientist, Nanyang Technological University, Singapore. We talk about her coauthored paper Hard to Read and Understand Pythonic Idioms? DeIdiom and Explain Them in Non-Idiomatic Equivalent Code (ICSE 2024). Zejun Zhang : "Following my presentation of the paper at ICSE, it was interesting. I mean, there was, first off, a lot of positive response, but then some people in the audience were asking why we would research the readability of Pythonic idioms, and also, why we would translate those idioms into non-idiomatic code. Now, these questions were coming in relation to our previous work on idiomatic code. Nonetheless, the effect for me was that, for future work, we need to further explore this line of the research and really explain Pythonic idioms so that developers can deeply understand them." Learn more about your ad choices. Visit megaphone.fm/adchoices Support our show by becoming a premium member! https://newbooksnetwork.supportingcast.fm/new-books-network

Listen to this interview of Zejun Zhang, Research Scientist, Nanyang Technological University, Singapore. We talk about her coauthored paper Hard to Read and Understand Pythonic Idioms? DeIdiom and Explain Them in Non-Idiomatic Equivalent Code (ICSE 2024). Zejun Zhang : "Following my presentation of the paper at ICSE, it was interesting. I mean, there was, first off, a lot of positive response, but then some people in the audience were asking why we would research the readability of Pythonic idioms, and also, why we would translate those idioms into non-idiomatic code. Now, these questions were coming in relation to our previous work on idiomatic code. Nonetheless, the effect for me was that, for future work, we need to further explore this line of the research and really explain Pythonic idioms so that developers can deeply understand them." Learn more about your ad choices. Visit megaphone.fm/adchoices

Halloween isn't just about collecting candy and cringy Wednesday costumes. Music plays a big part of the spooky season.   Whether it's awesome horror film soundtracks or horror related guitar rock n roll, music provides the perfect atmosphere sounds for your October. But over the years we've noticed some people's idea of "Halloween" music doesn't quite cut a drunken teenager at Summer camp. So this week we compiled some songs that are debatable and we our "Yay" or "Nay".   In our "News, Views and Tunes", we're off and running with our annual October Horror Movie Challenge and address the Taylor Swift charity guitar incident. Musically, we crank some Municipal Waste, Hallows Eve, Piledriver, Slasher Dave, Subhumans, Messer Chups and introduce Pythonic in our "Indie Spotlight".   Horns Up and Happy Halloween!!

Track Listing: 1. Positive Deconstruction (5:19) 2. Love H8 (4:02) 3. Pills (3:09) 4. Practise What You Preach (5:13) 5. Epica (5:00) Learn more about your ad choices. Visit megaphone.fm/adchoices

PYTHONIC's Takes A Thrashy Look At Addiction In New Single “Pills” Off Upcoming “EP 1” Out Sept 27, 2024.Take some zany and serious journeys with The Pipeman aka Dean K. Piper, CST on The Adventures of Pipeman also known as Pipeman Radio syndicated globally “Where Who Knows And Anything Goes”.Check out our segment Positively Pipeman dedicated to Business, Motivation, Spiritual, and Health & Wellness.Check out our segment Pipeman in the Pit dedicated to Music, Artistry and EntertainmentSubscribe to The Adventures of Pipeman at https://www.spreaker.com/podcast/the-adventures-of-pipeman--941822/supportWould you like to be a sponsor of the show?Would you like to have your business, products, services, merch, programs, books, music or any other professional or artistic endeavors promoted on the show?Would you like interviewed as a professional or music guest on The Adventures of Pipeman, Positively Pipeman and/or Pipeman in the Pit?Would you like to host your own Radio Show, Streaming TV Show, or Podcast?Contact the Pipeman:Phone/Text Contact – 561-506-4031Email Contact – dean@talk4media.com Follow @pipemanradio on all social media outlets Visit Pipeman Radio on the Web at linktr.ee/pipemanradio, theadventuresofpipeman.com, pipemanradio.com, talk4media.com, w4cy.com, talk4tv.com, talk4podcasting.com. Download The Pipeman Radio APP The Adventures of Pipeman is broadcast live Wednesdays at 1PM ET and Music & Positive Interviews daily at 8AM ET on W4CY Radio (www.w4cy.com) and replays on K4HD Radio (www.k4hd.com – Hollywood Talk Radio part of Talk 4 Radio (www.talk4radio.com) on the Talk 4 Media Network (www.talk4media.com). The Adventures of Pipeman TV Show is viewed on Talk 4 TV (www.talk4tv.com). The Adventures of Pipeman Podcast is also available on www.theadventuresofpipeman.com and www.pipemanradio.com, Talk 4 Media (www.talk4media.com), Talk 4 Podcasting (www.talk4podcasting.com), iHeartRadio, Amazon Music, Pandora, Spotify, Audible, and over 100 other podcast outlets.

PYTHONIC's Takes A Thrashy Look At Addiction In New Single “Pills” Off Upcoming “EP 1” Out Sept 27, 2024.Take some zany and serious journeys with The Pipeman aka Dean K. Piper, CST on The Adventures of Pipeman also known as Pipeman Radio syndicated globally “Where Who Knows And Anything Goes”.Check out our segment Positively Pipeman dedicated to Business, Motivation, Spiritual, and Health & Wellness.Check out our segment Pipeman in the Pit dedicated to Music, Artistry and EntertainmentSubscribe to The Adventures of Pipeman at https://www.spreaker.com/podcast/the-adventures-of-pipeman--941822/supportWould you like to be a sponsor of the show?Would you like to have your business, products, services, merch, programs, books, music or any other professional or artistic endeavors promoted on the show?Would you like interviewed as a professional or music guest on The Adventures of Pipeman, Positively Pipeman and/or Pipeman in the Pit?Would you like to host your own Radio Show, Streaming TV Show, or Podcast?Contact the Pipeman:Phone/Text Contact – 561-506-4031Email Contact – dean@talk4media.com Follow @pipemanradio on all social media outlets Visit Pipeman Radio on the Web at linktr.ee/pipemanradio, theadventuresofpipeman.com, pipemanradio.com, talk4media.com, w4cy.com, talk4tv.com, talk4podcasting.com. Download The Pipeman Radio APP The Adventures of Pipeman is broadcast live Wednesdays at 1PM ET and Music & Positive Interviews daily at 8AM ET on W4CY Radio (www.w4cy.com) and replays on K4HD Radio (www.k4hd.com – Hollywood Talk Radio part of Talk 4 Radio (www.talk4radio.com) on the Talk 4 Media Network (www.talk4media.com). The Adventures of Pipeman TV Show is viewed on Talk 4 TV (www.talk4tv.com). The Adventures of Pipeman Podcast is also available on www.theadventuresofpipeman.com and www.pipemanradio.com, Talk 4 Media (www.talk4media.com), Talk 4 Podcasting (www.talk4podcasting.com), iHeartRadio, Amazon Music, Pandora, Spotify, Audible, and over 100 other podcast outlets.

