Podcasts about Uis

Enterprise software is changing. I sat down with Brian Landsman, CEO of AgentExchange at Salesforce, to talk about what an agent-first future actually looks like. #salesforcepartnerThis wasn't a surface-level conversation. We went deep into what's coming next.Here's what stood out:* AgentExchange evolved from a marketplace directory into a commerce and discovery layer, is rethinking how enterprises deploy software* Headless architectures could fundamentally reshape how people interact with enterprise systems since traditional UIs matter less* Agents are moving from assistants to becoming the primary interface* Workflows need to be redesigned from the ground up for an agent-first world* Success will be defined by agents executing end-to-end tasks, not just supporting humans* The gap between AI pilots and production is finally starting to closeWe also discussed how individuals can go to market faster with $50M AgentExchange Builders Initiative.Watch the full conversation and let me know what you think.#data #ai #tdx26 #salesforce #workflows #api #headless360 #agentexchange #apps #theravitshow

We're announcing AIEWF speakers this week! Take the AI Engineering Survey!Today's guest Ethan first joined us for the LS Paper Club as the lead on NVIDIA Cosmos World Model, but then joined xAI and built Grok Imagine in 3 months:He comes back on Latent Space with some nuclear hot takes: that Video Models primarily get their intelligence from LLMs, not from training on video data, and that the next frontier for truly interactive, realtime, long-horizon world models is to work on LLMs (perhaps Interaction Models as well…)Put it this way: In the near term, the next Sora won't be a better video model, but a video agent.Generative Media may more closely follow the evolution of AI coding which went from focusing on one-shot output performance and cost, to multiturn reasoning and planning models for agents and systems that can plan, edit, test, debug, and submit PRs.At a certain point, coding models got so good that the only significant next step to improve performance was handling the orchestration of these models.Now as the performance of video models increases significantly across realism, consistency, & prompt adherence while becoming more cost efficient, the next evolution of video generation may also be systems that can plan, generate, edit, critique, and iterate across an entire creative task. In this episode, Ethan joins swyx and Vibhu to unpack what it actually takes to build frontier image and video systems: data, VAEs, diffusion transformers, audio-video alignment, inference speedups, and the hidden cost of storing and moving massive video datasets. From building NVIDIA's Cosmos world model to joining xAI as Grok Imagine was being built from zero to one, Ethan He has been at the center of some of the most important work in video generation, multimodal models, and real-time world models.We go deep on Grok Imagine, how a small xAI team shipped its first multimodal video model in three months, why iteration speed matters more than almost anything in model development, and why many of the biggest gains come from fixing tiny bugs in data and training pipelines. Flipbook: The future of VideomaxxingVideo agents are almost a sure bet to be the trend in the coming year. We end with a glance at what's beyond video agents:Flipbook caused a minor sensation this year when it was released, but most treat it as a fun demo. Ethan takes it very seriously — with the speed and cost of inference coming down every year, the future of custom video JIT UI is closer than you think. We talked about why videogen models may become the front end of AI, how generative UI could replace traditional HTML/CSS, why world models need to be real-time, interactive, and long-horizon, and why the future of video generation may depend more on language models and agents than on diffusion alone.We discuss:* Why fast iteration mattered more than meetings* Why small training bugs can drive huge model quality gains* Why coding models may make compute the bottleneck again* How image and video models are trained with synthetic captions* The role of VAEs and latent space in frontier video models* Why image models are the foundation for video models* The tradeoff between temporal compression and real-time interactivity* Flipbook, Neural OS, and the future of generative UI* Why future interfaces may go from user intent to pixels* The hidden cost of training video models: storage, egress, and GPU hours* How step distillation and consistency models (like OpenAI sCM) makes video inference orders of magnitude faster* Grok Imagine 0.9 and large-scale audio-video generation* Why audio-video alignment is harder than text-video alignment* Ethan's definition of world models* Reference-to-video, video extension, and long-context video generation* Why xAI's research communication undersells Grok Imagine* How xAI culture shaped the speed of development* AI watermarking, SynthID, and detecting generated media* Why prompt rewriting matters for video models* Grok Imagine Agent and the rise of video agents* Why language models may unlock better video generation* Robotics, physical AI, and embodied world models* Why Ethan left xAI and shifted focus toward LLMs* Self-managed context, memory, and the next frontier for language modelsEthan He* LinkedIn: https://www.linkedin.com/in/ethanhe42* X: https://x.com/EthanHe_42Timestamps00:00:00 Introduction00:01:25 From NVIDIA Cosmos to xAI00:03:24 Building Grok Imagine from Zero to One00:10:07 How Image and Video Models Are Trained00:18:53 Video Compression, VAEs, and Real-Time Tradeoffs00:22:10 Generative UI, Flipbook, and Neural OS00:32:10 The Cost of Training Large Video Models00:37:04 Distillation, GANs, and Fast Video Inference00:41:21 Audio-Video Generation and Grok Imagine 0.900:48:34 What Makes a World Model?00:55:51 Reference Videos, Long Context, and Video Memory01:00:11 xAI Culture, Research, and First-Principles Building01:09:45 AI Safety, Watermarking, and Prompt Rewriting01:13:10 Video Agents and AI-Assisted Creation01:27:32 Why Language Models Unlock Better Video01:31:15 Robotics, Physical AI, and Embodied World Models01:32:38 Why Ethan Left xAI01:34:16 Self-Managed Context and the Future of LLMs01:38:43 Ethan's Career Path and Closing ThoughtsTranscriptIntroduction: Ethan He, Latent Space, and the Path to xAISwyx [00:00:00]: We're here in the studio with Ethan He, most recently of xAI. Welcome.Ethan [00:00:10]: Thank you. Glad being here.Swyx [00:00:11]: We're also here with Vibhu. you were first coming to us or joining the latent space world because you were working on Kosmos at NVIDIA, and you did a paper. We loved it. you presented it as well, so thank you for doing that.Ethan [00:00:23]: I've actually, I also presented the MoEs twice at latent space.Swyx [00:00:29]: How did you actually hear about us? Did we reach out to you? Is that how it worked?Ethan [00:00:33]: No, actually, I-- the community. Like I realized, oh, there is this online community that people talk about AI and also learn from each other through papers every week through the Paperclip. It's very nice.Ethan [00:00:49]: I learned a lot.Swyx [00:00:49]: I think three years stop. We haven't stopped even on Christmas and New Years. many weeks I want to stop but it keeps going.Vibhu [00:00:58]: No, that was good. I think you had posted that you worked on a paper, and I was “Oh, very cool. We have Paperclip. Present then.”Vibhu [00:01:04]: But I might have reached out to you after.Swyx [00:01:05]: you-- because it's an amateur club, right?Swyx [00:01:08]: so it's very unusual and but we have sometimes paper authors come by and actually explain the paper. Today we just did, the poolside paper, which was apparently very good.Vibhu [00:01:18]: Came out yesterday.Vibhu [00:01:19]: pretty interesting, right? Fully open. They talk about everything, systems. So it's a good one. We'll, we'll recommend people to read it.Swyx [00:01:25]: Bring us up to speed on your transition to xAI, ‘cause I actually don't even know when you joined. just like tell the, tell the story about the sort of transition.From NVIDIA Cosmos to xAI: Scaling Video and World ModelsEthan [00:01:34]: Before xAI, I was working on Kosmos world model as in-- at NVIDIA. So Kosmos is, it's a giant video foundation models that can-- that aims to simulate the world and for-- it serves as a foundation of-- for all of the roboticists to build on top of. There, once I built the Kosmos one, I realized as this thing also has a scaling law similar to language model, we need to scale up the video models further. that's, that's why I realized I need to move to somewhere with much more compute resources. That's how ISwyx [00:02:13]: Than NVIDIA?Vibhu [00:02:14]: The GPU rich came themselves.Vibhu [00:02:19]: And timeline-wise, when was Kosmo? It was pretty early, right? It was open world model, open paper, everything.Ethan [00:02:25]: It was end of twenty-four.Vibhu [00:02:28]: End of twenty-four.Ethan [00:02:30]: Then at mid twenty-five, I moved to xAI. At that time-- I joined about the time when xAI was about to build video models and in multi-model models. There were no infra, no data, and no model, and it just-- as a few engineers, we built it in three months and released the first model, Grok Imagine zero point nine.Ethan [00:02:55]: And since then, I keep working on video models and move more from training and to post-training of the video models. For example, like a reference to videos, kind of like the cameo feature and, video extensions. And, before I left, I worked on a world model, leading a small team to focus on the real-time long horizon video generation.Building Grok Imagine From Scratch in Three MonthsSwyx [00:03:24]: Can you give like a rough roadmap of okay, you're on a brand-new team. Grok previously was only text, or they partnered with BFL for their image gen stuff. What do you-- what are the building blocks, right? You have compute, data you can procure somewhere. Like just what are like the sequence of things that people should think about when you're setting up a new team?Vibhu [00:03:43]: actually even deeper, not just data you can procure. You guys had to go through getting the data too, right? So you shipped it pretty fast, but yeahSwyx [00:03:51]: three months is likeVibhu [00:03:52]: From everythingSwyx [00:03:52]: actually like very surprisingly fast.Ethan [00:03:55]: One thing I say like thanks to my experience at NVIDIA, ‘cause first time when we were building Kosmos together, we built it, for about a year. So this is like the second time I do it. Roughly have an idea, what to do. I say the most important thing is the talent. Everyone were very strong and clever, very close with each other towards a common goal. So that speed up things a lot. So you reduce the communication bandwidth among people, and everyone can work towards the same goal. It's, it's like every day there's not that much meetings on the calendar, like maybe like a, like a sync a day, and after that it's, it's just all building. It was pretty fun at that time.Ethan [00:04:47]: And another thing is that xAI has very strong foundations of like data inference, model inference, and the supporting there can help the model develop a lot. When I look at, training models, I don't so actually the top important thing is like how many, how many iterations can you do, per day? and the more iteration can you do, you can, you can train the model much faster. So if you have very strong infra and you have a lot of compute, you can, you can train these models in very short period of time. That can give you a much larger buffer to, for errors, and it also gives you the opportunity to spot more bugs.Iteration Speed, Compute, and Debugging Model PipelinesSwyx [00:05:46]: What is an iteration? Is it like a few hundred steps or what are youEthan [00:05:50]: Let's say just the train-training the model, like from acquire new data and maybe design new algorithms and train a new model, maybe at smaller scale orSwyx [00:06:01]: So cycle time for like any hyperparam that you're searching.Ethan [00:06:04]: Cycle time and tune to like eval this model. Is this model better than my previous iteration?Ethan [00:06:11]: SoSwyx [00:06:11]: So it's like before you, someone had already set this up that you can iterate very quickly.Ethan [00:06:15]: I think the foundation there is extremely good forDeveloping and research models.Ethan [00:06:23]: And often I find is it-- this is kind of boring, but like a lot of the improvements does not come from new algorithms. It comes from finding small bugs here and there in the data pipeline, in the, in the model training pipeline. Those give, those give the biggest boost to the model quality.Vibhu [00:06:46]: It's interesting, right? So you say it's like small team, less communication bandwidth, but also a lot of quality is like find little bugs. It seems counterintuitive, right? You have a lot of people, you can iron out more of those, but it's interesting to see the other side, right?Swyx [00:07:00]: I also wonder, have you-- do you try using LLMs to look for bugs? I don't know.Ethan [00:07:05]: I remember at that time it was mid two thousand and twenty-five, so it's the coding model wasn't quite there yet. I remem- I remember like December two thousand and twenty-five, it was extremely good. Yeah, I've been, I've been using it at that time. It's, it's helpful. sometimes it produce codes that are kind of difficult to maintain, even though like the first time it built something extremely fast. But it gave the, like a spaghetti code, thousands of lines that I couldn't maintain, and the LLM itself couldn't figure out what's, what's wrong and how to improve on top of it. But now I find it much better. Yeah, I want to bring up another point here is now coding models are much more efficient and can help us implement stuff much faster. Compute might become a bottleneck again because previously, like if you want to train a new model, say you want to generate new synthetic data and then or write a new algorithm, it might take a few weeks. And during that period of time, you don't-- you might not have experiments to run. But now you can build that thing within a few hours, then you can immediately train a model.Ethan [00:08:24]: Now you have to have enough compute to try all of the ideas. So compute might be the bottleneck of iterating speed again.Swyx [00:08:36]: yeah, I actually, honestly, I think it's like kind of a stressful job because you're “Well, I should be trying everything, and if I'm not, then I'm not doing my job well.”Vibhu [00:08:48]: there's also the stress of you're eating thousands of GPUs per hour, which is very expensive and, compute can go to other researchers.Swyx [00:08:56]: You got the daddy Elon toVibhu [00:08:57]: You got daddy Elon.Ethan [00:08:59]: It wasVibhu [00:09:00]: But there's still finite amount of compute, like you want to use it, you want to use it well, you want more of it.Ethan [00:09:06]: That was quite stressful indeed. Yeah, I think one thing is the-- with coding models now, like a lot of these jobs can be automated, which is much better. A second, it's a, it's a marathon, so you got to maintain good health and, a regular schedule.Vibhu [00:09:28]: It's, it's hard to hear that when you shift from zero to nothing in two months.Swyx [00:09:32]: and, I think obviously the culture at xAI is very famously, people work very hard. one thing I did want to dive into, in our-- in the notes that you, that you sent ahead of time, you had specific comments about the cost of Video Gen training. presumably this is on the Colossus-1, right? the two hundred megawatt cluster. Any whatever you want to just share on that.Vibhu [00:09:54]: I think there's, there's three things we're talking about, right? So there's Video Gen, there's also the Image Gen model that you put out. Do you want to like complete the, okay, so zero to one, you have a few months. Just what are the stages of create Image Gen model?Swyx [00:10:06]: Oh, yeah, maybe I got distracted.How Image and Video Models Are Trained: Synthetic Captions, Tokenizers, and VAEsVibhu [00:10:07]: Sorry. and then, from there's Video Gen, there's Audio Gen. Would love to get into those next. But what is that first few months like? So small team, a lot of bugs, iterations, but what does it look like? Do we take something off the shelf? Do we just get data compute? What's, what's the few months like? How do you go to state-art Image Gen model? How do you just start?Ethan [00:10:28]: I cannot comment specifically how xAI did, but it's, it's a quite standard process. I can draw some, examples from Cosmos. So mainly it's building a video model, you actually need to build a image model first. And building these two models, the data you need is a hundred percent synthetic pair of language and image or language to video. Because on the, on the internet, actually, the videos don't naturally associate with text. So you can say, oh, like on YouTube, you have the title and you have the description and the commentsSwyx [00:11:11]: TitleEthan [00:11:11]: of a video, but usually they're not relevant to the video itself. And say maybe like the video is a natural scene of mountains or something, and the title is, I'm so happy today.Ethan [00:11:26]: So they have they have no correlation at all. So the first step is to, you have to generate synthetic pair of language with the videos. So you gather videos from the internet, and you use a VLM to caption the videos. So that part, here's a question, like how do you, how do you gather VLM to begin with? So if there's noSwyx [00:11:55]: You, so you fuse the model, right? LikeEthan [00:11:57]: Say if there's no like VLM exists, like how do you generate the text to the beginning, right? It's, it's impossible.Swyx [00:12:04]: I see.Ethan [00:12:05]: In the beginning, it's like you ask human to describe the video as detailed as possible.For example, you ask them to describe everything, like all objects, all characters, and all interaction and dialogues in the, in the videos. So that's in the protocol of Cosmos labeling. We require the objective we give to the labelers was that you have to describe the video as detailed as possible, such that a blind person hears a blob of text can reconstruct what the video is like from their head.Swyx [00:12:43]: Video or image? You're talking about images.Ethan [00:12:44]: Video or image, either one of them.Vibhu [00:12:47]: This was pretty common when we went from clip and DALL-E, right?Vibhu [00:12:51]: It's all training on really detailed captioning of images. So same is applied to video, but insteadEthan [00:12:57]: same appliedVibhu [00:12:57]: of using multimodal model to pass in video images and write rich descriptions, you can alsoSwyx [00:13:04]: I think there's this traditional perspective of supervised, or, very highly human curated thing. I feel like there's a unlock with unsupervised, right? Where like you have enough to bootstrap that you can just throw common corpus on it or, whatever. like unsupervised vision and language pairing, right? Like where you just have, interspersed image and text and it just learns. To me, that is the VLM breakthrough that is different from the clip, different from the LM era.Ethan [00:13:36]: It's interesting to see that you kind of need both data.Ethan [00:13:41]: For example, for theSwyx [00:13:41]: You need it to bootstrap it up. YeahEthan [00:13:43]: for the generative model training, there's also usually like a small percentage of unlabeled data. So the model is instructed to generate a video without any text instruction. That can also help the model generalize. So after this stage of generative synthetic pair, so, one important common step is to train a compressor or a tokenizer of the image or videos. So because, if you train-- If you can technically, theoretically train image or video models on pure pixels, but the problem is that the, it's, it's a lot of tokens. So like one image, it's, a thousand by a thousand, it's like one million tokens, one million pixels. It's impossible to train transformer on that. So it's, you need to train a tokenizer, which can go from image to latent space and latent space back to image.Swyx [00:14:45]: That's why we named the podcast.Swyx [00:14:48]: But, basically, you're talking about vocabulary science.Ethan [00:14:50]: so vocab.Swyx [00:14:51]: And so, what is, what is imp-- like a million is impossible?Ethan [00:14:54]: In generative models, the vocab is continuous. It's a continuous space. We can think about like you map an image to a vector. It's a, it's a fixed length vector. It's sixteen or forty-eight, something like that. And then you map that vector back to the image space. And the mapping is, has-- The mapping is patch-based. So you say you haveEthan [00:15:22]: a sixteen by sixteen patch and you match, you map that patch of pixels into this latent space.Swyx [00:15:29]: We've covered thisVibhu [00:15:30]: This is like the vision transformersSwyx [00:15:32]: VAEs,Ethan [00:15:33]: VAEs.Vibhu [00:15:34]: You basically compress your input, you do your generation, you're reasoning all that generation in smaller dimension, and then you project back out.Swyx [00:15:43]: VAE is a form compression, but I think the for me, the patching thing is from VIT, right?Ethan [00:15:48]: You can make those.Swyx [00:15:49]: Literally the, yeah, the paper is titled like sixteen by sixteen is all you need. something like that. and then I think also, people make a lot of comparisons with this kind of patching with convolutions.Swyx [00:16:02]: Which is you're, you're kind of re- reconstructing the old paradigm with the new.Ethan [00:16:05]: Actually, in VAEs, there are, there are both convolution networks and transformers. You can actually do both.Ethan [00:16:14]: After this VAE, so what you've got is you've got latent space tokens and you've got the language tokens. So now the training of the diffusion transformer, usually generative models use diffusion transformers. It is actually quite standard. It's, it's very similar to how you train a language transformer models. It's not that much difference. It's just the tokens, the visual tokens in, visual tokens out. The only difference is there's a denoising process. So you train the model to unmask some of the noise. So you add, you add random noise to the visual tokens, and then you train the model to remove those noise to generate the clean tokens. Any inference, the model can iteratively remove noise from a hundred percent noise.Swyx [00:17:12]: And then there's also, to speed things along on the tech tree of diffusion, there's CFG, and then there's, there's also, latent diffusion that, there's, there's someone in there. I think, somewhere along the line, obviously, like stability and all these other guys, pioneered a lot of this, architecture. I don't know if you want to get into that or just, or do the video side up to you.Bootstrapping Video from Image Models and Temporal CompressionEthan [00:17:37]: After you train such model, such image model, the reason it's a, it's a foundation for video models is that image models are cheaper to train, and they have much denser connection between language and text. So, sorry, language and images. For example, you train a billion, you train on a billion images, and there's a mapping from the text to the image. And the cost to train the same, like the, a billion, a billion text to a billion videos, that's much more expensive because videosNaturally have more tokens than images. Because the diffusion models, their understanding of, language purely come from this mapping. So if you don't have enough mapping, so if you only train on like a ten million videos or something, there-- you might not see enough language tokens in your training, so your model does not understand human intention enough. So that's why you really-- you train-- you first train this image diffusion models, and then you bootstrap the video model from there.Swyx [00:18:53]: One thing I did want to ask, because I-- actually, I think you're, you're the first per-- video model person I've ever talked to, I think. we've, we've like talked to Luma and all those folks. There's all these tricks in video compression where basically frame by frame there's not that much difference, so actually you don't have to regenerate or save the whole frame, right? but I think MP4 compression or something else like that.Swyx [00:19:16]: is it tempting to use that? Or as far as I can tell, everyone just treats it as, “No, we would just generate every frame.” Is that roughly the state-art?Ethan [00:19:27]: There are a few different approaches. Let's say first, like you want to just directly use MP4 compression and use that as the tokens for the transformers to train, right? So people actually have tried that, but the main challenge is the latent space for the MP4 tokens were not, were not very comprehensible for the models. It's, it's extremely hard to train on that. And there's aEthan [00:20:01]: So that's why they created VAEs, which creates more continuous, latent space, so the models can understand that latent space and learn from it much easier. Even within the VAEs, there are different difficulties of the latent space. So you can imagine something the simplest, the most naive VAE is like you have an image, and you just shuffle all of the images into a, into a vector. So you don't need to train any VAEs, right? But that latent space is extremely hard for models to train on top of. That's why there are some debate on like how do you compress the tokens. So you mentioned like you can compress frame by frame. Also, you can compress, the temporal dimension.Ethan [00:20:52]: The difference is if you compress the temporal dimension, you get a much higher compression rate. Because there's temporal redundancy between frames, because, this frame and the last frame, likely they are mostly similar, so there's only some small difference. for example, I think in 12.1 VAE, they have like a eight by eight by four compression rate. So the four temporal tokens are compressed into one tokens. That can save a lot of, save a lot of the context length. If you do it frame by frame, you have to do maybe like eight by eight by one. Your context length will be four times larger. That being said, the benefit of the frame-- per frame compression, we might come back to this later, is, real-timeness and interactivity. ‘Cause if you, if you strain the output of the model, frame by frame, you can-- the model can respond to any user request immediately. So if you have like a temporal four compression, four times compression, thenSwyx [00:22:06]: It might be laggyEthan [00:22:07]: there's a lag there in nature.Swyx [00:22:10]: So you're very pilled on this. let's just go ahead and bring it up ‘cause we have the visual prepared anyway. There's some frontier applications of real-time video gen. So Flipbook is one of the examples that went viral recently, right? What is Flipbook?Real-Time Generative UI: Flipbook, Neural OS, and Diffusion Front EndsEthan [00:22:23]: Flipbook is kind of like a web brow- web browser. You can see like it has the web bro- browser UI on top. The difference is all of the UIs are generated by generative image model in real time, and anything here are fake. But you can, you can explore inside this wor- this imaginary world. Say like we-- here we have engineering the Great Pyramid. Like the model generates this for us to understand how it works, and if we want to navigate around and understand further, we can click on some of the, some of the description here, and the model will generate a new page, new subpage describing the details we want to know about.Swyx [00:23:14]: So it's basically kind of we're playing a video, but it's pausing for our next interaction, and then it just plays the next thing based on our interaction.Swyx [00:23:23]: Which is kind of cool.Vibhu [00:23:25]: and you kind of decide your story. So this was, how do you make a pyramid? levering technique seemed interesting, right? It shows how do you take Okay, I want to know what is thisSwyx [00:23:35]: The demo, the demo tweet had more animation between frames.Vibhu [00:23:38]: I think it's just skipping,Swyx [00:23:39]: Oh, it's just skipping a lot of frames.Ethan [00:23:40]: they also have a video modeVibhu [00:23:42]: It takes a lot. There's a lot of peopleEthan [00:23:42]: but, a lot of people are using it.Ethan [00:23:45]: So it's not available.Vibhu [00:23:46]: There's a live video stream. We can try,Swyx [00:23:50]: So this is an example of the kind of future that you see at the extreme. We don't-- we're obviously not in it today.Swyx [00:23:56]: But in a world where inference is completely free this is better than generating code and text?Ethan [00:24:02]: So this is, this is a final state of where Viva will be at for word model, I think. Imagine internet doesn't exist, and then you type in google.com. Like what should, what should, what should a model show you?the model can imagine something, and this is what the model imagine. And these web pages, they completely do not exist. So I think as the inference costs come down, we are going to have generative UI for everything. If you think about how the coding model works, so they write code for a web page, and they render the code might be con- converted into binary, and the binary render the pixels on the screen. So we in machine learning, every time we have some breakthrough, obviously it's, it's more intuit. So why don't we have like user instruction to the pixel directly? So the generative UI will be user intention to the pixels directly. And say like even if I want email, let's say everyone have the same interface, but I want, I want it slightly different. I want the email to show to me like a TikTok, so I can swipe left and right for the emails. And or maybe you want something else. We can have completely different things. Or like I have I'm looking at, Instagram stories, and I don't like the Like button. I always may click it. And, generative UI resolved it. So it's going to be a revolutionary replacement of the interface. So in the future, we might have much more powerfulEthan [00:25:50]: LLMs and coding models running behind the scene. And in the, in the front-end, the diffusion model will actually be the front-end to show stuff to you. That's how I imagine it.Swyx [00:26:02]: Diffusion front-end, deterministic back-end.Swyx [00:26:04]: Something like that. I find that very expensive, but,Vibhu [00:26:08]: I find it interesting you called LLMs writing code on the back end deterministic, but okay.Swyx [00:26:14]: you write it onceVibhu [00:26:15]: Compare it toSwyx [00:26:16]: And then you execute.Ethan [00:26:17]: If you think about the cost, say, let's say H100 costs $1 per hour, and if you use this eight hours a day and thirty days, so, every month you're paying this two forty, you'll actually not wanna pay for that. That's even more expensive than Cloud Code Max. But if you think about the compute costs come down like two times every year, and I think the future will likely arrive like within few years.Vibhu [00:26:49]: It's everything, right? compute cost comes down, compute gets faster, model gets smarterEthan [00:26:54]: More efficientVibhu [00:26:54]: model gets smaller.Swyx [00:26:55]: I don't know why you say two times, ‘cause I think it's like 100 times. In language models, it is roughly one hundred to a thousand times every twelve to eighteen months, for the same given level of LMSys, ELO.Vibhu [00:27:08]: That's a net of everything, right? That's model performance alongside compute. So different than just compute costs come down. But, a very interesting future.Swyx [00:27:19]: So the web designers will have to shout out that accessibility is an issue, right? how do you deal with screen readers or whatever. But yes, this is higher bandwidth storytelling than anything you can possibly generate with code, right? So I think that's the rough idea.Ethan [00:27:34]: And I'd like to add a little bit that so human naturally have the maximum bandwidth when we are looking at things, look at videos, and we also have maximum output bandwidth when we are talking. So in the future, it might be something like we talk to AI models, and the AI model responds back with a generative UI. So that would be the maximum input and output bandwidth to interact with AI models before neural link happens.Vibhu [00:28:06]: And it's also very custom, right? Some people are very visual, some people are not as visual, right? They prefer the text. But the best thing about generative UI, right, it can also be text.Swyx [00:28:17]: There's another project that we wanted to highlight, which is the Neural OS. Kinda similar idea, but here you're literally operating, simulating an operating system with a video model.Swyx [00:28:27]: and you can play Doom, you can do Firefox. I find this like mildly less impressive, obviously, because it's an OS that I can run.Swyx [00:28:37]: But here everything is imagined.Vibhu [00:28:40]: I was, used to the Command+W to close the Firefox tab. It didn't crash. That's why I saidSwyx [00:28:45]: It's too immersive.Vibhu [00:28:46]: It's, it's too immersive for me.Swyx [00:28:47]: Too immersive.Vibhu [00:28:48]: I wanted to close the tab.Vibhu [00:28:49]: But yes, I can play generated diffusion.Swyx [00:28:51]: this is shockingly fast.Swyx [00:28:54]: Because I remember there was a demo about like maybe one to two years ago. Someone tried to do the first-person shooter with a image model. There was no consistency. It was very slow. But here it looks like realistically it's-- this is Doom.Vibhu [00:29:07]: I think there's two sides to that, right? There's okay, what is running a game? The heavy part of it is actually the game engine, all the lighting, all that stuff, the graphics. This is just kind of video, right? Like we've solved consistency. This is still, it looks like a few years old image generation. There's some temporal consistency, but it's, it's kind of just images stitched together as frame video. But it's a good visual representation to pi- to picture the future you wanna see, right? that's, that's what I see in these more so.Ethan [00:29:38]: This reminds me of how the video models gets better and better. So Neural OS is kinda if you just look at it feels like it's just a crappy version of the, like the Windows we could have, right? And, but the difference is, so the model, this model is overfitted on the existing operating systems. It can generate nothing different than that. But it's actually also similar to video models. So when we are training these video model, image model, we train them on internet. There's no imaginary supernatural stuff on the internet. But once we train this model, you can prompt the model to generate something supernatural that have never existed in the data set. So if you train your Neural OS or neural computer on the standard screen recordings on the entire internet. The model can imagine completely new interface to interact with the computer.Swyx [00:30:43]: This is one of those things that is magical to me. usually generalizing out of distribution is bad, but somehow we have learned some kind of internal world model that you say, this plus, but it looks like rainbows and butterflies, it'll do it and it will kind of make sense.Swyx [00:31:03]: So yeah, that's kind of cool. Yeah, I don't know if there's any comment more on there. I do, I do wanted to, I did wanted to touch a little bit more on the model architecture stuff, which I think you were getting. It's, really fascinating. We don't get a chance to talk about this enough. So one of the papers that we covered, we've covered every annual, segment anything release. and I don't know if you follow-- you're a computer vision guy, so youEthan [00:31:26]: I knowSwyx [00:31:27]: . So they did memory attention, which is kind of interesting. And I always think, anything where you can, across the temporal dimension, keep some consistency, I think it's, very fascinating, and I don't know if Basically, does that-- the CV side bleeding into video gen side, I think is underexplored, right? we talk about it for labeling, but actually you can borrow the architecture itself.Ethan [00:31:50]: There's, there's also complete different approaches, right? you brought up the term world model, so we went from video model to world model. There is diffusion, but there's also other approaches that people are doing. So maybe we get into those after as well,?Swyx [00:32:03]: He has a whole definition of world models and stuff. I feel like we threw a lot at you. Whatever you want to comment on.Why Video Models Are Expensive: Storage, I/O, and Training ScaleEthan [00:32:10]: I think one thing that we should actually comment back on is okay, so we were talking about the steps to train image gen to video model. One thing we don't see as much of is okay, you brought up the delta in training data, right? SoEthan [00:32:24]: you won't have as much a video model might not generalize, but what is the cost of training a large video model? So we know for LLMs roughly, okay, even like the poolside thing that came out today, right? It's a Gemma level model trained on roughly forty trillion tokens at this many H200s over this much time, right? You can see what is the exact cost of that. So how many GPU hours over how much H200 costs? So how do we do the back-end math of, same thing for video models, image models. How do you, how do you kind of break that down? I can share some back-envelope calculation. So surprisingly, video models is-- the cost is very-- is comparable to language models and obviously the largest scale is language model, maybe like a medium scale to language models. I said just storing the videos alone, it costs a lot. You can, you can maybe look up on AWS or something.Ethan [00:33:20]: You really, say if you have a billion videos and let's say, let's just say like each video, like five megabyte, then you need five petabyte to just store those videos. And also remember we talk about you use a VAE to compress the videos, and you also need to store, typically you need to store those continuous feature, in-- also in your storage. That's also comparable size with the videos themselves. So just storing these videos and the features is tens of petabytes alone. And,Swyx [00:33:58]: I just, I just looked up the calculation. Five petabytes on S3 Standard is one hundred K per month.Ethan [00:34:05]: AndSwyx [00:34:05]: It's comparableEthan [00:34:05]: and you needSwyx [00:34:06]: AndEthan [00:34:06]: And then like tens of petabytes, two hundred K. And even more expensive is you have the ingress and egress.Swyx [00:34:13]: Oh, yeah.Ethan [00:34:14]: Like you-- through the internet. You have to just to download those videos, I believe it's, it's more expensive on AWS than just storing those videos.Swyx [00:34:25]: Storing, yeah.Ethan [00:34:25]: And each training runs, you probably need to pull them once. If you train multiple times, it's, it's even more than that. So it's like just storing the network, those costs is just, it would be a few, a few millions per month to just storing everything, not to mention the GPU cost.Ethan [00:34:45]: AndSwyx [00:34:45]: my side tangent, the compute rental, like GPU rental is very efficient. There's one side, okay, you can be XAI and build your data center. Should we not just build our, storage compute as well? LikeEthan [00:34:57]: Of courseSwyx [00:34:57]: cloud cost compared to just,Ethan [00:34:59]: You save so muchSwyx [00:35:00]: store. Yeah, exactly.Swyx [00:35:01]: Especially with like egress and stuff. So.Ethan [00:35:04]: That's a good idea, but it also comes to-- there are some of its own challenges.Swyx [00:35:09]: Of course, of course.Ethan [00:35:10]: like people who build the GPU data centers, they might not expect this much, storage. And yeah, people build storage, typically they just build it somewhere with just CPUs.Swyx [00:35:23]: I just looked it up. Five-- AWS only charges for egress, not ingress. Tier five for five petabytes is two hundred and thirty K.Ethan [00:35:32]: Even more expensive than the storage.Swyx [00:35:34]: But storing is per month, right? You check in, then you cannot check out. so it's so cool. It's okay. So there's that side.Ethan [00:35:41]: So the TLDR, my backhand mathSwyx [00:35:42]: Data is larger than you think. Yes.Ethan [00:35:44]: my backhand math of GPU hours times GPU cost is also very much, I'm missing some storage.Swyx [00:35:49]: You're also-- you're basically like also more IO bound than normal training.Swyx [00:35:55]: Yes. ‘Cause like data loading, so caching everything, it becomes super important.Ethan [00:36:00]: So in Cosmos, we did a lot of optimizations to make it not IO bound. So, speaking of the training, actually training the model, the GPU cost, if you look up like the open source model, how big these video models are, I think like LTX has nineteen B parameters. That's a dense model. And people are also exploring, MoEs, so it might be twenty B active and, like a hun- hundreds B, total. So that's, that's even-- that's similar size as medium-sized LLM models. And if you, if you look at number of tokens-Uh, we disclose that in Cosmos. It's also like tens of trillions of tokens on the visual tokens. So putting this together, the cost of, training these video models, it's actually comparable with LLMs. Not to mention, the infra is slightly different from LLM, so it might be less efficient to train these models.Inference Speedups: Step Distillation, Consistency Models, and GANsSwyx [00:37:04]: Do you get the benefits of traditional diffusion speed-up? So for, images, there's LCM, LoRAs for, fine-tuning. There's, there's a lot of stuff that's beenEthan [00:37:15]: Flow matching.Swyx [00:37:16]: there's flow matching. There's a lot of stuff that's been done. there's some overlap that applies to diffusion on the inference side and stuff or?Ethan [00:37:23]: so the difference-- the inference side is a completely different story.Ethan [00:37:28]: I think for the training side, it might be a little bit hard to reduce that cost. And for the inference side, the biggest gain is from the distillation of these models. You can-- It's called step distillation, slightly different from knowledge distillation in LLMs. So you-- Typically, for flow matching models, you need like 100 steps or something. Like a distortion model even need even more, like 1,000 steps to generate a good image or video. A step distillation is try to learn to generate fewer step from the model itself. It's kind of like now we-- you use the full model to generate in 100 steps, and then you take a model that only generate 10 steps and let that model to learn from the perfect one.Ethan [00:38:25]: why this workSwyx [00:38:27]: Strong to weak seemingly.Ethan [00:38:28]: It is. It's kind ofSwyx [00:38:29]: DistillationEthan [00:38:29]: kind of like strong to weak. the-- from the modeling perspective, the strong model, the teacher model is trying to model the image and videos of inter-internet, and that distribution is extremely complex. But the step distilled model is just trying to learn from the teacher. The teacher is a model, and the size is fixed, as the distribution is much simpler than the whole internet. That's the intuition I have why step distillation can work. So usually these models serve in productions, they only run in a few steps. In Cosmos, I believe we have, we have like four step and eight steps. If you do some simpler task, image-image translation, it can even run in fewer step, like one step in Cosmos Transfer.Swyx [00:39:22]: I think this is the same intuition that guides a lot of the consistency model work. I sent you a link for, SCM. I don't know if you covered that. To me, that was actually one of, the most impressive papers I've ever seen from OpenAI.Swyx [00:39:34]: That this is the unifying grand concept of consistency models. I don't know if you have any comments on this.Ethan [00:39:41]: So there are, there are a few different approaches,Swyx [00:39:46]: Oh, yeah. Here it is.Swyx [00:39:47]: Two steps versus twenty or 100 steps, whatever. It's already done.Ethan [00:39:52]: So there are, there are a few different approaches, for example, consistency model, and there are also Actually, we shouldn't forget GAN. So GAN, actually, that was, that was the OG ofSwyx [00:40:05]: OGEthan [00:40:05]: step distillation ‘cause it trained just one step to begin with. So actually, a lot of, uh-- For example, there's a distribution matching distillation which use, which uses GAN, as one of the laws for distillation. It-- GAN just tells you, “Hey, generate an image,” and thenEthan [00:40:31]: it has a discriminator to tell, is this image real or not? So the model, the model just need to learn one of the distribution, not the full distribution. Because in training, the model is asked to reconstruct the ground truth image from the internet, which is extremely hard. And in-- When you're training GAN, it's a step process. It's just a, “Hey, you generate image. Does this image look as real as the image from the internet?” Which is a much simpler task. And, yeah, combining a lot of these approaches together, people typically do that, like consistency model and distribution matching and GAN, and we can get these few step models.Audio-Video Generation and Time AlignmentSwyx [00:41:21]: Then there's one step I wanted to add, which is audio and video.Ethan [00:41:26]: So, Grok Imagine zero point nine, I believe it's, it's a first audio video transmodel deployed at a large scale. SoSwyx [00:41:39]: And that was your first model?Ethan [00:41:40]: that was, Grok Imagine's first model. It's, it's audio video, joint generation. I think the hard part is, the modality alignment, ‘cause before this transmodel, we have, we have text to video alignment. We have this, correspondence between text and video. Typically, most of the VLMs, they understand images and videos. Video's very rare, and they don't understand audio mostly. And if you look at the audio generation on the LLM side, you can talk to them perfectly fine, but if you ask them to sing a song or something, it typically is not very good. Also, they don't have, they don't have music either. The hard part is thatUh, actually audio has two component. It has like a discrete component, a continuous component. The discrete component is like the language.Ethan [00:42:44]: So when we speak, it's just, someSwyx [00:42:47]: It's an ASR issue, yeah.Ethan [00:42:49]: It's, it's text token with some characteristics, I would say.Ethan [00:42:54]: But musicSwyx [00:42:56]: I think the speech guys would disagree with this.Swyx [00:42:57]: Like disfluencies and then,Vibhu [00:43:00]: There's tones you can get angry.Ethan [00:43:01]: Well, I say largely.Ethan [00:43:03]: the mu- but the music is completely different. It's, it's very continuous, and you cannot model them like discrete tokens in language models. this is like the hard part for models is, not to mention we have to align text, video, and audio together.Ethan [00:43:26]: SoVibhu [00:43:26]: How?Ethan [00:43:28]: So significant-- some significant challenges are like-- So first, like we talk about as the VLMs, they cannot understand most of them cannot understand audio.Ethan [00:43:39]: So you have to have some way to do the synthetic data generation for audio. You have to caption the model, and that involve, that involve synthetic data and human data effort a lot. And not just surprisingly, most of the LLMs are very bad at recognizing, like the beat, tone, and the details of the of music. They can, they can give some general prediction of which song is this, but it's very hard to describe the details of the music. like we mentioned in image generation, like you have to describe image as detailed as possible so that someone blind can reconstruct that. So here is like someoneVibhu [00:44:32]: DeafEthan [00:44:32]: someone deaf can reconstruct how the music sounds like without actually listening to it. Maybe you can think of it need to have the-- or they call the script.Vibhu [00:44:49]: Subtitles, yeah.Ethan [00:44:49]: You gotta have all the details of the music, and the dialogue.Vibhu [00:44:55]: So is the challenge there typically stuff like music and audio, or is it just Like is there a baseline? Okay, there's enough data where we can understand, narration, conversation, but there's nuances in audio that's where you hit all the data issues or is it just from stage zero, you just do it all right?Ethan [00:45:15]: So one important thing is like the alignment. So the model, the model has to know like the video and audio, the, uh-- it has to have a time-based alignment, like at which time step the video and the audio token correspond to each other. But we actually don't have this kind of alignment for most of the other modalities. If you think about like text and image, text and video, they are loosely aligned. So you can, you can have a description of what's going on in the video, but you don't have to exactly, You typically don't have exact description, oh, at, time step one second like what happened?Vibhu [00:46:02]: It's veryEthan [00:46:03]: At time step two second what happenedVibhu [00:46:03]: coarse. Yeah.Swyx [00:46:05]: So what was the ideal time step? You have to oblate it, and then it's like four seconds or something.Ethan [00:46:09]: So that comes down to how you design the model to, for the model to be aware of as a time, as a time modality. So the model is like a time aware. And that's something pretty unique if you think about LLMs. So if you ask LLM to complete a task, say they, uh-- you ask them and they will say, “Oh, this task will probably take twelve hours to complete,” and they come back in one hour. Say “I've already spent two days on this and I've exhausted everything.”Ethan [00:46:47]: So the LLMs them-themselves, they don't have a sense of time there.Vibhu [00:46:53]: I actually don't think that's just them not having a sense of time. I think it's somewhat based, right?Vibhu [00:46:58]: Like you tell someone, “Okay, go work on this feature. Go implement this,” there's a general understanding you would have of how long that would take without LLMs working at LLM speed, right? So you think back like two years ago, if I tell you to like build me like a new front end for latent space, have a search bar, have all this, you'll estimate that it'll take a few days, right?Vibhu [00:47:19]: So you tell an LLM, “Go build this.” It'll take me a few days. But I think it's somewhat grounded as opposed to them not having the best-- Not saying that they have a great understanding, but I think that example is like you can see where it comes from, right? You're trained on all over the text.Swyx [00:47:35]: They're, they're trying to estimate what a human would say.Vibhu [00:47:37]: because that's what the, that's what the data kind of represents. It's not themEthan [00:47:41]: It came from the corpus on the internet. People have a estimate of how much time.Vibhu [00:47:45]: And not even just in direct like training samples, right? Just your world understanding of tokens of how long stuff takes, right? Go read a book. It'll take you a while, right?Vibhu [00:47:56]: Even if you do nothing but read a book, it takes a few days. So yeah, LLM, I read it took me a few hours.Vibhu [00:48:01]: It'll take me a few hours to go through this research. But this is a tangent.Swyx [00:48:05]: Somewhat, yeah.Swyx [00:48:06]: This is a train of thought I haven't really expressed until now is, which is basically like a full world model must also be recursive, meaning that the participant in the world model must also be aware that they have a world model. which is like this whole recursive thing down the, down the line. but yes, and that the world model can be wrong and that they need to update it and blah. Yeah. We've, argued this on the, newsletter as well, that there needs to be sort of recursive or adversarial world models.World Models: Real-Time, Long-Horizon, Interactive VideoVibhu [00:48:34]: just, to ask, how do you define world model?Swyx [00:48:38]: Oh, yeah, let's go there.Ethan [00:48:40]: SoVibhu [00:48:40]: So just for context, we talked about, video generation, and then there's a-- if you say there's a distinction between world models, what's your, what's your definition? How do you see the two?Ethan [00:48:53]: So disclaimer, I'm not going to debate, what is world model. Yeah. there are many definitions, so I'll just talk about my definition. Since I came from the multi-model, multi-model domain, so mainly talking from video. So world model is like real-time interactive long horizon videos. So there are three parts. so we-- let's talk about them one by one. So the so interaction, so we just, we just look at Facebook and neural computer. So the interaction part of it, so you, world model can allow you to interact with them through keyboard, mouse, and maybe also voice. So these all is-- all is a modality. You can, you can interact with the model, and the model should respond reasonably. Second part is real time. So once you, once, say, you move your mouse, if, say, the world model generate a game, how fast can the game respond? So if you're like professional CS: GO players- -my say, oh, you have to respond- He's beginner within sub ten milliseconds or- Yeah even less. So that's not most of the- No, sixty FPS. Let's go. Oh, three hundred FPS. Oh, five hundred FPS. Wait. okay, yeah. I didn't do the math, but yeah, okay. Uh- Yeah, three hundred FPS, that's a three millisecond. So you have to respond- Oh, s**t. Okay. YeahEthan [00:50:29]: within a millisecond. Most of the video models cannot do that. Yeah. And, but if you, say, if you have a video model that is, say, like a digital human, the response time might be more generous. Maybe typically, for real-time voice interaction, it's like two hundred millisecond. So that's, that's much more generous. But even two hundred millisecond is pretty, it is pretty tricky, ‘cause remember we mentionedEthan [00:51:01]: you have this, temporal compression coming from the VAE. So if you, if you don't compress the temporal dimension, your sequence length is going to explode. So if you want to have this real-time, real-timeness in your model, you have to do is one context problem. And the third part is long horizon, ‘cause we-- if you're not going to just play with, video games just, a few seconds, most video models only a few seconds. We're going to play with minutes, hours. The model have to be able to generate long-form content.Ethan [00:51:42]: So putting these three together, it's, real-time, long horizon interactive videos. I think the final state will be, for example, like a video, a video version of Playbook, where you can, you can interact with, a neural computer. You move your mouse, and you click on the generative interface, and it will reply to you through pixels- generating in real time. But getting there, it's, it's a very long way to get there. So one of the first step, at Grok Imagine, where I led a small world model team there, was to build video extension. So, video extension- it's the first step of interactivity. Yeah. It's, it's the first step. Yeah. So it's the first step- You have it here, video editing, yeah. Yeah. Yeah. So the first step is because, this unlocks long horizon videos. Typically, for most of the video generation models, you give it a prompt or an image as an initial frame. You generate video, that's it. That's just, one time, done. And some creators would try to, use the last frame as a first frame for the second video. It can-- sometimes it works, but if you do it a few times, it says the quality would decrease. And- It doesn't have that context- Yeah over the full video, so the temporal- Yeah, exactly. Yeah, ‘cause you only gave it the last frame, of course, right? Yeah. Exactly. And- it's actually a pretty fun hack. if you've seen like- Oh, no, he's saying something better. Yeah. And for example, like Vue, I remember Vue 3 has like a second context of the last video. It is slightly better than using the last frame, but it has the same problem-- similar problem that it, the quality would decrease. if you extend a few times to, one minute, the video quality would look much worse than the first video. Second, another problem is that the model doesn't have long-range knowledge of, what's happening before. Say, if they generate some dialogue, some, two people speaking, and their voice might change, over some time, especially if the second conditioning, it does not cover the previous context. So these are the core challenges. So the Grok Imagine video extension, it has historical context of all of the previous generated videos. It can, It has, it has the context of, who is speaking and what objects have appeared and everything, having that to generate the next video. So if we naively do this, you can imagine, just, put all of the previous history video tokens into the context. The context lens will easily explode. Especially for video models, that can be like a few, a few million context, I would imagine- context lens. Yes.Yeah.Swyx [00:54:58]: Let's run with that.Ethan [00:54:59]: for example, like in Cosmos, I think just five seconds of video is like a fifty K or sixty K number of tokens. So like if you do, if you do fifty second, that's a five hundred K tokens. If you do longer than that, easily explode. This long horizon, problem was the first step we're trying to solve world model. It turns out people, yeah, people love video extension. Like a lot, a lot of the creators love using video extension to create longer form videos. This is the part I liked that you have a, you have an intermediate step toward the final goal instead of just a straight shot to the final version very much.Swyx [00:55:48]: But I can see you have a strong vision of where we want to end up.Long Context, Redundancy, and Efficient Interactive VideoVibhu [00:55:51]: Does it seem like it's an efficiency issue? okay, we're at a few million tokens context,. If you draw the parallel to language models, we had very short context, two thousand, eight thousand, then, you scale it up one million, ten million. sure, there's effective context, but at the end of the day, it's just what's it worth? sure, there's a whole training data side. In video, it might be slightly easier ‘cause we have a hundred million token video, right? Just take a movie with the full context there. Like is this efficiency from an inference standpoint that like it's expensive, but we know how to solve it? Or like why is this not the approach? So like my broader point was on your second point of world models, you say it needs to be interactive and live, right? You should be able to play a game and see the interaction live. So one thing I see with research is a lot of what you actually serve is different than what you build, right? So we talked about distillation. You train big model, you distill it, you do quantization, speculative decoding. We do all this stuff to serve it efficiently. Should we not just have a solution, like a world model that can interact well, do inference optimization, serve it, distill it secondary, so make it real time after you solve it? So like a-- another parallel is say, continual learning, right? What we need is someone to solve it and show it works inefficiently. Give it a few years, people will make it efficient. Same thing with regular attention, right? It worked. Over a few years, people have different forms of attention, and we've scaled it to be efficient at log context,? So kind of two things there, right? One is it seems like it works. You've scaled it. Can we not just scale it a lot more efficiently over time? Do we need a separate approach if this works? And same thing with interaction, right? if we can get it done, like if we can solve some way that it works, we can solve making it more efficient from an inference standpoint later.Ethan [00:57:53]: that's actually a very good point. So in videos, there's actually a lot of redundancies. So we solve a lot of the pixel redundancy from VE, but there's more redundancy in long range and long horizon videos. Say, if a character appear in the first clip and then it disappeared, it only reappear at the end of the video, you probably don't need the-- the context, like in the middle of the generation. So you only need that character, where you need. So that's why, I helped build another feature. It's a reference video.Vibhu [00:58:36]: Is it here?Swyx [00:58:36]: is it the same model release or different one?Ethan [00:58:39]: It's a different one.Ethan [00:58:41]: You probably need to search onSwyx [00:58:43]: I'll find itEthan [00:58:43]: X reference to video.Ethan [00:58:46]: So reference video allow you to like upload up to seven images as condition and generate the video. Say, if like I want-- it can, it can be characters or objects or even scenes. Say like I want, I want condition on, Sean's selfie and holding a bladeSwyx [00:59:07]: We have a dogEthan [00:59:08]: or whatever.Swyx [00:59:08]: We put the dog in the thing.Ethan [00:59:09]: you can put them there and the video models will generate the video from and copies the context over. So that can solve a lot of the problems there, like the long context problem. It doesn't need to have a very long context, but it's-- I feel like it's an intermediate solution. The modelSwyx [00:59:29]: It's cheating.Ethan [00:59:30]: the model should be able to like selectively know, where should I draw the references. So say if I want to generate a movie, I generate it autoregressive, like a ten second at a time or something. And now this character appear, I can look back to where it first appear and, bring that back. Yeah, this one, I put the references. Yeah, that's, Optimus, Einstein myself, Annie.Vibhu [01:00:02]: Oddly enough, I used Grok Search to find it, and it pulled your LinkedIn post. But yeah we found it.Ethan [01:00:08]: Interesting.Vibhu [01:00:10]: ButxAI's Underrated Work, Culture, and WatermarkingSwyx [01:00:11]: this is a problem. This is not your fault, but like XAI doesn't communicate all this work that you do very well because they just have the model release and then that's it. But actually, these details are very good.Swyx [01:00:22]: As far as I understand, everything you just described is state-art, like no one else has done it.Vibhu [01:00:30]: A lot of-- yeah, I have a lot moreSwyx [01:00:32]: And then, and then you just put this blog post with the cookies. I'm this is not enough,?Swyx [01:00:37]: but I, obviously this is like the high level numbers that people want to know. But no, okay, soVibhu [01:00:42]: And I wonder, like part of that is also some labs don't share research into what happens. And ifSwyx [01:00:50]: No, but this is literally bragging about how good they are, right?Swyx [01:00:54]: Like, why would you not say that you are capable of extending with full context? this is not a secret sauce. This is like we did the work. yeah, I don't know.Ethan [01:01:02]: different labs have slightly different communication styles.Swyx [01:01:07]: Anyway, if anyone from XAI is listening we are always happy to help you tell your story. Yeah, okay, so you did references, and I think, I think kind of the point you're, you're making is it is sort of like a kludge, right? this is-- you can do seven, but what about 100?Swyx [01:01:23]: Right? Then you need a completely different thing.Ethan [01:01:26]: So I think it's-- this is, a mechanism to, select the context from the history, and you might not put the entire history into the context. for example, there's a paper called Frame Pack, which haveEthan [01:01:41]: a heuristic that the latest history, the last one second, I put the entire history, and the history before that, I would, compress it and makes the video smaller. So they follow this pattern, this build overall pattern that the maximum sequence length is fixed. So the further you are from the current frame, you have a smaller image. So this is just a heuristic. I think it can be more automatic. The model is aware like which history part of it can be select. So this part of the research is actually being actively, worked on by a lot of people. It's also quite interesting. I feel this is actually, this part of long context is a little bit ahead of the LLM part.Ethan [01:02:31]: So for example, like in LLMs, if you-- so contexts keep growing. Let's say if you call tool and the tool call history is extremely long, that's still in context, and keep growing, keep growing. Even if you switch the topic to something else, the whole context was there. There are some agentic harnesses that help you to, say, prune the tool results and, prune Like when you, when you query a file, only show like the top 200 lines or something. Those were very heuristic-driven.Swyx [01:03:08]: For listeners, we did a write-up on the cloud code, leak where there are eight different kinds of pruning, including like you prune the tool results and all that. So you can, you can read up on that kind of thing.Ethan [01:03:17]: I think, one breakthrough in continual learning might be like a way to automatically, manage its own context.Swyx [01:03:27]: These are all heuristics, and they will be replaced by machine learning.Ethan [01:03:30]: InterestinglyVibhu [01:03:32]: TheEthan [01:03:32]: the same thing is being researched in both LLMs and video models.Vibhu [01:03:36]: The interesting thing is also like in the paper you showed, it's actually happening at the model level, right? Compared to like language models, sure, we have base attention, but we'll do our own compression, we'll do our own pruning, which is separate from model error.Vibhu [01:03:49]: Eventually, it all just boils in, hopefully.Swyx [01:03:52]: I think this is a form of like attention, but like also know sort of reasoning attention. I feel like that's different than normal attention.Swyx [01:04:03]: Does that, does that make sense?Ethan [01:04:04]: It's, it's different in the sense that attention, not to mention, set sparse attention aside,

