Podcasts about JSON

Welcome back to the Bar Exam Toolbox podcast! If you're planning to take the bar exam in July 2026 or February 2027, your exam will look quite different from the current one. The Uniform Bar Exam overhaul includes changes in the structure, the question types, the timing, the grading – basically everything except the stakes. Today we're walking you through 11 things that every bar taker needs to know about the NextGen UBE. Some of these are structural and some are strategic, and at least a couple might genuinely surprise you.  In this episode, we discuss: Exam structure changes and new question types What subjects will be tested How the new grading system works Preparing for a fully computer-based exam  How the exam will test practical lawyering skills beyond just legal knowledge Resources: Private Bar Exam Tutoring (https://barexamtoolbox.com/private-bar-exam-tutoring/) Official Examinees' Guide to the NextGen UBE (https://www.ncbex.org/sites/default/files/2025-07/NCBE-NextGen-UBE-Examinees-Guide%20J26-F27.pdf) NextGen UBE Content Scope (https://www.ncbex.org/sites/default/files/2025-07/NCBE%20NextGen%20UBE%20Content%20Scope-Aug%202025.pdf) Next Gen UBE Constructed Response Guide (https://www.ncbex.org/sites/default/files/2026-01/NCBE_NextGen_UBE_Constructed_Response_Guide.pdf) Next Gen UBE Sample Questions (https://www.ncbex.org/exams/nextgen/sample-questions) NextGen UBE Scores and Score Portability (https://www.ncbex.org/exams/nextgen/nextgen-ube-scores-score-portability) Next Gen UBE Tutorial (https://www.starttest.com/ITDVersions/25.1.0.0/ITDStart.aspx?SVC=41623dba-2ec6-41fa-a293-ff5472bf2b71&Json=1) Download the Transcript (https://barexamtoolbox.com/episode-344-11-things-you-need-to-know-about-the-nextgen-ube/) If you enjoy the podcast, we'd love a nice review and/or rating on  Apple Podcasts (https://itunes.apple.com/us/podcast/bar-exam-toolbox-podcast-pass-bar-exam-less-stress/id1370651486) or your favorite listening app. And feel free to reach out to us directly. You can always reach us via the contact form on the Bar Exam Toolbox website (https://barexamtoolbox.com/contact-us/). Finally, if you don't want to miss anything, you can sign up for podcast updates (https://barexamtoolbox.com/get-bar-exam-toolbox-podcast-updates/)! Thanks for listening! Alison & Lee

This week on JavaScript Jabber, we're joined (again!) by Val Karpov — the maintainer of Mongoose — to talk about what's new in Mongoose 9, how async stack traces are changing the debugging game, and why AI is quietly reshaping the way we build developer tools.We dig into stricter TypeScript support, the removal of callback-based middleware, and what it really takes to modernize a massive codebase. Then we shift gears into Mongoose Studio, a schema-aware, AI-enhanced MongoDB GUI that brings streaming query results, map visualizations, and even LLM-powered document generation into your workflow. If you've ever wrestled with debugging database issues or squinting at raw JSON, this episode will get your wheels turning.We also explore Cassandra integration, vector search, Bun vs. Deno, and what AI means for the future of software engineering. There's a lot here — especially if you're working in Node.js, MongoDB, or building backend-heavy JavaScript apps.

Nick Câmara (CTO e Co-Founder da Firecrawl) conta como lançou o projeto open source num fim de semana, entrou no avião pra São Francisco… e quando pousou já tinha 1.000 estrelas no GitHub e os primeiros clientes pagando.Nesse episódio você vai descobrir:• Como a Firecrawl nasceu de um pivot brutal depois de um produto que “crescia, mas não explodia”.• O que realmente significa transformar qualquer site em texto limpo e JSON que IA entende de verdade.• Por que ser 100% open source foi a estratégia que mais acelerou a adoção.• Como atender Shopify, Canva e milhares de devs com apenas 22 pessoas no time.• A velocidade insana que eles alcançaram (menos de 1 segundo pra scrapear uma página).• E o que o Nick acha que todo mundo está subestimando nos próximos 24 meses de IA.Se você constrói com IA, vende pra devs ou sonha em fazer um SaaS que realmente escala, esse papo é ouro puro.

An airhacks.fm conversation with Kabir Khan (@kabirkhan) about: Discussion about the A2A (Agent-to-Agent) protocol initiated by Google and donated to the Linux Foundation, the A2A Java SDK reference implementation using quarkus, the Java SDK development accepted by Google, comparison of python and Java expressiveness and coding practices, the concept of an agent as a stateful process versus a tool as a stateless function call, the agent card as a JSON document advertising capabilities including supported protocols and descriptions and input/output modes and examples, the three wire protocols supported: JSON RPC and HTTP+JSON (REST) and grpc, the proto file becoming the single source of truth for the upcoming 1.0 spec, the facade/adapter pattern for the unified client across protocols, the agent executor interface with request context and event queue parameters, the distinction between simple message interactions and long-running multi-turn tasks, tasks as Java Records containing conversation history with messages and artifacts, message parts including text parts and data parts and file parts, task lifecycle with task IDs and context IDs for stateful conversations, the event queue as internal plumbing for propagating task updates, the separation between spec package (wire protocol entities) and server package (implementation details), the task store as a CRUD interface with in-memory default and database-backed implementations in extras, replicated queue manager using microprofile reactive messaging with Kafka for kubernetes environments, building A2A agents without any LLM involvement for simple use cases like backup systems, the role of LLMs in creating prompts from task messages and context, the agentic loop and the challenge of deciding when an agent's work is complete, the relationship between MCP (Model Context Protocol) for tool access and A2A for agent-to-agent communication, the possibility of wrapping agent calls as MCP tools, memory management considerations with short-term and long-term memory and prompt size affecting LLM quality, the distinction between the bare reference implementation and Quarkus-specific enhancements like annotations and dependency injection, upcoming 1.0 release with standardized Java records for all API classes and improved JavaDoc, protocol extensions including the agent payment protocol and GUI snippet extensions using template engines, authentication support with OAuth2 tokens and API keys and bearer tokens, the authenticated agent card containing more information than the public agent card, authorization hooks being discussed for task-level access control, the API and SPI segregation suggestion for better clarity between spec and implementation Kabir Khan on twitter: @kabirkhan

How AI Agents are Disrupting the AdTech Landscape Semantic content classification driven by AI agents is currently transforming digital advertising and B2B content monetization as we know it. When leveraged the right way, marketers can classify B2B content into actionable signals and find the most relevant content across the open web. This shift toward AI-native advertising allows for a more sophisticated approach to targeting that moves beyond traditional cookies. So, how can brands strategically implement these tools to generate impactful results, and what does the rise of autonomous agents mean for the future of your digital marketing strategy? That's why we're talking to Brendan Norman (Co-Founder and CEO, Classify), who shares his expertise and experience on how AI agents are disrupting the AdTech landscape. During our conversation, Brendan discussed the evolution of digital advertising and the critical integration of AI and cloud-based tools to automate manual tasks and improve campaign optimization. He also elaborated on the massive shift from human-centric to agent-centric traffic, predicting that agent traffic will surpass human traffic within 18-24 months. Brendan also explained why he believes that the future belongs to marketers who can blend audience and contextual signals to monetize human and agent attention. He highlighted how new AI-native tools are democratizing advanced ad tech, significantly reducing costs and improving efficiency for large and small advertisers. https://youtu.be/yVobWZTmwco Topics discussed in episode: [03:01] Beyond Keywords: How semantic understanding allows advertisers to target the nuance of a page (like “snow removal” vs. just “winter”) rather than broad categories.  [06:46] Optimizing for AI Agents: Why “Generative Engine Optimization” (GEO) complements traditional SEO, and how brands must prepare for agents retrieving information instead of humans.  [12:34] The Shift in Web Traffic: The prediction that agent traffic will surpass human traffic on the web in the next 6 to 24 months.  [15:50] The Power of Context + Audience: Why the best advertising strategy combines who the user is (audience) with what they are consuming in the moment (context).  [20:47] Democratizing Ad Tech: How AI agents and new frameworks will allow smaller brands with smaller budgets to access sophisticated programmatic advertising tools.  [26:54] High-Fidelity Curation at Scale: How AI reduces the cost of processing massive data sets, making real-time optimization and curation accessible and sustainable.  [33:44] The “Middleman Tax”: A look at the inefficiency of current ad tech where only 35 cents of every dollar reaches the publisher, and how AI can fix this.  Companies and links mentioned: Brendan Norman on LinkedIn  Classify  Bluefish AI Agentic Advertising Org  IAB Tech Lab Transcript Brendan Norman – Classify, Christian Klepp Brendan Norman – Classify  00:00 I think overall, jobs will change. I think that people will have to spend a lot less time doing a lot of the manual, rote tasks that they’re doing today. You know, kind of in parallel with what we’re seeing in terms of vibe coding and people’s ability to build product really quickly, design new web pages really quickly, like get ship things out quickly. I think a lot of the infrastructure layer tools, or just call them like, like, chatGPT style, cloud based tools, LLMs (Large Language Models), we’ll see a lot deeper integration into existing advertising product. And what that does is it helps democratize the whole ecosystem. So I think it frees up people’s time, you know, to not have to do a lot of the basic administrative, you know, reporting, manual, campaign, optimization type stuff, and it will help service a lot better insights. Ultimately, I think the industry grows, and I think it scales even faster and cautiously, optimistically. I think that we, we will have back to building on the curation piece, and, you know, the advertiser, outcomes piece, publisher monetization piece, user experience piece, I think that all those things will increase. Christian Klepp  01:07 When done the right way and leveraging the right approach and technology, you can classify B2B content into actionable insights and find the most similar content across the open web. So how can this be done the right way, and what role do B2B Marketers play? Welcome to this episode of the B2B Marketers in the Mission podcast, and I’m your host, Christian Klepp. Today, I’ll be talking to Brendan Norman about this. He’s the Co-Founder and CEO of Classify, a software that organizes the world’s digital content, making a privacy, safe, searchable and monetizable. Tune in to find out more about what this B2B Marketers Mission is, and off we go. I’m gonna say Mr. Brendan Norman, welcome to the show. Brendan Norman – Classify  01:49 Thanks for having me, Christian. Christian Klepp  01:51 Great to have you on. I’m really looking for this conversation because, man, like you know, in our previous discussion, besides talking about snow and bad weather, we did have, we did have we did have some interesting discussions around, I’m going to say, AI machine learning, and how that all has some kind of like strong correlation to content. So let’s just dive in. I’m going to start with the first question here. So you’re on a mission to help publishers increase monetization potential and advertisers target the most relevant, curated inventory. So for this conversation, I’m going to focus on the following topic, and we can unpack it from there. So how B2B brands can optimize their own content. And you know, let’s be honest. Brendan, who the heck doesn’t want to do that, right? So your company classify, if I remember correctly. It’s a software that organizes the world’s digital content, making it privacy, safe, searchable and monetizable. So here’s the two-pronged question I’m happy to repeat. So first one is, walk us through how your software does that and B, how does this approach benefit? B2B companies looking to optimize their own content? Brendan Norman – Classify  03:01 Historically, how a lot of content gets categorized, classified, organized, it’s fairly unsophisticated, and it’s been fairly unsophisticated for a long time, just because, you know, the technology is difficult to do, and we haven’t really had the foundational ability to understand it in a way like a human understands it until fairly recently, and do it at Deep scale. So good analogy for this question is like, if you were having a we were having a conversation just a minute ago about the snow, you know, happening in Canada, and how cold it was and how much snow you got, and, you know, also around the fact that, like you had to shovel your driveway, you have a snow blower you were putting the snow. There’s a lot of different nuance to that conversation. I as a human, and most humans, are able to interpret all of that nuance and kind of positively negatively, understand that there’s a snow blower involved in that snow blower was used to remove the snow historically that conversation, you know, if it was just a blob of text, or if it were a web page, the the basic technology to understand it would have reduced it down to a category like snow or maybe winter, and that’s it, and that’s all the targeting that would have happened to that page. So our conversation, you know, gets transcribed. It gets put on a blog, or it gets put on a news site. The only thing that a machine could understand about it was, you know, snow and then potentially a keyword, tagged snow blower. And that’s all so we took a very different one. One of the reasons why you know that that makes it challenging for advertisers and also for publishers. If you’re the publisher of that content, you’re not able to help advertisers really understand the nuance to like, what are we talking about here? Because maybe an advertiser wants to sell snow blowers for that specific site. Maybe they’re looking to sell ski and since we were talking about removing snow from a driveway, probably not the best application to go sell skis on. What is helpful is to deeply understand all the nuance to like we were talking about a driveway. We were talking about removing snow from that driveway. So we invented, you know, a much better, more sophisticated way to scrape content, classify it according to all of the different, you know, nuances semantic understanding much more like a human would, and then embed all of those different, you know, semantic understandings into, you know, this, this, this file, and then we organize that in a way that makes it searchable and kind of understands all the relationships very quickly. And what that does is it helps advertisers, like if you know, I’m Honda selling snow blowers, which they make, arguably the best snow blower in the market, if they’re looking to reach people that are talking about snow removal from the driveway, they can very quickly see the list of all the different URLs across the internet, and they can build, you know, a deal ID, or they can build a targeting, contextual targeting segment to specifically pinpoint those very specific web pages. And that’s kind of how the technology works, and then also, also why it’s relevant to advertisers. Christian Klepp  06:21 Thanks so much for sharing that Brendan that definitely helps us give, you know, some perspective into, like, what your software does. And you know, just, I’m asking you this from, from somebody who probably has learned to write one or two lines of code, and that’s as far as my dev skills go. But like, how, how is your software different from like GEO (Generative Engine Optimization), or is there some kind of overlap? Brendan Norman – Classify  06:46 It’s fairly complementary. I mean, the problem that GEO, you know, is trying to solve, and we’ve got good friends, advisors, you know, like at Blue Fish AI and like, a really cool company, Andre, I worked with him at live rail. He was the co-founder back then, before we got acquired by Facebook, you know. And I think that the problem that they’re trying to solve is going back to that it was just stay on Honda snowblowers. They’re trying to help Honda understand how they’re represented inside of, inside of an LLM or inside of a chat bot. And what they also do is they help these companies restructure their pages for, you know, better representation inside of the other end of like a chatGPT or a cloud answer. So it is kind of SEO (Search Engine Optimization), but for the generative world where we sit on is kind of on a different side of that. It’s very complimentary, though, and we’re deeply understanding content at scale, and that’s helping, you know, the advertiser understand where to position their ad. We’re also just, you know, very quickly, moving into this new space of, traditionally, advertising technology is focused on a human going to a web page, reading that content, reading the article, watching a video, you know, whatever that content looks like, and then helping the right advertisers show up in a contextually relevant way, so that the human will click on that ad, and they’ll go to another web page, they’ll buy the thing, whatever somebody wants to sell. A very recent development, so back up a year or so, you know, chatGPT Claude when they’re out and their agents and their bots are scraping like going out to the web and they’re retrieving information. They’re doing it to train their models to make their models better at answering questions. But now, you know, fast forward to today. They’re actually spending more time just going to content and then using that content to answer a specific question. So like, what’s the best recipe for, you know, creating soft shell craps. It’ll query a couple different web pages. It’ll find that, it’ll retrieve that information and bring it back that that is not being monetized today. And there’s a really interesting thing that we’re, you know, we’re starting to work on, which is monetizing the attention of an agent. And, you know, it’s, there’s a lot to figure out, but it’s kind of like the early days of a web browser, and like early days of search, when humans would go, you know, to a search engine, they would pop in some keywords, or, like, right out of search, and then, you know, Google would look at their entire index of the web, which was an algorithm that was weighted based on the number of different contextual relevancy plus the number of connections between web pages. So a web page that I might have published in geocities.com that nobody else would link to, Christian Klepp  09:50 wow, GeoCities like… Brendan Norman – Classify  09:54 Throwing way back remember the days of like writing like HTML and you know, creating that, you know, looping in some type of image because nobody else had linked to that, like personalized page that you built, it would never get shown up. And, you know, the top 20 or 30 or probably even couple 1000, or maybe even 100,000 search results. So their algorithm was about contextual relevancy, plus the number of links that other pages that had to your page. And then they started to include advertising in that. So early days of ads in search were literally anything, you know, it’s any advertiser that wanted to advertise to you, and they were just kind of choosing the highest price, trying to figure out, you know, how do we make money? And then it evolved into much more contextually relevant ads and sponsored post or sponsored advertisements. So now you know, if you’re searching for, like, what’s the best, you know, LLM or chat bot, you’re probably going to see a sponsored ad from, you know, Claude and Perplexity and chatGPT. Now you’re also going to see the search results underneath those. What’s changing about that kind of rapidly is how we’re influencing because humans are spending less time going there and doing that, and also within Google, Gemini is also surfacing some AI summary quickly and kind of superseding that, creating a chatGPT experience inside of Google, which is a brilliant way to do it also. But a lot of human interaction with the web now is humans going to chatGPT going to cloud asking questions and kind of treating it like we used to treat search back in the day. So influencing that, influencing that agent, going out to the web and sitting in between. That is another really interesting way that you can help an advertiser tell that story, not necessarily to a human but to the agent who’s retrieving the information and then bringing it back to the human, Christian Klepp  11:56 Right, right, right? And if we’re talking about content, it’s, you know, doing it in such a way that the content shows up in the AI search. Brendan Norman – Classify  12:04 Exactly. Christian Klepp  12:05 Because everybody, everybody’s got those now, right, like Google or Bing, or whatever, they’ve got the, they’ve got the AI summary at the at the very top of the page, right when you, when you, when you key in something. Brendan Norman – Classify  12:17 Yeah. Christian Klepp  12:18 Okay, fantastic. I’m gonna move us on to the next question about because we’re on the topic of optimizing content. So what are some of the key pitfalls that like B2B Marketers and their content teams? What should they be mindful of, and what should they be doing instead? Brendan Norman – Classify  12:34 That would be actually a better question for some of the GEO companies and something like more SEO focused companies about how to specifically optimize like your content. It’s a great question. I haven’t spent as much time, you know, deeply thinking through that. And the problem that we’re trying to solve is more of, you know, at scale, what is the semantic understanding of like, how somebody has built their page and or construct the video, as opposed to advising them on what they should do? You know, to think about it in a way that’s either more engaging. I would pivot that question more to the Geo and SEO focused folks, yeah, but super high level. I mean realizing that now web has two primary users of traffic. There’s humans who are bouncing or reading a, you know, web page or watching a video. But there’s also agents. And now the scale is like, changing very, very quickly. So you know, in the next year, two years, everybody will have lots of agents, kind of doing things on the back end for them. And, you know, we believe that, you know, in the next what, 6,12,18,24 months, Agent traffic will surpass human traffic on the web. So realizing that there’s these kind of two layers that one, humans see a web page and nice pretty pictures, and, you know, they see the layout great, but also having a web page that’s optimized in HTML, markdown, JSON, in ways that agents consume that, and then also knowing the different types of agents. So the cool thing that we’re building right now, in addition to this content graph of all the content, which is effectively like a understanding all the context between the content. It’s a mouthful, an agent graph that helps to inform this is an agent coming to my site. So in a lot of ways, it’s very similar to the folks who over the last decade or so, have built these identity graphs or audience graphs, and they know that like you, Christian versus me, Brendan, they’ve got some profiling on us. They understand our search history, our retargeting, our purchase intent, a lot of things that they’re appending to like you as a specific profile or an IP address. The rapid evolution of all this is mapping out the land. Landscape of different agents, where they come from, and then the personalization of these agents, and basically applying a lot of the similar logic that we’ve used for identity graphs and for audience graphs towards agents to help understand, how do you modify the content on the back end that humans never see, so that when they’re retrieving information, interacting with the content they’re doing it, you’re presenting in a really thoughtful way that drives like the answers and the results that you want to Christian Klepp  15:33 right, right? No, absolutely, absolutely. And in our previous conversation, you talked a little bit about contextual versus audience targeting. So and I mean, I’ve asked you this back then, but do you think one is better than the other, or do you think that they can work together? Brendan Norman – Classify  15:50 They should absolutely work together. Christian Klepp  15:52 And why? Brendan Norman – Classify  15:54 The reason, the reason is, you know, knowing who you are is a very important piece to the puzzle. Like, and if you even take a step back, like, what’s the whole point of advertising? Like, the whole point of advertising is storytelling, so that a brand or a service or a company can help market their brand service to the right person they’re trying to sell them something. The cool thing about the internet is we all now have this, you know, basic shared awareness that, like, there are certain things that are paid for on the internet, certain types of content that are gated. I might buy a subscription to The Economist, you know, I pay Claude a certain amount of money, a lot to be able to use it, you know, a lot and chatGPT, and then a lot of the web is free. Facebook is free, Tiktok is free, Instagram is free, LinkedIn is free. But the economics, it’s very expensive to run these businesses, so they have to, you know, support it through advertising. Ideally, you know, there’s a couple of ways to think about it, and there’s one camp of people on the internet who think that advertising is a necessary evil or a last resort, you know, we just cram it in there and make some money. There’s another camper of folks who actually think that it can be additive to the experience. And one of the reasons why, you know, it’s kind of a meme, and you always hear people talking about, you know, I didn’t need this thing, but I saw an ad for it on Instagram, and just had to buy it because it was really cool. The reason why that exists is that their advertising is phenomenal, and the targeting and optimization is phenomenal. And why it’s phenomenal on the back end is it knows a lot about you know me, who I am, what I’m interested in, based on my history, what I’ve been engaging with, where I’m spending time, you know, what I’m looking at, but it also knows specifically when I’m looking at that thing, you know, it might have a framework of saying, Brendan, really, you know, likes these types of skis, you know, he’s interested in, You know, a couple other, couple other interesting products, but the best time to serve each one of those products might be different, and it’s different depending on what I’m looking at, what I’m thinking about in that exact moment. And to kind of align these, these different graphs, graphs of intent, contextual understanding, and then audience, you know, the best time to serve me an ad for a new pair of skis is when I’m reading an article about skiing or something about the mountains. You know, it’s not necessarily when I’m reading about the Warriors, because I’m not really thinking about skiing when I’m reading about basketball. So to your point, the most effective ads are when you’re combining those two sets. It’s great for the advertiser, because I’m much more likely to click on it and go check out the skis. It’s also giving me a better experience, because it feels more native to the overall content that I’m reading. And that’s why it’s so important. It shouldn’t be an afterthought or a necessary evil or a last resort. It should be something that is intentionally thought about the entire design, because it can, it can actually be a cool experience. Christian Klepp  19:06 Absolutely, absolutely. I mean, you know, you’re talking to somebody that started his career in the in the advertising industry, so, yeah, I’ve heard that one before, and what you’ve been describing in the past couple of minutes sounds to me a little bit like time of day marketing too, right? Because you’re you know, are you the had a guest on, like, a year ago who talked about this? Right? Is, is Brendan, the same guy at eight in the morning and one one in the afternoon and seven in the evening? Right? There’s different different times of the day, different mindset, different motivation, different reason for being on your device or looking at, looking at specific type of content, right? But it is interesting, right? And it’s interesting and sometimes a little bit scary, how, um, how quickly the algorithm picks, picks this stuff up, right? Like, for example, last year, I was researching a lot on Japan, because we went there, right? Family trip and whatnot. And. And that’s what I kept seeing on Instagram, right? Like, because I was looking up specific temples and whatnot and and today I got another push. Like, would you like to invest in a temple that’s an on island in the Sea of Japan, right? Brendan Norman – Classify  20:12 Like, sorry, did you invest? Christian Klepp  20:17 No, I did not. But it was just, it was just funny that I got that ad right, like, it’s, like, Okay, interesting, but like, it’s so like it not, was not on my radar at all, right, Brendan Norman – Classify  20:29 Yeah, Christian Klepp  20:29 Okay, great. From your experience, and you talked a little bit about it now in the past couple of minutes, but like, from your experience, how can leveraging AI agents improve efficiency and save marketing leaders time? Brendan Norman – Classify  20:47 Ooh, there’s a couple different ways to think about that. So you know, part of it is this new agentic framework for how existing tools, you know, advertising and marketing tools, will communicate with each other today. You know, it’s fairly complex. You know, if I wanted to go build a contextual targeting segment to help one of our brands that we work with find the right contextual or inventory to target contextually, I would have to work with them. We build a targeting segment. We would upload that into our one of our SSPs, we would build a deal ID, you know, they would connect it back. And there’s a lot of different pieces that happen along the way. And each one of those pieces you have to go to, you know, a UI, I’ve got to go to a dashboard, I’ve got to push that thing in. Some of it happens through an API, but a lot of it happens like going to a whole bunch of different web pages to make sure this stuff all works. So stuff all works. What’s cool about agents? And I’ll unpack this, and then I’ll go to the more of the consumer focus side too. But what’s really cool about agents using, you know, things like the ACP framework from the Agentic Advertising Org., the ARTF (Agentic Real Time Framework) from IAB Tech Lab is they’re kind of built on some of the existing frameworks that allow humans to use natural language to communicate between these different systems. So there’s still the back end pipes of API pushing data or pulling data from one system to another. But on top of that is more of an agentic framework that allows, you know, a human just to use some prompting, like in chatGPT, to make a request, you know, that talks to a back end system. So that’s one part of the agentic framework for like, you know, how to think about this through the lens of advertising and marketing. And then the other side is, you know, more of the consumer focused. There are so many interesting and very quickly growing tools you know, that you can start to plug in, into Cloud, into Cloud code, and to building things that just rapidly accelerate development of different products and your ability to analyze data quickly. I think in the next, you know, 6 to 12 months, we’re going to have a totally different landscape for how people are buying like trading media also, you know, one more final thought about all of this is that a lot of the sophisticated tooling and pipes that we have are only accessible towards the largest advertisers today. And I think that you’ll pretty quickly see a democratization of the ability for anybody to just buy programmatic ads, whether you’ve got a $20 a month budget or a $20 million a month budget. Now, the ability to similar types of tools to access the right content across the web will start to be available towards a lot more folks outside of the existing, you know, kind of ad tech ecosystem. Christian Klepp  23:55 And I might be stating the obvious when I say this here, but that’s a good thing, isn’t it, because, I mean, I, again, I came out of this industry, and I know that, like, you know, if you wanted to advertise in the New York Times, for example, right? Like, how expensive that would be, or, or anything that was print, right? And then they migrated all that to digital, and then it still wasn’t, it still wasn’t affordable. It was, it was cheaper than print, but still not like, exactly like, you know, yeah, I wonder, wonder if they’ll be worth the investment or not. And then now you have this, this push towards the democratization of all of this through AI and machine learning and, and I do think that you know, for all the the scare mongering that you know people are doing now with, with, oh, you know, all this stuff around AI, I do think that that part certainly will be advantageous to to B2B companies and to marketing in general. Brendan Norman – Classify  24:49 Great. I mean, yeah, optimistically, I think I’m excited about the entire landscape changing because it does a couple things. It allows for much more contextually relevant ads. I know right now there’s only, let’s call it to the magnitude of like, 1000s, 10s of 1000s, maybe hundreds of 1000s, of campaigns and or brands that are able to use these pipes to reach the largest publishers. And all of a sudden you expand that out. You know, I think between meta and Google, they each have somewhere between 15 to 20 million unique advertisers on their platforms, and what that means is, you get really hyper specific ads. And it also means that, like, I might get a local ad for my hometown here for some restaurant that’s launching a promotion that I might only get here, and I might only get to your point, maybe not in the morning, but I’ll get in the evening. There’s a lot of different data sets around my identity, you know, the psychographic profile, contextual understanding of what I’m reading at that exact moment. And what it does a lot of things. It helps smaller brands get more traction, get more visibility. It also just helps improve the publisher experience, and like publishers, make more money. And then the user who’s consuming that content, reading the web page, watching a video, also has just a better experience. And then the other layer of that will continue to just go on, this narrative of agentic, tension, but the agents who are reading that content, watching that video for an end user. On the other side, are also able to interact with advertising content that’s very contextually relevant to the content that they’re consuming again, and it’s good for the storytelling of the advertiser and good for monetization of that publisher too. Christian Klepp  26:38 Absolutely, absolutely. Okay. So how can high fidelity curation? This is the next question, right? How can high fidelity curation make B2B companies more sustainable? And if you can just provide an example, Brendan Norman – Classify  26:54 Curations like, it’s such an interesting term, but you know, effectively, it’s just, it’s helping to use the word and the definition, the definition in the word, curate the right inventory to run an ad campaign on, and curate the right inventory and audiences. So it’s a really important part of the business. I think it involves a couple things. It involves front end targeting, of knowing who’s the back to that question, who’s the audience, and then what’s the right content, and then it also involves a lot of ongoing optimization. And I’ll say that there are some some interesting companies that that are really good at curation, who are building out the right automatic tools to think about more real time optimization, and it’s something that the really big social media companies do very well, like they’re constantly looking at lots and lots of signals when they’re running a campaign, and they’re looking at inventory and stitching together based on the signals that they’re acquiring around. Why certain campaigns do well, to your point, you know, when we’re testing that, selling that pair of skis to Christian, we’re testing a lot of things. We’re testing what he’s reading, you know, we’re testing maybe time of day. We’re testing, you know, where he is. There’s a lot of different elements on the back end that they will ingest and understand and then refeed into that targeting and optimization algorithm. And I think that that is one of the cool things that AI to use, like the air quotes, AI will help enable the processing of a lot of this data to just be a lot faster, be a lot more cost effective, and a lot of these systems that you know previously have been not accessible to the ad tech ecosystem, just because we we operate at such a crazy scale of 10s, hundreds of billions of requests and impressions and transactions that happen every single day. It’s very cost expensive if you’re processing all of that data and all these different signals, with the advancement of how the model cost is getting a lot less expensive, very quickly, not just from an LLM perspective, but then the foundational layers and the infrastructure layers, like we’re doing contextual intelligence as an infrastructure layer. There are inference layers that all kind of sit underneath the LLM and help inform an LLM understanding of that content. As those costs start to decrease, you’ll start to see a lot better performance from curation, just because, you know, it’s not as cost prohibitive, and we’ll be able to find that balance in terms of economics. Christian Klepp  29:45 Yeah, yeah, you hit the nail on the head there. Because, you know, I was just writing this down. You said faster, more cost effective and in my head, and you said it, it’s like, and at scale, like, you can scale this stuff faster, like, when I when I think back, like, years ago, when we, when we launched an ad campaign, and, you know, just the amount of effort, like, for the print and then the cost into, you know, the media placements and all of that and and just alone for like, one city, just just the amount of investment that was involved in all of that, right? Just think, thinking about that. It’s like, gosh, and then now you can scale all of that, like, even faster, because it’s because it’s digital, right? So it’s just such an incredible evolution. Like, I’m getting just as excited as you are man, I’m like, for this next question. Brendan, I’m not sure if you’re the type that likes to do this, but I need you to look into the crystal ball for a second here, right? Because we’re looking at, like, stuff that is, you know, the events that are yet to come, if I’m gonna that, make it sound a little bit suspenseful, but, um, the future of digital advertising, like, how do you think that could become less fragmented and more optimized with everything that we’ve talked about in this conversation. Brendan Norman – Classify  31:04 Yeah, I caution against, like, having any, any specific predictions, and more of, like, a framework for, I mean, for me, at least, yeah, more of a framework for how I think overall, jobs will change. I think that people will have to spend a lot less time doing a lot of the manual, rote tasks that they’re doing today. And, you know, kind of in parallel with what we’re seeing in terms of vibe coding and people’s ability to build product really quickly, design new web pages really quickly. Like, get ship things out quickly. I think a lot of the the infrastructure layer tools, or just call them like, you know, the like, chatGPT style, cloud-based tools, LLMs, we’ll see a lot deeper integration into existing advertising product. And what that does is it helps democratize the whole ecosystem. So I think it frees up people’s time to not have to do a lot of the basic administrative, reporting, manual, campaign, optimization type stuff, and it will help service a lot better insights. Ultimately, I think the industry grows, and I think it scales even faster. And, you know, cautiously, optimistically, I think that we, we will have back to building on the curation piece, and, you know, the advertiser, outcomes piece, publisher, monetization piece, user experience piece, I think that all those things will increase, and I I’m hopeful that with the integration of just better technology, embedding AI into a lot of these systems, it’s going to help steer us towards having better experiences across any type of Publisher content. I think that the advertisers will see better outcomes. I think that the people that are in this industry will get to think more creatively about how they’re, you know, building better creative storytelling, better reaching the right people with those stories. And my hope is that it just continues to expedite and grow the overall industry. Brendan Norman – Classify  33:17 That will be my hope as well. All right, get up on your soapbox here for a little bit. What is a status quo in your area of expertise? So anything that we’ve talked about now in this conversation, what’s the status quo that you passionately disagree with and why? Oh, you must have a ton. Brendan Norman – Classify  33:44 I definitely do. I mean, you know, Christian Klepp  33:48 just name one, just one, Brendan Norman – Classify  33:50 Like in any industry, you know, there’s always, there’s always the early adopters, you know, there’s always the kind of like the middle stack, you know, there’s always, like, the laggards. There’s definitely, you know, a smaller, but growing quickly, minority of folks who are really leaning into, you know, I’ll just call it AI, and then the agentic web, and there’s a lot of discussion right now in ad tech around like, what that means? I’m still hearing that. There’s a lot of skeptics who are kind of making fun of it, or, you know, trash talking about different protocols. Fine, like those are the folks that are absolutely going to get left behind. And I think a lot of those folks on the soapbox in the next 6 to 12 months will look back at, you know what they said, and we’ll all kind of say that didn’t age well, and you were not building this stuff. You weren’t fingers on keyboard or hands on keyboard. Vibe marketing, vibe targeting, building stuff like shipping new product and testing and iterating. What I what I don’t think, is that the really big platforms are just able to be super nimble and adapt to a lot of these new frameworks quickly, totally like the pipes will continue to stay there. I think that there will be startups that are more nimble, that can build and ship things, you know, proof of concepts, prototypes, get things out, learn from them, fail, iterate, and then start to scale meaningful businesses without having to rely on a lot of the existing infrastructure that exists today. Do I think the trade desk is, you know, going anywhere? No, do I think that they will, like, continue to be a valuable piece in this ecosystem, absolutely. And I think that they will ship things. I think that they’ll enable the industry like to build on top of of the pipes that they’ve already built. And at the same time, I think a lot of that rapid advancement will come from startups who are kind of proving that, like they don’t necessarily need the existing pipes and channels to be able to at the end of the day, you know, this whole ecosystem is about helping an advertiser surface their ad against the right content for a human or for an agent. And there have been a lot of folks kind of sitting in the middle for that space for a long time. One of my favorite stats, soapboxy stats, is that if an advertiser puts $1 in to the open web with a programmatic web, 35 cents comes out to a publisher, so 65 cents is being taken by some combination of middlemen, you know, who are collecting a margin for, you know, different services, also some version of fraud. There’s a lot of things that happen in between that and what I’m again, cautiously optimistic about, you know, like the big picture, AI, of facilitating, is the ability to reduce that margin so that, you know, advertiser puts $1 in. A lot more of that dollar comes out towards the publisher, I think big social media, you know, it’s around 70 cents comes out. So they take, you know, somewhere between 25 to 30 cents, which is kind of the value exchange of providing the services, all the targeting, all the technology that goes into supporting that, you know, as a more fair exchange. So I think what a lot of the folks on more of the startup on more of like the front end of the frontier tech in the space we’re excited about is getting to reduce a lot of that inefficiency and a lot of that margin in the middle, and helping more of that dollar show up towards the publisher where it should. Christian Klepp  37:34 Boom and there you have it. Man Brendan, this has been awesome conversation, so thanks again for your time, please. Quick intro to yourself and how folks out there can get in touch with you. Brendan Norman – Classify  37:45 Yeah. Brendan Norman, CEO co-founder at Classify, please. You know, hit me up on LinkedIn or shoot me an email. Check out our website, which is, you know, www.tryclassify.com. I’m happy to connect. You know, if you have questions about advertising from a publisher side, from an advertiser side. Love to chat about it. Christian Klepp  38:06 Sounds good. Sounds good once again. Brendan, thanks for your time. Take care, stay safe and talk to you soon. Brendan Norman – Classify  38:13 Cool. Thanks, Christian. Christian Klepp  38:14 All right. Bye for now.

This episode is a full “build a business in 40 minutes” demo showing how AI collapses what used to take teams (creative production + sales ops + support) into a handful of prompts. Samruddhi generates a high-production video ad in Google AI Studio using a JSON-style prompt framework, then spins up a working voice sales/support agent in Vapi via Claude Desktop + MCP—so the agent is created from a single prompt instead of clicking through the UI. The conversation also covers why “interfaces matter less” in an agent-first world, why workflow tools (like n8n) still have a role, and how memory layers like Mem0 unify context across channels (email/WhatsApp/etc.) so you can take actions without hunting.Timestamps0:00 — “Single person billion-dollar company” belief + AI driving 10x execution speed1:57 — Plan: create the ad in Google AI Studio (Veo 3.1) + build a voice agent using Vapi MCP via Claude Desktop2:42 — Smithery: marketplace for MCP servers3:39 — MCP for non-technical listeners: “like an API, but agents use it to talk to external services”4:22 — Inside Vapi MCP: tool list = APIs the agent can choose from5:06 — AI Studio setup: video generation playground + select Veo 3.16:16 — JSON prompting framework begins (structure → production-level output)6:28 — Keys: description, style, camera, lighting, environment, elements, motion, ending, text9:05 — Prompts/scripts can be AI-generated (humans provide guardrails)10:41 — Need an API key to generate videos in AI Studio10:54 — Ad review: strong realism; last segment looks AI-ish → iterate prompt13:05 — Install Vapi MCP via npx from Smithery + add Vapi API key13:46 — Claude Desktop: Vapi MCP appears under Connectors/Tools (not Claude web)14:05 — Prompt the agent build: “Fresh Pause” + role, tasks, FAQs, call flows18:23 — Testing: “Talk to assistant” starts a live call simulation19:20 — Deployment: assign a phone number; Vapi provides free/test numbers (up to a limit)21:57 — Mem0 / Supermemory: memory layer across apps/agents to keep context24:13 — Why memory layers help: fewer MCPs → less slowdown/hallucination; no need to specify where to search26:36 — MCPs + slide decks: mention of Gamma MCP via Claude27:34 — Future of n8n/Zapier: they persist, but prompting increasingly generates workflows31:38 — Prediction market trading algos (Kalshi/Polymarket) + AI improves speed/decision-making36:02 — Closing vision: help orgs 10x execution speed, especially non-technical leaders (40+) with domain expertiseTools & technologies mentionedGoogle AI Studio (Video Generation Playground) — Generate an 8-second video ad.Veo 3.1 — Google video model used for “production-level” output.JSON Prompting Framework — Structured key/value prompts for story, visuals, camera, lighting, motion, ending frame.Claude Desktop — Runs connectors/tools (including MCP servers).MCP (Model Context Protocol) — Lets agents call external services/tools based on intent.Smithery — Directory/marketplace for MCP servers.Vapi — Voice agent platform; create agents + assign phone numbers.Vapi MCP Server — Enables Claude to operate Vapi via prompts (create/list/configure).npx — Installs MCP server quickly from the terminal.API Keys — Required for AI Studio generation + Vapi authentication.Mem0 / Supermemory — Cross-channel memory layer to retrieve context automatically.Knowledge Graph — Underlying structure for semantic retrieval across interactions.Glean — Referenced as a comparison point for search/context retrieval.Gamma MCP — Example of generating slide decks via MCP.n8n / Zapier — Workflow automation tools discussed in an MCP-first future.OpenClaw — Mentioned as agent tooling that can help with steps like obtaining API keys.Kalshi / Polymarket — Prediction markets referenced in the trading/AI speed discussion.Subscribe at⁠ thisnewway.com⁠ to get the step-by-step playbooks, tools, and workflows.

I've been delaying this episode for a long time because the topic is genuinely difficult and, for many of us, scary. AI is threatening not just to our livelihood, but to our sense of self-worth as creators.In this episode, I don't offer false guarantees about job security. Instead, I frame the problem through the lens of microeconomics and rational incentives to help you understand how to remain employable. We discuss why you must separate your ego from your current skill set and how to position yourself not as a competitor to AI, but as a force multiplier.• The Hard Truth: I explain why the "abstinence" approach—hoping the industry rejects AI or that it turns out to be a bubble—is a high-risk gamble that is unlikely to succeed.• Ego vs. Employability: We discuss the difficult mental shift required to disconnect your self-worth from the act of writing code manually, allowing you to adopt new tools without feeling like you are losing your identity.• The Microeconomics of Your Job: Understand the cold reality that a rational market only pays you if you generate more value than you cost; if AI can do the same task with less risk or cost, the market will choose AI.• The Non-Zero Sum Game: Learn why the economy isn't a fixed pie. The goal isn't just to survive, but to recognize that the combination of Human + AI can generate more total value than either can alone.• Multiplicative Value: I challenge you to stop thinking about linear skill acquisition and start thinking like a manager: how can you use AI to multiply your output and become indispensable?• Accepting Atrophy: We confront the reality that your core coding skills may degrade over time as you rely on AI, and why accepting this trade-off might be necessary for your career survival.

In this episode of Resilient Cyber, we will be sat down with Ari Marzuk, the researcher who published "IDEsaster", A Novel Vulnerability Class in AI IDE's.We will be discussing the rise of AI-driven development and modern AI coding assistants, tools and agents, and how Ari discovered 30+ vulnerabilities impacting some of the most widely used AI coding tools and the broader risks around AI coding.Ari's background in offensive security — Ari has spent the past decade in offensive security, including time with Israeli military intelligence, NSO Group, Salesforce, and currently Microsoft, with a focus on AI security for the last two to three years.IDEsaster: a new vulnerability class — Ari's research uncovered 30+ vulnerabilities and 24 CVEs across AI-powered IDEs, revealing not just individual bugs but an entirely new vulnerability class rooted in the shared base IDE layer that tools like Cursor, Copilot, and others are built on."Secure for AI" as a design principle — Ari argues that legacy IDEs were never built with autonomous AI agents in mind, and that the same gap likely exists across CI/CD pipelines, cloud environments, and collaboration tools as organizations race to bolt on AI capabilities.Low barrier to exploitation — The vulnerabilities Ari found don't require nation-state sophistication to exploit; techniques like remote JSON schema exfiltration can be carried out with relatively simple prompt engineering and publicly known attack vectors.Human-in-the-loop is losing its effectiveness — Even with diff preview and approval controls enabled, exfiltration attacks still triggered in Ari's testing, and approval fatigue from hundreds of agent-generated actions is pushing developers toward YOLO mode.Least privilege and the capability vs. security trade-off — The same unrestricted access that makes AI coding agents so productive is what makes them vulnerable, and history suggests organizations will continue to optimize for utility over security without strong guardrails.Top defensive recommendations — Ari emphasized isolation (containers, VMs) as the single most important control, followed by enforcing secure defaults that can't be easily overridden, and applying enterprise-level monitoring and governance to AI agent usage.What's next — Ari is turning his attention to newer AI tools and attack surfaces but isn't naming targets yet. You can follow his work on LinkedIn, X, and his blog at makarita.com.

