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State of the Yan 397 - AAC Cruiserweight Savage Discussion w/ Jitter & Biffolo by DREAM Network

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In this episode of the Semiconductor Insiders video series, Dan is joined by Samia Rashid, co-founder and president of Infinisim. Samia provides detailed background on clock jitter – what it is, what causes it and the various methods to address the problem. Samia describes the unique clock analysis technology developed… Read More

After a brief but important look into the state of milfs in Pokémon, we dive into our favorite demos from this year's quarter's Steam Next Fest.Timestamps(1:15) Talking about not talking bout Pokemon(04:00) Next Fest (05:20) Skulker(08:35) Neo Junk City(12:20) Moves of the Diamond Hand(13:50) Robin's roguelike corner (As We Descend, Shuffle Tactics, Creepy Redneck Dinosaur Mansion 3, Kaamos)(19:05) Demon Tides(22:00) Haste(24:50) Despelote(27:00) Wheel World(31:20) Static Dread(34:50) Desert Angels(37:20) To a T(39:45) Out and About(44:30) Is This Seat Taken?(47:00) Jitter(50:20) Dolls Nest(53:10) Children of Saturn(57:50) What else have Willa and Robin been up to this week? (feat. How Do We Relationship?, Split Fiction)TheWorstGarbage.onlineJoin The Worst Garbage Discord!Follow us and send us questions!Music Street Food by FASSoundsThings are bad right now, but you can help make them better. Please take some time to consider how you can help trans people, immigrants, and others targeted by our fascist government with this Big List Of Links. Hosted on Acast. See acast.com/privacy for more information.

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

Discussing the new iPhone iOS. What's the deal with Jitter? Pepper's hotel prank idea. Dylan's top three things including Diddy news.  September VS Nachos. Auto deposit VS Nachos. Pepper's road is closed. Why you should download the new "Seekr" app TODAY!

On What's Trending, Marvel has unveiled a new superhero, Jitter, a Singaporean teen named Sofia Yong, on Singapore's National Day. Jitter, who has the ability to hyper-focus and never slow down, is part of the Uncanny X-Men series. She can attain a skill set but not powers for up to a minute before she "crashes."   Disney has announced a US$60 billion investment in theme parks and cruises over the next decade, including the biggest expansion to Magic Kingdom, a new nighttime parade called Disney Starlight, a new "Monstropolis" land at Walt Disney World Resort in Florida, and the Tropical Americas area at Animal Kingdom park. The expansion will include the first ride based on the film "Encanto" and take place inside the magical "Casita" where the movie's Madrigal family lives.See omnystudio.com/listener for privacy information.

Visit our Patreon page to see the various tiers you sign up for today to get in on the ground floor of AIPT Patreon. We hope to see you chatting with us on our Discord soon!MCU recap, and Pornsak Pichetshote talks The Horizon Experiment NEWSMCU revealsDaredevil Born Again getting second seasonFirst season has Kingpin and Daredevil team upJohnny Storm sports a cozy sweater costumeIron Heart teaming up with…The Hood?!New Ultimate Black Cat, Hulk, and more in Ultimate Universe titles out November 2024Marvel spotlights new X-Men Calico, Deathdream, Jitter, and Ransom'Scarlet Witch' #6 introduces new protégé AmaranthSkybound launching ‘Creepshow: The Suspense Building Game'Our Top Books of the WeekDave:The Power Fantasy #1 (Kieron Gillen, Caspar Wijngaard)Cruel Universe #1 (Various)Nathan:The Power Fantasy #1 (Kieron Gillen, Caspar Wijngaard)Spider-Man: Black Suit & Blood #1 (Various)Standout KAPOW moment of the week:Nathan - Ain't No Grave #4 (Skottie Young, Jorge Corona)Dave - Spider-Man: Black Suit & Blood #1 (Dustin Nguyen)TOP BOOKS FOR NEXT WEEKDave: Ultimates #3 (Deniz Camp, Juan Frigeri)Nathan: Ultraman X Avengers #1 (Kyle Higgins, Matthew Groom, Francesco Manna)JUDGING BY THE COVER JR.Dave: Absolute Power: Task Force VII #4 (Steve Beach Card Stock Cover)Nathan: Star Wars: Darth Vader #49 (Dike Ruan)Interview: Pornsak Pichetshote/The Horizon Experiment - The Horizon Experiment: The Manchurian #1 - FOC September 2nd; September 25, 2024It's always exciting to have you on Pornsak, but even more so when there's something new going on in comics we haven't seen before. Can you tell our listeners what the Horizon Experiment is all about?We understand it started over the debate of Idris Elba as James Bond. Tell us a bit about that.- Do you feel that dream movie would still disappoint? Is it inherently impossible for an established IP to take those kind of risks?Something interesting about this project is the notion that it's “designed to act as pilots for follow-up books if sales merit.” Do you have a number in mind for a series to flourish from a one-shot, and if one of the one-shots does merit a series how will you let fans know?Tell us about your collaboration process with co-editor Will Dennis.Were there any pitches you got but they didn't make the cut for this first five?Tell us about working with the Dodsons and Jeff Powell on The Manchurian.We'd be remiss not to ask about Absolute Power Task Force VII #4, with a focus on Batman super robot Failsafe! This series has a different creative team each issue, and you're teaming with Claire Roe on this one, was DC actively accepting pitches for this or did it come about organically?Anything else you'd like to plug today?

Just-In Time Conversations: Dan Barletta, The Jitter Bus by WNHH Community Radio

Support us on Patron! https://www.patreon.com/MogTalkGuests: Woops, Jitter, & Wesk AlberDiscussion: Melee may be one of the most affected jobs coming in Dawntrail as it gets a new job, dragoon rework, and changes with the hitboxes. We bring on three great melee players to talk about the current state of the jobs and what we hope changes.Rating: 10/10ENJOYYYYYYYYYYYGuest Socials:Woops' Twitter: https://twitter.com/woopsWoops' Twitch: https://www.twitch.tv/woopsJitter's Twitter: https://twitter.com/jitter07Jitter's Twitch: https://www.twitch.tv/jitterWesk's Twitter:  https://twitter.com/weskalberWesk's Twitch: https://www.twitch.tv/weskalberShow Socials:MogTalk's Patreon: https://www.patreon.com/MogTalkMogTalk's Twitch: https://www.twitch.tv/MogTalkFrosty's Twitter: https://www.twitter.com/FrostyTVstream

1️⃣5️⃣3️⃣ En el programa 153 de Hadoken Rojo, nuestro querido Galleta sigue fuera (y le mandamos un abrazo fuerte) y en su lugar Borja Abadíe nos alegra con su presencia. 🎮 Empezamos con los nuevos anuncios de juegos como Star Wars Outlaws, Kingdom Come Deliverance II o el nuevo The Rogue Prince of Persia. 🔮 Después nos detenemos en los rumores que indican que habrá sendos State of Play y Nintendo Direct en mayo. ¿Qué esperamos de uno y otro? 📺 Y para acabar, comentamos gameplays de juegos como Jitter, Rauniot, Duckside o Escape Academy Tournament of Puzzles. 🍒 Y de guinda, un Top de los mejores juegos de ninjas.

Dans l'épisode de cette semaine, je suis heureux d'accueillir Sébastien Robaszkiewicz, plus affectueusement surnommé Robi, le CEO et cofondateur de Jitter, une plateforme innovante qui simplifie la création d'animations de qualité professionnelle. Jitter se positionne comme le "Figma du motion design", offrant une solution accessible et rapide pour les designers souhaitant intégrer des animations dans leurs vidéos, sites web, et applications sans la complexité habituellement associée à ces tâches. Au fil de notre discussion, Robi partage son parcours impressionnant, de ses débuts dans la Silicon Valley à la création de sa première entreprise au Brésil, jusqu'à son retour en France où il a contribué au succès de plusieurs startups en tant que designer produit. L'idée de Jitter est née de la rencontre entre Robi et son co-fondateur, Étienne, tous deux passionnés par la démocratisation des outils de création. Leur but : rendre le motion design aussi simple et accessible que possible. Pour y parvenir, ils ont dû repenser entièrement le modèle d'animation, s'éloignant du système complexe des keyframes pour adopter une approche plus intuitive basée sur des instructions simples, inspirée de logiciels comme Keynote. Depuis son lancement, Jitter a connu un succès viral grâce notamment à sa mise en avant par des influenceurs dans le monde du design, ce qui a permis à la plateforme de gagner rapidement en visibilité et en utilisateurs. Sébastien évoque également les défis techniques rencontrés pour développer un outil aussi performant et accessible directement depuis un navigateur web, soulignant l'importance de la R&D et de l'écoute active des besoins des utilisateurs. En termes de développement futur, Jitter se concentre sur l'amélioration de la collaboration en équipe et l'enrichissement de ses fonctionnalités pour répondre encore mieux aux attentes des designers. La plateforme envisage aussi de renforcer sa communauté en encourageant le partage et la création collective de contenus. Notre conversation aborde également la concurrence sur le marché des outils de design et l'évolution de l'écosystème avec des acteurs comme Figma. Sébastien partage sa vision d'un futur où des outils web modernes et collaboratifs pourraient venir concurrencer les suites créatives traditionnelles, en créant une nouvelle dynamique dans le monde du design. Pour soutenir Jitter et découvrir cet outil révolutionnaire de motion design, n'hésitez pas à visiter leur site et à suivre leur évolution. Bonne écoute ! _____ Mentionnés pendant l'épisode : Jitter Figma Adobe After Effects Canva Vid PlayPlay _____ Pour soutenir SaaS Connection en 1 minute⏱ (et 2 secondes) : Abonnez-vous à SaaS Connection sur votre plateforme préférée pour ne rater aucun épisode

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Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2023.03.22.533815v1?rss=1 Authors: Burger, T. S., Rule, M. E., O'Leary, T. Abstract: Dendritic action potentials exhibit long plateaus of many tens of milliseconds, outliving axonal spikes by an order of magnitude. The computational role of these slow events seems at odds with any need to rapidly integrate and relay information throughout large nervous systems. We propose that the timescale of dendritic potentials allows reliable integration of asynchronous inputs. We develop a physiologically grounded model in which the extended duration of dendritic spikes equips each dendrite with a resettable memory of incoming signals. This provides a tractable model for capturing dendritic nonlinearities observed in experiments and in more complex, detailed models. Using this model, we show that long-lived, nonlinear dendritic plateau potentials allow reliable integration of asynchronous spikes. We demonstrate this model supports non-trivial computations in a network solving an arbitrary association/discrimination task using sparse spiking that is subject to timing jitter. This demonstrates a computational role for the specific timecourse of dendritic potentials in situations where decisions occur quickly, reliably, and with a low number of spikes. Our results provide empirically testable hypotheses for the role of dendritic action potentials in cortical function as well as a potential bio-inspired means of realising neuromorphic spiking computations in analog hardware. Copy rights belong to original authors. Visit the link for more info Podcast created by Paper Player, LLC

