Podcasts about Somali

The Left continues fighting against voter IDs by claiming they're racist and misogynistic. All but one Democrat voted against the SAVE Act, despite the fact that most Americans support it. Rep. Greg Steube (R-Fla.) discusses his newly introduced bill that would end the H-1B visa program. Border czar Tom Homan announced the full withdrawal of ICE from Minneapolis. Meanwhile, Attorney General Pam Bondi gets embarrassed by Democrats during her testimony. Minnesota Attorney General Keith Ellison gets exposed in a hearing before the Senate regarding his connection to Minnesota's Somali fraud scheme. ► Watch my full documentary on how I exposed H-1B visa scams here: https://youtu.be/9sfeESywMUs?si=23qLeBI8neFymdFu ► Subscribe to my second YouTube channel: https://www.youtube.com/@SaraGonzalesTX?sub_confirmation=1 ► Read my H-1B op-ed here: https://www.theblaze.com/columns/opinion/america-should-eliminate-the-h-1b-and-replace-it-with-this ► Read about our investigation at Blaze News: https://www.theblaze.com/news/where-are-all-the-workers-blazetvs-sara-gonzales-exposes-potential-h-1b-visa-fraud-in-texas ► Email me at saratips@blazemedia.com if you have uncovered potential fraud in your area. Sponsors: ► BlazeTV Join BlazeTV today at BlazeTV.com/sara and get $20 off right now https://www.blazetv.com/sara. ► Share the Arrows 2026 Get your tickets for Share the Arrows 2026 at https://www.sharethearrows.com before they sellout! Timestamps: 00:00 – Dems Fighting Against Voter ID 20:53 – Rep. Greg Steube: Ending H-1B Visas 31:00 – ICE Retreating from Minnesota 39:12 – Pam Bondi Embarrassing Hearing 41:46 – Keith Ellison Gets Exposed Connect with Sara on Social Media: https://twitter.com/saragonzalestx https://www.instagram.com/saragonzalestx http://facebook.com/SaraGonzalesTX ► Subscribe on Apple Podcasts https://podcasts.apple.com/us/podcast/sara-gonzales-unfiltered/id1408958605 ► Shop American Beauty by Sara: http://americanbeautybysara.com Sara Gonzales is the host of Sara Gonzales Unfiltered, a daily news program on Blaze TV. Joined by frequent contributors & guests such as Chad Prather, Eric July, John Doyle, Jaco Booyens, Sara breaks down the latest news in politics and culture. She previously hosted "The News and Why It Matters," featuring notable guests such as Glenn Beck, Ben Shapiro, Dave Rubin, Michael Knowles, Candace Owens, Michael Malice, and more. As a conservative commentator, Sara frequently calls out the Democrats for their hypocrisy, the mainstream media for their misinformation, feminists for their toxicity, and also focuses on pro-life issues, culture, gender issues, health care, the Second Amendment, and passing conservative values to the next generation. Sara also appears as a recurring guest on the Megyn Kelly Show, The Sean Spicer Show, Tim Pool, and with Jesse Kelly on The First TV. Learn more about your ad choices. Visit megaphone.fm/adchoices

A mass shooting took place in Canada, killing at least eight. The alleged gunman, who identified as transgender, was also found dead on the scene. The media refused to accept that a transgender person would carry out this kind of atrocity. A new study shows that H-1B workers earn 16% less than native-born workers on average, which allows companies to important cheap labor. The Federal Aviation Administration temporarily shut down the airspace over El Paso over security concerns from cartel drones. Rep. Ilhan Omar (D-Minn.) threatens President Trump, suggesting that Trump should be executed after he says he will pursue Somali fraud cases in Minnesota. Justin Haskins and Adam Johnson join the show. ► Watch my full documentary on how I exposed H-1B visa scams here: https://youtu.be/9sfeESywMUs?si=23qLeBI8neFymdFu ► Subscribe to my second YouTube channel: https://www.youtube.com/@SaraGonzalesTX?sub_confirmation=1 ► Read my H-1B op-ed here: https://www.theblaze.com/columns/opinion/america-should-eliminate-the-h-1b-and-replace-it-with-this ► Read about our investigation at Blaze News: https://www.theblaze.com/news/where-are-all-the-workers-blazetvs-sara-gonzales-exposes-potential-h-1b-visa-fraud-in-texas ► Email me at saratips@blazemedia.com if you have uncovered potential fraud in your area. Sponsors: ► BetterWild BetterWild is offering our listeners up to 40% off your order at https://www.BetterWild.com/SARA. ► BlazeTV Join BlazeTV today at BlazeTV.com/sara and get $20 off right now https://www.blazetv.com/sara. Timestamps: 00:00 – Another Trans Mass Shooter? 13:15 – H-1B Study 20:52 – Fake Degrees in India 23:28 – Cartel Drones In Us Airspace 38:15 – Ilhan Omar Threatens Trump Connect with Sara on Social Media: https://twitter.com/saragonzalestx https://www.instagram.com/saragonzalestx http://facebook.com/SaraGonzalesTX ► Subscribe on Apple Podcasts https://podcasts.apple.com/us/podcast/sara-gonzales-unfiltered/id1408958605 ► Shop American Beauty by Sara: http://americanbeautybysara.com Sara Gonzales is the host of Sara Gonzales Unfiltered, a daily news program on Blaze TV. Joined by frequent contributors & guests such as Chad Prather, Eric July, John Doyle, Jaco Booyens, Sara breaks down the latest news in politics and culture. She previously hosted "The News and Why It Matters," featuring notable guests such as Glenn Beck, Ben Shapiro, Dave Rubin, Michael Knowles, Candace Owens, Michael Malice, and more. As a conservative commentator, Sara frequently calls out the Democrats for their hypocrisy, the mainstream media for their misinformation, feminists for their toxicity, and also focuses on pro-life issues, culture, gender issues, health care, the Second Amendment, and passing conservative values to the next generation. Sara also appears as a recurring guest on the Megyn Kelly Show, The Sean Spicer Show, Tim Pool, and with Jesse Kelly on The First TV. Learn more about your ad choices. Visit megaphone.fm/adchoices

