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

From a sequence starting in 2025. You can join, live, each Tuesday, 7.30 p.m. Ireland time (the same as UK time)! Information about the sequence can be found here: https://first164.blogspot.com/p/zoom164.html

On November 4, 2025, the Roman Catholic Dicastery for the Doctrine of the Faith issued a document titled Mater Populi Fidelis (MPF, Latin for “Mother of the Faithful People”). Signed by Pope Leo XIV, its primary purpose is indicated in Paragraph 22: Roman Catholics are no longer to refer to Mary as the Co-Redemptrix.This is a step in the right direction, but the title “Mother of the Faithful” alone indicates that this document is not Biblical. The Bible only knows one mother of all believers: “…Jerusalem which is above…is the mother of us all” (Galatians 4:26). Additionally, MPF favorably cites dozens of documents from the Magisterium that allege dozens of unbiblical doctrines regarding Mary. The meaning of MPF is also still a matter of debate within the Catholic Church.

On November 4, 2025, the Roman Catholic Dicastery for the Doctrine of the Faith issued a document titled Mater Populi Fidelis (MPF, Latin for “Mother of the Faithful People”). Signed by Pope Leo XIV, its primary purpose is indicated in Paragraph 22: Roman Catholics are no longer to refer to Mary as the Co-Redemptrix.This is a step in the right direction, but the title “Mother of the Faithful” alone indicates that this document is not Biblical. The Bible only knows one mother of all believers: “…Jerusalem which is above…is the mother of us all” (Galatians 4:26). Additionally, MPF favorably cites dozens of documents from the Magisterium that allege dozens of unbiblical doctrines regarding Mary. The meaning of MPF is also still a matter of debate within the Catholic Church.

In Episode 265, Christine Tulley offers practical advice for overwhelmed academic writers on how to make progress when time is limited by focusing on completing just one paragraph. She explains that paragraphs can serve various purposes—proposing new frameworks, introducing evidence, synthesizing literature, or challenging established views—and that working on a single paragraph is a manageable, non-threatening way to advance your writing even in small fragments of time. Tulley emphasizes that paragraphs have singular goals and typically take less than a full page, making them ideal units for focused work during brief writing sessions, and she encourages writers to start with key structural paragraphs or troublesome ones that need revision, noting that these small efforts accumulate toward completing larger projects. Resource Mentioned Lesson 13: Establish Originality (Tuesday Toolbox) Episode 233 - Paragraphing DPL Resources Tuesday Toolbox - contact christine@defendpublishandlead.com for subscription information to get more videos like Lesson 13 Set your writing goals with us! Try us out in a free consultation. Check out our current and past workshops at Eventbrite for writing support content. A FREE webinar is posted each month. Missed a workshop? Request a workshop or webinar recording from christine@defendandpublish.com Don't forget about the wonderful resources at Textbook and Academic Authors Association. The organization can be found at: https://www.taaonline.net New to TAA? Join for just $25 using discount code DP25! You will also receive a copy of the eBook, Guide to Making Time to Write: 100+ Time & Productivity Management Tips for Textbook and Academic Authors.

Letzter Tag heute zum Abstimmen: Jurios Podcast Voting (mit 2 Klicks erledigt). Danke!

Send us a textEver notice how quickly we blame our bodies for our feelings? We dig into A Course in Miracles' bold reminder that mind is cause and the body is a neutral device for learning and communication. By returning cause to thought, we dissolve the exhausting loop where symptoms, circumstances, and biographies get treated as the source of fear. That shift doesn't deny care; it frees care from panic and restores gentle responsibility: honest noticing, clear choosing, and willingness to let correction happen.From there, we tackle a subtle trap: awe. When admiration becomes awe among equals, hierarchy sneaks in. Pedestals feel inspiring at first, then quietly breed dependency, over-caring, and fear of loss. We share simple tells—monitoring someone's state, feeling responsible for their peace, or shrinking around their perceived power—and offer a clean replacement: recognition. Equal worth, shared mind, steady respect, genuine gratitude. This clears space for guidance to lead without specialness distorting the bond.We also re-anchor awe where it belongs: with the Creator. Properly placed, awe doesn't make us small; it confirms safety, source, and real power. Misplaced, it confuses awesome with fearful and keeps us braced against the very love that heals. Through stories, practical examples, and ACIM references, we show how to stop using the body as a defense, how to meet sensations as mirrors instead of meanings, and how to let miracles arise when defenses fall away. The result is less rumination, fewer bodily narratives, and more ease in everyday choices.If this conversation sparked clarity, subscribe, share with a friend who loves ACIM, and leave a review with your biggest takeaway. Your notes help others find the show and join the practice.Support the show

Wednesday // Pastor Ed Romero // Second London Baptist Confession of Faith Chapter 27