Top 100 AI Tools that are not ChatGPThttps://www.youtube.com/watch?v=9y8aDC6WbgkWhat Are You using ChatGPT For:Help with a business planHelp with e-BooksBlogsCreating a courseShow notes for a podcastList of topics to discussUpdate resume' and cover letterWrite speechesComplete outlinesResearch for booksFind grants for minority womenOutline for KaraokeWriting letters to politicsVideo Editors & GeneratorsSynthesia = https://www.synthesia.io/AI video creation is a time and cost-efficient alternative to the complex and costly traditional video creation processeshttps://www.youtube.com/watch?v=UVNUCBUrHL0Runway = https://runwayml.com/Runway is a new kind of creative suite. One where AI is a collaborator and anything you can imagine can be created.https://www.youtube.com/watch?v=trXPfpV5iRQDescript = https://www.descript.com/Descript is the only tool you need to write, record, transcribe, edit, collaborate, and share your videos and podcastsNova AI = https://wearenova.ai/Create stellar videos, cut, trim and collide your clips. Add subtitles, translate and more. Entirely online, no installation is needed.Trint = https://trint.com/a tool for generating captions from voice in your video through quick speech recognition, auto-generating simple captions that can be easily altered and styled with different fonts, borders, and shadows.Unscreen = https://www.unscreen.com/Unscreen is an AI-powered online tool that helps you remove the background from videos and GIFs. With Unscreen, you can easily extract the foreground object and place it onto a new background of yourAimages = https://aimages.ai/Aimages is an AI-powered platform that provides a range of image editing and processing services. It offers tools for image restoration, enhancement, colorization, and more.Bhuman = https://www.bhuman.ai/Bhuman is an AI-powered platform that helps companies optimize their hiring processes. It offers tools for resume screening, candidate ranking, and interview scheduling, among other features.Kaiber = https://www.kaiber.ai/Kaiber is an AI-powered platform that helps businesses automate their customer support operations. It uses natural language processing and machine learning to analyze customer queries and provide personalized responses in real-time.Make-A-Video = https://makeavideo.studio/a meta AI system for creating videos based on textual input by generating one-of-a-kind videos with just a few words or lines of text. Papercup = https://www.papercup.com/a tool for translating videos with expressive AI voices, enabling content owners and creators to reach large audiences in days without stretching using AI dubbing localization.Reface: Face Swap Videos = https://hey.reface.ai/a face-swap smartphone app for swapping faces with friends or celebrities, putting your face into a pre-made film, and including various effects, gifts, and amusing videos.Topaz Video AI = https://www.topazlabs.com/topaz-video-aia video enhancement tool for de-interlacing, upscaling, and motion interpolation with optimized processing times for modern workstations. Image & ArtsMidJourney = https://www.midjourney.com/MidJourney is a platform that uses AI to create personalized employee training and development programs. It uses natural language processing and machine learning to analyze employee skills and knowledge gaps, and then creates customized training plans to help them achieve their goals.Dall-E2 = https://openai.com/product/dall-e-2Dall-E2 is an AI-powered image generation tool developed by OpenAI. It uses a neural network to generate high-quality images from textual descriptions, allowing users to create realistic images of objects that don't exist in the real world.Stable Diffusion = https://stablediffusionweb.com/Stable Diffusion is an AI-powered platform that provides image and video editing services. It uses machine learning to generate high-quality visual effects, such as slow motion, time-lapse, and stabilization.Night Cafe Studio = https://nightcafe.studio/Night Cafe Studio is an AI-powered platform that provides photo and video editing services. It uses machine learning to enhance and stylize images and videos, and also offers tools for removing backgrounds and adding special effects.Gaugan = http://gaugan.org/gaugan2/Gaugan is an AI-powered platform that allows users to create photorealistic landscapes using a simple paintbrush interface. It uses machine learning to generate realistic textures and lighting effects, enabling users to create complex natural scenes without any prior knowledge of 3D modeling or rendering.This Beach Does Not Exist = https://thisbeachdoesnotexist.com/This Beach Does Not Exist is a website that uses AI to generate high-quality images of beaches that don't exist in the real world. Each time the page is refreshed, a new beach image is generated using a machine learning algorithm.Neural.Love = https://neural.love/Neural.Love is an AI-powered platform that allows users to generate personalized love letters using natural language processing and machine learning. It analyzes user input and generates customized love letters that are tailored to their individual preferences.The Next Rembrandt = https://www.nextrembrandt.com/The Next Rembrandt is an AI-powered project that used machine learning to create a new Rembrandt painting. The project analyzed Rembrandt's style and techniques and used that data to create a completely new and original painting in his style.Let's Enhance = https://letsenhance.io/Let's Enhance is an AI-powered platform that allows users to enhance and upscale their images without losing quality. It uses machine learning to remove noise and artifacts from images, increase resolution, and improve sharpness and detail.Auto Draw = https://www.autodraw.com/Auto Draw is an AI-powered drawing tool that uses machine learning to help users create professional-looking illustrations. It suggests relevant shapes and icons as users draw, making it easy to create complex designs quickly and easily.Playground AI = https://playgroundai.com/Playground AI is an AI-powered platform that allows users to create and train their own machine learning models. It offers a range of pre-built models for image and speech recognition, and also allows users to upload their own data to train custom models.Imagen = https://imagen-ai.com/Imagen is an AI-powered platform that provides image analysis and classification services. It uses machine learning to identify and classify objects within images, making it a useful tool for a range of applications, from security and surveillance to marketing and advertising.Artbreeder = https://www.artbreeder.com/Artbreeder is an AI-powered platform that allows users to generate and manipulate images using machine learning. It allows users to mix and blend different images together to create unique and original artwork, and also offers tools for facial recognition and character creation.ProductivityChatGPT = https://openai.com/ChatGPT is an open AI chatbot that uses the transformer architecture to generate human-like text in various styles and formats. It launched in November 2022 and has become a versatile tool for many use cases. Best of all, it's free to use!Jasper = https://www.jasper.ai/Jasper is a generative AI platform for businesses that helps teams create content 10x faster. With over 50 templates and AI trained on industry best practices, Jasper is a powerful tool for content creation.Rewind = https://www.rewind.ai/Rewind is a search engine that records everything you've seen, said, or heard on your computer and makes it searchable. With mind-boggling compression, you can easily find what you need.TLDR This = https://tldrthis.com/AI writing tool that helps you summarize any piece of text into concise, easy-to-digest content. You can choose between short and detailed summaries to free yourself from information overload.Notion AI = https://www.notion.so/Notion AI is an AI-powered tool that can be directly leveraged within any Notion page. It helps automate tedious tasks, write faster, and even handle the first draft to augment your creativity.Lyric Studio = https://lyricstudio.com/Lyric Studio is a tool for songwriters and musicians that generates unique lyrics for any music genre. It offers multiple options based on your selected topic and helps you find rhymes for specific words with its smart suggestion feature. Collaborate in real-time with your co-writers using Lyric Studio.Noty.AI = https://noty.ai/Noty.AI is a platform that uses AI to automate the process of generating high-quality marketing copy for businesses. With its AI-powered copywriting technology, Noty.AI enables businesses to create marketing messages, ads, and product descriptions quickly and efficiently. The platform also offers a variety of tools to help businesses optimize their marketing campaigns, including analytics, testing, and targeting capabilities.Shortly = https://www.shortlyai.com/AI-powered writing tool that continues your writing for you when you run out of ideas or aren't sure about your writing style. It uses GPT-3 and can help you rewrite, shorten, or expand your sentences with simple commands.Rationale = https://rationale.jina.ai/Rationale is a revolutionary AI tool that assists business owners, managers, and individuals in making tough decisions. With this app, simply enter your pending decision and its AI-powered system will list pros and cons or generate a SWOT analysis to help you weigh your options.INK = https://inkforall.com/combines an AI writer, an SEO optimizer, and a content planner. Its technology crafts natural language optimization AI models to understand the meaning of content and uncover the nuances of what makes it perform. This app aims to replace multiple tools that writers already use and provide a smooth user experience that covers different aspects of writing. Vowel = https://www.vowel.com/a tool for remote teams to host, summarize, search, and share video meetings without any add-ons required. This AI tool helps you save time and catch up on meetings in seconds. Copy.AI = https://www.copy.ai/text generator perfect for marketers who write different types of copy. You can write 10x faster, engage your audience, and never struggle with the blank page again. Just tell it what you want, and the AI will create the marketing copy for you. Provide some input data and choose the right tone, and the AI will generate a few different versions of copy for you to choose from. DeepL = https://www.deepl.com/translatorDeepL is an exceptional machine translation tool that provides unparalleled accuracy and nuance. With DeepL Pro, you can translate quickly and focus on your work, no matter the language or location. DeepL Pro is secure, accurate, and customizable to meet your needs.WordTune = https://www.wordtune.com/WordTune uses advanced AI tools and language models to understand the context and meaning of written text. As the first AI-based writing companion, WordTune goes beyond simple grammar and spelling fixes to help you express your thoughts in writing. Piggy To = https://piggy.ai/Piggy is a mobile-friendly tool that helps you create engaging and shareable content. With just a prompt, Piggy generates multiple slides in a visually appealing format, saving you time and energy.Sudowrite = https://www.sudowrite.com/Sudowrite is a web-based writing tool that uses AI to assist users in improving their writing skills. It provides features such as grammar and style suggestions, tone analysis, and personalized feedback to help users enhance the clarity, coherence, and impact of their writing. Article Forge = https://www.articleforge.com/Article Forge is an AI writing tool that uses advanced deep learning to write entire articles automatically. From product descriptions to blog posts, Article Forge delivers high-quality, SEO-optimized content about any topic with just a single click.Grammarly = https://www.grammarly.comGrammarly with Grammar Lease is a new AI-powered app that helps you write with confidence. With auto-suggestions, you can go beyond grammar and spelling and work on style and tone. Whether you're writing emails, documents, or social media posts, Grammarly with Grammar Lease has got your back.Copy Monkey: https://www.copymonkey.ai/Copy Monkey is an AI-powered writing assistant that helps you write more effectively and efficiently by suggesting better phrasing and providing real-time feedback on your writing.Elephas: https://github.com/maxpumperla/elephasElephas is an open-source library that allows you to distribute deep learning models using Apache Spark. With Elephas, you can easily scale up your deep learning models to work with large datasets. MusicSoundDraw: https://www.sounddraw.com/SoundDraw is an AI-powered music creation platform that lets you draw your own melodies and rhythms, and then automatically generates full songs based on your inputs.JukeBox: https://openai.com/blog/jukebox/JukeBox is an AI-powered music generator developed by OpenAI. It can create original music in a variety of genres, and even generate lyrics to go along with the music.Harmonai: https://harmonai.com/Harmonai is an AI-powered tool that helps you write better harmonies for your music compositions. It uses machine learning algorithms to analyze your melodies and suggest harmonies that complement them.Aiva: https://www.aiva.ai/Aiva is an AI-powered music composer that can create original music for a variety of applications, including films, video games, and advertisements.Reffusion: https://reffusion.com/Reffusion is an AI-powered marketing platform that uses machine learning to optimize your marketing campaigns and improve your ROI.Supertone: https://www.supertone.ai/Supertone is an AI-powered sound design tool that helps you create custom sound effects for your film, television, or video game projects.Beatoven AI: https://beatoven.ai/Beatoven AI is an AI-powered music generator that lets you create original music using your voice or any other sound you can produce.Boomy: https://boomy.com/Boomy is an AI-powered music production platform that allows you to create original songs in minutes using simple drag-and-drop tools.Mubert: https://mubert.com/Mubert is an AI-powered music streaming platform that generates unique electronic music in real-time based on the listener's preferences.Design/Graphic DesignDesign Beast: https://designbeast.io/Design Beast is an AI-powered graphic design platform that helps you create professional-quality designs for your business or personal projects.FontJoy: https://fontjoy.com/FontJoy is an AI-powered font pairing tool that helps you choose the perfect font combinations for your design projects.Profile Picture AI: https://profilepicture.ai/Profile Picture AI is an AI-powered tool that automatically generates high-quality profile pictures for your social media accounts.Looka: https://looka.com/Looka is an AI-powered logo design platform that helps you create professional-quality logos for your business or personal projects.Beautiful AI: https://beautiful.ai/Beautiful AI is an AI-powered presentation design platform that helps you create stunning and effective presentations in minutes.Flair AI: https://github.com/flairNLP/flairFlair AI is an open-source natural language processing (NLP) library that allows you to perform a variety of NLP tasks, including sentiment analysis, named entity recognition, and text classification.Khroma: https://khroma.co/Khroma is an AI-powered color palette generator that helps you choose the perfect colors for your design projects.FontPair: https://fontpair.co/FontPair is an AI-powered font pairing tool that helps you choose the perfect font combinations for your design projects.Pikazo: https://pikazoapp.comPikazo is a mobile app that uses artificial intelligence to transform your photos into unique works of art.Jitter = https://jitter.video/Jitter helps you create animated designs in seconds. Perfect for animating interfaces or creating social media posts.BusinessResume.IO = Resume.io Helps you create a great resume and cover letter to set you apart from other job applicants. They offer 18 templates to choose from. Visit their website here: https://resume.io/NameLicks = https://namelicks.com/Name Licks uses artificial intelligence to generate short, brandable business names. Just enter your keywords, choose the level of randomness, and pick a naming style.  Durable Gig - https://www.durablegig.com/ Durable Gig is an AI-powered platform for solo business owners to create a fully designed website with copy, images, and contact form in under a minute.Textio - https://textio.com/ Textio provides gold-standard recruiting guidance using AI to optimize job posts, email, social posts, and more with data-driven inclusion guidance, expanding the candidate pool and establishing a consistent candidate experience.Timely - https://timelyapp.com/ Timely automates company time tracking, tracking time spent in every web and desktop app automatically for precise daily recordkeeping.Zia - https://www.zoho.com/creator/zia/ Zia is an AI-powered assistant for your business that can collect customer data, write documents, and help you find sales numbers easily.Cresta - https://www.cresta.ai/ Cresta uses machine learning algorithms to provide real-time guidance to sales and service agents to improve customer service, increase sales, and improve customer satisfaction.Ferret - https://ferret.ai/ Ferret is an AI app that provides exclusive relationship intelligence to help businesses avoid high-risk individuals and spot promising opportunities.EchoWin - https://echowin.ai/ EchoWin uses AI to automate incoming calls, assisting clients in obtaining answers to their questions, completing business tasks, or connecting them to the appropriate person if necessary.Boost.ai - https://www.boost.ai/ Boost.ai allows businesses to create customized virtual assistants that can handle tasks such as answering frequently asked questions, providing customer support, or processing transactions, and can be integrated with various messaging channels.Scale - https://scale.com/ Scale helps businesses deliver value from their AI investments faster by providing better data, leading to more performant models and faster deployment.RAD AI - https://radai.ventures/ RAD AI blends information with authentic content across all marketing platforms, generating emotional interactions with target audiences by analyzing previous performance and devising tactics for future content.Adobe Sensei - https://www.adobe.com/sensei.htmlAdobe Sensei uses AI and machine learning to help businesses create effortlessly, make informed decisions, and target marketing for better results, creating and offering the ideal customer experience.Poly AI (https://www.polyai.com/) Poly AI's voice assistant can engage in a natural conversation for as long as it takes to solve the customer's problem. Improve customer experience, achieve accurate resolution, and uncover data-driven business opportunities with Poly AI.DigitalGenius (https://www.digitalgenius.com/) DigitalGenius automates responses to common customer queries and proactively identifies issues to resolve them faster. This AI tool enables faster response times, quicker resolutions, and improved customer satisfaction.AudioVoice Maker (https://www.voicemaker.in/) Voice Maker uses text-to-speech systems and related tools to generate speech. Register to use the free plan with 100 converts per week, or purchase basic, premium, and business plans for full access to all features and voices. Voice Maker supports over 130 languages worldwide.Podcast Castle (https://www.podcastcastle.com/)Podcast Castle is a multimedia creation platform that allows you to create high-quality audio interviews and provides AI-powered sound editing. It offers studio-quality recording, AI-powered editing, and seamless exporting all in a single web-based interface.VoiceMod (https://www.voicemod.net/)VoiceMod is a voice transformer and modifier with effects that make you sound like a girl, boy, demon, or robot.CereProc (https://www.cereproc.com/) CereProc's text-to-speech technology offers more than 5,000 expressive voices for your voiceover needs. It also allows you to clone your own voice.Cleanvoice AI (https://cleanvoice.ai/)Cleanvoice AI is an artificial intelligence tool that removes filler sounds, stuttering, and mouth sounds from your podcast or audio recording, saving you hours of editing time.LaLa AI = https://lala-ai.com/LALA AI is an AI-powered language learning app that uses natural language processing (NLP) technology to help users learn foreign languages. Real EstateInterior AI (https://www.interiorai.com/) nterior AI provides interior design ideas using artificial intelligence and allows you to virtually stage interiors for real estate listings with different interior styles.AI Room Planner (https://www.airplanner.com/) AI Room Planner offers hundreds of interior design ideas for your room for free, with no limit.GetFloorPlan (https://getfloorplan.com/) GetFloorPlan can convert your 2D floor plan into a fully furnished 3D layout with a 360 virtual tour, with a capacity of up to thousands per day.Cool AIed Interior design ideas (https://www.architecturelab.net/cool-aied-interior-design-ideas/) This article showcases AI-generated interior design ideas for those looking to decorate or get inspiration.Learn & ResearchPerplexity AI = Perplexity (https://perplexity.ai/) Perplexity is an AI tool that condenses difficult topics and questions into a concise summary of four to five sentences. It provides sources and allows for further questioning.Genei https://genei.io/Genei is an AI-powered search and summarization tool that helps users quickly find and digest relevant information from documents and web pages. IRIS AI (https://iris.ai/) IRIS AI is a research workspace that uses AI to filter and extract data, understand situations, and generate summaries.Consensus (https://www.useconsensus.com/) Consensus helps with finding research studies to back up arguments by allowing users to search for points they are trying to make.Scholar C (https://scholarc.com/) Scholar C is a summarizer tool that breaks down essays, reports, and books into bite-sized sections to save time.Semantic Scholar (https://www.semanticscholar.org/)Semantic Scholar indexes over 2 million scholarly publications and extracts significant conclusions to keep users up to date on recent research trends.Wisdom AI (https://www.wisdomai.ai/) Wisdom AI is a natural language processing platform that extracts insights from data to help with decision making.Cactus (https://getcactus.app/) Cactus is a hub of various tools for students to save time with tasks such as reading, writing, and language learning.E l i five (https://eli5.ai/) E l i five is an AI-powered tutor available 24/7 to have conversations with users about any topic.Elicit (https://elicit.org/) Elicit is an AI tool for researchers that uses GPT-3 to quickly find relevant papers and provide answers for free.LegalDo Not Pay (https://donotpay.com/) Do Not Pay is an app that includes the world's first robot lawyer. It helps users fight corporations, beat bureaucracy, and sue anyone at the press of a buttonSupport this podcast at — https://redcircle.com/the-secret-to-success/exclusive-contentAdvertising Inquiries: https://redcircle.com/brandsPrivacy & Opt-Out: https://redcircle.com/privacy

Jitter Garcia, Vice President of Event Marketing & Brand Experiences, joins Amanda Ma, CEO of Innovate Marketing Group to discuss everything you need to know about diversity within events and what goes into producing engaging experiences for the new year! Listen Now on EventUp! Jitter is the Vice President of Event Marketing and Brand Experiences at TelevisaUnivision, overseeing the team that strategizes, curates, and executes culturally-relevant engagements that deepen both internal and external client affinity for its brands, content, and the community it serves. Before coming to TelevisaUnivision, Jitter worked in-house events at the recognizable brands of Dow Jones, The Wall Street Journal, and Discovery Communications, creating experiences for both B2B and consumer audiences. Jitter was named one of Connect x BizBash's 2022 40 Under 40, Event Marketer's 2021 Women in Events as featured on the cover of their December issue and was BizBash's first Event Master in the inaugural episode of their docuseries of the same name. She has been featured in numerous publications as a thought leader in the events industry and her team's work has been celebrated and recognized with several industry awards over the years.

Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2022.11.30.518625v1?rss=1 Authors: Iwama, S., Takemi, M., Eguchi, R., Hirose, R., Morishige, M., Ushiba, J. Abstract: Multimodal recording using electroencephalogram (EEG) and other biological signals (e.g., electromyograms, eye movement, pupil information, or limb kinematics) is ubiquitous in human neuroscience research. However, the precise time alignment of data from heterogeneous sources is limited due to variable recording parameters of commercially available research devices and experimental setups. Here, we introduced the versatility of a Lab Streaming Layer (LSL)-based application for multimodal recordings of high-density EEG and other devices such as eye trackers or hand kinematics. To introduce the benefit of recording multiple devices in a time-synchronized manner, we discuss two common issues in measuring multimodal data: jitter and latency. The LSL-based system can be used for research on precise time-alignment of datasets, such as detecting stimulus-induced transient neural responses and testing hypotheses well-formulated in time by leveraging the millisecond time resolution of the system. Copy rights belong to original authors. Visit the link for more info Podcast created by Paper Player, LLC

GotTechED the Podcast Episode #123:17 EdTech Tools to Get Ahead of 2023 (Part 2) Welcome back to GotTechED the podcast this is Episode 123 called “17 EdTech Tools to Get Ahead of 2023 (Part 2)” In this episode, we'll share 17 unique and fun edtech tools in a variety of educational categories. Guise will also talk about his experience at the 2022 Teach Better Conference and give us a sneak peek at an upcoming bonus episode of GotTechEd. This is another episode you don't want to miss, check it out. Segment 1: UpdatesGuise talks Teach Better  Segment 2: Edtech Tools, Resources, etc. to check out  https://www.playphrase.me/ (Playphrase.me) - type a phrase and see what movies says it    https://neal.fun/ (Neal.fun) - Type in your bday and see what has happened since https://gethuman.com/ (Gethuman.com) - get the number for any company to speak to a human https://privnote.com/ (Privnote.com) - send a note that destructs after its read https://temp-mail.org/ (Temp-email.org) - temporary emails https://psdfreebies.com/ (Psdfreebies) - editable templates of flyers/posters https://www.beautiful.ai/ (Beautiful.ai) - presentation templates  https://fakeyou.com/ (Fakeyou.com) - type a message and pick a famous voice https://learn-anything.xyz/ (learn anything xyz) - populates articles basics of that topic https://jitter.video/ (Jitter.video) - motion graphic templates https://svgartista.net/ (svg artista) - animation logos https://vocalremover.org/ (Vocalremover.org) - separate music and voice https://www.calligraphr.com/ (Calligraphr.com) - turn any sentence into calligraphy https://360gigapixels.com/ (360gigapixels.com) - 360 degree pictures https://spik.ai/ (BigSpeak.ai) - voice-over https://incompetech.com/ (Incompetech.com) - royalty free music http://www.oldversion.com/ (Oldversion.com) - old versions of software Segment 3: Where to Find GotTechEDDo us 3 favors Subscribe to GotTechED the Podcast https://itunes.apple.com/us/podcast/gotteched/id1358366637?mt=2 (Apple Podcasts) https://open.spotify.com/show/7zyzfCkSDNHkKdqxmh9XLB?si=YhSdMa6BQVmcLHbSrYxE9Q (Spotify) https://play.google.com/music/listen?u=0#/ps/Indeizidhz4h37mawfylwdgco4y (Google Podcasts)   https://www.stitcher.com/search?q=gotteched (Stitcher)   https://www.youtube.com/channel/UCMIQwu39Tkow3kduRQAH85w?view_as=subscriber (YouTube)   https://twitter.com/WeGotTechED (Twitter)    https://www.facebook.com/WeGotTechED/ (Facebook) Write us an Apple Podcast Review! Tell your friends about http://www.gotteched.com (www.gotteched.com) Tell your friends about the Teach Better Podcast Network Music Credits:The Degs: Shotgunhttp://freemusicarchive.org/music/The_Degs/ ( http://freemusicarchive.org/music/The_Degs/) @bensoundshttps://www.bensound.com/ ( https://www.bensound.com/) Subscribe to our Podcasthttps://itunes.apple.com/us/podcast/gotteched/id1358366637?mt=2 (Apple Podcasts) https://open.spotify.com/show/7zyzfCkSDNHkKdqxmh9XLB?si=YhSdMa6BQVmcLHbSrYxE9Q (Spotify) https://play.google.com/music/listen?u=0#/ps/Indeizidhz4h37mawfylwdgco4y (Google Podcasts)   https://www.stitcher.com/search?q=gotteched (Stitcher)   https://www.youtube.com/channel/UCMIQwu39Tkow3kduRQAH85w?view_as=subscriber (YouTube)   https://twitter.com/WeGotTechED (Twitter)    https://www.facebook.com/WeGotTechED/ (Facebook) Connect with us on Social MediaGuise on Twitterhttps://twitter.com/GuiseGotTechEd ( @guisegotteched) Nick on Twitterhttps://twitter.com/NickGotTechEd ( @nickgotteched) GotTechED the Podcast on Twitterhttps://twitter.com/WeGotTechEd ( @wegotteched) Join the Conversation and our PLNOur favorite part of recording a live podcast each week is participating in the great conversations that happen on our https://www.facebook.com/WeGotTechED/ (Facebook Group Page).    Need a...

Ludington Bay Vanilla Jitter Bean is a cream ale by style. A light bodied, american cream ale with vanilla bean and coffee. This craft beer is 5.6% ABV and 28 IBUs. In this craft beer review, we will take a look at the color, smell, and taste.

Mid Era: new Alba Ecstacy (Romania), Fritz Mayr (Austria), Elkin Sergey (Russia), Mac (Italy) PsyTrance: new discoveries including Ozric Tentalces founder Ed Wynne (England) TIME ARTIST TRACK RELEASE       0:00 Intro [Mid Era]           0:00 AEM cathedral Light Landscapes     14:00 Alba Ecstacy black velvet i Black Velvet, Vol. 1     28:27 Fritz Mayr magnetic forge Delta Eusebeia     36:44 Elkin Sergey atmosphere       42:02 J-Walt aquarius Spontaneous Fantasia     47:43 Mac energiya (excerpt) Energiya     52:14 L.S.G. risin The Singles Reworked     58:52 break [PsyTrance]      1:01:12 Rukirek avreya Butterfly Tales For Trees  1:11:15 Martins Garden genesis Odyssey-A Space Symphony  1:18:08 Ed Wynne travel dust Shimmer Into Nature (Expanded)  1:26:12 Ozric Tentacles lemon kush Paper Monkeys  1:32:15 Cabeiri shivlock Temple Within  1:40:51 Basit Subhani pyar mein intezaar mein Just 1437  1:44:58 Juno Reactor playing with fire (Jitter rmx) The Golden Sun...Remixed  1:52:50 Echo Season proprioception Solarmetric  2:02:12 outro       Keywords: International electronic music internet electronic artists unsigned electronic artists Low Orbit Satellite Ambient Symphonic Rock Progressive Rock Art Rock Tribal Trance PsyTrance Ethno/PsyTrance IDM Nonima Dub Step Mid Era Berlin School

Soundart · Hörstück zur mouse on mars-Klanginstallation im Kunstbau/Lenbachhaus München. Ein rotierbarer Hornlautsprecher katapultiert Sounds wie Flipperkugeln in den 100 Meter langen Raum, macht ihn zum Resonanzkörper. Die Klänge brechen sich, verkanten sich - eine Aufforderung zum aktiven Hören. // Komposition und Realisation: mouse on mars / BR in Zusammenarbeit mit Lenbachhaus München 2022 // Exklusive Hörspiel- und Kultur-Tipps unter br.de/kultur-newsletter

Вместе с Егором Гришечко (https://t.me/egorikas) обсудим то, как сделать микросервисные приложения надёжными. Разберёмся, что значит «надежное приложение»? Обсудим преимущества использования IHttpClientFactory. Как правильно использовать политику повторных запросов и что такое Jitter? Обсудим важность Health check и их отличие от Readiness check. Присоединяйтесь к обсуждению выпусков в канале книжного клуба: https://t.me/bookclubdotnet Книга .NET Microservices: Architecture for Containerized .NET Applications (https://aka.ms/microservicesebook) В выпуске - Implement resilient applications (292 - 318 стр.): https://docs.microsoft.com/en-us/dotnet/architecture/microservices/implement-resilient-applications/ Выпуск на других платформах: https://bookclub-dotnet.mave.digital/ep-16 Выпуск на YouTube: https://www.youtube.com/watch?v=fH_o-1vpa3I&list=PLbxr_aGL4q3SAMvtA4ZTPdHPrX0YRutxyКанал книжного клуба: https://t.me/bookclubdotnet Сайт книжного клуба: https://bookclub.dotnet.ru Exponential backoff and Jitter: https://aws.amazon.com/blogs/architecture/exponential-backoff-and-jitter/ Нейгард Майкл “Release it! Проектирование и дизайн ПО для тех, кому не всё равно”: https://www.piter.com/collection/A31172/product/release-it-proektirovanie-i-dizayn-po-dlya-teh-komu-ne-vsyo-ravno Бёрнс Брендан, «Распределенные системы. Паттерны проектирования»: https://www.piter.com/product/raspredelennye-sistemy-patterny-proektirovaniya

Lefties freak as Elon Musk announces that he has declined a seat on Twitter's board, raising speculation he wants to avoid the 14% cap on his stake, which would allow him to take further control.     

Ein Museum als Resonanzkörper - im Kunstbau des Lenbachhauses in München lässt sich das jetzt erleben. Das Künstlerduo Mouse on Mars hat den Kunstbau in eine Sound-Installation verwandelt. Wie das klingt, und vor allem, wie sich das anfühlt, hat Tobias Stosiek erkundet.

Just one more game of the regular season to go, but no let up in the action! Scott, Ben and Joris talk through all the action, with play off 1 and 2 almost confirmed, we do now know that Seraing will face Molenbeek in the relegation play off. The trio also pay their respects to Miguel Van Damme, who tragically passed away during the week.

In this episode we are going to look at Network Transmission Quality.We will be discussing Prioritizing Traffic, Bandwidth, Congestion, Delay, and Jitter, and finally Packet Loss.Thank you so much for listening to this episode of my series on Enterprise Networking, Security, and Automation for the Cisco Certified Network Associate (CCNA).Once again, I'm Kevin and this is KevTechify. Let's get this adventure started.All my details and contact information can be found on my website, https://KevTechify.com-------------------------------------------------------Cisco Certified Network Associate (CCNA)Enterprise Networking, Security, and Automation v3 (ENSA)Episode 9 - QoS ConceptsPart A - Network Transmission QualityPodcast Number: 43-------------------------------------------------------Equipment I like.Home Lab ►► https://kit.co/KevTechify/home-labNetworking Tools ►► https://kit.co/KevTechify/networking-toolsStudio Equipment ►► https://kit.co/KevTechify/studio-equipment 

This week the crew doubles down on turkeys. Luke welcomes Ron Jitter on his Thursday podcast debut along with Evan, Perry, and Carter. Topics discussed are pre-season scouting, preparation, gear set ups, decoy setups, and other tactics they have found useful. Over here at Hunt Lift Eat we are beyond pumped for turkey season, it cannot get here soon enough! Please subscribe to the pod and drop us a rating and review! www.huntlifteat.com Instagram @huntlifteatofficial Jon- @ ron_jitter_hle Luke- @luke.d.cox Carter- @thehomestead_ga Evan- @evan.d.isner Perry- @perry.r.isner Sponsors Casey Burns: Loan Officer PrimeLending   (919) 710-1864   Casey.Burns@Primelending.com   www.CloseWithCasey.com Learn more about your ad choices. Visit megaphone.fm/adchoices

Artiste : @jitterparty EP : Jitter Edits Vol.2 Label : Jitter Sortie : 3/02/21 Acheter : https://bit.ly/3KGvkiL

GotTechED the PodcastEpisode #103: 10 EdTech Swiss Army Knife ToolsWelcome back to GotTechED the podcast this is Episode #103 called “__” In this episode, Guise and I will discuss 11 “swiss army knife style” edtech tools that are a one-stop shop of amazing learning opportunities. We'll also discuss some of our go-to edtech tools for 2022. This is another episode you don't want to miss. Check it out. Segment 1: The importance of building your edtech toolkithttp://www.gotteched.com/edtech2022 (www.gotteched.com/edtech2022) Segment 2: https://pdfcandy.com/ (Pdf candy) https://www.pdfescape.com/ (PDFescape) https://www.funbrain.com/ (Fun Brain) - Games, videos, and online books for Pre-K through 8th grade https://www.bookwidgets.com/ (Bookwidgets) https://www.geogebra.org/ (Geogebra) - Free digital tools for teaching math. Many different digital calculators, activities, books, and a collaborative whiteboard space. Students enter with a simple classroom code https://gothinktech.com/ (Think Tech) - Visualize student thoughts by generating whole class word clouds., Sort iT: Build flash cards with terms or images for students to classify., Check iT: Collect written responses to open ended questions., Sketch iT: Annotate PDFs and images or give students a blank canvas to draw., Speak iT: Give every student a voice by collecting audio responses., Poll iT: Foster whole class discussion by collecting student opinions in real time., Quiz iT: Formally assess students through a variety of question types., Flip iT: Have students create their own content though video responses., TEAMS: Have students work collaboratively and submit as a group. https://tools.techjunkie.com/login (https://tinywow.com/) https://www.ixl.com/ (IXL) - hundreds of interactive lessons and activities, organized by grade level, ready to use along with data analytics and diagnostics https://jitter.video/ (Jitter video) https://www.tinytap.com/ (Tiny Tap) - thousands of personalized learning apps organized by grade level Segment 3: Where to Find GotTechEDDo us 3 favors Subscribe to GotTechED the Podcast https://itunes.apple.com/us/podcast/gotteched/id1358366637?mt=2 (Apple Podcasts) https://open.spotify.com/show/7zyzfCkSDNHkKdqxmh9XLB?si=YhSdMa6BQVmcLHbSrYxE9Q (Spotify) https://play.google.com/music/listen?u=0#/ps/Indeizidhz4h37mawfylwdgco4y (Google Podcasts)   https://www.stitcher.com/search?q=gotteched (Stitcher)   https://www.youtube.com/channel/UCMIQwu39Tkow3kduRQAH85w?view_as=subscriber (YouTube)   https://twitter.com/WeGotTechED (Twitter)    https://www.facebook.com/WeGotTechED/ (Facebook) Write us an Apple Podcast Review! Tell your friends about http://www.gotteched.com (www.gotteched.com) Music Credits:The Degs: Shotgunhttp://freemusicarchive.org/music/The_Degs/ ( http://freemusicarchive.org/music/The_Degs/) @bensoundshttps://www.bensound.com/ ( https://www.bensound.com/) Subscribe to our Podcasthttps://itunes.apple.com/us/podcast/gotteched/id1358366637?mt=2 (Apple Podcasts) https://open.spotify.com/show/7zyzfCkSDNHkKdqxmh9XLB?si=YhSdMa6BQVmcLHbSrYxE9Q (Spotify) https://play.google.com/music/listen?u=0#/ps/Indeizidhz4h37mawfylwdgco4y (Google Podcasts)   https://www.stitcher.com/search?q=gotteched (Stitcher)   https://www.youtube.com/channel/UCMIQwu39Tkow3kduRQAH85w?view_as=subscriber (YouTube)   https://twitter.com/WeGotTechED (Twitter)    https://www.facebook.com/WeGotTechED/ (Facebook) Connect with us on Social MediaGuise on Twitterhttps://twitter.com/GuiseGotTechEd ( @guisegotteched) Nick on Twitterhttps://twitter.com/NickGotTechEd ( @nickgotteched) GotTechED the Podcast on Twitterhttps://twitter.com/WeGotTechEd ( @wegotteched) Join the Conversation and our PLNOur favorite part of recording a live podcast each week is participating in the great conversations that happen on our...