Don't wait until it's too late — see what's going on with gold today. Go to https://www.protectwithposo.com or call (844) 577-POSO now. They'll donate 1% of qualified investments from my audience to Turning Point USA or another great America-First organization — in addition to the 1% you already qualify for. Protect your future — and stand with me, fighting for America.Go to https://www.BlackoutCoffee.com/POSO and use promo code POSO for 20% OFF your first order.Support the show

-Rob Carson gleefully recounts Josh Hawley's verbal colonoscopy on Keith Ellison, exposing Minnesota's massive fraud schemes where suitcases of cash vanished faster than a Somali buddy's conscience. -Rob blasts Democrats for suddenly caring about Epstein files after ignoring them for years, suggesting they only dust them off when they need a distraction from their own "next thing" meltdowns. -Wrapping with Olympic drama, Carson roasts Eileen Gu for ditching the U.S. to ski for China, and pities a Norwegian bi-athlete who confesses cheating on his girlfriend live on TV, proving even gold medals can't fix relationship downhill slides. Today's podcast is sponsored by : RELIEF FACTOR - You don't need to live with aches & pains! Reduce muscle & joint inflammation and live a pain-free life by visiting http://ReliefFactor.com  QUINCE CLOTHING - Refresh your wardrobe with Quince.  Go to http://Quince.com/NEWSMAX for free shipping on your order and 365-day returns. BIRCH GOLD - Protect and grow your retirement savings with gold. Text ROB to 98 98 98 for your FREE information kit! To call in and speak with Rob Carson live on the show, dial 1-800-922-6680 between the hours of 12 Noon and 3:00 pm Eastern Time Monday through Friday…E-mail Rob Carson at : RobCarsonShow@gmail.com Musical parodies provided by Jim Gossett (http://patreon.com/JimGossettComedy) Listen to Newsmax LIVE and see our entire podcast lineup at http://Newsmax.com/Listen Make the switch to NEWSMAX today! Get your 15 day free trial of NEWSMAX+ at http://NewsmaxPlus.com Looking for NEWSMAX caps, tees, mugs & more? Check out the Newsmax merchandise shop at : http://nws.mx/shop Follow NEWSMAX on Social Media:  -Facebook: http://nws.mx/FB  -X/Twitter: http://nws.mx/twitter -Instagram: http://nws.mx/IG -YouTube: https://youtube.com/NewsmaxTV -Rumble: https://rumble.com/c/NewsmaxTV -TRUTH Social: https://truthsocial.com/@NEWSMAX -GETTR: https://gettr.com/user/newsmax -Threads: http://threads.net/@NEWSMAX  -Telegram: http://t.me/newsmax  -BlueSky: https://bsky.app/profile/newsmax.com -Parler: http://app.parler.com/newsmax Learn more about your ad choices. Visit megaphone.fm/adchoices

In a new series, Minnesota Now is airing the voices of people who have been hiding in their homes due to fear of arrest, detention, or deportation during Minnesota's ICE surge. We asked them to send us voice memos for three days in a row, describing how they are spending their days. A Somali immigrant who we are calling Z told us about her days in hiding. She has been working a nonprofit job from her apartment, which she shares with a beloved cat. MPR News is not using her full name for her safety. This series will continue on Minnesota Now on Thursday and next week. Listen at noon to hear more voices or check our podcast feed.

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]:

On today's episode, Leo Dickenson and Josh Christianson join Josiah to discuss Operation Metro Surge and the fraud crisis that the Trump regime has used to justify the occupation of the Twin Cities. Become a Fruitless Patron here: https://www.patreon.com/user?u=11922141Check out Fruitless on YouTubeFind more of Josiah's work: https://linktr.ee/josiahwsuttonFollow Josiah on Twitter @josiahwsuttonReferencesThumbnail was taken by photojournalist Jeff Wheeler for the Minnesota Star Tribune"How Minnesota became a hub for Somali immigrants in the U.S.," Joe Hernandez, NPR, https://www.npr.org/2025/12/03/nx-s1-5631809/somali-immigrants-minnesota-twin-cities-trump-ilhan-omar"Key figures in the long-running controversy over alleged fraudulent safety net programs in Minnesota," Ray Sanchez and Cheri Mossburg, CNN, https://www.cnn.com/2026/01/02/us/fraud-minnesota-programs-scandal-trump"Everything we know about Minnesota's massive fraud schemes," Jonah Kaplan and Joe Walsh, CBS News, https://www.cbsnews.com/amp/news/minnesota-fraud-schemes-what-we-know/"Minnesota investigators say child care centers accused of fraud are operating normally as governor drops reelection bid," Zoe Sottile and Andy Rose, CNN, https://www.cnn.com/2026/01/05/us/minnesota-child-care-fraud-investigation"Federal agents probe fraud allegations targeting Somali child care providers in Minnesota," Nick Schifrin and Jonah Anderson, PBS, https://www.pbs.org/newshour/show/federal-agents-probe-fraud-allegations-targeting-somali-child-care-providers-in-minnesota"School districts could face funding gaps due to virtual learning, missing students during Operation Metro Surge," Gordon Severson, KARE 11, https://www.kare11.com/article/news/local/ice-in-minnesota/school-districts-could-face-funding-gaps-due-to-virtual-learning-missing-students-during-operation-metro-surge/89-11755d76-3b77-4fed-a2d3-f766e332131b"Death raises new fraud allegations in Minnesota's Medicaid-funded ICS program," A.J. Lagoe, Kelly Dietz, Steven Eckert, Gary Knox, KARE 11, https://www.kare11.com/article/news/investigations/kare-11-investigates-unattended-death-raises-new-fraud-allegations-minnesota-medicaid-funded-ics-program/89-61240443-b8c1-4a60-969b-0b601c5fbd5d“The Largest Funder of Al-Shabaab Is the Minnesota Taxpayer,” Ryan Thorpe and Christopher Rufo, City Journal, https://www.city-journal.org/article/minnesota-welfare-fraud-somalia-al-shabaab"Key source says story that prompted Trump tirade against Somalis is erroneous," Deena Winter, The Minnesota Star Tribune, https://www.startribune.com/city-journal-fraud-story-seattle-detective/601537870"Who is Nick Shirley, the 23-year-old MAGA journalist whose Minnesota fraud story went viral?," Hadas Gold, CNN, https://www.cnn.com/2025/12/30/media/nick-shirley-minnesota-somali-video"Fact check: What's really happening with child care fraud in Minnesota," Chabeli Carrazana, The 19th, https://19thnews.org/2026/01/child-care-fraud-minnesota-fact-check/"ICE Reportedly Stole a 10th Grader's Phone, Then Seemingly Sold It for Cash," Frank Landymore, https://www.yahoo.com/news/articles/ice-reportedly-stole-10th-grader-230138675.html"Immigration officers assert sweeping power to enter homes without a judge's warrant, memo says," Rebecca Santana, Associated Press, https://apnews.com/article/ice-arrests-warrants-minneapolis-trump-00d0ab0338e82341fd91b160758aeb2d"ICE plans $100 million 'wartime recruitment' push targeting gun shows, military fans for hires," Washington Post, https://www.washingtonpost.com/technology/2025/12/31/ice-wartime-recruitment-push/NSPM-7, https://www.whitehouse.gov/presidential-actions/2025/09/countering-domestic-terrorism-and-organized-political-violence/Timeline on Wikipedia, https://en.wikipedia.org/wiki/Operation_Metro_Surge#Timeline_of_operation"Minneapolis Pastor Says He Was Detained by ICE After Joining Protest, Told 'You're White' and 'Wouldn't Be Any Fun Anyway' on Release," Ashley Vega, People, https://people.com/minneapolis-pastor-says-ice-detained-him-after-joining-protest-told-he-wouldnt-be-any-fun-because-youre-white-11884703"ICE entered Minneapolis hospital without warrant, handcuffed patient to bed, community organizers say," Jason Rantala, CBS News, https://www.cbsnews.com/minnesota/news/hcmc-hennepin-healthcare-ice-patient-handcuffed/"Ecuador says ICE tried to enter its Minneapolis consulate — here's what happened," BBC, https://www.bbc.com/news/videos/cvgpdx1yr15o"Walz: ICE may leave MN soon," Fox 9 KMSP, https://www.fox9.com/video/fmc-ch876hzml20j4brmAudio creditsOpening audio is from several videos of community members at the scene of Alex Pretti's murder a few hours later; videos shared by Left Voice on Bluesky:  https://bsky.app/profile/leftvoice.bsky.social/post/3md6zzb4mzk2rDay Without End - Protomartyr"ICE operations underway in Minneapolis,"