From a sequence starting in 2025. You can join, live, each Tuesday, 7.30 p.m. Ireland time (the same as UK time)! Information about the sequence can be found here: https://first164.blogspot.com/p/zoom164.html

Title: The Greatest Paragraph Ever Written Preacher: H B Charles, Jr. Series: Words From Old Friends Passage: Romans 3:21-26

2nd London Baptist Confession of Faith, Chapter 2, Paragraph 3 (Part 2)

LG Köln 25.09.2025 – 6 S 117/25: Abonnieren und weiter empfehlen! Instagram: rechtsprechung_newsJura; Urteil; Rechtsprechung; News; Referendariat;Rechtswissenschaften; Prozess; Recht; Gericht; Gesetz; Klage;Rechtsanwalt; Staatsexamen; Paragraf; Jurist; Examen; StEx;Rechtsreferendariat; Anwalt; Ref; Paragraph; Referendar; Justiz; Bundesverfassungsgericht; Rechtsreferendar; Richter; law; Justiz; Jurastudent; Jurapodcast; Staatsanwalt; Rechtswissenschaft; Streit; Verurteilung; Polizei; Beamte; Polizist; Klage; Kläger; Beklagte; Klausur; Erstesexamen; Assessorexamen; Erstesstaatsexamen; Repetitor;Repetitorium; Assessor; Zivilrecht; BGB; BGH; Bundesgerichthof; Landgericht; Oberlandesgericht; OLG; LG; Amtsgericht; AG; ZPO; Strafrecht; StGB; Strafgesetzbuch; Strafe; StPO; strafbar; Bewährung; Beschlagnahme; Prozess; Hund; Haustier; Hundehalter; Eigentümer; Hundeeigentümer; Hundepass; Tier;

Wednesday // Pastor Ed Romero // Second London Baptist Confession of Faith Chapter 27

Send us a textSummary of Cameo 14 & Cameo 9, Chapter 3, Part I, Paragraph 4, Sentence 2 & 3 | Summary of Key Points in Chapters 1 and 2A tiny, unwatched fear can hijack a whole day. We trace ACIM's chain of miscreation from its first faint flicker—what Jesus calls a “will-o'-the-wisp”—through strain, irritation, self-protection, and the cascade of missed guidance that follows. Using vivid stories (a botched cab ride, a cold doorway, even cat-and-meat drama), we show how events are irrelevant; the real lesson is how the mind slides from fear to reaction when it isn't watched.We lay down seven foundations that make everything else click: miracles are shifts in perception, not outcomes; cause and effect live in the mind; fear is self-generated; the body is neutral; guidance replaces control; circular miracles differ from corrective ones; and a unified will ends the sense of coercion. From relationships to sickness, we examine how level confusion turns bodies into causes and life into negotiation. Then we flip it: when the mind pauses and pardons, time shortens, efficiency appears, and action becomes light and clear for everyone involved.Along the way, we unpack a jarring but useful ACIM phrase—“mental retardation” as a defense—not to label people, but to name the tendency to feign confusion to avoid responsibility. The cure is simple willingness: study what matters, watch the first hint of strain, offer pardon, and let guidance carry you. If you're ready to stop wasting time with urgency and start saving time with miracles, this conversation will give you language, tools, and lived examples to practice today.If this resonated, subscribe, share with a friend who studies ACIM, and leave a review so more seekers can find the show. Your notes and questions shape future deep dives—join us and add your voice.Support the show

In this episode, Nathan Fabian, Chief Sustainable Systems Officer at the PRI, explores how global policy frameworks are evolving to unlock private capital for sustainable development. He is joined by Helena Viñes Fiestas, Commissioner at the Spanish Financial Markets Authority and Co-Chair of the Taskforce on Net Zero Policy, and Eric Usher, Head of the UN Environment Programme Finance Initiative (UNEP FI) and PRI Board member.The discussion focuses on the outcomes of the Fourth International Conference on Financing for Development in Seville and the significance of Paragraph 34 of the Seville Commitment, a milestone recognising the role of well-functioning financial markets in delivering the Sustainable Development Goals.OverviewAs public finance comes under pressure, governments are increasingly focused on creating enabling environments that attract long-term private investment, particularly in emerging and developing economies.Helena and Eric explain why Paragraph 34 marks an important shift: embedding issues such as transparency, disclosures, taxonomies and market integrity into a multilateral development framework. They discuss how this convergence of development, climate and financial policy could help mobilise capital at scale, if implemented effectively.Detailed coverageFrom development aid to market-based solutionsEric explains how financing for sustainable development has traditionally focused on public finance, debt and governance, but is now recognising the need for private capital and functioning financial markets to deliver long-term outcomes.Policy momentum beyond Europe and North AmericaHelena shares findings from the Taskforce on Net Zero Policy, showing that most new sustainable finance policies adopted last year emerged outside Europe and North America, particularly across Asia-Pacific. She highlights why global companies and investors will increasingly need to align with these frameworks.What's inside Paragraph 34The guests outline how Paragraph 34 references a broad set of tools, from sustainability disclosures and taxonomies to market transparency, covering environmental and social objectives across the SDGs.Development banks, DFIs and private capitalBoth guests reflect on the growing role of development finance institutions (DFIs) in de-risking investments and creating pathways for pension funds and asset managers to invest in emerging markets.Taxonomies and interoperabilityWith over 50 taxonomies now in development globally, the discussion explores why interoperability, rather than a single global standard, is essential for attracting international capital while reflecting local economic realities.From policy design to implementationHelena highlights lessons from Europe's experience: the need for better engagement with industry, tailored approaches for SMEs, capacity building for supervisors, and a stronger balance between incentives and regulation.The responsibility of investingIn closing reflections, Eric emphasises dynamic materiality and the role of science in understanding long-term risk, while Helena highlights the growing responsibility of investors, and citizens, to align capital with sustainable outcomes.For more information on the compromiso de sevilla, see our...