Rich Embury's R3TROGR4D3 returns to CRANK IT LOUD with more brand NEW Hard Rock & Metal from Nothing More / A Killer's Confession / Tuk Smith & The Restless Hearts / Crossbone Skully / Snarm / We're Wolves / Rich Kid Express / Stone Broken / Majestica / Ill Niño / Shallow Side / Black […]

ThursdAI October 26thTimestamps and full transcript for your convinience## [00:00:00] Intro and brief updates## [00:02:00] Interview with Bo Weng, author of Jina Embeddings V2## [00:33:40] Hugging Face open sourcing a fast Text Embeddings## [00:36:52] Data Provenance Initiative at dataprovenance.org## [00:39:27] LocalLLama effort to compare 39 open source LLMs +## [00:53:13] Gradio Interview with Abubakar, Xenova, Yuichiro## [00:56:13] Gradio effects on the open source LLM ecosystem## [01:02:23] Gradio local URL via Gradio Proxy## [01:07:10] Local inference on device with Gradio - Lite## [01:14:02] Transformers.js integration with Gradio-lite## [01:28:00] Recap and bye byeHey everyone, welcome to ThursdAI, this is Alex Volkov, I'm very happy to bring you another weekly installment of

Are you writing efficient Python with as few lines of code as possible? Are you familiar with the many built-in language features that will simplify your code and make it more Pythonic? Christopher Trudeau is back on the show this week, bringing another batch of PyCoder's Weekly articles and projects.

Watch on YouTube About the show Sponsored by us! Support our work through: Our courses at Talk Python Training Test & Code Podcast Patreon Supporters Connect with the hosts Michael: @mkennedy@fosstodon.org Brian: @brianokken@fosstodon.org Show: @pythonbytes@fosstodon.org Join us on YouTube at pythonbytes.fm/live to be part of the audience. Usually Tuesdays at 11am PT. Older video versions available there too. Brian #1: Plumbum: Shell Combinators and More Suggested by Henry Schreiner last week. (Also, thanks Michael for the awesome search tool on PythonBytes.fm that includes transcripts, so I can find stuff discussed and not just stuff listed in the show notes.) Plumbum is “ a small yet feature-rich library for shell script-like programs in Python. The motto of the library is “Never write shell scripts again”, and thus it attempts to mimic the shell syntax (shell combinators) where it makes sense, while keeping it all Pythonic and cross-platform.” Supports local commands piping redirection working directory changes in a with block. So cool. lots more fun features Michael #2: Our plan for Python 3.13 The big difference is that we have now finished the foundational work that we need: Low impact monitoring (PEP 669) is implemented. The bytecode compiler is a much better state. The interpreter generator is working. Experiments on the register machine are complete. We have a viable approach to create a low-overhead maintainable machine code generator, based on copy-and-patch. We plan three parallelizable pieces of work for 3.13: The tier 2 optimizer Enabling subinterpreters from Python code (PEP 554). Memory management Details on superblocks Brian #3: Some blogging myths Julia Evans myths (more info of each in the blog post): you need to be original you need to be an expert posts need to be 100% correct writing boring posts is bad you need to explain every concept page views matter more material is always better everyone should blog I'd add Write posts to help yourself remember something. Write posts to help future prospective employers know what topics you care about. You know when you find a post that is outdated and now wrong, and the code doesn't work, but the topic is interesting to you. Go ahead and try to write a better post with code that works. Michael #4: Jupyter AI A generative AI extension for JupyterLab An %%ai magic that turns the Jupyter notebook into a reproducible generative AI playground. This works anywhere the IPython kernel runs (JupyterLab, Jupyter Notebook, Google Colab, VSCode, etc.). A native chat UI in JupyterLab that enables you to work with generative AI as a conversational assistant. Support for a wide range of generative model providers and models (AI21, Anthropic, Cohere, Hugging Face, OpenAI, SageMaker, etc.). Official project from Jupyter Provides code insights Debug failing code Provides a general interface for interaction and experimentation with currently available LLMs Lets you collaborate with peers and an Al in JupyterLab Lets you ask questions about local files Video presentation: David Qiu - Jupyter AI — Bringing Generative AI to Jupyter | PyData Seattle 2023 Extras Brian: Textual has some fun releases recently Textualize youtube channel with 3 tutorials so far trogon to turn Click based command line apps into TUIs video example of it working with sqlite-utils. Python in VSCode June Release includes revamped test discovery and execution. You have to turn it on though, as the changes are experimental: "python.experiments.optInto": [ "pythonTestAdapter", ] I just turned it on, so I haven't formed an opinion yet. Michael: Michael's take on the MacBook Air 15” (black one) Joke: Phishing

Spoiler Alert = Mind-Blowing ContentHave you ever asked yourself if someone has thought about how to unwrap and simplify stream processing?Or, would stream processing be available in Python using the best-of-breed libraries and Pythonic code and yet scale horizontally?If you're about to listen to this episode, this one may not only change your mind but also would drastically change the way you look at Python.Zander Matheson is a distinguished Python engineer and founder of the Bytewax company, which aims to simplify real-time stream processing using today's lingua franca, Python.Bytewax is an Open-Source Python framework that simplifies end-to-end data pipeline workflows. It offers a non-JVM approach to abstract and solves complex data problems.Hence, it's wicked fast and straightforward to use. It also offers a CLI where you can easily deploy the DataFlows.In this episode, we wade through the following topics:Python for Data Engineers and ScientistsPopularity and LibrariesBytewax & MotivatorsMain CapabilitiesAdoptionUpcoming FeaturesZander Matheson = https://www.linkedin.com/in/alexandermatheson/Bytewax = https://bytewax.io/   Luan Moreno = https://www.linkedin.com/in/luanmoreno/