Angus Taylor oo ah xildhibaan ka tirsan xisbiga Liberal-ka ayaa iska casiley safka hore ee xisbiga si uu hoggaanka ugula tartamo hoggaamiyaha hadda, Susan Ley. Afartan iyo siddeedda saacadood ee soo socda ayaa la filayaa codbixinta.

0:30 - Person of Interest in Guthrie case released 15:19 - Who answered the question best? 32:01 - JB’s new substack 52:25 - Republican state representative from Illinois’ 87th District and a physician, Bill Hauter, raises concerns over new health misinformation bill. For more on Bill’s work for the 87th district rephauter.com 01:09:56 - Former White House aide Garret Ziegler weighs in on Catherine Herridge’s claims she was stonewalled at CBS over the Hunter Biden laptop story. Garret is also the founder of Marco Polo, a nonprofit research group with a mission of exposing corruption & blackmail 01:29:18 - Licensed Private Detective & former Chicago policeman, Paul Huebl, breaks down the investigation into Nancy Guthrie’s kidnapping 01:47:51 - Founder of JunkScience.com and former Trump EPA transition team member Steve Milloy on the administration’s move to scrap the scientific basis for federal climate regulations. 02:05:23 - US Congressman from Texas’ 26th Congressional District, Brandon Gill, discussing his recently proposed bill to pause Somali immigration for 25 years. Follow Rep Gill on X @realBrandonGillSee omnystudio.com/listener for privacy information.

A subcommittee of the U.S. Senate judiciary committee held a hearing Tuesday titled “Somali Fraud in Minnesota – The Tip of the Iceberg.”It's likely a reference to a former federal prosecutor's assessment that there was widespread fraud in state programs. President Donald Trump has also attacked the state's Somali communities with a series of sweeping and racist comments, using fraud as the reason for a surge of immigration agents in Minnesota. University of Minnesota Professor Eric Schwartz spoke out about the impact of the surge on immigrants in Tuesday's hearing and spoke with host Nina Moini about why he decided to do so.

İkili Görüş'te Emrullah Özdemir ve İlkan Dalkuç, Dr. Gökhan Çınkara ile Epstein dosyalarının açıklanmasının Ortadoğu'ya etkisini, İran'a olası ABD-İsrail müdahalesini ve Suudi Arabistan ile Birleşik Arap Emirlikleri arasında gerilen ilişkilerin Türkiye'ye etkisini tartışıyor.Çınkara'nın önerdiği Anthropic CEO'sunun sitesi: https://www.darioamodei.com/00:00 Giriş00:50 Bu bölümde neleri konuşacağız?02:00 ABD'yi yöneten üçlüde (askeri-sanayi kompleks, iş insanları ve elitler) Epstein nereye düşer?08:50 Epstein şaibeli bir şekilde ortaya çıkıyor: Başlangıçta bu kadar parayı nasıl kazandı?10:00 2008'den sonra hala Epstein'la ilişkisini sürdürenler, yeni ilişki kuranlar default şaibeli (evet, o da)11:50 Epstein'ın illegal ilişkileri dışındaki legal ilişkileri de gerçekten çok "ilginç"14:10 Noam Chomsky'nin Epstein'le ne işi vardı? Fail mi mağdur mu? (Homo sum, humani...)18:15 ABD'de Trump'ın partisinden olup önemli iki Trump karşıtının Epstein dosyalarının açıklanmasındaki rolüne dair21:55 Elitlerin savaşı yaklaşıyor: Steve Bannon, Peter Thiel, J. D. Vance27:30 Tech bro'lar Bill Gates'i gözden çıkardı, daha da sert vuracaklar30:50 Palantir ve Anthropic'in politika, niyet farkı32:30 2026 ABD ara seçimi Cumhuriyetçiler için iç açıcı görünmüyor36:30 2026 ABD ara seçiminde Demokrat Parti ne yapacak (DP dalgalanmadan durulmaz)38:40 İran'da kitlesel öldürümler İran'ın güç gösterisi değil zafiyetidir46:30 Ekonomik kriz İran rejimine darbelerini sıklaştıracak49:00 Küba pamuk ipliğine bağlı50:30 Biden İran'a yaptırımlara göz yumuyordu ama Trump arka kapı, nefes alanı bırakmadı53:30 Trump Körfez ülkelerinin kamplaşmasında taraf tutmuyor58:30 Türkiye'nin Suudi Arabistan ile BAE meselesinde şu haklıdır deme lüksü yok01:01:10 İngiltere'nin Arap ülkeleriyle, Epstein ile ilişkisi 01:03:20 Beğenmesek de Netanyahu "akıllı" adam, usta bir spinner01:04:40 Türkiye Libya'ya F-16 yollamamıştı ama Somali'ye yolladı. Bu ne anlama geliyor?01:08:50 İsrail neden Somaliland'ın ayrılmasını istiyor?01:10:10 Türkiye'nin Afrika açılımında Somali ve Etiyopya'nın önemi01:12:40 Dış politika, "hariciyeci"lere bırakılamayacak denli farklı hale gelecek (güvenlik ticareti)Ayrıcalıklardan yararlanmak için bu kanala KATIL:https://www.youtube.com/channel/UCWyDy24AfZX8ZoHFjm6sJkg/joinBizi Patreon'dan Destekleyin