Pastor David continues our series with his exposition of 1 Peter 2:1-3.(v1)  put away CORRUPT HEARTEDNESS1 Peter 1:22-231 Peter 1:14-15Acts 8:20-221 Peter 2:16Ephesians 4:25 (NASB)Colossians 3:9Galatians 2:12-14Revelation 12:10, John 8:44bPsalm 141:3 (CSB)James 4:1James 1:21 (NASB)Galatians 5:24-26John 17:17(v2)  long for SPIRITUAL NUTRITIONHebrews 5:12b-131 Corinthians 14:20Psalm 42:1-2 (CSB)Psalm 84:2 (CSB)2 Timothy 3:16-17Second London Baptist ConfessionChapter 1, Paragraph 1a(v3)  (and) experience THE LORD'S GOODNESS.Psalm 34:8Psalm 34:13-14(1 Samuel 21)Psalm 119:68Exodus 33:18-19Psalm 31:19Romans 8:28Proverbs 16:20

The Chicago Bulls found themselves at the center of NBA controversy after a heated Bulls vs Bucks matchup ended with a late-game windmill dunk by Giannis Antetokounmpo that sparked a postgame brawl. On this episode of Horns Over Hoops, Sal Bass, Dan Gracie, and Scott Berman break down everything that went wrong — and what it might mean for the Bulls moving forward. Despite the loss to Milwaukee, the Bulls showed real signs of growth, competing hard against one of the Eastern Conference's top teams. Chicago's recent five-game winning streak, improved ball movement, and defensive effort suggest the Bulls season may finally be turning around. We analyze the Bucks game, Giannis' minutes restriction, Bobby Portis' role in the scuffle, and whether the Bulls earned respect around the league. The conversation then shifts to the future of the Chicago Bulls roster. Is Kobe White worth a $30 million contract? Should the Bulls trade him before the deadline? What role should Patrick Williams, Matas Buzelis, and Jalen Smith play going forward? The crew debates lineup changes, offensive rebounding issues, and why Chicago struggles against physical superstars. Paragraph 4 (Bulls Season Outlook + Fan Engagement) With the Bulls sitting near the Play-In Tournament, questions loom about whether this team should push forward or pivot toward development. We also cover Tuesday Trivia, Bulls vs Bucks history, and New Year's resolutions for the team as Chicago heads into the most important stretch of the season.

A short reflection on releasing unassigned responsibilities and allowing others the dignity to handle their own things.