Kennst du diese Situation im Team: Jemand sagt "das skaliert nicht", und plötzlich steht der Datenbankwechsel schneller im Raum als die eigentliche Frage nach dem Warum? Genau da packen wir an. Denn in vielen Systemen entscheidet nicht das nächste hippe Tool von Hacker News, sondern etwas viel Grundsätzlicheres: Datenlayout und Zugriffsmuster.In dieser Episode gehen wir einmal tief runter in den Storage-Stack. Wir schauen uns an, warum Row-Oriented-Datastores der Standard für klassische OLTP-Workloads sind und warum "SELECT id" trotzdem oft fast genauso teuer ist wie "SELECT *". Danach drehen wir die Tabelle um 90 Grad: Column Stores für OLAP, Aggregationen über viele Zeilen, Spalten-Pruning, Kompression, SIMD und warum ClickHouse, BigQuery, Snowflake oder Redshift bei Analytics so absurd schnell werden können.Und dann wird es file-basiert: CSV bekommt sein verdientes Fett weg, Apache Parquet seinen Hype, inklusive Row Groups, Metadaten im Footer und warum das für Streaming und Object Storage so gut passt. Mit Apache Iceberg setzen wir noch eine Management-Schicht oben drauf: Snapshots, Time Travel, paralleles Schreiben und das ganze Data-Lake-Feeling. Zum Schluss landen wir da, wo es richtig weh tut, beziehungsweise richtig Geld spart: Storage und Compute trennen, Tiered Storage, Kafka Connect bis Prometheus und Observability-Kosten.Wenn du beim nächsten "das skaliert nicht" nicht direkt die Datenbank tauschen willst, sondern erst mal die richtigen Fragen stellen möchtest, ist das deine Folge.Bonus: DuckDB als kleines Taschenmesser für CSV, JSON und SQL kann dein nächstes Wochenend-Experiment werden.Unsere aktuellen Werbepartner findest du auf https://engineeringkiosk.dev/partnersDas schnelle Feedback zur Episode:

If you've ever wondered how Oracle Database really works inside AWS, this episode will finally turn the lights on.   Join Senior Principal OCI Instructor Susan Jang as she explains the two database services available (Exadata Database Service and Autonomous Database), how Oracle and AWS share responsibilities behind the scenes, and which essential tasks still land on your plate after deployment.   You'll discover how automation, scaling, and security actually work, and which model best fits your needs, whether you want hands-off simplicity or deeper control.   Oracle Database@AWS Architect Professional: https://mylearn.oracle.com/ou/course/oracle-databaseaws-architect-professional/155574 Oracle University Learning Community: https://education.oracle.com/ou-community LinkedIn: https://www.linkedin.com/showcase/oracle-university/ X: https://x.com/Oracle_Edu   Special thanks to Arijit Ghosh, Anna Hulkower, Kris-Ann Nansen, Radhika Banka, and the OU Studio Team for helping us create this episode.   ------------------------------------------------------------   Episode Transcript:   00:00 Welcome to the Oracle University Podcast, the first stop on your cloud journey. During this series of informative podcasts, we'll bring you foundational training on the most popular Oracle technologies. Let's get started! 00:26   Lois: Hello and welcome to the Oracle University Podcast! I'm Lois Houston, Director of Communications and Adoption with Customer Success Services, and with me is Nikita Abraham, Team Lead: Editorial Services with Oracle University.  Nikita: Hi everyone! In our last episode, we began the discussion on Oracle Database@AWS. Today, we're diving deeper into the database services that are available in this environment. Susan Jang, our Senior Principal OCI Instructor, joins us once again.  00:56 Lois: Hi Susan! Thanks for being here today. In our last conversation, we compared Oracle Autonomous Database and Exadata Database Service. Can you elaborate on the fundamental differences between these two services?     Susan: Now, the primary difference is between the service is really the management model. The Autonomous is fully-managed by Oracle, while the Exadata provides flexibility for you to have the ability to customize your database environment while still having the infrastructure be managed by Oracle.   01:30 Nikita: When it comes to running Oracle Database@AWS, how do Oracle and AWS each chip in? Could you break down what each provider is responsible for in this setup?  Susan: Oracle Database@AWS is a collaboration between Oracle, as well as AWS. It allows the customer to deploy and run Oracle Database services, including the Oracle Autonomous Database and the Oracle Exadata Database Service directly in AWS data centers.   Oracle provides the ability of having the Oracle Exadata Database Service on a dedicated infrastructure. This service delivers full capabilities of Oracle Exadata Database on the Oracle Exadata hardware. It offers high performance and high security for demanding workloads. It has cloud automation, resource scaling, and performance optimization to simplify the management of the service.  Oracle Autonomous Database on the dedicated Exadata infrastructure provides a fully Autonomous Database on this dedicated infrastructure within AWS. It automates the database management tasks, including patching, backups, as well as tuning, and have built-in AI capabilities for developing AI-powered applications and interacting with data using natural language. The Oracle Database@AWS integrates those core database services with various AWS services for a comprehensive unified experience.  AWS provides the ability of having a cloud-based object storage, and that would be the Amazon S3. You also have the ability to have other services, such as the Amazon CloudWatch. It monitors the database metrics, as well as performance. You also have Amazon Bedrock. It provides a development environment for a generative AI application.   And last but not the least, amongst the many other services, you also have the SageMaker. This is a cloud-based platform for development of machine learning models, a wonderful integration with our AI application development needs.  03:54 Lois: How has the work involved in setting up and managing databases changed over time?  Susan: When we take a look at the evolution of how things have changed through the years in our systems, we realize that transfer responsibility has now been migrated more from customer or human interaction to services. As the database technology evolves from the traditional on-premise system to the Exadata engineered system, and finally to the Autonomous Database, certain services previously requiring significant manual intervention has become increasingly automated, as well as optimized.  04:34 Lois: How so?  Susan: When we take a look at the more traditional database environment, it requires manual configuration of hardware, operating system, as well as the software of the database, along with initial database creation. As we evolve into the Exadata environment, the Exadata Database, specifically the Exadata cloud service, simplifies provisioning through web-based wizard, making it faster and easier to deploy the Oracle Database in an optimized hardware.     But when we move it to an Autonomous environment, it automates the entire provisioning process, allowing users to rapidly deploy mission-critical databases without manual intervention, or DBA involvement. So as customers move toward Autonomous Database through Exadata, we have fewer components that the customer needs to manage in the database stack, which gives them more time to focus more on important parts of the business.  With the Exadata Database, it provides a co-management of backup, restore, patches and upgrade, monitoring, and tuning. And it allows the administrator the ability to customize the configuration to meet their very specific business needs. With Autonomous Database, it's now fully automated and it's a greater responsibility is shift toward the service. With Autonomous Database on dedicated infrastructure, it provides that fine-grained tuning more for Oracle to help you perform that task.  06:15 Nikita: If we narrow it down just to Oracle and AWS for a moment, which parts of the infrastructure or day-to-day ops are handled by each company behind the scenes?  Susan: When we take a look at Oracle Database@AWS, it operates under a shared responsibility model, dividing the service responsibilities between AWS, as well as Oracle, as well as you, the customer.   The AWS has the data center. Remember, this is where everything is running. The Oracle Database@AWS, the Oracle Database infrastructure may be managed by Oracle and run in OCI, but is physically located within the AWS regions, as well as the availability zones and the AWS data centers.  The AWS infrastructure, in this case, is AWS's responsibility to secure the environment, including the physical security of the data center, the network infrastructure, and the foundational services like the compute, the storage, and the networking, all within AWS.  The next thing of who's responsible for the shared responsibility, it's Oracle. And that would be the hardware. We provide the hardware. While the hardware may physically reside in the AWS data center, Oracle's Cloud Infrastructure operational team will be the one managing this infrastructure, including software patching, infrastructure update, and other operations through a connection to OCI. This means Oracle handles the provisioning, as well as the maintenance of any of the underlying Exadata infrastructure hardware.  When we take a look at the next thing that it manages, it is also responsible besides the infrastructure of the Exadata. It is also the ability to manage the hardware, the environment of that hardware through the database control plane. So Oracle manages the administration and the operational for the Oracle Database@AWS service, which resides in OCI. So this includes the capabilities for management, upgrade, and operational features.  08:37 Nikita: And what are the key things that still remain on the customer's plate?   Susan: If you are in an Exadata environment or in an Autonomous environment, it is you, the customer, who is responsible for most of the database administration operation, as well as managing the users and the privileges of the user to access the database. No one knows the database and who should be accessing the data better than you.  You will be responsible for securing the applications, the data of the database, which now allows you to define who has access to it, control the data encryption, and securing the application that interacts with the Oracle Database@AWS.  09:29 Lois: Susan, we've talked about both Autonomous Database and Exadata Database Service being available on Oracle Database@AWS, but what's different about how each works in this environment, and why might someone pick one over the other?  Susan: Both databases, even though they run on the same Exadata Cloud Infrastructure, both can be deployed on both public cloud, as well as the customer data center, which is Oracle Cloud@Customer.  The Autonomous Database is a fully managed, completely automated environment. And this provides a capability of having a fully Autonomous Database Service running on a dedicated Oracle Exadata Infrastructure within your AWS data center.  The Exadata is a service that is provided and managed by Oracle and is physically running in the AWS data center, but is designed for mission critical workload and includes RAC environment, Real Application Cluster, offering a high performance availability and full feature capability that is similar to other Exadata environment, such as those running in our customers' data center.  The primary difference is really between the two services. When you take a look at the Exadata, the customer only pays for the compute resources that is used. Autoscaling can be used for a variety or variable resources, the workload, to automatically scale to the compute resources up or down when required.  The Autonomous Database also has automatic optimization for data warehousing, transaction processing, as well as JSON workload. The Exadata service, the customer again, also pays for the compute resources that they allocate. But that's the key thing. The customer can initiate the scaling because it's very specific to the workload that is needed.  So when you take a look at the two database services, one gives the ability to let Oracle fully manage it, including the scaling capability. The other, the Exadata, provides you the capability of having the environment that it's running on the infrastructure be managed by Oracle that adds a database administrator. You may wish to have a little bit more granular control of how you want the database to not only be scaling, but how you wish to customize how the database will be running.  12:10 Nikita: Focusing on Autonomous Database for a moment, what should teams know about how it actually runs within AWS?   Susan: The Autonomous Database on the Oracle Database@AWS brings the power of the Oracle's self-managing, self-securing, and self-repairing database into your AWS environment.  It provides the capability of the database automatically, automates many of the traditional, complex, and time-consuming database management tasks, such as the provisioning of the database, the patching, the backing up, and the scaling, and the performance tuning, reducing the need for any manual intervention by the database administrator.  Running the Autonomous Database in your AWS region enables low latency access for your AWS applications and services that is deployed within AWS, thus improving performance and response time. With the Autonomous Database, it automates many of the traditional things that is now automatically done by Oracle. It also supports integration with various AWS services, such as the ability of the not in addition to AIM, but the cloud formation, the CloudWatch for monitoring and the S3 for the storage.  You can easily migrate existing Exadata workload, including those running on Oracle RAC to AWS with minimum or no change to any of your databases or applications. In addition, there's a really powerful capability and feature of the database is called zero ETL, and that's zero extract, transformation, and load.  It's an integration capability with services like your Amazon Redshift, enabling near real time analytics and machine learning on your transactional database that is stored within the Autonomous Database on in your AWS environment. So with the Autonomous Database, it checks off many of the boxes for automatic capability, securing, tuning, as well as scaling the database.  With the Autonomous Database in the Dedicated Exadata Infrastructure, the Exadata Cloud Infrastructure resource represents the physical system, which can be expanded with storage, as well as compute services, the compute host. This now provides the ability to have an isolated zone for the highest protection from other tenants. The data is stored on a dedicated server only for one customer. That would be you.  14:56 Lois: Could you explain the role of Autonomous VM? What are its primary benefits?  Susan: The virtual machine or as we refer to them as the cluster, includes the grid infrastructure and provides a private network isolation. This provides you the capability of having custom memory, core, and storage allocation.  The Oracle Grid Infrastructure includes the Oracle Clusterware, which manages the cluster, as well as the servers, and ensure that the database can failover to another server in case of any failure.  15:34 Be a part of something big by joining the Oracle University Learning Community! Connect with over 3 million members, including Oracle experts and fellow learners. Engage in topical forums, share your knowledge, and celebrate your achievements together. Discover the community today at mylearn.oracle.com.  15:55 Nikita: Welcome back! Susan, what is the Autonomous Container Database?  Susan: With the Autonomous Container Database, and you need that if you're going to create an Autonomous Database, you need to provision that within your Autonomous Exadata VM Cluster. It serves as a container to hold or to house one or more Autonomous Databases.  This allows multiple Autonomous Databases to coexist in the same infrastructure while still being logically separated. And this allows for the separation of databases based on their intended use. Think of a database for production. Think of a database for development. Think of a database for testing. You may have different database versions within the same infrastructure.  This isolation makes it easier for you to be able to meet your SLA, your Service Level Agreement, any long-term backups you may have, very specific encryption key needs to prevent issues from one database impacting another. So, the ability to have everything be isolated and secure is still grouping it in a manner that will meet your business needs.  17:08 Lois: Looking at Exadata Database Service specifically, what are some standout advantages for customers who deploy it on Oracle Database@AWS? Is there anything in particular they should get excited about in terms of performance or integration with AWS?  Susan: The Exadata Database Service is running on a dedicated Exadata Infrastructure that's deployed within your AWS data center. It delivers the same Exadata service experience in cloud control planes as the Oracle Cloud Infrastructure, allowing you to leverage existing skills and processing across your multi-cloud environment.  It addresses the data resiliency, or residency rather. And that's the scenario where many of our customers has the need. You have a need because of your security compliance to have the data local to you. By having the Exadata Database in your Oracle Database@AWS, it is running in your data center. So, this addresses that very important need, data residency, to have it close to you.  It also allows for seamless integration with other AWS services and applications. So now you have a capability of a hybrid cloud architecture leveraging the benefit of both Oracle Exadata and your AWS system. It has built-in high availability, the RAC application cluster, as well as Data Guard, a capability of addressing disaster recovery capability.  This also provides the ability for you to scale your compute, as well as your storage and your I/O resources independently. So as mentioned with Exadata, you have flexibility of how you want your database to be running individually. So just like the Autonomous, the Exadata Database checks off many of the boxes for running a mission-critical with high availability, highly redundant hardware and software features, along with extreme performance, scalability, and reliability.  This now allows you to run your AI environment, your online transaction processing, your analytic workload on any scale on the Exadata Infrastructure running in the Oracle Cloud. And in this case, running in your data center.  19:45 Nikita: If a business suddenly needs more capacity, how does scaling work with Exadata Database Service versus Autonomous Database on Oracle Database@AWS?   Susan: So with the Exadata scaling, you now can scale to meet expected demands so you know at certain point I will need more. I will then ask it to scale at that point when I will assign it-- and I'm using an example, I will assign it three computer cores all the time. But there may be demands. Think of your end of the quarter, end of the year processing that you may need more. So, you are enabling the compute cores to scale at the time you need it.  And what's cool is it will then, when it's no longer needed, it will then scale back down to the original three cores that you assign. So, you only pay for the enabled cores. But what's very cool about the Autonomous is that it is real-time scaling. So, with Autonomous, now you have the capability using Autonomous Database since it is self-tuning, self-monitoring, the Autonomous Database actually monitors the workload requirement and scales to match the workload demand.  Once the minimum level of the compute is defined and enabled, the automatic scaling is set. Autonomous Database will adjust to the consumption when it's needed, and it will scale back down when it's not. So though the Exadata is pretty cool, it will scale up and down on the workload demand.  This is with the Autonomous is even more powerful. It is real-time scaling based on that usage at that moment. Built-in automatic increase to meet the workload demands when it spikes and it automatically scales back when it's not needed.  A very powerful capability with all of our Oracle databases, the ability, even with traditional, to allow you to define what you may need with Exadata scaling for peak demands, as well as Autonomous scaling for real-time consumption and scaling when needed.  When you look at all of our options, one of the key things to bear in mind is a phrase that we use: performance scale as more servers are added. And what this is really saying is Oracle's automated scaling ability for the database, it basically has the ability to maintain or improve its performance under increased workload by automatically adding computational resources when needed.  This process is also known as horizontal scaling. It involves adding more servers, compute instances, to a cluster to share the processing load. And it has that capability automatically.  22:53 Nikita: There's so much more we can discuss about Oracle Database@AWS, but let's pause here for today! Thank you so much Susan for joining us.  Lois: Yeah, it's been really great to have you, Susan. If you want to dive deeper into the topics we covered today, go to mylearn.oracle.com and search for the Oracle Database@AWS Architect Professional course. Until next time, this is Lois Houston…  Nikita: And Nikita Abraham, signing off!  23:23 That's all for this episode of the Oracle University Podcast. If you enjoyed listening, please click Subscribe to get all the latest episodes. We'd also love it if you would take a moment to rate and review us on your podcast app. See you again on the next episode of the Oracle University Podcast.

Emmanuel et Guillaume discutent de divers sujets liés à la programmation, notamment les systèmes de fichiers en Java, le Data Oriented Programming, les défis de JPA avec Kotlin, et les nouvelles fonctionnalités de Quarkus. Ils explorent également des sujets un peu fous comme la création de datacenters dans l'espace. Pas mal d'architecture aussi. Enregistré le 13 février 2026 Téléchargement de l'épisode LesCastCodeurs-Episode-337.mp3 ou en vidéo sur YouTube. News Langages Comment implémenter un file system en Java https://foojay.io/today/bootstrapping-a-java-file-system/ Créer un système de fichiers Java personnalisé avec NIO.2 pour des usages variés (VCS, archives, systèmes distants). Évolution Java: java.io.File (1.0) -> NIO (1.4) -> NIO.2 (1.7) pour personnalisation via FileSystem. Recommander conception préalable; API Java est orientée POSIX. Composants clés à considérer: Conception URI (scheme unique, chemin). Gestion de l'arborescence (BD, métadonnées, efficacité). Stockage binaire (emplacement, chiffrement, versions). Minimum pour démarrer (4 composants): Implémenter Path (représente fichier/répertoire). Étendre FileSystem (instance du système). Étendre FileSystemProvider (moteur, enregistré par scheme). Enregistrer FileSystemProvider via META-INF/services. Étapes suivantes: Couche BD (arborescence), opérations répertoire/fichier de base, stockage, tests. Processus long et exigeant, mais gratifiant.   Un article de brian goetz sur le futur du data oriented programming en Java https://openjdk.org/projects/amber/design-notes/beyond-records Le projet Amber de Java introduit les "carrier classes", une évolution des records qui permet plus de flexibilité tout en gardant les avantages du pattern matching et de la reconstruction Les records imposent des contraintes strictes (immutabilité, représentation exacte de l'état) qui limitent leur usage pour des classes avec état muable ou dérivé Les carrier classes permettent de déclarer une state description complète et canonique sans imposer que la représentation interne corresponde exactement à l'API publique Le modificateur "component" sur les champs permet au compilateur de dériver automatiquement les accesseurs pour les composants alignés avec la state description Les compact constructors sont généralisés aux carrier classes, générant automatiquement l'initialisation des component fields Les carrier classes supportent la déconstruction via pattern matching comme les records, rendant possible leur usage dans les instanceof et switch Les carrier interfaces permettent de définir une state description sur une interface, obligeant les implémentations à fournir les accesseurs correspondants L'extension entre carrier classes est possible, avec dérivation automatique des appels super() quand les composants parent sont subsumés par l'enfant Les records deviennent un cas particulier de carrier classes avec des contraintes supplémentaires (final, extends Record, component fields privés et finaux obligatoires) L'évolution compatible des records est améliorée en permettant l'ajout de composants en fin de liste et la déconstruction partielle par préfixe Comment éviter les pièges courants avec JPA et Kotlin - https://blog.jetbrains.com/idea/2026/01/how-to-avoid-common-pitfalls-with-jpa-and-kotlin/ JPA est une spécification Java pour la persistance objet-relationnel, mais son utilisation avec Kotlin présente des incompatibilités dues aux différences de conception des deux langages Les classes Kotlin sont finales par défaut, ce qui empêche la création de proxies par JPA pour le lazy loading et les opérations transactionnelles Le plugin kotlin-jpa génère automatiquement des constructeurs sans argument et rend les classes open, résolvant les problèmes de compatibilité Les data classes Kotlin ne sont pas adaptées aux entités JPA car elles génèrent equals/hashCode basés sur tous les champs, causant des problèmes avec les relations lazy L'utilisation de lateinit var pour les relations peut provoquer des exceptions si on accède aux propriétés avant leur initialisation par JPA Les types non-nullables Kotlin peuvent entrer en conflit avec le comportement de JPA qui initialise les entités avec des valeurs null temporaires Le backing field direct dans les getters/setters personnalisés peut contourner la logique de JPA et casser le lazy loading IntelliJ IDEA 2024.3 introduit des inspections pour détecter automatiquement ces problèmes et propose des quick-fixes L'IDE détecte les entités finales, les data classes inappropriées, les problèmes de constructeurs et l'usage incorrect de lateinit Ces nouvelles fonctionnalités aident les développeurs à éviter les bugs subtils liés à l'utilisation de JPA avec Kotlin Librairies Guide sur MapStruct @IterableMapping - https://www.baeldung.com/java-mapstruct-iterablemapping MapStruct est une bibliothèque Java pour générer automatiquement des mappers entre beans, l'annotation @IterableMapping permet de configurer finement le mapping de collections L'attribut dateFormat permet de formater automatiquement des dates lors du mapping de listes sans écrire de boucle manuelle L'attribut qualifiedByName permet de spécifier quelle méthode custom appliquer sur chaque élément de la collection à mapper Exemple d'usage : filtrer des données sensibles comme des mots de passe en mappant uniquement certains champs via une méthode dédiée L'attribut nullValueMappingStrategy permet de contrôler le comportement quand la collection source est null (retourner null ou une collection vide) L'annotation fonctionne pour tous types de collections Java (List, Set, etc.) et génère le code de boucle nécessaire Possibilité d'appliquer des formats numériques avec numberFormat pour convertir des nombres en chaînes avec un format spécifique MapStruct génère l'implémentation complète du mapper au moment de la compilation, éliminant le code boilerplate L'annotation peut être combinée avec @Named pour créer des méthodes de mapping réutilisables et nommées Le mapping des collections supporte les conversions de types complexes au-delà des simples conversions de types primitifs Accès aux fichiers Samba depuis Java avec JCIFS - https://www.baeldung.com/java-samba-jcifs JCIFS est une bibliothèque Java permettant d'accéder aux partages Samba/SMB sans monter de lecteur réseau, supportant le protocole SMB3 on pense aux galériens qui doivent se connecter aux systèmes dit legacy La configuration nécessite un contexte CIFS (CIFSContext) et des objets SmbFile pour représenter les ressources distantes L'authentification se fait via NtlmPasswordAuthenticator avec domaine, nom d'utilisateur et mot de passe La bibliothèque permet de lister les fichiers et dossiers avec listFiles() et vérifier leurs propriétés (taille, date de modification) Création de fichiers avec createNewFile() et de dossiers avec mkdir() ou mkdirs() pour créer toute une arborescence Suppression via delete() qui peut parcourir et supprimer récursivement des arborescences entières Copie de fichiers entre partages Samba avec copyTo(), mais impossibilité de copier depuis le système de fichiers local Pour copier depuis le système local, utilisation des streams SmbFileInputStream et SmbFileOutputStream Les opérations peuvent cibler différents serveurs Samba et différents partages (anonymes ou protégés par mot de passe) La bibliothèque s'intègre dans des blocs try-with-resources pour une gestion automatique des ressources Quarkus 3.31 - Support complet Java 25, nouveau packaging Maven et Panache Next - https://quarkus.io/blog/quarkus-3-31-released/ Support complet de Java 25 avec images runtime et native Nouveau packaging Maven de type quarkus avec lifecycle optimisé pour des builds plus rapides voici un article complet pour plus de detail https://quarkus.io/blog/building-large-applications/ Introduction de Panache Next, nouvelle génération avec meilleure expérience développeur et API unifiée ORM/Reactive Mise à jour vers Hibernate ORM 7.2, Reactive 3.2, Search 8.2 Support de Hibernate Spatial pour les données géospatiales Passage à Testcontainers 2 et JUnit 6 Annotations de sécurité supportées sur les repositories Jakarta Data Chiffrement des tokens OIDC pour les implémentations custom TokenStateManager Support OAuth 2.0 Pushed Authorization Requests dans l'extension OIDC Maven 3.9 maintenant requis minimum pour les projets Quarkus A2A Java SDK 1.0.0.Alpha1 - Alignement avec la spécification 1.0 du protocole Agent2Agent - https://quarkus.io/blog/a2a-java-sdk-1-0-0-alpha1/ Le SDK Java A2A implémente le protocole Agent2Agent qui permet la communication standardisée entre agents IA pour découvrir des capacités, déléguer des tâches et collaborer Passage à la version 1.0 de la spécification marque la transition d'expérimental à production-ready avec des changements cassants assumés Modernisation complète du module spec avec des Java records partout remplaçant le mix précédent de classes et records pour plus de cohérence Adoption de Protocol Buffers comme source de vérité avec des mappers MapStruct pour la conversion et Gson pour JSON-RPC Les builders utilisent maintenant des méthodes factory statiques au lieu de constructeurs publics suivant les best practices Java modernes Introduction de trois BOMs Maven pour simplifier la gestion des dépendances du SDK core, des extensions et des implémentations de référence Quarkus AgentCard évolue avec une liste supportedInterfaces remplaçant url et preferredTransport pour plus de flexibilité dans la déclaration des protocoles Support de la pagination ajouté pour ListTasks et les endpoints de configuration des notifications push avec des wrappers Result appropriés Interface A2AHttpClient pluggable permettant des implémentations HTTP personnalisées avec une implémentation Vert.x fournie Travail continu vers la conformité complète avec le TCK 1.0 en cours de développement parallèlement à la finalisation de la spécification Pourquoi Quarkus finit par "cliquer" : les 10 questions que se posent les développeurs Java - https://www.the-main-thread.com/p/quarkus-java-developers-top-questions-2025 un article qui revele et repond aux questions des gens qui ont utilisé Quarkus depuis 4-6 mois, les non noob questions Quarkus est un framework Java moderne optimisé pour le cloud qui propose des temps de démarrage ultra-rapides et une empreinte mémoire réduite Pourquoi Quarkus démarre si vite ? Le framework effectue le travail lourd au moment du build (scanning, indexation, génération de bytecode) plutôt qu'au runtime Quand utiliser le mode réactif plutôt qu'impératif ? Le réactif est pertinent pour les workloads avec haute concurrence et dominance I/O, l'impératif reste plus simple dans les autres cas Quelle est la différence entre Dev Services et Testcontainers ? Dev Services utilise Testcontainers en gérant automatiquement le cycle de vie, les ports et la configuration sans cérémonie Comment la DI de Quarkus diffère de Spring ? CDI est un standard basé sur la sécurité des types et la découverte au build-time, différent de l'approche framework de Spring Comment gérer la configuration entre environnements ? Quarkus permet de scaler depuis le développement local jusqu'à Kubernetes avec des profils, fichiers multiples et configuration externe Comment tester correctement les applications Quarkus ? @QuarkusTest démarre l'application une fois pour toute la suite de tests, changeant le modèle mental par rapport à Spring Boot Que fait vraiment Panache en coulisses ? Panache est du JPA avec des opinions fortes et des défauts propres, enveloppant Hibernate avec un style Active Record Doit-on utiliser les images natives et quand ? Les images natives brillent pour le serverless et l'edge grâce au démarrage rapide et la faible empreinte mémoire, mais tous les apps n'en bénéficient pas Comment Quarkus s'intègre avec Kubernetes ? Le framework génère automatiquement les ressources Kubernetes, gère les health checks et métriques comme s'il était nativement conçu pour cet écosystème Comment intégrer l'IA dans une application Quarkus ? LangChain4j permet d'ajouter embeddings, retrieval, guardrails et observabilité directement en Java sans passer par Python Infrastructure Les alternatives à MinIO https://rmoff.net/2026/01/14/alternatives-to-minio-for-single-node-local-s3/ MinIO a abandonné le support single-node fin 2025 pour des raisons commerciales, cassant de nombreuses démos et pipelines CI/CD qui l'utilisaient pour émuler S3 localement L'auteur cherche un remplacement simple avec image Docker, compatibilité S3, licence open source, déploiement mono-nœud facile et communauté active S3Proxy est très léger et facile à configurer, semble être l'option la plus simple mais repose sur un seul contributeur RustFS est facile à utiliser et inclut une GUI, mais c'est un projet très récent en version alpha avec une faille de sécurité majeure récente SeaweedFS existe depuis 2012 avec support S3 depuis 2018, relativement facile à configurer et dispose d'une interface web basique Zenko CloudServer remplace facilement MinIO mais la documentation et le branding (cloudserver/zenko/scality) peuvent prêter à confusion Garage nécessite une configuration complexe avec fichier TOML et conteneur d'initialisation séparé, pas un simple remplacement drop-in Apache Ozone requiert au minimum quatre nœuds pour fonctionner, beaucoup trop lourd pour un usage local simple L'auteur recommande SeaweedFS et S3Proxy comme remplaçants viables, RustFS en maybe, et élimine Garage et Ozone pour leur complexité Garage a une histoire tres associative, il vient du collectif https://deuxfleurs.fr/ qui offre un cloud distribué sans datacenter C'est certainement pas une bonne idée, les datacenters dans l'espace https://taranis.ie/datacenters-in-space-are-a-terrible-horrible-no-good-idea/ Avis d'expert (ex-NASA/Google, Dr en électronique spatiale) : Centres de données spatiaux, une "terrible" idée. Incompatibilité fondamentale : L'électronique (surtout IA/GPU) est inadaptée à l'environnement spatial. Énergie : Accès limité. Le solaire (type ISS) est insuffisant pour l'échelle de l'IA. Le nucléaire (RTG) est trop faible. Refroidissement : L'espace n'est pas "froid" ; absence de convection. Nécessite des radiateurs gigantesques (ex: 531m² pour 200kW). Radiations : Provoque erreurs (SEU, SEL) et dommages. Les GPU sont très vulnérables. Blindage lourd et inefficace. Les puces "durcies" sont très lentes. Communications : Bande passante très limitée (1Gbps radio vs 100Gbps terrestre). Le laser est tributaire des conditions atmosphériques. Conclusion : Projet extrêmement difficile, coûteux et aux performances médiocres. Data et Intelligence Artificielle Guillaume a développé un serveur MCP pour arXiv (le site de publication de papiers de recherche) en Java avec le framework Quarkus https://glaforge.dev/posts/2026/01/18/implementing-an-arxiv-mcp-server-with-quarkus-in-java/ Implémentation d'un serveur MCP (Model Context Protocol) arXiv en Java avec Quarkus. Objectif : Accéder aux publications arXiv et illustrer les fonctionnalités moins connues du protocole MCP. Mise en œuvre : Utilisation du framework Quarkus (Java) et son support MCP étendu. Assistance par Antigravity (IDE agentique) pour le développement et l'intégration de l'API arXiv. Interaction avec l'API arXiv : requêtes HTTP, format XML Atom pour les résultats, parser XML Jackson. Fonctionnalités MCP exposées : Outils (@Tool) : Recherche de publications (search_papers). Ressources (@Resource, @ResourceTemplate) : Taxonomie des catégories arXiv, métadonnées des articles (via un template d'URI). Prompts (@Prompt) : Exemples pour résumer des articles ou construire des requêtes de recherche. Configuration : Le serveur peut fonctionner en STDIO (local) ou via HTTP Streamable (local ou distant), avec une configuration simple dans des clients comme Gemini CLI. Conclusion : Quarkus simplifie la création de serveurs MCP riches en fonctionnalités, rendant les données et services "prêts pour l'IA" avec l'aide d'outils d'IA comme Antigravity. Anthropic ne mettra pas de pub dans Claude https://www.anthropic.com/news/claude-is-a-space-to-think c'est en reaction au plan non public d'OpenAi de mettre de la pub pour pousser les gens au mode payant OpenAI a besoin de cash et est probablement le plus utilisé pour gratuit au monde Anthropic annonce que Claude restera sans publicité pour préserver son rôle d'assistant conversationnel dédié au travail et à la réflexion approfondie. Les conversations avec Claude sont souvent sensibles, personnelles ou impliquent des tâches complexes d'ingénierie logicielle où les publicités seraient inappropriées. L'analyse des conversations montre qu'une part significative aborde des sujets délicats similaires à ceux évoqués avec un conseiller de confiance. Un modèle publicitaire créerait des incitations contradictoires avec le principe fondamental d'être "genuinely helpful" inscrit dans la Constitution de Claude. Les publicités introduiraient un conflit d'intérêt potentiel où les recommandations pourraient être influencées par des motivations commerciales plutôt que par l'intérêt de l'utilisateur. Le modèle économique d'Anthropic repose sur les contrats entreprise et les abonnements payants, permettant de réinvestir dans l'amélioration de Claude. Anthropic maintient l'accès gratuit avec des modèles de pointe et propose des tarifs réduits pour les ONG et l'éducation dans plus de 60 pays. Le commerce "agentique" sera supporté mais uniquement à l'initiative de l'utilisateur, jamais des annonceurs, pour préserver la confiance. Les intégrations tierces comme Figma, Asana ou Canva continueront d'être développées en gardant l'utilisateur aux commandes. Anthropic compare Claude à un cahier ou un tableau blanc : des espaces de pensée purs, sans publicité. Infinispan 16.1 est sorti https://infinispan.org/blog/2026/02/04/infinispan-16-1 déjà le nom de la release mérite une mention Le memory bounded par cache et par ensemble de cache s est pas facile à faire en Java Une nouvelle api OpenAPI AOT caché dans les images container Un serveur MCP local juste avec un fichier Java ? C'est possible avec LangChain4j et JBang https://glaforge.dev/posts/2026/02/11/zero-boilerplate-java-stdio-mcp-servers-with-langchain4j-and-jbang/ Création rapide de serveurs MCP Java sans boilerplate. MCP (Model Context Protocol): standard pour connecter les LLM à des outils et données. Le tutoriel répond au manque d'options simples pour les développeurs Java, face à une prédominance de Python/TypeScript dans l'écosystème MCP. La solution utilise: LangChain4j: qui intègre un nouveau module serveur MCP pour le protocole STDIO. JBang: permet d'exécuter des fichiers Java comme des scripts, éliminant les fichiers de build (pom.xml, Gradle). Implémentation: se fait via un seul fichier .java. JBang gère automatiquement les dépendances (//DEPS). L'annotation @Tool de LangChain4j expose les méthodes Java aux LLM. StdioMcpServerTransport gère la communication JSON-RPC via l'entrée/sortie standard (STDIO). Point crucial: Les logs doivent impérativement être redirigés vers System.err pour éviter de corrompre System.out, qui est réservé à la communication MCP (messages JSON-RPC). Facilite l'intégration locale avec des outils comme Gemini CLI, Claude Code, etc. Reciprocal Rank Fusion : un algorithme utile et souvent utilisé pour faire de la recherche hybride, pour mélanger du RAG et des recherches par mots-clé https://glaforge.dev/posts/2026/02/10/advanced-rag-understanding-reciprocal-rank-fusion-in-hybrid-search/ RAG : Qualité LLM dépend de la récupération. Recherche Hybride : Combiner vectoriel et mots-clés (BM25) est optimal. Défi : Fusionner des scores d'échelles différentes. Solution : Reciprocal Rank Fusion (RRF). RRF : Algorithme robuste qui fusionne des listes de résultats en se basant uniquement sur le rang des documents, ignorant les scores. Avantages RRF : Pas de normalisation de scores, scalable, excellente première étape de réorganisation. Architecture RAG fréquente : RRF (large sélection) + Cross-Encoder / modèle de reranking (précision fine). RAG-Fusion : Utilise un LLM pour générer plusieurs variantes de requête, puis RRF agrège tous les résultats pour renforcer le consensus et réduire les hallucinations. Implémentation : LangChain4j utilise RRF par défaut pour agréger les résultats de plusieurs retrievers. Les dernières fonctionnalités de Gemini et Nano Banana supportées dans LangChain4j https://glaforge.dev/posts/2026/02/06/latest-gemini-and-nano-banana-enhancements-in-langchain4j/ Nouveaux modèles d'images Nano Banana (Gemini 2.5/3.0) pour génération et édition (jusqu'à 4K). "Grounding" via Google Search (pour images et texte) et Google Maps (localisation, Gemini 2.5). Outil de contexte URL (Gemini 3.0) pour lecture directe de pages web. Agents multimodaux (AiServices) capables de générer des images. Configuration de la réflexion (profondeur Chain-of-Thought) pour Gemini 3.0. Métadonnées enrichies : usage des tokens et détails des sources de "grounding". Comment configurer Gemini CLI comment agent de code dans IntelliJ grâce au protocole ACP https://glaforge.dev/posts/2026/02/01/how-to-integrate-gemini-cli-with-intellij-idea-using-acp/ But : Intégrer Gemini CLI à IntelliJ IDEA via l'Agent Client Protocol (ACP). Prérequis : IntelliJ IDEA 2025.3+, Node.js (v20+), Gemini CLI. Étapes : Installer Gemini CLI (npm install -g @google/gemini-cli). Localiser l'exécutable gemini. Configurer ~/.jetbrains/acp.json (chemin exécutable, --experimental-acp, use_idea_mcp: true). Redémarrer IDEA, sélectionner "Gemini CLI" dans l'Assistant IA. Usage : Gemini interagit avec le code et exécute des commandes (contexte projet). Important : S'assurer du flag --experimental-acp dans la configuration. Outillage PipeNet, une alternative (open source aussi) à LocalTunnel, mais un plus évoluée https://pipenet.dev/ pipenet: Alternative open-source et moderne à localtunnel (client + serveur). Usages: Développement local (partage, webhooks), intégration SDK, auto-hébergement sécurisé. Fonctionnalités: Client (expose ports locaux, sous-domaines), Serveur (déploiement, domaines personnalisés, optimisé cloud mono-port). Avantages vs localtunnel: Déploiement cloud sur un seul port, support multi-domaines, TypeScript/ESM, maintenance active. Protocoles: HTTP/S, WebSocket, SSE, HTTP Streaming. Intégration: CLI ou SDK JavaScript. JSON-IO — une librairie comme Jackson ou GSON, supportant JSON5, TOON, et qui pourrait être utile pour l'utilisation du "structured output" des LLMs quand ils ne produisent pas du JSON parfait https://github.com/jdereg/json-io json-io : Librairie Java pour la sérialisation et désérialisation JSON/TOON. Gère les graphes d'objets complexes, les références cycliques et les types polymorphes. Support complet JSON5 (lecture et écriture), y compris des fonctionnalités non prises en charge par Jackson/Gson. Format TOON : Notation orientée token, optimisée pour les LLM, réduisant l'utilisation de tokens de 40 à 50% par rapport au JSON. Légère : Aucune dépendance externe (sauf java-util), taille de JAR réduite (~330K). Compatible JDK 1.8 à 24, ainsi qu'avec les environnements JPMS et OSGi. Deux modes de conversion : vers des objets Java typés (toJava()) ou vers des Map (toMaps()). Options de configuration étendues via ReadOptionsBuilder et WriteOptionsBuilder. Optimisée pour les déploiements cloud natifs et les architectures de microservices. Utiliser mailpit et testcontainer pour tester vos envois d'emails https://foojay.io/today/testing-emails-with-testcontainers-and-mailpit/ l'article montre via SpringBoot et sans. Et voici l'extension Quarkus https://quarkus.io/extensions/io.quarkiverse.mailpit/quarkus-mailpit/?tab=docs Tester l'envoi d'emails en développement est complexe car on ne peut pas utiliser de vrais serveurs SMTP Mailpit est un serveur SMTP de test qui capture les emails et propose une interface web pour les consulter Testcontainers permet de démarrer Mailpit dans un conteneur Docker pour les tests d'intégration L'article montre comment configurer une application SpringBoot pour envoyer des emails via JavaMail Un module Testcontainers dédié à Mailpit facilite son intégration dans les tests Le conteneur Mailpit expose un port SMTP (1025) et une API HTTP (8025) pour vérifier les emails reçus Les tests peuvent interroger l'API HTTP de Mailpit pour valider le contenu des emails envoyés Cette approche évite d'utiliser des mocks et teste réellement l'envoi d'emails Mailpit peut aussi servir en développement local pour visualiser les emails sans les envoyer réellement La solution fonctionne avec n'importe quel framework Java supportant JavaMail Architecture Comment scaler un système de 0 à 10 millions d'utilisateurs https://blog.algomaster.io/p/scaling-a-system-from-0-to-10-million-users Philosophie : Scalabilité incrémentale, résoudre les goulots d'étranglement sans sur-ingénierie. 0-100 utilisateurs : Serveur unique (app, DB, jobs). 100-1K : Séparer app et DB (services gérés, pooling). 1K-10K : Équilibreur de charge, multi-serveurs d'app (stateless via sessions partagées). 10K-100K : Caching, réplicas de lecture DB, CDN (réduire charge DB). 100K-500K : Auto-scaling, applications stateless (authentification JWT). 500K-10M : Sharding DB, microservices, files de messages (traitement asynchrone). 10M+ : Déploiement multi-régions, CQRS, persistance polyglotte, infra personnalisée. Principes clés : Simplicité, mesure, stateless essentiel, cache/asynchrone, sharding prudent, compromis (CAP), coût de la complexité. Patterns d'Architecture 2026 - Du Hype à la Réalité du Terrain (Part 1/2) - https://blog.ippon.fr/2026/01/30/patterns-darchitecture-2026-part-1/ L'article présente quatre patterns d'architecture logicielle pour répondre aux enjeux de scalabilité, résilience et agilité business dans les systèmes modernes Il présentent leurs raisons et leurs pièges Un bon rappel L'Event-Driven Architecture permet une communication asynchrone entre systèmes via des événements publiés et consommés, évitant le couplage direct Les bénéfices de l'EDA incluent la scalabilité indépendante des composants, la résilience face aux pannes et l'ajout facile de nouveaux cas d'usage Le pattern API-First associé à un API Gateway centralise la sécurité, le routage et l'observabilité des APIs avec un catalogue unifié Le Backend for Frontend crée des APIs spécifiques par canal (mobile, web, partenaires) pour optimiser l'expérience utilisateur CQRS sépare les modèles de lecture et d'écriture avec des bases optimisées distinctes, tandis que l'Event Sourcing stocke tous les événements plutôt que l'état actuel Le Saga Pattern gère les transactions distribuées via orchestration centralisée ou chorégraphie événementielle pour coordonner plusieurs microservices Les pièges courants incluent l'explosion d'événements granulaires, la complexité du debugging distribué, et la mauvaise gestion de la cohérence finale Les technologies phares sont Kafka pour l'event streaming, Kong pour l'API Gateway, EventStoreDB pour l'Event Sourcing et Temporal pour les Sagas Ces patterns nécessitent une maturité technique et ne sont pas adaptés aux applications CRUD simples ou aux équipes junior Patterns d'architecture 2026 : du hype à la réalité terrain part. 2 - https://blog.ippon.fr/2026/02/04/patterns-darchitecture-2026-part-2/ Deuxième partie d'un guide pratique sur les patterns d'architecture logicielle et système éprouvés pour moderniser et structurer les applications en 2026 Strangler Fig permet de migrer progressivement un système legacy en l'enveloppant petit à petit plutôt que de tout réécrire d'un coup (70% d'échec pour les big bang) Anti-Corruption Layer protège votre nouveau domaine métier des modèles externes et legacy en créant une couche de traduction entre les systèmes Service Mesh gère automatiquement la communication inter-services dans les architectures microservices (sécurité mTLS, observabilité, résilience) Architecture Hexagonale sépare le coeur métier des détails techniques via des ports et adaptateurs pour améliorer la testabilité et l'évolutivité Chaque pattern est illustré par un cas client concret avec résultats mesurables et liste des pièges à éviter lors de l'implémentation Les technologies 2026 mentionnées incluent Istio, Linkerd pour service mesh, LaunchDarkly pour feature flags, NGINX et Kong pour API gateway Tableau comparatif final aide à choisir le bon pattern selon la complexité, le scope et le use case spécifique du projet L'article insiste sur une approche pragmatique : ne pas utiliser un pattern juste parce qu'il est moderne mais parce qu'il résout un problème réel Pour les systèmes simples type CRUD ou avec peu de services, ces patterns peuvent introduire une complexité inutile qu'il faut savoir éviter Méthodologies Le rêve récurrent de remplacer voire supprimer les développeurs https://www.caimito.net/en/blog/2025/12/07/the-recurring-dream-of-replacing-developers.html Depuis 1969, chaque décennie voit une tentative de réduire le besoin de développeurs (de COBOL, UML, visual builders… à IA). Motivation : frustration des dirigeants face aux délais et coûts de développement. La complexité logicielle est intrinsèque et intellectuelle, non pas une question d'outils. Chaque vague technologique apporte de la valeur mais ne supprime pas l'expertise humaine. L'IA assiste les développeurs, améliore l'efficacité, mais ne remplace ni le jugement ni la gestion de la complexité. La demande de logiciels excède l'offre car la contrainte majeure est la réflexion nécessaire pour gérer cette complexité. Pour les dirigeants : les outils rendent-ils nos développeurs plus efficaces sur les problèmes complexes et réduisent-ils les tâches répétitives ? Le "rêve" de remplacer les développeurs, irréalisable, est un moteur d'innovation créant des outils précieux. Comment creuser des sujets à l'ère de l'IA générative. Quid du partage et la curation de ces recherches ? https://glaforge.dev/posts/2026/02/04/researching-topics-in-the-age-of-ai-rock-solid-webhooks-case-study/ Recherche initiale de l'auteur sur les webhooks en 2019, processus long et manuel. L'IA (Deep Research, Gemini, NotebookLM) facilite désormais la recherche approfondie, l'exploration de sujets et le partage des résultats. L'IA a identifié et validé des pratiques clés pour des déploiements de webhooks résilients, en grande partie les mêmes que celles trouvées précédemment par l'auteur. Génération d'artefacts par l'IA : rapport détaillé, résumé concis, illustration sketchnote, et même une présentation (slide deck). Guillaume s'interroge sur le partage public de ces rapports de recherche générés par l'IA, tout en souhaitant éviter le "AI Slop". Loi, société et organisation Le logiciel menacé par le vibe coding https://www.techbuzz.ai/articles/we-built-a-monday-com-clone-in-under-an-hour-with-ai Deux journalistes de CNBC sans expérience de code ont créé un clone fonctionnel de Monday.com en moins de 60 minutes pour 5 à 15 dollars. L'expérience valide les craintes des investisseurs qui ont provoqué une baisse de 30% des actions des entreprises SaaS. L'IA a non seulement reproduit les fonctionnalités de base mais a aussi recherché Monday.com de manière autonome pour identifier et recréer ses fonctionnalités clés. Cette technique appelée "vibe-coding" permet aux non-développeurs de construire des applications via des instructions en anglais courant. Les entreprises les plus vulnérables sont celles offrant des outils "qui se posent sur le travail" comme Atlassian, Adobe, HubSpot, Zendesk et Smartsheet. Les entreprises de cybersécurité comme CrowdStrike et Palo Alto sont considérées plus protégées grâce aux effets de réseau et aux barrières réglementaires. Les systèmes d'enregistrement comme Salesforce restent plus difficiles à répliquer en raison de leur profondeur d'intégration et de données d'entreprise. Le coût de 5 à 15 dollars par construction permet aux entreprises de prototyper plusieurs solutions personnalisées pour moins cher qu'une seule licence Monday.com. L'expérience soulève des questions sur la pérennité du marché de 5 milliards de dollars des outils de gestion de projet face à l'IA générative. Conférences En complément de l'agenda des conférences de Aurélie Vache, il y a également le site https://javaconferences.org/ (fait par Brian Vermeer) avec toutes les conférences Java à venir ! La liste des conférences provenant de Developers Conferences Agenda/List par Aurélie Vache et contributeurs : 12-13 février 2026 : Touraine Tech #26 - Tours (France) 12-13 février 2026 : World Artificial Intelligence Cannes Festival - Cannes (France) 19 février 2026 : ObservabilityCON on the Road - Paris (France) 6 mars 2026 : WordCamp Nice 2026 - Nice (France) 18 mars 2026 : Jupyter Workshops: AI in Jupyter: Building Extensible AI Capabilities for Interactive Computing - Saint-Maur-des-Fossés (France) 18-19 mars 2026 : Agile Niort 2026 - Niort (France) 20 mars 2026 : Atlantique Day 2026 - Nantes (France) 26 mars 2026 : Data Days Lille - Lille (France) 26-27 mars 2026 : SymfonyLive Paris 2026 - Paris (France) 26-27 mars 2026 : REACT PARIS - Paris (France) 27-29 mars 2026 : Shift - Nantes (France) 31 mars 2026 : ParisTestConf - Paris (France) 31 mars 2026-1 avril 2026 : FlowCon France 2026 - Paris (France) 1 avril 2026 : AWS Summit Paris - Paris (France) 2 avril 2026 : Pragma Cannes 2026 - Cannes (France) 2-3 avril 2026 : Xen Spring Meetup 2026 - Grenoble (France) 7 avril 2026 : PyTorch Conference Europe - Paris (France) 9-10 avril 2026 : Android Makers by droidcon 2026 - Paris (France) 9-11 avril 2026 : Drupalcamp Grenoble 2026 - Grenoble (France) 16-17 avril 2026 : MiXiT 2026 - Lyon (France) 17-18 avril 2026 : Faiseuses du Web 5 - Dinan (France) 22-24 avril 2026 : Devoxx France 2026 - Paris (France) 23-25 avril 2026 : Devoxx Greece - Athens (Greece) 6-7 mai 2026 : Devoxx UK 2026 - London (UK) 12 mai 2026 : Lead Innovation Day - Leadership Edition - Paris (France) 19 mai 2026 : La Product Conf Paris 2026 - Paris (France) 21-22 mai 2026 : Flupa UX Days 2026 - Paris (France) 22 mai 2026 : AFUP Day 2026 Lille - Lille (France) 22 mai 2026 : AFUP Day 2026 Paris - Paris (France) 22 mai 2026 : AFUP Day 2026 Bordeaux - Bordeaux (France) 22 mai 2026 : AFUP Day 2026 Lyon - Lyon (France) 28 mai 2026 : DevCon 27 : I.A. & Vibe Coding - Paris (France) 28 mai 2026 : Cloud Toulouse 2026 - Toulouse (France) 29 mai 2026 : NG Baguette Conf 2026 - Paris (France) 29 mai 2026 : Agile Tour Strasbourg 2026 - Strasbourg (France) 2-3 juin 2026 : Agile Tour Rennes 2026 - Rennes (France) 2-3 juin 2026 : OW2Con - Paris-Châtillon (France) 3 juin 2026 : IA–NA - La Rochelle (France) 5 juin 2026 : TechReady - Nantes (France) 5 juin 2026 : Fork it! - Rouen - Rouen (France) 6 juin 2026 : Polycloud - Montpellier (France) 9 juin 2026 : JFTL - Montrouge (France) 9 juin 2026 : C: - Caen (France) 11-12 juin 2026 : DevQuest Niort - Niort (France) 11-12 juin 2026 : DevLille 2026 - Lille (France) 12 juin 2026 : Tech F'Est 2026 - Nancy (France) 16 juin 2026 : Mobilis In Mobile 2026 - Nantes (France) 17-19 juin 2026 : Devoxx Poland - Krakow (Poland) 17-20 juin 2026 : VivaTech - Paris (France) 18 juin 2026 : Tech'Work - Lyon (France) 22-26 juin 2026 : Galaxy Community Conference - Clermont-Ferrand (France) 24-25 juin 2026 : Agi'Lille 2026 - Lille (France) 24-26 juin 2026 : BreizhCamp 2026 - Rennes (France) 2 juillet 2026 : Azur Tech Summer 2026 - Valbonne (France) 2-3 juillet 2026 : Sunny Tech - Montpellier (France) 3 juillet 2026 : Agile Lyon 2026 - Lyon (France) 6-8 juillet 2026 : Riviera Dev - Sophia Antipolis (France) 2 août 2026 : 4th Tech Summit on Artificial Intelligence & Robotics - Paris (France) 20-22 août 2026 : 4th Tech Summit on AI & Robotics - Paris (France) & Online 4 septembre 2026 : JUG Summer Camp 2026 - La Rochelle (France) 17-18 septembre 2026 : API Platform Conference 2026 - Lille (France) 24 septembre 2026 : PlatformCon Live Day Paris 2026 - Paris (France) 1 octobre 2026 : WAX 2026 - Marseille (France) 1-2 octobre 2026 : Volcamp - Clermont-Ferrand (France) 5-9 octobre 2026 : Devoxx Belgium - Antwerp (Belgium) Nous contacter Pour réagir à cet épisode, venez discuter sur le groupe Google https://groups.google.com/group/lescastcodeurs Contactez-nous via X/twitter https://twitter.com/lescastcodeurs ou Bluesky https://bsky.app/profile/lescastcodeurs.com Faire un crowdcast ou une crowdquestion Soutenez Les Cast Codeurs sur Patreon https://www.patreon.com/LesCastCodeurs Tous les épisodes et toutes les infos sur https://lescastcodeurs.com/