„Ježiš Kristus ten istý včera i dnes i naveky.“ List Židom 13:8 Aký bol Ježiš včera? V tejto otázke sa pozrime na vlastnú skúsenosť s Ježišom. Zažil som včera Ježišovu prítomnosť a pomoc? Mohlo sa stať, že si dostal výnimočný dôkaz, že Ježiš žije a stará sa o teba. Preto nás povzbudzuje myšlienka, že je taký istý aj dnes. Je tiež možné, že včera bol len jeden z bežných dní života, a preto si nemyslíš, že by veľmi pomohla skutočnosť, že Ježiš bude dnes ten istý. Preto nemáš v prvom rade rozmýšľať o vlastných skúsenostiach. Môžu sa zmeniť. Nemôžu ti zabezpečiť trvalú pomoc. Nie, obráť sa na Božie slovo. Aký bol Ježiš včera? Bol presne taký, ako o Ňom hovorí Božie slovo. Ani jedno Ježišovo slovo, či slovo o Ježišovi neprestalo včera platiť. Všetko, čo sa o Ňom píše, bola pravda. To platí aj dnes. Ježiš sa nezmenil. Ak premýšľaš o budúcnosti smerom k večnosti, pochopíš, že Ježiš bude navždy taký, aký bol, keď chodil po zemi. Ak si táto myšlienka nájde miesto v tvojom srdci, naplní ťa hlboké, tiché šťastie. Našiel si Ježiša, ktorý je vždy ten istý vo svete, kde sa všetko upravuje a mení. Budúcnosť už viac nie je neistá. Ježiš ťa do nej sprevádza. Nenechá ťa samého. Vždy bude s tebou. Svojou krvou prikryje tvoje hriechy, aby ťa nemohli odsúdiť. Ak ti nepomohol vyhnúť sa problémom, pomôže ti dostať sa cez ne. Ježiš je taký doteraz. Uisťuje ťa o tom Jeho slovo. Bude taký istý aj v budúcnosti. Božie slovo hovorí, že je ten istý včera i dnes i naveky. Hans Erik Nissen

HELLO TEAM, today we're doing something very brave. we're discussing the most unethical things you can do in world of warcraft, and ranking it based on just how unethical it actually is. Max, Dratnos & Dorki tackle all the important topics of the game, like boosting, and selling UIs and exploiting and much more - hope you enjoy!

Tiago Azevedo is the CIO of OutSystems, one of the largest low-code development platforms in the world. In this episode, he sits down with Matt Paige to talk about what it actually looks like to lead through the chaos of enterprise AI adoption, why the old playbook of re-architecting legacy systems is dead, and how his team is building agentic solutions that bypass the mess instead of trying to fix it.Tiago shares his philosophy that saying no to AI is the easy path, and that the real job of a CIO is to open the doors while learning to manage the risk. He breaks down why everything that isn't agentic is already legacy work, how his team uses AI to figure out where AI fits, and why companies should stop adding more fields and screens to broken systems and start building agents that do the work.The conversation also covers OutSystems' latest launch, OutSystems Mentor, which brings natural language vibe coding into the platform so users can describe what they want and build it conversationally. Tiago explains the architecture behind it, including how the platform combines probabilistic AI with deterministic code generation, one-click deployment, and built-in enterprise integrations.The episode closes with Tiago's advice for overwhelmed CIOs: identify the biggest problem your company needs to solve, feed it to an LLM with as much context as possible, and iterate from there. Think big, start small, scale fast.In this episode, you'll hear about:How Tiago approaches change management and AI adoption across a large organization. Why he believes everything non-agentic is already legacy. The "agents over apps" philosophy and what it means for enterprise systems. How OutSystems built Deal Mate, a team of agents that prepares sales reps for meetings. Why OutSystems achieved 40% automation in customer service after AI, up from under 10% before. The launch of OutSystems Mentor and what natural language app-building looks like inside the platform. The gap between a wow demo and enterprise-grade production. Why CIOs should try everything but be careful with divergence. Tiago's "think big, start small, scale fast" framework for AI transformation.Key Moments:01:17 — Tiago on the pace of change and what makes this moment unlike anything before06:20 — "Saying no is the easiest solution — managing the risk is the hard part"07:49 — Bypass the mess: why agents fill the gaps legacy modernization never could09:10 — "Everything that is not agentic is literally legacy work"10:15 — Use AI to figure out where AI fits: the meta approach to use cases11:30 — Deal Mate: the team of agents that prepares sales reps for meetings15:07 — "We were adding more fields to Salesforce when we should've been building agents"16:25 — Mark Zuckerberg building an agent to do his job17:23 — OutSystems' 20-year journey from visual development to agentic systems engineering19:58 — The deterministic magic behind OutSystems Mentor22:04 — One platform: infrastructure, integrations, UIs, agent skills, and deployment30:19 — 40% customer service automation with AI (vs. under 10% before)33:48 — How AI is augmenting, not replacing, engineering and product roles39:41 — "That's 2008 and this is 2026 — you have to change"41:27 — The wow factor vs. enterprise reality: why prototyping isn't the hard part46:17 — Tiago's advice: identify the biggest problem, feed it to an LLM, build the solution48:42 — "Think big, start small, scale fast"Key Links:OutSystemsConnect with Tiago on LinkedInMentioned in this episode:AI Opportunity FinderFeeling overwhelmed by all the AI noise out there? The AI Opportunity Finder from HatchWorks cuts through the hype and gives you a clear starting point. In less than 5 minutes, you'll get tailored, high-impact AI use cases specific to your business—scored by ROI so you know exactly where to start. Whether you're looking to cut costs, automate tasks, or grow faster, this free tool gives you a personalized roadmap built for action.

James and Frank unpack AI-driven development shifts—agent SDKs, session management, and the rise of agent-first UIs like Google's anti-gravity and GitHub Copilot—showing how VS Code's Agents window, worktrees, sub-sessions and tunnels help manage multi-repo cloud and local workflows. They share practical takeaways—why SDKs are essential, when to stay code-first, how subsessions and remote tunnels protect your machine, and what to watch for in sandboxing and integration gaps. Follow Us Frank: Twitter, Blog, GitHub James: Twitter, Blog, GitHub Merge Conflict: Twitter, Facebook, Website, Chat on Discord Music : Amethyst Seer - Citrine by Adventureface ⭐⭐ Review Us ⭐⭐ Machine transcription available on http://mergeconflict.fm

Guests: Gal Ordo, Co-founder & CPO @ Native  Topics:  In Episode 186, we debated 'Native vs. Third-Party' as a binary choice. Native seems to be a third-party vendor whose entire existence depends on the belief that cloud-native controls are superior. Does your platform validate the 'Cloud Provider' side of the debate (that their controls are enough), or does the fact that you exist prove the 'Third-Party' side (that native interfaces aren't enough)? A key argument against native controls is an AWS WAF and a Google Cloud Armor don't behave the same way. If your tool manages native controls across multi-cloud, how do you handle the 'lowest common denominator' problem? Do you dumb down the policy to fit all clouds, or do you expose the unique complexity of each one? GuardDuty and SCC produce similar but meaningfully different results. How do you abstract across that so an analyst or IR team isn't having to dig into the exact meaning of the different JSON fields in their output? We often say native tools are 'good enough' for 80% of use cases but lack the depth of specialized third-party vendors (like a dedicated CNAPP or DLP). By betting your company on orchestrating native controls, are you effectively betting that 'good enough' is the future of the market? What happens when a customer needs a feature that the CSP hasn't built yet? What fraction of your users are taking this from a "I'm 80% this one cloud, I need great coverage there and good enough elsewhere" vs "I'm truly multi-cloud" or even scarier "I have a workload that is active spanning clouds"?  Do your customers push you towards helping with the kinds of SaaS platforms that SSPM vendors cover? If AWS and Google Cloud suddenly decided to make their native security UIs perfect and unified tomorrow, would your company cease to exist? Or is the complexity of the cloud strictly increasing, guaranteeing you job security forever? Related: Video version EP186 Cloud Security Tools: Trust the Cloud Provider or Go Third-Party? An Epic Debate, Anton vs Tim EP160 Don't Cloud Your Judgement: Security and Cloud Migration, Again! The Great Cloud Security Debate: CSP vs. Third-Party Security Tools native.security blog

Inligting is nog skraps na ‘n ongeluk waar ‘n voertuig ‘n olifantkalf getref het ongeveer 4 kilometer buite Uis. Die trop diere is volgens oproerig en die kalf het beserings opgedoen. Die omgewingsministerie se woordvoerder Vilho Hangula het met Kosmos 94.1 Nuus gepraat.

Wie hat dir die Folge gefallen?Gut

Today, we're joined by Uri Haramati, CEO and Founder of Torii, the governance platform for SaaS and AI. We talk about:The blurring of lines between AI and SaaSChallenge of gaining enterprise-wide, centralized policies and management of AI and SaaS The amazing opportunities of AI, with many simultaneous new challenges and risksHow users will interact less with UIs and more with AI agentsThe identity management complexity of increasing non-human identitiesRead Torii's SaaS Benchmark Annual Report 2026, which takes a data-backed look at what companies actually use: sanctioned apps, shadow apps, and the AI tools taking over our workspace.

Hey, Alex here, I'll try to catch you up, but it's one of the more intense weeks in AI in recent memory. Here's the TL;DR - OpenAI dominates across the board this week! Finally launches “spud”, called it GPT 5.5 (and 5.5 Pro), and it's SOTA on most things,nearly matching the mysterious Claude Mythos but released and we can actually use it (we tested it extensively). OpenAI also took the crown in image generate with the incredible GPT-image-v2 release, beating Nano Banana 2 and pro by a significant margin, the images are incredible, this model can generate working QR codes and 360 images it's quite bonkers. Codex was updated with Computer Use (which I told you about last week), in-app browser and a bunch of other tools that match GPT 5.5 intelligence. Meanwhile, Anthropic launched an incredible research preview of Claude Design, finally admitted that Claude was dumb and reset quotas across the board, while breaking the trust of the community with removing Claude code from the pro plan. We've also got great open source updates, Kimi K2.6 and Qwen 3.6 27B are both great performers! We were live on the stream for almost 4 hours today waiting for GPT 5.5 and finally got it and tested it live on the show + had Peter Gostev on from Arena who had early access and shared with us his insights. Let's get into it! ThursdAI - Highest signal weekly AI news show is a reader-supported publication. To receive new posts and support my work, consider becoming a free or paid subscriber.OpenAI's GPT 5.5 is here - SOTA AI intelligence you can actually use (Release Blog)OpenAI finally gave us all access to their latest intelligence boost, GPT 5.5 thinking (and GPT 5.5 Pro). These models take the crown across many benchmarks, including TerminalBench (82.7%), GPDval (84%) and more. You can see the highlited versions on the image above. Though, its not uncommon for OpenAI to do some chart crimes, so @d4m1n created a chart that also showed the full benchmarks, including the ones GPT 5.5 is not beating Opus at, as you can see below, it underperforms on Humanity's Last Exam, and scaled tool use. But, benchmarks don't tell the full story. GPT 5.5 uses significantly less tokens, compared to 5.4, about 40% less. It's also more expensive, but given the lower token usage, it nets out at about ~20% price increase, while being more intelligence and faster. Tons of folks who had early access are reporting the same things, this model excels in long running tasks, Peter Gostev from Arena, who joined our live stream, showed us an incredible demo that ran overnight for over 8h! This model can work until the task is done, no longer just pausing in the middel asking for your input. The real highlight is, paired with the recent GPT-image-2 (which I'll expand on later in this newsletter), GPT 5.5 becomes an excellent UI designer. This is a big area in which Claude still has moat and OpenAI is trying to catch up here, and the real alpha now is to use both the Image gen and 5.5 in tandem to create beautiful visuals and UIs. The main thing is, after testing it quite a few times, this only works if you generate an image outside of the session that builds the actual UI. we tried a couple of times to do it in 1 session, and the resulting UI doesn't seem to be remotely close to the generated image. Only after sending this image to a completely fresh session and asking for a “pixel perfect” implementation, did GPT 5.5 start to resemble the input image and rebuild the whole ui in pixel perfect fidelity! GPT Image v2 - SOTA thinking image model, finally beating Nano Banana (Blog, Live)Like we said, OpenAI is dominating this week, and in both instances those are great models. Though, apples to apples comparison, GPT-image-v2 is a much higher jump — from previous models — than GPT 5.5! According to Artificial Analysis, the jump in how many people prefer GPT-image-2 in blind tests compared to other model is the higest we've ever seen, over 250 points. And you can clearly see it in the generations as well. Previously this week, we did a live streaming session with Peter Gostev (from Arena) and we did a deep dive comparing this new model to GPT Image 1.5, Nano Banana and Grok Imagine, and it's a clear winner across most categories.Character consistency is immaculate, high resolution imagery, instruction following, are all so so good it's a bit hard to explain in text. Reasoning visual intelligence Like with Nano Banana, this model is likely based on a big GPT image, it's no longer just diffusion, as you can see, it reasons! And apparently the more reasoning you give it (if you choose GPT pro) the better it'll be. The examples are indeed wild, the model can generate images of code that works, generate functional QR codes and bar codes! The craziest thing people figured out it can do, is functional 360 imagery (equirectangular format), you can just ask the model to create a 360 image of “scene” and then drop this in to a 360 viewer! Peter shows us on the show how he combined GPT 5.5 and Image v2 to create a sort of “street view” from a bunch of 360 images, it blew our minds. He literally spun up an overnight GPT 5.5 task in Codex that planned out the hanging gardens of Babylon, generated hundreds of equirectangular images, stitched them into a walkable interface, and had it running 8+ hours without babysitting. A street view of a place we don't actually know what it looked like, hallucinated from latent space. What a time.Day one availability is wide: Figma, Canva, Adobe Firefly, fal.ai, and Microsoft Foundry all have it. Nano Banana dominated for what felt like an eternity in AI time (it was really only a few months

Steve Newman, creator of Writely and founder of the Golden Gate Institute for AI, shares the personal AI toolkit and vibe-coding practices that have reshaped how he works. He walks through bespoke tools including an attention firewall, a reading app for surfacing new ideas, a coding-agent dashboard, workflow automations, and a universal logging system for debugging with Claude. They also discuss information security, mobile and voice workflows, Steve's “anti-tokenmaxxing” philosophy, and his views on AI takeoff, robotics, and climate change. Google: Try Gemini's Nano Banana image generation model in Google AI Studio or the Gemini app to create custom illustrated worksheets in seconds, and explore the app's quizzes and guided learning features. Sponsors: AvePoint: AvePoint is building the control layer for AI agents so you can securely govern, audit, and recover every action at scale. Design trusted agentic outcomes from day one at https://avpt.co/tcr VCX: VCX, by Fundrise, is the public ticker for private tech, giving everyday investors access to high-growth private companies in AI, space, defense tech, and more. Learn how to invest at https://getvcx.com Claude: Claude is the AI collaborator that understands your entire workflow, from drafting and research to coding and complex problem-solving. Start tackling bigger problems with Claude and unlock Claude Pro's full capabilities at https://claude.ai/tcr Tasklet: Build your own Cognitive Revolution monitoring agent in one click.Try it for free and use code COGREV for 50% off your first month at https://tasklet.ai CHAPTERS: (00:00) About the Episode (03:25) Special Sponsor (04:47) Building personal productivity tools (Part 1) (14:23) Sponsors: AvePoint | VCX (16:45) Building personal productivity tools (Part 2) (17:32) Security tradeoffs and caution (26:00) Touring the custom toolkit (Part 1) (26:05) Sponsors: Claude | Tasklet (29:56) Touring the custom toolkit (Part 2) (38:01) Stack choices and dashboards (45:12) Hooks, repos, and syncing (58:08) Logging, agents, and tools (01:11:18) Hard parts and iteration (01:18:57) Mobile workflows and UIs (01:26:19) AI-era engineering changes (01:35:54) Software jobs outlook (01:41:35) Thresholds, Mythos, and RSI (01:57:07) AI and climate (02:01:37) Golden Gate mission (02:07:50) Episode Outro (02:12:01) Outro PRODUCED BY: https://aipodcast.ing

Hey ya'll, Alex here with your weekly AI news catch up. It's one of those Thursday's where no matter how well I prep, the big AI labs are hell bent to show up before each other. Alibaba dropped Qwen 3.6 with Apache 2, confirming their commitment to Open Source, then Anthropic released Claude Opus 4.7 (not quite Mythos) and OpenAI followed with a huge Codex update that includes Computer Use among other things. The highlight of Computer User is the background usage, more on that below. This is all just from today!Previously in the week we had 2 incredible 3D world generators, Lyra 2.0 from Nvidia and HYWorld 2 from Tencent, Windsurf dropping 2.0 version with Devin integration and Google releasing a Gemini TTS, with over 90+ languages support and incredible emotions range, and Baidu open sources Ernie Image, rivaling Nano Banana. Today on the show we had 3 awesome guests, Theodor from Cognition joined to cover the new Windsurf, Kwindla is back on the show to talk about “the side project that escaped containment” Gradient-Bang, a multi agent, voice based space game and Trevor from Marimo joined to talk about pairing your agents with a Marimo notebook. Let's dive in!