Defending the proclamation without contention

In recent court filings, Sean "Diddy" Combs' legal team has argued that videos of his so-called "Freak Off" parties demonstrate consensual sexual activities among adults, countering allegations of coercion and misconduct. The defense contends that the footage shows participants engaging willingly, without evidence of force or manipulation, challenging the prosecution's portrayal of these events as exploitative.Combs faces serious charges, including sex trafficking and racketeering, with prosecutors alleging that he orchestrated drug-fueled sex parties involving non-consenting individuals. His attorneys have requested fewer restrictions on viewing the videos to prepare their defense, asserting that the government's case is unjustly criminalizing consensual adult behavior. Combs, who has pleaded not guilty, remains detained without bail, with a trial scheduled for May 2025.In United States v. Combs, Case No. 24-cr-542 (AS), Sean Combs's legal team has filed a request for a modification to the Protective Order issued by the court. The current order restricts the defense from receiving electronic copies of video evidence referenced in Paragraphs 12(a) and 12(c) of the indictment, permitting only inspection of the footage. Combs's attorneys argue that this restriction hinders their ability to fully investigate the evidence and demonstrate its exculpatory value. They contend that the videos strongly support Combs's innocence and must be electronically produced for proper evaluation and use in his defense.Citing Rule 16(a)(1)(E), which mandates the government to provide access to relevant evidence, and Rule 16(d)(1), which limits restrictions on such evidence to cases with demonstrated "good cause," the defense asserts that no valid justification exists for withholding electronic copies. They emphasize that the videos are critical to ensuring a fair trial and argue that the government's restrictions undermine the defense's ability to effectively utilize the material alongside other Rule 16 and Brady disclosures. The motion urges the court to modify the Protective Order and allow for standard electronic production of the videos.In United States v. Combs, Case No. 24 Cr. 542 (AS), the government has requested that the court direct Sean Combs's defense team to remove and refile their January 14, 2025, motion to amend the Protective Order. The government argues that the defense's filing violated the existing Protective Order by failing to appropriately redact sensitive information. The motion in question seeks to modify restrictions on video evidence, which is currently limited to inspection by counsel and the defendant, without allowing for electronic production.The government asserts that the defense's incomplete redactions breach the terms of the Protective Order (Dkt. 26), which is designed to safeguard the handling of specific evidence in the case. While acknowledging the defense's request to amend the order regarding the video evidence, the government emphasizes that compliance with the current protective measures is essential. They request the court to ensure the filing is re-submitted with redactions that fully adhere to the established rules.to contact me:bobbycapucci@protonmail.comsource:gov.uscourts.nysd.628425.126.0.pdf

Send us a textWhat if everything you've been taught to fear about the Last Judgment is backward? We take a fresh, practical look at A Course in Miracles, exploring how judgment isn't God condemning anyone but a healing of perception we undertake with Christ's help. Instead of punishment and dread, we talk about right evaluation: learning to distinguish what is worthy of us—love, peace, joining, forgiveness—from what is unworthy—fear, guilt, attack, and specialness. That shift dissolves the fear of judgment at its root and restores right-mindedness, where innocence and clarity feel natural.Together, we unpack why punishment never corrects the mind, how it secretly reinforces separation, and how to turn every trigger into a doorway to healing. You'll hear simple practices to use in real time: pause when you brace for consequences, ask for help to sort false from true, and notice how the body's energy field relaxes when you choose love. We also redefine “apocalypse” as a constructive division—an unveiling that gently separates reality from illusion. No divine wrath, no tallying of sins; just the mind preserving only what is good and releasing what was never real.As belief is withdrawn from miscreations, they lose their grip—and often vanish. Relationships shift, either transforming to match peace or naturally falling away when their lesson completes. You stop chasing pleasure in forms and start bringing joy to them, trusting guidance rather than forcing outcomes. By valuing only what's eternal, the exhausting swing between free will and imprisoned will ends. If you're ready to disown self-attack, welcome correction without fear, and experience the quiet strength of Christ vision in daily life, this deep dive will meet you where you are and invite you home to what you've always been.If this resonates, subscribe, share with a friend, and leave a review to help others find the show. Then tell us: which thought are you willing to release today?Support the show

Wednesday // Pastor Ed Romero // Second London Baptist Confession of Faith Chapter 26

Pastor David begins our advent series with his exposition of Genesis 3:14-15.(v14) the judgement of THE SERPENT;Genesis 3:1,Revelation 12:9,(Matthew 13:19, John 12:31, 14:30, 16:11),John 8:44, Genesis 2:17,Genesis 3:4, 6-7, (8-13),Romans 3:23Second London Baptist Confession,Chapter 6, Paragraph 2(v15) the promise of THE SEED.(Genesis 4, Exodus 7:-12, 1 Samuel 19, Esther 3, Matthew 12:34, 23:33), Genesis 4:25, Galatians 4:4-5, Matthew 4:4, Hebrews 2:14-15,Romans 3:10-12,Romans 5:19 (CSB),Matthew 1:20b-21, Luke 2:30-32,Romans 16:20Second London Baptist Confession,Chapter 7, Paragraph 3Chapter 20, Paragraph 1

Wednesday // Pastor Ed Romero // Second London Baptist Confession of Faith Chapter 26

The caretaker of the campground hit by a tornado in Manawatu yesterday says they're lucky no one was more seriously injured, or killed; Investigators have identified a defect that led to an Airbus A320 aircraft engine suddenly shutting off en route from Wellington to Sydney a year ago; As peace talks with Russia stall once again, New Zealand is committing $15 million to help arm Ukraine's soldiers; After years of anticipation, IKEA is about to open its doors to the New Zealand public; New data out today shows almost a third of kiwi workers often dread going to work, rising to 40% among Gen Z workers. Paragraph locked by Dan Lake

Today we are coming to beating heart of scripture.