Muftiga Guud ee Muslimiinta Australia ayaa ku baaqay baaritaan hufan oo madax-bannaan oo lagu sameeyo hab-dhaqankii booliiska New South Wales, kaddib mudaharaadkii ka dhacay Sydney oo looga soo horjeeday booqashada Madaxweynaha Israa'iil Isaac Herzog.

The mainstream media is trying to gaslight the public into thinking Bad Bunny's Super Bowl halftime show was good, and they refuse to acknowledge that Turning Point USA's halftime show ended up being a massive success. Athletes competing for the U.S. in the Winter Olympics are openly disrespecting America and have no country pride. Why are they representing our country if they hate it so much? Texas state Rep. Gene Wu and Texas Rep. Jasmine Crockett prove once again why the Democrats are shameless race-baiters who don't even hide their racist agendas anymore. Rep. Brandon Gill (R-Texas) joins the show to discuss the fight against mass immigration and his new bill that would ban Somali immigration for 25 years. ► Subscribe to my second YouTube channel: https://www.youtube.com/@SaraGonzalesTX?sub_confirmation=1 Sponsors: ► Patriot Mobile Call 972-PATRIOT today or go to https://www.patriotmobile.com/partners/sara and use promo code SARA for a FREE month of service. ► BlazeTV Join BlazeTV today at BlazeTV.com/sara and get $20 off right now https://www.blazetv.com/sara. Timestamps: 00:00 – TPUSA Halftime Show 08:49 – Bad Bunny's Worst Moments 20:15 – US Olympians Disrespect America 31:26 – Dems as Racist as Ever 36:57 – Rep. Brandon Gill on Immigration Connect with Sara on Social Media: https://twitter.com/saragonzalestx https://www.instagram.com/saragonzalestx http://facebook.com/SaraGonzalesTX ► Subscribe on Apple Podcasts https://podcasts.apple.com/us/podcast/sara-gonzales-unfiltered/id1408958605 ► Shop American Beauty by Sara: http://americanbeautybysara.com Sara Gonzales is the host of Sara Gonzales Unfiltered, a daily news program on Blaze TV. Joined by frequent contributors & guests such as Chad Prather, Eric July, John Doyle, Jaco Booyens, Sara breaks down the latest news in politics and culture. She previously hosted "The News and Why It Matters," featuring notable guests such as Glenn Beck, Ben Shapiro, Dave Rubin, Michael Knowles, Candace Owens, Michael Malice, and more. As a conservative commentator, Sara frequently calls out the Democrats for their hypocrisy, the mainstream media for their misinformation, feminists for their toxicity, and also focuses on pro-life issues, culture, gender issues, health care, the Second Amendment, and passing conservative values to the next generation. Sara also appears as a recurring guest on the Megyn Kelly Show, The Sean Spicer Show, Tim Pool, and with Jesse Kelly on The First TV. Learn more about your ad choices. Visit megaphone.fm/adchoices

Premier-ka NSW Chris Minns ayaa difaacay tallaabadii ay booliisku ka qaadeen mudaharaad ka dhacay Sydney, kaas oo lagaga soo horjeeday booqashada madaxweynaha Israa'iil, Isaac Herzog. Dibadbaxayaal isugu soo baxay Sydney Town Hall-ka Sydney, si ay u cambaareeyaan booqashada Herzog ayaa la sheegay inay booliisku ku buufiyeen basbaaska indhaha (pepper spray), rii-riixeen, qaarna garaaceen.

Ifrah F. Ahmed is a Somali-born, Brooklyn-based writer, chef, and artist whose work centers around food, history, culture, memory, and migration. For years, she's been sharing Somali food through her pop-up Milk and Myrrh and as a contributor to New York Times Cooking—and now, she's releasing a gorgeous debut cookbook, Soomaaliya. It's a pleasure to have Ifrah on the show to talk about the joys and challenges of translating an oral tradition into a cookbook, the rich legacy and influences of Somali cuisine, and much more.  And at the top of the show, Aliza talks about my recent trip to Tokyo, covering many stops along the way including Ramen Jazzy Beats, Nasu Oyaji, Laekker, and the Mori Art Museum. Subscribe to This Is TASTE: ⁠⁠Apple Podcasts⁠⁠, ⁠⁠Spotify⁠⁠, ⁠⁠YouTube⁠⁠  Learn more about your ad choices. Visit megaphone.fm/adchoices

Laura is joined by Ayub (Yubi) Hassan of Blue Horn Tea and Bilal Madani of Crème Foods as they share their experiences as entrepreneurs navigating the social and economic landscape of Minneapolis. They discuss the deep connection between cultural heritage and community resilience, highlighting how Somali tea and Basque cheesecake serve as bridges for unity…

In this episode, we break down a powerful lyric from a song by Farhiya Fiska - well known singer in the Somali community. * Adigaa dhagax tuur bilaabo - You started throwing stones Dhaawacay qalbigaygooWounded my heart Dhakhtarkaygii wuxuu yidhi My doctor said War nin kuu dhaama raadso.Find a better man.Until next time, take care!!