#oracle #shamanism #ascensionThis month we ask the earth Serpent 'How do we perceive reality?'Now is our time of transition, individually and collectively, within the liminal space of ‘becoming'.  We, as Pythia, come together to offer wisdom teachings from the earth serpent herself; Python.  Our wish is that these woven utterings will assist you on your unfolding journey as we work together to co-create a world born of our deepest most loving visions.  Each of us has been trained in the ancient oracular art of Seership and as we bring forth information from deep within the earth, the invitation is to allow the collective weave of our ‘seeing' to meld with your being in whatever way serves you best, in this moment, in this space, in this time.  We recommend you listen with your eyes closed or covered and that - for added potency - you breathe the weave into every cell of your body as we speak it. Each month we will bring a new question to Python and if you have a question you would like answered please feel free to send it to us: landscape_elms@yahoo.co.uk or; solhengeretreats@gmail.com.Interesting Greek info:    In Ancient Greece, Pythia is the High Priestess and Oracle of Delphi residing in the Temple of Apollo with a sacred stone, the Omphalos.  Covered in a protective weave and hollow inside, the Omphalos stands as the centre of power, a womb haven for healing, replenishment and revelation.  It is also the mystical dwelling place of Python herself and the sacred cave into which Pythia descends in order to commune with the great serpent.  Pre patriarchy Pythia is the interface between humankind and Spirit.  Mistress of the liminal space, Pythia honours Earth and Spirit alike and as a potent governing force she stands on the fissure of betwixt and between bringing to light prophecies and wisdoms from the depths of the  Pythonic realms.Emma Smart - Shamanic Healer, Teacher and Practitioner; Specialising in Women's ancient healing and sexual energy praxisAuthor of  "The Sacred Dance". A short tale of Shamanic Pilgrimage. landscape_elms@yahoo.co.uk,Sarita Cameron – Shamanic Healer, Teacher. Meditation Teacher and conduit for Integration into Galatic Knosis www.solhenge.com, https://www.patreon.com/Sarita_SolHengeJoy Ellis - Shamanic Practitioner, Homeopath, Oneiric work.www.homeopathy4all.co.uk  +44 7989147050Sarah-Jayne Grace - Therapeutic Embodiment Coach, empowering women to reclaim their wild, authentic selves and create a life they love. www.thewildembrace.com Chelsy Arber is an artist and seer specialising in oracular work, dream illumination, and transformative, talismanic objects; gnosticatemple@gmail.com and IG @ars.limina

How do you test whether your web sites are working well? Unit tests are great. But for web apps, the number of pieces that have to click together "just so" are many. You have databases, server code (such as a Flask app), server templates (Jinja for example), CSS, Javascript, and even deployment topologies (think nginx + uvicorn). Unit tests won't cover all of that integration. But Playwright does. Playwright is a modern, Pythonic take on testing webs apps using code driving a browser core to interact with web apps the way real users and API clients do. I think you'll find a lot to like there. And we have Pandy Knight from Automation Panda here to break it down for us. Links from the show Pandy's Twitter: @AutomationPanda Pandy's blog: automationpanda.com Playwright: playwright.dev Pandy's Playwright tutorial: github.com pytest: pytest.org applitools: applitools.com Screenplay package: pypi.org/project/screenplay Watch this episode on YouTube: youtube.com Episode transcripts: talkpython.fm --- Stay in touch with us --- Subscribe to us on YouTube: youtube.com Follow Talk Python on Twitter: @talkpython Follow Michael on Twitter: @mkennedy Sponsors Microsoft RedHat AssemblyAI Talk Python Training

#oracle #shamanism #ascensionThis month we ask the earth Serpent 'What is the brain?'Now is our time of transition, individually and collectively, within the liminal space of ‘becoming'.  We, as Pythia, come together to offer wisdom teachings from the earth serpent herself; Python.  Our wish is that these woven utterings will assist you on your unfolding journey as we work together to co-create a world born of our deepest most loving visions.  Each of us has been trained in the ancient oracular art of Seership and as we bring forth information from deep within the earth, the invitation is to allow the collective weave of our ‘seeing' to meld with your being in whatever way serves you best, in this moment, in this space, in this time.  We recommend you listen with your eyes closed or covered and that - for added potency - you breathe the weave into every cell of your body as we speak it. Each month we will bring a new question to Python and if you have a question you would like answered please feel free to send it to us: landscape_elms@yahoo.co.uk or; solhengeretreats@gmail.com.Interesting Greek info:    In Ancient Greece, Pythia is the High Priestess and Oracle of Delphi residing in the Temple of Apollo with a sacred stone, the Omphalos.  Covered in a protective weave and hollow inside, the Omphalos stands as the centre of power, a womb haven for healing, replenishment and revelation.  It is also the mystical dwelling place of Python herself and the sacred cave into which Pythia descends in order to commune with the great serpent.  Pre patriarchy Pythia is the interface between humankind and Spirit.  Mistress of the liminal space, Pythia honours Earth and Spirit alike and as a potent governing force she stands on the fissure of betwixt and between bringing to light prophecies and wisdoms from the depths of the  Pythonic realms.Emma Smart - Shamanic Healer, Teacher and Practitioner; Specialising in Women's ancient healing and sexual energy praxisAuthor of  "The Sacred Dance". A short tale of Shamanic Pilgrimage. landscape_elms@yahoo.co.uk,Sarita Cameron – Shamanic Healer, Teacher. Meditation Teacher and conduit for Integration into Galatic Knosis www.solhenge.com, https://www.patreon.com/Sarita_SolHengeJoy Ellis - Shamanic Practitioner, Homeopath, Oneiric work.www.homeopathy4all.co.uk  +44 7989147050Sarah-Jayne Grace - Therapeutic Embodiment Coach, empowering women to reclaim their wild, authentic selves and create a life they love. www.thewildembrace.com Chelsy Arber is an artist and seer specialising in oracular work, dream illumination, and transformative, talismanic objects; gnosticatemple@gmail.com and IG @ars.limina