Have you ever thought about writing the perfect legal brief? Guest David N. Greenwald has, so much so that the retired Cravath, Swaine & Moore partner wrote a book on the subject: Sentence, Paragraph, Argument, Brief: Meeting the Four Challenges of Legal Writing. The book is the culmination of a 30-year legal career, beginning with a clerkship and the lessons learned under the guidance of the Hon. Richard A. Posner, Chief Judge of the United States Court of Appeals for the Seventh Circuit. Reading, digesting, and understanding everything related to each brief proved to be the foundation of good legal writing, Greenwald says. Throughout his career, Greenwald intentionally honed his skills, from writing briefs to eventually, as a partner, editing them. With each paragraph and edit, he focused on the construction and flow of each argument. Writing, Greenwald explains, is a linear process, putting ideas and sentences in a logical progression. A brief, he says, is a special kind of writing that must be learned. It starts with a statement of fact or history, building a narrative. But it's also a work focused on clarity, without surprises or suspense. Hear Greenwald's discussion of the art, and science, of legal writing and the principles of a clear, persuasive argument. Have a question, comment, or suggestion for an upcoming episode? Get in touch at MRogson@SkywardInsurance.com and JAReeder@JonesDay.com. Resources: Hon. Paul R. Michel, Chief Judge (Retired), U.S. Court of Appeals for the Federal Circuit on C-SPAN  2026 Women in Litigation CLE Conference American Bar Association American Bar Association Litigation Section  “Sentence, Paragraph, Argument, Brief: Meeting the Four Challenges of Legal Writing,” by David N. Greenwald

Good morning, good afternoon, and good evening, investors! Scott Carson here, and boy, do we have a treat for you! Ever wanted to tackle Texas creative financing—think subject-to, wrap-around mortgages, and assumptions—without, shall we say, foul-ups? Good, because I've brought on the man, the myth, the legend himself: T Alan Ceshker from the Ceshker Law Firm! With over 30 years in the game and a proud 5th-generation Austinite, Alan is the go-to guy for navigating the Lone Star State's unique legal landscape. He's helped thousands of investors get these specialized transactions right, so you don't end up with an "Amityville Horror" on your hands. If you're eyeing those sweet, low-interest mortgages and distressed borrowers, this episode is your official "Don't Screw It Up" guide!In this episode, you'll learn:Demystifying Texas Creative Financing (Alan's Way!): Alan cuts through the jargon, explaining why he calls everything a "wrap" (even assumptions and sub-to deals!) from a legal standpoint, and why "sub-to" is a term he strategically avoids. Learn the core concept: it's "just seller financing" where the existing mortgage stays, and the seller dons a new "lender" hat.Bulletproof Contracting for Texas Wraps & Assumptions: Discover Alan's ingenious "math word problem" solution for drafting contracts that account for fluctuating payoff amounts in assumptions, bypassing Paragraph 3 headaches. For wraps, it's as simple as standard seller finance! Plus, get the crucial "disclose, disclose, disclose" mantra to avoid those pesky investor amnesia cases years down the road.Taming the Due-on-Sale Beast with Trusts: Unpack the infamous due-on-sale clause—what it means, why lenders usually don't call it (but sometimes do!), and how Alan's proprietary "due on sale trust" structure leverages the Garn-St. Germain Act for protection. You'll hear about specific lenders (looking at you, Home Loan Servicing!) that raise flags and why downloading generic trust forms is a bad idea.Non-Negotiable Insurance & Legal Compliance: This is HUGE. Alan reveals the #1 reason wraps fail: incorrect insurance. Learn the exact structure (seller as additional insured, not just interest; lender as mortgagee clause) and why you must use a proven provider. Plus, understand the critical legal compliance points for Texas: RMLO requirements, the 5.016 disclosure, and the "no balloons, no ARMs" rule for baseline compliance.Pro-Tips for a Smooth Ride (and Avoiding Foreclosure): Get actionable advice for managing your deals: conference calls with sellers for lender contact, the strategic use of Power of Attorney for checks, and Alan's "6% down" rule of thumb to mitigate default risk. He also stresses the importance of continuous communication with all parties to ensure smooth sailing and happy campers.This episode with Alan Ceshker is an absolute masterclass in navigating the legal and operational intricacies of Texas creative financing. He's not just talking theory; he's giving you the battle-tested strategies to build a robust portfolio and avoid painful (and costly) mistakes. So, stop drawing deals on napkins, reach out to Alan's team, and get ready to close some rock-solid transactions! Go out, take some action, and we'll see you at the top!Watch the Original VIDEO HERE!Connect with Alan's Team HERE!Love the show? Subscribe, rate, review, and share!Here's How »Join Note Night in America community today:WeCloseNotes.comScott Carson FacebookScott Carson TwitterScott Carson LinkedInNote Night in America YouTubeNote Night in America VimeoScott Carson InstagramWe Close Notes PinterestBook a call with Scott today at HTTP://TalkWithScottCarson.com to see if 1:1 Note Coaching is right for you!