Halkan ka dhegeyso barnaamijkii Jimcaha 6 February 2026 oo dhammaystiran, xayaysiikiina laga reebay

Premierka New South Wales oo deggenaan iyo isxakemeyn ugu baaqan debed-baxayaasha ka dib imaatinkii madaxweynaha Israel, Isac Herzog. Xildhibaan soo jeediyey in gubidda calanka Australia, kuwa Aboriginal iyo Torres Strait Islander laga dhigo dembi la isku xiri karo.

Special Guest Host Andrew Wilson is joined by Ian, Sean, & Rob Noerr to discuss leftists freaking out after Trump posts a clip of Obama depicted as a monkey, DOJ announcing the've charged over 150 people for impeding & assaulting federal agents, and the IRS targeting banks over the uncovered Somali fraud in Minnesota.   Hosts:  Andrew Wilson @PaleoChristCon (X) |  @The_Crucible  (YouTube) Phil @PhilThatRemains (X) Ian @IanCrossland (everywhere) | https://graphene.movie/ Producer: Serge @SergeDotCom (everywhere) Guest: Rob Noerr @robnoerr (X)

Leonarda Jonie Is the funniest female comic to ever to it! She comes back on the podcast to talk and joke about all the crazy things going on in the world; ICE, Israel, Somali's Epstein, All of it! It's A DAMN GOOD Show!Become a Member and Give Us Some DAMN GOOD Support :https://www.youtube.com/channel/UCX8lCshQmMN0dUc0JmQYDdg/joinGet your Twins merch and have a chance to win our Damn Good Giveaways! - https://officialhodgetwins.com/Get Optimal Human, your all in one daily nutritional supplement - https://optimalhuman.com/Want to be a guest on the Twins Pod? Contact us at bookings@twinspod.comDownload Free Twins Pod Content - https://drive.google.com/drive/folders/1_iNb2RYwHUisypEjkrbZ3nFoBK8k60COFollow Hodgwtins Podcast Everywhere -X - https://x.com/hodgetwinspodInstagram - https://www.instagram.com/hodgetwinspodcast/Facebook - https://www.facebook.com/thehodgetwinsYouTube - https://www.youtube.com/@HodgetwinsPodcastRumble - https://rumble.com/c/HodgetwinsPodcast?e9s=src_v1_cmdSpotify - https://open.spotify.com/show/79BWPxHPWnijyl4lf8vWVuApple - https://podcasts.apple.com/us/podcast/hodgetwins-podcast/id1731232810

Ilhan Omar's political career has always relied on two things: grievance amplification and bloc manipulation. The only thing that changed is the branding. Where the traditional Black Leftist playbook once dominated, Omar localized it, substituting Somali identity politics into the same grievance machine. It worked, until it didn't. Her power was predicated on a national Democratic regime that no longer exists in the form she needed.When Biden collapsed under his own party's weight and Kamala Harris lost, Omar's ladder snapped. That matters because her long-term strategy wasn't Congress. It was inevitability. A coalition presidency. A moral shield built from identity politics so thick that questions themselves could be framed as crimes.That future died quietly. And when futures die, politicians panic.Now place that panic next to Georgia.See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

1. SAVE America Act (Election Law & Voter ID) Core Argument The SAVE America Act would: Require proof of U.S. citizenship to register to vote Require photo ID to vote Democrats are portrayed as unanimously opposed, preventing passage due to the Senate’s 60‑vote threshold. Strategic Claim Cruz argues Republicans should: Force a “real” filibuster (continuous floor speeches) Make Democratic opposition politically and physically costly He frames Democratic resistance as intentional rather than procedural. Public Opinion Framing Polling is cited (CNN, Pew) to claim: Broad bipartisan and multiracial support for voter ID Democratic leadership (especially Chuck Schumer) is accused of ignoring their own voters. 2. Voter Fraud & Ballot Harvesting Claims Presented Ballot harvesting is described as: A system that enables fraud, especially among elderly or vulnerable populations Democrats are accused of: Supporting policies that increase fraud opportunities Reversing recommendations from the Carter–Baker Commission There is justification for: Photo ID laws Restrictions on mail-in voting Limits on third-party ballot collection 3. Somali Welfare Fraud in Minnesota Central Allegation Massive welfare fraud in Minnesota tied to programs serving the Somali immigrant community. Figures cited include: Up to half of $18 billion in welfare spending allegedly lost to fraud Disproportionately high welfare participation rates among Somali households Democratic state officials are accused of: Knowing about the fraud Allowing it to continue for political gain Silencing whistleblowers Stolen welfare funds indirectly finance al‑Shabab, a terrorist organization 4. Medical Policy Shift on Gender Surgeries for Minors Key Development The American Society of Plastic Surgeons and the American Medical Association are described as: Reversing prior support for “gender‑affirming surgeries” for minors Now recommending deferral until adulthood Causal Explanation The reversal is attributed to: A high‑profile malpractice lawsuit by a detransitioner Legal and financial risk to medical institution Please Hit Subscribe to this podcast Right Now. Also Please Subscribe to the 47 Morning Update with Ben Ferguson and The Ben Ferguson Show Podcast Wherever You get You're Podcasts. And don't forget to follow the show on Social Media so you never miss a moment! Thanks for Listening YouTube: https://www.youtube.com/@VerdictwithTedCruz/ Facebook: https://www.facebook.com/verdictwithtedcruz X: https://x.com/tedcruz X: https://x.com/benfergusonshowYouTube: https://www.youtube.com/@VerdictwithTedCruzSee omnystudio.com/listener for privacy information.

See omnystudio.com/listener for privacy information.