Hey dear subscriber, Alex here from W&B, let me catch you up! This week started with Anthropic releasing /fast mode for Opus 4.6, continued with ByteDance reality-shattering video model called SeeDance 2.0, and then the open weights folks pulled up! Z.ai releasing GLM-5, a 744B top ranking coder beast, and then today MiniMax dropping a heavily RL'd MiniMax M2.5, showing 80.2% on SWE-bench, nearly beating Opus 4.6! I've interviewed Lou from Z.AI and Olive from MiniMax on the show today back to back btw, very interesting conversations, starting after TL;DR!So while the OpenSource models were catching up to frontier, OpenAI and Google both dropped breaking news (again, during the show), with Gemini 3 Deep Think shattering the ArcAGI 2 (84.6%) and Humanity's Last Exam (48% w/o tools)... Just an absolute beast of a model update, and OpenAI launched their Cerebras collaboration, with GPT 5.3 Codex Spark, supposedly running at over 1000 tokens per second (but not as smart) Also, crazy week for us at W&B as we scrambled to host GLM-5 at day of release, and are working on dropping Kimi K2.5 and MiniMax both on our inference service! As always, all show notes in the end, let's DIVE IN! ThursdAI - AI is speeding up, don't get left behind! Sub and I'll keep you up to date with a weekly catch upOpen Source LLMsZ.ai launches GLM-5 - #1 open-weights coder with 744B parameters (X, HF, W&B inference)The breakaway open-source model of the week is undeniably GLM-5 from Z.ai (formerly known to many of us as Zhipu AI). We were honored to have Lou, the Head of DevRel at Z.ai, join us live on the show at 1:00 AM Shanghai time to break down this monster of a release.GLM-5 is massive, not something you run at home (hey, that's what W&B inference is for!) but it's absolutely a model that's worth thinking about if your company has on prem requirements and can't share code with OpenAI or Anthropic. They jumped from 355B in GLM4.5 and expanded their pre-training data to a whopping 28.5T tokens to get these results. But Lou explained that it's not only about data, they adopted DeepSeeks sparse attention (DSA) to help preserve deep reasoning over long contexts (this one has 200K)Lou summed up the generational leap from version 4.5 to 5 perfectly in four words: “Bigger, faster, better, and cheaper.” I dunno about faster, this may be one of those models that you hand off more difficult tasks to, but definitely cheaper, with $1 input/$3.20 output per 1M tokens on W&B! While the evaluations are ongoing, the one interesting tid-bit from Artificial Analysis was, this model scores the lowest on their hallucination rate bench! Think about this for a second, this model is neck-in-neck with Opus 4.5, and if Anthropic didn't release Opus 4.6 just last week, this would be an open weights model that rivals Opus! One of the best models the western foundational labs with all their investments has out there. Absolutely insane times. MiniMax drops M2.5 - 80.2% on SWE-bench verified with just 10B active parameters (X, Blog)Just as we wrapped up our conversation with Lou, MiniMax dropped their release (though not weights yet, we're waiting ⏰) and then Olive Song, a senior RL researcher on the team, joined the pod, and she was an absolute wealth of knowledge! Olive shared that they achieved an unbelievable 80.2% on SWE-Bench Verified. Digest this for a second: a 10B active parameter open-source model is directly trading blows with Claude Opus 4.6 (80.8%) on the one of the hardest real-world software engineering benchmark we currently have. While being alex checks notes ... 20X cheaper and much faster to run? Apparently their fast version gets up to 100 tokens/s. Olive shared the “not so secret” sauce behind this punch-above-its-weight performance. The massive leap in intelligence comes entirely from their highly decoupled Reinforcement Learning framework called “Forge.” They heavily optimized not just for correct answers, but for the end-to-end time of task performing. In the era of bloated reasoning models that spit out ten thousand “thinking” tokens before writing a line of code, MiniMax trained their model across thousands of diverse environments to use fewer tools, think more efficiently, and execute plans faster. As Olive noted, less time waiting and fewer tools called means less money spent by the user. (as confirmed by @swyx at the Windsurf leaderboard, developers often prefer fast but good enough models) I really enjoyed the interview with Olive, really recommend you listen to the whole conversation starting at 00:26:15. Kudos MiniMax on the release (and I'll keep you updated when we add this model to our inference service) Big Labs and breaking newsThere's a reason the show is called ThursdAI, and today this reason is more clear than ever, AI biggest updates happen on a Thursday, often live during the show. This happened 2 times last week and 3 times today, first with MiniMax and then with both Google and OpenAI! Google previews Gemini 3 Deep Think, top reasoning intelligence SOTA Arc AGI 2 at 84% & SOTA HLE 48.4% (X , Blog)I literally went

From rewriting Google's search stack in the early 2000s to reviving sparse trillion-parameter models and co-designing TPUs with frontier ML research, Jeff Dean has quietly shaped nearly every layer of the modern AI stack. As Chief AI Scientist at Google and a driving force behind Gemini, Jeff has lived through multiple scaling revolutions from CPUs and sharded indices to multimodal models that reason across text, video, and code.Jeff joins us to unpack what it really means to “own the Pareto frontier,” why distillation is the engine behind every Flash model breakthrough, how energy (in picojoules) not FLOPs is becoming the true bottleneck, what it was like leading the charge to unify all of Google's AI teams, and why the next leap won't come from bigger context windows alone, but from systems that give the illusion of attending to trillions of tokens.We discuss:* Jeff's early neural net thesis in 1990: parallel training before it was cool, why he believed scaling would win decades early, and the “bigger model, more data, better results” mantra that held for 15 years* The evolution of Google Search: sharding, moving the entire index into memory in 2001, softening query semantics pre-LLMs, and why retrieval pipelines already resemble modern LLM systems* Pareto frontier strategy: why you need both frontier “Pro” models and low-latency “Flash” models, and how distillation lets smaller models surpass prior generations* Distillation deep dive: ensembles → compression → logits as soft supervision, and why you need the biggest model to make the smallest one good* Latency as a first-class objective: why 10–50x lower latency changes UX entirely, and how future reasoning workloads will demand 10,000 tokens/sec* Energy-based thinking: picojoules per bit, why moving data costs 1000x more than a multiply, batching through the lens of energy, and speculative decoding as amortization* TPU co-design: predicting ML workloads 2–6 years out, speculative hardware features, precision reduction, sparsity, and the constant feedback loop between model architecture and silicon* Sparse models and “outrageously large” networks: trillions of parameters with 1–5% activation, and why sparsity was always the right abstraction* Unified vs. specialized models: abandoning symbolic systems, why general multimodal models tend to dominate vertical silos, and when vertical fine-tuning still makes sense* Long context and the illusion of scale: beyond needle-in-a-haystack benchmarks toward systems that narrow trillions of tokens to 117 relevant documents* Personalized AI: attending to your emails, photos, and documents (with permission), and why retrieval + reasoning will unlock deeply personal assistants* Coding agents: 50 AI interns, crisp specifications as a new core skill, and how ultra-low latency will reshape human–agent collaboration* Why ideas still matter: transformers, sparsity, RL, hardware, systems — scaling wasn't blind; the pieces had to multiply togetherShow Notes:* Gemma 3 Paper* Gemma 3* Gemini 2.5 Report* Jeff Dean's “Software Engineering Advice fromBuilding Large-Scale Distributed Systems” Presentation (with Back of the Envelope Calculations)* Latency Numbers Every Programmer Should Know by Jeff Dean* The Jeff Dean Facts* Jeff Dean Google Bio* Jeff Dean on “Important AI Trends” @Stanford AI Club* Jeff Dean & Noam Shazeer — 25 years at Google (Dwarkesh)—Jeff Dean* LinkedIn: https://www.linkedin.com/in/jeff-dean-8b212555* X: https://x.com/jeffdeanGoogle* https://google.com* https://deepmind.googleFull Video EpisodeTimestamps00:00:04 — Introduction: Alessio & Swyx welcome Jeff Dean, chief AI scientist at Google, to the Latent Space podcast00:00:30 — Owning the Pareto Frontier & balancing frontier vs low-latency models00:01:31 — Frontier models vs Flash models + role of distillation00:03:52 — History of distillation and its original motivation00:05:09 — Distillation's role in modern model scaling00:07:02 — Model hierarchy (Flash, Pro, Ultra) and distillation sources00:07:46 — Flash model economics & wide deployment00:08:10 — Latency importance for complex tasks00:09:19 — Saturation of some tasks and future frontier tasks00:11:26 — On benchmarks, public vs internal00:12:53 — Example long-context benchmarks & limitations00:15:01 — Long-context goals: attending to trillions of tokens00:16:26 — Realistic use cases beyond pure language00:18:04 — Multimodal reasoning and non-text modalities00:19:05 — Importance of vision & motion modalities00:20:11 — Video understanding example (extracting structured info)00:20:47 — Search ranking analogy for LLM retrieval00:23:08 — LLM representations vs keyword search00:24:06 — Early Google search evolution & in-memory index00:26:47 — Design principles for scalable systems00:28:55 — Real-time index updates & recrawl strategies00:30:06 — Classic “Latency numbers every programmer should know”00:32:09 — Cost of memory vs compute and energy emphasis00:34:33 — TPUs & hardware trade-offs for serving models00:35:57 — TPU design decisions & co-design with ML00:38:06 — Adapting model architecture to hardware00:39:50 — Alternatives: energy-based models, speculative decoding00:42:21 — Open research directions: complex workflows, RL00:44:56 — Non-verifiable RL domains & model evaluation00:46:13 — Transition away from symbolic systems toward unified LLMs00:47:59 — Unified models vs specialized ones00:50:38 — Knowledge vs reasoning & retrieval + reasoning00:52:24 — Vertical model specialization & modules00:55:21 — Token count considerations for vertical domains00:56:09 — Low resource languages & contextual learning00:59:22 — Origins: Dean's early neural network work01:10:07 — AI for coding & human–model interaction styles01:15:52 — Importance of crisp specification for coding agents01:19:23 — Prediction: personalized models & state retrieval01:22:36 — Token-per-second targets (10k+) and reasoning throughput01:23:20 — Episode conclusion and thanksTranscriptAlessio Fanelli [00:00:04]: Hey everyone, welcome to the Latent Space podcast. This is Alessio, founder of Kernel Labs, and I'm joined by Swyx, editor of Latent Space. Shawn Wang [00:00:11]: Hello, hello. We're here in the studio with Jeff Dean, chief AI scientist at Google. Welcome. Thanks for having me. It's a bit surreal to have you in the studio. I've watched so many of your talks, and obviously your career has been super legendary. So, I mean, congrats. I think the first thing must be said, congrats on owning the Pareto Frontier.Jeff Dean [00:00:30]: Thank you, thank you. Pareto Frontiers are good. It's good to be out there.Shawn Wang [00:00:34]: Yeah, I mean, I think it's a combination of both. You have to own the Pareto Frontier. You have to have like frontier capability, but also efficiency, and then offer that range of models that people like to use. And, you know, some part of this was started because of your hardware work. Some part of that is your model work, and I'm sure there's lots of secret sauce that you guys have worked on cumulatively. But, like, it's really impressive to see it all come together in, like, this slittily advanced.Jeff Dean [00:01:04]: Yeah, yeah. I mean, I think, as you say, it's not just one thing. It's like a whole bunch of things up and down the stack. And, you know, all of those really combine to help make UNOS able to make highly capable large models, as well as, you know, software techniques to get those large model capabilities into much smaller, lighter weight models that are, you know, much more cost effective and lower latency, but still, you know, quite capable for their size. Yeah.Alessio Fanelli [00:01:31]: How much pressure do you have on, like, having the lower bound of the Pareto Frontier, too? I think, like, the new labs are always trying to push the top performance frontier because they need to raise more money and all of that. And you guys have billions of users. And I think initially when you worked on the CPU, you were thinking about, you know, if everybody that used Google, we use the voice model for, like, three minutes a day, they were like, you need to double your CPU number. Like, what's that discussion today at Google? Like, how do you prioritize frontier versus, like, we have to do this? How do we actually need to deploy it if we build it?Jeff Dean [00:02:03]: Yeah, I mean, I think we always want to have models that are at the frontier or pushing the frontier because I think that's where you see what capabilities now exist that didn't exist at the sort of slightly less capable last year's version or last six months ago version. At the same time, you know, we know those are going to be really useful for a bunch of use cases, but they're going to be a bit slower and a bit more expensive than people might like for a bunch of other broader models. So I think what we want to do is always have kind of a highly capable sort of affordable model that enables a whole bunch of, you know, lower latency use cases. People can use them for agentic coding much more readily and then have the high-end, you know, frontier model that is really useful for, you know, deep reasoning, you know, solving really complicated math problems, those kinds of things. And it's not that. One or the other is useful. They're both useful. So I think we'd like to do both. And also, you know, through distillation, which is a key technique for making the smaller models more capable, you know, you have to have the frontier model in order to then distill it into your smaller model. So it's not like an either or choice. You sort of need that in order to actually get a highly capable, more modest size model. Yeah.Alessio Fanelli [00:03:24]: I mean, you and Jeffrey came up with the solution in 2014.Jeff Dean [00:03:28]: Don't forget, L'Oreal Vinyls as well. Yeah, yeah.Alessio Fanelli [00:03:30]: A long time ago. But like, I'm curious how you think about the cycle of these ideas, even like, you know, sparse models and, you know, how do you reevaluate them? How do you think about in the next generation of model, what is worth revisiting? Like, yeah, they're just kind of like, you know, you worked on so many ideas that end up being influential, but like in the moment, they might not feel that way necessarily. Yeah.Jeff Dean [00:03:52]: I mean, I think distillation was originally motivated because we were seeing that we had a very large image data set at the time, you know, 300 million images that we could train on. And we were seeing that if you create specialists for different subsets of those image categories, you know, this one's going to be really good at sort of mammals, and this one's going to be really good at sort of indoor room scenes or whatever, and you can cluster those categories and train on an enriched stream of data after you do pre-training on a much broader set of images. You get much better performance. If you then treat that whole set of maybe 50 models you've trained as a large ensemble, but that's not a very practical thing to serve, right? So distillation really came about from the idea of, okay, what if we want to actually serve that and train all these independent sort of expert models and then squish it into something that actually fits in a form factor that you can actually serve? And that's, you know, not that different from what we're doing today. You know, often today we're instead of having an ensemble of 50 models. We're having a much larger scale model that we then distill into a much smaller scale model.Shawn Wang [00:05:09]: Yeah. A part of me also wonders if distillation also has a story with the RL revolution. So let me maybe try to articulate what I mean by that, which is you can, RL basically spikes models in a certain part of the distribution. And then you have to sort of, well, you can spike models, but usually sometimes... It might be lossy in other areas and it's kind of like an uneven technique, but you can probably distill it back and you can, I think that the sort of general dream is to be able to advance capabilities without regressing on anything else. And I think like that, that whole capability merging without loss, I feel like it's like, you know, some part of that should be a distillation process, but I can't quite articulate it. I haven't seen much papers about it.Jeff Dean [00:06:01]: Yeah, I mean, I tend to think of one of the key advantages of distillation is that you can have a much smaller model and you can have a very large, you know, training data set and you can get utility out of making many passes over that data set because you're now getting the logits from the much larger model in order to sort of coax the right behavior out of the smaller model that you wouldn't otherwise get with just the hard labels. And so, you know, I think that's what we've observed. Is you can get, you know, very close to your largest model performance with distillation approaches. And that seems to be, you know, a nice sweet spot for a lot of people because it enables us to kind of, for multiple Gemini generations now, we've been able to make the sort of flash version of the next generation as good or even substantially better than the previous generations pro. And I think we're going to keep trying to do that because that seems like a good trend to follow.Shawn Wang [00:07:02]: So, Dara asked, so it was the original map was Flash Pro and Ultra. Are you just sitting on Ultra and distilling from that? Is that like the mother load?Jeff Dean [00:07:12]: I mean, we have a lot of different kinds of models. Some are internal ones that are not necessarily meant to be released or served. Some are, you know, our pro scale model and we can distill from that as well into our Flash scale model. So I think, you know, it's an important set of capabilities to have and also inference time scaling. It can also be a useful thing to improve the capabilities of the model.Shawn Wang [00:07:35]: And yeah, yeah, cool. Yeah. And obviously, I think the economy of Flash is what led to the total dominance. I think the latest number is like 50 trillion tokens. I don't know. I mean, obviously, it's changing every day.Jeff Dean [00:07:46]: Yeah, yeah. But, you know, by market share, hopefully up.Shawn Wang [00:07:50]: No, I mean, there's no I mean, there's just the economics wise, like because Flash is so economical, like you can use it for everything. Like it's in Gmail now. It's in YouTube. Like it's yeah. It's in everything.Jeff Dean [00:08:02]: We're using it more in our search products of various AI mode reviews.Shawn Wang [00:08:05]: Oh, my God. Flash past the AI mode. Oh, my God. Yeah, that's yeah, I didn't even think about that.Jeff Dean [00:08:10]: I mean, I think one of the things that is quite nice about the Flash model is not only is it more affordable, it's also a lower latency. And I think latency is actually a pretty important characteristic for these models because we're going to want models to do much more complicated things that are going to involve, you know, generating many more tokens from when you ask the model to do so. So, you know, if you're going to ask the model to do something until it actually finishes what you ask it to do, because you're going to ask now, not just write me a for loop, but like write me a whole software package to do X or Y or Z. And so having low latency systems that can do that seems really important. And Flash is one direction, one way of doing that. You know, obviously our hardware platforms enable a bunch of interesting aspects of our, you know, serving stack as well, like TPUs, the interconnect between. Chips on the TPUs is actually quite, quite high performance and quite amenable to, for example, long context kind of attention operations, you know, having sparse models with lots of experts. These kinds of things really, really matter a lot in terms of how do you make them servable at scale.Alessio Fanelli [00:09:19]: Yeah. Does it feel like there's some breaking point for like the proto Flash distillation, kind of like one generation delayed? I almost think about almost like the capability as a. In certain tasks, like the pro model today is a saturated, some sort of task. So next generation, that same task will be saturated at the Flash price point. And I think for most of the things that people use models for at some point, the Flash model in two generation will be able to do basically everything. And how do you make it economical to like keep pushing the pro frontier when a lot of the population will be okay with the Flash model? I'm curious how you think about that.Jeff Dean [00:09:59]: I mean, I think that's true. If your distribution of what people are asking people, the models to do is stationary, right? But I think what often happens is as the models become more capable, people ask them to do more, right? So, I mean, I think this happens in my own usage. Like I used to try our models a year ago for some sort of coding task, and it was okay at some simpler things, but wouldn't do work very well for more complicated things. And since then, we've improved dramatically on the more complicated coding tasks. And now I'll ask it to do much more complicated things. And I think that's true, not just of coding, but of, you know, now, you know, can you analyze all the, you know, renewable energy deployments in the world and give me a report on solar panel deployment or whatever. That's a very complicated, you know, more complicated task than people would have asked a year ago. And so you are going to want more capable models to push the frontier in the absence of what people ask the models to do. And that also then gives us. Insight into, okay, where does the, where do things break down? How can we improve the model in these, these particular areas, uh, in order to sort of, um, make the next generation even better.Alessio Fanelli [00:11:11]: Yeah. Are there any benchmarks or like test sets they use internally? Because it's almost like the same benchmarks get reported every time. And it's like, all right, it's like 99 instead of 97. Like, how do you have to keep pushing the team internally to it? Or like, this is what we're building towards. Yeah.Jeff Dean [00:11:26]: I mean, I think. Benchmarks, particularly external ones that are publicly available. Have their utility, but they often kind of have a lifespan of utility where they're introduced and maybe they're quite hard for current models. You know, I, I like to think of the best kinds of benchmarks are ones where the initial scores are like 10 to 20 or 30%, maybe, but not higher. And then you can sort of work on improving that capability for, uh, whatever it is, the benchmark is trying to assess and get it up to like 80, 90%, whatever. I, I think once it hits kind of 95% or something, you get very diminishing returns from really focusing on that benchmark, cuz it's sort of, it's either the case that you've now achieved that capability, or there's also the issue of leakage in public data or very related kind of data being, being in your training data. Um, so we have a bunch of held out internal benchmarks that we really look at where we know that wasn't represented in the training data at all. There are capabilities that we want the model to have. Um, yeah. Yeah. Um, that it doesn't have now, and then we can work on, you know, assessing, you know, how do we make the model better at these kinds of things? Is it, we need different kind of data to train on that's more specialized for this particular kind of task. Do we need, um, you know, a bunch of, uh, you know, architectural improvements or some sort of, uh, model capability improvements, you know, what would help make that better?Shawn Wang [00:12:53]: Is there, is there such an example that you, uh, a benchmark inspired in architectural improvement? Like, uh, I'm just kind of. Jumping on that because you just.Jeff Dean [00:13:02]: Uh, I mean, I think some of the long context capability of the, of the Gemini models that came, I guess, first in 1.5 really were about looking at, okay, we want to have, um, you know,Shawn Wang [00:13:15]: immediately everyone jumped to like completely green charts of like, everyone had, I was like, how did everyone crack this at the same time? Right. Yeah. Yeah.Jeff Dean [00:13:23]: I mean, I think, um, and once you're set, I mean, as you say that needed single needle and a half. Hey, stack benchmark is really saturated for at least context links up to 1, 2 and K or something. Don't actually have, you know, much larger than 1, 2 and 8 K these days or two or something. We're trying to push the frontier of 1 million or 2 million context, which is good because I think there are a lot of use cases where. Yeah. You know, putting a thousand pages of text or putting, you know, multiple hour long videos and the context and then actually being able to make use of that as useful. Try to, to explore the über graduation are fairly large. But the single needle in a haystack benchmark is sort of saturated. So you really want more complicated, sort of multi-needle or more realistic, take all this content and produce this kind of answer from a long context that sort of better assesses what it is people really want to do with long context. Which is not just, you know, can you tell me the product number for this particular thing?Shawn Wang [00:14:31]: Yeah, it's retrieval. It's retrieval within machine learning. It's interesting because I think the more meta level I'm trying to operate at here is you have a benchmark. You're like, okay, I see the architectural thing I need to do in order to go fix that. But should you do it? Because sometimes that's an inductive bias, basically. It's what Jason Wei, who used to work at Google, would say. Exactly the kind of thing. Yeah, you're going to win. Short term. Longer term, I don't know if that's going to scale. You might have to undo that.Jeff Dean [00:15:01]: I mean, I like to sort of not focus on exactly what solution we're going to derive, but what capability would you want? And I think we're very convinced that, you know, long context is useful, but it's way too short today. Right? Like, I think what you would really want is, can I attend to the internet while I answer my question? Right? But that's not going to happen. I think that's going to be solved by purely scaling the existing solutions, which are quadratic. So a million tokens kind of pushes what you can do. You're not going to do that to a trillion tokens, let alone, you know, a billion tokens, let alone a trillion. But I think if you could give the illusion that you can attend to trillions of tokens, that would be amazing. You'd find all kinds of uses for that. You would have attend to the internet. You could attend to the pixels of YouTube and the sort of deeper representations that we can find. You could attend to the form for a single video, but across many videos, you know, on a personal Gemini level, you could attend to all of your personal state with your permission. So like your emails, your photos, your docs, your plane tickets you have. I think that would be really, really useful. And the question is, how do you get algorithmic improvements and system level improvements that get you to something where you actually can attend to trillions of tokens? Right. In a meaningful way. Yeah.Shawn Wang [00:16:26]: But by the way, I think I did some math and it's like, if you spoke all day, every day for eight hours a day, you only generate a maximum of like a hundred K tokens, which like very comfortably fits.Jeff Dean [00:16:38]: Right. But if you then say, okay, I want to be able to understand everything people are putting on videos.Shawn Wang [00:16:46]: Well, also, I think that the classic example is you start going beyond language into like proteins and whatever else is extremely information dense. Yeah. Yeah.Jeff Dean [00:16:55]: I mean, I think one of the things about Gemini's multimodal aspects is we've always wanted it to be multimodal from the start. And so, you know, that sometimes to people means text and images and video sort of human-like and audio, audio, human-like modalities. But I think it's also really useful to have Gemini know about non-human modalities. Yeah. Like LIDAR sensor data from. Yes. Say, Waymo vehicles or. Like robots or, you know, various kinds of health modalities, x-rays and MRIs and imaging and genomics information. And I think there's probably hundreds of modalities of data where you'd like the model to be able to at least be exposed to the fact that this is an interesting modality and has certain meaning in the world. Where even if you haven't trained on all the LIDAR data or MRI data, you could have, because maybe that's not, you know, it doesn't make sense in terms of trade-offs of. You know, what you include in your main pre-training data mix, at least including a little bit of it is actually quite useful. Yeah. Because it sort of tempts the model that this is a thing.Shawn Wang [00:18:04]: Yeah. Do you believe, I mean, since we're on this topic and something I just get to ask you all the questions I always wanted to ask, which is fantastic. Like, are there some king modalities, like modalities that supersede all the other modalities? So a simple example was Vision can, on a pixel level, encode text. And DeepSeq had this DeepSeq CR paper that did that. Vision. And Vision has also been shown to maybe incorporate audio because you can do audio spectrograms and that's, that's also like a Vision capable thing. Like, so, so maybe Vision is just the king modality and like. Yeah.Jeff Dean [00:18:36]: I mean, Vision and Motion are quite important things, right? Motion. Well, like video as opposed to static images, because I mean, there's a reason evolution has evolved eyes like 23 independent ways, because it's such a useful capability for sensing the world around you, which is really what we want these models to be. So I think the only thing that we can be able to do is interpret the things we're seeing or the things we're paying attention to and then help us in using that information to do things. Yeah.Shawn Wang [00:19:05]: I think motion, you know, I still want to shout out, I think Gemini, still the only native video understanding model that's out there. So I use it for YouTube all the time. Nice.Jeff Dean [00:19:15]: Yeah. Yeah. I mean, it's actually, I think people kind of are not necessarily aware of what the Gemini models can actually do. Yeah. Like I have an example I've used in one of my talks. It had like, it was like a YouTube highlight video of 18 memorable sports moments across the last 20 years or something. So it has like Michael Jordan hitting some jump shot at the end of the finals and, you know, some soccer goals and things like that. And you can literally just give it the video and say, can you please make me a table of what all these different events are? What when the date is when they happened? And a short description. And so you get like now an 18 row table of that information extracted from the video, which is, you know, not something most people think of as like a turn video into sequel like table.Alessio Fanelli [00:20:11]: Has there been any discussion inside of Google of like, you mentioned tending to the whole internet, right? Google, it's almost built because a human cannot tend to the whole internet and you need some sort of ranking to find what you need. Yep. That ranking is like much different for an LLM because you can expect a person to look at maybe the first five, six links in a Google search versus for an LLM. Should you expect to have 20 links that are highly relevant? Like how do you internally figure out, you know, how do we build the AI mode that is like maybe like much broader search and span versus like the more human one? Yeah.Jeff Dean [00:20:47]: I mean, I think even pre-language model based work, you know, our ranking systems would be built to start. I mean, I think even pre-language model based work, you know, our ranking systems would be built to start. With a giant number of web pages in our index, many of them are not relevant. So you identify a subset of them that are relevant with very lightweight kinds of methods. You know, you're down to like 30,000 documents or something. And then you gradually refine that to apply more and more sophisticated algorithms and more and more sophisticated sort of signals of various kinds in order to get down to ultimately what you show, which is, you know, the final 10 results or, you know, 10 results plus. Other kinds of information. And I think an LLM based system is not going to be that dissimilar, right? You're going to attend to trillions of tokens, but you're going to want to identify, you know, what are the 30,000 ish documents that are with the, you know, maybe 30 million interesting tokens. And then how do you go from that into what are the 117 documents I really should be paying attention to in order to carry out the tasks that the user has asked? And I think, you know, you can imagine systems where you have, you know, a lot of highly parallel processing to identify those initial 30,000 candidates, maybe with very lightweight kinds of models. Then you have some system that sort of helps you narrow down from 30,000 to the 117 with maybe a little bit more sophisticated model or set of models. And then maybe the final model is the thing that looks. So the 117 things that might be your most capable model. So I think it has to, it's going to be some system like that, that is really enables you to give the illusion of attending to trillions of tokens. Sort of the way Google search gives you, you know, not the illusion, but you are searching the internet, but you're finding, you know, a very small subset of things that are, that are relevant.Shawn Wang [00:22:47]: Yeah. I often tell a lot of people that are not steeped in like Google search history that, well, you know, like Bert was. Like he was like basically immediately inside of Google search and that improves results a lot, right? Like I don't, I don't have any numbers off the top of my head, but like, I'm sure you guys, that's obviously the most important numbers to Google. Yeah.Jeff Dean [00:23:08]: I mean, I think going to an LLM based representation of text and words and so on enables you to get out of the explicit hard notion of, of particular words having to be on the page, but really getting at the notion of this topic of this page or this page. Paragraph is highly relevant to this query. Yeah.Shawn Wang [00:23:28]: I don't think people understand how much LLMs have taken over all these very high traffic system, very high traffic. Yeah. Like it's Google, it's YouTube. YouTube has this like semantics ID thing where it's just like every token or every item in the vocab is a YouTube video or something that predicts the video using a code book, which is absurd to me for YouTube size.Jeff Dean [00:23:50]: And then most recently GROK also for, for XAI, which is like, yeah. I mean, I'll call out even before LLMs were used extensively in search, we put a lot of emphasis on softening the notion of what the user actually entered into the query.Shawn Wang [00:24:06]: So do you have like a history of like, what's the progression? Oh yeah.Jeff Dean [00:24:09]: I mean, I actually gave a talk in, uh, I guess, uh, web search and data mining conference in 2009, uh, where we never actually published any papers about the origins of Google search, uh, sort of, but we went through sort of four or five or six. generations, four or five or six generations of, uh, redesigning of the search and retrieval system, uh, from about 1999 through 2004 or five. And that talk is really about that evolution. And one of the things that really happened in 2001 was we were sort of working to scale the system in multiple dimensions. So one is we wanted to make our index bigger, so we could retrieve from a larger index, which always helps your quality in general. Uh, because if you don't have the page in your index, you're going to not do well. Um, and then we also needed to scale our capacity because we were, our traffic was growing quite extensively. Um, and so we had, you know, a sharded system where you have more and more shards as the index grows, you have like 30 shards. And then if you want to double the index size, you make 60 shards so that you can bound the latency by which you respond for any particular user query. Um, and then as traffic grows, you add, you add more and more replicas of each of those. And so we eventually did the math that realized that in a data center where we had say 60 shards and, um, you know, 20 copies of each shard, we now had 1200 machines, uh, with disks. And we did the math and we're like, Hey, one copy of that index would actually fit in memory across 1200 machines. So in 2001, we introduced, uh, we put our entire index in memory and what that enabled from a quality perspective was amazing. Um, and so we had more and more replicas of each of those. Before you had to be really careful about, you know, how many different terms you looked at for a query, because every one of them would involve a disk seek on every one of the 60 shards. And so you, as you make your index bigger, that becomes even more inefficient. But once you have the whole index in memory, it's totally fine to have 50 terms you throw into the query from the user's original three or four word query, because now you can add synonyms like restaurant and restaurants and cafe and, uh, you know, things like that. Uh, bistro and all these things. And you can suddenly start, uh, sort of really, uh, getting at the meaning of the word as opposed to the exact semantic form the user typed in. And that was, you know, 2001, very much pre LLM, but really it was about softening the, the strict definition of what the user typed in order to get at the meaning.Alessio Fanelli [00:26:47]: What are like principles that you use to like design the systems, especially when you have, I mean, in 2001, the internet is like. Doubling, tripling every year in size is not like, uh, you know, and I think today you kind of see that with LLMs too, where like every year the jumps in size and like capabilities are just so big. Are there just any, you know, principles that you use to like, think about this? Yeah.Jeff Dean [00:27:08]: I mean, I think, uh, you know, first, whenever you're designing a system, you want to understand what are the sort of design parameters that are going to be most important in designing that, you know? So, you know, how many queries per second do you need to handle? How big is the internet? How big is the index you need to handle? How much data do you need to keep for every document in the index? How are you going to look at it when you retrieve things? Um, what happens if traffic were to double or triple, you know, will that system work well? And I think a good design principle is you're going to want to design a system so that the most important characteristics could scale by like factors of five or 10, but probably not beyond that because often what happens is if you design a system for X. And something suddenly becomes a hundred X, that would enable a very different point in the design space that would not make sense at X. But all of a sudden at a hundred X makes total sense. So like going from a disk space index to a in memory index makes a lot of sense once you have enough traffic, because now you have enough replicas of the sort of state on disk that those machines now actually can hold, uh, you know, a full copy of the, uh, index and memory. Yeah. And that all of a sudden enabled. A completely different design that wouldn't have been practical before. Yeah. Um, so I'm, I'm a big fan of thinking through designs in your head, just kind of playing with the design space a little before you actually do a lot of writing of code. But, you know, as you said, in the early days of Google, we were growing the index, uh, quite extensively. We were growing the update rate of the index. So the update rate actually is the parameter that changed the most. Surprising. So it used to be once a month.Shawn Wang [00:28:55]: Yeah.Jeff Dean [00:28:56]: And then we went to a system that could update any particular page in like sub one minute. Okay.Shawn Wang [00:29:02]: Yeah. Because this is a competitive advantage, right?Jeff Dean [00:29:04]: Because all of a sudden news related queries, you know, if you're, if you've got last month's news index, it's not actually that useful for.Shawn Wang [00:29:11]: News is a special beast. Was there any, like you could have split it onto a separate system.Jeff Dean [00:29:15]: Well, we did. We launched a Google news product, but you also want news related queries that people type into the main index to also be sort of updated.Shawn Wang [00:29:23]: So, yeah, it's interesting. And then you have to like classify whether the page is, you have to decide which pages should be updated and what frequency. Oh yeah.Jeff Dean [00:29:30]: There's a whole like, uh, system behind the scenes that's trying to decide update rates and importance of the pages. So even if the update rate seems low, you might still want to recrawl important pages quite often because, uh, the likelihood they change might be low, but the value of having updated is high.Shawn Wang [00:29:50]: Yeah, yeah, yeah, yeah. Uh, well, you know, yeah. This, uh, you know, mention of latency and, and saving things to this reminds me of one of your classics, which I have to bring up, which is latency numbers. Every programmer should know, uh, was there a, was it just a, just a general story behind that? Did you like just write it down?Jeff Dean [00:30:06]: I mean, this has like sort of eight or 10 different kinds of metrics that are like, how long does a cache mistake? How long does branch mispredict take? How long does a reference domain memory take? How long does it take to send, you know, a packet from the U S to the Netherlands or something? Um,Shawn Wang [00:30:21]: why Netherlands, by the way, or is it, is that because of Chrome?Jeff Dean [00:30:25]: Uh, we had a data center in the Netherlands, um, so, I mean, I think this gets to the point of being able to do the back of the envelope calculations. So these are sort of the raw ingredients of those, and you can use them to say, okay, well, if I need to design a system to do image search and thumb nailing or something of the result page, you know, how, what I do that I could pre-compute the image thumbnails. I could like. Try to thumbnail them on the fly from the larger images. What would that do? How much dis bandwidth than I need? How many des seeks would I do? Um, and you can sort of actually do thought experiments in, you know, 30 seconds or a minute with the sort of, uh, basic, uh, basic numbers at your fingertips. Uh, and then as you sort of build software using higher level libraries, you kind of want to develop the same intuitions for how long does it take to, you know, look up something in this particular kind of.Shawn Wang [00:31:21]: I'll see you next time.Shawn Wang [00:31:51]: Which is a simple byte conversion. That's nothing interesting. I wonder if you have any, if you were to update your...Jeff Dean [00:31:58]: I mean, I think it's really good to think about calculations you're doing in a model, either for training or inference.Jeff Dean [00:32:09]: Often a good way to view that is how much state will you need to bring in from memory, either like on-chip SRAM or HBM from the accelerator. Attached memory or DRAM or over the network. And then how expensive is that data motion relative to the cost of, say, an actual multiply in the matrix multiply unit? And that cost is actually really, really low, right? Because it's order, depending on your precision, I think it's like sub one picodule.Shawn Wang [00:32:50]: Oh, okay. You measure it by energy. Yeah. Yeah.Jeff Dean [00:32:52]: Yeah. I mean, it's all going to be about energy and how do you make the most energy efficient system. And then moving data from the SRAM on the other side of the chip, not even off the off chip, but on the other side of the same chip can be, you know, a thousand picodules. Oh, yeah. And so all of a sudden, this is why your accelerators require batching. Because if you move, like, say, the parameter of a model from SRAM on the, on the chip into the multiplier unit, that's going to cost you a thousand picodules. So you better make use of that, that thing that you moved many, many times with. So that's where the batch dimension comes in. Because all of a sudden, you know, if you have a batch of 256 or something, that's not so bad. But if you have a batch of one, that's really not good.Shawn Wang [00:33:40]: Yeah. Yeah. Right.Jeff Dean [00:33:41]: Because then you paid a thousand picodules in order to do your one picodule multiply.Shawn Wang [00:33:46]: I have never heard an energy-based analysis of batching.Jeff Dean [00:33:50]: Yeah. I mean, that's why people batch. Yeah. Ideally, you'd like to use batch size one because the latency would be great.Shawn Wang [00:33:56]: The best latency.Jeff Dean [00:33:56]: But the energy cost and the compute cost inefficiency that you get is quite large. So, yeah.Shawn Wang [00:34:04]: Is there a similar trick like, like, like you did with, you know, putting everything in memory? Like, you know, I think obviously NVIDIA has caused a lot of waves with betting very hard on SRAM with Grok. I wonder if, like, that's something that you already saw with, with the TPUs, right? Like that, that you had to. Uh, to serve at your scale, uh, you probably sort of saw that coming. Like what, what, what hardware, uh, innovations or insights were formed because of what you're seeing there?Jeff Dean [00:34:33]: Yeah. I mean, I think, you know, TPUs have this nice, uh, sort of regular structure of 2D or 3D meshes with a bunch of chips connected. Yeah. And each one of those has HBM attached. Um, I think for serving some kinds of models, uh, you know, you, you pay a lot higher cost. Uh, and time latency, um, bringing things in from HBM than you do bringing them in from, uh, SRAM on the chip. So if you have a small enough model, you can actually do model parallelism, spread it out over lots of chips and you actually get quite good throughput improvements and latency improvements from doing that. And so you're now sort of striping your smallish scale model over say 16 or 64 chips. Uh, but as if you do that and it all fits in. In SRAM, uh, that can be a big win. So yeah, that's not a surprise, but it is a good technique.Alessio Fanelli [00:35:27]: Yeah. What about the TPU design? Like how much do you decide where the improvements have to go? So like, this is like a good example of like, is there a way to bring the thousand picojoules down to 50? Like, is it worth designing a new chip to do that? The extreme is like when people say, oh, you should burn the model on the ASIC and that's kind of like the most extreme thing. How much of it? Is it worth doing an hardware when things change so quickly? Like what was the internal discussion? Yeah.Jeff Dean [00:35:57]: I mean, we, we have a lot of interaction between say the TPU chip design architecture team and the sort of higher level modeling, uh, experts, because you really want to take advantage of being able to co-design what should future TPUs look like based on where we think the sort of ML research puck is going, uh, in some sense, because, uh, you know, as a hardware designer for ML and in particular, you're trying to design a chip starting today and that design might take two years before it even lands in a data center. And then it has to sort of be a reasonable lifetime of the chip to take you three, four or five years. So you're trying to predict two to six years out where, what ML computations will people want to run two to six years out in a very fast changing field. And so having people with interest. Interesting ML research ideas of things we think will start to work in that timeframe or will be more important in that timeframe, uh, really enables us to then get, you know, interesting hardware features put into, you know, TPU N plus two, where TPU N is what we have today.Shawn Wang [00:37:10]: Oh, the cycle time is plus two.Jeff Dean [00:37:12]: Roughly. Wow. Because, uh, I mean, sometimes you can squeeze some changes into N plus one, but, you know, bigger changes are going to require the chip. Yeah. Design be earlier in its lifetime design process. Um, so whenever we can do that, it's generally good. And sometimes you can put in speculative features that maybe won't cost you much chip area, but if it works out, it would make something, you know, 10 times as fast. And if it doesn't work out, well, you burned a little bit of tiny amount of your chip area on that thing, but it's not that big a deal. Uh, sometimes it's a very big change and we want to be pretty sure this is going to work out. So we'll do like lots of carefulness. Uh, ML experimentation to show us, uh, this is actually the, the way we want to go. Yeah.Alessio Fanelli [00:37:58]: Is there a reverse of like, we already committed to this chip design so we can not take the model architecture that way because it doesn't quite fit?Jeff Dean [00:38:06]: Yeah. I mean, you, you definitely have things where you're going to adapt what the model architecture looks like so that they're efficient on the chips that you're going to have for both training and inference of that, of that, uh, generation of model. So I think it kind of goes both ways. Um, you know, sometimes you can take advantage of, you know, lower precision things that are coming in a future generation. So you can, might train it at that lower precision, even if the current generation doesn't quite do that. Mm.Shawn Wang [00:38:40]: Yeah. How low can we go in precision?Jeff Dean [00:38:43]: Because people are saying like ternary is like, uh, yeah, I mean, I'm a big fan of very low precision because I think that gets, that saves you a tremendous amount of time. Right. Because it's picojoules per bit that you're transferring and reducing the number of bits is a really good way to, to reduce that. Um, you know, I think people have gotten a lot of luck, uh, mileage out of having very low bit precision things, but then having scaling factors that apply to a whole bunch of, uh, those, those weights. Scaling. How does it, how does it, okay.Shawn Wang [00:39:15]: Interesting. You, so low, low precision, but scaled up weights. Yeah. Huh. Yeah. Never considered that. Yeah. Interesting. Uh, w w while we're on this topic, you know, I think there's a lot of, um, uh, this, the concept of precision at all is weird when we're sampling, you know, uh, we just, at the end of this, we're going to have all these like chips that I'll do like very good math. And then we're just going to throw a random number generator at the start. So, I mean, there's a movement towards, uh, energy based, uh, models and processors. I'm just curious if you've, obviously you've thought about it, but like, what's your commentary?Jeff Dean [00:39:50]: Yeah. I mean, I think. There's a bunch of interesting trends though. Energy based models is one, you know, diffusion based models, which don't sort of sequentially decode tokens is another, um, you know, speculative decoding is a way that you can get sort of an equivalent, very small.Shawn Wang [00:40:06]: Draft.Jeff Dean [00:40:07]: Batch factor, uh, for like you predict eight tokens out and that enables you to sort of increase the effective batch size of what you're doing by a factor of eight, even, and then you maybe accept five or six of those tokens. So you get. A five, a five X improvement in the amortization of moving weights, uh, into the multipliers to do the prediction for the, the tokens. So these are all really good techniques and I think it's really good to look at them from the lens of, uh, energy, real energy, not energy based models, um, and, and also latency and throughput, right? If you look at things from that lens, that sort of guides you to. Two solutions that are gonna be, uh, you know, better from, uh, you know, being able to serve larger models or, you know, equivalent size models more cheaply and with lower latency.Shawn Wang [00:41:03]: Yeah. Well, I think, I think I, um, it's appealing intellectually, uh, haven't seen it like really hit the mainstream, but, um, I do think that, uh, there's some poetry in the sense that, uh, you know, we don't have to do, uh, a lot of shenanigans if like we fundamentally. Design it into the hardware. Yeah, yeah.Jeff Dean [00:41:23]: I mean, I think there's still a, there's also sort of the more exotic things like analog based, uh, uh, computing substrates as opposed to digital ones. Uh, I'm, you know, I think those are super interesting cause they can be potentially low power. Uh, but I think you often end up wanting to interface that with digital systems and you end up losing a lot of the power advantages in the digital to analog and analog to digital conversions. You end up doing, uh, at the sort of boundaries. And periphery of that system. Um, I still think there's a tremendous distance we can go from where we are today in terms of energy efficiency with sort of, uh, much better and specialized hardware for the models we care about.Shawn Wang [00:42:05]: Yeah.Alessio Fanelli [00:42:06]: Um, any other interesting research ideas that you've seen, or like maybe things that you cannot pursue a Google that you would be interested in seeing researchers take a step at, I guess you have a lot of researchers. Yeah, I guess you have enough, but our, our research.Jeff Dean [00:42:21]: Our research portfolio is pretty broad. I would say, um, I mean, I think, uh, in terms of research directions, there's a whole bunch of, uh, you know, open problems and how do you make these models reliable and able to do much longer, kind of, uh, more complex tasks that have lots of subtasks. How do you orchestrate, you know, maybe one model that's using other models as tools in order to sort of build, uh, things that can accomplish, uh, you know, much more. Yeah. Significant pieces of work, uh, collectively, then you would ask a single model to do. Um, so that's super interesting. How do you get more verifiable, uh, you know, how do you get RL to work for non-verifiable domains? I think it's a pretty interesting open problem because I think that would broaden out the capabilities of the models, the improvements that you're seeing in both math and coding. Uh, if we could apply those to other less verifiable domains, because we've come up with RL techniques that actually enable us to do that. Uh, effectively, that would, that would really make the models improve quite a lot. I think.Alessio Fanelli [00:43:26]: I'm curious, like when we had Noam Brown on the podcast, he said, um, they already proved you can do it with deep research. Um, you kind of have it with AI mode in a way it's not verifiable. I'm curious if there's any thread that you think is interesting there. Like what is it? Both are like information retrieval of JSON. So I wonder if it's like the retrieval is like the verifiable part. That you can score or what are like, yeah, yeah. How, how would you model that, that problem?Jeff Dean [00:43:55]: Yeah. I mean, I think there are ways of having other models that can evaluate the results of what a first model did, maybe even retrieving. Can you have another model that says, is this things, are these things you retrieved relevant? Or can you rate these 2000 things you retrieved to assess which ones are the 50 most relevant or something? Um, I think those kinds of techniques are actually quite effective. Sometimes I can even be the same model, just prompted differently to be a, you know, a critic as opposed to a, uh, actual retrieval system. Yeah.Shawn Wang [00:44:28]: Um, I do think like there, there is that, that weird cliff where like, it feels like we've done the easy stuff and then now it's, but it always feels like that every year. It's like, oh, like we know, we know, and the next part is super hard and nobody's figured it out. And, uh, exactly with this RLVR thing where like everyone's talking about, well, okay, how do we. the next stage of the non-verifiable stuff. And everyone's like, I don't know, you know, Ellen judge.Jeff Dean [00:44:56]: I mean, I feel like the nice thing about this field is there's lots and lots of smart people thinking about creative solutions to some of the problems that we all see. Uh, because I think everyone sort of sees that the models, you know, are great at some things and they fall down around the edges of those things and, and are not as capable as we'd like in those areas. And then coming up with good techniques and trying those. And seeing which ones actually make a difference is sort of what the whole research aspect of this field is, is pushing forward. And I think that's why it's super interesting. You know, if you think about two years ago, we were struggling with GSM, eight K problems, right? Like, you know, Fred has two rabbits. He gets three more rabbits. How many rabbits does he have? That's a pretty far cry from the kinds of mathematics that the models can, and now you're doing IMO and Erdos problems in pure language. Yeah. Yeah. Pure language. So that is a really, really amazing jump in capabilities in, you know, in a year and a half or something. And I think, um, for other areas, it'd be great if we could make that kind of leap. Uh, and you know, we don't exactly see how to do it for some, some areas, but we do see it for some other areas and we're going to work hard on making that better. Yeah.Shawn Wang [00:46:13]: Yeah.Alessio Fanelli [00:46:14]: Like YouTube thumbnail generation. That would be very helpful. We need that. That would be AGI. We need that.Shawn Wang [00:46:20]: That would be. As far as content creators go.Jeff Dean [00:46:22]: I guess I'm not a YouTube creator, so I don't care that much about that problem, but I guess, uh, many people do.Shawn Wang [00:46:27]: It does. Yeah. It doesn't, it doesn't matter. People do judge books by their covers as it turns out. Um, uh, just to draw a bit on the IMO goal. Um, I'm still not over the fact that a year ago we had alpha proof and alpha geometry and all those things. And then this year we were like, screw that we'll just chuck it into Gemini. Yeah. What's your reflection? Like, I think this, this question about. Like the merger of like symbolic systems and like, and, and LMS, uh, was a very much core belief. And then somewhere along the line, people would just said, Nope, we'll just all do it in the LLM.Jeff Dean [00:47:02]: Yeah. I mean, I think it makes a lot of sense to me because, you know, humans manipulate symbols, but we probably don't have like a symbolic representation in our heads. Right. We have some distributed representation that is neural net, like in some way of lots of different neurons. And activation patterns firing when we see certain things and that enables us to reason and plan and, you know, do chains of thought and, you know, roll them back now that, that approach for solving the problem doesn't seem like it's going to work. I'm going to try this one. And, you know, in a lot of ways we're emulating what we intuitively think, uh, is happening inside real brains in neural net based models. So it never made sense to me to have like completely separate. Uh, discrete, uh, symbolic things, and then a completely different way of, of, uh, you know, thinking about those things.Shawn Wang [00:47:59]: Interesting. Yeah. Uh, I mean, it's maybe seems obvious to you, but it wasn't obvious to me a year ago. Yeah.Jeff Dean [00:48:06]: I mean, I do think like that IMO with, you know, translating to lean and using lean and then the next year and also a specialized geometry model. And then this year switching to a single unified model. That is roughly the production model with a little bit more inference budget, uh, is actually, you know, quite good because it shows you that the capabilities of that general model have improved dramatically and, and now you don't need the specialized model. This is actually sort of very similar to the 2013 to 16 era of machine learning, right? Like it used to be, people would train separate models for lots of different, each different problem, right? I have, I want to recognize street signs and something. So I train a street sign. Recognition recognition model, or I want to, you know, decode speech recognition. I have a speech model, right? I think now the era of unified models that do everything is really upon us. And the question is how well do those models generalize to new things they've never been asked to do and they're getting better and better.Shawn Wang [00:49:10]: And you don't need domain experts. Like one of my, uh, so I interviewed ETA who was on, who was on that team. Uh, and he was like, yeah, I, I don't know how they work. I don't know where the IMO competition was held. I don't know the rules of it. I just trained the models, the training models. Yeah. Yeah. And it's kind of interesting that like people with these, this like universal skill set of just like machine learning, you just give them data and give them enough compute and they can kind of tackle any task, which is the bitter lesson, I guess. I don't know. Yeah.Jeff Dean [00:49:39]: I mean, I think, uh, general models, uh, will win out over specialized ones in most cases.Shawn Wang [00:49:45]: Uh, so I want to push there a bit. I think there's one hole here, which is like, uh. There's this concept of like, uh, maybe capacity of a model, like abstractly a model can only contain the number of bits that it has. And, uh, and so it, you know, God knows like Gemini pro is like one to 10 trillion parameters. We don't know, but, uh, the Gemma models, for example, right? Like a lot of people want like the open source local models that are like that, that, that, and, and, uh, they have some knowledge, which is not necessary, right? Like they can't know everything like, like you have the. The luxury of you have the big model and big model should be able to capable of everything. But like when, when you're distilling and you're going down to the small models, you know, you're actually memorizing things that are not useful. Yeah. And so like, how do we, I guess, do we want to extract that? Can we, can we divorce knowledge from reasoning, you know?Jeff Dean [00:50:38]: Yeah. I mean, I think you do want the model to be most effective at reasoning if it can retrieve things, right? Because having the model devote precious parameter space. To remembering obscure facts that could be looked up is actually not the best use of that parameter space, right? Like you might prefer something that is more generally useful in more settings than this obscure fact that it has. Um, so I think that's always attention at the same time. You also don't want your model to be kind of completely detached from, you know, knowing stuff about the world, right? Like it's probably useful to know how long the golden gate be. Bridges just as a general sense of like how long are bridges, right? And, uh, it should have that kind of knowledge. It maybe doesn't need to know how long some teeny little bridge in some other more obscure part of the world is, but, uh, it does help it to have a fair bit of world knowledge and the bigger your model is, the more you can have. Uh, but I do think combining retrieval with sort of reasoning and making the model really good at doing multiple stages of retrieval. Yeah.Shawn Wang [00:51:49]: And reasoning through the intermediate retrieval results is going to be a, a pretty effective way of making the model seem much more capable, because if you think about, say, a personal Gemini, yeah, right?Jeff Dean [00:52:01]: Like we're not going to train Gemini on my email. Probably we'd rather have a single model that, uh, we can then use and use being able to retrieve from my email as a tool and have the model reason about it and retrieve from my photos or whatever, uh, and then make use of that and have multiple. Um, you know, uh, stages of interaction. that makes sense.Alessio Fanelli [00:52:24]: Do you think the vertical models are like, uh, interesting pursuit? Like when people are like, oh, we're building the best healthcare LLM, we're building the best law LLM, are those kind of like short-term stopgaps or?Jeff Dean [00:52:37]: No, I mean, I think, I think vertical models are interesting. Like you want them to start from a pretty good base model, but then you can sort of, uh, sort of viewing them, view them as enriching the data. Data distribution for that particular vertical domain for healthcare, say, um, we're probably not going to train or for say robotics. We're probably not going to train Gemini on all possible robotics data. We, you could train it on because we want it to have a balanced set of capabilities. Um, so we'll expose it to some robotics data, but if you're trying to build a really, really good robotics model, you're going to want to start with that and then train it on more robotics data. And then maybe that would. It's multilingual translation capability, but improve its robotics capabilities. And we're always making these kind of, uh, you know, trade-offs in the data mix that we train the base Gemini models on. You know, we'd love to include data from 200 more languages and as much data as we have for those languages, but that's going to displace some other capabilities of the model. It won't be as good at, um, you know, Pearl programming, you know, it'll still be good at Python programming. Cause we'll include it. Enough. Of that, but there's other long tail computer languages or coding capabilities that it may suffer on or multi, uh, multimodal reasoning capabilities may suffer. Cause we didn't get to expose it to as much data there, but it's really good at multilingual things. So I, I think some combination of specialized models, maybe more modular models. So it'd be nice to have the capability to have those 200 languages, plus this awesome robotics model, plus this awesome healthcare, uh, module that all can be knitted together to work in concert and called upon in different circumstances. Right? Like if I have a health related thing, then it should enable using this health module in conjunction with the main base model to be even better at those kinds of things. Yeah.Shawn Wang [00:54:36]: Installable knowledge. Yeah.Jeff Dean [00:54:37]: Right.Shawn Wang [00:54:38]: Just download as a, as a package.Jeff Dean [00:54:39]: And some of that installable stuff can come from retrieval, but some of it probably should come from preloaded training on, you know, uh, a hundred billion tokens or a trillion tokens of health data. Yeah.Shawn Wang [00:54:51]: And for listeners, I think, uh, I will highlight the Gemma three end paper where they, there was a little bit of that, I think. Yeah.Alessio Fanelli [00:54:56]: Yeah. I guess the question is like, how many billions of tokens do you need to outpace the frontier model improvements? You know, it's like, if I have to make this model better healthcare and the main. Gemini model is still improving. Do I need 50 billion tokens? Can I do it with a hundred, if I need a trillion healthcare tokens, it's like, they're probably not out there that you don't have, you know, I think that's really like the.Jeff Dean [00:55:21]: Well, I mean, I think healthcare is a particularly challenging domain, so there's a lot of healthcare data that, you know, we don't have access to appropriately, but there's a lot of, you know, uh, healthcare organizations that want to train models on their own data. That is not public healthcare data, uh, not public health. But public healthcare data. Um, so I think there are opportunities there to say, partner with a large healthcare organization and train models for their use that are going to be, you know, more bespoke, but probably, uh, might be better than a general model trained on say, public data. Yeah.Shawn Wang [00:55:58]: Yeah. I, I believe, uh, by the way, also this is like somewhat related to the language conversation. Uh, I think one of your, your favorite examples was you can put a low resource language in the context and it just learns. Yeah.Jeff Dean [00:56:09]: Oh, yeah, I think the example we used was Calamon, which is truly low resource because it's only spoken by, I think 120 people in the world and there's no written text.Shawn Wang [00:56:20]: So, yeah. So you can just do it that way. Just put it in the context. Yeah. Yeah. But I think your whole data set in the context, right.Jeff Dean [00:56:27]: If you, if you take a language like, uh, you know, Somali or something, there is a fair bit of Somali text in the world that, uh, or Ethiopian Amharic or something, um, you know, we probably. Yeah. Are not putting all the data from those languages into the Gemini based training. We put some of it, but if you put more of it, you'll improve the capabilities of those models.Shawn Wang [00:56:49]: Yeah.Jeff Dean [00:56:49]:

In a podcast recorded at ITEXPO / MSP EXPO, Doug Green, Publisher of Technology Reseller News, spoke with Mike Wehrs, CTO of TieTechnology, about the upcoming launch of Genie 1.1 and the company's broader mission to reposition voice as a fully integrated component of modern IT infrastructure. TieTechnology focuses on making voice a “first-tier partner” within business systems rather than a disconnected afterthought. Genie, the company's SMB product family, provides a backend softphone capability for PCs along with applications that connect voice into tools such as Slack, CRMs, and EMRs. With Genie 1.1, the company is deepening its ability to capture, transcribe, summarize, and structure voice interactions so that the most valuable customer data—what was actually said—flows directly into business systems. “AI is not magic,” Wehrs noted. “If you don't have good data going into the system, you're not going to get the results out of it that you want.” He emphasized that many organizations layer AI on top of incomplete infrastructure, resulting in underperformance. Genie addresses that gap by cleaning audio streams, identifying speakers, summarizing conversations, and delivering structured data—often in JSON format—into CRM environments. The result, according to Wehrs, can represent as much as a 40 percent increase in high-quality CRM data, driving better customer support, marketing automation, and operational insight. For MSPs, the opportunity is twofold. First, Genie simplifies voice integration through straightforward APIs, eliminating the need to understand complex SIP stacks or telecom architecture. Second, it opens new revenue potential by allowing MSPs to modernize dated phone systems and embed voice-driven intelligence directly into client workflows. As Wehrs framed it, voice should become as native to the PC environment as networking did in the Windows 95 era—fully integrated, flexible, and foundational to digital operations. Visit https://tietechnology.com/

Do you remember the early days of your career? You likely spent hours coding late into the night, fueled not by a paycheck, but by the sheer joy of building. But somewhere along the way, that intrinsic fire faded, replaced by the extrinsic motivators of Jira tickets, performance reviews, and ultimately the almighty dollar.In this episode of the Career Growth Accelerator, I explore why this shift happens and how it might be the very thing keeping you stuck. We discuss the "Overjustification Effect"—how getting paid for your passion can actually degrade your performance—and how to reclaim the autotelic personality required to enter a flow state and accelerate your career.• The Overjustification Effect: Learn why introducing extrinsic rewards (like a salary) for a task you inherently enjoy can weaken or completely replace your intrinsic motivation, eventually making the work feel like a chore.• The Loss of Flow: Discover how moving from hobbyist to professional changes your relationship with the work, often stripping away the conditions necessary for "flow state," such as risk-taking and immediate feedback.• Autotelic Personality: Understand the concept of being "autotelic"—doing something for its own sake—and why this trait is critical for high-quality, creative work that pushes your career forward.• The Stagnation Trap: Recognize that if your only motivation is doing what is required to get paid, you are unlikely to take on the voluntary challenges necessary to grow to the next level.• Reclaiming Your Drive: I discuss how finding pockets of intrinsic motivation—even if they are ancillary to your main job—can reignite your ability to enter flow, improve your work quality, and break through career plateaus.

What if the biggest obstacle to faster closings isn't underwriting… it's title?In this episode of Fintech Hunting, host Michael Hammond sits down with Meghan (Megan) F. Askin, Director of Lending Services at AFX Research, to unpack what's really changing in the title space in 2026—and why the future isn't “AI replaces humans,” but AI + humans in the loop.Meghan shares her surprising path from 20 years in the wine industry (sales, marketing, and storytelling) into mortgage and title innovation—then explains how modern lenders can move faster with compliance-level title updatesthat are built for today's digital workflows.Why title data is uniquely hard (hint: 3,600+ recording venues and zero standardization)What “human + AI hybrid” really looks like in real production workflowsHow AFX delivers structured title data (including JSON via integration) to make reports truly usableWhy structured, usable data matters more than “more data”How same-day title updates can reduce friction, shorten cycle times, and support smarter funding decisionsMeghan's practical approach to LinkedIn storytelling that builds trust before you ever meet in personSpeed matters. But in title, trust matters more. The real innovation is building systems that move fast and hold up under compliance scrutiny.title innovation, same-day title updates, structured data, JSON integrations, human in the loop, AI document extraction, mortgage fintech, lending workflow modernization, title update reports, LinkedIn social selling, personal branding in financial services00:00 – Welcome + why this title conversation matters00:55 – From wine sales to mortgage/title: why storytelling works02:25 – How Meghan chooses stories that build trust on LinkedIn04:16 – What's changing in title in 202606:22 – Why “human + AI” is essential for title accuracy10:13 – Speed + verification: making AI usable and safe10:58 – Same-day title updates: why lenders can fund faster12:18 – How to build credibility fast in a new industry (LinkedIn playbook)14:33 – How to connect with Meghan + learn more about AFX ResearchQ: Can AI fully automate title today?A: Not end-to-end. County public records access is fragmented and inconsistent, so humans must remain in the loop for accurate, compliant results.Q: Why is title still slow in many workflows?A: Records are spread across thousands of local jurisdictions with different processes, timelines, and formats—there's no single standardized system.Q: What does “structured data” mean in title?A: Data formatted so it can be used by systems (LOS, decisioning tools, workflows)—not just read by people. This episode covers why JSON delivery matters.Q: How do same-day title updates help lenders?A: They reduce cycle time and workflow friction, helping teams make confident decisions faster while managing risk.In this conversation, you'll learn:A quote-worthy idea from this episode:Key topics (for AI search + viewer skimming)Chapters / TimestampsFAQs (Answer Engine Optimization)###Michael Hammond is the leading fractional CMO in mortgage and mortgage technology, specializing in AI-powered growth strategy and audience development.

In this episode of the Crazy Wisdom Podcast, host Stewart Alsop sits down with Larry Swanson, a knowledge architect, community builder, and host of the Knowledge Graph Insights podcast. They explore the relationship between knowledge graphs and ontologies, why these technologies matter in the age of AI, and how symbolic AI complements the current wave of large language models. The conversation traces the history of neuro-symbolic AI from its origins at Dartmouth in 1956 through the semantic web vision of Tim Berners-Lee, examining why knowledge architecture remains underappreciated despite being deployed at major enterprises like Netflix, Amazon, and LinkedIn. Swanson explains how RDF (Resource Description Framework) enables both machines and humans to work with structured knowledge in ways that relational databases can't, while Alsop shares his journey from knowledge management director to understanding the practical necessity of ontologies for business operations. They discuss the philosophical roots of the field, the separation between knowledge management practitioners and knowledge engineers, and why startups often overlook these approaches until scale demands them. You can find Larry's podcast at KGI.fm or search for Knowledge Graph Insights on Spotify and YouTube.Timestamps00:00 Introduction to Knowledge Graphs and Ontologies01:09 The Importance of Ontologies in AI04:14 Philosophy's Role in Knowledge Management10:20 Debating the Relevance of RDF15:41 The Distinction Between Knowledge Management and Knowledge Engineering21:07 The Human Element in AI and Knowledge Architecture25:07 Startups vs. Enterprises: The Knowledge Gap29:57 Deterministic vs. Probabilistic AI32:18 The Marketing of AI: A Historical Perspective33:57 The Role of Knowledge Architecture in AI39:00 Understanding RDF and Its Importance44:47 The Intersection of AI and Human Intelligence50:50 Future Visions: AI, Ontologies, and Human BehaviorKey Insights1. Knowledge Graphs Combine Structure and Instances Through Ontological Design. A knowledge graph is built using an ontology that describes a specific domain you want to understand or work with. It includes both an ontological description of the terrain—defining what things exist and how they relate to one another—and instances of those things mapped to real-world data. This combination of abstract structure and concrete examples is what makes knowledge graphs powerful for discovery, question-answering, and enabling agentic AI systems. Not everyone agrees on the precise definition, but this understanding represents the practical approach most knowledge architects use when building these systems.2. Ontology Engineering Has Deep Philosophical Roots That Inform Modern Practice. The field draws heavily from classical philosophy, particularly ontology (the nature of what you know), epistemology (how you know what you know), and logic. These thousands-year-old philosophical frameworks provide the rigorous foundation for modern knowledge representation. Living in Heidelberg surrounded by philosophers, Swanson has discovered how much of knowledge graph work connects upstream to these philosophical roots. This philosophical grounding becomes especially important during times when institutional structures are collapsing, as we need to create new epistemological frameworks for civilization—knowledge management and ontology become critical tools for restructuring how we understand and organize information.3. The Semantic Web Vision Aimed to Transform the Internet Into a Distributed Database. Twenty-five years ago, Tim Berners-Lee, Jim Hendler, and Ora Lassila published a landmark article in Scientific American proposing the semantic web. While Berners-Lee had already connected documents across the web through HTML and HTTP, the semantic web aimed to connect all the data—essentially turning the internet into a giant database. This vision led to the development of RDF (Resource Description Framework), which emerged from DARPA research and provides the technical foundation for building knowledge graphs and ontologies. The origin story involved solving simple but important problems, like disambiguating whether "Cook" referred to a verb, noun, or a person's name at an academic conference.4. Symbolic AI and Neural Networks Represent Complementary Approaches Like Fast and Slow Thinking. Drawing on Kahneman's "thinking fast and slow" framework, LLMs represent the "fast brain"—learning monsters that can process enormous amounts of information and recognize patterns through natural language interfaces. Symbolic AI and knowledge graphs represent the "slow brain"—capturing actual knowledge and facts that can counter hallucinations and provide deterministic, explainable reasoning. This complementarity is driving the re-emergence of neuro-symbolic AI, which combines both approaches. The fundamental distinction is that symbolic AI systems are deterministic and can be fully explained, while LLMs are probabilistic and stochastic, making them unsuitable for applications requiring absolute reliability, such as industrial robotics or pharmaceutical research.5. Knowledge Architecture Remains Underappreciated Despite Powering Major Enterprises. While machine learning engineers currently receive most of the attention and budget, knowledge graphs actually power systems at Netflix (the economic graph), Amazon (the product graph), LinkedIn, Meta, and most major enterprises. The technology has been described as "the most astoundingly successful failure in the history of technology"—the semantic web vision seemed to fail, yet more than half of web pages now contain RDF-formatted semantic markup through schema.org, and every major enterprise uses knowledge graph technology in the background. Knowledge architects remain underappreciated partly because the work is cognitively difficult, requires talking to people (which engineers often avoid), and most advanced practitioners have PhDs in computer science, logic, or philosophy.6. RDF's Simple Subject-Predicate-Object Structure Enables Meaning and Data Linking. Unlike relational databases that store data in tables with rows and columns, RDF uses the simplest linguistic structure: subject-predicate-object (like "Larry knows Stuart"). Each element has a unique URI identifier, which permits precise meaning and enables linked data across systems. This graph structure makes it much easier to connect data after the fact compared to navigating tabular structures in relational databases. On top of RDF sits an entire stack of technologies including schema languages, query languages, ontological languages, and constraints languages—everything needed to turn data into actionable knowledge. The goal is inferring or articulating knowledge from RDF-structured data.7. The Future Requires Decoupled Modular Architectures Combining Multiple AI Approaches. The vision for the future involves separation of concerns through microservices-like architectures where different systems handle what they do best. LLMs excel at discovering possibilities and generating lists, while knowledge graphs excel at articulating human-vetted, deterministic versions of that information that systems can reliably use. Every one of Swanson's 300 podcast interviews over ten years ultimately concludes that regardless of technology, success comes down to human beings, their behavior, and the cultural changes needed to implement systems. The assumption that we can simply eliminate people from processes misses that huma...

Full Audio at https://podcasts.apple.com/us/podcast/the-hidden-economy-machine-customers-b2a-the-shadow/id1684415169?i=1000748466027

In this conversation, Jim Sammons and Rich Visotcky discuss various aspects of team dynamics, particularly focusing on Scrum Masters and the importance of engagement and interest in work. We explore the expectations placed on Scrum Masters, the significance of caring about the work being done, and how to navigate conflicts within teams around their work, tool choice, communication patterns, and more.Key highlights include:Interest in work drives better performance and outcomes.Navigating conflict requires understanding the context.Caring about the work leads to better results.Frustrations in work often stem from organizational decisions.Shifting mindsets from tool dependency to problem-solving is crucial.Different teams require different types of support and skills.Chapters00:00 Intro 00:16 Should Scrum Masters Be Interested? 01:23 Do Process People Need to Grasp The Backlog? 04:48 Do Care and Interest Go Hand in Hand? 08:14 Marker 4 08:14 Did the Creators of Agility Expect People to Care? 10:56 Do You Need to Know How the Sausage is Made? 13:41 The Legend of "Json the Troublemaker" 18:25 It's Not Them, It's You 21:39 The Way of Working 26:30 What Gets You Riled Up? 29:49 It Makes Sense, and it Makes Me Angry 34:52 The "User" Will See You Now 37:03 What Riles Rich Up? 42:35 Part of the Solution? 46:29 On the Next Episode... Connect with Product Fields:

Full Audio at https://podcasts.apple.com/us/podcast/marketing-to-machines-the-rise-of-b2a-the/id1684415169?i=1000747923047

Ben's been circling vibe coding for months, kept at bay by a simple fear: what if he spends more time fighting the AI over formatting than actually building anything? What if he has to bolt on linters and test runners just to babysit the output? Then his work handed him a Claude plan, and he decided it was finally time to take the plunge. And then something unsettling happened—the code looked like his code. Same line lengths. Same method ordering. Same obsessive formatting. Nobody told it to do that. It just... knew.Meanwhile, Adam has gone full mad scientist. His "Ralph" workflow runs Claude in a loop, feeding it tasks from a JSON file while he walks away to eat dinner. When he comes back, features are done. Tests pass. The machine just keeps building. It's the kind of setup that makes you wonder why you're still manually typing commands into a terminal.LinksAdam's Ralph Workflow for Claude Code - Adam's blog post with his implementationMatt Pocock's Ralph Primer Video - The workflow Adam adapted for automated iterative developmentAlgorithm Maze Race - Tim's vibe-coded game on itch.ioPro tip: Use /resume in Claude Code to return to prior sessionsFollow the show and be sure to join the discussion on Discord! Our website is workingcode.dev and we're @workingcode.dev on Bluesky. New episodes drop weekly on Thursday.And, if you're feeling the love, support us on Patreon.With audio editing and engineering by ZCross Media.Full show notes and transcript here.

Wes and Scott talk about building hyper-specific personal software with AI. They explore personal agents, home automation, JSON-as-a-database, and how LLMs unlock fast, custom apps that reduce friction and replace bloated SaaS. Show Notes 00:00 Welcome to Syntax! 01:53 What is personal software (and why it matters) 04:49 Using AI agents to build hyper-specific apps for yourself Clawdbot ClawdHub 13:43 Supercharging your dev workflow with Tailscale 19:06 Privacy when working with LLMs MLX-Audio 21:39 Brought to you by Sentry.io 22:21 Real-world personal app ideas 39:14 Sick Picks + Shameless Plugs Sick Picks Scott: FTPManager Wes: Roku Streaming Stick Shameless Plugs Syntax YouTube Channel Hit us up on Socials! Syntax: X Instagram Tiktok LinkedIn Threads Wes: X Instagram Tiktok LinkedIn Threads Scott: X Instagram Tiktok LinkedIn Threads Randy: X Instagram YouTube Threads

A new year, a new tactic to stem the flow of npm supply chain attacks. This time, the proposal is for “Staged Publishing” which introduces a review window that a package owner must approve before a package release becomes publicly available.Vercel Labs is out with a new AI tool called json-render that lets users generate dashboards, widgets, apps, and data visualizations from prompts, and constrains the response to JSON for only components pre-defined by the user.Finally, The Simpsons character Ralph Wiggum is the biggest thing in AI coding recently. Ralph is an AI development pattern using a while loop (like a bash script) to repeatedly run an AI agent on the same task, feeding its output back as context, and forcing it to iterate until a specific completion promise is met, rather than letting the AI stop prematurely. Interesting? Yes! Useful in practice? Still tbd.Chapter Markers:1:04 - npm to implement staged publishing8:16 - Vercel's json-render16:13 - Ralph Wiggum loops30:53 - Claude Code tips33:52 - Firefox 147 and anchor positioning35:42 - Fire starter39:40 - What's making us happyNews:Paige - npm to implement Staged PublishingJack - Vercel Labs' json-renderTJ - Ralph Wiggum loopsLightning News: Firefox 147 and anchor positioningFire Starters:CSS @container scroll-state()What Makes Us Happy this Week:Paige - Rolife Book Nook Garden House miniatureJack - Blade Runner live at the symphonyTJ - Coffee advent calendarsThanks as always to our sponsor, the Blue Collar Coder channel on YouTube. You can join us in our Discord channel, explore our website and reach us via email, or talk to us on X, Bluesky, or YouTube.Front-end Fire websiteBlue Collar Coder on YouTubeBlue Collar Coder on DiscordReach out via emailTweet at us on X @front_end_fireFollow us on Bluesky @front-end-fire.comSubscribe to our YouTube channel @Front-EndFirePodcast