Hugo speaks with Peter Wang, CEO of Anaconda, about how Python became so big in data science, machine learning, and AI. They jump into many of the technical and sociological beginnings of Python being used for data science, a history of PyData, the conda distribution, and NUMFOCUS. They also talk about the emergence of online collaborative environments, particularly with respect to open source, and attempt to figure out the movings parts of PyData and why it has had the impact it has, including the fact that many core developers were not computer scientists or software engineers, but rather scientists and researchers building tools that they needed on an as-needed basis They also discuss the challenges in getting adoption for Python and the things that the PyData stack solves, those that it doesn't and what progress is being made there. People who have listened to Hugo podcast for some time may have recognized that he's interested in the sociology of the data science space and he really considered speaking with Peter a fascinating opportunity to delve into how the Pythonic data science space evolved, particularly with respect to tooling, not only because Peter had a front row seat for much of it, but that he was one of several key actors at various different points. On top of this, Hugo wanted to allow Peter's inner sociologist room to breathe and evolve in this conversation. What happens then is slightly experimental – Peter is a deep, broad, and occasionally hallucinatory thinker and Hugo wanted to explore new spaces with him so we hope you enjoy the experiments they play as they begin to discuss open-source software in the broader context of finite and infinite games and how OSS is a paradigm of humanity's ability to create generative, nourishing and anti-rivlarous systems where, by anti-rivalrous, we mean things that become more valuable for everyone the more people use them! But we need to be mindful of finite-game dynamics (for example, those driven by corporate incentives) co-opting and parasitizing the generative systems that we build. These are all considerations they delve far deeper into in Part 2 of this interview, which will be the next episode of VG, where we also dive into the relationship between OSS, tools, and venture capital, amonh many others things. LInks Peter on twitter (https://twitter.com/pwang) Anaconda Nucleus (https://anaconda.cloud/) Calling out SciPy on diversity (even though it hurts) (https://ilovesymposia.com/2015/04/03/calling-out-scipy-on-diversity/) by Juan Nunez-Iglesias Here Comes Everybody: The Power of Organizing Without Organizations (https://en.wikipedia.org/wiki/Here_Comes_Everybody_(book)) by Clay Shirky Finite and Infinite Games (https://en.wikipedia.org/wiki/Finite_and_Infinite_Games) by James Carse Governing the Commons: The Evolution of Institutions for Collective Action (https://www.cambridge.org/core/books/governing-the-commons/7AB7AE11BADA84409C34815CC288CD79) by Elinor Olstrom Elinor Ostrom's 8 Principles for Managing A Commmons (https://www.onthecommons.org/magazine/elinor-ostroms-8-principles-managing-commmons)

Pythonic is an adjective that means able to predict the future. The word python, best known as a deadly type of snake, comes directly from Greek. It meant ‘spirit of divination.' Here's an example of pythonic in use: Mindy's pythonic powers always impressed me. She predicted our horrible softball team would lose every game, and she was right every time.

Do you know the difference between creating a class instance and initializing it? Would you like an interactive tour of the Python Pillow library? This week on the show, Christopher Trudeau is here, and he's brought another batch of PyCoder's Weekly articles and projects.

Allen Wyma talks with Brenden Matthews, the author of the book Code Like a Pro in Rust. Contributing to Rustacean Station Rustacean Station is a community project; get in touch with us if you'd like to suggest an idea for an episode or offer your services as a host or audio editor! Twitter: @rustaceanfm Discord: Rustacean Station Github: @rustacean-station Email: hello@rustacean-station.org Timestamps [@0:41] - Brenden's Bio [@1:30] - Where the idea to write the book came from [@4:32] - Pythonic, Rustacious/Idiomatic Rust and other coding style terms [@6:25] - Writing idiomatic code [@10:19] - Helper methods [@12:34] - From trait [@15:20] - Into trait [@17:00] - Errors in Rust [@26:59] - Other languages borrowing Rust's ideas for memory safety and no null type [@29:21] - Kotlin, Dart, Swift & Zig [@30:58] - LLVM, Swift & Rust and evolution of languages [@35:32] - Backwards compatibility in Rust [@39:00] - Experiences and the improvements in Rust [@42:44] - Components are added manually, but should they be installed by default? [@48:16] - Knowing when to use libc and adding a C runtime [@59:58] - Who Code Like a Pro in Rust is written for Other Resources Brenden's Blog Credits Intro Theme: Aerocity Audio Editing: Plangora Hosting Infrastructure: Jon Gjengset Show Notes: Plangora Hosts: Allen Wyma

#oracle #shamanism #ascensionThis month we ask the earth Serpent How do we navigate change.Now is our time of transition, individually and collectively, within the liminal space of ‘becoming'.  We, as Pythia, come together to offer wisdom teachings from the earth serpent herself; Python.  Our wish is that these woven utterings will assist you on your unfolding journey as we work together to co-create a world born of our deepest most loving visions.  Each of us has been trained in the ancient oracular art of Seership and as we bring forth information from deep within the earth, the invitation is to allow the collective weave of our ‘seeing' to meld with your being in whatever way serves you best, in this moment, in this space, in this time.  We recommend you listen with your eyes closed or covered and that - for added potency - you breathe the weave into every cell of your body as we speak it. Each month we will bring a new question to Python and if you have a question you would like answered please feel free to send it to us: landscape_elms@yahoo.co.uk or; solhengeretreats@gmail.com.Interesting Greek info:    In Ancient Greece, Pythia is the High Priestess and Oracle of Delphi residing in the Temple of Apollo with a sacred stone, the Omphalos.  Covered in a protective weave and hollow inside, the Omphalos stands as the centre of power, a womb haven for healing, replenishment and revelation.  It is also the mystical dwelling place of Python herself and the sacred cave into which Pythia descends in order to commune with the great serpent.  Pre patriarchy Pythia is the interface between humankind and Spirit.  Mistress of the liminal space, Pythia honours Earth and Spirit alike and as a potent governing force she stands on the fissure of betwixt and between bringing to light prophecies and wisdoms from the depths of the  Pythonic realms.Emma Smart - Shamanic Healer, Teacher and Practitioner; Specialising in Women's ancient healing and sexual energy praxisAuthor of  "The Sacred Dance". A short tale of Shamanic Pilgrimage. landscape_elms@yahoo.co.uk,Sarita Cameron – Shamanic Healer, Teacher. Meditation Teacher and conduit for Integration into Galatic Knosis www.solhenge.com, https://www.patreon.com/Sarita_SolHengeJoy Ellis - Shamanic Practitioner, Homeopath, Oneiric work.www.homeopathy4all.co.uk  +44 7989147050Sarah Bullock - Therapeutic Embodiment Coach, empowering women to reclaim their wild, authentic selves and create a life they love. www.thewildembrace.com Chelsy Arber is an artist and seer specialising in oracular work, dream illumination, and transformative, talismanic objects; gnosticatemple@gmail.com and IG @ars.limina

I always enjoy a look into the lives and activities of those fortunate professionals who carry the title of 'Evangelist.' Hugo Bowne-Anderson, Head of Data Science Evangelism and Marketing for Coiled joined me from the future, Sydney, Australia for a conversation on all things Coiled, Dask, and the Pythonic ecosystem. Hugo shares a unique perspective, having been a practicing data scientist in the world of molecular cell biology, as well as an instructor, developing and delivering online interactive data sciences classes that have been enjoyed by over half a million learners. A big part of Hugo's mission is the capture and share the stories of how people are using data science to improve our lives, one application at a time. Thanks, Hugo. Show Notes LinkedIn Article YouTube Video (DISCLOSURE*: This interview was sponsored by Coiled. Neither Coiled nor other sponsors have editorial control over the content)