Psst, hey... got any of that bean juice? That bitter crush? That dank jitter? If you want to get involved send your question in to griefburrito@gmail.com or to any of our socials You can now get your Pokemon Cards direct from our website too! Use code pokemon10 for 10% off your order! Join Our Community Here We hope you enjoy this episode and if you have any feedback or comments please contact us at griefburrito@gmail.com Our Socials  Want up to 90% off all the latest games? Click here for huge video game discounts!

Sébastien Robaszkiewicz is the cofounder of Jitter. Jitter is the easiest motion design tool on the web.★ Support this podcast ★

The Cru discusses one of their favorite playwrights, attempting to imitate Oscar Wilde in this week's episode.Jitterbug. Originally a swing music enthusiast in the late 1930s, with the name probably coming from Cab Calloway's song 'Jitter bug' (1934), in which a 'jitter bug' was a person who drank regularly and so 'has the jitters ev'ry morning'. The name passed to a person who danced the jitterbug, a fast, twirling, whirling US dance to a jazz accompaniment, popular in the 1940s. Hence also more generally, a nervous person, one who 'has the jitters'.

#EdTech Thought (4:28) You need hobbies. Here are the five types of hobbies you should have in your life: Make money Keep you in shape Keep you creative Build your knowledge Evolve your mindset What are your hobbies? Leave a comment below. Featured Content (11:11) The following are tech tips and websites you should explore as the 2021 school year gets underway (or anytime). 4 New Google Docs Features Clear space in your Google account Mecabricks.com MyColor.space Animatron.com Jitter.video printfriendly.com justdeleteme.xyz pixelhunter.io FREE Canva for educators! Just Give It A Try (27:33) What are you looking forward to trying in the coming months in your classroom or school? Submit responses by August 27, 2021, by clicking Send a Voicemail on the right of this page. House of #EdTech VIP (30:03) Erin Cummings Marilena Neocleous

Antoine Dabrowski etait en direct hier du 109 à Nice pour l'évenement Belaprem', une émission de Talks, de Débats sur la scène éléctro locale avec Nicolas Masseyef, Benoît Géli, Rag, Ahmed et JC du Kwartz Club ainsi que Aïmen du trio Jitter.

I'm a piece of shit, Working for others, Jitter town, Dogecoin, Wet dreams and dry mackinns, Retaining sperms, Tittie pain > Ball pain, I hit a guy on a bike. --- Send in a voice message: https://anchor.fm/aiandi/message

A violent mob descends on the farmhouse of a family accused of witchcraft. Joe, a reluctant participant in the proceedings, encounters one of the family members while trying to escape the violence.    You’ve been listening to the Night’s End podcast which is a production of Dissonance Media.    For the Good of the People was written by Jay Adair.    Jay Adair is an office worker and music instructor. His work has appeared in Scare You to Sleep, Jitter, Escaped Ink, and Hawk & Cleaver’s The Other Stories. He is also a drummer and can be heard on recordings with Jon Creeden & The Flying Hellfish www.joncreeden.com, Chad McCoy www.chadmccoymusic.com, and Just in Time https://justintimeband.bandcamp.com/. Check out his profile at Goodreads or reach him directly at jayadairwriting@hotmail.com.    This episode was narrated by Falconetti.    Lovey was performed by Marianne Coleman, who is the host of Walking the Shadowlands. which is a podcast about all things paranormal, unknown, unexplained, and all things that go bump in the night. It may just haunt your dreams and sometimes your waking hours. Head to www.walkingtheshadowlands.com or search for it wherever you find your podcasts.    Jimmy Horrors was performed by James Barnett.  This episode was edited and produced by James Barnett.    Support the Night’s end on Patreon to receive bonus content and merch:  www.patreon.com/nightsendpodcast  Or support us by purchasing directly from our shop:  www.nightsendpodcast.com/shop  Donations:  www.ko-fi.com/nightsendpodcast  And as always, stay horrific everyone.

This episode is also available as a blog post: https://datapane.wordpress.com/2021/04/25/tableau-how-to-create-a-circle-jitter-plot/

Radio Monaco donne Carte Blanche avec Crossover et Jitter !

Le dimanche soir, le duo formé par Aurélien Haas et Jeff Lasson re-visitent le Grand Mix avec des nouveaux morceaux, des samples d'actualité, des edits, de la musique de film, de la librairie… et un dimanche soir pépouze sur Radio Nova.Visuel © Radio Nova

In our debut episode we look at some ways we can handle this new year without going crazy. --- This episode is sponsored by · Anchor: The easiest way to make a podcast. https://anchor.fm/app

A young horror film buff accesses a disturbing website at the encouragement of his friend. But this mysterious site doesn’t contain just any run-of-the-mill frightening content; it provides him with an experience that is truly horrifying.Written by Jay AdairNarrated by Josh Curran (https://twitter.com/jcurranwriter)Edited by Karl Hughes (https://twitter.com/karlhughes)With music by JCM [Canada] (https://soundcloud.com/j_c_m/)And Thom Robson (https://www.thomrobsonmusic.com/)And sound effects provided by Freesound.org (https://freesound.org/)The episode illustration was provided by Luke Spooner of Carrion House (https://carrionhouse.com/)Jay Adair is an office worker and music instructor. His work has appeared in Scare You to Sleep, Jitter, Escaped Ink and previously in The Other Stories. He is also a drummer and can be heard on recordings with Jon Creeden & The Flying Hellfish (www.joncreeden.com), Chad McCoy (www.chadmccoymusic.com), and Just in Time (https://justintimeband.bandcamp.com/). He can be reached at jayadairwriting@hotmail.com.Josh Curran is a narrator and writer. He has narrated many episodes of The Other Stories over the show’s lifetime. He is also the creator of the horror Audio-Drama podcast, Miscreation. You can follow him on twitter, @jcurranwriterYou can help support the show over at Patreon.com/HawkandCleaverYou can join our bookclub and movie club and chat about the podcast over at Facebook.com/groups/hawkandcleaverT-shirts and mugs are available at www.gumroad.com/hawkandcleaverThe Other Stories is a production the story studio, Hawk & Cleaver, and is brought to you with a Creative Commons – Attribution-NonCommercial-NoDerivatives license. Don’t change it. Don’t sell it. But by all means… share the hell out of it. See acast.com/privacy for privacy and opt-out information.

AVANTIKA BAKSHI (New Delhi, INDIA) For Avantika Bakshi it wasn't just divine intervention but an amalgamation of realizations that led to a rather unusual career path. It's never easy to give up a tried and tested path to chase what the heart desires. Avantika, a business school graduate, has always been inclined towards creative spaces, be it music, dance or performing on stage; she's always been on the lookout for doing something 'out of the box' knowingly or unknowingly. Her biggest decision was to move away from a comfortable routine to become an artist, producer and deejay. Her music is far more than what meets the ears, it is the physical presence of the woman behind the heavy electronic deck. The fearlessness that burns through her eyes, the energy that moves the audience, often orchestrated by her hands. But, the meteoric rise isn't only attributed to good fortune but to hard work and sincerity. The willingness to try new sounds and the ability to roll the dice, over and over again. Her mix sets are very expressive and promise to lead the listener through high-emotion quotient. The beats that find tunes in ones head on the drive to work, or the adrenaline that it provides to others in their moment of hysteria. There's a bit for all those listening, there always has been. Where does she get such philosophy from? Her deep and insightful readings of the Bhagwad Gita. The last year has seen her supporting some of the most respected names in the International dance music industry such as Alex Metric, Darius, 16 Bit Lolitas, Macromism, Dubspeeka, Uner, Sally Doolally, as well as local acts such as Kohra, Jitter, Ankytrixx, Praveen Achary, Bullzeye. She has also performed at the most sought after venues across India and made her debut at some of the most renowned festivals such as Sunburn, Krank, Ctrl Alt Dance & Blurr. She might have only arrived on the scene but there's little doubt that she's here to serve the people the music they desire, the sounds that move soul and body. This is Avantika. Deep, emotive, euphoric. Links: Soundcloud: https://soundcloud.com/ avantikabakshi Mixcloud: https://www.mixcloud.com/ avantikabakshi/ Mixcrate: http://www.mixcrate.com/

Eric Krapf is the Publisher of No Jitter and General Manager for Enterprise Connect. Eric joins the ConnectThe2 podcast to discuss his extensive career in tech journalism, how the pandemic is shaping enterprise technology and collaboration, and his favorite holiday recipes (potato latkes, yum!). Here are the biggest takeaways from our conversation. If you like what you’re reading, be sure to listen to the whole episode, linked at the bottom of this page.