For all those who missed out on London, see you in Miami next week!Notion, the knowledge work decacorn, has been building AI tooling since before ChatGPT, with many hits from Q&A in 2023 and unified AI in 2024 and Meeting Notes in 2025. At the end of their last Make user conference, Ryan Nystrom teased Notion 3.0's Custom Agents - and they are finally embracing the Agent Lab playbook!Sarah Sachs and Simon Last of Notion join us for a deep dive into how Notion built Custom Agents, why it took years and multiple rebuilds to get right, and what it means to turn a productivity tool into an agent-native system of record for enterprise work.We go inside the product, engineering, evals, pricing, and org design decisions behind one of the most ambitious AI product efforts in software today — from early failed tool-calling experiments in 2022 to agent harnesses, progressive tool disclosure, meeting notes as data capture, and the long-term vision for software factories and agentic work.We discuss:* Sarah and Simon's path to launching Notion Custom Agents, and why the feature was rebuilt four or five times before it was ready for production* Why early agent attempts failed: no tool-calling standard, short context windows, unreliable models, and too much complexity exposed to the model* The “Agent Lab” thesis: not just wrapping a model, but understanding how people collaborate and building the right product system around frontier capabilities* How Notion thinks about roadmap timing: not swimming upstream against model limitations, but also building early enough that the product is ready when the models are* Why coding agents feel like the kernel of AGI, and how Notion is thinking about “software factories” made up of agents that spec, code, test, debug, review, and maintain codebases together* How Sarah runs AI engineering at Notion (“notes from Token Town”): objective-setting over idea ownership, low-ego teams comfortable deleting their own work, and a culture designed to swarm around fast-changing opportunities* The “Simon Vortex,” company hackathons, and why security gets pulled in early rather than late* How Notion organizes AI: core AI capabilities and infrastructure, product packaging teams, and a broader company mandate that every product surface must increasingly work for both humans and agents* Why prototypes have become much easier to build internally, and how “demos over memos” changes product development inside a tool the whole company already uses every day* Notion's eval philosophy: regression tests, launch-quality evals, and “frontier/headroom” evals that intentionally only pass ~30% of the time so the company can see where model capabilities are going* What a “Model Behavior Engineer” is, and why Notion treats eval writing, failure analysis, and model understanding as a distinct function rather than just software engineering* The changing role of software engineers in the age of coding agents, and why the new job looks less like typing code and more like supervising a rigorous outer system of agents, PRs, and verification loops* How the “software factory” should work: specs, self-verification, bug flows, subagents, and minimizing human intervention while preserving the invariants that matter* A live walkthrough of a Notion Custom Agent handling coworking space tenant applications by triaging email, enriching applicants with web search, and writing structured data into a Notion database* How agents compose inside Notion: shared databases as primitives, agents invoking other agents, “manager agents” supervising dozens of specialized agents, and memory implemented simply as pages and databases* Notion's take on MCP vs CLI: why Simon is bullish on CLI's self-debugging nature, where MCP still makes sense, and how Sarah thinks about capability, determinism, permissioning, and pricing alignment* The evolution of Notion's internal agent harness: from early JavaScript coding agents, to custom XML, to Markdown and SQL-like abstractions, to tool definitions, progressive disclosure, and a much shorter system prompt* Why Notion cares about teaching “the top of the class,” building for sophisticated operators rather than abstracting away too much capability for everyone* How agent setup works today: agents that can configure themselves, inspect their own failures, and edit their own instructions — with guardrails around permissions* How Notion prices Custom Agents: credits as an abstraction over tokens, model type, serving tier, web search, and future sandbox costs; why usage-based pricing was necessary; and how “auto” tries to match the right model to the right task* Why Notion is not eager to train a foundation model, where they do fine-tune and optimize today, and why retrieval/ranking is one of the most important investment areas as more searches come from agents rather than humans* Why Meeting Notes became one of Notion's strongest growth loops: not just as transcription, but as high-signal data capture that powers search, custom agents, follow-up workflows, and the broader system of record for company collaboration* Why Notion is more interested in being the place where collaboration data lives than in building hardware themselves — and how wearables or other capture devices may eventually feed into that systemSarah SachsLinkedIn: https://www.linkedin.com/in/sarahmsachsX: https://x.com/sarahmsachsSimon LastLinkedIn: https://www.linkedin.com/in/simon-last-41404140X: https://x.com/simonlastFull Video EpisodeTimestamps* 00:00:00 Introduction and launching Notion Custom Agents* 00:01:17 Why Notion rebuilt agents four or five times* 00:03:35 Building for where models are going, not just where they are* 00:05:32 The Agent Lab thesis, wrappers, and product intuition* 00:08:07 User journeys, leadership, and low-ego AI teams* 00:13:16 The Simon Vortex, hackathons, and bringing security in early* 00:16:39 Team structure, demos over memos, and building for agents* 00:20:25 Evals, Notion's Last Exam, and the Model Behavior Engineer role* 00:27:37 Evals as an agent harness and the changing role of software engineers* 00:30:42 The software factory: specs, verification, and agent workflows* 00:32:18 Live demo: a custom agent for coworking space applications* 00:35:08 Composing agents, manager agents, and memory as pages* 00:38:15 Notion Mail, Gmail, native integrations, and tools* 00:39:43 MCP vs CLI and the cost of capability* 00:44:13 When Notion uses MCP vs building its own integrations* 00:47:43 The history of Notion's agent harness rebuilds* 00:55:35 Power users, public tools, and the setup agent* 00:58:01 Self-fixing agents, permissions, and “flippy”* 01:01:13 Pricing, credits, and choosing the right model automatically* 01:09:01 Why Notion isn't training its own frontier model* 01:14:07 Retrieval, ranking, and search built for agents* 01:17:27 Meeting Notes as data capture and workflow automation* 01:21:18 Wearables, hardware, and Notion as the system of record* 01:23:45 OutroTranscript[00:00:00] Alessio: Hey everyone. Welcome to the Latent Space podcast. This is Alessio founder of Kernel Labs and I'm joined by swyx, editor of the Latent Space.[00:00:11] swyx: Hello. Hello. We're back in the beautiful studio that, uh, Alessio has set up for us with Simon and Sarah from Notion. Welcome.[00:00:18] Sarah Sachs: Thanks for having us.[00:00:19] Alessio: Thanks for having us. Yeah.[00:00:20] swyx: Congrats on the launch recently the custom agents, finally it's here. How's it feel?[00:00:26] Sarah Sachs: We ship things slowly. So it had been in Alpha for a little bit and at the point at which is it's an alpha, um, there's a group of people that are making sure it's ready for prod, and then there's a group of people working on the next thing.So sometimes some of these launches are a bit delayed satisfaction, so it's quite nice to remind yourself all the work you did because we do have a habit of like. Being two or three milestones ahead. Uh, just ‘cause you have to be, you know, you can't get complacent. Um, but it's been great that people understood how this is helpful.And I think that's just easier in general building AI tools today than it was two, three years ago. People kind of get it and so that user education, um, there's just, it was our most successful launch in terms of free trials and converting people and things like that. It was really successful, so yeah.But there's a lot to build.[00:01:12] swyx: Making it free for three months helps.[00:01:16] Sarah Sachs: Yep.[00:01:17] Simon Last: It was definitely super exciting for me because it's probably the fourth or fifth time that we rebuilt that.[00:01:22] swyx: Yes.[00:01:23] Simon Last: And I mean,[00:01:24] swyx: you've been building this since like 20, 22.[00:01:26] Simon Last: Yeah, I mean, like, it was even right when we got access to like GPT four in late 20 22, 1 of the first ideas we had is like, oh, okay, let's make an agent that I, we used the word assistant at the time, there wasn't really the word, the word agent yet, but, oh, we'll give an access to all the tools the notion can do, and then it, we run in the background like, like do work for us.And then we just tried that many times and it just. Was too early. Um,[00:01:48] swyx: I need to force you to like double click on that. What is too early? What didn't work?[00:01:52] Sarah Sachs: We were fine to, like, before function calling came out. We were trying to fine tune with the Frontier Labs and with fireworks, like a function calling model on notion functions.This is right when I joined. I joined because, um, we needed a manager as Simon was needed to be able to go on vacation. So, uh, that's, that's around when I joined, so you can speak much more to it.[00:02:11] Simon Last: Yeah, we did partnerships with both philanthropic and open AI at different times, uh, to try to, at the time the, I mean, when we first tried, there wasn't even a constant of like tools yet.We, we sort of designed our own like, like tool calling framework and then we tried to fine tune the models to, uh, to use it over multiple turns. Um, and because it, it didn't work well out the box, I think. Yeah. The models are just too dumb and the context thing was also way too short.[00:02:37] Alsesio: Yeah.[00:02:37] Simon Last: Um, and yeah, we just kind of banged our head against it for a long time.Uh, unfortunately it was always like, there was always like sort of. Glimmers that it was working, but um, it never felt quite robust enough to be like a useful, delightful thing. Um, until I would say, uh, the big unlock was probably like Sonic 3.6 or seven, uh, early last year. And that's when we started working on our agent, which we shipped last year.Um, and then, and then uh, uh, custom agents, kinda a similar capability and that, that one just took longer because we, we just wanted to get the reliability up a lot higher. ‘cause it's actually running in the background.[00:03:14] Sarah Sachs: And the product interface of like permissions and understanding, you know, this custom agent is shared in a Slack channel with X group of people and has access to documents that are surfaced to Y group of people.And the intersect experts, Y might not be whole. And so how do you build the product around making sure administrators understand that permissioning took multiple swings.[00:03:35] Alsesio: Everything is hard back at the end of the day. Yeah. I'm curious, like when the models are not working, how do you inform the product roadmap of like, okay, we should probably build, expecting the models to be better at some reasonable pace, but at the same time we need to, you know, you had a lot of customers in 2022.It's not like you were a new company or like no user base.[00:03:54] Simon Last: Yeah, I mean I think there's always the balance of, you know, like you want to be a GI pilled and thinking ahead and building for where things are going. Uh, but also you wanna be like shipping useful things. And so we always try to like, like keep a balance there.You know, we. We try to take clear, like a portfolio approach. You know, we're always working on multiple projects and, and we're always trying to work on, you know, maintaining things where that have already shipped, like, like shipping new things that are like eminently working well and make them really good.And, and then we wanna always have a few projects that are a little bit crazy. Um,[00:04:23] Alsesio: and what are the a GI peel projects that you have today? I'm curious about, uh, you don't have to share exactly what you're working on, but I'm curious what are things today that maybe in 18 months people will be like, oh, obviously this was gonna work[00:04:35] Sarah Sachs: 18 months.[00:04:37] Alsesio: Yeah, 18 months is, you know,[00:04:37] Sarah Sachs: it's a long time and Yeah. Yeah.[00:04:39] Simon Last: I mean, there's a number of things happening. I think one thing that's becoming more clear is I think like, like, uh, coding agents are the kernel of EGI, sort of, everything is a coding agent. Mm-hmm. I think that's, that's sort of one, one direction.Um, and then, yeah, the exciting thing about that is sort of your agent can sort of bootstrap its own software and capabilities and actually debug and maintain them. And so yeah, we're, we're, we're thinking a lot about that. And then, yeah, like, like another category of things that I'm, I'm really excited about is like, uh, we call the software factory also.People are using this, uh, this, this sort of word. Um, basically it just means can you create sort of like a, as automated as possible, a workflow for developing debugging. Mm-hmm. Merging, reviewing, and maintaining a code base and a service where there's a bunch of agents working together inside, and like, like how does that work?[00:05:28] Sarah Sachs: If you think back to your initial question, like, why did this take so long? I think something,[00:05:32] swyx: I didn't say that, but Yes. Okay. Go ahead.[00:05:34] Sarah Sachs: Why, what, what changed over the three and half years of trying[00:05:37] swyx: it? Exactly. Right. Because most people always say like, it didn't work yet. Then reasoning models came, then it worked.I was like, okay, let's go a little[00:05:43] Sarah Sachs: bit. That's, I mean, that's part of it, but I think the other part of it that I actually think is really what will set notion apart for every new capability is we have like. Two skills that are crucial when it comes to frontier capabilities. One is not letting yourself swim upstream.So like quickly realizing if you're just pressing against model capabilities versus not exposing the model to the right information, not having the right infrastructure set up. That and of itself is the skill of intuition. And the second is to see, okay, you're not swimming upstream. Which direction is the river flowing and what is like, how do we think ahead about the product and start building it even if it's not great yet, so that when it is there, we're ready for it.Right? And like those can sometimes feel like counterintuitive things. Like we can be trying to fine tune a tool calling model when they don't exist yet. And that the trick is to not do that for too long, but realize that there was something there. And we've had a lot of things which like, um, we're just like not swimming in the right direction with the streams.I think we had multiple versions of transcription before we got meeting notes, right? Oh, I gotta talk[00:06:39] swyx: about that. Yeah.[00:06:40] Sarah Sachs: Yeah. Um, and so. I, I, I think that like we, we really closely partner with the Frontier Labs on capabilities and we also have to have strong conviction on, as those capabilities move.Notion is about being the best place for you to collaborate and do your work. And how does that narrative change if the way that we work changes?Yeah.[00:06:58] swyx: Yeah. You told me you were a fan of the Agent Lab thesis, and this is, this is kind of it, right?[00:07:02] Sarah Sachs: Right. I show that thesis to so many candidates. Like I have it as like micro chrome autofill.Um, at this point, like it's one of my most visitations[00:07:10] swyx: because like, is this the, here's why you should work in notion and not open, open eye. I, it's like,[00:07:14] Sarah Sachs: here's, here's what's different about it.[00:07:16] swyx: Yeah.[00:07:16] Sarah Sachs: And here's why. It's not just a rapper. I actually think more and more people understand it's not just a wrapper.[00:07:21] swyx: Yeah.[00:07:22] Sarah Sachs: Um, and by the way, like in the beginning, parts of what we build are wrappers on functionality. That works well, of course, but that's not really the most, um. I would say that's not the product that, that drives revenue. And that's not necessarily always what users need.[00:07:35] swyx: I mean, you know, notion is the AWS wrapper, but like the, the wrapper is very beautiful and like very, very well polished.So[00:07:40] Sarah Sachs: like the analogy,[00:07:41] swyx: like[00:07:42] Sarah Sachs: the analogy that I've been coming back to his Datadog in AWS[00:07:45] swyx: Yeah.[00:07:46] Sarah Sachs: So, uh, Datadog could not exist with, without cloud storage. Right. That it's kind of fundamental that that works. Um, and AWS has like a CloudWatch product, but Datadog is an expert on understanding how people want observability on the products they launch.And we're experts in understanding how people wanna collaborate, and that's really where our expertise lies.[00:08:04] swyx: Totally.[00:08:04] Sarah Sachs: Um, regardless of the tools that we use,[00:08:07] Alsesio: I'm kind of curious how you think about implicit versus explicit expertise. I feel like Datadog is half and half implicit and explicit. It's like they understand across markets and industries what engineering teams usually look for.With notion, it's almost like more of the expertise is at the edge because you as a platform, you're like so horizontal that the end user is not really the same. Mm-hmm. Like with Datadog, the end user is always like, yeah, an engineering lead, a kinda like SRE related person with notion. It can be anything.So I'm curious how you put that expertise into a product versus, you know, obviously it, WS cannot build notion. It's, that doesn't quite work in this case, but[00:08:44] Simon Last: it's, it's a little bit differently shaped. I think, you know, a classic vertical SaaS, like the data is kind of like that. They understand their individual customer very deeply.It's kinda a narrow slice, um, notion has always been super horizontal. And our, our task has always been to sort of balance these two somewhat opposing forces of like, we're listening to our customers and what they want us to build. It's a broad slice. And then also we're thinking about like, okay, how do we decompose what they want into, uh, nice primitives that are, that are really nice to use and we'll, we'll get us like as much bang for the buck as possible.And then, you know. Maintain the whole system, make it all like, like super clean and nice to use.[00:09:22] Sarah Sachs: We still have user journeys. I mean, we still focus on like core. I actually think the failure of our team is when we focus too much on what are cools that are, what are tools that are[00:09:31] Simon Last: mm-hmm.[00:09:31] Sarah Sachs: Cool tools. I actually think that's when we make have the least velocity because you still need some sort of focus on a user journey.So like for instance, we'll all sit down every Friday and look at the P 99 of like the most token exhaustive custom agent transcript and just look at why it didn't do well and cut a bunch of tasks. Like we still focus on like, this has, like this should work. Email triaging should work. Mm-hmm. Right. And similarly, like when we're talking about before building, um, chatting, um, before we started filming about, okay, how can I do PDF export?Well that's functionality that then merits. Maybe we should build a tool that has access to a computer sandbox in a file system and the ability to write code. Right? Right. Um, but it's because we're thinking about the fact that our users to do their, to do their daily work, need to export PDFs, not because we're like, Hmm, I think a computer tool could be cool.Like, let's just see what happens. Mm-hmm. Like we, we have to focus on some user journeys, otherwise we just don't have like, enough strategy to, to prioritize.[00:10:29] swyx: I think there's a lot of like really strong opinions that you've had. Do you have like sort of like a towel of Sarah Sachs? Like, you know, like what, how do you run your team?Like I feel like you just have accumulated all these strong opinions. Obviously part, part of this is your, your token town thing.[00:10:43] Sarah Sachs: I think the TAs working with Service X is, um, you'd have to, it depends who you ask. Um, I think it depends if you're on my team or a partner Right. Or a vendor.[00:10:54] swyx: Yeah. There other people want to run their teams the way that you're Yeah.You're like bringing these things. And then also similarly, uh, Simon, when you did the custom agents demo, you had like, well, we've been using custom agents and here's the super long list of everything that we do. No humans ever read it. Right? That's what you said. I was like,[00:11:07] Sarah Sachs: yeah. So I think for, for me, um, something that I learned very quickly and became very comfortable with was that my job was not to be the ideas per person or the technical expert.My job was to make it so that everybody understood the objective, had a resource to help prioritize what they should work on, and had an avenue to prioritize what they thought was important. And I think that's true with all, all leadership, but I think especially on the AI team. Almost all of our best ideas come from prototypes, from people that have a cool idea because they saw a user problem, and it's a huge disservice if all of those ideas have to pass, like the sniff test of what me and a product partner or Simon and Ivan decided were the direction, right?Because a lot of what we're doing is leaning into capabilities, so. I think that's the first thing is like, I don't really view like the role of engineering leadership as like, uh, hierarchical, nor has it ever been, but especially now, like very willing to change direction based on, um, like proof is in the pudding.Yeah. And like, and I think we have rebuilt our harness three or four times. And when you do that, then the second rule of engineering leadership is like you need to build a team that's comfortable deleting their own code and is very low ego and is driven by what's best for the company. And, um, doesn't write design docs because they think it's their promotion packet.Right. And that's a culture that notion had long before I joined, but like our willingness to just swarm on different problems and um, redo things that we've built before because something has changed. Like, there's a lot of friction that can happen at companies when you do that. And it doesn't happen at Notion.And because it doesn't happen when new people join. Like they don't wanna be the ones that are saying, we shouldn't do this. I wrote that code. So then it's, you know, you, you create a culture that everyone thoughts and that culture comes directly, I think from Simon and Ivan though, um, because they're very open-minded.[00:12:50] swyx: Anything that you,[00:12:50] Simon Last: you'd add? I'm not a manager, like, like, like Sarah is. Um, a lot of my role is really to try to think a little bit ahead, make sure that we're, we're building on the right capabilities and then like the prototyping stuff. And yeah, it's really, really critical to always just be starting again.It's like, okay, this is new thing. What does this mean? What if we just rethought everything or wrote everything? And so I, I'm, I'm basically just doing that in a loop every six months.[00:13:16] swyx: Yeah. Do you believe in internal hackathons for this stuff?[00:13:19] Sarah Sachs: I think there's like two different versions. So one is like, we just have a, a, a solid bench of senior engineers that come and go on what we call the Simon Vortex and Productionizing what we built, right?Because when you're in the Simon Vortex, the velocity is super high. The direction changes daily, and it's meant to be like the equivalent of a SC Works lab. We don't need to do hackathons for that. We need to have senior engineers that we trust to come in and out of those projects. For instance, like management boundaries are really loose.Like you report to him, but you work for her right now. Yeah. That's something that when we hire managers, it's important they don't care about because we tend to form more structures. Yeah. Don't be too[00:13:54] swyx: territorial.[00:13:55] Sarah Sachs: We form more. It's after we ship things, not not before, just historically. Um, the second thing is we do have companywide hackathons.Actually we just had our demos day for the hackathon we had last week this morning. That's more for people that aren't directly working on the project, feeling like they have the time to pause and learn how to make themselves more productive or how they would use notion custom agents to build something.Or part of the hackathon was actually encouraging everyone across the company to build their own agentic tool loop, calling from scratch. Follow like an every blog post on how to do what I think because we want[00:14:26] swyx: just with the compound engineering one. Yeah.[00:14:28] Sarah Sachs: We want everyone to use cloud code in the company or whatever the coding agent they please and understand that fundamental.So we set aside a day and a half. We're all leadership, encourage everyone on their teams across the company to do it. So we have hackathons like that. I would say like kind of facetiously, like everything we build is a little bit like a hackathon until it graduates and puts on big boy pants and as a product ops rollout leader and has a assigned data scientists and stuff like that,[00:14:54] swyx: security review enterprise stuff,[00:14:56] Sarah Sachs: actually security reviews one of the things that we bring in first because it just slows us down way more and, um, causes a lot of tension and they build better product if they're involved early.So, um, that is probably the first person to get involved in something that's the[00:15:09] swyx: right PR approved answer.[00:15:10] Sarah Sachs: No, but it's not just PR approved. It like, um, um, it's[00:15:13] swyx: actually real. It's actually real. It's like, um, I'm just saying scar[00:15:15] Sarah Sachs: tissue.[00:15:15] swyx: Yeah,[00:15:16] Sarah Sachs: because like, you know, my background's also, I worked at Robinhood for a number of years.Yes. So like, uh, compliance and things like that, um, are a little bit more, you learn the hard way when it doesn't come naturally.[00:15:26] Simon Last: Yeah. I think the. The hackathon is really important for uplifting the general population, but like, if that's the only way you can build new things, you're kind of toast. I mean, it, it has to be like the daily processes, like, you know, building these new things.Um, and it has to be about, I think like, I think in the AI era a lot more leverage accumulates to the most curious and excited people. And so it's like we're all about just like activating that energy. You know, like if someone's protesting something on the weekend that they're excited about and it's important, that should be the main thing that we're doing.Yeah. Um, it's not a hackathon that we schedule once a quarter, it's just like, yeah. Daily process. Part of the culture.[00:16:02] Sarah Sachs: I mean, that's how we shift image generation and notion now. It was always this thing that would be kind of nice to have, but it wasn't really clear where that was necessarily aligned in product priorities.It'd be a lot of work. And we had someone on the database collections team, Jimmy, who was like. I really wanna do image generation for cover photos and inside notion. And we're like, if you wanna build it, like it's, do it please. Like we encourage you. We gave ‘em all the resources of working directly with Gemini and being able to like track the token usage and it working through endpoints.We gave them eval, support, everything, and then became a, a full project.[00:16:34] Alsesio: Yeah.[00:16:35] Sarah Sachs: That's why you can't have like ego as a, a leader. Like that's, that's how we work.[00:16:39] Alsesio: What's the size of the team today, both engineering and overall?[00:16:43] Sarah Sachs: I manage, uh, the team. That's what we'll call it. Core AI capabilities and infrastructure.That's about 50 people. But then we have per i partner teams that do packaging. So how it shows up in the corner chat versus custom agents versus meeting notes, that's another 30, 40 people. And, and then every team that has a product service at Notion that a user can interface with owns the tool that the agent interfaces with the editor team.The team that did CRDT for offline mode is the same team that handles how two agents, um, edit competing blocks. Mm-hmm. Right? It's the same problem. The team that built the underlying SQL engine is the same team that owns how the agent asks it to run a SQL query, and it does it performantly. And so from that regard, anyone working on product engineering is tasked with making them work for customers that are humans and agents because over time the majority of our traffic will be coming from agencies using in our interface, not humans.And so. Our objective is to make it so that the whole product org is building for agents.[00:17:40] Alsesio: Yeah. How has it changed internally? The activation bar is kind of lowered a lot. Like anybody can kind of create a prototype very, somewhat easily, especially if you're like an existing code base. Have you raised the bar on like what type of prototype people need to bring forward to gonna be taken?Not like seriously, but like, you know what I[00:17:58] Simon Last: mean? Yeah. I think the bar is lowered in many ways. Be like, one thing our, uh, our team built that is really cool is our, uh, our, our design team made a whole separate GitHub repo, uh, called the, the design Playground. And it's basically just to create a bunch of like, like helper components and you, uh, for, for quickly a throwing together UIs.And it's become like actually quite sophisticated. Like it has like an agent in there and like, uh, that's pretty fun. So like, we pretty much, like, they don't do mocks, they just make like, like full, full prototypes.[00:18:27] swyx: Here it is. It works.[00:18:28] Simon Last: They give you like a u rl. They're like, okay, all right. So we have to make the, like the real production version of that.Um, and then for engineers. A prototype looks like just making it a feature flag that actually works. Like that's sort of the bar.[00:18:39] Sarah Sachs: Something to understand that's really unique about notion. One of the reasons I joined we're super lucky is no one uses Notion in their job as much as people that work at Notion.[00:18:46] Simon Last: Of course.[00:18:47] Sarah Sachs: So I think there's very few companies, maybe if you worked on Chrome I guess, but like everything that we ship, we ship internally first and get a lot of really quick feedback. And also sometimes our dev instance is totally borked and you have to change a bunch of flags to get things done. And that's kind of like, but everyone, so people that do it ticketing, people that do supply chain procurement, recruiting, everyone is using the same instance of notion with like a lot of flags on for these prototypes people build.Um, and so we have this, Brian Levin, one of the designers on our team, I think evangelize this concept of demos over memos.[00:19:18] swyx: Ooh, too[00:19:20] Sarah Sachs: good. Um, which has been, uh, very good for building demos, and I think it's put a big pressure point on us to have really strong product conviction, because if anything can be demoed, you really need a strong filter of making sure that if you know, you're doing X amount of work, you're making the, you're, you're focusing on one tower, you're not just building a really flat hill.Right. That's actually where I think there has to be more conviction from our PMs, um, and our designers and, and well, the company really to have conviction of what journey we're going on.[00:19:52] Simon Last: But overall, I feel like it works pretty well. Like people, almost all the engineers have good enough taste to realize that like, this prototype doesn't actually make sense in the product, or, or it does.So it's not that common that I would see a prototype. It's like, oh, this makes no sense. Mm-hmm. It's like, you know, people are doing reasonable things and, and, and then it's just a matter of. Which things we build first and then often just, just figuring out how to turn it on and off. There's our, in the, in our like experimental chat ui, there's this, there's probably like, like a hundred check boxes in there.[00:20:22] Sarah Sachs: Kills me[00:20:23] Simon Last: the things you could turn on and off.[00:20:25] Sarah Sachs: Uh, but I think that, okay, so that is kind of true, Simon, but like being the person that manages the evals team, like there is a level of intensity that it adds to the platform team. So, you know, if we're gonna do image generation and notion, all of a sudden the way that we do attachments and the way that we, um, our LLM completion like cortex talks and expects tokens back and now it's getting images back.Like there's a lot of platform work that we do need to, like solidify a little bit. So sometimes it'll be in dev for a couple weeks before it makes it to prod just because we still have to like, make it robust, make it HIPAA compliant, ZDR compliant, figure out the right contracting with the vendor, whatever it is.And we need to eval it because we want the team. To still maintain what they build. That's the one thing is like if we have a bunch of prototypes, it can't just be like a small group of people that then maintain whatever end prototypes. So we have invested a lot of people in an eval and model behavior understanding teams that, we call it agent dev velocity.So your dev velocity building agents can be faster if we invest in that platform. And so we have a whole org dedicated to Asian, um, platform velocity so that you can build your own eval and then maintain it once you ship it. So if a new model release comes out and we, every[00:21:38] swyx: team maintains their own eval,[00:21:40] Sarah Sachs: we maintain the eval framework.Every team owns their own evals and a lot of them we've integrated to Optin, to ci, or we run them nightly and we have a team, uh, a custom agent that triggers to a team to look at the major failures. That's really critical because if we have like all these different surfaces now, a lot of it's on the same agent harness, so it's easier to maintain.It's just packaging of different agent harnesses, but new functionality of the agent. Let's say that like we wanna update like. Uh, you know, they deprecated, sonnet, um, four or whatever it is and we need to auto update. Are[00:22:11] swyx: they already? That's so, okay. Yeah. Actually wasn't that long ago.[00:22:14] Alsesio: Theywere[00:22:14] Alsesio: just 3.5.[00:22:15] Sarah Sachs: 3.537. Just got deprecated.[00:22:18] swyx: 3 7, 5 0.2 or, yeah. No,[00:22:20] Sarah Sachs: it's not. 5.2 is five point. Five point no. Yeah, five four is 40% more expensive than five two. So if they deprecated five two, you would hear they can, you would hear from me about that one. Um, but, uh, another conversation to have.[00:22:35] swyx: I have a cheeky evals question for you.Have you noticed any secret degradation from any of the major model providers?[00:22:40] Sarah Sachs: Secret degradation,[00:22:42] swyx: like. During the War Bay, when it's high traffic, it suddenly gets dumber.[00:22:47] Sarah Sachs: Yeah. I mean, not just between the, I mean, we definitely notice flakiness, we've definitely noticed, particularly for some providers, that things are slower during working hours and[00:22:57] swyx: there's a latency argument.Yes. Not a quality argument.[00:22:59] Sarah Sachs: No. I think the quality difference that's interesting is, um, even though companies that say they're selling the same, a, it's really into like quanti quantization, but like companies that say they're selling the same model through different vendors, whether it be through first party or Bedrock, Azure, et cetera.We do see different qualities sometimes, and that's not necessarily what's advertised.[00:23:21] swyx: Yeah. Kidney went to the point of like, if we, they shipped like this, like eval across all the providers and it was like very obvious we were secret equalizing and it was very,[00:23:28] Sarah Sachs: yeah. But[00:23:29] swyx: that's very embarrassing.[00:23:30] Sarah Sachs: You know, um, we hire Subprocess to figure that out for us.So we just wanna understand where it's regressing or where it's optimized. And sometimes we're okay with regressions that optimize latency if they're the appropriate regressions. Our job is to make sure we have the evals to understand the changes that are important to us. And even like when we're partnering with labs on pre-releasees of models, they'll send us multiple snapshots.And this is less about quantization, but more just regressions. Like they have shipped models that were not the snapshots that we wanted, and they have changed the snapshots that they shipped based on the feedback that we give. Because our feedback tends to be more enterprise work focused and not coding agent focused.And definitely those can be bummers, like, you know, uh, we know that this wasn't the version you wanted, but we'll help you make it work. I mean, we always make it work, but that definitely happens.[00:24:16] Alsesio: Yeah. Do you have, um, failing evals that you're just hoping, oh, that will have success eventually when a good model comes out?[00:24:23] Sarah Sachs: Uh, I mean, yeah. So I think. I mean, I could talk about this for 60 minutes, so I will limit myself. I think it's a real issue when people say evals and it's just like, that's quality, that's like unit, I mean, it's like saying testing. It's not just unit tests, right? So. We have the equivalent of unit test.Regression test. Those live in ci, those have to pass a certain percent, you know, within some stochastic error rate. Then we have, as you're building a product, evals of these aren't passing right now, and this is launch quality. So we have a report card and we need to, on these categories, you know, be it 80 or 90% of all of these user journeys to launch, and then what we have what we call frontier or headroom evals, where we actively wanna be at 30% pass rate.And that's actually been a effort that we took in partnership with philanthropic and OpenAI in the past maybe two or three months, because we actually hit a point where our evals were saturated and we weren't able to really give insightful feedback other than it wasn't worse. And not only is that not helpful for our partners, it's not helpful for us to understand where the stream is going.You know, going back to that analogy. And so we spent a lot of time thinking about. What notions last exam looks like, right? Mm-hmm. Not just humanities, last exam. Ooh, notions last exam. Mm-hmm. And, um, there's a lot of, you know, dreams about what that would look like. I know we've talked a lot about benchmarking, um, swix, but, uh, yeah.Notions last exam is a big thing inside the company and we have people, full-time staff to it exclusively. Mm. We have a data scientist, a model behavior engineer, and an full-time, um, evals engineer just dedicated to the evals that we pass 30% of the time.[00:25:56] swyx: What you're hiring for[00:25:57] Sarah Sachs: MBEs? I am hiring[00:25:58] swyx: What is an MBEA[00:25:59] Sarah Sachs: model?Behavior Engineer Model. Behavior engineers started with a title data specialist before I joined when they were working with Simon on like, uh, Google Sheets and like Simon just needed someone to look through Google Sheets and say, yes, no, this looks bad. This looks good. Right? And so we hired people with kind of diverse linguistics background.We had like a linguistics PhD dropout. Mm-hmm. And a Stanford ate new grad. And they're amazing. And they formed a new function basically. And over time we've built a whole team, um, with a manager who's now kind of reinventing what that role is with coding agents. So they used to be kind of manually inspecting code.Now they're primarily building agents that can write evals for themselves or LLM judges. There's a really funny day I can send you the picture where Simon, about a year and a half ago, was teaching them how to use GitHub. Um, and they're on the whiteboard and it was like, okay, I think it would be so much faster if our data specialists learned how to use GitHub and like learned how to commit these things in Dakota.And, and that was then and now I think, you know, coding has been a lot more accessible. Um, but moving forward it's this mix of like data scientist PM and prompt engineer because there's craft in understanding like even like what models can and can't do things. How do we define like that headroom? How do we define like what a good journey is?Um, is this model better or not? Why is this failing? There's some qualitative work, but then there's also like a lot of instinct and taste to it, and that's not necessarily software engineering. And so we have like very firm conviction and we have had for a number of years now that that is its own career path and we have always welcomed the misfits, so to speak.So we really firmly believe that you don't need an engineering background to be the best at this job. And that's what's quite unique about this particular role.[00:27:37] Simon Last: Yeah, this is something that I've been pretty excited about recently is we made an effort basically to treat the eval system as like an agent harness.So if you think about it, like, you know, you should be able to have an agent end-to-end, download a dataset, run an eval, iterate on a failure, debug, and, and then implement a fix. And ultimately you should be able to, you know, drive the full time process with a human sort of observing the, you know, the outer uh, system.So yeah, we went, went pretty hard on that. And that's, that's worked extremely well so far. It's like basically just to turn it into a coding agent, uh, uh, problem.[00:28:11] swyx: Your coding agent or just whatever[00:28:13] Simon Last: harness No coding agent. Yeah, code, cloud code. It should be totally general. Yeah. I think if it would be a mistake to like, like fix it on any, any particular coding agent.At the end of the day, it's just like CLI tools.[00:28:21] Sarah Sachs: It's like the same way that you would've a coding agent write the unit test. You should have a coding agent write the eval.[00:28:26] swyx: Yeah.[00:28:26] Sarah Sachs: But there's a lot of supervision in that still. We just don't believe that supervision has to come from software engineers because a lot of it is like, um, kind of you XREE and whatever, and these are the people that also triage failures and tell us where we should be investing next.[00:28:40] swyx: Yeah. I'm gonna go ahead and ask a spicy question. Is there a data, there are no software engineers at Notion.[00:28:46] Simon Last: Um,[00:28:46] Sarah Sachs: what does it mean to be a software engineer?[00:28:47] swyx: Exactly.[00:28:48] Simon Last: I mean, I think the way things are going is like we're on some continuum where. If, if you look back three years ago, humans were typing all the code and then we had auto complete, you're typing list of the code.Then we had sort of like filling agents, filling lines, and now we're getting into like agents doing longer range tasks where you can debug and implement a fix and then verify it works and you know, get your, get your PR even like, like Merion deployed. I think we're sort of just moving up the abstraction ladder and then the human role becomes more about observing and maintaining the outer system.There's a string of agents flowing through, like me prs what's going off the rails. Like what do I need to approve? Is there like a learning or memory mechanism that that works? So it's kind of a hard engineering problem. There's a, you know, there's, there's a lot to do there. I think we're just sort of moving up stack[00:29:34] Sarah Sachs: the same transition machine learning engineers have made, right?Like I haven't looked at a PR curve in a while.[00:29:39] swyx: Yeah. You used to do this stuff and now, um, auto research can do it,[00:29:42] Sarah Sachs: right? Like I think it depends on what you define as a software engineer.[00:29:46] swyx: Yes. It's, that's changing for sure.[00:29:49] Sarah Sachs: I think every software engineer in notion this summer went through like this, um, sheer, um, one of our engineering leads of the company called it, like every software engineer is going through the, the, uh, identity crisis that every manager goes through, where all of a sudden they realize their ability to write code is less important than their ability to delegate in context switch.And I think that is a transition out of being a software engineer. But[00:30:12] Simon Last: yeah. Yeah, there's a critical difference to being a manager, which is that like, it is actually very deeply technical. The problem, you know, humans are very like, like, like fuzzy and you can't like treat a team of humans like a, like a rigorous system where like, you know, prs like, like flow through and can be in like a block status and then what happens when they're blocked, right.With a set of agents, you actually can do that. And, and, and I think it's actually, there's a lot of interesting technical rigor that that goes into that it's like it's a technical design problem. Ultimately.[00:30:42] Alsesio: What is the design of the software factory that you're building?[00:30:46] Simon Last: Yeah, I mean, I think we're. Trying a lot of different things.I mean, ultimately you want to design a system that requires as little human intervention as possible, but like still maintaining the in variance that, that you care about. So yeah, we're exploring a lot different ideas there. I mean, I think I could talk about a few things I think are important there.Like, one thing I think is really important is, um, having some kind of like specification layer you can just commit marked on files. Mm-hmm. That works pretty well, but[00:31:15] swyx: it's nice to be notion man. I'm just saying like the spec, like Yeah. The natural home for specs is notion.[00:31:21] Simon Last: Yeah. Right. It can be a database of pages.Yeah. I mean, it needs to be something that is, you know, human readable and I viewable and I think that's pretty key. Another really key component is like the, the self verification loop. Yes. You need really, really good testing layers, basically. And that's a really deep, uh, uh, problem. But by getting that right, you know, and then, and then it's kinda like the workflow of like.What happens when there's a bug? How does it flow into the system? Like, is it like a subagent working on it? How does it make a PR and how does that get reviewed? And me, and then, you know, so there's like the, the flow or process.[00:31:56] swyx: Yeah. Cool. Uh, you know, one thing we did work out before you guys came in was this demo or this[00:32:01] Simon Last: agents[00:32:02] swyx: agent demo.Uh,[00:32:03] Simon Last: so every,[00:32:04] Alsesio: every time we do an episode, we try the product. Right. I don't think there's ever been an episode that I haven't tried. Yeah. Um,[00:32:11] swyx: and we, we try, try is a, a big word. Like since day one lane space has been on Notion, but this is the, this is the net new thing. Yes.[00:32:18] Alsesio: So this is for Nel Labs, which is the space we're in.So next week we're opening applications for tenants. So there's a web form, let me, we got this form done here. Uh, so, uh, before. Uh, the workflow would be I get an email, then I look at the person. It was like, should I spend time talking to this person? Then I respond, they respond back. So I build this. So the name it came up for on its own.Can you maybe h how do, how does it come up with its own name?[00:32:43] Simon Last: Yeah, that's a pretty app name. It's, it, it is just a random, it's a random, a name generator.[00:32:47] Alsesio: Oh, that's funny. It just came,[00:32:49] Simon Last: the fact that it picked that is, is kind of hilarious. I'm pretty sure it's just determined,[00:32:54] Sarah Sachs: resilient collector. I, I think I've never looked at the code for that.I've never second guessed it. I think it's kind of like a madlib situation.[00:33:00] Simon Last: Yeah, I think you're right. Yeah. It's, it's totally a, a deterministic. Oh, I thought it was great. Yes. Although, although when the, if you use the AI to set itself up, it can update its own name, so. Okay. Um,[00:33:11] Sarah Sachs: how did you create it? It, did you just do[00:33:12] Alsesio: classroom?I,[00:33:13] Sarah Sachs: okay.[00:33:13] Alsesio: I did, yeah. I'll say just check my inbox for applications for a coworking space. Keep a people, so it created the database for me. Which I have here. And I guess database is like an notion table because everything is notion. Um, and then whenever um, an email comes in, like here, it just creates a new role for the person.Mm-hmm. And then it uses web search to enrich the mm-hmm. The profile. So it kind of like searches the web and it's like, this is who this person is, this is when they say they wanna move in and kind of updates everything else. This is, I mean, it's not a GI, but to me, I don't wanna do this work. So it feels like, I mean, it took me maybe like 15 minutes to set up the whole thing.Um, and I really like that most of the information should live here. You know, it is not like some other tool asking me[00:34:01] Sarah Sachs: Yeah.[00:34:01] Alsesio: To like, bring my stuff there. It's like I would've probably already created an ocean thing.[00:34:06] Sarah Sachs: Mm-hmm.[00:34:06] Alsesio: So[00:34:07] Sarah Sachs: most of our biggest use cases and gains are from. That extra layer of human involvement in the process to make it so right.And so like one of our biggest use cases is bug triaging. So if someone posts something in Slack, can you just have a custom agent that lives there that has its own routing constitution of what team this belongs to, creates a task in your task database and then posts in that Slack channel, right? Like that's like one of the first things that we built internally, I think.And it's completely changed the way that notion functions as a company. Nothing falls through, well, most things don't fall through the crack. We don't know what we don't know. But it's not replacing people, it's replacing processes.[00:34:44] Alsesio: Yeah.[00:34:44] Sarah Sachs: Right.[00:34:45] Alsesio: And I'm curious how you think about composability of these things.So the other one I was working on is like a. These filler. So whenever somebody signs up as a tenant, kind of he'll sell the lease for them. There should probably some agent that is like office manager agent mm-hmm. That can handle the request, make the lease, and then, uh, give them a ADA access to the office and all of that.How do you think about that feature?[00:35:08] Simon Last: Yeah, so I mean, there's, there's two ways you can compose. One way is by using like the data primitives. So you can, you know, you, you could give, you have one agent, uh, be writing to the database and there's another agent that's walked in the database. So that's, that's one way that they, they can coordinate that's like a little bit more decoupled and mm-hmm.Works really well. Or you, you can couple them. So I, I think it's actually not released yet. Releasing it like next week is, uh, in the settings for an agent, you can give access to invoke any other agent.[00:35:34] swyx: Hmm.[00:35:34] Simon Last: So you can have them just. Just, uh, uh, talk directly. So[00:35:37] swyx: you, was there a limit on like, number of recursions or just,[00:35:40] Simon Last: um, probably,[00:35:42] swyx: you know what I mean?Like, you can just get an infinite loop that way there's[00:35:45] Simon Last: some kind of Yeah,[00:35:46] Sarah Sachs: I think it's, there is actually a number somewhere.[00:35:49] swyx: I believe I'm just, you know, like, you're, you're, someone's gonna screw up. You[00:35:51] Simon Last: should you try to see[00:35:53] swyx: Yeah. I mean, everything's gonna be paperclips.[00:35:55] Simon Last: Oh, yeah. Yeah. But, uh, but, but that's really useful.Yeah. So we, you know, like I just, I, I helped, uh, someone internally the other day, they had, they had built like over 30 custom agents for, uh, for our go to market team doing all kinds of different things. You know, for example, like researching, you know, like, like filling information about, about a customer or like, like triaging customer feedback or like, uh, something like that.Literally over 30 of them. And, and then he, and then he even made like a database of all the agents and then he is like, okay, and, and now I'm getting 70, over 70 notifications per day with just the agents are blocked on various things. Uh, and then I was like, oh, okay, cool. You know, the obvious thing to do there is to make a manager agent,[00:36:32] Sarah Sachs: right?[00:36:33] Simon Last: That's gonna sort of blocks be another abstraction layer in between your, your, uh, uh, 30 agents. Uh, so yeah, we, we send out with like a manager agent and then has access to invoke all the other agents and it's sort of like, like watching and observing them and then it sort of, it just creates a layer of abstraction.So instead of 70 notifications per day, it's like, like five. And then, and then the manager agent can help like, uh, debug and fix any problems with the,[00:36:54] swyx: does this is a concept of like an inbox or something like piece, you're basically saying that they can message each other?[00:37:00] Simon Last: Yeah.[00:37:01] Sarah Sachs: Well[00:37:01] swyx: they use the system of record, which, which is[00:37:02] Sarah Sachs: notion, so we[00:37:03] Simon Last: actually, yeah, we didn't make any special concepts at all.[00:37:06] swyx: They're interested to the motion notifications that I would've got,[00:37:09] Sarah Sachs: they can just like write a task to a database that the other agent's task to listening to, or they can actually call a web book to the agent, like they can just add the agent. Okay.[00:37:17] Simon Last: Yeah, I mean, this is something that, that we're still working on.I, I think we, you know, like, like generally, generally the way we do these things is, you know, you first make it possible, maybe like a sort of janky way. So I, I, I think the way I set ‘em up is like, you know, we created like a new database that was sort of like issues mm-hmm. That the custom agents were, were experiencing, and then gave them all access to file an issue and then the manager has access to, to read the issues.Um, and that works pretty well, essentially like, like give it its own like internal issue tracker just for the agents. And then, you know, if that becomes a, a concept that seems useful, generally maybe we will think of how to package it in. But I mean, generally we try to just keep it to composing the primitive if we can.You know, another example of this is we have no built-in memory concept. Memory is, is just pages and databases. And so if you wanna give a memory, just give it a page and give it. Edit access to that page and the[00:38:03] swyx: human can edit it. Agent can edit[00:38:04] Simon Last: it. Yeah. And so that works, that pattern works extremely well on it.And you know, depending this case, you can have it be just a page or it could be an entire database with, you know, or, you know, I can have sub pages is is pretty on what you can do with that.[00:38:15] Alsesio: So when I was setting this up, uh, I connected my inbox and it was like, do you wanna use Gmail or Notion Mail? And I'm like, I don't wanna use Eater, I just want you to do it.I'm curious how you think about, you know, notion, mail, notion, calendar, all of these kind of ui ux interfaces, full stack[00:38:29] Simon Last: notion.[00:38:30] Alsesio: Yeah. When like at the same time you have the agents abstracting them away from you in a way, you know, how do you spend like the product calories so to speak?[00:38:37] Simon Last: Yeah, I mean, I think it's pretty important that you don't have to use, not your mail to connect to the mail capability.So we can just connect to Gmail or, or whatever you want, uh, to use. And we're thinking of the mail service as being really great to the extent that it's really agent built, right? So maybe the mail app is just sort of a prepackaged agent that helps you automate your, your inbox.[00:39:00] Alsesio: Yeah, the auto labeling is great.Think[00:39:03] Sarah Sachs: the, when we, um, integrate with Gmail for instance, we have a series of tools available that are available via MCP or API to Gmail. When we integrate with Notion Mail, we have the Notion Mail engineering team to build us the, um, exact right tools that optimize latency, optimize performance and quality.They own that quality. Um, there's product leads there. They're directly thinking about the user problems that happen in mail. So it tends to be when we build integrations and connections, we build natively first. Um, and then think about, um, extending them generally just because it's also easier. Mm-hmm. Um, um, to build natively first.Um, so that tends to be how we phase things out.[00:39:43] swyx: Talking about integrations, you prompted me, so I gotta ask. M-C-P-C-L-I. What's going on? What's the[00:39:48] Simon Last: Yeah. Opinion. I think, I mean, I'm, I'm definitely bullish and excited about cli. I think there's a few really cool things about cli. So one really cool thing is like, um, is that it's in the terminal environment, so it gets a bunch of extra power.So it, you know, for example, it can like, like paginating and cursor through like long outputs. Um, and it has a progressive disclosure inherently. Uh, so, you know, you don't see all the tools at once. It's just, you see the CLI wrapper and you can like use the, the help commands and, and, and read files. And then I think the most important thing that's, that's super cool is that there, it's also inherently a, a bootstrapped.So if there's an issue, uh, the agent can debug and fix itself within the same environment that it uses the tool.[00:40:30] swyx: Mm.[00:40:30] Simon Last: Right. Like, you know, I think I saw a tweet this morning. Someone said, you know, my agent didn't have a browser, so I asked it to make all a browser tool and within a hundred lines of code, it gave itself a little browser, like, like wrapping the, the, the chromium API, um.That's pretty incredible. And then if there was a bug, it would just immediately try to fix it. Mm-hmm. Right. On the other hand, if you use an, you know, if you use like of, of the Chrome dev tools, MCP, I've had this issue where like, like sometimes the transport gets like messed up. If it gets messed up, the agent has no way to fix itself.It, it no longer has a browser, it's, it's not broken. Right. I think that's, that's pretty fundamental, but I would say like a lot of the, the bad things about it can be fixed. Uh, so I think like, as a progressive disclosure, that can be fixed with, with right harness. Like, it, it obviously doesn't make sense to show it all the tools all the time.That's not really inherent to the MCP protocol. It's just like how you wrap it and use it.[00:41:16] swyx: There's many poorly built MCPs because we didn't know.[00:41:19] Simon Last: Yeah, yeah. I mean it was just early, like, like the obvious thing is, uh, you know, to start with is, is to just show it all the tools and it's like, okay, now we have a hundred tools.Yeah. And like the tool calling actually works. So let's of[00:41:28] swyx: your success[00:41:29] Simon Last: give it a way to like, like filter to source the tools. So yeah, I would say like broadly speaking, I'm really bullish on cli. I'm still bullish on CPS and in a certain environment. I think in, in particular, CP is really great for when you want sort of like a narrow, lightweight agent.I think there's, there's definitely a lot of use cases where, where you don't want like a full coding agent with a compute run time. And also you want it to be like more tightly permissioned. MCP inherently has a really strong permission model, like all you can do is call the tools. A CLI is a little bit murkier.It's like, can I access the, if PI token are you, like, properly sort of like re-encrypt the token so it can't like exfiltrate it, it introduce a lot of like, like new issues, which are. Real and hard to solve. And MCP is just like the dumb simple thing that works and it that it's pretty good.[00:42:12] Sarah Sachs: I'll add two more perspectives, not from it working well for Notion, but how notion like commits to both platforms.Notion is dedicated to being the best system of record for where people do their enterprise work. So we will always support our MCP and so far as other people are using cps, right? So regardless of our perspective, we've put a lot of effort into our MCP and we have a fantastic team that we're building, um, to do more there.And the second thing I'll say, I think, um, we all think a lot, but lately I've been thinking a lot about making sure there's a value alignment and pricing, um, with capability.[00:42:43] swyx: Literally our next question[00:42:44] Sarah Sachs: and. Needing language to execute deterministic tasks feels wasteful and requiring on a language model to interface with third party providers seems wasteful for tasks that don't require it.And particularly because our custom agents are using usage-based pricing. We think of pricing as like the barrier of entry for use of our product, and we're quite committed to making sure that it's not wasteful. Um, not just because it's a bad deal for our customers, but it's also bad business. We wanna have as many buyers, like there's a, there's an elasticity of demand and so if we can have our agents properly execute code that calls on CLI deterministically, it's a one-time cost, right?Versus constantly having a language model integrate with an MCP over and over and over and paying those like repeated token fees and it's happening outside the cash window, then you're paying for it over and over and over and it's just kind of unnecessary and less deterministic when it doesn't have to be.[00:43:36] Alessio: Yeah, the open-endedness I think is like, the main thing is like, well, if I go write code to just call an API, I would never use an MCP. But then you need an NCP sometimes when you know what to call, but you don't want it to restart versus like, I think the it built a browser from scratch is like, it's great when you're doing it on your own, but like if your customers were having your AI write a browser from scratch every time and you had to pay the token cost of that, yeah.You'd be like, no, no. The Chrome dev tools CP is actually pretty great. Just use that. I'm curious, how do you make that decision? Like should it be. Just straight API call very narrow. Should it be an MCP? Should it be super open-ended?[00:44:10] Sarah Sachs: Do you mean for when we ship notion capabilities or when we add capabilities to[00:44:13] Alessio: notion[00:44:14] Sarah Sachs: AI or,[00:44:14] Alessio: I mean, you might have a capability that the only way to do is an open-ended agent, like an agent with a coding sandbox.[00:44:21] Sarah Sachs: Yeah. In Notion ai they're not explicit, not We also ship an MCP.[00:44:24] Alsesio: Yeah. Yeah. In B,[00:44:25] Sarah Sachs: yeah.[00:44:26] Alsesio: Internally. Okay. Like is there ever a discussion of like, we're not gonna ship it because we're not able to tie it down? Or are you happy to just like,[00:44:33] Sarah Sachs: um, no. I mean, there are a lot of things where we choose not to use MCP because we wanna add more high touch to quality.I think search an agent to find is like the largest instance of that, where we have. Um, slack and linear and Jira search and notion that is not using necessarily the search MCP functionality that is provided by those companies. And that's because it's quite critical we think, to how our agent trajectories work is for us to have a little bit more control on the functionality of the search journey.And so it usually comes from quality and there's a long tail of things and that's why we built an MCP client or an MCP server, excuse me, so that people can connect whatever they want. There's that long tail, right. But we, for search particularly, I would say that's like the primary entry point, but there are other connections as well that it's a little bit of secret sauce a