This week we talk with another member of our PyBites community: Robin Beer.He shares his coding journey, going from physicist to cloud software architect and co-founder of an energy startup.Some takeaways from our conversation:- Learning core Python and having a constant curious mindset gets you really far.- You have to become comfortable stepping outside of your comfort zone, specially in a high-paced startup environment. - How  his perception of Pythonic / quality code changed going from a coding physicist to a professional software architect.- How Python gave him more fulfilment in life, even to the point of becoming a digital nomad :)Reach out to Robin:- Twitter: https://twitter.com/R_E_Beer- Our community: https://pybit.es/community

Getting to grips with the HAFNIUM gang/vulnerabilities/exploits/webshells/attacks. Why it's important to think before you share those home-based selfies. What you need to know about social engineering. How (not!) to prove a point when you're a programmer. With Kimberly Truong and Paul Ducklin Original music by Edith Mudge Got questions/suggestions/stories to share? Email tips@sophos.com Twitter @NakedSecurity Instagram @NakedSecurity

Getting to grips with the HAFNIUM cybercrooks/vulnerabilities/exploits/webshells/attacks. Why it's important to think before you share those home-based selfies. What you need to know about social engineering. How (not!) to prove a point when you're a programmer. https://nakedsecurity.sophos.com/serious-security-webshells-explained-in-the-aftermath-of-hafnium https://nakedsecurity.sophos.com/i-see-you-your-home-working-photos-reveal-more-than-you-think https://nakedsecurity.sophos.com/s3-ep12-a-chat-with-social-engineering-hacker-rachel-tobac https://nakedsecurity.sophos.com/poison-packages-supply-chain-risks-user-hits-python-community With Kimberly Truong and Paul Ducklin. Original music by Edith Mudge (https://www.edithmudge.com) Got questions/suggestions/stories to share? Email: tips@sophos.com Twitter: NakedSecurity (https://twitter.com/nakedsecurity) Instagram: NakedSecurity (https://instagram.com/nakedsecurity)

Sponsored by Datadog: pythonbytes.fm/datadog Special guest: Brett Cannon Brian #1: Keeping up with Rich Will McGugan has been building Rich It looks like it’s on its way to becoming a full fledged TUI (text user interface) December: Live view: no blog post on that, I don’t think. January: Tree view: Rendering a tree view in the terminal with Python and Rich February: Layouts: Building Rich terminal dashboards fun fullscreen.py example, uses Live view Also, python -m rich will display a demo screen that shows tons of the stuff that Rich can do Many of the features also have a stand alone demo built in, like: $ python -m rich.layout $ python -m rich.tree $ python -m rich.live Although I haven’t figured out how to kill the live demo. it doesn’t seem to time out, and it eats Ctrl-C in my terminal. I’d really like to use Rich for interactive stuff, like keyboard interrupts and arrow keys and tab and such. It’d be fun. Which brings me to the bottom right corner of the python -m rich output. It includes a GitHub Sponsor link for Will. Also, Will, unless it’s a contradiction to RTD TOS, I think you should include a Sponsor link in the Rich documentation. Let’s convince Will to make Rich a full TUI. Michael #2: 12 requests per second If you take a look around the blogosphere at various benchmarks for Python web frameworks, you might start to feel pretty bad about your own setup. The incredible work of the guys at magic stack, getting 100,000 requests per second from uvloop in a single thread. There’s the FastAPI benchmarks Even more mind-blowing is Japronto which claims an insane 1.2 million requests per-second in a single thread But what about your “boring” Flask or Django app? And how realistic are these benchmarks? Usually, not very. Here’s an article diving into this for a “proper” ORM style app. 12 - 80 requests per sec: Both our sync workers are now hitting a blazing 12 requests per second

Zen coding in python

So you're excited about that next app you're about to build. You can visualize the APIs with the smooth scalability taking to the mobile apps. You can see how, finally, this time, you'll get deployment right and it'll be pure continuous delivery out of GitHub with zero downtime. What you're probably not dreaming about is writing yet another password reset form and integrating mail capabilities just for this purpose. Or how you'll securely store user accounts the right way this time. Don't worry, we got you covered. Our guests, Christos Matskas and John Patrick Dandison are here to cover a bunch of different libraries and techniques we can use for adding identity to our Python applications. Links from the show Christos on Twitter: @christosmatskas John Patrick Dandison on Twitter: @azureandchill shhgit live: shhgit.com Twitch channel for Christos and JP: twitch.tv/425show Passlib & Folding: passlib.readthedocs.io Microsoft Authentication Library: github.com/AzureAD authlib - JavaScript Object Signing and Encryption draft implementation: github.com django-allauth - Authentication app for Django that "just works": github.com django-oauth-toolkit - OAuth 2 goodies for Django: github.com python-oauth2 - A fully tested, abstract interface to creating OAuth clients and servers: github.com python-social-auth - An easy-to-setup social authentication mechanism: github.com Sponsors Talk Python Training Linode

Video Version: https://youtu.be/pgzEqhuGBd0 Subscribe here to the newsletter: https://tinyletter.com/sanyambhutani In this episode, Sanyam Bhutani talks Python to the amazing podcaster, content creator and educator: Michael Kennedy, the host of talk Python podcast and Python Bytes podcast, In this episode, they talk about Michael's journey in programming and with Python. Michael has been hosting the podcast for five years and has been in the programming world for even longer, they dive into what he's learned through this and his perspective, how it has evolved through creating content, and to creating these courses and, and eventually a business around it as well. Links: https://talkpython.fm/home Automating the saw: https://www.youtube.com/watch?v=JEImn7s7x1o The ML leads to 50 exoplanet discovery: https://www.techrepublic.com/article/machine-learning-algorithm-confirms-50-new-exoplanets-in-historic-first/ Follow: Michael Kennedy: https://twitter.com/mkennedy https://www.linkedin.com/in/mkennedy/ Sanyam Bhutani: https://twitter.com/bhutanisanyam1 Blog: sanyambhutani.com About: https://sanyambhutani.com/tag/chaitimedatascience/ A show for Interviews with Practitioners, Kagglers & Researchers and all things Data Science hosted by Sanyam Bhutani. You can expect weekly episodes every available as Video, Podcast, and blogposts. Intro track: Flow by LiQWYD https://soundcloud.com/liqwyd