חדש! ביום רביעי 16 בדצמבר נקיים ״הכה את המומחה״ או ״שאל אותי כל דבר״ AMA עם דותן, אלון ואני בדיסקורד פה https://discord.gg/cJYX7f2j, מוזמנים להאזין, להצטרף ולתחיל כבר לשאול שאלות מראש. פרק מספר 399 (!) של רברס עם פלטפורמה - באמפרס מספר 70 (!!).באולפן (הוירטואלי) רן, דותן נחום ואלון נתיב - בוקר טוב, מלא זמן שלא הקלטנו, ובדרך עוד היה לנו כנס: Reversim Summit 2020, שאליו נרשמו מלא אנשים וצפו בוידאו מלא אנשים - מקווה שהייתם שם, וגם אם לא אז אתם מוזמנים ללכת ולצפות, כל ההרצאות זמינות עכשיו ב-YouTube, פשוט לכו ל-Reversim Summit 2020 וחפשו את ההקלטות או פשוט חפשו ב-YouTube את ה-Playlist, זה גם פורסם ברשתות השונות וכל זה, בקיצור - קל למצוא.נגיד בהזדמנות זו תודה לכל המודרטורים (Moderators), כולל דותן שלקח חלק במאמץ הזה, וזהו:היה בסך הכל מאוד מוצלח, פעם ראשונה בעצם שאנחנו עושים כנס וירטואלי - היו בסך הכל משהו כמו 12 הרצאות בשישה טרקים (Tracks) שוניםעשינו את זה במשך שלושה ימים ברצףה-Q&A היה מאוד צפוף ומעניין, היו הרבה מאוד אנשים שהגיעו ופתחנו את זה ככה גם לשיחה פתוחה באחד הימים שפשוט לא נגמרה . . . היה כיף.מקווה שבפעם הבאה ניפגש פנים אל פנים, אבל עד אז - נמשיך במסורת ה-Zoom.[וזה באמפרס - סדרה של קצרצרים שבה כל אחד מאיתנו מספר על הדברים המעניינים שהוא נתקל בהם בחודש (או קצת יותר) האחרון - בלוג-פוסטים מעניינים, Repos מעניינים ב-GitHub, כתבות מעניינות וכו’].רן - ואולי ככה מעניין לעניין באותו עניין - קצת עדכונים מהרשת על עבודה מרחוק בחברות השונות, שני עדכונים קטנים שיצא לי לתפוס בחודש-חודשיים האחרונים - הראשון - Dropbox מכריזה שהם עוברים למדיניות של Remote Work - לתמידמה שהם עושים בעצם זה הופכים את כל חללי העבודה שלהם לסוג של חלל עבודה משותף, זאת אומרת - לא יהיו שולחנות קבועים, לפחות לפי ההכרזה או הכתבה ב-Business Insider.סוג של WeWork (כביטוי): מרחבי עבודה שאתה יכול להגיע אליהם - אבל אתה לא מחוייב להגיע אליהם - במשרדים השונים, כשאתה יכול לעבוד מהבית מתי שאתה רוצה.האמת היא שלהרבה חברות יש חדשות בתחום הזה, ממש הבאתי מקבץ מאוד קטן - חדשה נוספת מחברה גדולה אחרת כמו Microsoft, שגם הם מכריזים על Policy רשמי של Remote workבו הם אומרים “אתם יכולים לעבוד עד 50% מהזמן מהבית - ובאישור מנהל אפילו ב-100% מהזמן מהבית.יכול להיות, דרך אגב, שזה כבר השתנה, אני יודע שדברים משתנים כל הזמן - אבל בגדול רק רציתי לבוא ולהראות את המגמה, שחברות נפתחות יותר ויותר לסיפור של Remote Work, ולא רק בהקשר של הקורונה.זאת אומרת, מן הסתם עכשיו יש אילו-שהם אילוצים - אבל הם גם מדברים על העתיד, לא מדברים רק על הקורונה אלא מדברים גם על העתיד.דרך אגב, יצא לי לדבר עם חבר שנמצא עכשיו בסאן-פרנסיסקו, והוא אומר שהעיר ממש “מתה” - הכל סגור: מסעדות סגורות, חנויות סגורות, כל האנשים עובדים מרחוק - לא רק עובדים מהבית אלא ממש נסעו למקום אחרהשכירות בעיר ירדה ב30% - לפי מה שהוא אומר, לא באמת בדקתי את הנתונים סטטיסטית - אבל בהחלט מרגישים את השינוי: העיר הפכה לכמעט “עיר רפאים”.זה מעניין - אני מניח שזה לא ישאר ככה לתמיד, אבל אני חושב שזה טרנד מעניין והוא בהחלט מורגש.האמריקאים כנראה תמיד מגיבים מהר, ואולי לפעמים מגיבים, ככה, “בתגובת יתר”; הישראלים מגיבים קצת יותר לאט, אז אצלם אולי הדברים ילכו קצת יותר לאט - אבל בכל אופן, אני חושב שזה שינוי משמעותי שאני חושב שאי אפשר להתעלם ממנו.(דותן) יש לכם מושג מה עושים עובדי Microsoft, שצריכים להתעסק עם חומרה? בדיוק אני חושב על זה . . .(אלון) אני שמעתי על אינטל . . . אני יכול להגיד לך שבאינטל, מה שקרה הוא שבנו להם “מעבדה בבית” . . .(דותן) באמת?(אלון) אנשים הפכו את המטבח למעבדה, עם כל “הציוד המכאני הכבד”, ציוד לפעמים באיזה $100K שיושב להם בבית, לבדיקות . . . אני לא יודע איך Microsoft, אבל אני יודע שבאינטל, חלקם לפחות, עושים בבית.(דותן) גם פה יש איזשהו סימן שאלה של . . . תכל’ס, בית זה שטח פרטי, ועכשיו לקחת לי חדר מהבית, שזה יכול להיות מאוד יקר לאנשים . . .(רן) אני שמעתי, אני חושב שבהולנד או מדינה אירופאית אחרת כלשהי . . . אולי זה היה גרמניה? מציעים למסות את המעבידים ב-5%, או משהו כזה, על כל עובד מהבית - זאת אומרת: לא למסות את העובדים עצמם, אבל למסות את המעביד, כאילו הוא “מרוויח” נדל”ן, אז בוא תשלם על זה איפשהו במקום אחר.זה מעניין - מעניין איזו חקיקה או איזה מיסוי חדש הולך להיות על כל הסיפור הזה . . .(דותן) זה כאילו פותח לי איזושהי תיבת פנדורה . . . נגיד שיש לך ציוד כזה בבית - מה קורה אם הילדים נוגעים בציוד הזה, או חס וחלילה תאונה - מה קורה עם הביטוחים? אם אתה Apple, ומישהו פורץ לך לבית ולוקח את הדגם של ה-iPhone הבא . . . וואו, זה מטורף.(אלון) אני מאמין ש-Apple לא . . . במדיניות שלהם הם לא יעשו את זה מהבית . . . זה אני מאמין שלא יהיהאתה יודע - לא כל החברות עברו לעבוד מהבית, נגיד - וזו דוגמא לזה שהם דווקא יכולים לעבוד מהבית - אבל המנכ”ל של Netflix אמר שהעבודה מהבית זה הדבר הכי גרוע שקרה, וביום הראשון שאפשר לחזור הוא מחזיר את כולם . . .אז הוא הפוך מהמגמה, ואני לא אתפלא אם Netflix קנו לעובדים שלהם חיסונים, אפילו ברמה הזו, כי הוא אמר . . . הוא אפילו הצהיר על זה איזשהו משהו מעורפל, שלעובדים שלו - הם הראשונים שיתחסנו, אני לא יודע אם הוא באמת רכש חיסונים או לא, אבל זה יהיה מעניין.(רן) Amazon השקיעו, בזמנו, כבר ממש בתחילת המשבר, הם השקיעו הרבה מאוד בסניטזציה (Sanitization) של (בשביל) העובדים שלהם - אז עדיין לא דובר על חיסונים, אבל הם הכריזו על סכום, אני לא זוכר מה היה הסכום אבל זה היה סכום מאוד גדול, משהו כמו $100M או אפילו יותר מזה - בבריאות של עובדים ושמירה על הבטחון שלהם וכו’.ומעניין לעניין באותו עניין (II) - אם כבר דיברנו קצת על Microsoft, אז Guido van Rossum, הידוע לנו מתהילתו כיוצר של Python וה”דיקטטור-לעד” - איך אומרים את זה? ה - Benevolent Dictator For Life של Python? - שכזכור לכם מפרקים קודמים של העלילה (לפני 20 באמפרים) התפטר מהתפקיד שלו כ”הדיקטטור-לעד-של-Python” ואמר “אוקיי חבר’ה, הספיק לי - אני יוצא לחופשה”אז מסתבר שהספיק לו מהחופשה הזאת ועכשיו הוא חזר לעבוד - והוא חזר לעבוד ב-Microsoft.הוא למעשה הודיע שהוא התגייס לעבוד בשורות Microsoft, הוא הולך להמשיך לעבוד על Python ולשפר את השפה ואת הכלים של השפה, תחת המטרייה של Python (של Microsoft)וזהו - חדשות מעניינות, יכול להיות שפשוט היה משמעם לו לעשות בטן-גב, או שהוא החליט שהספיק לו, והוא חייב עכשיו לבוא ולהשפיע . . . מעניין איך הקהילה תגיב לכל הסיפור הזה, אני עוד לא ראיתי תגובות מהקהילה, רק ראיתי את ההכרזה שלו ושל Microsoftאתה זוכר בטח שבפרקים קודמים יצא לנו לדבר על “אוקיי - מה עושים עכשיו, אחרי שהוא פרש?”’ וכבר קמו מנהיגים לקהילה - אז מה יקרה עכשיו כשהוא חזר? מעניין, נראה מה יהיה.(דותן) אפשר לעשות מזה סדרה . . . זה נראה מהטוויט שלו, שעכשיו אני קורא את זה בתוך המאמר שבלינק, שהוא אמר פשוט שמשעמם לו . . .(רן) כן - הוא אמר שמשעמם לו, “בואו תנו לי כסף ואני אעבוד” או משהו כזה . . .(אלון) לא, הוא אמר משהו יותר מעניין - הוא לא בדיוק אמר . . הוא אמר “אני לא יודע מה אני אעשה, אבל זה יהיה קשור ל- Python” - הוא לא בדיוק אמר “אני חוזר להנהיג את השפה”.(רן) נכון(אלון) “אני אעשה משהו כדי לקדם את הקהילה של Python”, אבל זה היה די מעורפל כזה של “אני לא בהכרח חוזר להוביל את הקהילה”.(דותן) הנה - הוא עושה ממש עכשיו Twit מעניין - azure.pythonlabs.com - ואז הוא כתב “למדתי לעשות משהו ב-Azure” . . .(אלון) דותן - הפרק של 1 באפריל? אז בדרך כלל אנחנו מקליטים אותו ב-1 באפריל . . . נשמע לי שצריך לחתוך את זה . . .(דותן) אה, נכון . . . נשמור את זה.ד”ש ל-Werner Vogels.(רן) נושא אחר לחלוטין הפעם - Application Load Balancers for gRPCבעצם AWS מכריזים על תמיכה ב-HTTP2 וב-gRPC, תחת ה-Load balancer שלהם, שזה בעצם Feature שאני חושב שהרבה זמן חיכינו לו.בעצם gRPC עובד מעל פרוטוקול שנקרא HTTP2, שזו הגרסא המתקדמת יותר של HTTP, עם פיצ’רים שונים - יש הרבה דברים שונים בין HTTP ו-HTTP2, ועד עכשיו ה-Load Balancers של AWS תמכו ב-TCP וב-HTTP1 או ב-HTTP1.1, אבל לא ב-HTTP2עכשיו הם למעשה תומכים ב-HTTP2 וב-gRPC שרוכב עליו - וזה נחמד, למי שצריך . . . לא תמיד צריך - אם זה gRPC שנמצא בתוך ה-Datacenter, ואתם לא בהכרח רוצים להכניס Load Balancer בפנים,אבל אם זה משהו שמגיע מבחוץ, או לפעמים, במקרים מסויימים, בתוך ה-Data Center - אז זה Feature מעניין שאני חושב שנמצא לו שימוש.ובעניין אחר לגמרי: תורים ישראליים - כולם מכירים את המושג הזה של “תור ישראלי”, כזה שאם אתה מוצא חבר בתור אז אתה מקודם אוטומטית קדימה?אז מסתבר שזו לא רק אנקדוטה ישראלית, אלא שממש יש מבנה נתונים כזה שנקרא “תורים ישראליים”נתנו לזה אולי שם קצת יותר פוליטיקלי-קורקט, אבל השם החביב הוא “תורים ישראליים”, ופה אני מצרף איזשהו מאמר, שמסביר את המוטיבציה למתי נצטרך “תורים ישראליים” כאלה - כשאתם רוצים לצוות כמה Work Items ביחד, כשזמן ה-Setup של כל אחד ארוך . . .אז הוא בא ואומר “אוקיי, יש תורים, ויש גם Priority Queues - אבל לפעמים יש Work Items שהסוג שלהם דומה” - נגיד שאתם רוצים לעשות איזשהו Setup ל-Web Scrapper, או לאיזשהו עיבוד של Data שהוא מאסיבי, ואתם רוצים לעשות Setup מיוחד, אז עדיף לכם לצמד כמה Work Items מאותו סוג ולעבוד עליהם ביחד - ובשביל זה תורים ישראליים, תורים שבהם אתם מצמדים את ה-Work Items לפי הסוגים שלהם, כשהסוגים הם לכאורה “חברים” - תורים כאלה יכולים להיות מאוד יעילים.אז זהו - מאמר נחמד, עם הרבה תרשימים, ובעיקר שם חמוד - “תורים ישראליים”.(אלון) אגב - זה ממש לא חדש. . . (רן) לא, זה לא חדש - רק ההצגה של זה כמדע פופלארי והשם היפה הזה של “תורים ישראליים” - זה כן נחמד.(דותן) אני פשוט רואה פה מאמר של שני חבר’ה ישראליים - ניר פרל ואורי יחיאלי, מאוניברסיטת תל אביב - שנקרא The Israeli Queue with Priorities, שהוא עושה לו רפרנס זה המאמר שכאילו נתן את השם, או שזה בא אחרי שכבר יש את הדבר הזה?(רן) אני לא יודע היסטורית, פשוט נתקלתי בבלוג-פוסט הזה, ואני מסכים שזאת לא עבודה חדשה ולא קונספט חדש, זה נכון, אבל פשוט בלוג-פוסט שמסביר את זה בצורה נחמדה, קצת הומוריסטית וקל לקריאה.(דותן) מעניין(רן) ובעניין אחר - הפעם איזושהי כתבה שפורסמה ב-Geektime, קצת משעשעת, על מהנדס בריטי שהחליט לפתוח חברת ייעוץ ולקרוא לה בשם, שימו לב - “”> LTD”בקיצור - ניסה לעשות Cross sites Script Injection באמצעות השם של החברה . . .(דותן) מדהים . . .(רן) . . . מתוך מחשבה שאם מישהו . . .אולי רשם החברות הבריטי לא יפול בפח, אבל בכל מקום אחר שהדבר הזה יוצג, יכול להיות שהוא ייצר Cross Site Script Attack.אבל זה לא הלך לו . . . אז קודם כל היה לו אחלה חוש הומור, אבל רשם החברות הבריטי כנראה היה מספיק עירני ועצר אותו - אבל הרעיון נחמד.בסופו של דבר הוא שינה את השם פשוט לשם של “החברה שהיה לה Script HTML ורשם החברות סירב” - וזה השם של החברה, פחות או יותר.סתם, סיפור ככה משעשע, וזה כמובן מזכיר לנו את הסיפור על Little Bobby DROP TABLE (כמובן עם קישור ל-xkcd)אחזור על זה, כיוון שזה משעשע - מנהל בית הספר מתקשר לאמא ושואל “הי! זו אמא של בובי? הייתה בעיה קטנה במחשב . . . ““מה בובי שוב שבר?”מנהל בית הספר עונה “זה לא שהוא באמת שבר משהו, אבל . . . האם באמת קראת לבן שלך Robert’); DROP TABLE Students;-- ? . . .”אז היא אומרת “כן, כן - אנחנו קוראים לו Little Bobby Tables” . . . המנהל אומר “רק שתדעי- כל טבלת הסטודנטים של השנה האחרונה נמחקה, אני מקווה שזה ילמד אותך לא לקרוא לילדים שלך בשמות כאלה”אז היא עונה לו בחזרה - “אני מקווה שזה ילמד אותך לקח, לפעם הבאה לסנן את ה-Inputs שלך” . . .כן, אז זהו - xkcd משעשע . . .(אלון) היה אגב, באותו הקשר, מישהו שהשם משפחה שלו NULL, או . . . וב-SOA, מה שהיה עם ה-Web Services, היו מעבירים XML, והייצוג של NULL היה פשוט לרשום String NULL . . .(רן) זה היה ב-SOAP לדעתי . . .(אלון) כן, נכון, ב-SAOP - ואז כאילו אני זוכר שזה לא עבד באיזה משרד ממשלתי או איזו שטות כזאת . . .(רן) כן, אז הייתה שאלה כזאת ב - Stack Overflow - מה עושים אם השדה באמת NULL, והיו כל כך הרבה הצעות שם . . . זה היה Thread, אה, מאוד ממצא, ב-Stack Overflow.אז הנה - עוברים אליך - דותן - קח את זה:דותן - טוב - אז האייטם הראשון שלי הוא Repository שנקרא code-serverבא מחברה בשם Coder, שאני יודע שהם עושים . . . יש להם פתרון של “בוא תפתח לא במחשב שלך, אלא בסביבה, כזאת, וירטואלית”. “בעצם לא באמת צריך את המחשב שלך וכל ה-IDE והכלים כולם אצלנו, והכל יהיה יותר קל” - זה הפתרון שלהם.מה שמגניב פה זה שהם לקחו את VS Code וגרמו לו לרוץ על Browser בשלמותו.תמיד ידעתי של-VS Code יש איזשהו פוטנציאל להיות הרבה יותר ממה שהוא, במיוחד שאני די מת על ה-Vim Mode שלו - מרשים.אני לא יודע אם אפשר לעשות עם זה משהו כרגע פיזית או האם אני ממליץ לעבור ל-VS Code ב- Browser, אבל די מרשים לראות את VS Code בשלמותו עובד ב-Browser.(רן) נזכיר, דרך אגב, שזה לא ה-IDE הראשון שרץ ב-Browser, אמאזון אפילו קנו חברה שעושה את זה, ויש לא מעט חברות אחרות . . .(דותן) כן, Cloud 9 . . .(רן) Cloud 9 . . . יש לא מעט חברות כאלה, אבל אתה אומר שמבחינת ביצוע, יש כאן ביצוע טוב במיוחד?(דותן) כן, יש Editor שהוא כולו על טהרת הנקרא-לזה-Frontend, שנארז ב-Electron, שזה VS Code - ותמיד אתה שואל את עצמך “האם אני יכול לקרוא את הדבר הזה ולדחוף אותו ל-Browser, ושזה עדיין יעבוד?”, האם יש פה איזושהי הפגנת יכולות טכנית מאוד מרשימה?והתשובה היא “כן” - הם עשו את זה.ועוד פעם, ב-VS Code ה-Editor, לפחות החלק של ה-Editing, מבוסס על טכנולוגיה של Microsoft, תעזרו לי אם איך קראו לה - Monaco? משהו כזה? כבר לא זוכר . . .(רן) Monaco זה Front, אבל יכול להיות שיש גם טכנולוגיה כזאת, אני לא מכיר . . .(דותן) בכל מקרה, שם-של-עיר כלשהי שמתחיל ב-”מ” . . . אבל נחמד לראות את זה קורה ממש במציאות, ואני מניח שהם, אותה החברה - יש להם אינטרס שזה יעבוד והם משתמשים בזה בצורה כזו.(אלון) הוספתי פה לינק ל - StackBlitz - האמת היא שכבר דיברנו עליו פעם בעבר, אבל אם תראה אותו עכשיו, אז זה השתפר מאודעכשיו אתה ממש . . פשוט זה “VS Code online” - האמת שבראיונות האחרונים - אני מוכרח כאן גילוי נאות שאנחנו מראיינים - פשוט רצינו שיעשו איזה משהו Frontend ב-React - פשוט תגישו דרך זה, זה הדבר הכי נוח, כאילו . . . תלחץ על React ויש לך Editor, יש לך הכל בפנים . . .(רן) אבל זה רק Frontend, נכון? . . . (אלון) כן, אבל זה מדהים, פשוט מדהים - כי זה עובד: אתה יכול להוסיף Dependencies בקליק, הכלי הזה ניהיה פשוט מפלצתהאמת היא שרציתי לדבר עליו אח”כ, אבל דותן עקף אותי . . .אז זה ממש מגניב: לוחצים בקליק ויש לך אפליקציה עובדת והכל מתעדכן - וזה VS Codeאפילו יש את ה-Extensions . . . אני לא יודע אם כל ה-Extensions של VS Code נתמכים בזה או לא, אבל אתה ממש יכול להעשיר את זה, אם חסר לך איזה Extension של VS Code אז אתה יכול להוסיף אותו.מומלץ בחום, אפילו אם אתם לא צריכים כלום, סתם לשחק עם זה, כי זה באמת פותח את הראש וזה ממש מגניב.(דותן) אני מניח שה-Use case העיקרי, או לפחות המיידי, הוא לכל מיני חברות שיש להן פלטרפורמות לראיונות ב-Real time וכל מיני דברים כאלה - Cloud 9 היה מאוד מוקדםזאת אומרת - יש את התופעה של חברות שמקדימות את זמנן וכל מיני דברים כאלה, אני זוכר את Cloud 9 ממש לפני המון שנים, ואז AWS קנו אותם - אני עדיין לא יודע בדיוק למה . . .