Have you ever thought about writing the perfect legal brief? Guest David N. Greenwald has, so much so that the retired partner from the firm Cravath, Swaine & Moore wrote a book on the subject, titled “Sentence, Paragraph, Argument, Brief: Meeting the Four Challenges of Legal Writing.” The book is the culmination of a 30-year legal career, beginning with a clerkship and the lessons learned under the guidance of the Hon. Richard A. Posner, Chief Judge of the United States Court of Appeals for the Seventh Circuit. Reading, digesting, and understanding everything related to each brief proved to be the foundation of good legal writing, Greenwald says. Throughout his career, Greenwald intentionally honed his skills, from writing briefs to eventually, as a partner, editing them. With each paragraph and edit, he focused on the construction and flow of each argument. Writing, Greenwald explains, is a linear process, putting ideas and sentences in a logical progression. A brief, he says, is a special kind of writing that must be learned. It starts with a statement of fact or history, building a narrative. But it's also a work focused on clarity, without surprises or suspense. Hear Greenwald's discussion of the art, and science, of legal writing and the principles of a clear, persuasive argument. Have a question, comment, or suggestion for an upcoming episode? Get in touch at MRogson@SkywardInsurance.com and JAReeder@JonesDay.com. Resources: Hon. Paul R. Michel, Chief Judge (Retired), U.S. Court of Appeals for the Federal Circuit on C-SPAN  2026 Women in Litigation CLE Conference American Bar Association American Bar Association Litigation Section  “Sentence, Paragraph, Argument, Brief: Meeting the Four Challenges of Legal Writing,” by David N. Greenwald Learn more about your ad choices. Visit megaphone.fm/adchoices

Send us a textWhat if “the Last Judgment” isn't a cosmic trial but the clearest act of love your mind can make? We take a grounded, ACIM‑based tour through one of spirituality's most misunderstood ideas and uncover a liberating frame: God does not judge, and judgment itself is a temporary tool we repurpose to sort illusion from truth. When we keep only what is real—what is created in love—we end the ego's loop of self‑evaluation and fear.We start by drawing the essential line between creating and making. Creations are eternal; what we “make” through belief in separation appears real only because we value it. From that lens, the Last Judgment becomes discernment: recognizing fear as nothing and love as everything. We explore how fear is evidence of confusion, not danger, and how exposing our darkness without drama lets correction in. Justice gets redefined too—not as punishment, but as restoration of innocence, a learning device that returns the mind to balance and refuses to let guilt replace truth.Then we zoom out to time. Miracles collapse intervals of delay that would otherwise stretch across lifetimes. That's the “celestial speedup”: when enough of us become truly miracle‑minded—choosing forgiveness over attack, vision over judgment—we compress suffering for the whole. This isn't about striving; it's about relaxing into willingness, asking for Christ's vision, and practicing a simple sort: keep the eternal, release the unreal. Confidence grows through happy learning, and peace becomes a reliable signal that we're on purpose.If you've ever felt haunted by judgment, stuck in guilt, or tangled in the ego's stories, this conversation offers a practical way out. Trade the myth of a wrathful deity for the reality of mercy. Reclaim discernment. Help collapse time by choosing miracles where you stand. If this resonates, subscribe, share with a friend, and leave a review—what illusion are you ready to release today?Support the show

Wednesday // Pastor Ed Romero // Second London Baptist Confession of Faith Chapter 26

This week, cut out the weakest part of every paragraph, and your story or novel will sing.Book recommendation: HEARTWOOD, by Amity Gaige.Like us on Apple Podcasts and help us grow!