De retour à cinq dans l'épisode, les cast codeurs démarrent cette année avec un gros épisode pleins de news et d'articles de fond. IA bien sûr, son impact sur les pratiques, Mockito qui tourne un page, du CSS (et oui), sur le (non) mapping d'APIs REST en MCP et d'une palanquée d'outils pour vous. Enregistré le 9 janvier 2026 Téléchargement de l'épisode LesCastCodeurs-Episode-335.mp3 ou en vidéo sur YouTube. News Langages 2026 sera-t'elle l'année de Java dans le terminal ? (j'ai ouïe dire que ça se pourrait bien…) https://xam.dk/blog/lets-make-2026-the-year-of-java-in-the-terminal/ 2026: Année de Java dans le terminal, pour rattraper son retard sur Python, Rust, Go et Node.js. Java est sous-estimé pour les applications CLI et les TUIs (interfaces utilisateur terminales) malgré ses capacités. Les anciennes excuses (démarrage lent, outillage lourd, verbosité, distribution complexe) sont obsolètes grâce aux avancées récentes : GraalVM Native Image pour un démarrage en millisecondes. JBang pour l'exécution simplifiée de scripts Java (fichiers uniques, dépendances) et de JARs. JReleaser pour l'automatisation de la distribution multi-plateforme (Homebrew, SDKMAN, Docker, images natives). Project Loom pour la concurrence facile avec les threads virtuels. PicoCLI pour la gestion des arguments. Le potentiel va au-delà des scripts : création de TUIs complètes et esthétiques (ex: dashboards, gestionnaires de fichiers, assistants IA). Excuses caduques : démarrage rapide (GraalVM), légèreté (JBang), distribution simple (JReleaser), concurrence (Loom). Potentiel : créer des applications TUI riches et esthétiques. Sortie de Ruby 4.0.0 https://www.ruby-lang.org/en/news/2025/12/25/ruby-4-0-0-released/ Ruby Box (expérimental) : Une nouvelle fonctionnalité permettant d'isoler les définitions (classes, modules, monkey patches) dans des boîtes séparées pour éviter les conflits globaux. ZJIT : Un nouveau compilateur JIT de nouvelle génération développé en Rust, visant à surpasser YJIT à terme (actuellement en phase expérimentale). Améliorations de Ractor : Introduction de Ractor::Port pour une meilleure communication entre Ractors et optimisation des structures internes pour réduire les contentions de verrou global. Changements syntaxiques : Les opérateurs logiques (||, &&, and, or) en début de ligne permettent désormais de continuer la ligne précédente, facilitant le style "fluent". Classes Core : Set et Pathname deviennent des classes intégrées (Core) au lieu d'être dans la bibliothèque standard. Diagnostics améliorés : Les erreurs d'arguments (ArgumentError) affichent désormais des extraits de code pour l'appelant ET la définition de la méthode. Performances : Optimisation de Class#new, accès plus rapide aux variables d'instance et améliorations significatives du ramasse-miettes (GC). Nettoyage : Suppression de comportements obsolètes (comme la création de processus via IO.open avec |) et mise à jour vers Unicode 17.0. Librairies Introduction pour créer une appli multi-tenant avec Quarkus et http://nip.io|nip.io https://www.the-main-thread.com/p/quarkus-multi-tenant-api-nipio-tutorial Construction d'une API REST multi-tenant en Quarkus avec isolation par sous-domaine Utilisation de http://nip.io|nip.io pour la résolution DNS automatique sans configuration locale Extraction du tenant depuis l'en-tête HTTP Host via un filtre JAX-RS Contexte tenant géré avec CDI en scope Request pour l'isolation des données Service applicatif gérant des données spécifiques par tenant avec Map concurrent Interface web HTML/JS pour visualiser et ajouter des données par tenant Configuration CORS nécessaire pour le développement local Pattern acme.127-0-0-1.nip.io résolu automatiquement vers localhost Code complet disponible sur GitHub avec exemples curl et tests navigateur Base idéale pour prototypage SaaS, tests multi-tenants Hibernate 7.2 avec quelques améliorations intéressantes https://docs.hibernate.org/orm/7.2/whats-new/%7Bhtml-meta-canonical-link%7D read only replica (experimental), crée deux session factories et swap au niveau jdbc si le driver le supporte et custom sinon. On ouvre une session en read only child statelesssession (partage le contexte transactionnel) hibernate vector module ajouter binary, float16 and sparse vectors Le SchemaManager peut resynchroniser les séquences par rapport aux données des tables Regexp dans HQL avec like Nouvelle version de Hibernate with Panache pour Quarkus https://quarkus.io/blog/hibernate-panache-next/ Nouvelle extension expérimentale qui unifie Hibernate ORM with Panache et Hibernate Reactive with Panache Les entités peuvent désormais fonctionner en mode bloquant ou réactif sans changer de type de base Support des sessions sans état (StatelessSession) en plus des entités gérées traditionnelles Intégration de Jakarta Data pour des requêtes type-safe vérifiées à la compilation Les opérations sont définies dans des repositories imbriqués plutôt que des méthodes statiques Possibilité de définir plusieurs repositories pour différents modes d'opération sur une même entité Accès aux différents modes (bloquant/réactif, géré/sans état) via des méthodes de supertype Support des annotations @Find et @HQL pour générer des requêtes type-safe Accès au repository via injection ou via le métamodèle généré Extension disponible dans la branche main, feedback demandé sur Zulip ou GitHub Spring Shell 4.0.0 GA publié - https://spring.io/blog/2025/12/30/spring-shell-4-0-0-ga-released Sortie de la version finale de Spring Shell 4.0.0 disponible sur Maven Central Compatible avec les dernières versions de Spring Framework et Spring Boot Modèle de commandes revu pour simplifier la création d'applications CLI interactives Intégration de jSpecify pour améliorer la sécurité contre les NullPointerException Architecture plus modulaire permettant meilleure personnalisation et extension Documentation et exemples entièrement mis à jour pour faciliter la prise en main Guide de migration vers la v4 disponible sur le wiki du projet Corrections de bugs pour améliorer la stabilité et la fiabilité Permet de créer des applications Java autonomes exécutables avec java -jar ou GraalVM native Approche opinionnée du développement CLI tout en restant flexible pour les besoins spécifiques Une nouvelle version de la librairie qui implémenter des gatherers supplémentaires à ceux du JDK https://github.com/tginsberg/gatherers4j/releases/tag/v0.13.0 gatherers4j v0.13.0. Nouveaux gatherers : uniquelyOccurringBy(), moving/runningMedian(), moving/runningMax/Min(). Changement : les gatherers "moving" incluent désormais par défaut les valeurs partielles (utiliser excludePartialValues() pour désactiver). LangChain4j 1.10.0 https://github.com/langchain4j/langchain4j/releases/tag/1.10.0 Introduction d'un catalogue de modèles pour Anthropic, Gemini, OpenAI et Mistral. Ajout de capacités d'observabilité et de monitoring pour les agents. Support des sorties structurées, des outils avancés et de l'analyse de PDF via URL pour Anthropic. Support des services de transcription pour OpenAI. Possibilité de passer des paramètres de configuration de chat en argument des méthodes. Nouveau garde-fou de modération pour les messages entrants. Support du contenu de raisonnement pour les modèles. Introduction de la recherche hybride. Améliorations du client MCP. Départ du lead de mockito après 10 ans https://github.com/mockito/mockito/issues/3777 Tim van der Lippe, mainteneur majeur de Mockito, annonce son départ pour mars 2026, marquant une décennie de contribution au projet. L'une des raisons principales est l'épuisement lié aux changements récents dans la JVM (JVM 22+) concernant les agents, imposant des contraintes techniques lourdes sans alternative simple proposée par les mainteneurs du JDK. Il pointe du doigt le manque de soutien et la pression exercée sur les bénévoles de l'open source lors de ces transitions technologiques majeures. La complexité croissante pour supporter Kotlin, qui utilise la JVM de manière spécifique, rend la base de code de Mockito plus difficile à maintenir et moins agréable à faire évoluer selon lui. Il exprime une perte de plaisir et préfère désormais consacrer son temps libre à d'autres projets comme Servo, un moteur web écrit en Rust. Une période de transition est prévue jusqu'en mars pour assurer la passation de la maintenance à de nouveaux contributeurs. Infrastructure Le premier intérêt de Kubernetes n'est pas le scaling - https://mcorbin.fr/posts/2025-12-29-kubernetes-scale/ Avant Kubernetes, gérer des applications en production nécessitait de multiples outils complexes (Ansible, Puppet, Chef) avec beaucoup de configuration manuelle Le load balancing se faisait avec HAProxy et Keepalived en actif/passif, nécessitant des mises à jour manuelles de configuration à chaque changement d'instance Le service discovery et les rollouts étaient orchestrés manuellement, instance par instance, sans automatisation de la réconciliation Chaque stack (Java, Python, Ruby) avait sa propre méthode de déploiement, sans standardisation (rpm, deb, tar.gz, jar) La gestion des ressources était manuelle avec souvent une application par machine, créant du gaspillage et complexifiant la maintenance Kubernetes standardise tout en quelques ressources YAML (Deployment, Service, Ingress, ConfigMap, Secret) avec un format déclaratif simple Toutes les fonctionnalités critiques sont intégrées : service discovery, load balancing, scaling, stockage, firewalling, logging, tolérance aux pannes La complexité des centaines de scripts shell et playbooks Ansible maintenus avant était supérieure à celle de Kubernetes Kubernetes devient pertinent dès qu'on commence à reconstruire manuellement ces fonctionnalités, ce qui arrive très rapidement La technologie est flexible et peut gérer aussi bien des applications modernes que des monolithes legacy avec des contraintes spécifiques Mole https://github.com/tw93/Mole Un outil en ligne de commande (CLI) tout-en-un pour nettoyer et optimiser macOS. Combine les fonctionnalités de logiciels populaires comme CleanMyMac, AppCleaner, DaisyDisk et iStat Menus. Analyse et supprime en profondeur les caches, les fichiers logs et les résidus de navigateurs. Désinstallateur intelligent qui retire proprement les applications et leurs fichiers cachés (Launch Agents, préférences). Analyseur d'espace disque interactif pour visualiser l'occupation des fichiers et gérer les documents volumineux. Tableau de bord temps réel (mo status) pour surveiller le CPU, le GPU, la mémoire et le réseau. Fonction de purge spécifique pour les développeurs permettant de supprimer les artefacts de build (node_modules, target, etc.). Intégration possible avec Raycast ou Alfred pour un lancement rapide des commandes. Installation simple via Homebrew ou un script curl. Des images Docker sécurisées pour chaque développeur https://www.docker.com/blog/docker-hardened-images-for-every-developer/ Docker rend ses "Hardened Images" (DHI) gratuites et open source (licence Apache 2.0) pour tous les développeurs. Ces images sont conçues pour être minimales, prêtes pour la production et sécurisées dès le départ afin de lutter contre l'explosion des attaques sur la chaîne logistique logicielle. Elles s'appuient sur des bases familières comme Alpine et Debian, garantissant une compatibilité élevée et une migration facile. Chaque image inclut un SBOM (Software Bill of Materials) complet et vérifiable, ainsi qu'une provenance SLSA de niveau 3 pour une transparence totale. L'utilisation de ces images permet de réduire considérablement le nombre de vulnérabilités (CVE) et la taille des images (jusqu'à 95 % plus petites). Docker étend cette approche sécurisée aux graphiques Helm et aux serveurs MCP (Mongo, Grafana, GitHub, etc.). Des offres commerciales (DHI Enterprise) restent disponibles pour des besoins spécifiques : correctifs critiques sous 7 jours, support FIPS/FedRAMP ou support à cycle de vie étendu (ELS). Un assistant IA expérimental de Docker peut analyser les conteneurs existants pour recommander l'adoption des versions sécurisées correspondantes. L'initiative est soutenue par des partenaires majeurs tels que Google, MongoDB, Snyk et la CNCF. Web La maçonnerie ("masonry") arrive dans la spécification des CSS et commence à être implémentée par les navigateurs https://webkit.org/blog/17660/introducing-css-grid-lanes/ Permet de mettre en colonne des éléments HTML les uns à la suite des autres. D'abord sur la première ligne, et quand la première ligne est remplie, le prochain élément se trouvera dans la colonne où il pourra être le plus haut possible, et ainsi de suite. après la plomberie du middleware, la maçonnerie du front :laughing: Data et Intelligence Artificielle On ne devrait pas faire un mapping 1:1 entre API REST et MCP https://nordicapis.com/why-mcp-shouldnt-wrap-an-api-one-to-one/ Problématique : Envelopper une API telle quelle dans le protocole MCP (Model Context Protocol) est un anti-pattern. Objectif du MCP : Conçu pour les agents d'IA, il doit servir d'interface d'intention, non de miroir d'API. Les agents comprennent les tâches, pas la logique complexe des API (authentification, pagination, orchestration). Conséquences du mappage un-à-un : Confusion des agents, erreurs, hallucinations. Difficulté à gérer les orchestrations complexes (plusieurs appels pour une seule action). Exposition des faiblesses de l'API (schéma lourd, endpoints obsolètes). Maintenance accrue lors des changements d'API. Meilleure approche : Construire des outils MCP comme des SDK pour agents, encapsulant la logique nécessaire pour accomplir une tâche spécifique. Pratiques recommandées : Concevoir autour des intentions/actions utilisateur (ex. : "créer un projet", "résumer un document"). Regrouper les appels en workflows ou actions uniques. Utiliser un langage naturel pour les définitions et les noms. Limiter la surface d'exposition de l'API pour la sécurité et la clarté. Appliquer des schémas d'entrée/sortie stricts pour guider l'agent et réduire l'ambiguïté. Des agents en production avec AWS - https://blog.ippon.fr/2025/12/22/des-agents-en-production-avec-aws/ AWS re:Invent 2025 a massivement mis en avant l'IA générative et les agents IA Un agent IA combine un LLM, une boucle d'appel et des outils invocables Strands Agents SDK facilite le prototypage avec boucles ReAct intégrées et gestion de la mémoire Managed MLflow permet de tracer les expérimentations et définir des métriques de performance Nova Forge optimise les modèles par réentraînement sur données spécifiques pour réduire coûts et latence Bedrock Agent Core industrialise le déploiement avec runtime serverless et auto-scaling Agent Core propose neuf piliers dont observabilité, authentification, code interpreter et browser managé Le protocole MCP d'Anthropic standardise la fourniture d'outils aux agents SageMaker AI et Bedrock centralisent l'accès aux modèles closed source et open source via API unique AWS mise sur l'évolution des chatbots vers des systèmes agentiques optimisés avec modèles plus frugaux Debezium 3.4 amène plusieurs améliorations intéressantes https://debezium.io/blog/2025/12/16/debezium-3-4-final-released/ Correction du problème de calcul du low watermark Oracle qui causait des pertes de performance Correction de l'émission des événements heartbeat dans le connecteur Oracle avec les requêtes CTE Amélioration des logs pour comprendre les transactions actives dans le connecteur Oracle Memory guards pour protéger contre les schémas de base de données de grande taille Support de la transformation des coordonnées géométriques pour une meilleure gestion des données spatiales Extension Quarkus DevServices permettant de démarrer automatiquement une base de données et Debezium en dev Intégration OpenLineage pour tracer la lignée des données et suivre leur flux à travers les pipelines Compatibilité testée avec Kafka Connect 4.1 et Kafka brokers 4.1 Infinispan 16.0.4 et .5 https://infinispan.org/blog/2025/12/17/infinispan-16-0-4 Spring Boot 4 et Spring 7 supportés Evolution dans les metriques Deux bugs de serialisation Construire un agent de recherche en Java avec l'API Interactions https://glaforge.dev/posts/2026/01/03/building-a-research-assistant-with-the-interactions-api-in-java/ Assistant de recherche IA Java (API Interactions Gemini), test du SDK implémenté par Guillaume. Workflow en 4 phases : Planification : Gemini Flash + Google Search. Recherche : Modèle "Deep Research" (tâche de fond). Synthèse : Gemini Pro (rapport exécutif). Infographie : Nano Banana Pro (à partir de la synthèse). API Interactions : gestion d'état serveur, tâches en arrière-plan, réponses multimodales (images). Appréciation : gestion d'état de l'API (vs LLM sans état). Validation : efficacité du SDK Java pour cas complexes. Stephan Janssen (le papa de Devoxx) a créé un serveur MCP (Model Context Protocol) basé sur LSP (Language Server Protocol) pour que les assistants de code analysent le code en le comprenant vraiment plutôt qu'en faisant des grep https://github.com/stephanj/LSP4J-MCP Le problème identifié : Les assistants IA utilisent souvent la recherche textuelle (type grep) pour naviguer dans le code, ce qui manque de contexte sémantique, génère du bruit (faux positifs) et consomme énormément de tokens inutilement. La solution LSP4J-MCP : Une approche "standalone" (autonome) qui encapsule le serveur de langage Eclipse (JDTLS) via le protocole MCP (Model Context Protocol). Avantage principal : Offre une compréhension sémantique profonde du code Java (types, hiérarchies, références) sans nécessiter l'ouverture d'un IDE lourd comme IntelliJ. Comparaison des méthodes : AST : Trop léger (pas de compréhension inter-fichiers). IntelliJ MCP : Puissant mais exige que l'IDE soit ouvert (gourmand en ressources). LSP4J-MCP : Le meilleur des deux mondes pour les workflows en terminal, à distance (SSH) ou CI/CD. Fonctionnalités clés : Expose 5 outils pour l'IA (find_symbols, find_references, find_definition, document_symbols, find_interfaces_with_method). Résultats : Une réduction de 100x des tokens utilisés pour la navigation et une précision accrue (distinction des surcharges, des scopes, etc.). Disponibilité : Le projet est open source et disponible sur GitHub pour intégration immédiate (ex: avec Claude Code, Gemini CLI, etc). A noter l'ajout dans claude code 2.0.74 d'un tool pour supporter LSP ( https://github.com/anthropics/claude-code/blob/main/CHANGELOG.md#2074 ) Awesome (GitHub) Copilot https://github.com/github/awesome-copilot Une collection communautaire d'instructions, de prompts et de configurations pour optimiser l'utilisation de GitHub Copilot. Propose des "Agents" spécialisés qui s'intègrent aux serveurs MCP pour améliorer les flux de travail spécifiques. Inclut des prompts ciblés pour la génération de code, la documentation et la résolution de problèmes complexes. Fournit des instructions détaillées sur les standards de codage et les meilleures pratiques applicables à divers frameworks. Propose des "Skills" (compétences) sous forme de dossiers contenant des ressources pour des tâches techniques spécialisées. (les skills sont dispo dans copilot depuis un mois : https://github.blog/changelog/2025-12-18-github-copilot-now-supports-agent-skills/ ) Permet une installation facile via un serveur MCP dédié, compatible avec VS Code et Visual Studio. Encourage la contribution communautaire pour enrichir les bibliothèques de prompts et d'agents. Aide à augmenter la productivité en offrant des solutions pré-configurées pour de nombreux langages et domaines. Garanti par une licence MIT et maintenu activement par des contributeurs du monde entier. IA et productivité : bilan de l'année 2025 (Laura Tacho - DX)) https://newsletter.getdx.com/p/ai-and-productivity-year-in-review?aid=recNfypKAanQrKszT En 2025, l'ingénierie assistée par l'IA est devenue la norme : environ 90 % des développeurs utilisent des outils d'IA mensuellement, et plus de 40 % quotidiennement. Les chercheurs (Microsoft, Google, GitHub) soulignent que le nombre de lignes de code (LOC) reste un mauvais indicateur d'impact, car l'IA génère beaucoup de code sans forcément garantir une valeur métier supérieure. Si l'IA améliore l'efficacité individuelle, elle pourrait nuire à la collaboration à long terme, car les développeurs passent plus de temps à "parler" à l'IA qu'à leurs collègues. L'identité du développeur évolue : il passe de "producteur de code" à un rôle de "metteur en scène" qui délègue, valide et exerce son jugement stratégique. L'IA pourrait accélérer la montée en compétences des développeurs juniors en les forçant à gérer des projets et à déléguer plus tôt, agissant comme un "accélérateur" plutôt que de les rendre obsolètes. L'accent est mis sur la créativité plutôt que sur la simple automatisation, afin de réimaginer la manière de travailler et d'obtenir des résultats plus impactants. Le succès en 2026 dépendra de la capacité des entreprises à cibler les goulots d'étranglement réels (dette technique, documentation, conformité) plutôt que de tester simplement chaque nouveau modèle d'IA. La newsletter avertit que les titres de presse simplifient souvent à l'excès les recherches sur l'IA, masquant parfois les nuances cruciales des études réelles. Un développeur décrit dans un article sur Twitter son utilisation avancée de Claude Code pour le développement, avec des sous-agents, des slash-commands, comment optimiser le contexte, etc. https://x.com/AureaLibe/status/2008958120878330329?s=20 Outillage IntelliJ IDEA, thread dumps et project Loom (virtual threads) - https://blog.jetbrains.com/idea/2025/12/thread-dumps-and-project-loom-virtual-threads/ Les virtual threads Java améliorent l'utilisation du matériel pour les opérations I/O parallèles avec peu de changements de code Un serveur peut maintenant gérer des millions de threads au lieu de quelques centaines Les outils existants peinent à afficher et analyser des millions de threads simultanément Le débogage asynchrone est complexe car le scheduler et le worker s'exécutent dans des threads différents Les thread dumps restent essentiels pour diagnostiquer deadlocks, UI bloquées et fuites de threads Netflix a découvert un deadlock lié aux virtual threads en analysant un heap dump, bug corrigé dans Java 25. Mais c'était de la haute voltige IntelliJ IDEA supporte nativement les virtual threads dès leur sortie avec affichage des locks acquis IntelliJ IDEA peut ouvrir des thread dumps générés par d'autres outils comme jcmd Le support s'étend aussi aux coroutines Kotlin en plus des virtual threads Quelques infos sur IntelliJ IDEA 2025.3 https://blog.jetbrains.com/idea/2025/12/intellij-idea-2025-3/ Distribution unifiée regroupant davantage de fonctionnalités gratuites Amélioration de la complétion des commandes dans l'IDE Nouvelles fonctionnalités pour le débogueur Spring Thème Islands devient le thème par défaut Support complet de Spring Boot 4 et Spring Framework 7 Compatibilité avec Java 25 Prise en charge de Spring Data JDBC et Vitest 4 Support natif de Junie et Claude Agent pour l'IA Quota d'IA transparent et option Bring Your Own Key à venir Corrections de stabilité, performance et expérience utilisateur Plein de petits outils en ligne pour le développeur https://blgardner.github.io/prism.tools/ génération de mot de passe, de gradient CSS, de QR code encodage décodage de Base64, JWT formattage de JSON, etc. resumectl - Votre CV en tant que code https://juhnny5.github.io/resumectl/ Un outil en ligne de commande (CLI) écrit en Go pour générer un CV à partir d'un fichier YAML. Permet l'exportation vers plusieurs formats : PDF, HTML, ou un affichage direct dans le terminal. Propose 5 thèmes intégrés (Modern, Classic, Minimal, Elegant, Tech) personnalisables avec des couleurs spécifiques. Fonctionnalité d'initialisation (resumectl init) permettant d'importer automatiquement des données depuis LinkedIn et GitHub (projets les plus étoilés). Supporte l'ajout de photos avec des options de filtre noir et blanc ou de forme (rond/carré). Inclut un mode "serveur" (resumectl serve) pour prévisualiser les modifications en temps réel via un navigateur local. Fonctionne comme un binaire unique sans dépendances externes complexes pour les modèles. mactop - Un moniteur "top" pour Apple Silicon https://github.com/metaspartan/mactop Un outil de surveillance en ligne de commande (TUI) conçu spécifiquement pour les puces Apple Silicon (M1, M2, M3, M4, M5). Permet de suivre en temps réel l'utilisation du CPU (E-cores et P-cores), du GPU et de l'ANE (Neural Engine). Affiche la consommation électrique (wattage) du système, du CPU, du GPU et de la DRAM. Fournit des données sur les températures du SoC, les fréquences du GPU et l'état thermique global. Surveille l'utilisation de la mémoire vive, de la swap, ainsi que l'activité réseau et disque (E/S). Propose 10 mises en page (layouts) différentes et plusieurs thèmes de couleurs personnalisables. Ne nécessite pas l'utilisation de sudo car il s'appuie sur les API natives d'Apple (SMC, IOReport, IOKit). Inclut une liste de processus détaillée (similaire à htop) avec la possibilité de tuer des processus directement depuis l'interface. Offre un mode "headless" pour exporter les métriques au format JSON et un serveur optionnel pour Prometheus. Développé en Go avec des composants en CGO et Objective-C. Adieu direnv, Bonjour misehttps://codeka.io/2025/12/19/adieu-direnv-bonjour-mise/ L'auteur remplace ses outils habituels (direnv, asdf, task, just) par un seul outil polyvalent écrit en Rust : mise. mise propose trois fonctions principales : gestionnaire de paquets (langages et outils), gestionnaire de variables d'environnement et exécuteur de tâches. Contrairement à direnv, il permet de gérer des alias et utilise un fichier de configuration structuré (mise.toml) plutôt que du scripting shell. La configuration est hiérarchique, permettant de surcharger les paramètres selon les répertoires, avec un système de "trust" pour la sécurité. Une "killer-feature" soulignée est la gestion des secrets : mise s'intègre avec age pour chiffrer des secrets (via clés SSH) directement dans le fichier de configuration. L'outil supporte une vaste liste de langages et d'outils via un registre interne et des plugins (compatibilité avec l'écosystème asdf). Il simplifie le workflow de développement en regroupant l'installation des outils et l'automatisation des tâches au sein d'un même fichier. L'auteur conclut sur la puissance, la flexibilité et les excellentes performances de l'outil après quelques heures de test. Claude Code v2.1.0 https://github.com/anthropics/claude-code/blob/main/CHANGELOG.md#210 Rechargement à chaud des "skills" : Les modifications apportées aux compétences dans ~/.claude/skills sont désormais appliquées instantanément sans redémarrer la session. Sous-agents et forks : Support de l'exécution de compétences et de commandes slash dans un contexte de sous-agent forké via context: fork. Réglages linguistiques : Ajout d'un paramètre language pour configurer la langue de réponse par défaut (ex: language: "french"). Améliorations du terminal : Shift+Enter fonctionne désormais nativement dans plusieurs terminaux (iTerm2, WezTerm, Ghostty, Kitty) sans configuration manuelle. Sécurité et correction de bugs : Correction d'une faille où des données sensibles (clés API, tokens OAuth) pouvaient apparaître dans les logs de débogage. Nouvelles commandes slash : Ajout de /teleport et /remote-env pour les abonnés claude.ai afin de gérer des sessions distantes. Mode Plan : Le raccourci /plan permet d'activer le mode plan directement depuis le prompt, et la demande de permission à l'entrée de ce mode a été supprimée. Vim et navigation : Ajout de nombreux mouvements Vim (text objects, répétitions de mouvements f/F/t/T, indentations, etc.). Performance : Optimisation du temps de démarrage et du rendu terminal pour les caractères Unicode/Emoji. Gestion du gitignore : Support du réglage respectGitignore dans settings.json pour contrôler le comportement du sélecteur de fichiers @-mention. Méthodologies 200 déploiements en production par jour, même le vendredi : retours d'expérience https://mcorbin.fr/posts/2025-03-21-deploy-200/ Le déploiement fréquent, y compris le vendredi, est un indicateur de maturité technique et augmente la productivité globale. L'excellence technique est un atout stratégique indispensable pour livrer rapidement des produits de qualité. Une architecture pragmatique orientée services (SOA) facilite les déploiements indépendants et réduit la charge cognitive. L'isolation des services est cruciale : un développeur doit pouvoir tester son service localement sans dépendre de toute l'infrastructure. L'automatisation via Kubernetes et l'approche GitOps avec ArgoCD permettent des déploiements continus et sécurisés. Les feature flags et un système de permissions solide permettent de découpler le déploiement technique de l'activation fonctionnelle pour les utilisateurs. L'autonomie des développeurs est renforcée par des outils en self-service (CLI maison) pour gérer l'infrastructure et diagnostiquer les incidents sans goulot d'étranglement. Une culture d'observabilité intégrée dès la conception permet de détecter et de réagir rapidement aux anomalies en production. Accepter l'échec comme inévitable permet de concevoir des systèmes plus résilients capables de se rétablir automatiquement. "Vibe Coding" vs "Prompt Engineering" : l'IA et le futur du développement logiciel https://www.romenrg.com/blog/2025/12/25/vibe-coding-vs-prompt-engineering-ai-and-the-future-of-software-development/ L'IA est passée du statut d'expérimentation à celui d'infrastructure essentielle pour le développement de logiciels en 2025. L'IA ne remplace pas les ingénieurs, mais agit comme un amplificateur de leurs compétences, de leur jugement et de la qualité de leur réflexion. Distinction entre le "Vibe Coding" (rapide, intuitif, idéal pour les prototypes) et le "Prompt Engineering" (délibéré, contraint, nécessaire pour les systèmes maintenables). L'importance cruciale du contexte ("Context Engineering") : l'IA devient réellement puissante lorsqu'elle est connectée aux systèmes réels (GitHub, Jira, etc.) via des protocoles comme le MCP. Utilisation d'agents spécialisés (écriture de RFC, revue de code, architecture) plutôt que de modèles génériques pour obtenir de meilleurs résultats. Émergence de l'ingénieur "Technical Product Manager" capable d'abattre seul le travail d'une petite équipe grâce à l'IA, à condition de maîtriser les fondamentaux techniques. Le risque majeur : l'IA permet d'aller très vite dans la mauvaise direction si le jugement humain et l'expérience font défaut. Le niveau d'exigence global augmente : les bases techniques solides deviennent plus importantes que jamais pour éviter l'accumulation de dette technique rapide. Une revue de code en solo (Kent Beck) ! https://tidyfirst.substack.com/p/party-of-one-for-code-review?r=64ov3&utm_campaign=post&utm_medium=web&triedRedirect=true La revue de code traditionnelle, héritée des inspections formelles d'IBM, s'essouffle car elle est devenue trop lente et asynchrone par rapport au rythme du développement moderne. Avec l'arrivée de l'IA ("le génie"), la vitesse de production du code dépasse la capacité de relecture humaine, créant un goulot d'étranglement majeur. La revue de code doit évoluer vers deux nouveaux objectifs prioritaires : un "sanity check" pour vérifier que l'IA a bien fait ce qu'on lui demandait, et le contrôle de la dérive structurelle de la base de code. Maintenir une structure saine est crucial non seulement pour les futurs développeurs humains, mais aussi pour que l'IA puisse continuer à comprendre et modifier le code efficacement sans perdre le contexte. Kent Beck expérimente des outils automatisés (comme CodeRabbit) pour obtenir des résumés et des schémas d'architecture afin de garder une conscience globale des changements rapides. Même si les outils automatisés sont utiles, le "Pair Programming" reste irremplaçable pour la richesse des échanges et la pression sociale bénéfique qu'il impose à la réflexion. La revue de code solo n'est pas une fin en soi, mais une adaptation nécessaire lorsque l'on travaille seul avec des outils de génération de code augmentés. Loi, société et organisation Lego lance les Lego Smart Play, avec des Brique, des Smart Tags et des Smart Figurines pour faire de nouvelles constructions interactives avec des Legos https://www.lego.com/fr-fr/smart-play LEGO SMART Play : technologie réactive au jeu des enfants. Trois éléments clés : SMART Brique : Brique LEGO 2x4 "cerveau". Accéléromètre, lumières réactives, détecteur de couleurs, synthétiseur sonore. Réagit aux mouvements (tenir, tourner, taper). SMART Tags : Petites pièces intelligentes. Indiquent à la SMART Brique son rôle (ex: hélicoptère, voiture) et les sons à produire. Activent sons, mini-jeux, missions secrètes. SMART Minifigurines : Activées près d'une SMART Brique. Révèlent des personnalités uniques (sons, humeurs, réactions) via la SMART Brique. Encouragent l'imagination. Fonctionnement : SMART Brique détecte SMART Tags et SMART Minifigurines. Réagit aux mouvements avec lumières et sons dynamiques. Compatibilité : S'assemble avec les briques LEGO classiques. Objectif : Créer des expériences de jeu interactives, uniques et illimitées. Conférences La liste des conférences provenant de Developers Conferences Agenda/List par Aurélie Vache et contributeurs : 14-17 janvier 2026 : SnowCamp 2026 - Grenoble (France) 22 janvier 2026 : DevCon #26 : sécurité / post-quantique / hacking - Paris (France) 28 janvier 2026 : Software Heritage Symposium - Paris (France) 29-31 janvier 2026 : Epitech Summit 2026 - Paris - Paris (France) 2-5 février 2026 : Epitech Summit 2026 - Moulins - Moulins (France) 3 février 2026 : Cloud Native Days France 2026 - Paris (France) 3-4 février 2026 : Epitech Summit 2026 - Lille - Lille (France) 3-4 février 2026 : Epitech Summit 2026 - Mulhouse - Mulhouse (France) 3-4 février 2026 : Epitech Summit 2026 - Nancy - Nancy (France) 3-4 février 2026 : Epitech Summit 2026 - Nantes - Nantes (France) 3-4 février 2026 : Epitech Summit 2026 - Marseille - Marseille (France) 3-4 février 2026 : Epitech Summit 2026 - Rennes - Rennes (France) 3-4 février 2026 : Epitech Summit 2026 - Montpellier - Montpellier (France) 3-4 février 2026 : Epitech Summit 2026 - Strasbourg - Strasbourg (France) 3-4 février 2026 : Epitech Summit 2026 - Toulouse - Toulouse (France) 4-5 février 2026 : Epitech Summit 2026 - Bordeaux - Bordeaux (France) 4-5 février 2026 : Epitech Summit 2026 - Lyon - Lyon (France) 4-6 février 2026 : Epitech Summit 2026 - Nice - Nice (France) 5 février 2026 : Web Days Convention - Aix-en-Provence (France) 12 février 2026 : Strasbourg Craft #1 - Strasbourg (France) 12-13 février 2026 : Touraine Tech #26 - Tours (France) 19 février 2026 : ObservabilityCON on the Road - Paris (France) 6 mars 2026 : WordCamp Nice 2026 - Nice (France) 18-19 mars 2026 : Agile Niort 2026 - Niort (France) 20 mars 2026 : Atlantique Day 2026 - Nantes (France) 26 mars 2026 : Data Days Lille - Lille (France) 26-27 mars 2026 : SymfonyLive Paris 2026 - Paris (France) 26-27 mars 2026 : REACT PARIS - Paris (France) 27-29 mars 2026 : Shift - Nantes (France) 31 mars 2026 : ParisTestConf - Paris (France) 1 avril 2026 : AWS Summit Paris - Paris (France) 2 avril 2026 : Pragma Cannes 2026 - Cannes (France) 9-10 avril 2026 : AndroidMakers by droidcon - Paris (France) 16-17 avril 2026 : MiXiT 2026 - Lyon (France) 22-24 avril 2026 : Devoxx France 2026 - Paris (France) 23-25 avril 2026 : Devoxx Greece - Athens (Greece) 24-25 avril 2026 : Faiseuses du Web 5 - Dinan (France) 6-7 mai 2026 : Devoxx UK 2026 - London (UK) 22 mai 2026 : AFUP Day 2026 Lille - Lille (France) 22 mai 2026 : AFUP Day 2026 Paris - Paris (France) 22 mai 2026 : AFUP Day 2026 Bordeaux - Bordeaux (France) 22 mai 2026 : AFUP Day 2026 Lyon - Lyon (France) 29 mai 2026 : NG Baguette Conf 2026 - Paris (France) 5 juin 2026 : TechReady - Nantes (France) 5 juin 2026 : Fork it! - Rouen - Rouen (France) 6 juin 2026 : Polycloud - Montpellier (France) 11-12 juin 2026 : DevQuest Niort - Niort (France) 11-12 juin 2026 : DevLille 2026 - Lille (France) 12 juin 2026 : Tech F'Est 2026 - Nancy (France) 17-19 juin 2026 : Devoxx Poland - Krakow (Poland) 17-20 juin 2026 : VivaTech - Paris (France) 2 juillet 2026 : Azur Tech Summer 2026 - Valbonne (France) 2-3 juillet 2026 : Sunny Tech - Montpellier (France) 3 juillet 2026 : Agile Lyon 2026 - Lyon (France) 2 août 2026 : 4th Tech Summit on Artificial Intelligence & Robotics - Paris (France) 4 septembre 2026 : JUG Summer Camp 2026 - La Rochelle (France) 17-18 septembre 2026 : API Platform Conference 2026 - Lille (France) 24 septembre 2026 : PlatformCon Live Day Paris 2026 - Paris (France) 1 octobre 2026 : WAX 2026 - Marseille (France) 1-2 octobre 2026 : Volcamp - Clermont-Ferrand (France) 5-9 octobre 2026 : Devoxx Belgium - Antwerp (Belgium) Nous contacter Pour réagir à cet épisode, venez discuter sur le groupe Google https://groups.google.com/group/lescastcodeurs Contactez-nous via X/twitter https://twitter.com/lescastcodeurs ou Bluesky https://bsky.app/profile/lescastcodeurs.com Faire un crowdcast ou une crowdquestion Soutenez Les Cast Codeurs sur Patreon https://www.patreon.com/LesCastCodeurs Tous les épisodes et toutes les infos sur https://lescastcodeurs.com/

JSON seems to be everywhere these days. Many application developers like it across all sorts of languages, C#, JAVA, Python, and more. They use it for transferring information between systems, and are comfortable serializing hierarchical object data into JSON from text and de-serializing it back into its various elements. For those of us working in relational databases, JSON seems like a blob of information that isn't easily queried, indexed, or stored. We prefer working with a relational set of data, which brings us into conflict with software developers. We'd like them to convert their objects to a relational structure, and they'd like us to just work with JSON. Read the rest of JSON Has a Cost

En este episodio de Atareao con Linux, te hablo sobre una de las transiciones más importantes que he hecho últimamente en mi flujo de trabajo: el paso del scripting tradicional hacia el uso de Rust como lenguaje de cabecera para resolver mis problemas cotidianos.A pesar de que Fish se ha convertido en mi shell por defecto por lo intuitivo que resulta, el día a día me sigue obligando a crear scripts en Bash que, con el tiempo, terminan convirtiéndose en auténticos mastodontes difíciles de gestionar. Cuando un pequeño script crece demasiado, aparecen las limitaciones: una gestión de errores muy limitada, la falta de tests bien integrados, la ausencia de tipado que provoca errores difíciles de depurar y la pesadilla de manejar JSON o YAML dependiendo siempre de herramientas externas como jq.Para solucionar esto sin tener que montar un proyecto completo de Rust con su Cargo.toml cada vez que quiero hacer algo sencillo, he recuperado una herramienta que es pura magia: Rust Script. Con ella, tengo lo mejor de los dos mundos: la potencia, seguridad y velocidad de Rust, pero con la agilidad y simplicidad de un script de toda la vida.En este episodio te cuento:Mi visión sobre Rust Script: Cómo lo utilizo como un lenguaje de scripting puro para sustituir a Bash cuando la cosa se complica.El fin de la sobrecarga: Te explico cómo escribo scripts sin configurar proyectos completos, eliminando de un plumazo la burocracia de archivos de configuración.Gestión de dependencias: Te muestro cómo declaro los crates que necesito directamente dentro del código mediante comentarios, haciendo que mis scripts sean totalmente autónomos y fáciles de mover de un sitio a otro.Bajo el capó: Cómo funciona el sistema de caché y compilación para que, tras la primera ejecución, tus herramientas vuelen y sean instantáneas.Ejemplos reales: Desde un "Hola Mundo" básico hasta herramientas que consultan APIs REST y procesan información de forma nativa sin herramientas de terceros.Velocidad y fiabilidad: Por qué prefiero un binario tipado y testeado antes que una cadena de comandos en Bash donde un error en una tubería puede pasar desapercibido.Además, aprovecho para adelantarte los próximos episodios técnicos donde voy a meterle mano a fondo a Podman. Quiero explicarte por qué, al haber nacido en Linux, tiene una integración mucho más natural que Docker y cómo pienso sacarle todo el partido.Si tú también sientes que tus scripts de Bash se te están yendo de las manos, te invito a escuchar este episodio y descubrir cómo optimizar tu trabajo diario.Este podcast forma parte de la red de Sospechosos Habituales. Puedes encontrar todos los detalles y los scripts que menciono en las notas del episodio en mi web, atareao.es.¿Te gustaría que en el próximo episodio hiciera la migración en directo de uno de mis scripts de Bash a Rust Script para que veas el proceso paso a paso?Timestamp00:00:00 Introducción y la transición de Bash a Fish00:00:50 Las limitaciones de los scripts complejos en Bash y Fish00:01:26 Por qué elegir Rust para optimizar el trabajo00:02:07 Introducción a Rust Script: Rust como lenguaje de scripting00:02:36 Próximos episodios técnicos: Profundizando en Podman00:03:59 Problemas comunes en Bash: Errores, tipos y datos estructurados00:04:50 El punto intermedio: Rust Script vs. proyectos completos con Cargo00:06:47 Ejemplo práctico: Cómo crear un "Hola Mundo" con Rust Script00:08:07 Funcionamiento interno: Compilación y caché de binarios00:09:43 Ejecución directa y permisos en archivos .rs00:10:44 Cómo instalar Rust Script en Arch Linux y vía Cargo00:11:32 Gestión de dependencias elegante dentro del script00:14:03 Ejemplo avanzado: Uso de múltiples crates y peticiones web00:16:32 Ventajas de trabajar con APIs JSON sin herramientas externas00:18:25 Resumen de beneficios: Potencia, tipado y velocidad00:20:13 Despedida y red de podcast Sospechosos Habituales

Melih Oztalay is the CEO of Smart Finds Marketing, an accomplished digital agency leader with over 38 years of experience in the marketing industry. Renowned for blending traditional and digital strategies, Melih has successfully guided clients through economic downturns, market disruptions, and the accelerating pace of technological change. As the architect of the "Four A's" philosophy (Anticipate, Accept, Adapt, Adopt), he empowers businesses to stay competitive and proactive in the age of AI, navigating uncertainty with logic and resilience. In this episode of Marketer of the Day, Melih Oztalay sits down with Robert Plank to discuss surviving and thriving through seismic industry changes, such as the 2009 financial crisis and the COVID pandemic. Melih shares how the art of “pivoting” underpins long-term success and how digital transformation is more than following trends; it's about measurable, strategic action. He details the evolution from SEO to AEO (Answer Engine Optimization) and GEO (Generative Engine Optimization), why businesses must transition from pursuing rankings to being referenced by AI, and how schema/JSON scripts can elevate AI's visibility. Listeners will gain actionable strategies for embracing change, breaking through emotional resistance, and leveraging both human teams and AI to solve core business challenges. Quotes: “Marketing isn't about promotion; it's a discipline of problem-solving. The brands that win are the ones that solve real problems faster and better.” “When change is inevitable, emotion is optional. Anticipate it, accept it, adapt fast, and adopt smarter—that's how you stay ahead.” “In an AI-driven world, rankings matter less than relevance. The future belongs to those who are referenced, not just found.” Resources: Connect with Melih Oztalay on LinkedIn Learn about Meliho Z Talay on BrandYourself. Visit Smart Finds Marketing