(רן) הם משתמשים בהם, נגיד - אתה יכול לערוך פונקציות Lambda ב-Cloud 9, לערוך את הטקסט שלהן . . .(אלון) זה יותר מזה - זה נותן לך Ecosystem - הרעיון שם הוא שזה נותן לך Ecosystem ל-Cloud, שאתה יכול בקליק לעשות Deploy ך-Cloud ואז לשנות - ואז אתה כאילו אומר . . . ה-Editor שלך מחובר ל-AWS, ואנשים נורא נקשרים גם ל-Editor, אז אם מחר אני אעביר אותך אז אתה גם לא תרצה לעבור Editor - ואז אתה גם לא תעבור Cloud . . .(דותן) בדיוק . . .(אלון) זה כאילו הפוך . . .(דותן) זה היה מאוד מעניין, שעוד לא ראיתי אותם אומרים את זה - זה קורה “בשקט בשקט”, אבל . . .(רן) אני משתמש המון ב-Jupyter בזמן האחרון, וזה גם סביבת עבודה . . עכשיו - זה לא באותה רמה של Visual Studio, אבל זה כן . . .זאת אומרת, יש הרבה אנשים שכל החיים שלהם רק חיים בתוך Jupyter, עם כל המגבלות של הכלי הזה, אבל זהו - זה כמובן בתוך הדפדפן, ה-Jupyter.(אלון) יש גם אנשים שחיים בתוך Emacs, זה לא הופך את זה לכלי ממש טוב . . רק אומר.(רן) כמעט אמרת VI, טוב שעצרת את עצמך . . . (דותן) ולאייטם הבא - יש פרויקט שנקרא urlhunter - בעצם זה סוג של כלי, נקרא לזה “כלי להאקרים”, לכל מיני חבר’ה “שמנסים את מזלם”.מה שזה עושה זה שולף קבצים שמכילים של Short URLs ל-URL המלאנגיד - Bitly זה שירות שעושה Shortening ל-URLsיש איזושהי חברה אחרת שעושה את ה-Scanning וה-Crawling וכל זה - והכלי הזה פשוט לוקח ומאנדקס (Index) אותם.בעצם נולד לך סוג של כלי שאתה יכול לחפש איזשהו Regular Expression - נגיד לינק יחסית-רגיש, שהוחבא פעם תחת Short Link - ולקבל אותו.הדוגמא שהם נותנים שם זה נגיד ב Google Docs Link, שאתה יכול ליפול על כל מיני מסמכים פומבייםו-Long Story Short, אתה יכול לייצר לעצמך איזושהי . . . אם אתה האקר שעושה את זה למחייתו אז לייצר איזושהי הכנסה, ואם אתה אתי, אז זה לייצר מאמר ב-TechCrunch על חברה שדלפה כל מיני דברים מעניינים . . .אז קחו, שחקו - ונסו את מזלכם.אייטם הבא - ספריה בשם rich, ב-Python, ולפחות מהתקופה שעשיתי המון ב-Python - היום אני עושה הרבה פחות - חיפשתי ספרייה שהיא דומה מאוד לספריות הפופלאריות ב-Node.js, שצובעת טקסט בטרמינל, שעושה Text-highlighting בטרמינל, שעושה מסגרות, טבלאות, כל מיני דברים נחמדים, נקרא לזה “Developer Experience” מאוד נחמד - ולא היה. ממש ממש לא היה, ודי התבאסתי מזה.והנה סוף סוף יוצאת ספרייה, שנראה שהיא עושה את זה בצורה טובה, שזה ממש ממש מגניב.(רן) דרך אגב, דותן - אתה אומר שהיום אתה כמעט שלא כותב ב-Python - איך נראה ה - Stack הטכנולוגי שלכם היום בחברה?(דותן) Rust ו-TypeScript.(רן) אוקיי . . .(דותן) יש גם Data Science שזה Python - אבל בתקופה הקודמת הייתי עושה פשוט Full-time, כמעט 100% Python - גם Frontend ו-Backend והכל.האייטם הבא נקרא Maddy - למי שזוכר את Caddy, אז יש כזה שרת HTTP שנקרא Caddy, שה-Value שלו כלפינו זה פשוט כשרת HTTP שאפשר להרים, והקונפיגורציה (Configuration) שלו היא מאוד אנושית ומאוד קלילה, והכל מרגיש כמו פלסטלינה.בניגוד, נגיד, ל-NGINX עכשיו ו-Apache וכאלהובא מישהו ואמר - “טוב, אני אחליף את האות הראשונה מ-C ל-N” - יצא לו Maddy - וזה אותו הרעיון, רק Mail Server . . .כלומר - זה עכשיו מחליף את ה Post-fix-ים וכל החבר’ה האלה של העולם.אם אתה במצב שאתה רוצה לבנות לעצמך איזשהו Mail Server in-House, אז האמת שזו אחלה אופציהבאילו מקרים תרצה לעשות את זה? אז אני יכול להגיד, שמהנסיון שלי, הרבה פעמים הייתי מקים מערכות שהמטרה שלהן זה לקבל מיילים, To process them ולעשות איזושהי אוטומציה - נגיד שאתה שולח מייל לאיזשהו בוט - “שלום, מחר תזכיר לי לקנות חלב”, ואז ב-Calendar שלך אתה פתאום רואה Invite לעצמך “לקנות חלב” , או משהו בסגנון.היום, נגיד, אם ניקח את Rails - הם כבר הקימו תשתית, הקימו Framework שעושה את ה Inbound email processing, יש SaaS-ים שעושים את זה, שאתה יכול לזרוק שם איזושהי פונקציהאבל עדיין לפעמים יש מצבים שאתה רוצה ממש Mail Server בכוחות עצמך, שרץ אצלך וכו’.(רן) אני חושב, אגב, שההבדל המשמעותי בין זה לבין ה-Mail Servers היותר מסורתיים ומוכרים זה שה-Mail Servers האלה אולי מממשים את הפרוטוקולים הבסיסיים של SMTP ו-POP3, אבל יש הרבה הרבה מאוד Extensions, בעיקר בתחום של Security ו Anti-Spam וכאלה, כמו DKIM ו-SPF וכאלה, שזה כאב ראש להוסיף ל Mail Server המסורתיים.ו-Maddy - ככה קראנו לו? - Maddy מגיע עם כל אלה Built-in, אז נחמד, זה חוסך לך הרבה מאוד עבודה ב-Setup.(דותן) כן, וכמובן גם Caddy וגם Maddy טובים ב-Go (השפה, לא זה), שזה אומר שאפשר לשחק איתם, לשנות אותם, לעשות Import לחלקים מהם . . . וזה נחמד, מה שאי אפשר תמיד לעשות עם NGINX ו-Apache וכו’.אייטם אחר, של Microsoft - פרוייקט שנקרא . . . אין לזה באמת שם, אבל ה-Repository נקרא Bringing-Old-Photos-Back-to-Lifeהפרויקט עצמו הוא פרויקט Data Science שנקרא Old Photo Restoration ובעצם, מה שהם עשו זה... יש פה איזשהו פרויקט Deep Learning שנותן לך את היכולת לקחת תמונה - “מעופשת, מקומטת וקצת דהויה” - ופשוט להעביר את זה דרך המנוע הזה, ואתה מקבל תמונה שהיא נראית חדשה, “בלי קמטים”, בלי טשטושים - מדהים.עברתי ממש על כל הדוגמאות שיש להם פה.(אלון) בדיוק רציתי להגיד שזה מדהים - אם אתה ב-2010 . . . כי בכל פלאפון (זה ממש 2001. . .) יש את ה-Auto-fix הזה של התמונות, וזה עושה את אותו אפקט . . . (דותן) אני אגיד לך למה זה מדהים - כי לפחות בתקופת הקורונה, כבר נתקלתי בכמה וכמה מופעים שאנשים סביב פשוט שולפים כל מיני תמונות מהבוידעם . . . אתה יודע, הסגר גורם לדברים האלה לקרות, אתה מנסה להוציא את הארגזים ולשלוף את התמונות הישנות ולהיזכר וכל מיני דברים כאלה.ולכן זה מדהים - זה אחלה כלי לבוא עכשיו לסרוק את התמונה - אתה יכול לסרוק או לצלם את התמונה או מה שבא לך.אבל אם יש לך תמונה באמת מיושנת, שבאמת הוצאת אותה מלפני 70 שנה, ששייכת לדורות אחורה - אז זה מאוד מעניין לבוא ולהעביר אותה דרך המנוע הזה, ולראות מה אתה מקבל.(אלון) מה שרציתי להגיד זה רק שב-Google Photos יש לך את ה-Magic Pen הזה, וגם ב-iPhone Photos . . . זה בול אותו אפקט, כאילו . . . (דותן) וואלה . . .(אלון) אז מגניב ש-Microsoft הגיעו לזה עכשיו, אבל . . . היה את זה ב-2010. אולי בלי Machine Learning, אבל . . .(דותן) רגע - אבל זה כולל קמטים? (זה לא קרם, כן?) - זאת אומרת, אם יש לך תמונה עם קמטים כאלה וחתכים . . זה מאחה לך את הכל?(אלון) יש לך Sharpen, שעושה אפקט כזה . . . יש כאן רק איזה אפקט אחד שאני חושב שהוא לא מטפל בו, האפקט של הפסים הלבנים האלה, שאני לא בטוח . . . אבל כל שאר האפקטים . . .(דותן) כן, פס לבן זה קמט או משהו, כשתמונה מתעקמת אז זה נשבר.(אלון) כן, אבל כל שאר האפקטים פה - זה לגמרי ה-Magic Fix עושה לבד, אז . . . לא יודע.(דותן) מה אתה אומר . . .(רן) באותה הזדמנות - יש פרויקט נחמד של My Heritage של צביעת תמונות, בעצם שירות שהחברה נותנת בחינם, למיטב זכרוניאתם יכולים להעלות תמונות בשחור-לבן ובאמצעות - עם גרשיים באוויר “Deep Learning” - או דברים אחרים, לא יודע בדיוק איך, אבל הם צובעים, בצורה אינטליגנטית, מבינים מה אמור להיות הצבע של כל חלק בתמונה, צובעים אותו, וזה נחמד.זה אולי לא מתקן קמטים או דברים כאלה, אבל כל מה שאתם רוצים זה לצבוע תמונות, אז זה נחמד.(דותן) אז זהו . . . כאן אין צביעה של התמונות בפרויקט הזה(אלון) יש על זה פטנט, אגב . . . על הצביעת תמונות יש פטנט, אני חושב של Facebook, שאתה לוקח תמונה בשחור-לבן, אבל נגיד שיש שם פחית קולה, ואתה יודע בדיוק מה הצבע של הפחית קולה ואז לפי זה אתה יכול לצבע את התמונה בצבעים האמיתיים שלה . .. אתה מוצא כמה Anchors על חפצים, נגיד עם Brand - שקית דוריטוס או אני לא יודע מה - ומתחיל לצבוע ככה את התמונה, ואז אתה מגיע באמת לצבעים האמיתיים , שם עובד בצורה אחרת, אני חושב, מהשיטה של . . .(דותן) כן, זה תחום אחר - למי שאוהב Netflix ואת מלחמת העולם השנייה, אז יש כזה מן סרט דוקומנטרי על מלחמת העולם השנייה בצבעים, שעשו Re-coloration לכל הדברים האלה - למי שאוהב את שני הדברים האלה אז זה שילוב מעניין.יש מישהו שנקרא 3Blue1Brown - זה הכינוי שלו בכלל ב-YouTube, ככה אני הכרתי אותו, ועם הזמן הייתי מקשיב לפרקים שלו בנושא מתימטיקה באוטו, במקום פודקאסט הייתי פשוט מקשיב לזה, ומדי פעם חוזר על חומרים בצורה ויזואלית.האיכות שלו . . . הוא לוקח נושא כמו הכפלת מטריצות או כל מיני דברים כאלה ומראה את זה בצורה אנימטיבית (Animated) מאוד מאוד אינטואיטיבית.אז אני Fan שלו - של הערוץ שלו בכלל ושל הוידאו שלו שם, שהם משהו כמו 5 דקות כל אחד אז זה גם טוב, נחמד שזה לא מעיק מדי.מה שהוא עשה - בוידאו שלו יש אנימציות, שהוא לוקח נגיד צירים ועושה להם סיבוב ועושה להם איזושהי טרנספורמציה, ואת כל הדברים האלה הוא לא בנה באיזושהי דרך מלאכותית, אלא הוא כתב קוד שעשה את האנימציות האלה.והוא פשוט משחרר את ה-Code Base שבעזרתו הוא בנה את האנימציות לווידאו שלו, ובעיני זה לא פחות ממדהים.ראיתי קצת קטעי קוד שבונים אנימציות, יש Tutorials ב-Repository למטה - וזה פשוט מדהים.אם אתה רוצה להבהיר רעיון מתימטי - זה ממש השאיר אותי ב”וואו” . . .אז נחמד, ומי שרוצה . . . לא יודע, אולי ברמה האינדיבידואלית קצת ללמד, נגיד - אם רוצים ללמד ילדים ככה, נגיד באיזור התיכון או טיפה לפני, ורוצים לתת אינטואיציה, ולהמחיש באמצעות ויזואליזציה ואנימציה אז זה ממש מעולה.(אלון) בלי קשר, הערוץ פשוט מדהים . . . הפרויקט הזה מגניב גם כן, אבל ה . . .(דותן) זה לגמרי משהו שהיה נחמד לראות בזמן האוניברסיטה, כי זה נותן את האינטואיציה שמאחורי כל התיאוריה - כשגם התיאוריה היא חשובה, אבל גם האינטואיציה.יש פרויקט נוסף שנקרא EUL - לא יודע אם אפשר לבטא את זה - וזה כמו בתקופה של Windows, כשהייתי, אז היו מלא Utilities כאלה מגניבים שמראים לך את ה-Performance של המערכת, וגם ב-Linux . . יש את זה קצת פחות ב-Mac.אז הוא מוציא Utility כזה מאוד מגניב, כשתחת כל Performance שאתה מוציא על המחשב שלך יש בטריה, Volts, מאווררים, מה שבא לך . . .לפריקים של Performance וחומרה ל-Mac בעצם.כמובן יש את הקוד - זה בנוי ב-Swift, והכל נורא מגניב.פרויקט נוסף ,גם באיזור הזה של Mac - למי שמתעסק בויזטואליזציה (Virtualization) של Mac, וצריך עכשיו “להקים Mac-ים מאפס” כזה, מה שיצא לי גם להתעסק איתו - לפעמים צריך “סביבה ריקה” לגמרי, נקייה.אז יש פה פרויקט שפשוט שולף את כל ה . . . אני לא יודע עד כמה זה רשמי, במובן של חוקי, אבל הפרויקט הזה הוא Open-source ומה שהוא עושה זה די יודע איך ה-Installer של Mac עובד, ונותן לך את זה בצורת Scripts של Pythonהוא פשוט . . . אתה אומר לו מה אתה רוצה, מהחבילות שיש ל Mac OS, והוא פשוט מביא לך את ה-Zip-ים ואת Tarball-ים ישירות, ואז אתה יכול פשוט לעשות אם זה מה שאתה רוצה, אם אתה בונה אוטומציות.אז לאנשי אוטומציה או אנשים שרוצים לעשות טסטים וכל מיני דברים כאלה, נראה לי שזה יכול מאוד מאוד להועיל, שזה מגניב.(אלון) עם ה-Mac החדש זה גם עובד?(דותן) וואלה - לא יודע . . . עם ה-CPU של ARM? (אלון) כן, סתם שאלה . . .(דותן) שאלה . . . האם אתה היית קונה את המחשב הראשון שיוצא עם CPU חדש? (אלון) תשמע . . . תראה . . . המחשב הספציפי, הדגם שיש לי של ה-Mac - לרדת מפה הם לא יצליחו, אז כן.(דותן) לא יודע, תראה - זה מעניין, כי אתה יכול לחשוב על זה שה-iPads שיש עכשיו עובדים עם ARM, ובעצם כל מה שצריך זה לקחת את אותו ה-CPU ורק לתת לו עוד קצת בשר, ולחבר לו מקלדת ועכבר ויש לך את אותה המערכת.אבל, מה שנקרא - אני באופן אישי, במיוחד כשזה קשור לכלים האישיים שלי, מה שעובד לי טוב אני לא כל כך רוצה להחליף, במיוחד כשזה קשור לחומרה.אבל בוא נראה איך זה יקרה, כאילו - יכול להיות שזה יקרה כמו שתמיד: הם תמיד מחליפים את ה-Macbook Air, לפחות זה מה שאני זוכר מהפעם שעברה, מהמרים על ה Macbook Airs של העולם, ואחרי זה הם ממשיכים לתוך ה-Pro, לתוך כל ה-Mac-ים שהם באמת למקצוענים שצריכים את זה בשביל היום יום שלהם לעבודה.האייטמים הבאים הם בנושא Rust - אחת הכתבות שהתפרסמו לאחרונה, שקצת יותר תפסו כותרת, היא על AWS, שקצת פרסמה “מאמרי אהבה ל-Rust”, אבל הם היו לטענתי קצת חלשים, כי האחרון שבהם היה “אנחנו תומכים ב-Rust, ונתנו להם אחסון S3 חינם” . . .אז עכשיו הם יוצאים עם מאמר הרבה יותר חזק - הם אומרים ש-Rust זה בעצם חלק מה-Core שלהם, והם חייבים - בצורה אסטרטגית, כמו ש-AWS יודעים לעשות - להשקיע ב-Rust.מה שמעניין פה זה Tokio, שזו בעצם תשתית Networking, הדור הבא בכל מובן ולדעתי גם בכל שפה, שמבוססת ושייכת ל-Rust.וזה חשוב להם - זה בעצם Runtime ל- async Programming.הם הולכים להשקיע בזה - הם לקחו, לפחות עשו Hiring למישהו שהיה ב-Core של Rust, ואני חושב שגם לעוד אנשים.הם בעצם מכריזים - “חבר’ה, אנחנו הולכים להיכנס ממש עמוק לתוך Rust” - שזה ממש טוב.ובלי קשר, באופן כללי, כבר לא מעט חברות, במיוחד מהסוג הזה, שצריך Performance וטכנולוגיה Hardcore עמוק בתוך התשתיות - הן כבר מושקעות ב-Rust, שזה ממש טוב.היה איזשהו Milestone לפני שבוע, ש-Rust הגיעה ל 50,000 Crates, שזה Libraries או Gens או npm Modules או מה שזה לא יהיה.זה לא הרבה במונחים של Ruby ו-Node.js, שם המספרים זה מאות אלפים ואולי מיליונים, אבל מה שאני יכול להגיד מניסיון אישי זה שכמעט כל אחד מ-50,000 האלה הם מאוד איכותיים, לפחות בשלב הזה.אין לי 8 ספריות של Logging . . .(אלון) הבעיה עם הכמות הזו . . הכמות, מה לעשות, מורידה את האיכות - והיה משהו טוב ב-Rust, שהיה לך בקושי ספריות מצד אחד, ומצד שני כל אחת הייתה, וואלה - חלק מהשפה.(דותן) בדיוק - אין לי 8 ספריות של Logging, אין לי חמש ספריות של . . . לא יודע, מה שלא תבחר - וזה לא מבלבל, אתה פשוט לוקח מתוך שניים, אחד.והשניים, שעושים ספריות של Logging, הם ממש שונים, בצורה כזאת שבאמת אתה צריך לבחור מה שמתאים לך, ולא “מה שטעים לך”, מה שהטעם שלך . . . פשוט מה שמתאים לסיטואציה.זה ממש נחמד, ונותן למח שלך לנוח, כי אתה יודע שאתה בוחר באופציה הטובה ביותר שאפשר לבחור.יצא גם ספר - The Rust Performance Book - למי שמכיר Rust, אז יש לו Performance מטורף, וגם יוצא ספר שקצת מדבר על Performance, שזה 3=1+1 כזה, קצת . . .וקצת למי שמתעניין, אז הוספתי גם שני פרויקטים ב-Rust - אחד גדול ואחד קטן:אחד מהם זה פרויקט שעושה משהו כמו 1password - זאת אומרת, עם UI, רק עם Linux, לצערי, GTK-based - מישהו לקח Rust ו-GTK ומימש משהו כמו 1password או LastPass, מה שאתם לא משתמשים בוהשני הוא אולי קצת יותר מעניין, גם בגלל שהוא קטן - נקרא simples, ואני אגיב את זה בצורה בוטה: זה כמו Kafka קטן שמישהו מימש ב-Rust . . .או באופן רשמי - event sourcing databaseזאת אומרת - זה לא באמת Kafka, בואו לא נשלה את עצמנו - אבל זה ממש אחלה פרויקט כדי לקרוא את הקוד שלוהם אומרים שאתה יכול לקחת את זה ל-Raspberry Pi . . . אני לא יודע אם מישהו משתמש במשהו כמו Kafka על Raspberry Pi, אולי ארגונים מחקריים או מישהו שרוצה לבדוק Distributed Systems וכאלהאבל זה באמת אחלה פרויקט בשביל לקרוא את הקוד שלו, לקמפל (Compile) אותו, לשנות אולי טיפה את הקוד, להריץ עוד טיפה וכאלה - למי שרוצה “ללמוד דרך הידיים”, מה שנקרא.זהו - אליך אלון!אלון - (“!Alon is on the Mike”) [דמיינו אפקט סאונד לבחירתכם]אתה (דותן) דיברת על כלי UI חמוד, אז אני אלך על כלי Terminal-י חמוד - DUFשלך היה EUL? - אז חמוד, שלוש אותיות גם כן, אולי דיברנו עליו פעם.למי שאוהב Terminal וגרפים ב-Terminal אז זה הכלי שלכם - אתם עושים ורואים את כל ה-Folders, גרפים, אחוזים, בארים - הכל בגרפיקת Terminal יפה.