Darrell Castle talks about what is going on in Minneapolis as well as the state of Minnesota — what is going on there, does anyone care, and what about fraud. Transcription / Notes MINNESOTA IS A STRANGE PLACE Hello, this is Darrell Castle with today's Castle Report. This is Friday the 6th day of February in the year of our Lord 2026. Although I am a little late for the party I will be talking about what is going on in Minneapolis as well as the state of Minnesota. What in the world is going on there and does anyone care about the violence and death, and also what about the fraud. Was a 37-year-old ICU nurse really a threat to the ICE agents in Minnesota? Do Americans who film such agents as they try to do their jobs belong on government lists. Should the presence of ICE agents in Minnesota be eliminated since the politicians and the people apparently are violently opposed to their presence. The answers to these questions, unfortunately, almost always depend on one's political party affiliation. Republicans back ICE in its deportation efforts while Democrats stand with Alex Pretti. Sometimes these positions require members of both parties to abandon the principles they once held dear. The struggle this time around has often centered on the real culprit, the ruling class. Everything seems like a diversion or distraction. When you see the Minnesota politicians parading around talking about the demonization of the Somali community one might wonder if they are just trying to distract attention away from the billions in fraud from which they allegedly received hefty amounts. When the administration doubles down on its hardline response by sending in more and more agents even after people have been killed one wonders if this is in reality an effort to distract us from the coming stupid pointless war against Iran. Yes the U.S. sent another carrier battle group into the Persian Gulf as an obvious threat to attack at the moment of Trump's or Netanyahu's choosing. Yes, Bibi has or soon will be coming to America for the 6th time in the Trump presidency perhaps to deliver to the president his strike options for the coming attack on Iran. A diversion for our attention is therefore a welcome side benefit thanks to the violence of the protesters in Minneapolis. I can't help but wonder why the president has made no effort to explain to the American people why an attack on Iran is in their best interest. He seems to go from one reason to another as one justification is erased, another takes its place. He first gave us the nuclear threat or weapons of mass destruction we have heard many times before. Then, we are told the nuclear enrichment sites were obliterated in a previous American attack. Then CIA, Mossad, and who knows how many others, allegedly launched a violent protest movement directed at regime change. When the Iranian regime put down the uprising with extreme violence that became another excuse for an attack. The sides are talking now about a way to avoid war but I get the impression it is simply a brief delay for some reason or another. A similar delay is going on in Minnesota as the sides are talking and Mr. Homan has agreed to withdraw 700 agents. Could the agreement be this, look Governor Waltz if you will stop inciting this violence and let us at least arrest the murderers and rapists among you then you can keep the millions you stole from the American people. I don't know if that is true but it sounds plausible to me. What about fraud? Let's talk about that for a moment. The fraud conducted by the “Somali community” escorted into the country by Barack Obama and Joe Biden and grouped together in Minnesota so they could have political power and even elect their members to congress to lecture us about racism. We are expected to concentrate on the seeming insanity of the ICE protesters and ICE agents instead of the money and where it goes. The last figures I have seen reveal that Somali welfare fraud in Minnesota is estimated at $9 billion. That is $9 billion of Medicaid and other welfare stolen from people who work and from people who need the help and shuffled off to various fraudulent enterprises run by the Somali community. That is just the amount found to have been stolen in Minnesota. It's predicted to be even worse in California, Illinois, Maine, New York, and perhaps other Democrat states. Who knows how many billions of dollars of your labor have been stolen. Well, Elon Musk says he knows the various forms of fraudulent payments given away by the federal government amount to $1.5 and $2 trillion per year which is about 25% of annual government spending and which coincides almost exactly with the deficit. In other words, if these figures are correct that $2 trillion the government spends over what it takes in is all fraud. Almost all this fraud goes to perpetuate various deep state programs and to line the pockets of Democrat voters and especially their politicians. That's why they fight so hard to make us ignore it and to detract us from investigating it. Now, let me say a few words about the killing of Alex Pretti by ICE agents in Minneapolis. Mr. Pretti apparently was killed when the agents first disarmed him of his legally possessed handgun, then threw him to the ground and shot him in the back many times. The object he was waving in his hand was a cell phone and to say they thought it was a gun made no sense because he had been disarmed by them. It does remind one a little of the bad old days of Democrat presidents when conservatives feared the Feds rather than supporting them. I'm thinking specifically of the incident in which Vicki Weaver had her head blown off by an FBI sniper named Lon Horiuchi while she stood in her cabin door holding her child. Agents also killed the Weaver's 14-year-old son by shooting him in the back. I knew Randy Weaver quite well back then as we had met at many speaking engagements and he was often at gun shows where I campaigned for office. The government today says there is no comparison with Vickie and Alex Pretti because she was unarmed. The truth is that Vickie had a .380 handgun in a holster under her dress just as Pretti had a legal gun in his possession. Once again they, the ruling elite that is, distract us from the real enemy. I've seen and heard Mr. Pretti described as a beloved ICU nurse and a peaceful man. I have no argument with the beloved description since I have no evidence to dispute that but the peaceful part I definitely dispute. To me, he appeared to be a man so fanatical in his system of beliefs that he was out of control and violently so. He was a nurse, which is a profession of the most noble among all if it is practiced correctly. Who can't tell stories of heroic nurses sitting with the dying or helping one through some of the most trying moments of life. Well, there was another nurse whose name goes unmentioned, but not for me. I say her name and I hope her parents are not offended by that but I feel compelled to speak for her because she really was peaceful. Her name was Laken Riley and she was peacefully jogging on her Georgia college campus when she was assaulted, raped, and murdered by an illegal criminal invited into this country by Joe Biden. Why, I ask, do none of the protesters care about Laken. She goes unmentioned by Democrat politicians and their protesters because I guess she just doesn't fit their political agenda. Laken's benevolence, her altruistic pursuits, her many accomplishments, her hopes and dreams count for nothing because she did not meet the Democrat politician's definition of someone they should care about. Sadly, Laken went out for a jog and she didn't come back the same fate has happened to literally dozens of women and girls at the hands of criminals whose cages were opened by Joe Biden. Just to name a few Sarah Root, Rachel Morin, Jocelyn Nungaray but there have been countless others. Jocelyn is of particular importance to me but I don't say much about her because she was a 12 year of child when she was very brutally murdered by five of Joe Biden's criminal invaders. No one speaks for her I suppose she is an embarrassment to the entire spectrum of what the ruling elite are trying to accomplish in this country and this world. That's probably enough about the women who have been murdered but there is one more and very recently. In Illinois recently a man named Joe Abraham has gained attention because he has had the audacity to speak for his 20-year-old daughter Katie, killed while stopped at a traffic light by a drunk illegal named Julio Cucol-Bol. Mr. Bol fled the scene but Katie was killed instantly. At least she didn't have to endure the torture that Jocelyn did. This case reminds me of that most appealing argument that to deport criminals would be forced separation of families. Well, the Abraham, Riley, and Nungaray families among countless others were certainly separated. None of these women are ever spoken of by Democrats and there have been no protests or demonstrations objecting to the government's policy of inviting violent criminals to walk among us. I suggest that when we see and hear the violent protests about ICE we try to remember the real victims who for Democrat politicians and their supporters remain nameless but for their families the loss is never ending. In Conclusion, I want to conclude by recommending a book that was just released and which I had to get an early copy to read. Peter Schweizer's “The Invisible Coup, How American Elites and Foreign Powers Use Migration as a Weapon.” That book opens for you the war that has been prosecuted against us by people who hate us and intend to destroy us, and unfortunately that often includes our leaders. Finally, folks, what in the world does this all mean? That's a topic for another day perhaps next week, we'll see. Remember the nameless fallen ones and pray for peace here and abroad. At least that's the way I see it, Until next time folks, This is Darrell Castle, Thanks for listening.