Sure, here’s a script from this audio: Interview Script Host: Dr. Bill Lampton, Business Communication Expert. Guest: Gina Carr, CEO of Stark Raving Entrepreneurs and AI Specialist. Bill Lampton: Hi there! Welcome to the Business Communication Show. I’m your host, Bill Lampton, the Biz Communication Guy, once again bringing you communication tips and strategies that will boost your business. Yet, this is not a solo act. I bring you those tips and strategies through a guest, a highly qualified business communication expert. Today, I am so delighted to introduce you to Gina Carr, the visionary CEO of Stark Raving Entrepreneurs, where she empowers leaders to harness AI-driven tools for transformative marketing and sales strategies. With an MBA from Harvard and an engineering degree from Georgia Tech, Gina—affectionately known as the Tribe Builder—specializes in cultivating passionate communities of raving fans. As a dynamic international speaker and serial entrepreneur, she’s founded multiple successful ventures, including an award-winning real estate firm and a chain of community magazines. I knew her way back then when she was in those ventures. Gina, formerly the CEO and speaker curator of TEDxDuPree Park, now resides in Orlando, Florida, with her fiancé, Terry Brock. She’s an advocate for animals, freedom, and plant-based living. Her zest for life inspires all those around her, including me, for a long time, I can assure you. So, let's now welcome Gina Carr to the stage! Hello there! Gina Carr: Hello there! I am delighted to be here. Bill Lampton: Well, it’s terrific to have you here. As the introduction qualified, you're a highly credentialed guest who has mastered business communication for a long time. Gina, many of our podcast viewers and listeners are entrepreneurs. Some are long-time entrepreneurs who started, as you and I did, about three decades ago. Some are new entrepreneurs—a few of those by choice, some because the business they were in no longer included them. For all of us who are entrepreneurs, I think of your MBA in business from Harvard University. Judging by what you’ve learned as a highly successful entrepreneur, what are some of the business and communication tips that you would give today to those who are entrepreneurs? How do they make it? Gina Carr: Well, that’s such a great question and something that I think many entrepreneurs ask themselves every day: what is the key? What am I going to do? What will make me successful? I would say the number one thing is that you really need to be clear about who you serve and what problem you solve. And as much as those are important, why you do that is also important to you, because there’s going to be a lot of ups and downs on the entrepreneurial road and you’re going to need to be clear so that it comes across to your potential clients as well as sustains you. You also need to be very clear about how you are going to make money, how you’re going to monetize. That may sound obvious, but it is not. Especially for people who are coaches, speakers, authors, people who are in professional services—but they’re not so much traditional professional services like an attorney or an accountant—how to price those services, I think, can be tough for a lot of entrepreneurs. And then my final main tip here might be a surprise, but it’s going to be: work out. And I say that because I know that it’s important to have that at least one nugget of your day—and I think it’s important to exercise every day—one nugget of your day that you are in total control and that provides stability and confidence to your life that comes across through so many other aspects of your business. It’s like powering up my battery on my phone; I need to charge it every day. It’s like a power-up session for your body and your mind, and I find if I do that, it really helps me in every area of my day, especially in my business. Bill Lampton: I would echo, underline, and emphasize everything you said. First of all, to be clear about what we are offering and to be able to state it with clarity. There are many wonderful consultants who can help us refine and define how we state our mission. It’s important to have help from others on that. Gina, this takes me back to the first year I was an entrepreneur. I remember so well having lunch with someone who also was a member of the National Speakers Association, and I knew that I needed some advice from him. So, I took along a draft of my website. He looked at it, and of course, with pride of authorship, I was thinking he’d say, “Oh, this looks great, this will be a real winner, you’ll attract clients.” But what he said to me was, “What you’re doing is just presenting your credentials,” which is a mistake that many entrepreneurs and even seasoned business people make. I put my degrees, I put clients I’d had, and it all focused primarily on my qualifications. But you and I have learned, and marketing experts have taught us, that as you say, it’s not so much who we are, it’s not so much what we’ve accomplished, but what really counts so much is: what can we do for other people? What is our service? How will they benefit from being with us? And I would like for you to give us some further tips on the business of pricing. There’s a wonderful expert friend of mine in Atlanta named John Ray; he specializes in helping people on their pricing. And one more note on that—I remember also, Gina, near the start of my consulting, speaking, and coaching career, I had somebody say that they would represent me in a speakers bureau. That sounded great. So I gave them my materials and then, after a year, nothing had happened. I called him and I said, “What’s the problem?” And he said, “The problem is you priced yourself so low everybody identifies you as a beginner.” So, give us some guidelines on pricing, please, and tell us how we get the courage and the fortitude to state that affirmatively without apology. Gina Carr: Oh boy, that’s a real tough one. I have certainly learned a lot about pricing over the years and generally, I tend to be too low on the pricing scale. So, I have actively worked to learn from people who are charging more and to confidently present a price that is probably higher than I am comfortable with. I think if it doesn’t make you feel like “ooh” when you say it, you’re probably pricing yourself too low. So, to your example of speaking in particular, speakers come out and they say, “Well, if I’m a brand-new speaker, yes, I should price myself on the low end.” For professional speakers—let’s just throw out some numbers here—generally, that would be in the $2,000 to $5,000 range. Even though that sounds like a lot of money—it is a lot of money for an organization to pay—for the professional speaker, for the ones who have more, it’s not just experience, it’s the background of the person who is doing the speaking and what they bring to the table. You were talking about earlier, what is that change that they help people make? What is that transformation? So, the transformation and the years of experience not necessarily as a speaker, but as an expert doing whatever it is that you do, could translate to right out of the gate a speaker could be in the $7,500 to $10,000 range or, if there’s some celebrityhood to it, in the $20,000 range for a single keynote. So, I hope that that’s helpful. It is hard to say and you really do need to do some research. I’ll tell you one of the ways that it’s making it easier for me to do the research when I’m making a proposal for a new service or to a new client: is using my AI tools, which I’m very big on, using AI tools—artificial intelligence—to say, “What’s a price range for this service? What do you think of this? What should I be asking for this?” And give me different levels, which in the past, that would have been hard for me to come up with, but because of AI, I can come up with those levels. And I do like to go back to a prospect with different levels of opportunities—a high, medium, and a low—and really give some options because you don’t know. A lot of times people will choose the higher price even though you think, “Well, that’s crazy, why would someone choose that?” It happens a lot. Bill Lampton: Well, they choose the higher price because they see the value in it. They choose the higher price because they know it’s going to make, as your introduction used the word, a transformative difference. And by the way, Gina, I couldn’t ask for a better transition to our next topic because you mentioned artificial intelligence. I know that over the past several years, you have been, along with your fiancé and business associate Terry Brock, not only learning artificial intelligence and how we can use it, but you’ve been teaching it ardently. Now, for those of us who maybe know the term and we’ve heard how some other people do it, what would you give as great starting points for really getting so involved in artificial intelligence that it not only makes your work easier, but it makes it far more productive? Gina Carr: Well, just think of AI, and let’s just talk about ChatGPT, which is one that many people have heard of and most people think of AI as ChatGPT, which it’s so much more than that. But even just ChatGPT is your new 24-hour, seven-day-a-week assistant that never goes on vacation, never needs a break, and never needs a raise. They can help you with so many things personally and professionally. Just personally, let’s just look at an example from a few months ago—maybe it was a year ago—there was a problem with our toilet. And we used our ChatGPT to turn on the mode that allows the ChatGPT phone to look into whatever you’re showing it and said, “Hey, what’s the problem here?” And it identified that it was a flapper problem. We ordered a new flapper for less than $10 on Amazon and then we asked ChatGPT to help us learn, show us how to install it. And so we saved ourselves hundreds of dollars, and that’s a personal issue. I use it all the time with recipes and those sorts of things. So, those are on the personal side. On the business side, I mentioned pricing of services, writing those proposals, thinking of what would be the name of a new workshop or the name of a new service. My goodness, I do a little demo in some of my presentations where in about five minutes, I go all the way from the idea of a product, naming the product, developing packaging, developing the marketing plan for the product—things that would have taken months in the past can now take about five minutes, and they’re really, really good. Bill Lampton: It is, it’s very much like a hired assistant that is so incredibly cost-effective. My favorite—and of course, I know you and Terry have mentioned, I think it’s Grok and some others—the one I use probably the most is Perplexity, which is sort of a confusing title because Perplexity sounds like you’re confused. But for an example of how I use it, let’s say I’m going to write an article or I’m going to do a YouTube short video. And I might ask Perplexity, “What are some famous quotes on this topic?” And then I’ve got some highly qualified, credible resources familiar to the public that I can quote. Now, of course, one thing that you and I and many people who are in the business communication arena would emphasize is that there’s an ethical problem involved, and that is that if you go to ChatGPT or if you go to Perplexity—the amazing thing about it for anyone who hasn’t used it is the second you get through typing your question, the answer starts being typed—one of the ethical problems here is that people will get a large printout of information and then they’ll just copy and paste that and publish it as their own. I suppose you’ve had to warn clients about that, Gina. Gina Carr: Yes, actually, I just was doing that yesterday with a client, and she was worried that she’s using AI too much. After reading the sample that she shared, I think she probably was. And so what we talk about in our program—and Terry Brock, my fiancé, coined this term—he calls it “UIs.” You take your initial story or your videos or whatever and you get AI to help you with that. If AI generates it initially, you still need to review it, you need to add your own stories, you need to ask it to make it more formal, make it shorter, make it more humorous, make it sound more like you. And the more that you feed it—that is, you’ve loaded in your own videos, your own transcripts, your own articles that are in your pre-AI voice—then the more it’s going to come back in your own voice. And AI has the ability to organize the thoughts very well, the ability to just polish and make things sound much better than what I sound initially. I’ll give it what I want it to say, but it makes it sound a little bit better. So, that’s one way to use AI. Bill Lampton: It is, for those who may not have ventured in that direction yet. Gina Carr is a great resource for teaching AI because she’s been doing that for several years. Gina, we mentioned in the introduction that you are known as a tribe builder. In just a few seconds, we’re going to come back and talk about that. (Brief break with an ad for Bill Lampton’s services) Bill Lampton: Hello again! You’re on the Business Communication Show with our distinguished, highly qualified guest, Gina Carr. Gina, I remember as long ago as 20 or 25 years, I interviewed you one time about your term “tribes.” Now, tell us please what a tribe is, how we develop it, and what are the benefits? Gina Carr: Well, a tribe is nothing more than a group of people that have bought into a particular philosophy or movement or leader. Typically, there is a leader that says, “Hey, let’s go—we’re at Point A, let’s go to Point B. This is how I think we need to go there, and do you want to follow me? If so, here’s how we’re going to go.” And building a successful tribe includes communication channels such as you have this great channel right now. So, you are a tribe builder, Bill, whether you think of yourself that way or not. You’re teaching people how to communicate professionally, how to communicate better. And so it’s so helpful. People are saying, “Do I want to communicate better? Well, yes, I do.” Well, watch Bill’s show. Bill is going to communicate with you through his show and other posts that he’s doing in the social media world and in the digital world. And so that’s one way that people have joined, not officially but effectively, they have joined your tribe and they are learning from you about how to communicate better. This is one of the so powerful ways to build a tribe because then you develop trust with the people who are following you. And when you want to help them, serve them, or work with them, they are more likely to know you better and to more easily make a decision as to whether they want to follow you to that next step. And as we talk about business here, is that next step a paid opportunity, is it a free opportunity? There’s a mix and match that you’re going to do as a tribe builder to help people. And often it is going to be a paid opportunity because you can serve people better when you’re making the money so that it allows you to serve people more. Bill Lampton: I don’t mind admitting some of the mistakes I made early in my entrepreneurial career. And probably the most blatant mistake—I wish, Gina, that I’d heard you talk about tribes before my first six months—because not knowing any better, I just picked up the phone and started calling people and telling them how fortunate they would be to have my services. But you and I know another benefit of the tribe is that it’s so much more effective when we have formed a tribe and we’ve associated with those people and they know what we do and they’ve benefited from it, and they become our advocates. It’s far more persuasive to have someone else talk about our credibility and the benefits of working with us than us doing it ourselves. And then a second thing I would say about tribes is we learn from those people. We learn business insights, we learn about leadership, we learn about networking. I would imagine as you look back, since you started out as a tribe builder, the reason that you are, in my judgment, very high in the pinnacle of success is not just because of your own doing, it’s because of your tribe. Gina Carr: Oh, completely. There’s an old African saying: “If you want to go fast, go alone. If you want to go far, go together.” And so that is definitely a fundamental philosophy for me of tribe building. Because of all the people that are in my life, including you, including Terry Brock, including the amazing members of our Stark Raving Entrepreneurs community that I’ve had the honor to serve, I’m able to do more. I’m able to impact more people, I’m able to serve a lot more people, and that makes my heart sing. That makes me happy. And I just want to help people have better lives. Bill Lampton: It’s just not a solo way to success, is it? Gina Carr: Not at all. And with AI tools and the agentic AI tools that people these days are having full suites of executives that are working for them that are all artificial, they are all AI agents. This is something that’s going to happen more and more. People are going to be able to build bigger companies with fewer real employees—in real-life employees. But the ones that they have now, they don’t necessarily need to fire them or lay them off, they free them up from tasks they were doing that were filled with more drudgery, that were boring. The AI can do those tasks now, and so the human can do things that are more creative and things that are going to be better for bringing in more business. And so it’s a win-win for everybody. That’s part of why I’m so excited about it. Bill Lampton: That’s a wonderful point for you to emphasize because so many who hear about AI think it’s gloom and doom for their careers. It’s not. It’s an opening to new skills and to new opportunities. And when people take the training that you and Terry Brock and other experts offer, they can have a far more productive, lucrative, and service-oriented business. Time for one more question, Gina. You are a veteran professional speaker. I remember we were in the Georgia chapter of the National Speakers Association back in the late 1990s. So you have been an in-demand speaker, even internationally. In just a few sentences, what tips would you give to business leaders—what are the two or three keys to highly effective speaking? Gina Carr: Well, one is to be clear on how you want your audience to be transformed when you’re done. What is the position? They’re at Point A and they’ll be at Point B after they have heard you speak. So what is that? Be very clear on that. Another key skill is to engage the audience. And as we’ve been speaking here, I’ve been using my hands, I use head gestures, I use smiles, I use voices lower, voices higher. Those are engagement techniques that mostly I do naturally, but I do work on those. And they’re much more engaging than the speaker who sits here and just talks like this in a monotone, right? So variety! People want variety. Exactly. So those are some of the main skills. Connecting with the audience and then actually selling. If you’re going to be a professional speaker and you want to get paid to show up to speak, you have to be able to sell yourself as a professional speaker. It’s a joke in the business that you’re actually a salesperson who gets to get on stage every now and then and give a speech. Bill Lampton: Yes, it doesn’t just happen, does it? Gina Carr: It does not. Bill Lampton: Gina, this has been a highly informative, helpful, and interesting conversation. I refer to the Business Communication Show not as an interview but as a conversation with an expert, which I’ve been fortunate to host today. I know that there are those who would like your contact information. So please share that with us. Gina Carr: Well, easiest way to reach me is at ginacarr.com—G-I-N-A-C-A-R-R dot com. And then also, I’d love you to check out my community: starkravingentrepreneurs.com. That’s a great place to find us. And I do have a tool that is available for free. It’s called aitools4biz.com—AI tools the number four B-I-Z dot com. And that has some of my favorite AI tools that I’m using right now and that we have videos on there that help you know how to use those tools better. So that’s a free resource for your community that you can share with your folks. Bill Lampton: Hey, free is everybody’s favorite word, isn’t it? I think so. I am going to order that and I encourage our viewers and listeners to order that. I know it’s going to be highly resourceful. And now that Gina has given her contact information, I’m happy to give mine. First, my YouTube channel: Bill Lampton PhD is the way it’s listed. I have been producing educational videos on the area of communication since 2007—don’t look at any of those earlier ones, please. And in the last eight years, most of my videos are for the Business Communication Show. So you will, by going through and, by the way, while you’re on my YouTube channel, please subscribe there. And then my website—since my tagline is Biz Communication Guy—logically my website is bizcommunicationguy.com. And while you’re there, you can subscribe to the podcast if you haven’t done that already. I welcome phone calls with no obligation for an exploratory call to talk about your communication challenges and problems and how I can assist you with them. And then I want to give credit to the co-producer of this show, Mike Stewart. Mike is based in Nashville. His website is localinternetpresence.com. Mike Stewart and I met, Gina, at the Georgia chapter of the National Speakers Association in the first year that I was getting going. And Mike said to me, “Have you got a website?” And I said, “Yes.” He said, “Does it have sound on it?” And I said, “No.” And of course, then we started out in print but we needed to get to sound, and now we include video. So localinternetpresence.com, I certainly encourage you to check with my long-time associate and long-time mentor, Mike Stewart. Gina, this has again been so intriguing, so informative, and such a wonderful pleasure to host you. What nugget of a minute or a minute and a half would you like to leave with our viewers and listeners? Gina Carr: Well, I love Stephen Covey’s philosophy or one of his principles of: begin with the end in mind. And I think it’s important to think about—at your eulogy, what will people say about you? And so think about that as you are planning your life for today and for the next year and for as long as you are going to be on this planet. Life is hard—and not in a bad or a sad way. Life is hard; choose your hard. If you want to be an entrepreneur, it’s going to be hard. But being an employee is also hard, and doing nothing—sitting on the couch watching TV all day—is also hard in other ways. It’s going to be very hard for you in financial and health and mental health and all of those ways. So, I just want to encourage you: begin with the end in mind, choose your hard, recognize that it’s going to be hard. But which hard is going to make you happier right now? What are you willing to go through—whatever the pain is—for the gain that’s going to make you the happiest and fulfill your life? Bill Lampton: Reminds me of a great piece of advice I heard years ago: they call it W-O-R-K, not P-L-A-Y. And we do have to do the things that are difficult. We have to do the things that might be unpleasant. Learning AI is not something you’ll do in one afternoon, but it will bring dividends when you get into it—you’re amazed already at what can happen. So thank you, Gina, for being our wonderful guest today. I’ve looked forward to this opportunity and keep your calendar handy because I know we will call on you again for the Business Communication Show. And to those of you who have been with us today on the Business Communication Show, we invite you to be with us again next week for another business communication strategies and tips session. Thank you. I’m Bill Lampton, the Biz Communication Guy.