אז לגיקי-הטרמינל (להקה חדשה?) בקהל, שאוהבים סטטיסטיקות . . .(רן) זה בעצם בא להחליף את du, נכון? כאילו - du, אבל עם קצת יותר ויזואליזציה ושיטה? קצת Norton Commander ל-du.(אלון) ממש . . האמת, נכון. אבל זה יותר יפה, Norton Commander היה כחול, וזה עם צבעים יותר יפים, נעימים, למה . . קצת לכלכת . . .(דותן) Norton Commander זה עם הצבע הנעים . . .(אלון) זה היה עם . . . היה כחול, והיה לו את הפונט הצהוב הזוהר הזה, שאתה צריך משקפי שמש . . .(דותן) כן . . . תקשיב, זה כחול-בורלנד (Borland-Blue), נקרא . . . (רן) “ב-DUF אתה תמצא לא פחות מ-256 צבעים שונים (!)” . . . אוקיי, יפה ונחמד.יש איזה כלי של Google שנקרא ko - זה כלי ל-Go, אז הם כנראה החליפו רק אות אחת ויצא להם ko . . .זה כלי לבנייה ו-Deploy של Go על Kubernetesאז אם Kubernetes זו אבסטרקציה, פתאום ניהיה עוד אבסטרקציה על האבסטרקציה . . . הרעיון הוא שאתה רק נותן Mode מעיין YAML-י כזה, של מה שצריך לעשות, והוא בונה לך כבר את ה-Image, ואתה יכול לעשות איתו Deployment.אז זה נראה מאוד מעניין, האמת - אז אם יש לכם איזה Kubernetes ו-Go ביחד, זה יכול להיות מעניין(רן) יש עוד איזה Framework של Functions-as-a-Service מעל Kubernetes, שכחתי איך קוראים לזה . . . אני זוכר שראיתי משהו בעבר . . .אולי זה יתפתח פשוט ל-ko בסוף?(אלון) יכול להיות . . . זה כאילו . . . לפי מה שרשום פה, זה מה שהם ממליצים או משתמשים או לא יודע.יש גם איזשהו כלי ב-Cloud, שהוא מבוסס על זה . . . של לבנות Image-ים, אז אני חושב שזה מבוסס על זה.(דותן) אני רואה שאחד ה-Highlights פה הוא שאתה כאילו לא נותן . . . כשאתה בונה את ה-YAML-ים הנהדרים של Kubernetes, אתה לא שם Docker Image בצד ובלה-בלה-בלה, אלא אתה פשוט נותן איזשהו Prefix מיוחד שמתחיל ב //:ko, כמו פרוטוקול כזה שלהם - ובעצם כל מה שקורא אחר כך זה פשוט ה-url ל-Package שלך ב-Go אני מניח שמה שהם עושים זה בונים את הפרויקט ב-Go ודוחפים את זה לאיזה Minimal Image ב-Alpine או משהו כזה, והופ! נולד לך Image . . .שזה, האמת, ממש משכנע . . .(אלון) זה חמוד - כי אתה לא צריך Docker . . . זה מוריד לך את ה-Docker, אתה רק מגדיר ב-YAML את מה שאתה רוצה, שהם כבר אומרים “אוקיי, זה ה-Repo שלך? אני בונה לך אותו” . . . (דותן) מעניין!(אלון) למה אני צריך לבנות לבד Docker? תמיד אני עושה בדיוק את אותם הדברים . . . בונה, לוקח את ה-Dependencies שלי, זורק עליהם . . . הרי אין פה איזה משהו מיוחדאלא אם כן יש לך איזה משהו ספציפי, אבל ב-90% מהמקרים אתה הרי סתם אומר “מה אני צריך?” - ודוחף את זה פנימה וזהו.אז חסכו לך את כל זה - וזה מעניין.(רן) אני אגב לא רואה מניעה שזה יהיה גם בשפות אחרות - אולי הגרסא הראשונה זה ב-Go, אבל לא נראה שיש פה משהו שהוא מאוד ספציפי ל-Go.(דותן) כן . . .קצת מזכיר לי את Buildpacks של Heroku(רן) כן, נכון(אלון) כן . . .אז Netlix הוציאו איזה מאמר, על הStreaming . . . על כל האבולוציה של השימוש שלהם ב-Node.js ב-Netlix - הוציאו על זה וידאו נחמד.אז מי שאוהב Node.js ומתעסק עם Performance יכול למצוא את זה מעניין.האמת שזה קצת מוזר, כי כולם לאחרונה רשמו שהם יורדים מ-Node.js ופתאום Netflix, שהם די גדולים ומשמעותיים . . . די מעניין, האמת, כי זו חברה מעניינת והכלים שלהם מעניינים והם פותרים דברים בצורה מעניינת - בגלל זה זה לא איזה מאמר צדדי כזה, שאתה אומר “עוד מישהו הצליח לעשות איזה משהו”, אז אני חושב שיש פה משהו נחמד, ומי שבעולם ה-Node.js ומחפש Performance אז זהנראה לי נחמד מאוד.ו-“Neflix הוציאו וידאו” זה אכן חדשות מטורפות(רן) ועוד Netflix?(אלון) עוד Netflix! איזה חיבור מדהים, הרצף! סתם . . . למי שרוצה לבדוק Speed-Test, אז גיליתי את זה לא מזמן - יש את Fast.comזה ממש נחמד, ואחד הדברים היפים הם שלא צריך ללחוץ על כלום - תמיד כשאתה רוצה לעשות Speed test, אתה ננכס לאתר ועושה “Start!” - למה? בוא תתחיל לבדוק . . . אז זה של Netflix - זה Fast.com, וזה בודק Performance נחמד.(רן) אז כמה מילים על זה - קודם כל הוא בודק רק Download, כי זה הדבר היחיד שמעניין את Netflix, רק כמה Download, ממש לא מעניין אותם Uploadוגם לא Ping - רק מראה לך Download.דבר שני - אני זוכר שכשהאתר הזה הוקם - Fast.com - זה הוקם בעקבות של הסיפור של Net Neutrality בארה”ב, לפני כמה שנים - אתם זוכרים את הסיפור הזה, שהיו כמה חברות גדולות שבאו ואמרו “מה זה? כל חברות ה-Streaming האלה שוברות לנו את האינטרנט! שימו להם מגבלות” וכל זה.ואז Netflix באו ואמרו “רגע, חבר’ה - Net Neutrality! אתם לא יכולים לשים מגבלות רק על חברה אחת ולא על חברה אחרת”.והם גילו באמת שהיו הרבה ISP שהגבילו את ה-Traffic ל-Netflix ולא הגבילו את ה-Traffic למקומות אחרים.אז הם החליטו לבנות את Fast.com ואמרו - “תקשיבו, אתם בעצמכם תמדדו את היכולת של ה-ISP שלכם, ואם אתם לא מרוצים ממנו, אז תעברו ל-ISP אחר” - וזה התחיל אז.אבל אני מסכים שזה אחלה כלי - כלי נורא פשוט למדידה של Download.מה שכן - הוא לא מישראל - אתה לא עושה Download מישראל - אולי זה לא מה שאתה רוצה למדוד, אבל כשאתה עושה Speed Test, הוא בדר”כ מחפש את ה-Download הקרוב ביותר, והרבה פעמים זה קורה בישראל.ו-Fast.com הולך, כנראה, ל-Netflix, באיזשהו מקום בעולם.(דותן) אני יכול לשלוח להם מייל, תלונה או משהו? כי אני רואה רק 960Mb . . . ולא 1000 גדול.(רן) אני לא רוצה להגיד לך מה יש לי על המסך . . . (אלון) בביטים . . .עוד משהו על זה - כן יש שם Upload, אני אתקן - יש שם איזה חץ כזה . . . זה לא ב-Default, כי זה פחות מעניין אותם, אבל אתה יכול לראות גם Upload, אז זה . . .רק שלא יתבעו אותך דיבה, אתה יודע . . .(רן) !I stand corrected, סבבה . . .עוד אתר נחמד, אם אנחנו כבר בבדיקות מהירות - האמת שהוא יותר חמוד - הוא של Cloudflare - ו-Cloudflare, עש להם גם אתר - Speed.Cloudflare.Com - שהם מראים לך Dashboard, שחוץ מזה שהוא יפה ומהיר ונחמד, הוא גם עובד מיד ועם Upload והכלוהוא גם נותן לך את הסטטיסטיקות של ה-Jitter וה-Latency ודברים כאלה, וגם מראה לך מאיפה הוא בודקאת ה-End-point הקרוב לביתינו הוא - לפחות מהבית שלי - הוא גרמניה . . . אולי לכם יש משהו בארץ, אבל אולי בחור השחור של פתח-תקווה זה הדבר הכי קרוב שהוא מוצא.(רן) אני הגעתי ל-TLV . . .(אלון) אני הגעתי לגרמניה . . . זה מה שיש אצלי. אני פרנקפורט . . . למרות שיש להם גם Nodes בארץ, אני לא יודע למה הוא . . .(רן) באמת Dashboard יפה, מראה כל מיני סטטיסטיקות ממש נחמדות.(אלון) כן, והוא גם נותן מידע שאין לך לפעמים - לפעמים אתה אומר שהאינטרנט שלך מהיר אבל הדברים לא זזים בגלל ה-Jitter, אז אתם יכולים להסתכל פה ולהבין אם יש לכם בעיה במחשב, ב-Router, וכו’((דותן) אני גם גרמניה . . . אלון, נראה לי שאני ואתה יוצאים ישר לאוקיאנוס, ישירות . . .(אלון) אנחנו על המהיר! נכון, הוא עובר ועוצר בחנייה פה, שכחתי את אינטרנט שלו . . .(רן) ככה זה באופטי, כן . . .(אלון) כן, אנחנו באופטי . . .(רן) אבל הי - קיבלתי יותר מ-Fast.com . . . (אלון) זה אומר שחוסמים לך את Netflix. . . (רן) לגמרי . . .(אלון) ראית מה זה?ול-NET.! אז NET 5.0 יצאה(דותן) וואו!(אלון) ומדברים פה על . . . זה NET Core 3.1. - מדברים פה על שיפורי Performance די מרשימים שיש בגרסא הזאת.באופן כללי, NET. - אם לא היה לא את העוול של פעם עם ה-Windows, נראה לי שהיום זה היה By-far אמור להיות ה-Framework הכי מצליחאין שום סיבה - יש לו את כל הנתונים להצליח: יש לו את #C, שזהכנראה שפה הכי מתקדמת ונחמדה וה-Framework פסיכי, ולדעתי זה סתם PR רע שהיה לו לכמה שנים כשהם היו באמת לא-להיט ומאז קשה לו להתרומם.אבל באמת - ה-Framework הזה מדהים, ה-NET. - הוא משתפר מרגע לרגע ו . . . לא יודע, אולי ב-NET. 7-8 זה כבר יהיה Framework פופולארי בחזרה(דותן) רגע, אני אשפוך שנייה מים קרים(דותן) לא, לא עכשיו . . .(דותן) זה נראה, לפחות מה-Screenshot, שהשיפורים הם סביב SQL ו-Caching של SQL וכל מיני דברים כאלה . . . אבל אני מקווה שיש יותר מזה.אני תמיד הייתי בעד ה-NET. - מהצד . . . כלומר, אני כבר לא בפנים, אבל מהצד.(אלון) אז קודם כל כן - הם מדברים פה שה-Output, שזה Caching sample, איזה Fusion’s “Caching” sample של איזה רכיב שהוסיפו פה.אני לא יודע אם זה ספציפית רק על הרכיב הזה או עוד דברים - אבל באופן כללי . . . יש שם גם איזה לינק למאמר יותר רחב ובפירוט . . .(דותן) אני רואה שזה באמת . . .זה Across the board - המאמר הבא הוא ממש וואו - GC ו-Jit ומלא מלא דברים יש לו פה . . .(אלון) כן - זה גם חופר פסיכי, כאילו במספרים, למי שזה מעניין אותו, זה חופר, יורד פה לפרטים ולכל המספרים, וזה נראנה פסיכי - מספיק להסתכל פה על הגרפים ולראות את השיפורי Performance . . .כמובן שזה “בדיקות מעבדה” - Disclaimer וכל הבלה-בלה-בלה - אבל ה-Framework הזה פשוט מתקדם מדהים.(דותן) אני אגיד לך מה - כשאני משווה את שני “ה-VM-ים הדינוזאורים” - Java ו-NET. - אז כש - NET. מפרסמים “שידרגנו גרסא ויש שיפורי Performance”, אז המאמר, שעכשיו אני מסתכל עליו ועומד מולו, הוא ממש כייפי לקריאהכלומר - יושב בנאדם, בנה פה מאמר ש . . . לא יודע, עם ה-Scroll-bar אני יכול להגיד שיש פה 50, אולי 70 עמודים - והוא כתב אותו טוב מלמעלה עד למטה, ואני ככה “צד” כל מיני תכנים בעיניים - וזה נראה אחלה חומר קריאה גם לללמידה, ככה בכיףהוא מלמד על לקחים שהם למדו, מה עבד ומה לא עבד - איך בונים שפה, בקיצור.בעיני זה סוג של הבדל, נגיד, בין לחיות בתוך ה-Ecosystem של Java לבין NET., שפעם היום מתחרים גדולים, אני לא יודע עד כמה זה נכון עכשיו.(אלון) אני לא יודע אם הם באמת היו מתחרים, כי תמיד היה להם את הבעיה של “רק Windows”, והעולם היה תמיד Linux, אז הם . . . אני לא יודע מתי הם באמת הובילו.(דותן) פעם שדה הקרב היה הרבה יותר מיושר - אם אתה מסתכל על 2008, נגיד עד 2010, אז הקרב היה . . . היה שם פייט רציני.היום הם כבר בטח לא.(אלון) כן, למרות ששוב, כמו שאמרתי - אני חש שזה Marketing issue - ומן הסתם הקהילה לא שם, אז זה הבעיה . . . רוב הקהילה ב-Java אז הכלים נכתבים שם.(דותן) כן, Windows Server היה משהו פעם . . . (אלון) כן, היום אף אחד כבר לא יודע מה זה . . . בסדר, לא יודע אם יש עוד דבר כזה בכלל(רן) בוא נבדוק אם יצאה גרסא חדשה ל-IIS, יכול להיות שיצא IIS 6 או 7 או 8, לא זוכר . . .(אלון) יו . . לא, היה 7(רן) היה 7?(אלון) היה עוד משהו, 7 אני זוכר שהיה, אחרי זה אני לא יודע.מי שעבד עם IIS יותר מתקדם מ-7 - 8 ומעלה - אנא שלחו לנו גלויה, ואנחנו נפתח אותה בפרק הבא, ונגיב!אז תודה למגיבים.אז נמשיך . . .כלי חמוד - דיברנו על UI ב-Terminal, אז progressbar ב-Goלמי שרוצה לעשות Progress Bar חמודים ב-Terminal, איזה יש פה ספריה חמודה ב-Goקחו, אמצו, השתמשו.ואגב, דרך פה יש את הפרויקט הזה, שאני לא זוכר, אני חושב שדברנו עליו, שנקרא crocדיברנו - ב-Bumpers הקודם . . .זה Client-to-Client, לשלוח קבצים - אז הוא פשוט משתמש בספרייה הזאת כדי לעשות את ה-Bar-ים החמודים שלו, אז זה כל מה שרציתי לציין פה.(רן) חמוד, באמת נראה נחמד(רן) אז אלון - תגיד לי: איך עובד DNS? אתה יכול להסביר לי איך עובד DNS?(אלון) או - טוב ששאלת! אני שמח . . . במקרה הכינותי מראש: יש אתר ממש חמוד בשם How DNS works (זה HowDNS.Works - זה הדומיין . . .)ואז יש Episode I, וזה ממש מסביר בצורה ציורית-קומיקסית כל שלב שקורה ב-DNS - מה קורה כשמקלידים ב-Browser, ואז ה-Browser עושה “עצור! מה זה הכתובת הזאת?”, הולך למערכת הפעלה, שואל את עצמו האם יש לו ב-Cache, שואל את המערכת הפעלה - שניהם מחפשים ב-Cacheלא מוצאים - הולכים ל-DNSוהכל בקומיקס ממש חמוד - אז מי שרוצה להבין DNS לעומק, יכול לראות את הקומיקס הזה, ואשכרה לדעת DNS לעומק.(רן) ואם זה קל לך מדי - אפשר לעשות את זה שוב, בספרדית. יש את זה גם באנגלית וגם בספרדית.(אלון) זה המבחן - במבחן אחרי זה אתה צריך להשלים . . . אתה מתרגם את הספרדית לאנגלית כדי לראות אם הצלחת . . .(רן) כמו שהיה בבית ספר “מפה עיוורת” - היית צריך להגיד איפה הישובים.(אלון) נכון, היה משהו כזה פעם (בית ספר?)היה בצבא גם . . במבחן בקורס קצינים פעם היה משהו כזה, לא יודע.(רן) כן . . . תגיד - מה מצב רשתות ה-GAN בזמן האחרון? הייתה התקדמות עם Generation של פרצופים?(אלון) שמע, זה נושא מעניין שאני רוצה בדיוק לדבר עליו . . . אז היה פעם את האתר הזה, שמייצר את הפרצופים הזה . . שכחתי את שמו - שהיה מייצר פרצופים רדנומליים?הכוונה ל-This Person Does Not Exist?אז עכשיו יש את זה ב-New York Times ,ממש יפה - זה קצת התקדם, ועכשיו הוא ייצר מלא פרצופים דמיוניים . . .המערכות האלה ב-AI, ואפשר לשחק באתר של ה-New York Times, בגלל זה הוא כל כך יפה . . . אתה יכול לשחק עם הגיל של הפרצופים שם, ועם העיניים . . .אתה מקבל אינסוף פרצופים, על אותו פרצוף אפילו . . .לסובב אותו, לחייך, אם אתה רוצה שהפרצוף יחייך או יהיה עצוב - זה ממש Gamification קלאפילו אפשר לשנות Race, מאדם שחור ללבן, או Gender, מאישה לגבר - על ידי sliderאז . . . ממש מגניב.(רן) למי שלא היה פה בפרקים האחרונים, מדובר על רשתות GAN, רשתות שבעצם מג’נרטות (Generates) תמונות של אנשים שהם לא אמיתיים - אבל התמונות נראות לחלוטין אמיתיותאתם תראו את התמונה ותגידו - “וואלה, זה בנאדם”, נראה כמו תצלום של בנאדםאף אחד מהאנשים שמופעים פה בתמונות לא באמת קיים - הכל מג’ונרט (Generated)ומה שיפה זה שהם עשו בכתבה הזו של ה-NYT זה שבאמצעות Scroll פשוט של העכבר אתם יכולים לעבור בהדרגה מפרצוף אחד לפרצוף שני, או כמו שאלון אמר קודם - לשנות Gender, לשנות Race, לשנות הבעת פנים וכו’.והכל נראה ממש טבעי - זה נראה כמו איזשהו סרטון של בנאדם שמתחיל לחייך, והכל נראה מאוד אמיתי ויפה, זה מגניב.נכון שהטכנולוגיה עצמה לא חדשה, והיא באמת מאוד השתפרה - והתצוגה של ה-NYT פשוט מאוד יפה.(אלון) אני חושב שהם באמת עשו פה מהלך יפה עם התצוגה - היו כל מיני אתרים, אבל זה באמת הכי מרשים שראיתי.(רן) ואנחנו לקראת סיום - נעבור למצחיקולים שלנו: יש לנו פה כמה פריטים קטנים - אז האייטם הראשון - בעצם שני האייטמים הראשונים - הם מאת מחבר בשם מיכאל ציון - Michael Zion - חבר שעובד איתי (רן) ב-Appsflyer - בחור מאוד יצרתי, והוא יצר כמה פרויקטים בקוד פתוח, הראשון שבהם נקרא okify - זה פרויקט ב-Go, קטן, שנותן לכם להרגיש טוב עם עצמכםלמה להרגיש רע כשאפשר להרגיש טוב? יותר חשוב . . .השורה שלו היא “הרגשות שלכם יותר חשובים מ-Production” - קודם כל תרגיעו, הכל בסדר.אפילו אם זה 404 - הכל בסדר, עשיתם הכל נכון, אין לכם מה לדאוגגם אם ה-CI נכשל - זה לא אשמתכם! אתם עשיתם כל מה שצריך!כל מה שאתם צריכים זה לקחת את ה-Output, להכניס אותו לתוך okify - והוא כבר יתן לכם איזושהי תפיכה נעימה על השכם.כמו שהוא אמר - “יותר חשובים הרגשות שלכם מאשר ה-Production או ה-CI או אחרים” . . .כלי מאוד נחמד - והוא אחר כך גם הולך ועושה בו קצת שימוש בכלים אחרים שהוא כותבוהכלי הבא נקרא singload - וזה למעשה Load Balancer שמפשט מאוד את הענייניםלמה לעשות Load Balancing להרבה מאוד Server-ים שונים, אם אפשר לפשט את הסיפור הזה, ותמיד לעשות Load Balancing לאותו Server, לאותו Backend Server?הוא אומר “Load Balancing זה קונספט נורא מורכב- בואו תקימו Cluster, שהוא Single-load, וכל ה-Cluster בסופו של דבר ינתב את כל ה-Traffic ל-Server אחד, ויהיה לכם מאוד ברור איזה Server הולך לקבל Traffic, בלי כל הסיבוך הזה של Load Balancing”.(אלון) נורא קל ל-Debugging . . . למי שמכיר את הבעיה עם Load Balancer - נורא ק