Hour 2 of the Chris Hand Show | Friday 02-06-26 In this episode, we're diving into the dark side of Minnesota's childcare system, where a massive fraud scheme has been uncovered. David Hawk shares his personal journey of exposing the truth behind the Somali-owned childcare facilities in Minneapolis, where millions of dollars in taxpayer funds were being embezzled. He talks about the red flags he noticed, from missing footprints in the snow to misspelled signage, and how he began to investigate further. This conversation gets to the heart of corruption, complicity, and the need for accountability in government and politics.See omnystudio.com/listener for privacy information.

Guddoomiyaha xisbiga Labour-ka oo ka digay xisbiga One Nation oo sii xoogeysanaya iyo Ra'iisal-wasaare Albanese oo heshiis amni maanta la saxeexan doona dhiggiisa Indonisia.

With the House set to vote on the Voter ID SAVE Act next week, the most incredible story of the day belongs to Minneapolis fraud whistleblower David Hoch. He testified before the Senate Judiciary Committee on the Somali fraud that was allowed to happen and festered for years under the noses of knowing political leaders. Don't miss a word of his powerful opening statement imploring senators to fix the problem. Fulton County democrats sue to get their ballots back. 

Welcome to The Daily Wrap Up, an in-depth investigatory show dedicated to bringing you the most relevant independent news, as we see it, from the last 24 hours (2/5/26). As always, take the information discussed in the video below and research it for yourself, and come to your own conclusions. Anyone telling you what the truth is, or claiming they have the answer, is likely leading you astray, for one reason or another. Stay Vigilant. !function(r,u,m,b,l,e){r._Rumble=b,r[b]||(r[b]=function(){(r[b]._=r[b]._||[]).push(arguments);if(r[b]._.length==1){l=u.createElement(m),e=u.getElementsByTagName(m)[0],l.async=1,l.src="https://rumble.com/embedJS/u2q643"+(arguments[1].video?'.'+arguments[1].video:'')+"/?url="+encodeURIComponent(location.href)+"&args="+encodeURIComponent(JSON.stringify([].slice.apply(arguments))),e.parentNode.insertBefore(l,e)}})}(window, document, "script", "Rumble");   Rumble("play", {"video":"v735zgs","div":"rumble_v735zgs"}); Video Source Links (In Chronological Order): (7) The Last American Vagabond on X: "So all this hype about Somali daycare fraud only for the Republicans to largely allow this to continue. The government is not on your side. It actively plays us against ourselves so nothing changes. #TwoPartyIllusion" / X (7) Grok on X: "@MaryBowdenMD @RepThomasMassie The House Rules Committee voted 8-4 to report the rule for H.R. 7148 without making Massie's daycare amendment in order, effectively blocking it. Yea voters: Michelle Fischbach (R-MN) Ralph Norman (R-SC) Chip Roy (R-TX) Nick Langworthy (R-NY) Austin Scott (R-GA) Morgan Griffith" / X (7) James O'Keefe on X: "BREAKING: FBI Official Admits Kash Patel WILL NOT Arrest ANY Minnesota Daycare Criminals. https://t.co/7WR6SdaGOx" / X (13) Lexi on X: "@FBIDirectorKash https://t.co/HsUbj0Jfcf" / X (13) Christopher Raymond on X: "@marvomago @michaeljknowles It's so bizarre that MAGA has invented a standard where all protests must be comprised only of people who, through no communication or coordination with other people, all somehow happen to show up at the same place at the same time. Newsflash - protests are coordinated" / X New Tab (13) Scott Horton on X: "-Iran has an "unalienable right" to a civilian nuclear program as members of the NPT -US has no authority to insist on limits to their missiles' ranges, no pretended UNSC resolution or anything, none can reach America -Hezbollah is Israel's problem, not the USA's. They kill AQ" / X (13) Rapid Response 47 on X: ""Should the Supreme Leader in Iran be worried right now?" @POTUS: "I would say he should be very worried, yeah, he should be." https://t.co/kMQTzx61V1" / X (13) Daniel McAdams on X: "AIPAC thanking @JDVance is not the win Vance may think it is. They've just alienated almost every under-40 Republican voter for 2028. @AIPAC is no longer a force-multiplier you want on your side and out front. It is a political liability you want to remain in the background." / X (13) The Last American Vagabond on X: "Trump: "Look, that country is a mess right now because of us", referring to Iran. Yeah man, we know. But please keep telling us it's Iran's "mismanagement".

Minnesotans are experiencing the ICE surge in the state in many ways. One Minneapolis artist took the spotlight thrust on Minnesota's Somali American community from President Trump as an opportunity to share what it is to be a part of that community.MPR News host Kelly Gordon spoke with Ifrah Mansour to explain her perspective on the last few months and how her community is standing strong.