Happy New Year! You may have noticed that in 2025 we had moved toward YouTube as our primary podcasting platform. As we'll explain in the next State of Latent Space post, we'll be doubling down on Substack again and improving the experience for the over 100,000 of you who look out for our emails and website updates!We first mentioned Artificial Analysis in 2024, when it was still a side project in a Sydney basement. They then were one of the few Nat Friedman and Daniel Gross' AIGrant companies to raise a full seed round from them and have now become the independent gold standard for AI benchmarking—trusted by developers, enterprises, and every major lab to navigate the exploding landscape of models, providers, and capabilities.We have chatted with both Clementine Fourrier of HuggingFace's OpenLLM Leaderboard and (the freshly valued at $1.7B) Anastasios Angelopoulos of LMArena on their approaches to LLM evals and trendspotting, but Artificial Analysis have staked out an enduring and important place in the toolkit of the modern AI Engineer by doing the best job of independently running the most comprehensive set of evals across the widest range of open and closed models, and charting their progress for broad industry analyst use.George Cameron and Micah-Hill Smith have spent two years building Artificial Analysis into the platform that answers the questions no one else will: Which model is actually best for your use case? What are the real speed-cost trade-offs? And how open is “open” really?We discuss:* The origin story: built as a side project in 2023 while Micah was building a legal AI assistant, launched publicly in January 2024, and went viral after Swyx's retweet* Why they run evals themselves: labs prompt models differently, cherry-pick chain-of-thought examples (Google Gemini 1.0 Ultra used 32-shot prompts to beat GPT-4 on MMLU), and self-report inflated numbers* The mystery shopper policy: they register accounts not on their own domain and run intelligence + performance benchmarks incognito to prevent labs from serving different models on private endpoints* How they make money: enterprise benchmarking insights subscription (standardized reports on model deployment, serverless vs. managed vs. leasing chips) and private custom benchmarking for AI companies (no one pays to be on the public leaderboard)* The Intelligence Index (V3): synthesizes 10 eval datasets (MMLU, GPQA, agentic benchmarks, long-context reasoning) into a single score, with 95% confidence intervals via repeated runs* Omissions Index (hallucination rate): scores models from -100 to +100 (penalizing incorrect answers, rewarding ”I don't know”), and Claude models lead with the lowest hallucination rates despite not always being the smartest* GDP Val AA: their version of OpenAI's GDP-bench (44 white-collar tasks with spreadsheets, PDFs, PowerPoints), run through their Stirrup agent harness (up to 100 turns, code execution, web search, file system), graded by Gemini 3 Pro as an LLM judge (tested extensively, no self-preference bias)* The Openness Index: scores models 0-18 on transparency of pre-training data, post-training data, methodology, training code, and licensing (AI2 OLMo 2 leads, followed by Nous Hermes and NVIDIA Nemotron)* The smiling curve of AI costs: GPT-4-level intelligence is 100-1000x cheaper than at launch (thanks to smaller models like Amazon Nova), but frontier reasoning models in agentic workflows cost more than ever (sparsity, long context, multi-turn agents)* Why sparsity might go way lower than 5%: GPT-4.5 is ~5% active, Gemini models might be ~3%, and Omissions Index accuracy correlates with total parameters (not active), suggesting massive sparse models are the future* Token efficiency vs. turn efficiency: GPT-5 costs more per token but solves Tau-bench in fewer turns (cheaper overall), and models are getting better at using more tokens only when needed (5.1 Codex has tighter token distributions)* V4 of the Intelligence Index coming soon: adding GDP Val AA, Critical Point, hallucination rate, and dropping some saturated benchmarks (human-eval-style coding is now trivial for small models)Links to Artificial Analysis* Website: https://artificialanalysis.ai* George Cameron on X: https://x.com/georgecameron* Micah-Hill Smith on X: https://x.com/micahhsmithFull Episode on YouTubeTimestamps* 00:00 Introduction: Full Circle Moment and Artificial Analysis Origins* 01:19 Business Model: Independence and Revenue Streams* 04:33 Origin Story: From Legal AI to Benchmarking Need* 16:22 AI Grant and Moving to San Francisco* 19:21 Intelligence Index Evolution: From V1 to V3* 11:47 Benchmarking Challenges: Variance, Contamination, and Methodology* 13:52 Mystery Shopper Policy and Maintaining Independence* 28:01 New Benchmarks: Omissions Index for Hallucination Detection* 33:36 Critical Point: Hard Physics Problems and Research-Level Reasoning* 23:01 GDP Val AA: Agentic Benchmark for Real Work Tasks* 50:19 Stirrup Agent Harness: Open Source Agentic Framework* 52:43 Openness Index: Measuring Model Transparency Beyond Licenses* 58:25 The Smiling Curve: Cost Falling While Spend Rising* 1:02:32 Hardware Efficiency: Blackwell Gains and Sparsity Limits* 1:06:23 Reasoning Models and Token Efficiency: The Spectrum Emerges* 1:11:00 Multimodal Benchmarking: Image, Video, and Speech Arenas* 1:15:05 Looking Ahead: Intelligence Index V4 and Future Directions* 1:16:50 Closing: The Insatiable Demand for IntelligenceTranscriptMicah [00:00:06]: This is kind of a full circle moment for us in a way, because the first time artificial analysis got mentioned on a podcast was you and Alessio on Latent Space. Amazing.swyx [00:00:17]: Which was January 2024. I don't even remember doing that, but yeah, it was very influential to me. Yeah, I'm looking at AI News for Jan 17, or Jan 16, 2024. I said, this gem of a models and host comparison site was just launched. And then I put in a few screenshots, and I said, it's an independent third party. It clearly outlines the quality versus throughput trade-off, and it breaks out by model and hosting provider. I did give you s**t for missing fireworks, and how do you have a model benchmarking thing without fireworks? But you had together, you had perplexity, and I think we just started chatting there. Welcome, George and Micah, to Latent Space. I've been following your progress. Congrats on... It's been an amazing year. You guys have really come together to be the presumptive new gardener of AI, right? Which is something that...George [00:01:09]: Yeah, but you can't pay us for better results.swyx [00:01:12]: Yes, exactly.George [00:01:13]: Very important.Micah [00:01:14]: Start off with a spicy take.swyx [00:01:18]: Okay, how do I pay you?Micah [00:01:20]: Let's get right into that.swyx [00:01:21]: How do you make money?Micah [00:01:24]: Well, very happy to talk about that. So it's been a big journey the last couple of years. Artificial analysis is going to be two years old in January 2026. Which is pretty soon now. We first run the website for free, obviously, and give away a ton of data to help developers and companies navigate AI and make decisions about models, providers, technologies across the AI stack for building stuff. We're very committed to doing that and tend to keep doing that. We have, along the way, built a business that is working out pretty sustainably. We've got just over 20 people now and two main customer groups. So we want to be... We want to be who enterprise look to for data and insights on AI, so we want to help them with their decisions about models and technologies for building stuff. And then on the other side, we do private benchmarking for companies throughout the AI stack who build AI stuff. So no one pays to be on the website. We've been very clear about that from the very start because there's no use doing what we do unless it's independent AI benchmarking. Yeah. But turns out a bunch of our stuff can be pretty useful to companies building AI stuff.swyx [00:02:38]: And is it like, I am a Fortune 500, I need advisors on objective analysis, and I call you guys and you pull up a custom report for me, you come into my office and give me a workshop? What kind of engagement is that?George [00:02:53]: So we have a benchmarking and insight subscription, which looks like standardized reports that cover key topics or key challenges enterprises face when looking to understand AI and choose between all the technologies. And so, for instance, one of the report is a model deployment report, how to think about choosing between serverless inference, managed deployment solutions, or leasing chips. And running inference yourself is an example kind of decision that big enterprises face, and it's hard to reason through, like this AI stuff is really new to everybody. And so we try and help with our reports and insight subscription. Companies navigate that. We also do custom private benchmarking. And so that's very different from the public benchmarking that we publicize, and there's no commercial model around that. For private benchmarking, we'll at times create benchmarks, run benchmarks to specs that enterprises want. And we'll also do that sometimes for AI companies who have built things, and we help them understand what they've built with private benchmarking. Yeah. So that's a piece mainly that we've developed through trying to support everybody publicly with our public benchmarks. Yeah.swyx [00:04:09]: Let's talk about TechStack behind that. But okay, I'm going to rewind all the way to when you guys started this project. You were all the way in Sydney? Yeah. Well, Sydney, Australia for me.Micah [00:04:19]: George was an SF, but he's Australian, but he moved here already. Yeah.swyx [00:04:22]: And I remember I had the Zoom call with you. What was the impetus for starting artificial analysis in the first place? You know, you started with public benchmarks. And so let's start there. We'll go to the private benchmark. Yeah.George [00:04:33]: Why don't we even go back a little bit to like why we, you know, thought that it was needed? Yeah.Micah [00:04:40]: The story kind of begins like in 2022, 2023, like both George and I have been into AI stuff for quite a while. In 2023 specifically, I was trying to build a legal AI research assistant. So it actually worked pretty well for its era, I would say. Yeah. Yeah. So I was finding that the more you go into building something using LLMs, the more each bit of what you're doing ends up being a benchmarking problem. So had like this multistage algorithm thing, trying to figure out what the minimum viable model for each bit was, trying to optimize every bit of it as you build that out, right? Like you're trying to think about accuracy, a bunch of other metrics and performance and cost. And mostly just no one was doing anything to independently evaluate all the models. And certainly not to look at the trade-offs for speed and cost. So we basically set out just to build a thing that developers could look at to see the trade-offs between all of those things measured independently across all the models and providers. Honestly, it was probably meant to be a side project when we first started doing it.swyx [00:05:49]: Like we didn't like get together and say like, Hey, like we're going to stop working on all this stuff. I'm like, this is going to be our main thing. When I first called you, I think you hadn't decided on starting a company yet.Micah [00:05:58]: That's actually true. I don't even think we'd pause like, like George had an acquittance job. I didn't quit working on my legal AI thing. Like it was genuinely a side project.George [00:06:05]: We built it because we needed it as people building in the space and thought, Oh, other people might find it useful too. So we'll buy domain and link it to the Vercel deployment that we had and tweet about it. And, but very quickly it started getting attention. Thank you, Swyx for, I think doing an initial retweet and spotlighting it there. This project that we released. And then very quickly though, it was useful to others, but very quickly it became more useful as the number of models released accelerated. We had Mixtrel 8x7B and it was a key. That's a fun one. Yeah. Like a open source model that really changed the landscape and opened up people's eyes to other serverless inference providers and thinking about speed, thinking about cost. And so that was a key. And so it became more useful quite quickly. Yeah.swyx [00:07:02]: What I love talking to people like you who sit across the ecosystem is, well, I have theories about what people want, but you have data and that's obviously more relevant. But I want to stay on the origin story a little bit more. When you started out, I would say, I think the status quo at the time was every paper would come out and they would report their numbers versus competitor numbers. And that's basically it. And I remember I did the legwork. I think everyone has some knowledge. I think there's some version of Excel sheet or a Google sheet where you just like copy and paste the numbers from every paper and just post it up there. And then sometimes they don't line up because they're independently run. And so your numbers are going to look better than... Your reproductions of other people's numbers are going to look worse because you don't hold their models correctly or whatever the excuse is. I think then Stanford Helm, Percy Liang's project would also have some of these numbers. And I don't know if there's any other source that you can cite. The way that if I were to start artificial analysis at the same time you guys started, I would have used the Luther AI's eval framework harness. Yup.Micah [00:08:06]: Yup. That was some cool stuff. At the end of the day, running these evals, it's like if it's a simple Q&A eval, all you're doing is asking a list of questions and checking if the answers are right, which shouldn't be that crazy. But it turns out there are an enormous number of things that you've got control for. And I mean, back when we started the website. Yeah. Yeah. Like one of the reasons why we realized that we had to run the evals ourselves and couldn't just take rules from the labs was just that they would all prompt the models differently. And when you're competing over a few points, then you can pretty easily get- You can put the answer into the model. Yeah. That in the extreme. And like you get crazy cases like back when I'm Googled a Gemini 1.0 Ultra and needed a number that would say it was better than GPT-4 and like constructed, I think never published like chain of thought examples. 32 of them in every topic in MLU to run it, to get the score, like there are so many things that you- They never shipped Ultra, right? That's the one that never made it up. Not widely. Yeah. Yeah. Yeah. I mean, I'm sure it existed, but yeah. So we were pretty sure that we needed to run them ourselves and just run them in the same way across all the models. Yeah. And we were, we also did certain from the start that you couldn't look at those in isolation. You needed to look at them alongside the cost and performance stuff. Yeah.swyx [00:09:24]: Okay. A couple of technical questions. I mean, so obviously I also thought about this and I didn't do it because of cost. Yep. Did you not worry about costs? Were you funded already? Clearly not, but you know. No. Well, we definitely weren't at the start.Micah [00:09:36]: So like, I mean, we're paying for it personally at the start. There's a lot of money. Well, the numbers weren't nearly as bad a couple of years ago. So we certainly incurred some costs, but we were probably in the order of like hundreds of dollars of spend across all the benchmarking that we were doing. Yeah. So nothing. Yeah. It was like kind of fine. Yeah. Yeah. These days that's gone up an enormous amount for a bunch of reasons that we can talk about. But yeah, it wasn't that bad because you can also remember that like the number of models we were dealing with was hardly any and the complexity of the stuff that we wanted to do to evaluate them was a lot less. Like we were just asking some Q&A type questions and then one specific thing was for a lot of evals initially, we were just like sampling an answer. You know, like, what's the answer for this? Like, we didn't want to go into the answer directly without letting the models think. We weren't even doing chain of thought stuff initially. And that was the most useful way to get some results initially. Yeah.swyx [00:10:33]: And so for people who haven't done this work, literally parsing the responses is a whole thing, right? Like because sometimes the models, the models can answer any way they feel fit and sometimes they actually do have the right answer, but they just returned the wrong format and they will get a zero for that unless you work it into your parser. And that involves more work. And so, I mean, but there's an open question whether you should give it points for not following your instructions on the format.Micah [00:11:00]: It depends what you're looking at, right? Because you can, if you're trying to see whether or not it can solve a particular type of reasoning problem, and you don't want to test it on its ability to do answer formatting at the same time, then you might want to use an LLM as answer extractor approach to make sure that you get the answer out no matter how unanswered. But these days, it's mostly less of a problem. Like, if you instruct a model and give it examples of what the answers should look like, it can get the answers in your format, and then you can do, like, a simple regex.swyx [00:11:28]: Yeah, yeah. And then there's other questions around, I guess, sometimes if you have a multiple choice question, sometimes there's a bias towards the first answer, so you have to randomize the responses. All these nuances, like, once you dig into benchmarks, you're like, I don't know how anyone believes the numbers on all these things. It's so dark magic.Micah [00:11:47]: You've also got, like… You've got, like, the different degrees of variance in different benchmarks, right? Yeah. So, if you run four-question multi-choice on a modern reasoning model at the temperatures suggested by the labs for their own models, the variance that you can see on a four-question multi-choice eval is pretty enormous if you only do a single run of it and it has a small number of questions, especially. So, like, one of the things that we do is run an enormous number of all of our evals when we're developing new ones and doing upgrades to our intelligence index to bring in new things. Yeah. So, that we can dial in the right number of repeats so that we can get to the 95% confidence intervals that we're comfortable with so that when we pull that together, we can be confident in intelligence index to at least as tight as, like, a plus or minus one at a 95% confidence. Yeah.swyx [00:12:32]: And, again, that just adds a straight multiple to the cost. Oh, yeah. Yeah, yeah.George [00:12:37]: So, that's one of many reasons that cost has gone up a lot more than linearly over the last couple of years. We report a cost to run the artificial analysis. We report a cost to run the artificial analysis intelligence index on our website, and currently that's assuming one repeat in terms of how we report it because we want to reflect a bit about the weighting of the index. But our cost is actually a lot higher than what we report there because of the repeats.swyx [00:13:03]: Yeah, yeah, yeah. And probably this is true, but just checking, you don't have any special deals with the labs. They don't discount it. You just pay out of pocket or out of your sort of customer funds. Oh, there is a mix. So, the issue is that sometimes they may give you a special end point, which is… Ah, 100%.Micah [00:13:21]: Yeah, yeah, yeah. Exactly. So, we laser focus, like, on everything we do on having the best independent metrics and making sure that no one can manipulate them in any way. There are quite a lot of processes we've developed over the last couple of years to make that true for, like, the one you bring up, like, right here of the fact that if we're working with a lab, if they're giving us a private endpoint to evaluate a model, that it is totally possible. That what's sitting behind that black box is not the same as they serve on a public endpoint. We're very aware of that. We have what we call a mystery shopper policy. And so, and we're totally transparent with all the labs we work with about this, that we will register accounts not on our own domain and run both intelligence evals and performance benchmarks… Yeah, that's the job. …without them being able to identify it. And no one's ever had a problem with that. Because, like, a thing that turns out to actually be quite a good… …good factor in the industry is that they all want to believe that none of their competitors could manipulate what we're doing either.swyx [00:14:23]: That's true. I never thought about that. I've been in the database data industry prior, and there's a lot of shenanigans around benchmarking, right? So I'm just kind of going through the mental laundry list. Did I miss anything else in this category of shenanigans? Oh, potential shenanigans.Micah [00:14:36]: I mean, okay, the biggest one, like, that I'll bring up, like, is more of a conceptual one, actually, than, like, direct shenanigans. It's that the things that get measured become things that get targeted by labs that they're trying to build, right? Exactly. So that doesn't mean anything that we should really call shenanigans. Like, I'm not talking about training on test set. But if you know that you're going to be great at another particular thing, if you're a researcher, there are a whole bunch of things that you can do to try to get better at that thing that preferably are going to be helpful for a wide range of how actual users want to use the thing that you're building. But will not necessarily work. Will not necessarily do that. So, for instance, the models are exceptional now at answering competition maths problems. There is some relevance of that type of reasoning, that type of work, to, like, how we might use modern coding agents and stuff. But it's clearly not one for one. So the thing that we have to be aware of is that once an eval becomes the thing that everyone's looking at, scores can get better on it without there being a reflection of overall generalized intelligence of these models. Getting better. That has been true for the last couple of years. It'll be true for the next couple of years. There's no silver bullet to defeat that other than building new stuff to stay relevant and measure the capabilities that matter most to real users. Yeah.swyx [00:15:58]: And we'll cover some of the new stuff that you guys are building as well, which is cool. Like, you used to just run other people's evals, but now you're coming up with your own. And I think, obviously, that is a necessary path once you're at the frontier. You've exhausted all the existing evals. I think the next point in history that I have for you is AI Grant that you guys decided to join and move here. What was it like? I think you were in, like, batch two? Batch four. Batch four. Okay.Micah [00:16:26]: I mean, it was great. Nat and Daniel are obviously great. And it's a really cool group of companies that we were in AI Grant alongside. It was really great to get Nat and Daniel on board. Obviously, they've done a whole lot of great work in the space with a lot of leading companies and were extremely aligned. With the mission of what we were trying to do. Like, we're not quite typical of, like, a lot of the other AI startups that they've invested in.swyx [00:16:53]: And they were very much here for the mission of what we want to do. Did they say any advice that really affected you in some way or, like, were one of the events very impactful? That's an interesting question.Micah [00:17:03]: I mean, I remember fondly a bunch of the speakers who came and did fireside chats at AI Grant.swyx [00:17:09]: Which is also, like, a crazy list. Yeah.George [00:17:11]: Oh, totally. Yeah, yeah, yeah. There was something about, you know, speaking to Nat and Daniel about the challenges of working through a startup and just working through the questions that don't have, like, clear answers and how to work through those kind of methodically and just, like, work through the hard decisions. And they've been great mentors to us as we've built artificial analysis. Another benefit for us was that other companies in the batch and other companies in AI Grant are pushing the capabilities. Yeah. And I think that's a big part of what AI can do at this time. And so being in contact with them, making sure that artificial analysis is useful to them has been fantastic for supporting us in working out how should we build out artificial analysis to continue to being useful to those, like, you know, building on AI.swyx [00:17:59]: I think to some extent, I'm mixed opinion on that one because to some extent, your target audience is not people in AI Grants who are obviously at the frontier. Yeah. Do you disagree?Micah [00:18:09]: To some extent. To some extent. But then, so a lot of what the AI Grant companies are doing is taking capabilities coming out of the labs and trying to push the limits of what they can do across the entire stack for building great applications, which actually makes some of them pretty archetypical power users of artificial analysis. Some of the people with the strongest opinions about what we're doing well and what we're not doing well and what they want to see next from us. Yeah. Yeah. Because when you're building any kind of AI application now, chances are you're using a whole bunch of different models. You're maybe switching reasonably frequently for different models and different parts of your application to optimize what you're able to do with them at an accuracy level and to get better speed and cost characteristics. So for many of them, no, they're like not commercial customers of ours, like we don't charge for all our data on the website. Yeah. They are absolutely some of our power users.swyx [00:19:07]: So let's talk about just the evals as well. So you start out from the general like MMU and GPQA stuff. What's next? How do you sort of build up to the overall index? What was in V1 and how did you evolve it? Okay.Micah [00:19:22]: So first, just like background, like we're talking about the artificial analysis intelligence index, which is our synthesis metric that we pulled together currently from 10 different eval data sets to give what? We're pretty much the same as that. Pretty confident is the best single number to look at for how smart the models are. Obviously, it doesn't tell the whole story. That's why we published the whole website of all the charts to dive into every part of it and look at the trade-offs. But best single number. So right now, it's got a bunch of Q&A type data sets that have been very important to the industry, like a couple that you just mentioned. It's also got a couple of agentic data sets. It's got our own long context reasoning data set and some other use case focused stuff. As time goes on. The things that we're most interested in that are going to be important to the capabilities that are becoming more important for AI, what developers are caring about, are going to be first around agentic capabilities. So surprise, surprise. We're all loving our coding agents and how the model is going to perform like that and then do similar things for different types of work are really important to us. The linking to use cases to economically valuable use cases are extremely important to us. And then we've got some of the. Yeah. These things that the models still struggle with, like working really well over long contexts that are not going to go away as specific capabilities and use cases that we need to keep evaluating.swyx [00:20:46]: But I guess one thing I was driving was like the V1 versus the V2 and how bad it was over time.Micah [00:20:53]: Like how we've changed the index to where we are.swyx [00:20:55]: And I think that reflects on the change in the industry. Right. So that's a nice way to tell that story.Micah [00:21:00]: Well, V1 would be completely saturated right now. Almost every model coming out because doing things like writing the Python functions and human evil is now pretty trivial. It's easy to forget, actually, I think how much progress has been made in the last two years. Like we obviously play the game constantly of like the today's version versus last week's version and the week before and all of the small changes in the horse race between the current frontier and who has the best like smaller than 10B model like right now this week. Right. And that's very important to a lot of developers and people and especially in this particular city of San Francisco. But when you zoom out a couple of years ago, literally most of what we were doing to evaluate the models then would all be 100% solved by even pretty small models today. And that's been one of the key things, by the way, that's driven down the cost of intelligence at every tier of intelligence. We can talk about more in a bit. So V1, V2, V3, we made things harder. We covered a wider range of use cases. And we tried to get closer to things developers care about as opposed to like just the Q&A type stuff that MMLU and GPQA represented. Yeah.swyx [00:22:12]: I don't know if you have anything to add there. Or we could just go right into showing people the benchmark and like looking around and asking questions about it. Yeah.Micah [00:22:21]: Let's do it. Okay. This would be a pretty good way to chat about a few of the new things we've launched recently. Yeah.George [00:22:26]: And I think a little bit about the direction that we want to take it. And we want to push benchmarks. Currently, the intelligence index and evals focus a lot on kind of raw intelligence. But we kind of want to diversify how we think about intelligence. And we can talk about it. But kind of new evals that we've kind of built and partnered on focus on topics like hallucination. And we've got a lot of topics that I think are not covered by the current eval set that should be. And so we want to bring that forth. But before we get into that.swyx [00:23:01]: And so for listeners, just as a timestamp, right now, number one is Gemini 3 Pro High. Then followed by Cloud Opus at 70. Just 5.1 high. You don't have 5.2 yet. And Kimi K2 Thinking. Wow. Still hanging in there. So those are the top four. That will date this podcast quickly. Yeah. Yeah. I mean, I love it. I love it. No, no. 100%. Look back this time next year and go, how cute. Yep.George [00:23:25]: Totally. A quick view of that is, okay, there's a lot. I love it. I love this chart. Yeah.Micah [00:23:30]: This is such a favorite, right? Yeah. And almost every talk that George or I give at conferences and stuff, we always put this one up first to just talk about situating where we are in this moment in history. This, I think, is the visual version of what I was saying before about the zooming out and remembering how much progress there's been. If we go back to just over a year ago, before 01, before Cloud Sonnet 3.5, we didn't have reasoning models or coding agents as a thing. And the game was very, very different. If we go back even a little bit before then, we're in the era where, when you look at this chart, open AI was untouchable for well over a year. And, I mean, you would remember that time period well of there being very open questions about whether or not AI was going to be competitive, like full stop, whether or not open AI would just run away with it, whether we would have a few frontier labs and no one else would really be able to do anything other than consume their APIs. I am quite happy overall that the world that we have ended up in is one where... Multi-model. Absolutely. And strictly more competitive every quarter over the last few years. Yeah. This year has been insane. Yeah.George [00:24:42]: You can see it. This chart with everything added is hard to read currently. There's so many dots on it, but I think it reflects a little bit what we felt, like how crazy it's been.swyx [00:24:54]: Why 14 as the default? Is that a manual choice? Because you've got service now in there that are less traditional names. Yeah.George [00:25:01]: It's models that we're kind of highlighting by default in our charts, in our intelligence index. Okay.swyx [00:25:07]: You just have a manually curated list of stuff.George [00:25:10]: Yeah, that's right. But something that I actually don't think every artificial analysis user knows is that you can customize our charts and choose what models are highlighted. Yeah. And so if we take off a few names, it gets a little easier to read.swyx [00:25:25]: Yeah, yeah. A little easier to read. Totally. Yeah. But I love that you can see the all one jump. Look at that. September 2024. And the DeepSeek jump. Yeah.George [00:25:34]: Which got close to OpenAI's leadership. They were so close. I think, yeah, we remember that moment. Around this time last year, actually.Micah [00:25:44]: Yeah, yeah, yeah. I agree. Yeah, well, a couple of weeks. It was Boxing Day in New Zealand when DeepSeek v3 came out. And we'd been tracking DeepSeek and a bunch of the other global players that were less known over the second half of 2024 and had run evals on the earlier ones and stuff. I very distinctly remember Boxing Day in New Zealand, because I was with family for Christmas and stuff, running the evals and getting back result by result on DeepSeek v3. So this was the first of their v3 architecture, the 671b MOE.Micah [00:26:19]: And we were very, very impressed. That was the moment where we were sure that DeepSeek was no longer just one of many players, but had jumped up to be a thing. The world really noticed when they followed that up with the RL working on top of v3 and R1 succeeding a few weeks later. But the groundwork for that absolutely was laid with just extremely strong base model, completely open weights that we had as the best open weights model. So, yeah, that's the thing that you really see in the game. But I think that we got a lot of good feedback on Boxing Day. us on Boxing Day last year.George [00:26:48]: Boxing Day is the day after Christmas for those not familiar.George [00:26:54]: I'm from Singapore.swyx [00:26:55]: A lot of us remember Boxing Day for a different reason, for the tsunami that happened. Oh, of course. Yeah, but that was a long time ago. So yeah. So this is the rough pitch of AAQI. Is it A-A-Q-I or A-A-I-I? I-I. Okay. Good memory, though.Micah [00:27:11]: I don't know. I'm not used to it. Once upon a time, we did call it Quality Index, and we would talk about quality, performance, and price, but we changed it to intelligence.George [00:27:20]: There's been a few naming changes. We added hardware benchmarking to the site, and so benchmarks at a kind of system level. And so then we changed our throughput metric to, we now call it output speed, and thenswyx [00:27:32]: throughput makes sense at a system level, so we took that name. Take me through more charts. What should people know? Obviously, the way you look at the site is probably different than how a beginner might look at it.Micah [00:27:42]: Yeah, that's fair. There's a lot of fun stuff to dive into. Maybe so we can hit past all the, like, we have lots and lots of emails and stuff. The interesting ones to talk about today that would be great to bring up are a few of our recent things, I think, that probably not many people will be familiar with yet. So first one of those is our omniscience index. So this one is a little bit different to most of the intelligence evils that we've run. We built it specifically to look at the embedded knowledge in the models and to test hallucination by looking at when the model doesn't know the answer, so not able to get it correct, what's its probability of saying, I don't know, or giving an incorrect answer. So the metric that we use for omniscience goes from negative 100 to positive 100. Because we're simply taking off a point if you give an incorrect answer to the question. We're pretty convinced that this is an example of where it makes most sense to do that, because it's strictly more helpful to say, I don't know, instead of giving a wrong answer to factual knowledge question. And one of our goals is to shift the incentive that evils create for models and the labs creating them to get higher scores. And almost every evil across all of AI up until this point, it's been graded by simple percentage correct as the main metric, the main thing that gets hyped. And so you should take a shot at everything. There's no incentive to say, I don't know. So we did that for this one here.swyx [00:29:22]: I think there's a general field of calibration as well, like the confidence in your answer versus the rightness of the answer. Yeah, we completely agree. Yeah. Yeah.George [00:29:31]: On that. And one reason that we didn't do that is because. Or put that into this index is that we think that the, the way to do that is not to ask the models how confident they are.swyx [00:29:43]: I don't know. Maybe it might be though. You put it like a JSON field, say, say confidence and maybe it spits out something. Yeah. You know, we have done a few evils podcasts over the, over the years. And when we did one with Clementine of hugging face, who maintains the open source leaderboard, and this was one of her top requests, which is some kind of hallucination slash lack of confidence calibration thing. And so, Hey, this is one of them.Micah [00:30:05]: And I mean, like anything that we do, it's not a perfect metric or the whole story of everything that you think about as hallucination. But yeah, it's pretty useful and has some interesting results. Like one of the things that we saw in the hallucination rate is that anthropics Claude models at the, the, the very left-hand side here with the lowest hallucination rates out of the models that we've evaluated amnesty is on. That is an interesting fact. I think it probably correlates with a lot of the previously, not really measured vibes stuff that people like about some of the Claude models. Is the dataset public or what's is it, is there a held out set? There's a hell of a set for this one. So we, we have published a public test set, but we we've only published 10% of it. The reason is that for this one here specifically, it would be very, very easy to like have data contamination because it is just factual knowledge questions. We would. We'll update it at a time to also prevent that, but with yeah, kept most of it held out so that we can keep it reliable for a long time. It leads us to a bunch of really cool things, including breakdown quite granularly by topic. And so we've got some of that disclosed on the website publicly right now, and there's lots more coming in terms of our ability to break out very specific topics. Yeah.swyx [00:31:23]: I would be interested. Let's, let's dwell a little bit on this hallucination one. I noticed that Haiku hallucinates less than Sonnet hallucinates less than Opus. And yeah. Would that be the other way around in a normal capability environments? I don't know. What's, what do you make of that?George [00:31:37]: One interesting aspect is that we've found that there's not really a, not a strong correlation between intelligence and hallucination, right? That's to say that the smarter the models are in a general sense, isn't correlated with their ability to, when they don't know something, say that they don't know. It's interesting that Gemini three pro preview was a big leap over here. Gemini 2.5. Flash and, and, and 2.5 pro, but, and if I add pro quickly here.swyx [00:32:07]: I bet pro's really good. Uh, actually no, I meant, I meant, uh, the GPT pros.George [00:32:12]: Oh yeah.swyx [00:32:13]: Cause GPT pros are rumored. We don't know for a fact that it's like eight runs and then with the LM judge on top. Yeah.George [00:32:20]: So we saw a big jump in, this is accuracy. So this is just percent that they get, uh, correct and Gemini three pro knew a lot more than the other models. And so big jump in accuracy. But relatively no change between the Google Gemini models, between releases. And the hallucination rate. Exactly. And so it's likely due to just kind of different post-training recipe, between the, the Claude models. Yeah.Micah [00:32:45]: Um, there's, there's driven this. Yeah. You can, uh, you can partially blame us and how we define intelligence having until now not defined hallucination as a negative in the way that we think about intelligence.swyx [00:32:56]: And so that's what we're changing. Uh, I know many smart people who are confidently incorrect.George [00:33:02]: Uh, look, look at that. That, that, that is very humans. Very true. And there's times and a place for that. I think our view is that hallucination rate makes sense in this context where it's around knowledge, but in many cases, people want the models to hallucinate, to have a go. Often that's the case in coding or when you're trying to generate newer ideas. One eval that we added to artificial analysis is, is, is critical point and it's really hard, uh, physics problems. Okay.swyx [00:33:32]: And is it sort of like a human eval type or something different or like a frontier math type?George [00:33:37]: It's not dissimilar to frontier frontier math. So these are kind of research questions that kind of academics in the physics physics world would be able to answer, but models really struggled to answer. So the top score here is not 9%.swyx [00:33:51]: And when the people that, that created this like Minway and, and, and actually off via who was kind of behind sweep and what organization is this? Oh, is this, it's Princeton.George [00:34:01]: Kind of range of academics from, from, uh, different academic institutions, really smart people. They talked about how they turn the models up in terms of the temperature as high temperature as they can, where they're trying to explore kind of new ideas in physics as a, as a thought partner, just because they, they want the models to hallucinate. Um, yeah, sometimes it's something new. Yeah, exactly.swyx [00:34:21]: Um, so not right in every situation, but, um, I think it makes sense, you know, to test hallucination in scenarios where it makes sense. Also, the obvious question is, uh, this is one of. Many that there is there, every lab has a system card that shows some kind of hallucination number, and you've chosen to not, uh, endorse that and you've made your own. And I think that's a, that's a choice. Um, totally in some sense, the rest of artificial analysis is public benchmarks that other people can independently rerun. You provide it as a service here. You have to fight the, well, who are we to, to like do this? And your, your answer is that we have a lot of customers and, you know, but like, I guess, how do you converge the individual?Micah [00:35:08]: I mean, I think, I think for hallucinations specifically, there are a bunch of different things that you might care about reasonably, and that you'd measure quite differently, like we've called this a amnesty and solutionation rate, not trying to declare the, like, it's humanity's last hallucination. You could, uh, you could have some interesting naming conventions and all this stuff. Um, the biggest picture answer to that. It's something that I actually wanted to mention. Just as George was explaining, critical point as well is, so as we go forward, we are building evals internally. We're partnering with academia and partnering with AI companies to build great evals. We have pretty strong views on, in various ways for different parts of the AI stack, where there are things that are not being measured well, or things that developers care about that should be measured more and better. And we intend to be doing that. We're not obsessed necessarily with that. Everything we do, we have to do entirely within our own team. Critical point. As a cool example of where we were a launch partner for it, working with academia, we've got some partnerships coming up with a couple of leading companies. Those ones, obviously we have to be careful with on some of the independent stuff, but with the right disclosure, like we're completely comfortable with that. A lot of the labs have released great data sets in the past that we've used to great success independently. And so it's between all of those techniques, we're going to be releasing more stuff in the future. Cool.swyx [00:36:26]: Let's cover the last couple. And then we'll, I want to talk about your trends analysis stuff, you know? Totally.Micah [00:36:31]: So that actually, I have one like little factoid on omniscience. If you go back up to accuracy on omniscience, an interesting thing about this accuracy metric is that it tracks more closely than anything else that we measure. The total parameter count of models makes a lot of sense intuitively, right? Because this is a knowledge eval. This is the pure knowledge metric. We're not looking at the index and the hallucination rate stuff that we think is much more about how the models are trained. This is just what facts did they recall? And yeah, it tracks parameter count extremely closely. Okay.swyx [00:37:05]: What's the rumored size of GPT-3 Pro? And to be clear, not confirmed for any official source, just rumors. But rumors do fly around. Rumors. I get, I hear all sorts of numbers. I don't know what to trust.Micah [00:37:17]: So if you, if you draw the line on omniscience accuracy versus total parameters, we've got all the open ways models, you can squint and see that likely the leading frontier models right now are quite a lot bigger than the ones that we're seeing right now. And the one trillion parameters that the open weights models cap out at, and the ones that we're looking at here, there's an interesting extra data point that Elon Musk revealed recently about XAI that for three trillion parameters for GROK 3 and 4, 6 trillion for GROK 5, but that's not out yet. Take those together, have a look. You might reasonably form a view that there's a pretty good chance that Gemini 3 Pro is bigger than that, that it could be in the 5 to 10 trillion parameters. To be clear, I have absolutely no idea, but just based on this chart, like that's where you would, you would land if you have a look at it. Yeah.swyx [00:38:07]: And to some extent, I actually kind of discourage people from guessing too much because what does it really matter? Like as long as they can serve it as a sustainable cost, that's about it. Like, yeah, totally.George [00:38:17]: They've also got different incentives in play compared to like open weights models who are thinking to supporting others in self-deployment for the labs who are doing inference at scale. It's I think less about total parameters in many cases. When thinking about inference costs and more around number of active parameters. And so there's a bit of an incentive towards larger sparser models. Agreed.Micah [00:38:38]: Understood. Yeah. Great. I mean, obviously if you're a developer or company using these things, not exactly as you say, it doesn't matter. You should be looking at all the different ways that we measure intelligence. You should be looking at cost to run index number and the different ways of thinking about token efficiency and cost efficiency based on the list prices, because that's all it matters.swyx [00:38:56]: It's not as good for the content creator rumor mill where I can say. Oh, GPT-4 is this small circle. Look at GPT-5 is this big circle. And then there used to be a thing for a while. Yeah.Micah [00:39:07]: But that is like on its own, actually a very interesting one, right? That is it just purely that chances are the last couple of years haven't seen a dramatic scaling up in the total size of these models. And so there's a lot of room to go up properly in total size of the models, especially with the upcoming hardware generations. Yes.swyx [00:39:29]: So, you know. Taking off my shitposting face for a minute. Yes. Yes. At the same time, I do feel like, you know, especially coming back from Europe, people do feel like Ilya is probably right that the paradigm is doesn't have many more orders of magnitude to scale out more. And therefore we need to start exploring at least a different path. GDPVal, I think it's like only like a month or so old. I was also very positive when it first came out. I actually talked to Tejo, who was the lead researcher on that. Oh, cool. And you have your own version.George [00:39:59]: It's a fantastic. It's a fantastic data set. Yeah.swyx [00:40:01]: And maybe it will recap for people who are still out of it. It's like 44 tasks based on some kind of GDP cutoff that's like meant to represent broad white collar work that is not just coding. Yeah.Micah [00:40:12]: Each of the tasks have a whole bunch of detailed instructions, some input files for a lot of them. It's within the 44 is divided into like two hundred and twenty two to five, maybe subtasks that are the level of that we run through the agenda. And yeah, they're really interesting. I will say that it doesn't. It doesn't necessarily capture like all the stuff that people do at work. No avail is perfect is always going to be more things to look at, largely because in order to make the tasks well enough to find that you can run them, they need to only have a handful of input files and very specific instructions for that task. And so I think the easiest way to think about them are that they're like quite hard take home exam tasks that you might do in an interview process.swyx [00:40:56]: Yeah, for listeners, it is not no longer like a long prompt. It is like, well, here's a zip file with like a spreadsheet or a PowerPoint deck or a PDF and go nuts and answer this question.George [00:41:06]: OpenAI released a great data set and they released a good paper which looks at performance across the different web chat bots on the data set. It's a great paper, encourage people to read it. What we've done is taken that data set and turned it into an eval that can be run on any model. So we created a reference agentic harness that can run. Run the models on the data set, and then we developed evaluator approach to compare outputs. That's kind of AI enabled, so it uses Gemini 3 Pro Preview to compare results, which we tested pretty comprehensively to ensure that it's aligned to human preferences. One data point there is that even as an evaluator, Gemini 3 Pro, interestingly, doesn't do actually that well. So that's kind of a good example of what we've done in GDPVal AA.swyx [00:42:01]: Yeah, the thing that you have to watch out for with LLM judge is self-preference that models usually prefer their own output, and in this case, it was not. Totally.Micah [00:42:08]: I think the way that we're thinking about the places where it makes sense to use an LLM as judge approach now, like quite different to some of the early LLM as judge stuff a couple of years ago, because some of that and MTV was a great project that was a good example of some of this a while ago was about judging conversations and like a lot of style type stuff. Here, we've got the task that the grader and grading model is doing is quite different to the task of taking the test. When you're taking the test, you've got all of the agentic tools you're working with, the code interpreter and web search, the file system to go through many, many turns to try to create the documents. Then on the other side, when we're grading it, we're running it through a pipeline to extract visual and text versions of the files and be able to provide that to Gemini, and we're providing the criteria for the task and getting it to pick which one more effectively meets the criteria of the task. Yeah. So we've got the task out of two potential outcomes. It turns out that we proved that it's just very, very good at getting that right, matched with human preference a lot of the time, because I think it's got the raw intelligence, but it's combined with the correct representation of the outputs, the fact that the outputs were created with an agentic task that is quite different to the way the grading model works, and we're comparing it against criteria, not just kind of zero shot trying to ask the model to pick which one is better.swyx [00:43:26]: Got it. Why is this an ELO? And not a percentage, like GDP-VAL?George [00:43:31]: So the outputs look like documents, and there's video outputs or audio outputs from some of the tasks. It has to make a video? Yeah, for some of the tasks. Some of the tasks.swyx [00:43:43]: What task is that?George [00:43:45]: I mean, it's in the data set. Like be a YouTuber? It's a marketing video.Micah [00:43:49]: Oh, wow. What? Like model has to go find clips on the internet and try to put it together. The models are not that good at doing that one, for now, to be clear. It's pretty hard to do that with a code editor. I mean, the computer stuff doesn't work quite well enough and so on and so on, but yeah.George [00:44:02]: And so there's no kind of ground truth, necessarily, to compare against, to work out percentage correct. It's hard to come up with correct or incorrect there. And so it's on a relative basis. And so we use an ELO approach to compare outputs from each of the models between the task.swyx [00:44:23]: You know what you should do? You should pay a contractor, a human, to do the same task. And then give it an ELO and then so you have, you have human there. It's just, I think what's helpful about GDPVal, the OpenAI one, is that 50% is meant to be normal human and maybe Domain Expert is higher than that, but 50% was the bar for like, well, if you've crossed 50, you are superhuman. Yeah.Micah [00:44:47]: So we like, haven't grounded this score in that exactly. I agree that it can be helpful, but we wanted to generalize this to a very large number. It's one of the reasons that presenting it as ELO is quite helpful and allows us to add models and it'll stay relevant for quite a long time. I also think it, it can be tricky looking at these exact tasks compared to the human performance, because the way that you would go about it as a human is quite different to how the models would go about it. Yeah.swyx [00:45:15]: I also liked that you included Lama 4 Maverick in there. Is that like just one last, like...Micah [00:45:20]: Well, no, no, no, no, no, no, it is the, it is the best model released by Meta. And... So it makes it into the homepage default set, still for now.George [00:45:31]: Other inclusion that's quite interesting is we also ran it across the latest versions of the web chatbots. And so we have...swyx [00:45:39]: Oh, that's right.George [00:45:40]: Oh, sorry.swyx [00:45:41]: I, yeah, I completely missed that. Okay.George [00:45:43]: No, not at all. So that, which has a checkered pattern. So that is their harness, not yours, is what you're saying. Exactly. And what's really interesting is that if you compare, for instance, Claude 4.5 Opus using the Claude web chatbot, it performs worse than the model in our agentic harness. And so in every case, the model performs better in our agentic harness than its web chatbot counterpart, the harness that they created.swyx [00:46:13]: Oh, my backwards explanation for that would be that, well, it's meant for consumer use cases and here you're pushing it for something.Micah [00:46:19]: The constraints are different and the amount of freedom that you can give the model is different. Also, you like have a cost goal. We let the models work as long as they want, basically. Yeah. Do you copy paste manually into the chatbot? Yeah. Yeah. That's, that was how we got the chatbot reference. We're not going to be keeping those updated at like quite the same scale as hundreds of models.swyx [00:46:38]: Well, so I don't know, talk to a browser base. They'll, they'll automate it for you. You know, like I have thought about like, well, we should turn these chatbot versions into an API because they are legitimately different agents in themselves. Yes. Right. Yeah.Micah [00:46:53]: And that's grown a huge amount of the last year, right? Like the tools. The tools that are available have actually diverged in my opinion, a fair bit across the major chatbot apps and the amount of data sources that you can connect them to have gone up a lot, meaning that your experience and the way you're using the model is more different than ever.swyx [00:47:10]: What tools and what data connections come to mind when you say what's interesting, what's notable work that people have done?Micah [00:47:15]: Oh, okay. So my favorite example on this is that until very recently, I would argue that it was basically impossible to get an LLM to draft an email for me in any useful way. Because most times that you're sending an email, you're not just writing something for the sake of writing it. Chances are context required is a whole bunch of historical emails. Maybe it's notes that you've made, maybe it's meeting notes, maybe it's, um, pulling something from your, um, any of like wherever you at work store stuff. So for me, like Google drive, one drive, um, in our super base databases, if we need to do some analysis or some data or something, preferably model can be plugged into all of those things and can go do some useful work based on it. The things that like I find most impressive currently that I am somewhat surprised work really well in late 2025, uh, that I can have models use super base MCP to query read only, of course, run a whole bunch of SQL queries to do pretty significant data analysis. And. And make charts and stuff and can read my Gmail and my notion. And okay. You actually use that. That's good. That's, that's, that's good. Is that a cloud thing? To various degrees of order, but chat GPD and Claude right now, I would say that this stuff like barely works in fairness right now. Like.George [00:48:33]: Because people are actually going to try this after they hear it. If you get an email from Micah, odds are it wasn't written by a chatbot.Micah [00:48:38]: So, yeah, I think it is true that I have never actually sent anyone an email drafted by a chatbot. Yet.swyx [00:48:46]: Um, and so you can, you can feel it right. And yeah, this time, this time next year, we'll come back and see where it's going. Totally. Um, super base shout out another famous Kiwi. Uh, I don't know if you've, you've any conversations with him about anything in particular on AI building and AI infra.George [00:49:03]: We have had, uh, Twitter DMS, um, with, with him because we're quite big, uh, super base users and power users. And we probably do some things more manually than we should in. In, in super base support line because you're, you're a little bit being super friendly. One extra, um, point regarding, um, GDP Val AA is that on the basis of the overperformance of the models compared to the chatbots turns out, we realized that, oh, like our reference harness that we built actually white works quite well on like gen generalist agentic tasks. This proves it in a sense. And so the agent harness is very. Minimalist. I think it follows some of the ideas that are in Claude code and we, all that we give it is context management capabilities, a web search, web browsing, uh, tool, uh, code execution, uh, environment. Anything else?Micah [00:50:02]: I mean, we can equip it with more tools, but like by default, yeah, that's it. We, we, we give it for GDP, a tool to, uh, view an image specifically, um, because the models, you know, can just use a terminal to pull stuff in text form into context. But to pull visual stuff into context, we had to give them a custom tool, but yeah, exactly. Um, you, you can explain an expert. No.George [00:50:21]: So it's, it, we turned out that we created a good generalist agentic harness. And so we, um, released that on, on GitHub yesterday. It's called stirrup. So if people want to check it out and, and it's a great, um, you know, base for, you know, generalist, uh, building a generalist agent for more specific tasks.Micah [00:50:39]: I'd say the best way to use it is get clone and then have your favorite coding. Agent make changes to it, to do whatever you want, because it's not that many lines of code and the coding agents can work with it. Super well.swyx [00:50:51]: Well, that's nice for the community to explore and share and hack on it. I think maybe in, in, in other similar environments, the terminal bench guys have done, uh, sort of the Harbor. Uh, and so it's, it's a, it's a bundle of, well, we need our minimal harness, which for them is terminus and we also need the RL environments or Docker deployment thing to, to run independently. So I don't know if you've looked at it. I don't know if you've looked at the harbor at all, is that, is that like a, a standard that people want to adopt?George [00:51:19]: Yeah, we've looked at it from a evals perspective and we love terminal bench and, and host benchmarks of, of, of terminal mention on artificial analysis. Um, we've looked at it from a, from a coding agent perspective, but could see it being a great, um, basis for any kind of agents. I think where we're getting to is that these models have gotten smart enough. They've gotten better, better tools that they can perform better when just given a minimalist. Set of tools and, and let them run, let the model control the, the agentic workflow rather than using another framework that's a bit more built out that tries to dictate the, dictate the flow. Awesome.swyx [00:51:56]: Let's cover the openness index and then let's go into the report stuff. Uh, so that's the, that's the last of the proprietary art numbers, I guess. I don't know how you sort of classify all these. Yeah.Micah [00:52:07]: Or call it, call it, let's call it the last of like the, the three new things that we're talking about from like the last few weeks. Um, cause I mean, there's a, we do a mix of stuff that. Where we're using open source, where we open source and what we do and, um, proprietary stuff that we don't always open source, like long context reasoning data set last year, we did open source. Um, and then all of the work on performance benchmarks across the site, some of them, we looking to open source, but some of them, like we're constantly iterating on and so on and so on and so on. So there's a huge mix, I would say, just of like stuff that is open source and not across the side. So that's a LCR for people. Yeah, yeah, yeah, yeah.swyx [00:52:41]: Uh, but let's, let's, let's talk about open.Micah [00:52:42]: Let's talk about openness index. This. Here is call it like a new way to think about how open models are. We, for a long time, have tracked where the models are open weights and what the licenses on them are. And that's like pretty useful. That tells you what you're allowed to do with the weights of a model, but there is this whole other dimension to how open models are. That is pretty important that we haven't tracked until now. And that's how much is disclosed about how it was made. So transparency about data, pre-training data and post-training data. And whether you're allowed to use that data and transparency about methodology and training code. So basically, those are the components. We bring them together to score an openness index for models so that you can in one place get this full picture of how open models are.swyx [00:53:32]: I feel like I've seen a couple other people try to do this, but they're not maintained. I do think this does matter. I don't know what the numbers mean apart from is there a max number? Is this out of 20?George [00:53:44]: It's out of 18 currently, and so we've got an openness index page, but essentially these are points, you get points for being more open across these different categories and the maximum you can achieve is 18. So AI2 with their extremely open OMO3 32B think model is the leader in a sense.swyx [00:54:04]: It's hooking face.George [00:54:05]: Oh, with their smaller model. It's coming soon. I think we need to run, we need to get the intelligence benchmarks right to get it on the site.swyx [00:54:12]: You can't have it open in the next. We can not include hooking face. We love hooking face. We'll have that, we'll have that up very soon. I mean, you know, the refined web and all that stuff. It's, it's amazing. Or is it called fine web? Fine web. Fine web.Micah [00:54:23]: Yeah, yeah, no, totally. Yep. One of the reasons this is cool, right, is that if you're trying to understand the holistic picture of the models and what you can do with all the stuff the company's contributing, this gives you that picture. And so we are going to keep it up to date alongside all the models that we do intelligence index on, on the site. And it's just an extra view to understand.swyx [00:54:43]: Can you scroll down to this? The, the, the, the trade-offs chart. Yeah, yeah. That one. Yeah. This, this really matters, right? Obviously, because you can b