Sponsored by Techmeme Ride Home podcast: pythonbytes.fm/ride Special guest: Steve Dower - @zooba Brian #1: Making Enums (as always, arguably) more Pythonic “I hate enums” Harry Percival Hilarious look at why enums are frustrating in Python and a semi-reasonable workaround to make them usable. Problems with enums of strings: Can’t directly compare enum elements with the values Having to use .value is dumb. Can’t do random choice of enum values Can’t convert directly to a list of values If you use IntEnum instead of Enum and use integer values instead of strings, it kinda works better. Making your own StringEnum also is better, but still doesn’t allow comparison. Solution: class BRAIN(str, Enum): SMALL = 'small' MEDIUM = 'medium' GALAXY = 'galaxy' def __str__(self) -> str: return str.__str__(self) Derive from both str and Enum, and add a *__str(self)__* method. Fixes everything except random.choice(). Michael #2: Python 3.10 will be up to 10% faster 4.5 years in the making, from Yury Selivanov work picked up by Pablo Galindo, Python core developer, Python 3.10/3.11 release manager LOAD_METHOD, CALL_METHOD, and LOAD_GLOBAL improved “Lot of conversations with Victor about his PEP 509, and he sent me a link to his amazing compilation of notes about CPython performance. One optimization that he pointed out to me was LOAD/CALL_METHOD opcodes, an idea first originated in PyPy.” There is a patch that implements this optimization Based on: LOAD_ATTR stores in its cache a pointer to the type of the object it works with, its tp_version_tag, and a hint for PyDict_GetItemHint. When we have a cache hit, LOAD_ATTR becomes super fast, since it only needs to lookup key/value in type's dict by a known offset (the real code is a bit more complex, to handle all edge cases of descriptor protocol etc). Steve #3: Python 3.9 and no more Windows 7 PEP 11 -- Removing support for little used platforms | Python.org Windows 7 - Microsoft Lifecycle | Microsoft Docs Default x64 download Brian #4: Writing Robust Bash Shell Scripts David Pashley Some great tips that I learned, and I’ve been writing bash scripts for decades. set -u : exits your script if you use an uninitialized variable set -e : exit the script if any statement returns a non-true return value. Prevents errors from snowballing. Expect the unexpected, like missing files, missing directories, etc. Be prepared for spaces in filenames. if [ "$filename" = "foo" ]; Using trap to handle interrupts, exits, terminal kills, to leave the system in a good state. Be careful of race conditions Be atomic Michael #5: Ideas for 5x faster CPython Twitter post by Anthony Shaw calling attention to roadmap by Mark Shannon Implementation plan for speeding up CPython: The overall aim is to speed up CPython by a factor of (approximately) five. We aim to do this in four distinct stages, each stage increasing the speed of CPython by (approximately) 50%: 1.5**4 ≈ 5 Each stage will be targeted at a separate release of CPython. Stage 1 -- Python 3.10: The key improvement for 3.10 will be an adaptive, specializing interpreter. The interpreter will adapt to types and values during execution, exploiting type stability in the program, without needing runtime code generation. Stage 2 -- Python 3.11: Improved performance for integers of less than one machine word. Faster calls and returns, through better handling of frames. Better object memory layout and reduced memory management overhead. Stage 3 -- Python 3.12 (requires runtime code generation): Simple "JIT" compiler for small regions. Stage 4 -- Python 3.13 (requires runtime code generation): Extend regions for compilation. Enhance compiler to generate superior machine code. Wild conversation over here. One excerpt, from Larry Hastings: Speaking as the Gilectomy guy: borrowed references are evil. The definition of the valid lifetime of a borrowed reference doesn't exist, because they are a hack (baked into the API!) that we mostly "get away with" just because of the GIL. If I still had wishes left on my monkey's paw I'd wish them away (1). (1) Unfortunately, I used my last wish back in February, wishing I could spend more time at home.* Steve #6: CPython core developer sprints Hosted by pythondiscord.com https://youtu.be/gXMdfBTcOfQ - Core dev Q&A Extras Brian: Tools I found recently that are kinda awesome in their own way - Brian mcbroken.com - Is the ice cream machine near you working? just a funny single purpose website vim-adventures.com - with a dash. Practice vim key bindings while playing an adventure game. Super cool. Joke: Hackobertfest 2020 t-shirt https://twitter.com/McCroden/status/1319646107790704640 5 Most Difficult Programming Languages in the World (Not really long enough for a full topic, but funny. I think I’ll cut short the last code example after we record) suggested by Troy Caudill Author: Lokajit Tikayatray malboge, intercal, brainf*, cow, and whitespace whitespace is my favorite: “Entire language depends on space, tab, and linefeed for writing any program. The Whitespace interpreter ignores Non-Whitespace characters and considers them as code comments.” Intercal is kinda great in that One thing I love about this article is that the author actually writes a “Hello World!” for each language. Examples of “Hello World!” malboge (=

Diesmal machen wir eine Testepisode zu Tests :). Wir sind zum ersten mal mit Aufnahmeequipment draussen unterwegs, weil es zuhause einfach zu heiss wurde. Dabei sind heute Ronny, Dominik und Jochen und wir reden über Tests in Python. Ist vielleicht ein bisschen django-lastig, aber viele der Punkte dürften auch auf andere Projekte übertragbar sein. Shownotes Unsere E-Mail für Fragen, Anregungen & Kommentare: hallo@python-podcast.de Wer und Wo Ambient Innovation PyCologne Meetup Django Meetup Köln Restaurant Spoerl Fabrik Zoom H6 HMC 660X Headset HA3D Kopfhörerverstärker News aus der Szene Django 3.1 Release Notes Django 3.1 Async Python 3.9 Release Candidate Buch zu Django: Two Scoops of Django 3.x Tests pytest Pythonic testing framework unittest built in testing framework Langsame Tests finden: django-slowtests Coverage für branch-coverage etc. xdist pytest plugin für verteilte Testausführung Buch von Adam Johnson: Speed Up Your Django Tests | Sein Blog Pareto Distribution kcachegrind Profiler Schnelleres Filesystem für Tests: dj-inmemorystorage django q für asynchrone Tasks Djangocon 2019 talk: Maintaning a Django codebase after 10k commits freezegun time mocking unittests.mock aus der Standardbibliothek cypress end to end tests für Javascript jest unittests für Javascript Öffentliches Tag auf konektom

Do you obsess about writing your code just the right way before you get started? Maybe you have some ugly code on your hands and you need to make it better. Either way, refactoring could be your ticket to happier days! On this episode, we'll talk through a powerful example of iteratively refactoring some code until we eventually turn our ugly duckling into a Pythonic beauty. Conor Hoekstra is our guest on this episode to talk us through refactoring some web scraping code. Links from the show The PyCon talk: youtube.com Presentation source code: github.com/codereport Conor on Twitter: @code_report Youtube channel: youtube.com/codereport Perf example exceptions vs. test: gist.github.com/mikeckennedy PyCon Online: us.pycon.org/2020/online RAPIDS AI project: rapids.ai Slides from presentation (with 9 refactoring steps): github.com/codereport Talk Python episode on Sourcery: talkpython.fm/266 pip for venv only environment variable PIP_REQUIRE_VIRTUALENV: docs.python-guide.org Sponsors Talk Python Training

Do you use Selenium, but have Cypress envy? What if you could have the same “Cypress” experience using Selenium WebDriver? In this episode Carlos Kidman, creator of Pylenium, will share his wrapper around the Selenium WebDriver Python API, which provides a more Pythonic interface. If you’re new to automation and/or are looking for an easier way to create automation tests, listen up!