Good morning, good afternoon, and good evening, investors! Scott Carson here, and boy, do we have a treat for you! Ever wanted to tackle Texas creative financing—think subject-to, wrap-around mortgages, and assumptions—without, shall we say, foul-ups? Good, because I've brought on the man, the myth, the legend himself: T Alan Ceshker from the Ceshker Law Firm! With over 30 years in the game and a proud 5th-generation Austinite, Alan is the go-to guy for navigating the Lone Star State's unique legal landscape. He's helped thousands of investors get these specialized transactions right, so you don't end up with an "Amityville Horror" on your hands. If you're eyeing those sweet, low-interest mortgages and distressed borrowers, this episode is your official "Don't Screw It Up" guide!In this episode, you'll learn:Demystifying Texas Creative Financing (Alan's Way!): Alan cuts through the jargon, explaining why he calls everything a "wrap" (even assumptions and sub-to deals!) from a legal standpoint, and why "sub-to" is a term he strategically avoids. Learn the core concept: it's "just seller financing" where the existing mortgage stays, and the seller dons a new "lender" hat.Bulletproof Contracting for Texas Wraps & Assumptions: Discover Alan's ingenious "math word problem" solution for drafting contracts that account for fluctuating payoff amounts in assumptions, bypassing Paragraph 3 headaches. For wraps, it's as simple as standard seller finance! Plus, get the crucial "disclose, disclose, disclose" mantra to avoid those pesky investor amnesia cases years down the road.Taming the Due-on-Sale Beast with Trusts: Unpack the infamous due-on-sale clause—what it means, why lenders usually don't call it (but sometimes do!), and how Alan's proprietary "due on sale trust" structure leverages the Garn-St. Germain Act for protection. You'll hear about specific lenders (looking at you, Home Loan Servicing!) that raise flags and why downloading generic trust forms is a bad idea.Non-Negotiable Insurance & Legal Compliance: This is HUGE. Alan reveals the #1 reason wraps fail: incorrect insurance. Learn the exact structure (seller as additional insured, not just interest; lender as mortgagee clause) and why you must use a proven provider. Plus, understand the critical legal compliance points for Texas: RMLO requirements, the 5.016 disclosure, and the "no balloons, no ARMs" rule for baseline compliance.Pro-Tips for a Smooth Ride (and Avoiding Foreclosure): Get actionable advice for managing your deals: conference calls with sellers for lender contact, the strategic use of Power of Attorney for checks, and Alan's "6% down" rule of thumb to mitigate default risk. He also stresses the importance of continuous communication with all parties to ensure smooth sailing and happy campers.This episode with Alan Ceshker is an absolute masterclass in navigating the legal and operational intricacies of Texas creative financing. He's not just talking theory; he's giving you the battle-tested strategies to build a robust portfolio and avoid painful (and costly) mistakes. So, stop drawing deals on napkins, reach out to Alan's team, and get ready to close some rock-solid transactions! Go out, take some action, and we'll see you at the top!Watch the Original VIDEO HERE!Connect with Alan's Team HERE!Book a Call With Scott HERE!Sign up for the next FREE One-Day Note Class HERE!Sign up for the WCN Membership HERE!Sign up for the next Note Buying For Dummies Workshop HERE!Love the show? Subscribe, rate, review, and share!Here's How »Join the Note Closers Show community today:WeCloseNotes.comThe Note Closers Show FacebookThe Note Closers Show TwitterScott Carson LinkedInThe Note Closers Show YouTubeThe Note Closers Show VimeoThe Note Closers Show InstagramWe Close Notes PinterestBook a call with Scott today at HTTP://TalkWithScottCarson.com to see if 1:1 Note Coaching is right for you!

Wednesday // Pastor Ed Romero // Second London Baptist Confession of Faith Chapter 26

It's Jam's birthday! To find out the first song they selected, go to Paragraph 2 (P2). To skip ahead to the ranking music, go to P5. P2: It was "Yule Shoot Your Eye Out" by Fall Out Boy! For their second selection, they give Ian two options -- past or future. If you think he should choose "future", go to P3. If you prefer "past", go to P4. P3: Ian chose the same thing! And the song was, unfortunately, "Feels Like Christmas" by Panic! At the Disco. All that's left is learning the ranking music -- go to P5! P4: Oh no! Ian didn't choose this option. Also, you slipped on an icy patch and fell down a well. Oops!!! P5: The ranking music in this episode is "Yule Shoot Your Eye Out" as performed by Skatune Network. Yay, you did it!

Pastor David preaches on 1 Peter 1:3-7.(V3) THE CAUSE OF NEW BIRTHJohn 3:3, 5,Ezekiel 36:25-27, Titus 3:5, John 1:12-13, Ephesians 2:12-13,Romans 6:4,Philippians 3:8-11 (NASB),Colossians 3:1-4 (CSB)(VV4-5) AN IMPERISHABLE AND SECURE INHERITANCE1 Peter 1:23, Deuteronomy 12:9-11 (NASB),Joshua 11:23,1 Corinthians 15:42-45, 53, Romans 8:17, Ephesians 1:11-14,John 10:27-29, Romans 8:30,Jude 24Second London Baptist Confession, Chapter 17, Paragraph 1b(VV6-7) (AND) THE REJOICING OF SUFFERING SAINTS2 Corinthians 4:17-18,James 1:2-3