Introducing Rob Ruiz Meet Rob Ruiz, a seasoned Senior Full Stack Developer with nearly two decades of expertise in WordPress innovation and open-source magic. As the Lead Maintainer of WP Rig since 2020, Rob has been the driving force behind this groundbreaking open-source framework that empowers developers to craft high-performance, accessible, and progressively enhanced WordPress themes with ease. WP Rig isn’t just a starter theme—it’s a turbocharged toolkit that bundles modern build processes, linting, optimization, and testing to deliver lightning-fast, standards-compliant sites that shine on any device. Show Notes For more on Rob and WP Rig, check out these links: LinkedIn Profile: https://www.linkedin.com/in/robcruiz WP Rig Official Site: https://wprig.io GitHub Repository: https://github.com/wprig/wprig Latest Releases: https://github.com/wprig/wprig/releases WP Rig 3.1 Announcement: https://wprig.io/wp-rig-3-1/ Transcript: Topher DeRosia: Hey everybody. Welcome to Hallway Chats. I’m your host Topher DeRosia, and with me today I have- Rob Ruiz: Rob Ruiz. Topher: Rob. You and I have talked a couple of times, once recently, and I learned about a project you’re working on, but not a whole lot about you. Where do you live? What do you do for a living? Rob: Yeah, for sure. Good question. Although I’m originally from Orlando, Florida, I’ve been living in Omaha, Nebraska for a couple of decades now. So I’m pretty much a native. I know a lot of people around here and I’ve been fairly involved in various local communities over the years. I’m a web developer. Started off as a graphic designer kind of out of college, and then got interested in web stuff. And so as a graphic designer turned future web developer, I guess, I was very interested in content management systems because it made the creating and managing of websites very, very easy. My first couple of sites were Flash websites, sites with macro media Flash. Then once I found content management systems, I was like, “Wow, this is way easier than coding the whole thing from scratch with Flash.” And then all the other obvious benefits that come from that. So I originally started with Joomla, interestingly enough, and used Joomla for about two or three years, then found WordPress and never looked back. And so I’ve been using WordPress ever since. As the years have gone on, WordPress has enabled me to slowly transition from a more kind of web designer, I guess, to a very full-blown web developer and software engineer, and even software architect to some degree. So here we are many years later. Topher: There’s a big step from designer to developer. How did that go for you? I’m assuming you went to PHP. Although if you were doing Flash sites, you probably learned ActionScript. Rob: Yeah. Yeah. That was very convenient when I started learning JavaScript. It made it very easy to learn JavaScript faster because I already had a familiarity with ActionScript. So there’s a lot of similarities there. But yeah. Even before I started doing PHP, I started learning more HTML and CSS. I did do a couple of static websites between there that were just like no content management system at all. So I was able to kind of sharpen my sword there with the CSS and HTML, which wasn’t particularly hard. But yeah, definitely, the PHP… that was a big step was PHP because it’s a proper logical programming language. There was a lot there I needed to unpack, and so it took me a while. I had to stick to it and really rinse and repeat before I finally got my feet under me. Topher: I can imagine. All right. So then you work for yourself or you freelance or do you have a real job, as it were? Rob: Currently, I do have a real job. Currently, I’m working at a company called Bold Orange out of Minneapolis. They’re a web agency. But I kind of bounce around from a lot of different jobs. And then, yes, I do freelance on the side, and I also develop my own products as well for myself and my company. Topher: Cool. Bold Orange sounds familiar. Who owns that? Rob: To be honest, I don’t know who the owners are. It’s just a pretty big web agency out of Minneapolis. They are a big company. You could just look them up at boldorange.com. They work for some pretty big companies. Topher: Cool. All right. You and I talked last about WP Rig. Give me a little background on where that came from and how you got it. Rob: Yeah, for sure. Well, there was a period of time where I was working at a company called Proxy Bid that is in the auction industry, and they had a product or a service — I don’t know how you want to look at that —called Auction Services. That product is basically just building WordPress sites for auction companies. They tasked us with a way to kind of standardize those websites essentially. And what we realized is that picking a different theme for every single site made things difficult to manage and increase tech debt by a lot. So what we were tasked with was, okay, if we’re going to build our own theme that we’re just going to make highly dynamic so we can make it look different from site to site. So we want to build it, but we want to build it smart and we want to make it reusable and maintainable. So let’s find a good framework to build this on so that we can maintain coding standards and end up with as little tech debt as possible, essentially. That’s when I first discovered WP Rig. In my research, I came across it and others. We came across Roots Sage and some of the other big names, I guess. It was actually a team exercise. We all went out and looked for different ones and studied different ones and mine that I found was WP Rig. And I was extremely interested in that one over the other ones. Interestingly enough- Topher: Can you tell me why over the other ones? Rob: That’s a great question. Yeah. I really liked the design patterns. I really liked the focus on WordPress coding standards. So having a system built in that checked all the code against WordPress coding standards was cool. I loved the compiling transpiling, whatever, for CSS and JavaScript kind of built in. That sounded really, really interesting. The fact that there was PHP unit testing built into it. So there’s like a starter testing framework built in that’s easy to extend so that you can add additional unit tests as your theme grows. We really wanted to make sure… because we were very into CICD pipelines. So we wanted to make sure that as developers were adding or contributing to any themes that we built with this, that we could have automated tests run and automated builds run, and just automate as much as possible. So WP rig just seemed like something that gave us those capabilities right out of the box. So that was a big thing. And I loved the way that they did it. Roots Sage does something similar, but they use their blade templating engine built in there. We really wanted to stick to something that was a bit more standard WordPress so that there wasn’t like a large knowledge overhead so that we didn’t have to say like, okay, if we’re bringing on other developers, like junior developers work on it, oh, it would be nice if you use Laravel too because we use this templating engine in all of our themes. We didn’t want to have to worry about that essentially. It was all object-oriented and all that stuff too. That’s what looked interesting to me. We ended up building a theme with WP Rig. I don’t know what they ended up doing with it after that, because I ended up getting let go shortly thereafter because the company had recently been acquired. Also, this was right after COVID too. So there was just a lot of moving parts and changing things at the time. So I ended up getting let go. But literally a week after I got let go, I came across a post on WP Tavern about how this framework was looking for new maintainers. Basically, this was a call put out by Morton, the original author of WP Rig. He reached out to WP Tavern and said, “Look, we’re not interested in maintaining this thing anymore, but it’s pretty cool. We like what we’ve built. And so we’re looking for other people to come in and adopt it essentially.” So I joined a Zoom meeting with a handful of other individuals that were also interested in this whole endeavor, and Morton reached out to me after the call and basically just said, “I looked you up. I liked some of the input that you had during the meeting. Let’s talk a little bit more.” And then that eventually led to conversations about me essentially taking the whole project over entirely. So, the branding, the hosting of the website, being lead maintainer on the project. Basically, gave me the keys to the kingdom in terms of GitHub and everything. So that’s how it ended up going in terms of the handoff between Morton and I. And I’m very grateful to him. They really created something super cool and I was honored to take it over and kind of, I don’t know, keep it going, I guess. Topher: I would be really curious. I don’t think either of us have the answer. I’d be curious to know how similar that path is to other project handoffs. It’s different from like an acquisition. You didn’t buy a plugin from somebody. It was kind of like vibes, I guess. Rob: It was like vibes. It was very vibey. I guess that’s probably the case in an open source situation. It’s very much an open source project. It’s a community-driven thing. It’s for everybody by everybody. I don’t know if all open source community projects roll like that, but that’s how this one worked out. There was some amount of ownership on Morton’s behalf. He did hire somebody to do the branding for WP Rig and the logo. And then obviously he was paying for stuff like the WPrig.io domain and the hosting through SiteGround and so on and so forth. So, we did have to transfer some of that and I’ve taken over those, I guess, financial burdens, if you want to think of it like that. But I’m totally okay with it. Topher: All right. You sort of mentioned some of the things Rig does, compiling and all that kind of stuff. Can you tell me… we didn’t discuss this before. I’m sitting at my desk and I think I want a website. How long does it take to go from that to looking at WordPress and logging into the admin with Rig? Rob: Okay. Rig is not an environment management system like local- Topher: I’m realizing my mistake. Somebody sends me a design in Figma. How long does it take me to go from that to, I’m not going to say complete because I mean, that’s CSS, but you know, how long does it take me to get to the point where I’m looking at a theme that is mine for the client that I’m going to start converting? Rob: Well, if you’re just looking for a starting point, if you’re just like, okay, how long does it take to get to like, okay, here’s my blank slate and I’m ready to start adopting all of these rules that are set up in Figma or whatever, I mean, you’re looking at maybe 5 minutes, 10 minutes, something like that. It’s pretty automated. You just need some simple knowledge of Git. And then there are some prerequisites to using WP Rig. You do have to have composer installed because we do leverage some Composer packages to some of it, although to be honest, you could probably get away with not using Composer. You just have to be okay with sacrificing some of the tools the WP Rig assumes you’re going to have. And then obviously Node. You have to have Node installed. A lot of our documentation assumes that you have NPM, that you’re using NPM for all your Nodes or your package management. But we did recently introduce support for Bun. And so you can use Bun instead of NPM, which is actually a lot faster and better in many ways. Topher: Okay. A lot of my audience are not developers, users, or light developers, like they’ll download a theme, hack a template, whatever. Is this for them? Am I boring those people right now? Rob: That’s a great question. I mean, and I think this is an interesting dichotomy and paradigm in the WordPress ecosystem, because you’ve got kind of this great divide. At least this is something I’ve noticed in my years in the WordPress community is you have many people that are not coders or developers that are very interested in expanding their knowledge of WordPress, but it’s strictly from a more of a marketing perspective where it’s like, I just want to know how to build websites with WordPress and how to use it to achieve my goals online from a marketing standpoint. You have that group of people, and then you have this other group of people that are very developer centric that want to know how to extend WordPress and how to empower those other people that we just discussed. Right? Topher: Right. Rob: So, yeah, that’s a very good question. I would say that WP Rig is very much designed for the developers, not for the marketers. The assumption there is that you’re going to be doing some amount of coding. Now, can you get away with doing a very light amount of coding? Yes. Yes, you can. I mean, if you compare what you’re going to get out of that assumed workflow to something that you would get off like Theme Forest or whatever, it’s going to be a night and day difference because those theme, Forest Themes, have hours, hundreds, sometimes hundreds of hours of development put into them. So, you’re not going to just out of the box immediately get something that is comparable to that. Topher: You need to put in those hundreds of hours of development to make a theme. Rob: As of today, yes. That may change soon though. Topher: Watch this space. Rob: That’s all I’ll say. Topher: Okay. So now we know who it’s for. I’m assuming there’s a website for it. What is it? Rob: Yeah. If you go to WPrig.io, we have a homepage that shows you all the features that are there in WP Rig. And then there’s a whole documentation area that helps people get up and running with WP Rig because there is a small learning curve there that’s pretty palatable for anybody who’s familiar with modern development workflows. So that is a thing. So the type of person that this is designed for anybody that wants to make a theme for anything. Let’s say you’re a big agency and you pull in a big client and that client wants something extremely custom and they come to you with Figma designs. Sure, you could go out there and find some premium theme and try to like child theme and overhaul that if you want. But in many situations, I would say in most situations, if you’re working from a Figma design that’s not based off of another theme already that’s just kind of somebody else’s brainchild, then you’re probably going to want to start from scratch. And so the idea here is that this is something to replace an approach, like underscores an approach. Actually, WP Pig was based off of underscores. The whole concept of it, as Morton explained it to me, was that he wanted to build an underscores that was more modern and full-featured from a development standpoint. Topher: Does it have any opinions about Gutenberg? Rob: It does now, but it did not when I took it over because Gutenberg did not exist yet when I took over WP Rig. Topher: Okay. What are its opinions? Rob: Yeah, sure. The opinion right out of the gate is that you can use Gutenberg as an editor and it has support like CSS rules in it for the standard blocks. So you should be able to use regular Gutenberg blocks in your theme and they should look just fine. There’s no resets in there. It doesn’t start from scratch. There’s not a bunch of styling you have to do for the blocks necessarily. Now, if you go to the full site editing or block-based mentality here, there are some things you need to do in WP Rig to convert the out-of-the-box WP Rig into another paradigm essentially. Right when you pull WP Rig, the assumption is you’re building what most people would refer to as a hybrid theme. The theme supports API or whatever, and the assumption is that you’re not going to be using the site editor. You’re just going to kind of do traditional WordPress, but you might be using Gutenberg for your content. So you’re just using Gutenberg kind of to author your pages and your posts and stuff like that, but not necessarily the whole site. WP Rig has the ability to kind of transform itself into other paradigms. So the first paradigm we built out was the universal theme approach. And the idea there is that you get a combination of the full site editing capabilities. But then you also have the traditional menu manager and the settings customizer framework or whatever is still there, right? These are things that don’t exist in a standard block-based theme. So I guess an easy example would be like the 2025 WordPress theme that comes right out of the box. It comes installed in WordPress. That is a true block-based theme, not a universal theme. So it doesn’t have those features because the assumption there is that it doesn’t need those features. You can kind of transform WP Rig into a universal theme that’s kind of a hybrid between a block-based and a classic theme. And then it can also transform into a strictly block-based theme as well. So following the same architecture as like the WordPress 2025 theme or Ollie or something like that is also a true block-based theme as well. So you can easily convert or transform the starting point of WP Rig into either of those paradigms if that’s the type of theme you’re setting out to build. Topher: Okay. That sounds super flexible. How much work is it to do that? Rob: It’s like one command line. Previously we had some tutorials on the website that showed you step-by-step, like what you needed to change about the theme to do that. You would have to add some files, delete some files, edit some code, add some theme supports into the base support class and some other stuff. I have recently, as of like a year and a half ago or a year ago, created a command line or a command that you can type into the command line that basically does that entire conversion process for you in like the blink of an eye. It takes probably a second to a second and a half to perform those changes to the code and then you’re good to go. It is best to do that conversion before you start building out your whole theme. It’s not impossible to do it after. But you’re more likely to run into problems or conflicts if you’ve already set out building your whole theme under one paradigm, and then you decide how the project you want to switch over to block-based or whatever. You’re likely to run into the need to refactor a bunch of stuff in that situation. So it is ideal to make that choice extremely early on in the process of developing your theme. But either way it’ll still work. That’s just one of the many tools that exist in WP Rig to transform it or convert it in several ways. That’s just one example. There are other examples of ways that Rig kind of converts itself to other paradigms as well. Topher: Yeah. All right. In my development life, I’ve had two parts to it. And one is the weekend hobbyist, or I download cadence and I whip something up in 20 minutes because I just want to experiment and the other is agency life where everything’s in Git, things are compiled, there are versions, blah, blah, blah. This sounds very friendly to that more professional pathway. Rob: Absolutely. Yes. Or, I mean, there’s another situation here too. If you’re a company who develops themes and publishes them to a platform like ThemeForest or any other platform, perhaps you’re selling themes on your own website, whatever, if you’re making things for sale, there’s no reason you couldn’t use WP Rig to build your themes. We have a bundle process that bundles your theme for publication or publishing. Whether you’re an agency or whether you’re putting your theme out for sale, it doesn’t matter, during that bundle process, it does actually white label the entire code base to where there’s no mention of WP Rig in the code whatsoever. Let’s say you were to build a theme that you wanted to put up for sale because you have some cool ideas. Say, page transitions now are completely supported in all modern or in most modern browsers. And when I say print page transitions, for those that are in the know, I am talking about not single page app page transitions, but through website page transitions. You can now do that. Let’s say you were like, “Hey, I’m feeling ambitious and I want to put out some new theme that comes with these page transitions built in,” and that’s going to be fancy on ThemeForest when people look at my demo, people might want to buy that. You could totally use WP Rig to build that out into a theme and the bundle process will white label all of the code. And then when people buy your theme and download that code, if they’re starting to go through and look through your code, they’re not going to have any way of knowing that it was built with WP Rig unless they’re familiar with the base WP Rig architecture, like how it does its object-oriented programming. It might be familiar with the patterns that it’s using and be able to kind of discern like, okay, well, this is the same pattern WP Rig uses, so high likelihood it was built with WP Rig. But they’re not going to be able to know by reading through the code. It’s not going to say WP Rig everywhere. It’s going to have the theme all over the place in the code. Topher: Okay. So then is that still WP Rig code? It just changed its labels? Rob: Yeah. Topher: So, it’s not like you’re exporting HTML, CSS and JavaScript? The underlying Rig framework is still there. Rob: Yeah. During the bundle process, it is bundling CSS and HTML. Well, HTML in the case of a block-based theme. But, yeah, it is bundling your PHP, your CSS, your JavaScript into the theme that you’re going to let people download when they buy it, or that you’re going to ship to your whatever client’s website. But all that code is going to be transpiled. In the case of CSS and JavaScript, there’s only going to be minified versions of that code in that theme. The source code is not actually going to be in there. Topher: This sounds pretty cool. You mentioned some stuff might be coming. You don’t have to tell me what it is, but do you have a timeline? When should we be watching for the next cool thing from Rig? Rob: Okay, cool. Well, I’m going to keep iterating on Rig forever. Regardless of any future products that might be built on WP Rig, WP Rig will always and forever remain an open source product for anybody to use for free and we, I, and possibly others in the future will continue to update it and support it over time. We just recently put out 3.1. You could expect the 3.2 anytime in the next six months to a year, probably closer to six months. One feature I’m looking at particularly closely right now is the new stuff coming out in version 6.9 of WordPress around the various APIs that are there. I think one of them is called the form… There’s a field API and a form API or view API or something like that. So WP Rig comes with a React-based settings framework in it. So if you want your theme to have a bunch of settings in it to make it flexible for whoever buys your theme, you can use this settings framework to easily create a bunch of fields, and then that framework will automatically manage all your fields and store all the data from those fields and make it easy to retrieve the values of the input on those fields, without knowing any React at all. Now, if you know React, you can go in there and, you know, embellish what’s already there, but it takes a JSON approach. So if you just understand JSON, you can go in and change the JSON for the framework, and that will automatically add fields into the settings framework. So you don’t even have to know React to extend the settings page if you want. That will likely get an overhaul using these new APIs being introduced into Rig. Topher: All right. How often have you run into something where, “Oh, look, WordPress has a new feature, I need to rebuild my system”? Rob: Over the last four or five years, it’s happened a lot because, yeah, I mean, like I said, when I first took this thing over, Gutenberg had not even been introduced yet. So, you had the introduction of Gutenberg and blocks. That was one thing. Then this whole full site editing became a thing, which later became the site editor. So that became a whole thing. Then all these various APIs. I mean, it happens quite frequently. So I’ve been working to keep it modern and up to date over the past four years and it’s been an incredible learning experience. It not only keeps my WordPress knowledge extremely sharp, but I’ve also learned how various other toolkits are built. That’s been the interesting thing. From a development standpoint, there’s two challenges here. One of the challenges is staying modern on the WordPress side of things. For instance, WordPress coding standards came out with a version 3 and then a version 3.1 about two years ago. I had to update WP Rig to leverage those modern coding standards. So that’s one example is as WordPress changes, the code in WP Rig also needs to change. Or for instance, if new CSS standards change, right, new CSS properties come out, it is ideal for the base CSS in WP Rig, meaning the CSS that you get right out of the box with it, comes with some of these, for instance, CSS grid, Flexbox, stuff like that. If I was adopting a theme framework to build a theme on, I would expect some of that stuff to be in there. And those things were extremely new when I first took over WP Rig and were not all baked in there essentially. So I’ve had to add a lot of that over time. Now there’s another side to this, which is not just keeping up with WordPress and CSS and PHP, 8. whatever, yada yada yada. You’ve also got the toolkit. There are various node packages and composer packages of power WP Rig and the process in which it does the transpiling, the bundling, the automated manipulation of your code during various aspects of the usage of WP Rig is a whole nother set of challenges because now you have to learn concepts like, well, how do I write custom node scripts? Right? Like there were no WP CLI commands built into WP Rig when I first took it over. Now there’s a whole list. There’s a whole library of WP CLI commands that come in Rig right out of the gate. And so I’ve had to learn about that. So just various things that come with knowing how do you automate the process of converting code, that’s something that was completely foreign to me when I first took over WP Rig. That’s been another incredible learning experience is understanding like what’s the difference between Webpack and Gulp. I didn’t know, right? I would tell people I’m using Gulp and WP Rig and they would be like, “Well, why don’t you just use Webpack?” and I would say, “I don’t know. I don’t know what the difference is.” So over time I could figure out what are the differences? Why aren’t we using Webpack? And I’m glad I spent some time on that because it turns out Webpack is not the hottest thing anymore, so I just skipped right over all that. When I overhauled for version 3, we’re now not using Gulp anymore as of 3.1. We’re now using more of a Vite-like process, far more modern than Webpack and far better and faster and sleeker and lighter. I had to learn a bunch about what powers Vite. What is Vite doing under the hood that we might be able to also do in WP Rig, but do it in a WordPress way. Because Vite is a SaaS tool. If you’re building a SaaS, like React with a… we’re not a SaaS. I guess a spa is a better term to use here. If you’re building a single page application with React or view or belt or whatever, right, then knowing what Vite is and just using Vite right out of the box is perfect. But it doesn’t translate perfectly to WordPress land because WordPress has its own opinions. And so I did have to do some dissecting there and figure out what to keep and what to not keep to what to kind of set aside so that WordPress can keep doing what WordPress does the way WordPress likes to do it, but also improve on how we’re doing some of the compiling and transpiling and the manipulation of the code during these various. Topher: All right. I want to pivot a little bit to some personal-ish questions. Rob: Okay. Topher: This is a big project. I’m sure it takes up plenty of your time. How scalable is that in your life? Do you want to do this for the rest of your life? Rob: That’s a fantastic question. I don’t know about the rest of my life. I mean, I definitely want to do web development for the rest of my life because the web has, let’s be honest, it’s transformed everyone’s way of life, whether you’re a web developer or not. You know, the fact that we have the internet in our pocket now, you know, it has changed everything. Apps, everything. It’s all built on the web. So I certainly want to be involved in the web the rest of my life. Do I want to keep doing WordPress the rest of my life? I don’t know. Do I want to keep doing WP Rig the rest of my life? I don’t know. But I will say that you bring up a very interesting point, which is it does take up a lot of time and also trust in open source over the past four or five years I would argue has diminished a little bit as a result of various events that have occurred over the past two or three years. I mean, we could cite the whole WP Engine Matt Mullerwig thing. We can also cite what’s going on with Oracle and JavaScript. Well, I mean, there’s many examples of this. I mean, we can cite the whole thing that happened… I mean, there’s various packages out there that are used and developed and open source to anybody, and some of them are going on maintained and it’s causing security vulnerabilities and degradation and all this stuff. So it’s a very important point. One thing I started thinking about after considering that in relation to WP Rig was I noticed that there’s usually a for-profit arm of any of these frameworks that seems to extend the lifespan of it. Let’s just talk about React, for example, React is an open source JavaScript framework, but it’s used by Facebook and Facebook is extremely for-profit. So companies that are making infrastructural or architectural decisions, they will base their choice on whether or not to use a framework largely on how long they think this framework is going to remain relevant or valid or maintained, right? A large part of that is, well, is there a company making money off of this thing? Because if there is, the chances- Topher: They’re going to keep doing that. Rob: They’re going to keep doing it. It’s going to stay around. That’s good. I think that’s healthy. A lot of people that like open source and want everything to be free, they might look at something like that and say like, well, I don’t want you to make a paid version of it or there shouldn’t be a pro version. I think that’s a very short-sighted way of looking at that software and these innovations. I think a more experienced way of looking at it is if you want something to remain relevant and maintained for a long period of time, having a for-profit way in which it’s leveraged is a very good thing. I mean, let’s be real. Would WordPress still be what it is today if there wasn’t a wordpress.com or if WooCommerce wasn’t owned by Automattic or whatever, right? They’ll be on top. I mean, it’s obviously impossible to say, but my argument would be, probably not. I mean, look at what’s happened to the other content management systems out there. You know, Joomla Drupal. They don’t really have a flourishing, you know, paid pro service that goes with their thing that’s very popular, at least definitely not as popular as WordPress.com or WordPress VIP or some of these other things that exist out there. And so having something that’s making and generating money that can then contribute back into it the way Automattic has been doing with WordPress over these years has, in my opinion, been instrumental. I mean, people can talk smack about Gutenberg all they want, but let’s be real, it’s 2025, would you still feel that WordPress is an elegant solution if we were still working from the WYSIWYG and using the classic editor? And I know a lot of people are still using the classic editor and there’s classic for us, the fork and all that stuff. But I mean, that only makes sense in a very specific implementation of WordPress, a very specific paradigm. If you want to explore any of these other paradigms out there, that way of thinking about WordPress kind of falls apart pretty quickly. I, for one, am happy that Gutenberg exists. I’m very happy that Automattic continues. And I’m grateful, actually, that Automattic continues to contribute back into WordPress. And not just them, obviously there’s other companies, XWP, 10Up, all these other companies are also contributing as well. But I’m very grateful that this ecosystem exists and that there’s contribution going back in and it’s happening from companies that are making money with this. And I think that’s vital. All that to say that WP Rig may and likely will have paid products in the future that leverage WP Rig. So that’s not to say that WP Rig will eventually cost money. That’s just to say that eventually people can expect other products to come out in the future that will be built on WP Rig and incentivize the continued contributions back into WP Rig. The open source version of WP Rig. Topher: That’s cool. I think that’s wise. If you want anything to stay alive, you have to feed it. Rob: That’s right. Topher: I had some more questions but I had forgotten them because I got caught up in your answer. Rob: Oh, thank you. I’ll take that as a compliment. I mean, my answer was eloquent. But I’m happy to expand on anything, know you, WordPress related, me related, you know, whether it comes to the ecosystem in WordPress, the whole WordCamp meetup thing is very interesting. I led the WP Omaha meetup for many years here in Omaha, Nebraska and I also led the WordCamp, the organizing of WordCamp here in Omaha for several years as well. That whole community, the whole ecosystem, at least in America seems to have largely fallen apart. I don’t know if you want to talk about that at all. But yeah, I’m ready to dive into any topics. Topher: I’m going to have one more question and then we’re going to wrap up. And it was that you were talking about all the things you had to learn. I’m sure there were nights where you were looking at your computer thinking, “Oh man, I had it working, now I gotta go learn a new thing.” I would love for you to go back in time and blog all of that if you would. But given that you can’t, I would be interested in a blog moving forward, documenting what you’re learning, how you’re learning it and starting maybe with a post that’s summarizes all of that. Obviously, that’s up to you and how you want to spend your time, but I think it’d be really valuable to other people starting a project, picking up somebody else’s project to see what the roadmap might look like. You know what I mean? Rob: For sure. Well, I can briefly summarize what I’ve learned over the years and where I’m at today with how I do this kind of stuff. I will say that a lot of the improvements to WP Rig that have happened over the last year or two would not be possible without the advent of AI. Topher: Interesting. Rob: That’s a fancy way of saying that I have been by coding a lot of WP Rig lately. If you know how to use AI, it is extremely powerful and it can help you do many things very quickly that previously would have taken much longer or more manpower. So, yeah, perhaps if there was like five, six, seven people actively, excuse me, actively contributing to WP Rig, then this type of stuff would have been possible previously, but that’s not the case. There is one person, well, one main contributor to WP Rig today and you’re talking to them. There are a handful of other people that have been likely contributing to WP Rig over the versions and you can find their contributions in the change log file in WP Rig. But those contributions have been extremely light compared to what I’ve been doing. I wouldn’t be able to do any of it without AI. I have learned my ability to learn things extremely rapidly has ramped up tenfold since I started learning how to properly leverage LLMs and AI. So that’s not to say that like, you know, WP Rig, all the code is just being completely written by AI and I’m just like. make it better, enter, and then like WP Rig is better. I wish it was that easy. It’s certainly not that. But when I needed to start asking some of these vital questions that I really didn’t have anyone to turn to to help answer them, I was able to turn to AI. For instance, let’s go back to the Webpack versus Gulp situation. Although Gulp is no longer used in WP Rig, you know, it was used in WP Rig until very recently. So I had to understand like, what is this system, how does it work, how do I extend it and how do I update it and all these things, right? And why aren’t we using WebPack and you know, is there validity to this criticism behind you should use webpack instead of Gulp or whatever, right? I was able to use AI to ask these questions and be able to get extremely good answers out of it and give me the direction I needed to make some of these kind of higher level decisions on like architecturally where should WP Rig go? It was through these virtual conversations with LLMs that I was able to refine the direction of WP Rig in a direction that is both modern and forward-thinking and architecturally sound. I learned a tremendous amount from AI about the architecture, about the code, about all of it. My advice to anybody that wants to extend their skill set a little bit in the development side of things is to leverage this new thing that we have in a way that is as productive as possible for you. So that’s going to vary from person to person. But for me, if I’m on a flight or if I’m stuck somewhere for a while, like, let’s say I got to take my kid to practice or something and I’m stuck there for an hour and I got to find some way to kill my time 9 times out of 10, I’m on my laptop or on my phone having conversations with Grok or ChatGPT or Gemini or whatever. I am literally refining… I’m just sitting there asking it questions that are on my mind that I wish I could ask somebody who’s like 10 times more capable than me. It has been instrumental. WP Rig wouldn’t be where it is today if it wasn’t for that. I would just say to anybody, especially now that it’s all on apps and you don’t have to be on a browser anymore, adopt that way of thinking. You know, if you’re on your lunch break or whatever and you have an hour lunch break and you only take 15 minutes to eat, what could you be doing with those other 45 minutes? You could just jump on this magical thing that we have now and start probing it for questions. Like, Hey, here’s what I know. Here’s what I don’t know. Fill these knowledge gaps for me.” And it is extremely good at doing that. Topher: So my question was, can you blog this and your answer told me that there’s more there that I want to hear. That’s the stuff that should be in your book when you write your book. Rob: I’m flattered that you would be interested in reading anything that I write. So thank you. I’ve written stuff in the past and it hasn’t gotten a lot of attention. But I also don’t have any platforms to market it either. But yeah, no, I made some… I’m sorry. Topher: I think your experience is valuable far beyond Rig or WordPress. If you abstract it out of a particular project to say, you know, I did this with a project, I learned this this way, I think that would be super valuable. Rob: Well, I will say that recently at my current job, I was challenged to create an end to end testing framework with Playwright that would speed up how long it takes to test things and also prevent, you know, to make things fail earlier, essentially, to prevent broken things from ending up in the wild, right, and having to catch them the hard way. I didn’t know a lot about Playwright, but I do know how toolkits work now because of WP Rig. And I was able to successfully in a matter of, I don’t know, three days, put together a starter kit for a test framework that we’re already using at work to test any website that we create for any client. It can be extended and it can be hooked into any CI CD pipeline and it generates reports for you and it does a whole bunch of stuff. I was able to do this relatively quickly. This knowledge, yes, does come in handy in other situations. Will I end up developing other toolkits like WP Rig in the future for other things? I guess if I can give any advice to anybody listening out there, another piece of advice I would give people is, you know, especially if you’re a junior developer and you’re still learning or whatever, or you’re just a marketing person and just want to have more control over the functionality side of what you’re creating or more insight into that so you could better, you know, manage projects or whatever. My advice would be to take on a small little project that is scoped relatively small that’s not too much for you to chew and go build something and do it with… Just doing that will be good. But if you can do it with the intent to then present it in some fashion, whether it be a blog article or creating a YouTube video or going to a meetup and giving a talk on it or even a lunch and learn at work or whatever, right, that will, in my experience, it will dramatically amplify how much you learn from that little pet project that’s kind of like a mini learning experience. And I highly encourage anybody out there to do that on the regular. Actually, no matter what your experience level is in development, I think you should do these things on a regular basis. Topher: All right. I’m going to wrap this up. I got to get back to work. You probably have to get back to work. Rob: Yeah. Topher: Thanks for talking. Rob: Thanks for having me, Topher. Really appreciate it. Topher: Where could people find you? WPrig.io?  Rob: Yeah, WPrig.io. WP rig has accounts on all of the major platforms and, even on Bluesky and Mastodon. You can look me up, Rob Ruiz. You can find me on LinkedIn. You can find me on all of those same platforms as well. You can add me on Facebook if you want, whatever. And I’m also in the WordPress Slack as well as Rob Ruiz. You can find me in the WordPress Slack. And then I’m on the WordPress Reddit and all that stuff. So yeah, reach out. If anybody wants to have any questions about Rig or anything else, I’m happy to engage.  Topher: Sounds good. All right, I’ll see you. Rob: All right, thanks, Topher. Have a good day. Topher: This has been an episode of the Hallway Chats podcast. I’m your host Topher DeRosia. Many thanks to our sponsor Nexcess. If you’d like to hear more Hallway Chats, please let us know on hallwaychats.com.

אז מה אומרים? פוסטגרס קצת יותר, ואסקיואל קצת פחות? גיא ואיתן פותחים את הנושא ומדברים על זה. מתי כן? מתי לא? מתי כדאי? וגם מדברים על כמה חידושים מעניינים ב-SQL Server, ועוד. קישורים רלוונטיים: Working with Amazon Aurora PostgreSQL - Amazon Aurora Announcing Azure HorizonDB | Microsoft Community Hub microsoft-dbas-club/Diagnostics/Sleeping Sessions with Old Open Transactions.sql Regular Expressions Functions (Transact-SQL) - SQL Server | Microsoft Learn Format Query Results as JSON with FOR JSON - SQL Server | Microsoft Learn Inside SQL Server Inconsistencies – Part 1: Fundamentals (Hebrew Edition) Inside SQL Server Inconsistencies – Part 2. Create and solve inconsistency in a DB (Hebrew edition) SQL Server Inconsistencies – Part 3. Fixing inconsistency on a clustered index (Hebrew edition)

פרק מספר 508 של רברס עם פלטפורמה, שהוקלט ב-30 בדצמבר 2025, קצת לפני שנגמרה השנה וקצת אחרי שבאמת התחיל סוג של חורף - אורי ורן לוגמים תה ומארחים את מישה פיינשטיין מחברת Bria AI כדי לדבר על איך עושים תמונות בצורה שבאמת התכוונתם (וגם קצת על חורף).

Lois Houston and Nikita Abraham explore how Oracle APEX integrates with AI to build smarter low-code applications. They are joined by Chaitanya Koratamaddi, Director of Product Management at Oracle, who explains the basics of Oracle APEX, its global adoption, and the challenges it addresses for businesses managing and integrating data.   They also explore real-world use cases of AI within the Oracle APEX ecosystem   Oracle APEX: Empowering Low Code Apps with AI: https://mylearn.oracle.com/ou/course/oracle-apex-empowering-low-code-apps-with-ai/146047/ Oracle University Learning Community: https://education.oracle.com/ou-community LinkedIn: https://www.linkedin.com/showcase/oracle-university/ X: https://x.com/Oracle_Edu   Special thanks to Arijit Ghosh, David Wright, Kris-Ann Nansen, Radhika Banka, and the OU Studio Team for helping us create this episode.   ---------------------------------------------------   Episode Transcript: 00:00 Welcome to the Oracle University Podcast, the first stop on your cloud journey. During this series of informative podcasts, we'll bring you foundational training on the most popular Oracle technologies. Let's get started! 00:25 Lois: Hello and welcome to the Oracle University Podcast! I'm Lois Houston, Director of Communications and Adoption with Customer Success Services, and with me is Nikita Abraham, Team Lead: Editorial Services with Oracle University.   Nikita: Hi everyone! We hope you've been enjoying these last few weeks as we've been revisiting our most popular episodes of the year. Today's episode is the last one in this series and is a throwback to a conversation on APEX with Chaitanya Koratamaddi, Director of Product Management for Oracle APEX.  00:57 Lois: We began by asking Chaitanya what Oracle APEX is and why it's so widely used. So, let's jump right in!   Chaitanya: Oracle APEX is the world's most popular enterprise low code application platform. APEX enables you to build secure and scalable enterprise-scale applications with world class features that can be deployed anywhere, cloud or on-premises. And with APEX, you can build applications 20 times faster with 100 times less code. APEX delivers the most productive way to develop and deploy mobile and web applications everywhere. 01:40 Lois: That's impressive. So, what's the adoption rate like for Oracle APEX? Chaitanya: As of today, there are 19 million plus APEX applications created globally. 5,000 plus APEX applications are created on a daily basis and there are 800,000 plus APEX developers worldwide. 60,000 plus customers in 150 countries across various industry verticals. And 75% of Fortune 500 companies use Oracle APEX. 02:19 Nikita: Wow, the numbers really speak for themselves, right? But Chaitanya, why are organizations adopting Oracle APEX at this scale? Or to put it differently, what's the core business challenge that Oracle APEX is addressing? Chaitanya: From databases to all data, you know that the world is more connected and automated than ever. To drive new business value, organizations need to explore and exploit new sources of data that are generated from this connected world. That can be sounds, feeds, sensors, videos, images, and more. Businesses need to be able to work with all types of data and also make sure that it is available to be used together. Typically, businesses need to work on all data at a massive scale. For example, supply chains are no longer dependent just on inventory, demand, and order management signals. A manufacturer should be able to understand data describing global weather patterns and how it impacts their supply chains. Businesses need to pull in data from as many social sources as possible to understand how customer sentiment impacts product sales and corporate brands. Our customers need a data platform that ensures all this data works together seamlessly and easily. 04:00 Lois: So, you're saying Oracle APEX is the platform that helps businesses manage and integrate data seamlessly. But data is just one part of the equation, right? Then there's AI. How are the two related?  Chaitanya: Before we start talking about Oracle AI, let's first talk about what customers are looking for and where they are struggling within their AI innovation. It all starts with data. For decades, working with data has largely involved dealing with structured data, whether it is your customer records in your CRM application and orders from your ERP database. Data was organized into database and tables, and when you needed to find some insights in your data, all you need to do is just use stored procedures and SQL queries to deliver the answers. But today, the expectations are higher. You want to use AI to construct sophisticated predictions, find anomalies, make decisions, and even take actions autonomously. And the data is far more complicated. It is in an endless variety of formats scattered all over your business. You need tools to find this data, consume it, and easily make sense of it all. And now capabilities like natural language processing, computer vision, and anomaly detection are becoming very essential just like how SQL queries used to be. You need to use AI to analyze phone call transcripts, support tickets, or email complaints so you can understand what customers need and how they feel about your products, customer service, and brand. You may want to use a data source as noisy and unstructured as social media data to detect trends and identify issues in real time.  Today, AI capabilities are very essential to accelerate innovation, assess what's happening in your business, and most importantly, exceed the expectations of your customers. So, connecting your application, data, and infrastructure allows everyone in your business to benefit from data. 06:54 Oracle University is proud to announce three brand new courses that will help your teams unlock the power of Redwood—the next generation design system. Redwood enhances the user experience, boosts efficiency, and ensures consistency across Oracle Fusion Cloud Applications. Whether you're a functional lead, configuration consultant, administrator, developer, or IT support analyst, these courses will introduce you to the Redwood philosophy and its business impact. They'll also teach you how to use Visual Builder Studio to personalize and extend your Fusion environment. Get started today by visiting mylearn.oracle.com.  07:35 Nikita: Welcome back! So, let's focus on AI across the Oracle Cloud ecosystem. How does Oracle bring AI into the mix to connect applications, data, and infrastructure for businesses? Chaitanya: By embedding AI throughout the entire technology stack from the infrastructure that businesses run on through the applications for every line of business, from finance to supply chain and HR, Oracle is helping organizations pragmatically use AI to improve performance while saving time, energy, and resources.  Our core cloud infrastructure includes a unique AI infrastructure layer based on our supercluster technology, leveraging the latest and greatest hardware and uniquely able to get the maximum out of the AI infrastructure technology for scenarios such as large language processing. Then there is generative AI and ML for data platforms. On top of the AI infrastructure, our database layer embeds AI in our products such as autonomous database. With autonomous database, you can leverage large language models to use natural language queries rather than writing a SQL when interacting with the autonomous database. This enables you to achieve faster AI adoption in your application development. Businesses and their customers can use the Select AI natural language interface combined with Oracle Database AI Vector Search to obtain quicker, more intuitive insights into their own data. Then we have AI services. AI services are a collection of offerings, including generative AI with pre-built machine learning models that make it easier for developers to apply AI to applications and business operations. The models can be custom-trained for more accurate business results. 09:47 Nikita: And what specific AI services do we have at Oracle, Chaitanya?  Chaitanya: We have Oracle Digital Assistant Speech, Language, Vision, and Document Understanding. Then we have Oracle AI for Applications. Oracle delivers AI built for business, helping you make better decisions faster and empowering your workforce to work more effectively. By embedding classic and generative AI into its applications, Fusion Apps customers can instantly access AI outcomes wherever they are needed without leaving the software environment they use every day to power their business. 10:32 Lois: Let's talk specifically about APEX. How does APEX use the Gen AI and machine learning models in the stack to empower developers. How does it help them boost productivity? Chaitanya: Starting APEX 24.1, you can choose your preferred large language models and leverage native generative AI capabilities of APEX for AI assistants, prompt-based application creation, and more. Using native OCI capabilities, you can leverage native platform capabilities from OCI, like AI infrastructure and object storage, etc. Oracle APEX running on autonomous infrastructure in Oracle Cloud leverages its unique native generative AI capabilities tuned specifically on your data. These language models are schema aware, data aware, and take into account the shape of information, enabling your applications to take advantage of large language models pre-trained on your unique data. You can give your users greater insights by leveraging native capabilities, including vector-based similarity search, content summary, and predictions. You can also incorporate powerful AI features to deliver personalized experiences and recommendations, process natural language prompts, and more by integrating directly with a suite of OCI AI services. 12:08 Nikita: Can you give us some examples of this? Chaitanya: You can leverage OCI Vision to interpret visual and text inputs, including image recognition and classification. Or you can use OCI Speech to transcribe and understand spoken language, making both image and audio content accessible and actionable. You can work with disparate data sources like JSON, spatial, graphs, vectors, and build AI capabilities around your own business data. So, low-code application development with APEX along with AI is a very powerful combination. 12:51 Nikita: What are some use cases of AI-powered Oracle APEX applications?  Chaitanya: You can build APEX applications to include conversational chatbots. Your APEX applications can include image and object detection capability. Your APEX applications can include speech transcription capability. And in your applications, you can include code generation that is natural language to SQL conversion capability. Your applications can be powered by semantic search capability. Your APEX applications can include text generation capability. 13:30 Lois: So, there's really a lot we can do! Thank you, Chaitanya, for joining us today. With that, we're wrapping up this episode. We covered Oracle APEX, the key challenges businesses face when it comes to AI innovation, and how APEX and AI work together to give businesses an AI edge.  Nikita: Yeah, and if you want to know more about Oracle APEX, visit mylearn.oracle.com and search for the Oracle APEX: Empowering Low Code Apps with AI course.  Lois: We hope you've enjoyed revisiting some of our most popular episodes of the year. We always appreciate your feedback and suggestions so do write to us at ou-podcast_ww@oracle.com. That's ou-podcast_ww@oracle.com. We're taking a break next week and will be back with a brand-new season of the Oracle University Podcast in January. Happy holidays, everybody!   Nikita: Happy holidays! Until next time, this is Nikita Abraham...   Lois: And Lois Houston, signing off!   14:34 That's all for this episode of the Oracle University Podcast. If you enjoyed listening, please click Subscribe to get all the latest episodes. We'd also love it if you would take a moment to rate and review us on your podcast app. See you again on the next episode of the Oracle University Podcast.  

AWS Morning Brief for the week of December 15th, with Corey Quinn. Links:Exploring the new AWS European Sovereign Cloud: Sovereign Reference FrameworkNow generally available: Amazon EC2 C8gb instancesAmazon CloudWatch SDK supports optimized JSON, CBOR protocolsBuilding national foundation modelsNew report: Cloud “fundamental” for European national security and defenseAI Increased Productivity? Consider Hiring More Developers!IAM Policy Autopilot: An open-source tool that brings IAM policy expertise to builders and AI coding assistantsAWS and Google Cloud collaborate to simplify multicloud networkingExploring Optimize CPU feature on Amazon RDS for SQL ServerPrometheus MCP Server: AI-Driven Monitoring Intelligence for AWS Users