Let's look at another programming language we use at bol.com: Python. At bol.com we use Python for Tools, Data Science and more. It has been around since 1991.What this episode coversIt was created by a Dutch guy: Guido van Rossum. Its design philosophy emphasizes code readability. The language's core philosophy is summarized in the document The Zen of Python (PEP 20), which includes aphorisms such as:Beautiful is better than ugly.Explicit is better than implicit.Simple is better than complex.Complex is better than complicated.Readability counts.All well, but what do we do with it? What do we use it for?In the earlier episode “How we tame Google Cloud resources with R2D2” we discussed the Deployment tool we built for our Software Engineers. This tool has been built in Python.But we do more great stuff in bol.com with python, at least that's what we heard in our tech community. We asked around and, not surprisingly, ended up in the Data Science area. So time to introduce our guests to the show….GuestsOlaf Meuwese – A software engineer who has worked mostly with and in Data Science teams in the last years.Bas Dunn – Data scientist, econometrician in Assortment and earlier in Partner Performance Management.Jasper Adegeest - Data scientist, since September 2019 at bol.com in the Personalization team.NotesThe zen of PythonPythonic: When a veteran Python developer (a Pythonista) calls portions of code not “Pythonic”, they usually mean that these lines of code do not follow the common guidelines and fail to express its intent in what is considered the best (hear: most readable) way.SKlearn: Machine learning module for PythonTensorflow: The core open source library to help you develop and train ML modelsDatawarehouse bigqueryJupyter Notebook: The Jupyter Notebook is an open-source web application that allows you to create and share documents that contain live code, equations, visualizations and narrative text.Flask: a lightweight WSGI web application frameworkFastapi: a modern, fast (high-performance), web framework for building APIs with Python 3.6+ based on standard Python type hints.Django: a high-level Python Web framework that encourages rapid development and clean, pragmatic designAxle - the opinionated framework to build Java apps within bol.com

Python for Parents or so you want your child to code? Sometimes it just takes a few creative ideas and a short amount of time to make some fun little projects. Sean and Kelly, with the help of Anthony Shaw and his lovely daughters, ‘ Zebra’ and ‘Jaguar’ have a little fun with this topic! The Shaws’ help us to dive into the topic of #pythonicparenting. Together we discuss things that a parent can do to help your child have the same passion for coding as you do. Or at least some interest! Whether you are an experienced coder or just a beginner, you can still find some projects that you can build with your kids at home. We encourage you to have a little fun, enjoy a lot of giggles and make loads of memories with your kids, while learning Python,. Special Guest: Anthony Shaw.

Sponsored by DigitalOcean: pythonbytes.fm/digitalocean - $100 credit for new users to build something awesome. Michael #1: Python visualization graph via Prayson Daniel The PyViz.org website is an open platform for helping users decide on the best open-source (OSS) Python data visualization tools for their purposes, with links, overviews, comparisons, and examples. Overviews of the OSS visualization packages High-level tools for getting started A live table for comparing maturity, popularity, and support. Dashboarding tools SciVis tools for rendering data embedded in three-dimensional space. Tutorials Topic examples of using Python viz tools to analyze or describe specific datasets Brian #2: Awesome Zen of Python A Rabbit Hole lot of Zen yes, I know, that’s a terrible mixed metaphor List of articles on “the Zen of Python” Well, articles, talks, tools, and “other?” Al Sweigart: The Zen of Python, Explained is a nice quick reference. Moshe Zadka: Meditations on the Zen of Python is slightly longer, but good and still a quick read. One line (“There should be one-- and preferably only one --obvious way to do it.”) is a joke making fun of pre-decrement, post-decrement in C. Abdur-Rahmaan Janhangeer: The Zen Of Python Is A Joke And Here Is Why is a must read. Michael #3: Jupytext via Matt Harrison Jupyter Notebooks as Markdown Documents, Julia, Python or R scripts Wished Jupyter notebooks were plain text documents? Wished you could edit them in your favorite IDE? And get clear and meaningful diffs when doing version control? Then... Jupytext may well be the tool you're looking for! Jupytext can save Jupyter notebooks as Markdown and R Markdown documents Scripts in many languages. The languages that are currently supported by Jupytext are: Julia, Python, R, Bash, Scheme, Clojure, Matlab, Octave, C++, q/kdb+, IDL, TypeScript, Javascript, Scala, Rust/Evxcr, PowerShell, C#, F#, and Robot Framework. Brian #4: Tour of Python Itertools Martin Heinz Very cool quick look at some of the cool-ness to be found in itertools and more_itertools. itertools compress - one iterator to another eliminating elements that fail a bool expression accumulate - like functools.reduce but returns all intermediate values cycle - so cool, create a never ending repeating iterable tee - multiple references to one iterable more_itertools divide - divides iterable into sub-iterables partition - split into two based on a predicate bool expression side_effect - attach a side effect function to an iterable that gets called with each element collapse - like flatten split_at - multiple iterables splitting at divider items, specified with predicate bucket - multiple iterables based on multi-return-value expression map_reduce - specify 3 functions: key function (for categorizing), value function (for transforming) and finally reduce function (for reducing). sort_together seekable filter_except unique_to_each Michael #5: justpy.io JustPy is an object-oriented, component based, high-level Python Web Framework that requires no front-end programming. JustPy has no front-end/back-end distinction. All programming is done on the back-end allowing a simpler, more productive, and more Pythonic web development experience. JustPy removes the front-end/back-end distinction by intercepting the relevant events on the front-end and sending them to the back-end to be processed. Elements on the web page are instances of component classes. A component in JustPy is a Python class that allows you to instantiate reusable custom elements whose functionality and design is encapsulated away from the rest of your code. Custom components can be created using other components as building blocks. Out of the box, JustPy comes with support for HTML and SVG components as well as more complex components such as charts and grids. Supports most of the components and the functionality of the Quasar library Based on solid libraries: Starlette, uvicorn, and Vue.js. Brian #6: Modularity for Maintenance Glyph A list of many automation tools you can use to help with the maintenance of open source projects. CI, tox, linting, type checking, dependencies, security, coverage, formatting, releasing with lots of options and links A request for some kind of tool to help automate all the automation when starting new projects. Maybe a cookie-cutter thing…. That would be cool. But frankly, the list is super helpful also. Extras: Brian: Sentry helping fund some OSS projects. black, pypi, pytest, structlog, gimli (last one is a Rust thing). Michael: Just launched a new 7.2 hour course: Python for absolute beginners Talk Python Training now streaming newest courses in HiDPI (nearly 4K) and it’s super crisp. More details here. AWS Cloud has decided to no longer publish awscli to #pypi pulling a 700M+ download package (via Anthony Sottile) The podcast RSS feed is a little smaller now. Joke: First law of software quality: e = mc^2 → errors = (more code)^2.