UIS student leaders take their stories to the Capitol, lobbying lawmakers for higher ed funding — and learning advocacy starts with using your voice.

At MVP Summit we dig into Agentic Workflows — write Markdown prompts that drive AI agents to run CI, open PRs, and automate cross‑repo tasks — and MAUI DevFlow, which lets agents interact with native UIs to click, screenshot and validate designs. Listen for practical takeaways on ditching brittle YAML/scripts and automating tedious maintenance and testing, plus the real caveats: security front‑matter, a compile/lock step and token costs. Follow Us Frank: Twitter, Blog, GitHub James: Twitter, Blog, GitHub Merge Conflict: Twitter, Facebook, Website, Chat on Discord Music : Amethyst Seer - Citrine by Adventureface ⭐⭐ Review Us ⭐⭐ Machine transcription available on http://mergeconflict.fm

University of Illinois Springfield's Innovate Springfield business incubator hosts Illinois Small Business Development Center for Central Illinois in their new partnership as the show visits the UIS location at Horace Mann headquarters on SBDC Day, with clients Little Lincoln's Toy Shop owner Ryan Leake and Heidi Clark of Wellthy Juice Company, plus election analysis from former State Journal-Register reporter Bernie Schoenburg. See omnystudio.com/listener for privacy information.

Ko'u Hopkins discuss the immigration story of UIS's Dr. Adrianna Crocker's, her life under a dictatorship and her life in the United States.

How can you move your MCP tools beyond plain text? How do you add interactive UI components directly inside chat conversations? This week on the show, Den Delimarsky from Anthropic joins us to discuss MCP Apps and interactive UIs in MCP.

We're keeping the AI Tools series rolling with Adir Traitel, entrepreneur, product leader, and early adopter of just about every vibe coding tool out there. Adir joins Matt and Moshe to share hard‑won lessons from building real apps with v0, Bolt, Replit, Figma Make, and more, all while running his own startup and consulting on product builds across industries.From his early days in project management and mobile app startups, through work with companies like Moovit and across FinTech, AgTech, and credit scoring, Adir has consistently been the “try it first” person for new build tools. In this episode, he breaks down what these platforms actually do well, where they fall short, and how product managers can use them responsibly for experiments, prototypes, and beyond.Join Matt, Moshe, and Adir as they explore:Adir's journey from PM and founder to heavy user of vibe coding tools in his current startupHis 3-layer view of the ecosystem: AI dev assistants (Cursor, Antigravity, Claude Code), front-end mockup tools (v0, Figma Make), and full‑product builders (Lovable, Base44, Bolt, Replit)V0: where it shines for quickly building functional UIs (like his electricity consumption app) and where it starts to crackLovable: great for sites and simple flows, but not ideal for complex SaaS or CRM‑like productsBolt: fun and fast for concepts, but why it never got him close to productionReplit: stronger agents and capabilities, but weaker UI output and surprising backend defaults that can get very expensive very quicklyFigma Make and Google Stitch: when design quality trumps everything else, especially for SaaS interfacesThe real costs of vibe coding: AI token spend, hosting/pricing traps, and why production economics matter as much as build speedWhat his “dream product” would look like, including multi‑agent environments, better security/privacy, and built‑in QA and CI/CDHow all this is reshaping the product management role, and why curiosity and tool fluency are becoming must‑have skillsAnd much more!Want to connect with Adir or learn more?LinkedIn: https://www.linkedin.com/in/adirtraitel/ Website: https://adirtraitel.com/You can also connect with us and find more episodes:Product for Product Podcast: http://linkedin.com/company/product-for-product-podcastMatt Green: https://www.linkedin.com/in/mattgreenproduct/Moshe Mikanovsky: http://www.linkedin.com/in/mikanovskyNote: Any views mentioned in the podcast are the sole views of our hosts and guests, and do not represent the products mentioned in any way.Please leave us a review and feedback ⭐️⭐️⭐️⭐️⭐️

In this episode of the Liquid Weekly Podcast, hosts Karl Meisterheim and Taylor Page recap Taylor's trip to New York City for the exclusive Shopify Winter Editions meetup. They break down the "Agentic Commerce" hype, the realities of the new Universal Commerce Protocol (UCP), and why the best way to optimize for AI is actually just using Standard Category Metafields.The episode features on-the-ground interviews with key Shopify staff members, including Liam Griffin, Darius (Senior Product Lead, Dev Platform), and Eitan (Shopify Flow team), offering deep dives into the new developer tools, Shop Minis, and the future of Shopify Flow.Interviews & Highlights Liam Griffin: Discusses the Model Context Protocol (MCP) for developers, using AI for mundane tasks like REST-to-GraphQL migration, and the "Pokemon generation" explosion of Shop Minis (including a virtual sommelier!). Darius: Breaks down the Universal Commerce Protocol (UCP), how the Catalog API works with agents, and why "Agentic Engine Optimization" isn't about hacks—it's about clean data and taxonomy. Eitan: Covers the "Flow 3.0" feel with new Sidekick-generated workflows, the highly anticipated testing layer, and moving toward declarative UIs.Timestamps00:00 Introduction, Stickers, and Winter Storm Stories06:40 Taylor's NYC Editions Trip Recap20:45 Key Takeaways: Agentic Commerce & UCP Realities31:00 Live Demo: Shopify Flow Sidekick Generation & Testing Layer38:30 Developer Tip: Running Queries from Dev Docs in GraphiQL43:00 Interview: Liam Griffin (Dev MCP, Shop Minis, Dev Forums)55:20 Interview: Darius (Universal Commerce Protocol, Metaobject Limits, Catalog API)01:07:50 Interview: Eitan (Shopify Flow Updates)01:20:30 Dev Changelog Highlights01:25:00 Picks of the WeekDev Changelog New article list input settings for themes: Link Migrate metafields with Shopify App Import custom data definitions: Link [Action Required] Shop Minis development now requires partner account permissions: Link [Action Required] Standardization of asset_url version params: Link Shopify App extensions now support Admin and Theme App Extensions: Link [Action Required] Ensuring POS UI extension stability by hardening callback handling: LinkPicks of the Week Karl: MagiQuest at Great Wolf Lodge – An interactive live-action game that turns the water park lodge into a questing adventure. Taylor: DJI Mic 3 – Wireless microphones that offer professional audio quality and ease of use for field recordings and interviews.Sign Up for Liquid WeeklyDon't miss out on expert insights and tips—subscribe to Liquid Weekly for more content like this: https://liquidweekly.com/

Robert Moore, one of the first Black U.S. Marshals, an Illinois State Police Trooper, and UIS student charts his pioneering path in law enforcement in his new book, Off My Neck.

UIS grad assistant Evie Rodenbaugh expands on her research into a half century of 91.9 FM in Springfield. In this first episode, she goes back to the beginning. Lincoln Library exhibit, now-May.

0000019b-c717-d481-a1ff-e7379f750000https://www.wvik.org/podcast/good-morning-from-wvik-news/2026-01-16/tuition-and-fees-increase-at-uis-and-other-university-of-illinois-campusesJoseph LeahyTuition and fees increase at UIS and other University of Illinois campuses

Visionary OpenAI audio model reimagines computing sans screens through hyper-realistic voice interaction. Advanced reasoning chains maintain context across hour-long conversations fluidly. Company bets multimodal audio eclipses visual UIs fundamentally transforming UX.Get the top 40+ AI Models for $20 at AI Box: ⁠⁠https://aibox.aiAI Chat YouTube Channel: https://www.youtube.com/@JaedenSchaferJoin my AI Hustle Community: https://www.skool.com/aihustleSee Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

Modern computers are faster than ever, yet much of our software feels slower, heavier, and more frustrating to use. In this episode of Hanselminutes, Scott talks with Vjekoslav Krajačić, creator of File Pilot, about bringing speed and responsiveness back to everyday tools.Vjekoslav built File Pilot as a reaction to bloated file managers and laggy interfaces, focusing on instant feedback, keyboard-first workflows, and a UI that feels immediate. We talk about what actually makes software feel fast, why modern frameworks often work against that goal, and how users instinctively know when an app respects their time.This is a conversation about restraint, craft, and why fast UIs still matter.https://filepilot.tech

One year ago, Anthropic launched the Model Context Protocol (MCP)—a simple, open standard to connect AI applications to the data and tools they need. Today, MCP has exploded from a local-only experiment into the de facto protocol for agentic systems, adopted by OpenAI, Microsoft, Google, Block, and hundreds of enterprises building internal agents at scale. And now, MCP is joining the newly formed Agentic AI Foundation (AAIF) under the Linux Foundation, alongside Block's Goose coding agent, with founding members spanning the biggest names in AI and cloud infrastructure.We sat down with David Soria Parra (MCP lead, Anthropic), Nick Cooper (OpenAI), Brad Howes (Block / Goose), and Jim Zemlin (Linux Foundation CEO) to dig into the one-year journey of MCP—from Thanksgiving hacking sessions and the first remote authentication spec to long-running tasks, MCP Apps, and the rise of agent-to-agent communication—and the behind-the-scenes story of how three competitive AI labs came together to donate their protocols and agents to a neutral foundation, why enterprises are deploying MCP servers faster than anyone expected (most of it invisible, internal, and at massive scale), what it takes to design a protocol that works for both simple tool calls and complex multi-agent orchestration, how the foundation will balance taste-making (curating meaningful projects) with openness (avoiding vendor lock-in), and the 2025 vision: MCP as the communication layer for asynchronous, long-running agents that work while you sleep, discover and install their own tools, and unlock the next order of magnitude in AI productivity.We discuss:* The one-year MCP journey: from local stdio servers to remote HTTP streaming, OAuth 2.1 authentication (and the enterprise lessons learned), long-running tasks, and MCP Apps (iframes for richer UI)* Why MCP adoption is exploding internally at enterprises: invisible, internal servers connecting agents to Slack, Linear, proprietary data, and compliance-heavy workflows (financial services, healthcare)* The authentication evolution: separating resource servers from identity providers, dynamic client registration, and why the March spec wasn't enterprise-ready (and how June fixed it)* How Anthropic dogfoods MCP: internal gateway, custom servers for Slack summaries and employee surveys, and why MCP was born from “how do I scale dev tooling faster than the company grows?”* Tasks: the new primitive for long-running, asynchronous agent operations—why tools aren't enough, how tasks enable deep research and agent-to-agent handoffs, and the design choice to make tasks a “container” (not just async tools)* MCP Apps: why iframes, how to handle styles and branding, seat selection and shopping UIs as the killer use case, and the collaboration with OpenAI to build a common standard* The registry problem: official registry vs. curated sub-registries (Smithery, GitHub), trust levels, model-driven discovery, and why MCP needs “npm for agents” (but with signatures and HIPAA/financial compliance)* The founding story of AAIF: how Anthropic, OpenAI, and Block came together (spoiler: they didn't know each other were talking to Linux Foundation), why neutrality matters, and how Jim Zemlin has never seen this much day-one inbound interest in 22 years—David Soria Parra (Anthropic / MCP)* MCP: https://modelcontextprotocol.io* https://uk.linkedin.com/in/david-soria-parra-4a78b3a* https://x.com/dsp_Nick Cooper (OpenAI)* X: https://x.com/nicoaicoprBrad Howes (Block / Goose)* Goose: https://github.com/block/gooseJim Zemlin (Linux Foundation)* LinkedIn: https://www.linkedin.com/in/zemlin/Agentic AI Foundation* https://agenticai.foundationFull Video EpisodeTimestamps00:00:00 Introduction: MCP's First Year and Foundation Launch00:01:17 MCP's Journey: From Launch to Industry Standard00:02:06 Protocol Evolution: Remote Servers and Authentication00:08:52 Enterprise Authentication and Financial Services00:11:42 Transport Layer Challenges: HTTP Streaming and Scalability00:15:37 Standards Development: Collaboration with Tech Giants00:34:27 Long-Running Tasks: The Future of Async Agents00:30:41 Discovery and Registries: Building the MCP Ecosystem00:30:54 MCP Apps and UI: Beyond Text Interfaces00:26:55 Internal Adoption: How Anthropic Uses MCP00:23:15 Skills vs MCP: Complementary Not Competing00:36:16 Community Events and Enterprise Learnings01:03:31 Foundation Formation: Why Now and Why Together01:07:38 Linux Foundation Partnership: Structure and Governance01:11:13 Goose as Reference Implementation01:17:28 Principles Over Roadmaps: Composability and Quality01:21:02 Foundation Value Proposition: Why Contribute01:27:49 Practical Investments: Events, Tools, and Community01:34:58 Looking Ahead: Async Agents and Real Impact Get full access to Latent.Space at www.latent.space/subscribe