The topics are on the move with ObservationTFR Show: Crazy Dee discusses his film making, his process, the use of light and shadow to tell his stories, and why the narratives he chooses to shine the light on, deal with relationships between a man and a woman; good bad, etc. Why it's important to him to shot a project, and finish the movie to release it. What is Jitter Ditter Action Camera Light Flicker? Plus, we look at some memes and take your phone calls; kicking-off a week of looking at independents, and why it's best to remain independent. Let's discuss, the phones lines are open @ 213.943.3358.

They may or may not have more presidential debates, but you’ll always at least have us!  Tune in for this week’s episode as Professors Chesney and Vladeck review the latest national security law developments: The...

jitter clicking maybe faster than drag clicking --- Support this podcast: https://anchor.fm/andrew-gajewski/support

Congrats again to Scott M. whose name was drawn from our list of Patreon Patrons to receive a model of a sci-fi spaceship from Andrew L.! Our Listeners of the Week are Derek and Manuel for their donations, and our 121 Patreon Patrons. And we also appreciate the notes of gratitude for keeping the podcast going that […] The post AV Rant #703: Jitter, Judder, Dumber appeared first on AV Rant.

On this episode, Dan and Ted are once again joined by Frank Wells who has a tremendous history in radio, recording studios, and with AES.  We discuss shifting from a technical studio career to an editorial career, joining and engaging with AES, the evolution of the Audio Engineering Society, supporting the future of the audio industry, $3m recording studios being bulldozed to build condos, and a whole lot more! Learn more about joining AES: www.aes.org/membershipTo learn more about the ‘World-First’ Cross-Border Interactive Performance that we discussed, click here. 

A continuously ill-tempered man turns to a self-help tape to control his anger, but instead obtains a unique skill. Subscribe to The Grayscale on iTunes. Directed by Jordan Goldston Written by Dylan James Amick Edited by Chelsea Rugg Theme by Sammy Pisano Art by Jackie Mullen Starring Dylan James Amick as Danny Wampler Coryn Carson as […]