Are you learning or helping someone else learn Python, why not make a game out of it? TwilioQuest is a game that doesn't treat you with kid-gloves while teaching you Python. Using your editor of choice, write code on your machine, and still play the game to solve Python challenges. In this episode, you'll meet Kevin Whinnery and Ryan Kubik from Twilio, who created TwiloQuest. They are here to tell us all about it. Links from the show TwilioQuest: twilio.com/quest/learn/python Ryan on Twitter: @ryrykubes Kevin on Twitter: @kevinwhinnery TwilioQuest on Twitter: @twilioquest Tiled map editor: mapeditor.org Phaser: phaser.io Free game assets: kenney.nl Open Game Art: opengameart.org Sponsors Datadog Linode Talk Python Training

Sponsored by us! Support us by visiting pythonbytes.fm/biz [courses] and pythonbytes.fm/pytest [book], or becoming a patron at patreon.com/pythonbytes Brian #1: Meditations on the Zen of Python Moshe Zadka The Zen of Python is not "the rules of Python" or "guidelines of Python". It is full of contradiction and allusion. It is not intended to be followed: it is intended to be meditated upon. Moshe give some of his thoughts on the different lines of the Zen of Python. Full Zen of Python can be found here or in a REPL with import this A few Beautiful is better than ugly Consistency helps. So black, flake8, pylint are useful. “But even more important, only humans can judge what humans find beautiful. Code reviews and a collaborative approach to writing code are the only realistic way to build beautiful code. Listening to other people is an important skill in software development.” Complex is better than complicated. “When solving a hard problem, it is often the case that no simple solution will do. In that case, the most Pythonic strategy is to go "bottom-up." Build simple tools and combine them to solve the problem.” Readability counts “In the face of immense pressure to throw readability to the side and just "solve the problem," the Zen of Python reminds us: readability counts. Writing the code so it can be read is a form of compassion for yourself and others.” Michael #2: nginx raided by Russian police Russian police have raided today the Moscow offices of NGINX, Inc., a subsidiary of F5 Networks and the company behind the internet's most popular web server technology. Russian search engine Rambler.ru claims full ownership of NGINX code. Rambler claims that Igor Sysoev developed NGINX while he was working as a system administrator for the company, hence they are the rightful owner of the project. Sysoev never denied creating NGINX while working at Rambler. In a 2012 interview, Sysoev claimed he developed NGINX in his free time and that Rambler wasn't even aware of it for years. Update Promptly following the event we took measures to ensure the security of our master software builds for NGINX, NGINX Plus, NGINX WAF and NGINX Unit—all of which are stored on servers outside of Russia. No other products are developed within Russia. F5 remains committed to innovating with NGINX, NGINX Plus, NGINX WAF and NGINX Unit, and we will continue to provide the best-in-class support you’ve come to expect. Brian #3: I'm not feeling the async pressure Armin Ronacher “Async is all the rage.” But before you go there, make sure you understand flow control and back pressure. “…back pressure is resistance that opposes the flow of data through a system. Back pressure sounds quite negative … but it's here to save your day.” If parts of your system are async, you have to make sure the entire flow throw the system doesn’t have overflow points. An example shown with reader/writer that is way hairier than you’d think it should be. “New Footguns: async/await is great but it encourages writing stuff that will behave catastrophically when overloaded.” “So for you developers of async libraries here is a new year's resolution for you: give back pressure and flow control the importance they deserve in documentation and API.” Michael #4: codetiming from Real Python via Doug Farrell A flexible, customizable timer for your Python code For a complete tutorial on how codetiming works, see Python Timer Functions: Three Ways to Monitor Your Code on Real Python. Time your code via A timer class A decorator A context manager Brian #5: Making Python Programs Blazingly Fast Martin Heinz Seemed like a good followup to the last topic Profiling with command line time python something.py python -m cProfile -s time something.py timing functions with wrapper Misses timeit, but see that also, https://docs.python.org/3.8/library/timeit.html How to make things faster: use built in types over custom types caching/memoization with lru_cache use local variables and local aliases when looping use functions… (kinda duh, but sure). don’t repeatedly access attributes in loops use f-strings over other formatting use generators. or at least experiment with them. the memory savings could result in speedup Michael #6: LocalStack via Graham Williamson and Jan 'oglop' Gazda A fully functional local AWS cloud stack. Develop and test your cloud & Serverless apps offline! LocalStack spins up the following core Cloud APIs on your local machine: S3, DynamoDB, Lambda, Elasticsearch see many more services paid one has more LocalStack builds on existing best-of-breed mocking/testing tools, most notably kinesalite/dynalite and moto. While these tools are awesome (!), they lack functionality for certain use cases. LocalStack combines the tools, makes them interoperable, and adds important missing functionality on top of them Has lots of config and knobs, but runs in docker so that helps Extras: Python Job Board Michael: Guido interviewed for JavaScript language! Microsoft: We're creating a new Rust-based programming language for secure coding New webcast: Python for the .NET developer Ace Python Interviews free course Joke: Types of software jobs.

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Izar Tarandach is a threat modeling pioneer, seen as one of the movers and shakers in the threat modeling world. Izar leads a small team that develops the pytm tool, which is self-described as a "A Pythonic framework for threat modeling". The GitHub page goes on to say define your system in Python using the [...] The post Izar Tarandach — Command line threat modeling with pytm appeared first on Security Journey Podcasts.

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