One year ago, Anthropic launched the Model Context Protocol (MCP)—a simple, open standard to connect AI applications to the data and tools they need. Today, MCP has exploded from a local-only experiment into the de facto protocol for agentic systems, adopted by OpenAI, Microsoft, Google, Block, and hundreds of enterprises building internal agents at scale. And now, MCP is joining the newly formed Agentic AI Foundation (AAIF) under the Linux Foundation, alongside Block's Goose coding agent, with founding members spanning the biggest names in AI and cloud infrastructure. We sat down with David Soria Parra (MCP lead, Anthropic), Nick Cooper (OpenAI), Brad Howes (Block / Goose), and Jim Zemlin (Linux Foundation CEO) to dig into the one-year journey of MCP—from Thanksgiving hacking sessions and the first remote authentication spec to long-running tasks, MCP Apps, and the rise of agent-to-agent communication—and the behind-the-scenes story of how three competitive AI labs came together to donate their protocols and agents to a neutral foundation, why enterprises are deploying MCP servers faster than anyone expected (most of it invisible, internal, and at massive scale), what it takes to design a protocol that works for both simple tool calls and complex multi-agent orchestration, how the foundation will balance taste-making (curating meaningful projects) with openness (avoiding vendor lock-in), and the 2025 vision: MCP as the communication layer for asynchronous, long-running agents that work while you sleep, discover and install their own tools, and unlock the next order of magnitude in AI productivity. We discuss: The one-year MCP journey: from local stdio servers to remote HTTP streaming, OAuth 2.1 authentication (and the enterprise lessons learned), long-running tasks, and MCP Apps (iframes for richer UI) Why MCP adoption is exploding internally at enterprises: invisible, internal servers connecting agents to Slack, Linear, proprietary data, and compliance-heavy workflows (financial services, healthcare) The authentication evolution: separating resource servers from identity providers, dynamic client registration, and why the March spec wasn't enterprise-ready (and how June fixed it) How Anthropic dogfoods MCP: internal gateway, custom servers for Slack summaries and employee surveys, and why MCP was born from "how do I scale dev tooling faster than the company grows?" Tasks: the new primitive for long-running, asynchronous agent operations—why tools aren't enough, how tasks enable deep research and agent-to-agent handoffs, and the design choice to make tasks a "container" (not just async tools) MCP Apps: why iframes, how to handle styles and branding, seat selection and shopping UIs as the killer use case, and the collaboration with OpenAI to build a common standard The registry problem: official registry vs. curated sub-registries (Smithery, GitHub), trust levels, model-driven discovery, and why MCP needs "npm for agents" (but with signatures and HIPAA/financial compliance) The founding story of AAIF: how Anthropic, OpenAI, and Block came together (spoiler: they didn't know each other were talking to Linux Foundation), why neutrality matters, and how Jim Zemlin has never seen this much day-one inbound interest in 22 years — David Soria Parra (Anthropic / MCP) MCP: https://modelcontextprotocol.io https://uk.linkedin.com/in/david-soria-parra-4a78b3a https://x.com/dsp_ Nick Cooper (OpenAI) X: https://x.com/nicoaicopr Brad Howes (Block / Goose) Goose: https://github.com/block/goose Jim Zemlin (Linux Foundation) LinkedIn: https://www.linkedin.com/in/zemlin/ Agentic AI Foundation https://agenticai.foundation Chapters 00:00:00 Introduction: MCP's First Year and Foundation Launch 00:01:17 MCP's Journey: From Launch to Industry Standard 00:02:06 Protocol Evolution: Remote Servers and Authentication 00:08:52 Enterprise Authentication and Financial Services 00:11:42 Transport Layer Challenges: HTTP Streaming and Scalability 00:15:37 Standards Development: Collaboration with Tech Giants 00:34:27 Long-Running Tasks: The Future of Async Agents 00:30:41 Discovery and Registries: Building the MCP Ecosystem 00:30:54 MCP Apps and UI: Beyond Text Interfaces 00:26:55 Internal Adoption: How Anthropic Uses MCP 00:23:15 Skills vs MCP: Complementary Not Competing 00:36:16 Community Events and Enterprise Learnings 01:03:31 Foundation Formation: Why Now and Why Together 01:07:38 Linux Foundation Partnership: Structure and Governance 01:11:13 Goose as Reference Implementation 01:17:28 Principles Over Roadmaps: Composability and Quality 01:21:02 Foundation Value Proposition: Why Contribute 01:27:49 Practical Investments: Events, Tools, and Community 01:34:58 Looking Ahead: Async Agents and Real Impact

PowerShell legend Jeff Hicks joins The PowerShell Podcast to talk about what it really means to live in PowerShell every day. From running his entire workflow in the console to building highly polished terminal tools, Jeff shares how PowerShell can be used far beyond infrastructure management—to organize your day, automate personal tasks, and multiply productivity. The conversation also dives deep into learning PowerShell long-term, embracing small wins, investing in your own career growth, and making yourself “available to luck.” Jeff introduces his newest project, PSIntro, designed to help absolute beginners get started with PowerShell through interactive, localized tutorials and a welcoming splash experience.   Key Takeaways: PowerShell fluency comes from time and repetition, not talent. Use it daily, even for small personal tasks, and progress will follow. PowerShell is a force multiplier. Thoughtful use of color, terminal UIs, verbose output, and helper functions can dramatically improve productivity. Investing in your own learning outside of work gives you career freedom. Your job is not your career—your skills are. Guest Bio: Jeff Hicks is a PowerShell author, educator, and community icon with nearly two decades of experience teaching automation to IT professionals. A long-time Microsoft MVP, Jeff has written multiple books, created countless tools and modules, and spoken at conferences around the world. Known for his practical approach and passion for teaching, Jeff continues to shape how people learn, use, and think about PowerShell.   Resource Links: Jeff Links - https://jdhitsolutions.github.io/ PSIntro Project – https://github.com/jdhitsolutions/PSIntro Spectre.Console for PowerShell – https://pwshspectreconsole.com/ PowerShell Summit – https://powershellsummit.org PDQ Discord – https://discord.gg/PDQ PowerShell Wednesdays – https://www.youtube.com/watch?v=ztKT2wK6EW4&list=PL1mL90yFExsix-L0havb8SbZXoYRPol0B&pp=gAQB The PowerShell Podcast on YouTube: https://youtu.be/lKKfmdDtBOU

What if understanding your codebase was no longer a blocker for great testing? Most testers were trained to work around the code — clicking through UIs, guessing selectors, and relying on outdated docs or developer explanations. In this episode, Playwright expert Ben Fellows flip that model on its head. Using AI tools like Cursor, testers can now explore the codebase directly — asking questions, uncovering APIs, understanding data relationships, and spotting risk before a single test is written. This isn't about becoming a developer. It's about using AI to finally see how the system really works — and using that insight to test smarter, earlier, and with far more confidence. If you've ever joined a new team, inherited a legacy app, or struggled to understand what really changed in a release, this episode is for you. Registration for Automation Guild 2026 Now: https://testguild.me/podag26

On this episode of Crazy Wisdom, I, Stewart Alsop, sit down with Dax Raad, co-founder of OpenCode, for a wide-ranging conversation about open-source development, command-line interfaces, the rise of coding agents, how LLMs change software workflows, the tension between centralization and decentralization in tech, and even what it's like to push the limits of the terminal itself. We talk about the future of interfaces, fast-feedback programming, model switching, and why open-source momentum—especially from China—is reshaping the landscape. You can find Dax on Twitter and check an example of what can be done using OpenCode in this tweet.Check out this GPT we trained on the conversationTimestamps00:00 Stewart Alsop and Dax Raad open with the origins of OpenCode, the value of open source, and the long-tail problem in coding agents. 05:00 They explore why command line interfaces keep winning, the universality of the terminal, and early adoption of agentic workflows. 10:00 Dax explains pushing the terminal with TUI frameworks, rich interactions, and constraints that improve UX. 15:00 They contrast CLI vs. chat UIs, discuss voice-driven reviews, and refining prompt-review workflows. 20:00 Dax lays out fast feedback loops, slow vs. fast models, and why autonomy isn't the goal. 25:00 Conversation turns to model switching, open-source competitiveness, and real developer behavior. 30:00 They examine inference economics, Chinese open-source labs, and emerging U.S. efforts. 35:00 Dax breaks down incumbents like Google and Microsoft and why scale advantages endure. 40:00 They debate centralization vs. decentralization, choice, and the email analogy. 45:00 Stewart reflects on building products; Dax argues for healthy creative destruction. 50:00 Hardware talk emerges—Raspberry Pi, robotics, and LLMs as learning accelerators. 55:00 Dax shares insights on terminal internals, text-as-canvas rendering, and the elegance of the medium.Key InsightsOpen source thrives where the long tail matters. Dax explains that OpenCode exists because coding agents must integrate with countless models, environments, and providers. That complexity naturally favors open source, since a small team can't cover every edge case—but a community can. This creates a collaborative ecosystem where users meaningfully shape the tool.The command line is winning because it's universal, not nostalgic. Many misunderstand the surge of CLI-based AI tools, assuming it's aesthetic or retro. Dax argues it's simply the easiest, most flexible, least opinionated surface that works everywhere—from enterprise laptops to personal dev setups—making adoption frictionless.Terminal interfaces can be richer than assumed. The team is pushing TUI frameworks far beyond scrolling text, introducing mouse support, dialogs, hover states, and structured interactivity. Despite constraints, the terminal becomes a powerful “text canvas,” capable of UI complexity normally reserved for GUIs.Fast feedback loops beat “autonomous” long-running agents. Dax rejects the trend of hour-long AI tasks, viewing it as optimizing around model slowness rather than user needs. He prefers rapid iteration with faster models, reviewing diffs continuously, and reserving slower models only when necessary.Open-source LLMs are improving quickly—and economics matter. Many open models now approach the quality of top proprietary systems while being far cheaper and faster to serve. Because inference is capital-intensive, competition pushes prices down, creating real incentives for developers and companies to reconsider model choices.Centralization isn't the enemy—lack of choice is. Dax frames the landscape like email: centralized providers dominate through convenience and scale, but the open protocols underneath protect users' ability to choose alternatives. The real danger is ecosystems where leaving becomes impossible.LLMs dramatically expand what individuals can learn and build. Both Stewart and Dax highlight that AI enables people to tackle domains previously too opaque or slow to learn—from terminal internals to hardware tinkering. This accelerates creativity and lowers barriers, shifting agency back to small teams and individuals.

Pavan Davuluri only spoke at one Ignite 2025 session, and it did not deserve the hate he got. But what did he really say? Copilot is a front-end for apps and cloud AI services, agents are background processes. Apps in Windows need to become programmatic so AI and agents can control them. You are in control. You being IT and the user. These experiences are off by default, opt-in, and optional. This is the end of whatever BS argument anyone has about this stuff. Copilot Voice because AI is better when you babble and is often more natural than typing Key concept: Apps, CLIs, etc. expect exact commands, AI is all about intent, just do what I want, not exactly what I say. This is why, yes, people WILL want to talk to their PCs (and other devices) UIs for these new features will look/feel natural in Windows Search box in Taskbar is getting updated to orchestrate between local/web search and Copilot capabilities, including agents Agents will appear as app icons in Taskbar when fired, can be check in on, can post notifications for you to attend to Integration of M365 Copilot capabilities with Windows - Better together story, with things like Writing Assistance for every text box Accessibility updates thanks to AI - Fluid Dictation, which is what makes Copilot Voice make so much sense All the security, privacy, and IT management the audience expects Windows Insider Program Dev and Beta builds include Full Screen Experience on all PCs, new Notepad app, more Hardware - Earnings Lenovo PC business up 12 percent to $15.1 billion, 25.6 percent unit share HP up 4 percent to $14.6 billion, but job cuts for AI are coming Dell PC business up 3 percent to $1.41 billion AI and Stuff Microsoft releases local Fara-7B agentic model for computer use ChatGPT's new coding model is optimized for Windows Dear God, you must see Nano Banana Pro to understand Google's lead Google is bringing AirDrop to Android, starting with Pixel. This is what happens when regulators "force design changes on OS makers." Xbox and Gaming Xbox Cloud Gaming usage is up 45 percent YOY. Sure. What's 45 percent of 3 people? Xbox Cloud Gaming is adding per-game resolution settings, to 1440p for Game Pass Ultimate customers ROG Xbox Ally is getting default game profiles, in preview for 40 titles now Microsoft open sources the source code for Zork, Zork II, and Zork III New Chromebook buyers get one year of Nvidia GeForce NOW with Fast Pass Tips and Picks Tip of the week: Finding experts is more important than ever We live in the age of stupid. Find the smart and never let go Also, Xbox is having a good Black Friday sale Also: Perplexity Comet on Android RunAs Radio this week: Christmas Gifts for SysAdmins with Joey Snow and Rick Claus https://runasradio.com/Shows/Show/1012 Brown liquor pick of the week: Sidetrack Stone Whisky https://www.huskdistillers.com/shop/sidetrack-stone-whisky These show notes have been truncated due to length. For the full show notes, visit https://twit.tv/shows/windows-weekly/episodes/960 Hosts: Leo Laporte, Paul Thurrott, and Richard Campbell Sponsors: outsystems.com/twit cachefly.com/twit

Pavan Davuluri only spoke at one Ignite 2025 session, and it did not deserve the hate he got. But what did he really say? Copilot is a front-end for apps and cloud AI services, agents are background processes. Apps in Windows need to become programmatic so AI and agents can control them. You are in control. You being IT and the user. These experiences are off by default, opt-in, and optional. This is the end of whatever BS argument anyone has about this stuff. Copilot Voice because AI is better when you babble and is often more natural than typing Key concept: Apps, CLIs, etc. expect exact commands, AI is all about intent, just do what I want, not exactly what I say. This is why, yes, people WILL want to talk to their PCs (and other devices) UIs for these new features will look/feel natural in Windows Search box in Taskbar is getting updated to orchestrate between local/web search and Copilot capabilities, including agents Agents will appear as app icons in Taskbar when fired, can be check in on, can post notifications for you to attend to Integration of M365 Copilot capabilities with Windows - Better together story, with things like Writing Assistance for every text box Accessibility updates thanks to AI - Fluid Dictation, which is what makes Copilot Voice make so much sense All the security, privacy, and IT management the audience expects Windows Insider Program Dev and Beta builds include Full Screen Experience on all PCs, new Notepad app, more Hardware - Earnings Lenovo PC business up 12 percent to $15.1 billion, 25.6 percent unit share HP up 4 percent to $14.6 billion, but job cuts for AI are coming Dell PC business up 3 percent to $1.41 billion AI and Stuff Microsoft releases local Fara-7B agentic model for computer use ChatGPT's new coding model is optimized for Windows Dear God, you must see Nano Banana Pro to understand Google's lead Google is bringing AirDrop to Android, starting with Pixel. This is what happens when regulators "force design changes on OS makers." Xbox and Gaming Xbox Cloud Gaming usage is up 45 percent YOY. Sure. What's 45 percent of 3 people? Xbox Cloud Gaming is adding per-game resolution settings, to 1440p for Game Pass Ultimate customers ROG Xbox Ally is getting default game profiles, in preview for 40 titles now Microsoft open sources the source code for Zork, Zork II, and Zork III New Chromebook buyers get one year of Nvidia GeForce NOW with Fast Pass Tips and Picks Tip of the week: Finding experts is more important than ever We live in the age of stupid. Find the smart and never let go Also, Xbox is having a good Black Friday sale App pick of the week: Copilot Mode in Microsoft Edge Also: Perplexity Comet on Android RunAs Radio this week: Christmas Gifts for SysAdmins with Joey Snow and Rick Claus https://runasradio.com/Shows/Show/1012 Brown liquor pick of the week: Sidetrack Stone Whisky https://www.huskdistillers.com/shop/sidetrack-stone-whisky These show notes have been truncated due to length. For the full show notes, visit https://twit.tv/shows/windows-weekly/episodes/960 Hosts: Leo Laporte, Paul Thurrott, and Richard Campbell Sponsors: outsystems.com/twit cachefly.com/twit

Pavan Davuluri only spoke at one Ignite 2025 session, and it did not deserve the hate he got. But what did he really say? Copilot is a front-end for apps and cloud AI services, agents are background processes. Apps in Windows need to become programmatic so AI and agents can control them. You are in control. You being IT and the user. These experiences are off by default, opt-in, and optional. This is the end of whatever BS argument anyone has about this stuff. Copilot Voice because AI is better when you babble and is often more natural than typing Key concept: Apps, CLIs, etc. expect exact commands, AI is all about intent, just do what I want, not exactly what I say. This is why, yes, people WILL want to talk to their PCs (and other devices) UIs for these new features will look/feel natural in Windows Search box in Taskbar is getting updated to orchestrate between local/web search and Copilot capabilities, including agents Agents will appear as app icons in Taskbar when fired, can be check in on, can post notifications for you to attend to Integration of M365 Copilot capabilities with Windows - Better together story, with things like Writing Assistance for every text box Accessibility updates thanks to AI - Fluid Dictation, which is what makes Copilot Voice make so much sense All the security, privacy, and IT management the audience expects Windows Insider Program Dev and Beta builds include Full Screen Experience on all PCs, new Notepad app, more Hardware - Earnings Lenovo PC business up 12 percent to $15.1 billion, 25.6 percent unit share HP up 4 percent to $14.6 billion, but job cuts for AI are coming Dell PC business up 3 percent to $1.41 billion AI and Stuff Microsoft releases local Fara-7B agentic model for computer use ChatGPT's new coding model is optimized for Windows Dear God, you must see Nano Banana Pro to understand Google's lead Google is bringing AirDrop to Android, starting with Pixel. This is what happens when regulators "force design changes on OS makers." Xbox and Gaming Xbox Cloud Gaming usage is up 45 percent YOY. Sure. What's 45 percent of 3 people? Xbox Cloud Gaming is adding per-game resolution settings, to 1440p for Game Pass Ultimate customers ROG Xbox Ally is getting default game profiles, in preview for 40 titles now Microsoft open sources the source code for Zork, Zork II, and Zork III New Chromebook buyers get one year of Nvidia GeForce NOW with Fast Pass Tips and Picks Tip of the week: Finding experts is more important than ever We live in the age of stupid. Find the smart and never let go Also, Xbox is having a good Black Friday sale App pick of the week: Copilot Mode in Microsoft Edge Also: Perplexity Comet on Android RunAs Radio this week: Christmas Gifts for SysAdmins with Joey Snow and Rick Claus https://runasradio.com/Shows/Show/1012 Brown liquor pick of the week: Sidetrack Stone Whisky https://www.huskdistillers.com/shop/sidetrack-stone-whisky These show notes have been truncated due to length. For the full show notes, visit https://twit.tv/shows/windows-weekly/episodes/960 Hosts: Leo Laporte, Paul Thurrott, and Richard Campbell Sponsors: outsystems.com/twit cachefly.com/twit

Pavan Davuluri only spoke at one Ignite 2025 session, and it did not deserve the hate he got. But what did he really say? Copilot is a front-end for apps and cloud AI services, agents are background processes. Apps in Windows need to become programmatic so AI and agents can control them. You are in control. You being IT and the user. These experiences are off by default, opt-in, and optional. This is the end of whatever BS argument anyone has about this stuff. Copilot Voice because AI is better when you babble and is often more natural than typing Key concept: Apps, CLIs, etc. expect exact commands, AI is all about intent, just do what I want, not exactly what I say. This is why, yes, people WILL want to talk to their PCs (and other devices) UIs for these new features will look/feel natural in Windows Search box in Taskbar is getting updated to orchestrate between local/web search and Copilot capabilities, including agents Agents will appear as app icons in Taskbar when fired, can be check in on, can post notifications for you to attend to Integration of M365 Copilot capabilities with Windows - Better together story, with things like Writing Assistance for every text box Accessibility updates thanks to AI - Fluid Dictation, which is what makes Copilot Voice make so much sense All the security, privacy, and IT management the audience expects Windows Insider Program Dev and Beta builds include Full Screen Experience on all PCs, new Notepad app, more Hardware - Earnings Lenovo PC business up 12 percent to $15.1 billion, 25.6 percent unit share HP up 4 percent to $14.6 billion, but job cuts for AI are coming Dell PC business up 3 percent to $1.41 billion AI and Stuff Microsoft releases local Fara-7B agentic model for computer use ChatGPT's new coding model is optimized for Windows Dear God, you must see Nano Banana Pro to understand Google's lead Google is bringing AirDrop to Android, starting with Pixel. This is what happens when regulators "force design changes on OS makers." Xbox and Gaming Xbox Cloud Gaming usage is up 45 percent YOY. Sure. What's 45 percent of 3 people? Xbox Cloud Gaming is adding per-game resolution settings, to 1440p for Game Pass Ultimate customers ROG Xbox Ally is getting default game profiles, in preview for 40 titles now Microsoft open sources the source code for Zork, Zork II, and Zork III New Chromebook buyers get one year of Nvidia GeForce NOW with Fast Pass Tips and Picks Tip of the week: Finding experts is more important than ever We live in the age of stupid. Find the smart and never let go Also, Xbox is having a good Black Friday sale Also: Perplexity Comet on Android RunAs Radio this week: Christmas Gifts for SysAdmins with Joey Snow and Rick Claus https://runasradio.com/Shows/Show/1012 Brown liquor pick of the week: Sidetrack Stone Whisky https://www.huskdistillers.com/shop/sidetrack-stone-whisky These show notes have been truncated due to length. For the full show notes, visit https://twit.tv/shows/windows-weekly/episodes/960 Hosts: Leo Laporte, Paul Thurrott, and Richard Campbell Sponsors: outsystems.com/twit cachefly.com/twit

Pavan Davuluri only spoke at one Ignite 2025 session, and it did not deserve the hate he got. But what did he really say? Copilot is a front-end for apps and cloud AI services, agents are background processes. Apps in Windows need to become programmatic so AI and agents can control them. You are in control. You being IT and the user. These experiences are off by default, opt-in, and optional. This is the end of whatever BS argument anyone has about this stuff. Copilot Voice because AI is better when you babble and is often more natural than typing Key concept: Apps, CLIs, etc. expect exact commands, AI is all about intent, just do what I want, not exactly what I say. This is why, yes, people WILL want to talk to their PCs (and other devices) UIs for these new features will look/feel natural in Windows Search box in Taskbar is getting updated to orchestrate between local/web search and Copilot capabilities, including agents Agents will appear as app icons in Taskbar when fired, can be check in on, can post notifications for you to attend to Integration of M365 Copilot capabilities with Windows - Better together story, with things like Writing Assistance for every text box Accessibility updates thanks to AI - Fluid Dictation, which is what makes Copilot Voice make so much sense All the security, privacy, and IT management the audience expects Windows Insider Program Dev and Beta builds include Full Screen Experience on all PCs, new Notepad app, more Hardware - Earnings Lenovo PC business up 12 percent to $15.1 billion, 25.6 percent unit share HP up 4 percent to $14.6 billion, but job cuts for AI are coming Dell PC business up 3 percent to $1.41 billion AI and Stuff Microsoft releases local Fara-7B agentic model for computer use ChatGPT's new coding model is optimized for Windows Dear God, you must see Nano Banana Pro to understand Google's lead Google is bringing AirDrop to Android, starting with Pixel. This is what happens when regulators "force design changes on OS makers." Xbox and Gaming Xbox Cloud Gaming usage is up 45 percent YOY. Sure. What's 45 percent of 3 people? Xbox Cloud Gaming is adding per-game resolution settings, to 1440p for Game Pass Ultimate customers ROG Xbox Ally is getting default game profiles, in preview for 40 titles now Microsoft open sources the source code for Zork, Zork II, and Zork III New Chromebook buyers get one year of Nvidia GeForce NOW with Fast Pass Tips and Picks Tip of the week: Finding experts is more important than ever We live in the age of stupid. Find the smart and never let go Also, Xbox is having a good Black Friday sale App pick of the week: Copilot Mode in Microsoft Edge Also: Perplexity Comet on Android RunAs Radio this week: Christmas Gifts for SysAdmins with Joey Snow and Rick Claus https://runasradio.com/Shows/Show/1012 Brown liquor pick of the week: Sidetrack Stone Whisky https://www.huskdistillers.com/shop/sidetrack-stone-whisky These show notes have been truncated due to length. For the full show notes, visit https://twit.tv/shows/windows-weekly/episodes/960 Hosts: Leo Laporte, Paul Thurrott, and Richard Campbell Sponsors: outsystems.com/twit cachefly.com/twit