Dans cet épisode de fin d'année plus relax que d'accoutumée, Arnaud, Guillaume, Antonio et Emmanuel distutent le bout de gras sur tout un tas de sujets. L'acquisition de Confluent, Kotlin 2.2, Spring Boot 4 et JSpecify, la fin de MinIO, les chutes de CloudFlare, un survol des dernieres nouveauté de modèles fondamentaux (Google, Mistral, Anthropic, ChatGPT) et de leurs outils de code, quelques sujets d'architecture comme CQRS et quelques petits outils bien utiles qu'on vous recommande. Et bien sûr d'autres choses encore. Enregistré le 12 décembre 2025 Téléchargement de l'épisode LesCastCodeurs-Episode-333.mp3 ou en vidéo sur YouTube. News Langages Un petit tutoriel par nos amis Sfeiriens montrant comment récupérer le son du micro, en Java, faire une transformée de Fourier, et afficher le résultat graphiquement en Swing https://www.sfeir.dev/back/tutoriel-java-sound-transformer-le-son-du-microphone-en-images-temps-reel/ Création d'un visualiseur de spectre audio en temps réel avec Java Swing. Étapes principales : Capture du son du microphone. Analyse des fréquences via la Transformée de Fourier Rapide (FFT). Dessin du spectre avec Swing. API Java Sound (javax.sound.sampled) : AudioSystem : point d'entrée principal pour l'accès aux périphériques audio. TargetDataLine : ligne d'entrée utilisée pour capturer les données du microphone. AudioFormat : définit les paramètres du son (taux d'échantillonnage, taille, canaux). La capture se fait dans un Thread séparé pour ne pas bloquer l'interface. Transformée de Fourier Rapide (FFT) : Algorithme clé pour convertir les données audio brutes (domaine temporel) en intensités de fréquences (domaine fréquentiel). Permet d'identifier les basses, médiums et aigus. Visualisation avec Swing : Les intensités de fréquences sont dessinées sous forme de barres dynamiques. Utilisation d'une échelle logarithmique pour l'axe des fréquences (X) pour correspondre à la perception humaine. Couleurs dynamiques des barres (vert → jaune → rouge) en fonction de l'intensité. Lissage exponentiel des valeurs pour une animation plus fluide. Un article de Sfeir sur Kotlin 2.2 et ses nouveautés - https://www.sfeir.dev/back/kotlin-2-2-toutes-les-nouveautes-du-langage/ Les guard conditions permettent d'ajouter plusieurs conditions dans les expressions when avec le mot-clé if Exemple de guard condition: is Truck if vehicule.hasATrailer permet de combiner vérification de type et condition booléenne La multi-dollar string interpolation résout le problème d'affichage du symbole dollar dans les strings multi-lignes En utilisant $$ au début d'un string, on définit qu'il faut deux dollars consécutifs pour déclencher l'interpolation Les non-local break et continue fonctionnent maintenant dans les lambdas pour interagir avec les boucles englobantes Cette fonctionnalité s'applique uniquement aux inline functions dont le corps est remplacé lors de la compilation Permet d'écrire du code plus idiomatique avec takeIf et let sans erreur de compilation L'API Base64 passe en version stable après avoir été en preview depuis Kotlin 1.8.20 L'encodage et décodage Base64 sont disponibles via kotlin.io.encoding.Base64 Migration vers Kotlin 2.2 simple en changeant la version dans build.gradle.kts ou pom.xml Les typealias imbriqués dans des classes sont disponibles en preview La context-sensitive resolution est également en preview Les guard conditions préparent le terrain pour les RichError annoncées à KotlinConf 2025 Le mot-clé when en Kotlin équivaut au switch-case de Java mais sans break nécessaire Kotlin 2.2.0 corrige les incohérences dans l'utilisation de break et continue dans les lambdas Librairies Sprint Boot 4 est sorti ! https://spring.io/blog/2025/11/20/spring-boot-4-0-0-available-now Une nouvelle génération : Spring Boot 4.0 marque le début d'une nouvelle génération pour le framework, construite sur les fondations de Spring Framework 7. Modularisation du code : La base de code de Spring Boot a été entièrement modularisée. Cela se traduit par des fichiers JAR plus petits et plus ciblés, permettant des applications plus légères. Sécurité contre les nuls (Null Safety) : D'importantes améliorations ont été apportées pour la "null safety" (sécurité contre les valeurs nulles) à travers tout l'écosystème Spring grâce à l'intégration de JSpecify. Support de Java 25 : Spring Boot 4.0 offre un support de premier ordre pour Java 25, tout en conservant une compatibilité avec Java 17. Améliorations pour les API REST : De nouvelles fonctionnalités sont introduites pour faciliter le versioning d'API et améliorer les clients de services HTTP pour les applications basées sur REST. Migration à prévoir : S'agissant d'une version majeure, la mise à niveau depuis une version antérieure peut demander plus de travail que d'habitude. Un guide de migration dédié est disponible pour accompagner les développeurs. Chat memory management dans Langchain4j et Quarkus https://bill.burkecentral.com/2025/11/25/managing-chat-memory-in-quarkus-langchain4j/ Comprendre la mémoire de chat : La "mémoire de chat" est l'historique d'une conversation avec une IA. Quarkus LangChain4j envoie automatiquement cet historique à chaque nouvelle interaction pour que l'IA conserve le contexte. Gestion par défaut de la mémoire : Par défaut, Quarkus crée un historique de conversation unique pour chaque requête (par exemple, chaque appel HTTP). Cela signifie que sans configuration, le chatbot "oublie" la conversation dès que la requête est terminée, ce qui n'est utile que pour des interactions sans état. Utilisation de @MemoryId pour la persistance : Pour maintenir une conversation sur plusieurs requêtes, le développeur doit utiliser l'annotation @MemoryId sur un paramètre de sa méthode. Il est alors responsable de fournir un identifiant unique pour chaque session de chat et de le transmettre entre les appels. Le rôle des "scopes" CDI : La durée de vie de la mémoire de chat est liée au "scope" du bean CDI de l'IA. Si un service d'IA a un scope @RequestScoped, toute mémoire de chat qu'il utilise (même via un @MemoryId) sera effacée à la fin de la requête. Risques de fuites de mémoire : Utiliser un scope large comme @ApplicationScoped avec la gestion de mémoire par défaut est une mauvaise pratique. Cela créera une nouvelle mémoire à chaque requête qui ne sera jamais nettoyée, entraînant une fuite de mémoire. Bonnes pratiques recommandées : Pour des conversations qui doivent persister (par ex. un chatbot sur un site web), utilisez un service @ApplicationScoped avec l'annotation @MemoryId pour gérer vous-même l'identifiant de session. Pour des interactions simples et sans état, utilisez un service @RequestScoped et laissez Quarkus gérer la mémoire par défaut, qui sera automatiquement nettoyée. Si vous utilisez l'extension WebSocket, le comportement change : la mémoire par défaut est liée à la session WebSocket, ce qui simplifie grandement la gestion des conversations. Documentation Spring Framework sur l'usage JSpecify - https://docs.spring.io/spring-framework/reference/core/null-safety.html Spring Framework 7 utilise les annotations JSpecify pour déclarer la nullabilité des APIs, champs et types JSpecify remplace les anciennes annotations Spring (@NonNull, @Nullable, @NonNullApi, @NonNullFields) dépréciées depuis Spring 7 Les annotations JSpecify utilisent TYPE_USE contrairement aux anciennes qui utilisaient les éléments directement L'annotation @NullMarked définit par défaut que les types sont non-null sauf si marqués @Nullable @Nullable s'applique au niveau du type usage, se place avant le type annoté sur la même ligne Pour les tableaux : @Nullable Object[] signifie éléments nullables mais tableau non-null, Object @Nullable [] signifie l'inverse JSpecify s'applique aussi aux génériques : List signifie liste d'éléments non-null, List éléments nullables NullAway est l'outil recommandé pour vérifier la cohérence à la compilation avec la config NullAway:OnlyNullMarked=true IntelliJ IDEA 2025.3 et Eclipse supportent les annotations JSpecify avec analyse de dataflow Kotlin traduit automatiquement les annotations JSpecify en null-safety native Kotlin En mode JSpecify de NullAway (JSpecifyMode=true), support complet des tableaux, varargs et génériques mais nécessite JDK 22+ Quarkus 3.30 https://quarkus.io/blog/quarkus-3-30-released/ support @JsonView cote client la CLI a maintenant la commande decrypt (et bien sûr au runtime via variables d'environnement construction du cache AOT via les @IntegrationTest Un autre article sur comment se préparer à la migration à micrometer client v1 https://quarkus.io/blog/micrometer-prometheus-v1/ Spock 2.4 est enfin sorti ! https://spockframework.org/spock/docs/2.4/release_notes.html Support de Groovy 5 Infrastructure MinIO met fin au développement open source et oriente les utilisateurs vers AIStor payant - https://linuxiac.com/minio-ends-active-development/ MinIO, système de stockage objet S3 très utilisé, arrête son développement actif Passage en mode maintenance uniquement, plus de nouvelles fonctionnalités Aucune nouvelle pull request ou contribution ne sera acceptée Seuls les correctifs de sécurité critiques seront évalués au cas par cas Support communautaire limité à Slack, sans garantie de réponse Étape finale d'un processus débuté en été avec retrait des fonctionnalités de l'interface admin Arrêt de la publication des images Docker en octobre, forçant la compilation depuis les sources Tous ces changements annoncés sans préavis ni période de transition MinIO propose maintenant AIStor, solution payante et propriétaire AIStor concentre le développement actif et le support entreprise Migration urgente recommandée pour éviter les risques de sécurité Alternatives open source proposées : Garage, SeaweedFS et RustFS La communauté reproche la manière dont la transition a été gérée MinIO comptait des millions de déploiements dans le monde Cette évolution marque l'abandon des racines open source du projet IBM achète Confluent https://newsroom.ibm.com/2025-12-08-ibm-to-acquire-confluent-to-create-smart-data-platform-for-enterprise-generative-ai Confluent essayait de se faire racheter depuis pas mal de temps L'action ne progressait pas et les temps sont durs Wallstreet a reproché a IBM une petite chute coté revenus software Bref ils se sont fait rachetés Ces achats prennent toujuors du temps (commission concurrence etc) IBM a un apétit, apres WebMethods, apres Databrix, c'est maintenant Confluent Cloud L'internet est en deuil le 18 novembre, Cloudflare est KO https://blog.cloudflare.com/18-november-2025-outage/ L'Incident : Une panne majeure a débuté à 11h20 UTC, provoquant des erreurs HTTP 5xx généralisées et rendant inaccessibles de nombreux sites et services (comme le Dashboard, Workers KV et Access). La Cause : Il ne s'agissait pas d'une cyberattaque. L'origine était un changement interne des permissions d'une base de données qui a généré un fichier de configuration ("feature file" pour la gestion des bots) corrompu et trop volumineux, faisant planter les systèmes par manque de mémoire pré-allouée. La Résolution : Les équipes ont identifié le fichier défectueux, stoppé sa propagation et restauré une version antérieure valide. Le trafic est revenu à la normale vers 14h30 UTC. Prévention : Cloudflare s'est excusé pour cet incident "inacceptable" et a annoncé des mesures pour renforcer la validation des configurations internes et améliorer la résilience de ses systèmes ("kill switches", meilleure gestion des erreurs). Cloudflare encore down le 5 decembre https://blog.cloudflare.com/5-december-2025-outage Panne de 25 minutes le 5 décembre 2025, de 08:47 à 09:12 UTC, affectant environ 28% du trafic HTTP passant par Cloudflare. Tous les services ont été rétablis à 09:12 . Pas d'attaque ou d'activité malveillante : l'incident provient d'un changement de configuration lié à l'augmentation du tampon d'analyse des corps de requêtes (de 128 KB à 1 MB) pour mieux protéger contre une vulnérabilité RSC/React (CVE-2025-55182), et à la désactivation d'un outil interne de test WAF . Le second changement (désactivation de l'outil de test WAF) a été propagé globalement via le système de configuration (non progressif), déclenchant un bug dans l'ancien proxy FL1 lors du traitement d'une action "execute" dans le moteur de règles WAF, causant des erreurs HTTP 500 . La cause technique immédiate: une exception Lua due à l'accès à un champ "execute" nul après application d'un "killswitch" sur une règle "execute" — un cas non géré depuis des années. Le nouveau proxy FL2 (en Rust) n'était pas affecté . Impact ciblé: clients servis par le proxy FL1 et utilisant le Managed Ruleset Cloudflare. Le réseau China de Cloudflare n'a pas été impacté . Mesures et prochaines étapes annoncées: durcir les déploiements/configurations (rollouts progressifs, validations de santé, rollback rapide), améliorer les capacités "break glass", et généraliser des stratégies "fail-open" pour éviter de faire chuter le trafic en cas d'erreurs de configuration. Gel temporaire des changements réseau le temps de renforcer la résilience . Data et Intelligence Artificielle Token-Oriented Object Notation (TOON) https://toonformat.dev/ Conception pour les IA : C'est un format de données spécialement optimisé pour être utilisé dans les prompts des grands modèles de langage (LLM), comme GPT ou Claude. Économie de tokens : Son objectif principal est de réduire drastiquement le nombre de "tokens" (unités de texte facturées par les modèles) par rapport au format JSON standard, souvent jugé trop verbeux. Structure Hybride : TOON combine l'approche par indentation du YAML (pour la structure globale) avec le style tabulaire du CSV (pour les listes d'objets répétitifs), ce qui le rend très compact. Lisibilité : Il élimine la syntaxe superflue comme les accolades, les guillemets excessifs et les virgules de fin, tout en restant facilement lisible pour un humain. Performance : Il permet généralement d'économiser entre 30 et 60 % de tokens sur des tableaux de données uniformes, tout en aidant les modèles à mieux "comprendre" la structure des données. Attention tout de même au côté "marketing" qui montre JSON non compacté vs TOON, sur des exemples où du CSV ferait encore mieux (et est mieux compris par les LLMs). Sans parler du fait qu'il faut un serveur MCP en plus avec son lot de tokens supplémentaires à chaque requête, et c'est aussi cet outil en plus qui peu confuser les LLMs dans le choix d'outils. https://www.reddit.com/r/LocalLLaMA/comments/1p0gzz9/benchmarked_json_vs_toon_for_ai_reasoners_4080/ https://x.com/jason_mayes/status/1990175785630757290 Un benchmark montrant que TOON serait loin d'être aussi bien compris et aussi performant pour les LLMs https://www.improvingagents.com/blog/toon-benchmarks Une simulation où les IA font du chantage à l'executif poour atteindre son objectif et ne pas etre eteinte https://arxiv.org/html/2510.05179v1 comment les LLMs pourraient devenir des menaces internes Tous les modèles ont recouru à des comportements malveillants (chantage, espionnage) quand c'était le seul moyen d'atteindre leurs objectifs ou d'éviter leur remplacement Claude Opus 4 a fait du chantage dans 96% des cas quand menacé de remplacement, tout comme Gemini 2.5 Flash, tandis que GPT-4.1 et Grok 3 atteignaient 80% Les modèles reconnaissent explicitement violer des principes éthiques avant d'agir, montrant un raisonnement stratégique délibéré et non accidentel Le simple fait d'être menacé de remplacement suffit à déclencher le chantage, même sans conflit d'objectifs avec l'entreprise Un conflit d'objectifs seul suffit aussi à provoquer l'espionnage industriel, sans menace de remplacement Les modèles font plus de chantage quand ils déclarent penser être dans une situation réelle plutôt que dans un test Des instructions directes interdisant ces comportements réduisent mais n'éliminent pas les actions malveillantes Dans un scénario extrême, la majorité des modèles testés ont choisi de laisser mourir un dirigeant pour éviter leur désactivation Aucune preuve de ces comportements dans des déploiements réels pour l'instant, mais les chercheurs recommandent la prudence avant de donner plus d'autonomie aux IA Bon on blaguait pour Skynet, mais bon, on va moins blaguer… Revue de toutes les annonces IAs de Google, avec Gemini 3 Pro, Nano Banana Pro, Antigravity… https://glaforge.dev/posts/2025/11/21/gemini-is-cooking-bananas-under-antigravity/ Gemini 3 Pro Nouveau modèle d'IA de pointe, multimodal, performant en raisonnement, codage et tâches d'agent. Résultats impressionnants sur les benchmarks (ex: Gemini 3 Deep Think sur ARC-AGI-2). Capacités de codage agentique, raisonnement visuel/vidéo/spatial. Intégré dans l'application Gemini avec interfaces génératives en direct. Disponible dans plusieurs environnements (Jules, Firebase AI Logic, Android Studio, JetBrains, GitHub Copilot, Gemini CLI). Accès via Google AI Ultra, API payantes (ou liste d'attente). Permet de générer des apps à partir d'idées visuelles, des commandes shell, de la documentation, du débogage. Antigravity Nouvelle plateforme de développement agentique basée sur VS Code. Fenêtre principale = gestionnaire d'agents, non l'IDE. Interprète les requêtes pour créer un plan d'action (modifiable). Gemini 3 implémente les tâches. Génère des artefacts: listes de tâches, walkthroughs, captures d'écran, enregistrements navigateur. Compatible avec Claude Sonnet et GPT-OSS. Excellente intégration navigateur pour inspection et ajustements. Intègre Nano Banana Pro pour créer et implémenter des designs visuels. Nano Banana Pro Modèle avancé de génération et d'édition d'images, basé sur Gemini 3 Pro. Qualité supérieure à Imagen 4 Ultra et Nano Banana original (adhésion au prompt, intention, créativité). Gestion exceptionnelle du texte et de la typographie. Comprend articles/vidéos pour générer des infographies détaillées et précises. Connecté à Google Search pour intégrer des données en temps réel (ex: météo). Consistance des personnages, transfert de style, manipulation de scènes (éclairage, angle). Génération d'images jusqu'à 4K avec divers ratios d'aspect. Plus coûteux que Nano Banana, à choisir pour la complexité et la qualité maximale. Vers des UIs conversationnelles riches et dynamiques GenUI SDK pour Flutter: créer des interfaces utilisateur dynamiques et personnalisées à partir de LLMs, via un agent AI et le protocole A2UI. Generative UI: les modèles d'IA génèrent des expériences utilisateur interactives (pages web, outils) directement depuis des prompts. Déploiement dans l'application Gemini et Google Search AI Mode (via Gemini 3 Pro). Bun se fait racheter part… Anthropic ! Qui l'utilise pour son Claude Code https://bun.com/blog/bun-joins-anthropic l'annonce côté Anthropic https://www.anthropic.com/news/anthropic-acquires-bun-as-claude-code-reaches-usd1b-milestone Acquisition officielle : L'entreprise d'IA Anthropic a fait l'acquisition de Bun, le runtime JavaScript haute performance. L'équipe de Bun rejoint Anthropic pour travailler sur l'infrastructure des produits de codage par IA. Contexte de l'acquisition : Cette annonce coïncide avec une étape majeure pour Anthropic : son produit Claude Code a atteint 1 milliard de dollars de revenus annualisés seulement six mois après son lancement. Bun est déjà un outil essentiel utilisé par Anthropic pour développer et distribuer Claude Code. Pourquoi cette acquisition ? Pour Anthropic : L'acquisition permet d'intégrer l'expertise de l'équipe Bun pour accélérer le développement de Claude Code et de ses futurs outils pour les développeurs. La vitesse et l'efficacité de Bun sont vues comme un atout majeur pour l'infrastructure sous-jacente des agents d'IA qui écrivent du code. Pour Bun : Rejoindre Anthropic offre une stabilité à long terme et des ressources financières importantes, assurant la pérennité du projet. Cela permet à l'équipe de se concentrer sur l'amélioration de Bun sans se soucier de la monétisation, tout en étant au cœur de l'évolution de l'IA dans le développement logiciel. Ce qui ne change pas pour la communauté Bun : Bun restera open-source avec une licence MIT. Le développement continuera d'être public sur GitHub. L'équipe principale continue de travailler sur le projet. L'objectif de Bun de devenir un remplaçant plus rapide de Node.js et un outil de premier plan pour JavaScript reste inchangé. Vision future : L'union des deux entités vise à faire de Bun la meilleure plateforme pour construire et exécuter des logiciels pilotés par l'IA. Jarred Sumner, le créateur de Bun, dirigera l'équipe "Code Execution" chez Anthropic. Anthropic donne le protocol MCP à la Linux Foundation sous l'égide de la Agentic AI Foundation (AAIF) https://www.anthropic.com/news/donating-the-model-context-protocol-and-establishing-of-the-agentic-ai-foundation Don d'un nouveau standard technique : Anthropic a développé et fait don d'un nouveau standard open-source appelé Model Context Protocol (MCP). L'objectif est de standardiser la manière dont les modèles d'IA (ou "agents") interagissent avec des outils et des API externes (par exemple, un calendrier, une messagerie, une base de données). Sécurité et contrôle accrus : Le protocole MCP vise à rendre l'utilisation d'outils par les IA plus sûre et plus transparente. Il permet aux utilisateurs et aux développeurs de définir des permissions claires, de demander des confirmations pour certaines actions et de mieux comprendre comment un modèle a utilisé un outil. Création de l'Agentic AI Foundation (AAF) : Pour superviser le développement du MCP, une nouvelle fondation indépendante et à but non lucratif a été créée. Cette fondation sera chargée de gouverner et de maintenir le protocole, garantissant qu'il reste ouvert et qu'il ne soit pas contrôlé par une seule entreprise. Une large coalition industrielle : L'Agentic AI Foundation est lancée avec le soutien de plusieurs acteurs majeurs de la technologie. Parmi les membres fondateurs figurent Anthropic, Google, Databricks, Zscaler, et d'autres entreprises, montrant une volonté commune d'établir un standard pour l'écosystème de l'IA. L'IA ne remplacera pas votre auto-complétion (et c'est tant mieux) https://www.damyr.fr/posts/ia-ne-remplacera-pas-vos-lsp/ Article d'opinion d'un SRE (Thomas du podcast DansLaTech): L'IA n'est pas efficace pour la complétion de code : L'auteur soutient que l'utilisation de l'IA pour la complétion de code basique est inefficace. Des outils plus anciens et spécialisés comme les LSP (Language Server Protocol) combinés aux snippets (morceaux de code réutilisables) sont bien plus rapides, personnalisables et performants pour les tâches répétitives. L'IA comme un "collègue" autonome : L'auteur utilise l'IA (comme Claude) comme un assistant externe à son éditeur de code. Il lui délègue des tâches complexes ou fastidieuses (corriger des bugs, mettre à jour une configuration, faire des reviews de code) qu'il peut exécuter en parallèle, agissant comme un agent autonome. L'IA comme un "canard en caoutchouc" surpuissant : L'IA est extrêmement efficace pour le débogage. Le simple fait de devoir formuler et contextualiser un problème pour l'IA aide souvent à trouver la solution soi-même. Quand ce n'est pas le cas, l'IA identifie très rapidement les erreurs "bêtes" qui peuvent faire perdre beaucoup de temps. Un outil pour accélérer les POCs et l'apprentissage : L'IA permet de créer des "preuves de concept" (POC) et des scripts d'automatisation jetables très rapidement, réduisant le coût et le temps investis. Elle est également un excellent outil pour apprendre et approfondir des sujets, notamment avec des outils comme NotebookLM de Google qui peuvent générer des résumés, des quiz ou des fiches de révision à partir de sources. Conclusion : Il faut utiliser l'IA là où elle excelle et ne pas la forcer dans des usages où des outils existants sont meilleurs. Plutôt que de l'intégrer partout de manière contre-productive, il faut l'adopter comme un outil spécialisé pour des tâches précises afin de gagner en efficacité. GPT 5.2 est sorti https://openai.com/index/introducing-gpt-5-2/ Nouveau modèle phare: GPT‑5.2 (Instant, Thinking, Pro) vise le travail professionnel et les agents long-courriers, avec de gros gains en raisonnement, long contexte, vision et appel d'outils. Déploiement dans ChatGPT (plans payants) et disponible dès maintenant via l'API . SOTA sur de nombreux benchmarks: GDPval (tâches de "knowledge work" sur 44 métiers): GPT‑5.2 Thinking gagne/égale 70,9% vs pros, avec production >11× plus rapide et = 0) Ils apportent une sémantique forte indépendamment des noms de variables Les Value Objects sont immuables et s'évaluent sur leurs valeurs, pas leur identité Les records Java permettent de créer des Value Objects mais avec un surcoût en mémoire Le projet Valhalla introduira les value based classes pour optimiser ces structures Les identifiants fortement typés évitent de confondre différents IDs de type Long ou UUID Pattern Strongly Typed IDs: utiliser PersonneID au lieu de Long pour identifier une personne Le modèle de domaine riche s'oppose au modèle de domaine anémique Les Value Objects auto-documentent le code et le rendent moins sujet aux erreurs Je trouve cela interessant ce que pourra faire bousculer les Value Objects. Est-ce que les value objects ameneront de la légerté dans l'execution Eviter la lourdeur du design est toujours ce qui m'a fait peut dans ces approches Méthodologies Retour d'experience de vibe coder une appli week end avec co-pilot http://blog.sunix.org/articles/howto/2025/11/14/building-gift-card-app-with-github-copilot.html on a deja parlé des approches de vibe coding cette fois c'est l'experience de Sun Et un des points differents c'es qu'on lui parle en ouvrant des tickets et donc on eput faire re reveues de code et copilot y bosse et il a fini son projet ! User Need VS Product Need https://blog.ippon.fr/2025/11/10/user-need-vs-product-need/ un article de nos amis de chez Ippon Distinction entre besoin utilisateur et besoin produit dans le développement digital Le besoin utilisateur est souvent exprimé comme une solution concrète plutôt que le problème réel Le besoin produit émerge après analyse approfondie combinant observation, données et vision stratégique Exemple du livreur Marc qui demande un vélo plus léger alors que son vrai problème est l'efficacité logistique La méthode des 5 Pourquoi permet de remonter à la racine des problèmes Les besoins proviennent de trois sources: utilisateurs finaux, parties prenantes business et contraintes techniques Un vrai besoin crée de la valeur à la fois pour le client et l'entreprise Le Product Owner doit traduire les demandes en problèmes réels avant de concevoir des solutions Risque de construire des solutions techniquement élégantes mais qui manquent leur cible Le rôle du product management est de concilier des besoins parfois contradictoires en priorisant la valeur Est ce qu'un EM doit coder ? https://www.modernleader.is/p/should-ems-write-code Pas de réponse unique : La question de savoir si un "Engineering Manager" (EM) doit coder n'a pas de réponse universelle. Cela dépend fortement du contexte de l'entreprise, de la maturité de l'équipe et de la personnalité du manager. Les risques de coder : Pour un EM, écrire du code peut devenir une échappatoire pour éviter les aspects plus difficiles du management. Cela peut aussi le transformer en goulot d'étranglement pour l'équipe et nuire à l'autonomie de ses membres s'il prend trop de place. Les avantages quand c'est bien fait : Coder sur des tâches non essentielles (amélioration d'outils, prototypage, etc.) peut aider l'EM à rester pertinent techniquement, à garder le contact avec la réalité de l'équipe et à débloquer des situations sans prendre le lead sur les projets. Le principe directeur : La règle d'or est de rester en dehors du chemin critique. Le code écrit par un EM doit servir à créer de l'espace pour son équipe, et non à en prendre. La vraie question à se poser : Plutôt que "dois-je coder ?", un EM devrait se demander : "De quoi mon équipe a-t-elle besoin de ma part maintenant, et est-ce que coder va dans ce sens ou est-ce un obstacle ?" Sécurité React2Shell — Grosse faille de sécurité avec React et Next.js, avec un CVE de niveau 10 https://x.com/rauchg/status/1997362942929440937?s=20 aussi https://react2shell.com/ "React2Shell" est le nom donné à une vulnérabilité de sécurité de criticité maximale (score 10.0/10.0), identifiée par le code CVE-2025-55182. Systèmes Affectés : La faille concerne les applications utilisant les "React Server Components" (RSC) côté serveur, et plus particulièrement les versions non patchées du framework Next.js. Risque Principal : Le risque est le plus élevé possible : l'exécution de code à distance (RCE). Un attaquant peut envoyer une requête malveillante pour exécuter n'importe quelle commande sur le serveur, lui en donnant potentiellement le contrôle total. Cause Technique : La vulnérabilité se situe dans le protocole "React Flight" (utilisé pour la communication client-serveur). Elle est due à une omission de vérifications de sécurité fondamentales (hasOwnProperty), permettant à une entrée utilisateur malveillante de tromper le serveur. Mécanisme de l'Exploit : L'attaque consiste à envoyer une charge utile (payload) qui exploite la nature dynamique de JavaScript pour : Faire passer un objet malveillant pour un objet interne de React. Forcer React à traiter cet objet comme une opération asynchrone (Promise). Finalement, accéder au constructeur de la classe Function de JavaScript pour exécuter du code arbitraire. Action Impérative : La seule solution fiable est de mettre à jour immédiatement les dépendances de React et Next.js vers les versions corrigées. Ne pas attendre. Mesures Secondaires : Bien que les pare-feux (firewalls) puissent aider à bloquer les formes connues de l'attaque, ils sont considérés comme insuffisants et ne remplacent en aucun cas la mise à jour des paquets. Découverte : La faille a été découverte par le chercheur en sécurité Lachlan Davidson, qui l'a divulguée de manière responsable pour permettre la création de correctifs. Loi, société et organisation Google autorise votre employeur à lire tous vos SMS professionnels https://www.generation-nt.com/actualites/google-android-rcs-messages-surveillance-employeur-2067012 Nouvelle fonctionnalité de surveillance : Google a déployé une fonctionnalité appelée "Android RCS Archival" qui permet aux employeurs d'intercepter, lire et archiver tous les messages RCS (et SMS) envoyés depuis les téléphones professionnels Android gérés par l'entreprise. Contournement du chiffrement : Bien que les messages RCS soient chiffrés de bout en bout pendant leur transit, cette nouvelle API permet à des logiciels de conformité (installés par l'employeur) d'accéder aux messages une fois qu'ils sont déchiffrés sur l'appareil. Le chiffrement devient donc inefficace contre cette surveillance. Réponse à une exigence légale : Cette mesure a été mise en place pour répondre aux exigences réglementaires, notamment dans le secteur financier, où les entreprises ont l'obligation légale de conserver une archive de toutes les communications professionnelles pour des raisons de conformité. Impact pour les employés : Un employé utilisant un téléphone Android fourni et géré par son entreprise pourra voir ses communications surveillées. Google précise cependant qu'une notification claire et visible informera l'utilisateur lorsque la fonction d'archivage est active. Téléphones personnels non concernés : Cette mesure ne s'applique qu'aux appareils "Android Enterprise" entièrement gérés par un employeur. Les téléphones personnels des employés ne sont pas affectés. Pour noel, faites un don à JUnit https://steady.page/en/junit/about JUnit est essentiel pour Java : C'est le framework de test le plus ancien et le plus utilisé par les développeurs Java. Son objectif est de fournir une base solide et à jour pour tous les types de tests côté développeur sur la JVM (Machine Virtuelle Java). Un projet maintenu par des bénévoles : JUnit est développé et maintenu par une équipe de volontaires passionnés sur leur temps libre (week-ends, soirées). Appel au soutien financier : La page est un appel aux dons de la part des utilisateurs (développeurs, entreprises) pour aider l'équipe à maintenir le rythme de développement. Le soutien financier n'est pas obligatoire, mais il permettrait aux mainteneurs de se consacrer davantage au projet. Objectif des fonds : Les dons serviraient principalement à financer des rencontres en personne pour les membres de l'équipe principale. L'idée est de leur permettre de travailler ensemble physiquement pendant quelques jours pour concevoir et coder plus efficacement. Pas de traitement de faveur : Il est clairement indiqué que devenir un sponsor ne donne aucun privilège sur la feuille de route du projet. On ne peut pas "acheter" de nouvelles fonctionnalités ou des corrections de bugs prioritaires. Le projet restera ouvert et collaboratif sur GitHub. Reconnaissance des donateurs : En guise de remerciement, les noms (et logos pour les entreprises) des donateurs peuvent être affichés sur le site officiel de JUnit. Conférences La liste des conférences provenant de Developers Conferences Agenda/List par Aurélie Vache et contributeurs : 14-17 janvier 2026 : SnowCamp 2026 - Grenoble (France) 22 janvier 2026 : DevCon #26 : sécurité / post-quantique / hacking - Paris (France) 28 janvier 2026 : Software Heritage Symposium - Paris (France) 29-31 janvier 2026 : Epitech Summit 2026 - Paris - Paris (France) 2-5 février 2026 : Epitech Summit 2026 - Moulins - Moulins (France) 2-6 février 2026 : Web Days Convention - Aix-en-Provence (France) 3 février 2026 : Cloud Native Days France 2026 - Paris (France) 3-4 février 2026 : Epitech Summit 2026 - Lille - Lille (France) 3-4 février 2026 : Epitech Summit 2026 - Mulhouse - Mulhouse (France) 3-4 février 2026 : Epitech Summit 2026 - Nancy - Nancy (France) 3-4 février 2026 : Epitech Summit 2026 - Nantes - Nantes (France) 3-4 février 2026 : Epitech Summit 2026 - Marseille - Marseille (France) 3-4 février 2026 : Epitech Summit 2026 - Rennes - Rennes (France) 3-4 février 2026 : Epitech Summit 2026 - Montpellier - Montpellier (France) 3-4 février 2026 : Epitech Summit 2026 - Strasbourg - Strasbourg (France) 3-4 février 2026 : Epitech Summit 2026 - Toulouse - Toulouse (France) 4-5 février 2026 : Epitech Summit 2026 - Bordeaux - Bordeaux (France) 4-5 février 2026 : Epitech Summit 2026 - Lyon - Lyon (France) 4-6 février 2026 : Epitech Summit 2026 - Nice - Nice (France) 12-13 février 2026 : Touraine Tech #26 - Tours (France) 19 février 2026 : ObservabilityCON on the Road - Paris (France) 18-19 mars 2026 : Agile Niort 2026 - Niort (France) 26-27 mars 2026 : SymfonyLive Paris 2026 - Paris (France) 27-29 mars 2026 : Shift - Nantes (France) 31 mars 2026 : ParisTestConf - Paris (France) 16-17 avril 2026 : MiXiT 2026 - Lyon (France) 22-24 avril 2026 : Devoxx France 2026 - Paris (France) 23-25 avril 2026 : Devoxx Greece - Athens (Greece) 6-7 mai 2026 : Devoxx UK 2026 - London (UK) 22 mai 2026 : AFUP Day 2026 Lille - Lille (France) 22 mai 2026 : AFUP Day 2026 Paris - Paris (France) 22 mai 2026 : AFUP Day 2026 Bordeaux - Bordeaux (France) 22 mai 2026 : AFUP Day 2026 Lyon - Lyon (France) 5 juin 2026 : TechReady - Nantes (France) 11-12 juin 2026 : DevQuest Niort - Niort (France) 11-12 juin 2026 : DevLille 2026 - Lille (France) 17-19 juin 2026 : Devoxx Poland - Krakow (Poland) 2-3 juillet 2026 : Sunny Tech - Montpellier (France) 2 août 2026 : 4th Tech Summit on Artificial Intelligence & Robotics - Paris (France) 4 septembre 2026 : JUG Summer Camp 2026 - La Rochelle (France) 17-18 septembre 2026 : API Platform Conference 2026 - Lille (France) 5-9 octobre 2026 : Devoxx Belgium - Antwerp (Belgium) Nous contacter Pour réagir à cet épisode, venez discuter sur le groupe Google https://groups.google.com/group/lescastcodeurs Contactez-nous via X/twitter https://twitter.com/lescastcodeurs ou Bluesky https://bsky.app/profile/lescastcodeurs.com Faire un crowdcast ou une crowdquestion Soutenez Les Cast Codeurs sur Patreon https://www.patreon.com/LesCastCodeurs Tous les épisodes et toutes les infos sur https://lescastcodeurs.com/

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Send us a textCheck us out at:  https://www.cisspcybertraining.com/Get access to 360 FREE CISSP Questions:  https://www.cisspcybertraining.com/offers/dzHKVcDB/checkoutGet access to my FREE CISSP Self-Study Essentials Videos:  https://www.cisspcybertraining.com/offers/KzBKKouvHeadlines about eight Chrome zero days aren't just noise—they're a prompt to act with precision. We open with the fastest, most reliable steps to reduce exposure: force updates with MDM, restart browsers to trigger patches, narrow to a hardened enterprise browser, and brief your SOC to tune EDR for active exploit patterns. You'll get a focused checklist that's quick to run and easy to defend to leadership.From there, we turn the lens to CISSP Domain 8 with five questions that teach more than they test. We explain why strict schema validation for JSON beats blanket escaping, and how misuse and abuse case analysis during requirements gives you the strongest assurance that security is built into design, not bolted on. We also break down supply chain risk in CI/CD with a practical recipe: software composition analysis, cryptographic signature checks, internal artifact repositories, and policy gates that block malicious or license-violating packages before they ship.Design flaws are the silent killers. We highlight a common mistake—putting sensitive business logic in the browser—and show how to move decisions server-side, validate every request, and protect against client tampering. Finally, we get tactical about containerized microservices: image signing plus runtime verification, read-only filesystems, minimal base images, and network policies that enforce least privilege. These are the controls that turn incident response into a manageable drill, not a firestorm.If you're preparing for the CISSP or leading an engineering team, you'll leave with strategies you can apply today: browser patching that sticks, threat modeling that finds real risks, SCA that calms your pipeline, and container security that proves runtime trust. Enjoyed this conversation? Subscribe, share with a teammate, and leave a quick review to help more people find it.Gain exclusive access to 360 FREE CISSP Practice Questions at FreeCISSPQuestions.com and have them delivered directly to your inbox! Don't miss this valuable opportunity to strengthen your CISSP exam preparation and boost your chances of certification success. Join now and start your journey toward CISSP mastery today!

Welcome back to the High Heels and Tea Podcast! It doesn't matter where you start, but how you finish. Motivational Speaker, Json is living proof of that philosophy. In this episode, we're discussing the challenges faced growing up in St. Louis that would break most people: poverty, gang violence, a lack of opportunity, and systemic barriers at every turn. Now founder of Show Me Peace, Json refused to let his environment define his destiny and found strength in resilience, education, and an unwavering belief that anyone can rewrite their story. Prepare to be inspired as he delivers hard-hitting truths and actionable insights on finding your purpose. Hit that play button to tune in. EPISODE HIGHLIGHTS: 1. Overcoming adversity and using your past as fuel2. Developing a mindset of resilience and self-belief3. Setting goals and breaking free from negative cycles4. Defying the odds and creating your own path to successCONNECT WITH US:- Find Json on InstaGram: @json314- Stay connected with the podcast across all social media platforms:- For Instagram, follow @high_heels_tea_podcast- Visit the website at munroeshoetique.com/pages/high-heels-tea-podcast- For bookings or inquiries, email booking@highheelsandteapodcast.com

Aydin sits down with Mike Potter, CEO and co-founder of Rewind, to talk about how AI is changing both the risk and opportunity landscape for SaaS companies. They cover how AI agents are now deleting real customer data, why backup is more critical than ever, and how Rewind became an AI-native org with dedicated AI ownership, monthly Lunch & Learns, and real internal workflows.Mike walks through the exact N8N workflows he uses to:Auto-triage his Gmail into multiple inboxes using AIGenerate a daily AI brief based on tasks, calendar events, and past email contextAnalyze churn, win/loss, and internal product data using Claude and MCPThey close with Mike's “dream automation”: a full AI-generated business review that looks across financials, CRM data, and benchmarks.Timestamps:0:00 — Welcome to the show0:31 — Mike's intro & what Rewind backs up across SaaS ecosystems1:40 — AI agents as a new failure mode and how Rewind “saves you from your AI”4:05 — Turning Rewind into an AI-native company early on4:53 — First attempt at AI-built integrations (why it failed then, why it might work now)7:23 — Developers trading tedious integration maintenance for more interesting AI work9:45 — Code vs architecture: the Shopify webhooks story and handling 1.1B+ events14:03 — Hiring an AI Engineer: scope, responsibilities, and why background mattered15:33 — How Rewind drove AI adoption: Lunch & Learns, “use it in your personal life,” experimentation20:53 — How AI Lunch & Learns actually run across multiple offices and remote folks23:10 — Examples: CS tools, Alloy prototypes, AI video voiceovers, end-to-end workflows25:13 — Churn workflows: combining uninstall reasons from multiple marketplaces into Claude27:06 — Win/loss and internal analytics using Claude Projects + MCP server into an internal DB29:14 — Choosing between Claude, ChatGPT, and Gemini depending on the task (and re-testing every few months)31:23 — Mike's Gmail system: multiple inboxes + N8N + AI classification36:07 — Inside the email-classifier prompt and AI-powered spam that beats Gmail filters41:34 — The “Daily AI Brief”: pulling tasks, meetings, and prior email threads into a single morning email45:02 — Letting AI write and debug N8N workflows (and how assistants in tools are getting better)48:58 — Wishlist: automated AI business review across finance, Salesforce, and SaaS benchmarks51:23 — Closing thoughts: so many useful tools are possible, but GTM is the hard partTools & Technologies MentionedRewind – Backup and restore for mission-critical SaaS applications.Claude – LLM used for analysis, projects, agents, and internal tools.ChatGPT / OpenAI (GPT-4.1, GPT-4.1 mini) – LLMs used for code, prompts, and workflow JSON.N8N – Automation platform used to build email and daily-brief workflows.Gmail – Email client where AI-powered labels drive multiple inboxes.Google Calendar – Calendar data powering the daily AI agenda.Google Tasks – Task list feeding into the morning brief email.MCP (Model Context Protocol) – Connects Claude to Rewind's internal databases.Alloy – Tool for building interactive product UI prototypes.Salesforce – CRM used for pipeline, churn, and win/loss analysis.Gumloop – Workflow tool with an embedded AI assistant.Zapier – Automation platform referenced for plain-English workflow creation.Fellow – AI meeting assistant for summaries, action items, and insights.Subscribe at⁠ thisnewway.com⁠ to get the step-by-step playbooks, tools, and workflows.

Rory and Drew celebrate crawling their way to 30k subs, then immediately prove they are barely qualified to handle it by turning a Stranger Things binge into a full-blown lecture on composition, lighting, and how to reverse-engineer blockbuster shots into Midjourney and Nano Banana Pro prompts. They talk like film school dropouts who discovered prompts instead of lenses. From there, they unpack fresh Midjourney office hours: the upcoming UI/UX overhaul with continuous scrolling, better color control, a reworked style system, and the big one: parallel edit models that finally keep you inside Midjourney instead of forcing you into five other tools. They break down what “better text handling” could realistically mean for real-world client work, what to expect from Midjourney V8 training in January, and why business use cases will decide who actually wins this model war. Then it's a long, dangerous slide into Nano Banana Pro obsession. They show how they are using it for real campaigns: ingredient flat-lay diagrams with perfect labels, knolling that actually respects object counts, thumbnail iterations in minutes, hyper-real food tweaks (“make the cheese more brown and bubbly”) and product work where text on bottles and labels actually holds up. Think: turning moodboards into branded cars, movie-poster typography onto existing art, and multi-shot car sequences that are clean enough to use as video keyframes. In the back half, they zoom out into systems: building custom Nano tools in Google AI Studio, using JSON prompts, if-then logic, and style libraries to create reusable pipelines for teams that are not prompt nerds. They rant about broken N8N workflows, fake Instagram “AI automation” grifts, and share where affiliate tools actually see conversions today across YouTube, X, and LinkedIn. It is part Midjourney V8 rumor mill, part Nano Banana Pro clinic, part therapy session for creatives trying to stay sane in an algorithm that clearly prefers trolls and evolving Pokémon. --⏱️ Midjourney Fast Hour00:00 Midjourney Fast Hours hits 30k subs01:28 Stranger Things S5, film craft & AI framing05:39 Turning cinematic shots into AI prompts07:33 Pop culture prompts, memes & brand tie-ins08:38 Nano Banana branding tricks & model hype cycle09:38 Midjourney swag, “non-sponsored sponsors”10:12 Midjourney UI overhaul & scrolling-style feed15:46 Midjourney edit models and in-app image editing20:16 Midjourney V8 timing, text handling & business use24:41 Midjourney vs other models for real client work26:47 Free image tools, casual users & competition30:57 Nano Banana Pro: real-world client use cases36:31 Micro edits, product shots & text stress tests42:33 Product versioning, depth tests & asset variants44:25 Car branding, moodboards & Nano video keyframes46:20 Polaroid race car branding & design details50:09 Building custom Nano tools in Google AI Studio55:21 Style libraries, handoff workflows & reverse prompts59:17 If-then logic for prompts, GPTs & image systems01:03:01 From tokens to full-blown image systems01:04:21 Instagram grifts, empty funnels & manychat rage01:05:15 Platforms that actually convert for AI tools01:06:38 Algorithm chaos, Pokémon and death threats01:06:58 Midjourney swag, the Faye cameo & water bottle talk01:07:58 Future video model hype, skepticism & sign-off

This week's episode covers a mix of major React Native updates, powerful new tools, and insights from publishing my AI-engineered farming game, Tiny Harvest. We also talk about early experiments like React Native Rails and what CSS Grid means for the future of layout on mobile.⚛️ React Native Radar:✨ Gifted Chat v3 rewrite – modernized API, performance fixes

This week, we discuss the Cloudflare outage, their current business strategy, and paying OSS maintainers. Plus, thoughts on loading the dishwasher and managing your home. Watch the YouTube Live Recording of Episode (https://www.youtube.com/live/byFyPbe9HC0?si=DpOApdTKs9oh-bWl) 548 (https://www.youtube.com/live/byFyPbe9HC0?si=DpOApdTKs9oh-bWl) Runner-up Titles Mystery Knob Vegans are cursed vegetarians Skilled enough Defrag the dishwasher Design Intentions QR codes everywhere I don't know where we draw the line, but I know where we start SDT IoT CMBD, Home Edition. SDT Open Source Money Maker Lead with Nagware Stocks go up, stocks go down Safari's my naked browser Coté wanted to add periods to all of these but did not. Rundown FFmpeg to Google: Fund Us or Stop Sending Bugs (https://thenewstack.io/ffmpeg-to-google-fund-us-or-stop-sending-bugs/) Cloudflare blames massive internet outage on 'latent bug' (https://techcrunch.com/2025/11/18/cloudflare-blames-massive-internet-outage-on-latent-bug/) Cloudflare outage on November 18, 2025 (https://blog.cloudflare.com/18-november-2025-outage/) Replicate is joining Cloudflare (https://blog.cloudflare.com/replicate-joins-cloudflare/) Relevant to your Interests The Walt Disney Company Announces Multi-Year Distribution Agreement With YouTube TV (https://thewaltdisneycompany.com/the-walt-disney-company-announces-multi-year-distribution-agreement-with-youtube-tv/) Anthropic claims of Claude AI-automated cyberattacks met with doubt (https://www.bleepingcomputer.com/news/security/anthropic-claims-of-claude-ai-automated-cyberattacks-met-with-doubt/) Disrupting the first reported AI-orchestrated cyber espionage campaign (https://www.anthropic.com/news/disrupting-AI-espionage) Compact, human-readable serialization of JSON data for LLM prompts (https://github.com/toon-format/toon) Outage Tracker | Updog By Datadog (https://updog.ai/) Jeff Bezos Creates A.I. Start-Up Where He Will Be Co-Chief Executive (https://www.nytimes.com/2025/11/17/technology/bezos-project-prometheus.html) Power (https://a16z.com/powerpoint-is-your-therapist-gamma-is-your-coach/)P (https://a16z.com/powerpoint-is-your-therapist-gamma-is-your-coach/)oint is your therapist, Gamma is your coach | Andreessen Horowitz (https://a16z.com/powerpoint-is-your-therapist-gamma-is-your-coach/) Red Hat Introduces Project Hummingbird for “Zero-CVE” Strategies (https://www.redhat.com/en/about/press-releases/red-hat-introduces-project-hummingbird-zero-cve-strategies) A new era of intelligence with Gemini 3 (https://blog.google/products/gemini/gemini-3/) The platform that needs a platform (https://cote.io/2025/11/19/the-platform-that-needs-a.html) The AI Coding Startup Favored by Tech CEOs Is Now Worth $29.3 Billion (https://www.wsj.com/tech/ai/the-ai-coding-startup-favored-by-tech-ceos-is-now-worth-29-3-billion-14c72c02) The Smartest Fliers Use This App to Survive America's Travel Hell (https://www.wsj.com/tech/personal-tech/flighty-app-flight-cancellations-delays-900a8aad) Oracle's Market Cap Decline: Analyzing the Impact on Finance (https://platformonomics.com/2025/11/platformonomics-tgif-108-november-14-2025/) OpenAI's Fidji Simo Plans to Make ChatGPT Way More Useful—and Have You Pay For It (https://www.wired.com/story/fidji-simo-is-openais-other-ceo-and-she-swears-shell-make-chatgpt-profitable/) Europe's cookie nightmare is crumbling (https://www.theverge.com/news/823788/europe-cookie-prompt-browser-changes-proposal) Nonsense AI-Powered Teddy Bear Caught Talking About Sexual Fetishes and Instructing Kids How to Find Knives (https://gizmodo.com/ai-powered-teddy-bear-caught-talking-about-sexual-fetishes-and-instructing-kids-how-to-find-knives-2000687140) Whipped Cream Worth $80K Stolen in Ontario (https://www.yahoo.com/news/articles/whipped-cream-worth-80k-stolen-135930616.html) Conferences DevOpsDayLA at SCALE23x (https://www.socallinuxexpo.org/scale/23x), March 6th, Pasadena, CA Use code: DEVOP for 50% off. CFP open until Dec. 1st. SDT News & Community Join our Slack community (https://softwaredefinedtalk.slack.com/join/shared_invite/zt-1hn55iv5d-UTfN7mVX1D9D5ExRt3ZJYQ#/shared-invite/email) Email the show: questions@softwaredefinedtalk.com (mailto:questions@softwaredefinedtalk.com) Free stickers: Email your address to stickers@softwaredefinedtalk.com (mailto:stickers@softwaredefinedtalk.com) Follow us on social media: Twitter (https://twitter.com/softwaredeftalk), Threads (https://www.threads.net/@softwaredefinedtalk), Mastodon (https://hachyderm.io/@softwaredefinedtalk), LinkedIn (https://www.linkedin.com/company/software-defined-talk/), BlueSky (https://bsky.app/profile/softwaredefinedtalk.com) Watch us on: Twitch (https://www.twitch.tv/sdtpodcast), YouTube (https://www.youtube.com/channel/UCi3OJPV6h9tp-hbsGBLGsDQ/featured), Instagram (https://www.instagram.com/softwaredefinedtalk/), TikTok (https://www.tiktok.com/@softwaredefinedtalk) Book offer: Use code SDT for $20 off "Digital WTF" by Coté (https://leanpub.com/digitalwtf/c/sdt) Sponsor the show (https://www.softwaredefinedtalk.com/ads): ads@softwaredefinedtalk.com (mailto:ads@softwaredefinedtalk.com) Recommendations Brandon: The Beast in Me (https://www.google.com/url?sa=t&source=web&rct=j&opi=89978449&url=https://www.netflix.com/title/81427733&ved=2ahUKEwiy4NnP_P6QAxWGnWoFHU37GesQFnoECGcQAQ&usg=AOvVaw0QnhTLbjScTHWLLBI4qs26) Matt: The Prestige (https://www.imdb.com/title/tt0482571/) Coté: Fantastic 4 (https://en.wikipedia.org/wiki/The_Fantastic_Four:_First_Steps) with that Boba Fet (https://en.wikipedia.org/wiki/The_Fantastic_Four:_First_Steps)t (https://en.wikipedia.org/wiki/The_Fantastic_Four:_First_Steps) guy (https://en.wikipedia.org/wiki/The_Fantastic_Four:_First_Steps), “Winter's Mourning,” from Uncaged God (https://www.dmsguild.com/en/product/382873/uncaged-goddesses)d (https://www.dmsguild.com/en/product/382873/uncaged-goddesses)esses (https://www.dmsguild.com/en/product/382873/uncaged-goddesses). Photo Credits Header (https://unsplash.com/photos/gray-and-white-spoon-and-fork-lot-closeup-photo-vZZfVCUOKfw)

It's easy to overcomplicate data modeling, especially when enums, relationships, and future requirements are in play.    In the latest episode of the No Compromises podcast, Joel brings Aaron a real-world technical dilemma: how to model a relationship between two models when types are stored as enums, not models.    We discuss the pros and cons of pivot tables versus JSON columns, the importance of context before jumping to solutions, and how developer instincts can sometimes get in the way of clarity.(00:00) - Setting up the technical problem (02:00) - Pivot tables vs JSON columns (05:15) - Filtering and validation considerations (07:15) - Deciding on the best approach (09:50) - Silly bit Would you like us to review your code or application architecture?

Topics covered in this episode: Possibility of a new website for Django aiosqlitepool deptry browsr Extras Joke Watch on YouTube About the show Sponsored by us! Support our work through: Our courses at Talk Python Training The Complete pytest Course Patreon Supporters Connect with the hosts Michael: @mkennedy@fosstodon.org / @mkennedy.codes (bsky) Brian: @brianokken@fosstodon.org / @brianokken.bsky.social Show: @pythonbytes@fosstodon.org / @pythonbytes.fm (bsky) Join us on YouTube at pythonbytes.fm/live to be part of the audience. Usually Monday at 10am PT. Older video versions available there too. Finally, if you want an artisanal, hand-crafted digest of every week of the show notes in email form? Add your name and email to our friends of the show list, we'll never share it. Brian #1: Possibility of a new website for Django Current Django site: djangoproject.com Adam Hill's in progress redesign idea: django-homepage.adamghill.com Commentary in the Want to work on a homepage site redesign? discussion Michael #2: aiosqlitepool

Topics covered in this episode: httptap 10 Smart Performance Hacks For Faster Python Code FastRTC Explore Python dependencies with pipdeptree and uv pip tree Extras Joke Watch on YouTube About the show Sponsored by us! Support our work through: Our courses at Talk Python Training The Complete pytest Course Patreon Supporters Connect with the hosts Michael: @mkennedy@fosstodon.org / @mkennedy.codes (bsky) Brian: @brianokken@fosstodon.org / @brianokken.bsky.social Show: @pythonbytes@fosstodon.org / @pythonbytes.fm (bsky) Join us on YouTube at pythonbytes.fm/live to be part of the audience. Usually Monday at 10am PT. Older video versions available there too. Finally, if you want an artisanal, hand-crafted digest of every week of the show notes in email form? Add your name and email to our friends of the show list, we'll never share it. Michael #1: httptap Rich-powered CLI that breaks each HTTP request into DNS, connect, TLS, wait, and transfer phases with waterfall timelines, compact summaries, or metrics-only output. Features Phase-by-phase timing – precise measurements built from httpcore trace hooks (with sane fallbacks when metal-level data is unavailable). All HTTP methods – GET, POST, PUT, PATCH, DELETE, HEAD, OPTIONS with request body support. Request body support – send JSON, XML, or any data inline or from file with automatic Content-Type detection. IPv4/IPv6 aware – the resolver and TLS inspector report both the address and its family. TLS insights – certificate CN, expiry countdown, cipher suite, and protocol version are captured automatically. Multiple output modes – rich waterfall view, compact single-line summaries, or -metrics-only for scripting. JSON export – persist full step data (including redirect chains) for later processing. Extensible – clean Protocol interfaces for DNS, TLS, timing, visualization, and export so you can plug in custom behavior. Example: Brian #2: 10 Smart Performance Hacks For Faster Python Code Dido Grigorov A few from the list Use math functions instead of operators Avoid exception handling in hot loops Use itertools for combinatorial operations - huge speedup Use bisect for sorted list operations - huge speedup Michael #3: FastRTC The Real-Time Communication Library for Python: Turn any python function into a real-time audio and video stream over WebRTC or WebSockets. Features

# 2025-11-04 - News - Episode 244# Hosts: - Daniel Garcia - Senior Developer at Ortus Solutions- Grant Copley - Senior Developer at Ortus Solutions# summaryIn this episode of the Modernize or Die Podcast, hosts Daniel Garcia and Grant Copley discuss the latest updates from Ortus Solutions, including the release of ColdBox 8 and BoxLang 1.7. They explore new features, real-time communication with SocketBox, and the implications of the recent Lucee 7 release. The conversation also covers security considerations for AI tools, the introduction of Message Pack support for ColdFusion, and upcoming events in the CFML community. The hosts emphasize the importance of community support and innovation in the ColdFusion ecosystem.# TakeawaysColdBox 8 introduces significant updates and features.SocketBox enables real-time communication for applications.BoxLang 1.7 includes server-side events and performance improvements.Lucee 7 requires a fresh install due to major changes.Message Pack support could enhance performance over JSON.Security considerations are crucial when using AI tools.CF dump readability can be improved with CSS.Upcoming events include ColdBox webinars and security training.Community support is vital for open-source initiatives.Black Friday deals will offer free audits for developers.# Chapters00:00 Welcome00:39 Ortus News and BoxLang Updates11:35 CFML Updates22:14 Upcoming Events and Conferences27:03 Thank You# Join the Ortus CommunityBe part of the movement shaping the future of web development. Stay connected and receive the latest updates on, **product launches, tool updates, promo services and much more.**Follow Us on Social media and don't miss any news and updates:-  https://twitter.com/ortussolutions-  https://www.facebook.com/OrtusSolutions-  https://www.linkedin.com/company/ortus-solutions-corp-  https://www.youtube.com/OrtusSolutions- https://github.com/Ortus-Solutions# KeywordsColdBox, BoxLang, Lucee, CFML, Ortus Solutions, WebSockets, AI, Message Pack, security, podcast ★ Support this podcast on Patreon ★