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Hey friends, and welcome back to the College Essay Guy podcast.  Today's episode is the third and final episode in our series called Inside the Personal Statement Process. If you're just tuning in, this series takes you behind the scenes as I work one-on-one with Alisha, a current high school senior applying for the Fall 2026 term. Find Part 1 here and Part 2 here.  This episode was recorded just a few days before Alisha submitted her early applications. The focus of this episode is on one of the most challenging—and perhaps, the least discussed—aspect of writing the personal statement: insight.  In the session, Alisha and I explore:  What is insight?  How do you find good insights?  What are the kinds of questions that can lead to insights? And more Whether you're a student working on your own essays right now, a parent supporting from the sidelines, or a counselor guiding students through this process, I hope you'll find something useful here. Alisha is a current high school senior going through the application process who loves science, movies, and discovering new places. When she's not studying the brain, she's mentoring younger students through her program Running Start or planning her next adventure. Hope you enjoy our session.   Play-by-Play: 1:16 – It's just days before her Early Decision deadline. How's Alisha feeling? 2:21 – What is "insight," and why does it matter in an essay? 3:30 – Alisha begins reading her latest draft, Wherever the Road Takes Us 4:08 – Ethan gives his thoughts on Alisha's intro 5:37 – Paragraph one: Curiosity 11:04 – Paragraph two: Creativity 11:52 – How does art make Alisha a better scientist? 14:20 – Paragraph three: Empathy 21:20 – Alisha uses the Values Exercise to identify potential new insights  28:19 – Paragraph four: Community 32:47 – Paragraph five: Conclusion and the "empty jar." 35:53 – Ethan recaps final notes and next steps 39:50 – Alisha and Ethan reflects on the writing process and Alisha's growth  41:34 – Closing thoughts    Resources: Inside the Personal Statement Process (Part 1): The Sand Essay with Alisha, HS Senior Inside the Personal Statement Process (Part 2): Discovering Values Through Revision with Alisha, HS Senior  The Values Exercise College Essay Guy's Personal Statement Resources College Essay Guy's College Application Hub  

Wednesday // Pastor Ed Romero // Second London Baptist Confession of Faith Chapter 26

Hey friends, and welcome back to the College Essay Guy podcast. Today's episode is part two of our series called Inside the Personal Statement Process. If you're just tuning in, this series takes you behind the scenes as I work one-on-one with Alisha, a current high school senior applying for the Fall 2026 term. In the first episode, we got to know Alisha through her brainstorming and outline. In this episode, we pick up right where we left off — Alisha's second draft. We get into: How did the new outline work for Alisha? How to approach trimming—not just words, but how Alisha can focus on a particular idea or value in the session to help her find her focus and what to trim How to align the insights that you have in your paragraphs with the examples And more Whether you're a student working on your own essays right now, a parent supporting from the sidelines, or a counselor guiding students through this process, I hope you'll find something useful here. Alisha is a current high school senior going through the application process who loves science, movies, and discovering new places. When she's not studying the brain, she's mentoring younger students through her program Running Start or planning her next adventure. Hope you enjoy our session. Play-by-Play: 1:24 – How is Alisha's writing coming along? 4:10 – Alisha shares her goals for feedback 5:50 – Alisha reads through her second draft 11:13 – Alisha shares her thoughts on the draft 15:10 – Ethan makes suggestions for trimming by focusing on values that Alisha wants to communicate to the reader 16:40 – Paragraph one: Curiosity 23:10 – Paragraph two: Empathy and nurturing 32:50 – Paragraph three: Creativity. 39:50 – Paragraph four: Pakistan. 46:40 – Ethan and Alisha map next steps for Draft 3 49:22 – Alisha shares new revision ideas and closing thoughts Resources: Inside the Personal Statement Process (Part 1): The Sand Essay with Alisha, HS Senior How to Cut Down Words in Your College Essay How to Write the Columbia University Supplemental Essays: Examples + Guide 2025/2026 College Essay Guy's Personal Statement Resources College Essay Guy's College Application Hub

Daniel Handler's sardonic sense of humor and deep pathos have engaged readers across genres for over twenty-five years. Handler's best known for his series of children's books A Series of Unfortunate Events under the pen name Lemony Snicket).  His books published under his own name include Why We Broke Up, We Are Pirates), and the memoir, And Then? And Then? What Else? which has just been published in paperback. Andrew Sean Greer's six works of fiction include the bestsellers The Story of a Marriage, The Confessions of Max Tivoli, Less (which earned him the Pulitzer Prize), and Less is Lost. On October 8, 2025, Daniel Handler and Andrew Sean Greer took to the stage of the Sydney Goldstein Theater in San Francisco, for a program they call “Paragraphs on Ice!” in which they dissect paragraphs written by other notable authors. It was a lesson in the art of writing – and the art of close reading. 

On this very special episode, we share some of the most important individual paragraphs of writing in our lives - paragraphs that have planted acorns in our brains that have grown over time into mighty oaks. We revisit Thomas Mann, Christopher Hitchens, Jean-Luc Godard, and other luminaries. PLUS: Checking in on Bari Weiss at CBS. Join us on Patreon for an extra episode every week - https://www.patreon.com/michaelandus Our classic Leonard Cohen episode - https://www.patreon.com/posts/186-new-skin-for-44083560 Past Essential Paragraphs discussions: https://www.patreon.com/posts/bonus-essential-28574337 https://www.patreon.com/posts/bonus-essential-36315183 The tweet that Luke cites in the opening discussion - https://x.com/mattpolprof/status/1976069728541245770