I sat down with James, the Boring Marketer, to stress-test Claude 4.5 Opus against Gemini 3 Pro for real-world coding and design work. Together we live-build and compare landing pages and clickable prototypes for an “EstateClear” probate-family dashboard, then zoom out into conversion copywriting frameworks, “elevated direct response” brand voice, and ad creative workflows. The episode is a practical walkthrough of how non-technical builders can go from idea → landing page → prototype → ads using modern AI tools without vibe-coded, low-converting sites.  Timestamps 00:00 – Intro 02:00 – Startup Idea: EstateClear 03:17 – Claude Opus 4.5 04:47 – Gemini 3.0 Pro 09:02 – Reviewing Opus' landing page 11:16 – Reviewing Gemini's version 12:17 – Comparing the two 17:29 – Reprompting Opus 4.5 and Gemini 19:02 – Google's vertically integrated stack (AI Studio, Anti-Gravity, TPUs, Workspace) 21:50 – Nano Banana Pro and ad creative experiments 27:35 – Opus 4.5 builds a clickable EstateClear prototype 31:38 – Gemini's prototype and comparing product depth 36:40 – Anti-Gravity + Nano Banana workflow for mockups and code 44:41 – Claude Skills Workflow and Best Practices 56:41 – Final Thoughts Key Points Claude 4.5 Opus can act like a senior engineer for non-technical builders when paired with a tight skill and tools setup. Conversion wins come more from “elevated direct response” copy and clear page architecture than from fancy visuals alone. Claude's front-end design skill meaningfully reduces “AI-looking” gradients and vibe-coded layouts, producing cleaner, production-grade UIs. Live tests show Opus 4.5 generates more refined layouts and deeper product thinking, while Gemini often injects clever AI product features. Google's integration of Anti-Gravity, Nano Banana, and AI Studio points to a powerful end-to-end environment for shipping and promoting products. The #1 tool to find startup ideas/trends - https://www.ideabrowser.com LCA helps Fortune 500s and fast-growing startups build their future - from Warner Music to Fortnite to Dropbox. We turn 'what if' into reality with AI, apps, and next-gen products https://latecheckout.agency/ The Vibe Marketer - Resources for people into vibe marketing/marketing with AI: thevibemarketer.com Startup Empire - get your free builders toolkit to build cashflowing business - https://startup-ideas-pod.link/startup-empire-toolkit Become a member - https://startup-ideas-pod.link/startup-empire FIND ME ON SOCIAL X/Twitter: https://twitter.com/gregisenberg Instagram: https://instagram.com/gregisenberg/ LinkedIn: https://www.linkedin.com/in/gisenberg/ FIND JAMES ON SOCIAL X/Twitter: https://x.com/boringmarketer LinkedIn: https://www.linkedin.com/in/jadickerson/

On today's episode I stress-test Gemini 3.0 in Google AI Studio to see how good it really is as a designer, not just a code generator. Across the episode, I ask Gemini to redesign my personal website in a Windows XP–inspired style, build a restaurant analytics SaaS dashboard, and create a workout mobile app inspired by the “Brain Rot” app. Along the way, I experiment with prompts, visual annotations, and reference images to see how well Gemini takes feedback. By the end, he's rating each build. Timestamps 00:00 – Intro 00:54 – Personal Website 15:48 – SaaS 21:52 – Mobile App 26:35 – AntiGravity 27:17 – Final rating and takeaways Key Points Gemini 3.0 can now generate full, styled web and mobile UIs (not just “purple Tailwind vibe-coded” layouts) when given strong prompts and references. greg-take-02 A Windows XP–themed personal site, built from a screenshot and a short prompt, impresses Greg enough that he considers redoing his actual homepage. greg-take-02 Visual annotation inside Google AI Studio (drawing on the canvas and commenting) is a powerful way to refine icons, backgrounds, and layout without “speaking designer.” greg-take-02 A restaurant analytics SaaS dashboard (“Chef OS”) shows how combining Dribbble shots + Teenage Engineering hardware as references pushes Gemini toward more tactile, “real button” UI. greg-take-02 The “Gains” workout app, modeled on the Brain Rot app, demonstrates that AI can remix an existing product pattern into a new behavior-change app with streaks, goals, and a reactive mascot. greg-take-02 Greg's big takeaway: good ideas + taste + references + Gemini 3.0 let non-designers ship highly differentiated experiences, raising their odds of standing out. greg-take-02 The #1 tool to find startup ideas/trends - https://www.ideabrowser.com LCA helps Fortune 500s and fast-growing startups build their future - from Warner Music to Fortnite to Dropbox. We turn 'what if' into reality with AI, apps, and next-gen products https://latecheckout.agency/ The Vibe Marketer - Resources for people into vibe marketing/marketing with AI: thevibemarketer.com Startup Empire - get your free builders toolkit to build cashflowing business - https://startup-ideas-pod.link/startup-empire-toolkit Become a member - https://startup-ideas-pod.link/startup-empire FIND ME ON SOCIAL X/Twitter: https://twitter.com/gregisenberg Instagram: https://instagram.com/gregisenberg/ LinkedIn: https://www.linkedin.com/in/gisenberg/

Get our free Google Gemini bundle with our favorite prompts + workflows: https://clickhubspot.com/sce Ep. 380 Is Gemini 3 better than GPT-5? Kipp and Kieran dive into the seismic release of Gemini 3 and what it means for the future of marketing, search, and AI-driven business tools. Learn more on how Gemini's state-of-the-art multimodal reasoning unlocks custom interactive apps in search, why dynamic AI-generated UIs signal the end of the blue links era, and how marketers can leverage Gemini to automate research, customer insights, and content more effortlessly than ever before. Mentions Gemini 3 https://gemini.google.com/ ChatGPT 5 https://chat.chatbot.app/gpt5 Claude https://claude.ai/ Replit https://replit.com/ Lovable https://lovable.dev/ Fiverr https://www.fiverr.com/ Get our guide to build your own Custom GPT: https://clickhubspot.com/customgpt We're creating our next round of content and want to ensure it tackles the challenges you're facing at work or in your business. To understand your biggest challenges we've put together a survey and we'd love to hear from you! https://bit.ly/matg-research Resource [Free] Steal our favorite AI Prompts featured on the show! Grab them here: https://clickhubspot.com/aip We're on Social Media! Follow us for everyday marketing wisdom straight to your feed YouTube: ​​https://www.youtube.com/channel/UCGtXqPiNV8YC0GMUzY-EUFg  Twitter: https://twitter.com/matgpod  TikTok: https://www.tiktok.com/@matgpod  Join our community https://landing.connect.com/matg Thank you for tuning into Marketing Against The Grain! Don't forget to hit subscribe and follow us on Apple Podcasts (so you never miss an episode)! https://podcasts.apple.com/us/podcast/marketing-against-the-grain/id1616700934   If you love this show, please leave us a 5-Star Review https://link.chtbl.com/h9_sjBKH and share your favorite episodes with friends. We really appreciate your support. Host Links: Kipp Bodnar, https://twitter.com/kippbodnar   Kieran Flanagan, https://twitter.com/searchbrat  ‘Marketing Against The Grain' is a HubSpot Original Podcast // Brought to you by Hubspot Media // Produced by Darren Clarke.

Linda Schatz, Director of AKASA, explains the role of Clinical Documentation Integrity (CDI) specialists in ensuring accurate coding and bridging the gap between clinical documentation and specific, accurate codes to ensure proper reimbursement. The complexity of medical coding often leads to errors, which can be nearly eliminated by using AI to review 100% of patient encounters to identify inconsistencies and help CDI and coding professionals process more accurate claims quickly. Accurate documentation is important for hospital revenue, patient care quality, and perception of the hospital's performance. Linda explains, "Well, the old adage, if it isn't documented, it wasn't done. If the doctor uses incorrect or perfectly acceptable medical terminology, it doesn't translate into an appropriate code. You've heard the term UIs, this is years ago, right? Grandma had UTIs and died. In the coding world, that used to code for a simple UTI. So the hospitals are getting paid for a patient that took care of a UTI, when in reality that patient was septic. To the outside world, it looks like Grandma came to the hospital, something that could have been treated outpatient, and she died. So the public perception of quality is less. So not only is it revenue, it's quality, but ultimately it's delivering patient care."   "I'm an old nurse. I've been in this field for over 40 years. I've worked across the NICU, PICU, and adult ICU. I've worked in access hospitals to large academics and all the way through hospice. That's pretty unique as a nurse to have that big of a background. Then I became a CDS, or clinical documentation specialist, or integrity specialist, and learned the documentation and coding aspect." "Then I moved into the consulting role and worked with organizations and physicians all across this nation, helping them learn how to do this. And so you've got the clinical background, the coding background, and now I understand how generative AI works. And so while you're a new nurse, you're a horse, right? When we hear a heartbeat, we think of a horse, and after years, you earn your stripes and you become a zebra, and then you add all of these multiple areas of expertise, you become uniquely valuable as a pink zebra."  #AKASA #GenAI #CDI #RevenueCycleManagement akasa.com Listen to the podcast here

Linda Schatz, Director of AKASA, explains the role of Clinical Documentation Integrity (CDI) specialists in ensuring accurate coding and bridging the gap between clinical documentation and specific, accurate codes to ensure proper reimbursement. The complexity of medical coding often leads to errors, which can be nearly eliminated by using AI to review 100% of patient encounters to identify inconsistencies and help CDI and coding professionals process more accurate claims quickly. Accurate documentation is important for hospital revenue, patient care quality, and perception of the hospital's performance. Linda explains, "Well, the old adage, if it isn't documented, it wasn't done. If the doctor uses incorrect or perfectly acceptable medical terminology, it doesn't translate into an appropriate code. You've heard the term UIs, this is years ago, right? Grandma had UTIs and died. In the coding world, that used to code for a simple UTI. So the hospitals are getting paid for a patient that took care of a UTI, when in reality that patient was septic. To the outside world, it looks like Grandma came to the hospital, something that could have been treated outpatient, and she died. So the public perception of quality is less. So not only is it revenue, it's quality, but ultimately it's delivering patient care."   "I'm an old nurse. I've been in this field for over 40 years. I've worked across the NICU, PICU, and adult ICU. I've worked in access hospitals to large academics and all the way through hospice. That's pretty unique as a nurse to have that big of a background. Then I became a CDS, or clinical documentation specialist, or integrity specialist, and learned the documentation and coding aspect." "Then I moved into the consulting role and worked with organizations and physicians all across this nation, helping them learn how to do this. And so you've got the clinical background, the coding background, and now I understand how generative AI works. And so while you're a new nurse, you're a horse, right? When we hear a heartbeat, we think of a horse, and after years, you earn your stripes and you become a zebra, and then you add all of these multiple areas of expertise, you become uniquely valuable as a pink zebra."  #AKASA #GenAI #CDI #RevenueCycleManagement akasa.com Download the transcript here

Get a private, on-screen walkthrough of Google's new Gemini 3.0 with Logan Kilpatrick. We vibe-code full apps, games, and product UIs in real time. You'll see how to go from raw idea to working product in a single prompt, then iterate visually with design, features, and AI workflows. They turn an IdeaBrowser concept into a live talent-matching platform, screenshot-clone the IdeaBrowser UI, wire up a “generate tomorrow's idea” feature grounded in Google Search, and even add co-founder matching on top. If you're building with AI or still on the fence, this episode shows what's now possible with Gemini 3.0 Pro in AI Studio. Timestamps 00:00 – Intro 01:00 – What Gemini 3 is and where it lives (Gemini app, AI Studio, API) 03:03 – Vibe Coding 3D games 09:27 – Vibe Coding an idea from IdeaBrowser 25:02 – Screenshot-cloning the IdeaBrowser UI and regenerating it in Gemini Key Points Gemini 3.0 Pro in AI Studio lets you “vibe code” full apps—UI, logic, and AI features—from natural-language prompts, then iteratively refine them. Games and complex simulations are a stress-test and showcase for the model's capabilities, not just toys. You can paste an entire business idea (like IdeaBrowser's generational talent-matching concept) into AI Studio and get a working, multi-screen product with AI-powered workflows. Gemini 3.0 Pro is free to use inside AI Studio up to generous limits, and the API is priced at $2 per million input tokens and $12 per million output tokens under 200K input tokens. Screenshot-driven UI cloning plus “add five more features” prompts are powerful loops for product and UX ideation. You can layer social features like co-founder matching directly on top of idea-discovery products with only a few additional prompts. The #1 tool to find startup ideas/trends - https://www.ideabrowser.com LCA helps Fortune 500s and fast-growing startups build their future - from Warner Music to Fortnite to Dropbox. We turn 'what if' into reality with AI, apps, and next-gen products https://latecheckout.agency/ Boringmarketing - Vibe Marketing for Companies: boringmarketing.com The Vibe Marketer - Join the Community and Learn: thevibemarketer.com Startup Empire - get your free builders toolkit to build cashflowing business - https://startup-ideas-pod.link/startup-empire-toolkit Become a member - https://startup-ideas-pod.link/startup-empire FIND ME ON SOCIAL X/Twitter: https://twitter.com/gregisenberg Instagram: https://instagram.com/gregisenberg/ LinkedIn: https://www.linkedin.com/in/gisenberg/ FIND LOGAN ON SOCIAL X/Twitter: https://x.com/OfficialLoganK  Youtube: https://www.youtube.com/@LoganKilpatrickYT  LinkedIn: https://www.linkedin.com/in/logankilpatrick/

In this episode of the HTML All The Things Podcast, Mike walks through the new web development tech that's been landing on his radar. From next-gen formatters and bundlers to emerging UI frameworks and terminal-UI toolkits, Mike breaks down what each tool is, why it matters, and where its limitations are today. In this episode Matt and Mike cover: BiomeJS - all-in-one formatter/linter with strong Prettier compatibility Ripple - an experimental TypeScript-first UI framework TanStack Start - a router-first full-stack framework for React/Solid Hono.js - tiny, blazing-fast multi-runtime web framework Rolldown - Rust-powered bundler with major Vite build speed gains Effect - type-safe effects/concurrency runtime for TypeScript OpenTUI - build rich terminal UIs using React/Solid renderers If you want a curated look at early-stage tools shaping how we might build for the web in 2025, Mike's got you covered. Show Notes: https://www.htmlallthethings.com/podcast/new-web-development-tech-thats-on-my-radar Powered by CodeRabbit - AI Code Reviews: https://coderabbit.link/htmlallthethings Use our Scrimba affiliate link (https://scrimba.com/?via=htmlallthethings) for a 20% discount!! Full details in show notes.

Mike Hanson, CTO at Clockwise, joins the show to break down how our relationship with computers is changing as language based systems reshape expectations. We explore why natural storytelling feels so intuitive with today's AI tools, how context is becoming the new currency of great software, and why narrow AI is often more powerful than the industry hype suggests.This conversation gives tech leaders a grounded look at what is real, what is noise, and what is coming fast.Key Takeaways• Natural storytelling is becoming the default way people communicate with AI, and products must adjust to that shift.• Context is the driving force behind great interaction design and LLM powered systems now surface and use context at a scale traditional UIs never could.• Most real world gains come from narrow AI that solves focused everyday problems, not from broad AGI promises.• Multi agent systems and multiplayer coordination are emerging as the next frontier for enterprise AI.• The biggest risk is not model weakness but user uncertainty about when an answer is trustworthy.Timestamped Highlights01:21 What Clockwise is building with its scheduling brain and how natural language creates new value04:13 Why humans default to storytelling and how LLMs finally make that instinct useful08:00 The rising expectation that software should understand context the way people do12:13 The shift away from feed centric design and toward multi person coordination in AI systems17:31 Why narrow AI delivers real value while wide AI often creates anxiety23:52 A real world example of how AI can remove busy work by orchestrating tasks across tools26:24 Why we do not need AGI to meaningfully improve everyday productivityA standout thoughtPeople have always tried to talk to computers in a natural way. The difference now is that the systems finally understand us well enough to meet us where we already are.Pro Tips• Look for AI that reduces busy work across tools rather than chasing broad capability.• Prioritize context rich interactions in your product planning. It will define user expectations for years to come.• Treat multi person workflows as the next major opportunity. Most teams still rely on manual coordination.Call to actionIf this episode helped you think differently about where AI is actually useful, follow the show and share it with someone who is building product in this space. And join me on LinkedIn for weekly insights on tech, people, and impact.

When you build a software business as a founder, you have a dream. Building. Features. APIs. UIs.But how much of that is JUST a dream, and what REALLY leads to paying customers?This episode of The Bootstraped Founder is sponsored by Paddle.comYou'll find the Black Friday Guide here: https://www.paddle.com/learn/grow-beyond-black-fridayThe blog post: https://thebootstrappedfounder.com/the-things-your-customers-dont-care-about/The podcast episode: https://tbf.fm/episodes/422-the-things-your-customers-dont-care-aboutCheck out Podscan, the Podcast database that transcribes every podcast episode out there minutes after it gets released: https://podscan.fmSend me a voicemail on Podline: https://podline.fm/arvidYou'll find my weekly article on my blog: https://thebootstrappedfounder.comPodcast: https://thebootstrappedfounder.com/podcastNewsletter: https://thebootstrappedfounder.com/newsletterMy book Zero to Sold: https://zerotosold.com/My book The Embedded Entrepreneur: https://embeddedentrepreneur.com/My course Find Your Following: https://findyourfollowing.comHere are a few tools I use. Using my affiliate links will support my work at no additional cost to you.- Notion (which I use to organize, write, coordinate, and archive my podcast + newsletter): https://affiliate.notion.so/465mv1536drx- Riverside.fm (that's what I recorded this episode with): https://riverside.fm/?via=arvid- TweetHunter (for speedy scheduling and writing Tweets): http://tweethunter.io/?via=arvid- HypeFury (for massive Twitter analytics and scheduling): https://hypefury.com/?via=arvid60- AudioPen (for taking voice notes and getting amazing summaries): https://audiopen.ai/?aff=PXErZ- Descript (for word-based video editing, subtitles, and clips): https://www.descript.com/?lmref=3cf39Q- ConvertKit (for email lists, newsletters, even finding sponsors): https://convertkit.com?lmref=bN9CZw

On September 30th, ServiceNow's AI Experience launched, and with it came one of the most transformative new features: Web Agents! On this week's episode of Break Point, Lauren McManamon was joined by returning guest, Jingfei Chen, to unveil how Web Agents automate tasks by navigating browser UIs like a human. Learn how this catch-all agent works, where it fits alongside APIs and RPA, and why it’s set to change how developers approach automation. ⏱️ Timestamps 00:00 Introduction 02:37 What ARE ServiceNow Web Agents? 07:09 So, Web Agents Are Not ONLY Accessible Through Chat? 09:41 How Are Web Agents Similar/Different From RPA? 12:25 What Are Some Cornerstone Use Cases for Web Agents? 17:19 What Are the V1 Capabilities / Limitations of Web Agents? 18:47 What Are Web Agent Best Practices? 22:18 Where Can People Try Out Web Agents? 23:55 What’s On the Roadmap for Web Agents? *SAFE HARBOR* 27:01 How Have Priorities Changed to Meet Market Expectations? 29:50 What Are Initial Customer Thoughts on Web Agents? 31:11 Where Can People Provide Feedback on Web Agents? 32:02 Call to Action 33:57 Conclusion

Nilay's back! And you can listen to The Vergecast with no ads, if you're a Verge subscriber! Big week, really. Nilay and David start the show by talking about ads, podcasts, platforms, and subscriptions. Then they talk a bunch about Apple's new M5-powered MacBook, iPad, and Vision Pro, and whether a chip bump is worth getting excited about. After that, Nilay reflects on a summer of using AI products, and explains why you can tell the whole story of this generation of AI just by talking about the smart home. Finally, in the lightning round, the hosts talk about AI song covers, Apple TV, TiVo, Roku, Cybertrucks, and the exploding Pixel 10 Pro Fold. Help us improve The Verge: Take our quick survey at theverge.com/survey. Further reading: Ad-free Verge podcasts have arrived Netflix is making a big bet on video podcasts Apple's 2025 iPad Pro comes with an M5 chip inside  Apple just upgraded the Vision Pro with an M5 chip and new strap  Apple's 14-inch MacBook Pro gets an M5 chip bump and faster storage  Logitech made an Apple Pencil-like stylus for the Vision Pro  Apple's rumored smart home display hub might start at $350  Samsung officially teases Moohan headset launch for next week  Apple's future smart glasses could have two separate UIs.  ChatGPT will soon help you shop at Walmart.  How OpenAI plans to make all its money.  Microsoft wants you to talk to your PC and let AI control it   As Microsoft bids farewell to Windows 10, millions of users won't  Spotify says it's working with labels on ‘responsible' AI music tools  DirecTV will soon bring AI ads to your screensaver  OpenAI partners with Broadcom to produce its own AI chips  Sam Altman says ChatGPT will soon sext with verified adults  Apple TV Plus is being rebranded to… Apple TV  Apple exec on Apple TV rebranding: ‘let's just do it' Google's Pixel 10 Pro Fold is the first to ‘go up in smoke during a bend test,' JerryRigEverything says Roku's AI-upgraded voice assistant can answer questions about what you're watching  DirecTV will soon bring AI ads to your screensaver  Soul Against the Machine TiVo has sold its last DVR  Tesla Cybertruck sales are flatlining  Subscribe to The Verge for unlimited access to theverge.com, subscriber-exclusive newsletters, and our ad-free podcast feed.We love hearing from you! Email your questions and thoughts to vergecast@theverge.com or call us at 866-VERGE11. Learn more about your ad choices. Visit podcastchoices.com/adchoices

Estudiantes de la UIS esperaron cinco horas a Petro para la inauguración del edificio del Instituto de LenguasPresidente Gustavo Petro aceptó la renuncia del ministro de Igualdad, Juan Carlos Florián SilvaPetro no erradicará forzosamente Juan Manuel Santos Cobardes

The 2025 generative AI image market is a trade-off between aesthetic quality, instruction-following, and user control. This episode analyzes the key platforms, comparing Midjourney's artistic output against the superior text generation and prompt adherence of GPT-4o and Imagen 4, the commercial safety of Adobe Firefly, and the total customization of Stable Diffusion. Links Notes and resources at ocdevel.com/mlg/mla-25 Try a walking desk - stay healthy & sharp while you learn & code Build the future of multi-agent software with AGNTCY. The State of the Market The market is split by three core philosophies: The "Artist" (Midjourney): Prioritizes aesthetic excellence and cinematic output, sacrificing precise user control and instruction following. The "Collaborator" (GPT-4o, Imagen 4): Extensions of LLMs that excel at conversational co-creation, complex instruction following, and integration into productivity workflows. The "Sovereign Toolkit" (Stable Diffusion): An open-source engine offering users unparalleled control, customization, and privacy in exchange for technical engagement. Table 1: 2025 Generative AI Image Tool At-a-Glance Comparison Tool Parent Company Access Method(s) Pricing Core Strength Best For Midjourney v7 Midjourney, Inc. Web App, Discord Subscription Artistic Aesthetics & Photorealism Fine Art, Concept Design, Stylized Visuals GPT-4o OpenAI ChatGPT, API Freemium/Sub Conversational Control & Instruction Following Marketing Materials, UI/UX Mockups, Logos Google Imagen 4 Google Gemini, Workspace, Vertex AI Freemium/Sub Ecosystem Integration & Speed Business Presentations, Educational Content Stable Diffusion 3 Stability AI Local Install, Web UIs, API Open Source Ultimate Customization & Control Developers, Power Users, Bespoke Workflows Adobe Firefly Adobe Creative Cloud Apps, Web App Subscription Commercial Safety & Workflow Integration Professional Designers, Agencies, Enterprise Core Platforms Midjourney v7: Premium choice for artistic quality. Features: Web UI with Draft Mode, user personalization, emerging video/3D. Weaknesses: Poor text generation, poor prompt adherence, public images on cheap plans, no API/bans automation. OpenAI GPT-4o: An intelligent co-creator for controlled generation. Features: Conversational refinement, superior text rendering, understands uploaded image context. Weaknesses: Slower than competitors, generates one image at a time, strict content filters. Google Imagen 4: Pragmatic tool focused on speed and ecosystem integration. Features: High-quality photorealism, fast generation, strong text rendering, multilingual. Weaknesses: Less artistic flair; value is dependent on Google ecosystem investment. Stable Diffusion 3: Open-source engine for maximum user control. Features: MMDiT architecture improves prompt/text handling, scalable models, vast ecosystem (LoRAs/ControlNet). Weaknesses: Steep learning curve, quality is user-dependent. Adobe Firefly: Focused on commercial safety and professional workflow integration. Features: Trained on Adobe Stock for legal indemnity, Generative Fill/Expand tools. Weaknesses: Creative range limited by training data, requires Adobe subscription/credits. Tools and Concepts In-painting: Modifying a masked area inside an image. Out-painting: Extending an image beyond its original borders. LoRA (Low-Rank Adaptation): A small file that applies a fine-tuned style, character, or concept to a base model. ControlNet: Uses a reference image (e.g., pose, sketch) to enforce the composition, structure, or pose of the output. A1111 vs. ComfyUI: Two main UIs for Stable Diffusion. A1111 is a beginner-friendly tabbed interface; ComfyUI is a node-based interface for complex, efficient, and automated workflows. Workflows "Best of Both Worlds": Generate aesthetic base images in Midjourney, then composite, edit, and add text with precision in Photoshop/Firefly. Single-Ecosystem: Work entirely within Adobe Creative Cloud or Google Workspace for seamless integration, commercial safety (Adobe), and convenience (Google). "Build Your Own Factory": Use ComfyUI to build automated, multi-step pipelines for consistent character generation, advanced upscaling, and video. Decision Framework Choose by Goal: Fine Art/Concept Art: Midjourney. Logos/Ads with Text: GPT-4o, Google Imagen 4, or specialist Ideogram. Consistent Character in Specific Pose: Stable Diffusion with a Character LoRA and ControlNet (OpenPose). Editing/Expanding an Existing Photo: Adobe Photoshop with Firefly. Exclusion Rules: If you need legible text, exclude Midjourney. If you need absolute privacy or zero cost (post-hardware), Stable Diffusion is the only option. If you need guaranteed commercial legal safety, use Adobe Firefly. If you need an API for a product, use OpenAI or Google; automating Midjourney is a bannable offense.