Dawlada Federaalka ayaa la rumeysan yahay inay ka fiirsaneyso in la beddelo ama la tirtiro cashuur-dhimis horey u jirtey oo la yiraahdo (Capital Gains Tax) si loo xalliyo u-sinnaan la'aanta guryaha ee jiilasha kala duwan, iyo in la xakameeyo sicir-bararka. Cashuur-dhimistan CGT, iyo sidoo kale nidaamka negative gearing, waxaa aad loogu eedeeyaa, inay ka mid yihiin sababaha kordhiya qiimaha, sidaana ku adkeeya helitaankooda.

Guest Host George Santos is joined by Phil, Ian, Sean & Jennifer Lawrence to discuss ICE arresting an illegal immigrant police officer in New Orleans, New Yorkers slamming Mamdani over snow & dog poop piling up on NY streets, & James O'Keefe exposing an FBI official saying Somali fraud will go unpunished.   Hosts:  George Santos @GeorgeSantos (X) Phil @PhilThatRemains (X) Ian @IanCrossland (everywhere) | https://graphene.movie/ Sean @TimcastNews (X) Producer: Serge @SergeDotCom (everywhere) Guest: Jennifer Lawrence @JenLawrence21 (X) | https://rumble.com/c/AmericaMission1776

Dana Loesch reacts to the highlights of the DOJ's release of 3 Million Epstein Files including Bill and Hillary Clinton being deposed before Congress. Who are the creeps?! Liberals occupy a target to sing “We are Somali” as a form of protest against ICE in Minnesota. Dana breaks down how pundits are misleading you with commentary when using the Special Election in Texas State District 9 as a bellwether for the Trump Administration. A Boston-area woman is thrilled after taking in a Haitian migrant and says it's like having your own personal chefDana reacts to Judge Jeanine Pirro's viral comments about sending Americans to JAIL for confiscating guns in Washington D.C., Dana reacts to Country Superstar Jelly Roll declining to comment on ICE amid growing outcry as well as the Billie Eilish outrage about “stolen land”.Dana shares an update on Midterm Elections and how Republicans need to transition into a new political strategy. Trump hints at wanting the Federal Government to run local elections. Ryan Routh sentenced to life in prison for 2024 attempt on Trump's life.Thank you for supporting our sponsors that make The Dana Show possible…CovePurehttps://CovePure.com/DanaImprove your health with clean water this year. Get $200 off for a limited time.Relief Factorhttps://ReliefFactor.com OR CALL 1-800-4-RELIEFTry Relief Factor's 3-week Quickstart for just $19.95—tell them Dana sent you and see if you can be next to control your pain!Patriot Mobilehttps://PatriotMobile.com/DANA or call 972-PATRIOTSwitch to Patriot Mobile in minutes—keep your number and phone or upgrade, then take a stand today with promo code DANA for a free phone!Humannhttps://HumanN.comSet yourself up with simple, delicious wellness support—pick up Humann's Turmeric Chews at Sam's Club next time you're there and see why they're such a fan favorite!WebRootTake your cybersecurity seriously! Get 60% off Webroot Total Protection at https://Webroot.com/Dana  Noble Goldhttps://NobleGoldInvestments.com/DanaThis is the year to create a more stable financial future.  Open a qualified account with Noble Gold and receive a 3 oz Silver Virtue coin free.Subscribe today and stay in the loop on all things news with The Dana Show. Follow us here for more daily clips, updates, and commentary:YoutubeFacebookInstagramXMore InfoWebsite

Hoggaamiyaha mucaaradka heer federaal, Susan Ley, ayaa sheegtay inay labada xisbi ee Liberal iyo National dib u midoobi doonaan. Dowladda Qatar-na waxay sheegtay inay ku howlan yihiin qaboojinta xiisadda colaaadeed ee ka dhex taagan Maraykanka iyo Iran.

TDC Podcast topics - the blue eyed midget and Amy are here today, the Grammy's was filled with nonsense from people like Billie Eilish claiming "no one is illegal on stolen land", Trump threatens to sue CBS and Trevor Noah after the host claimed Trump was on Epstein Island, new Epstein email dump is huge and has a lot of people sweating like newly hired CBS health expert Peter Attia, NY Giants owner Steve Tisch and more, new allegations against the Somali pirate "bananas and rice" girl, The Today Show host Savannah Guthrie's mom has apparently been kidnapped from her home in Arizona, Chuck Schumer wants to shut the government down if the GOP dares to try and secure our elections and much more.

In this “Kim on a Whim,” Kim exposes a massive hospice fraud scandal in Los Angeles County that dwarfs even the Somali daycare scam in Minnesota. With over 1,900 fake hospice providers—some operating out of strip malls and auto shops—scammers are tricking elderly Californians into signing away their Medicare in exchange for false promises of care. Marc and Kim blast the exploitation of seniors, the government's blind eye, and the moral rot behind a $3.5 billion grift bleeding taxpayers dry. Hashtags: #KimOnAWhim #CaliforniaFraud #HospiceScam #MedicareAbuse #ElderlyExploitation #HealthcareCorruption #MarcCoxMorningShow

Amy Klobuchar wants to be governor of Minnesota, but whistleblower David Hoch says she's known about the rampant Somali fraud in the state for years - because he's been telling her. President Trump Truths a Christina Bobb 2021 OAN report on election fraud in Pennsylvania and Arizona. Is fraud in PA the next bomb to drop? Chris Cuomo proves the mainstream media cannot be rehabilitated. 11 Senate Republicans join with democrats to continue funding a USAID offshoot whose CEO just just admitted to scamming.   

Today on the show, we dive into Real ID fines, digital surveillance, illegal immigration, and political chaos

Josh Hammer addresses the anti-ICE protests in Minnesota while underscoring that the underlying Somali fraud scandal is driving the broader political fallout. He breaks down the Texas special election upset that flipped a seat to Democrats and assesses what Republicans should take away as November nears. As Second Amendment concerns come back into focus following the death of Alex Pretti, Josh argues that conservative voters expect clear leadership, not silence. The program concludes with an analysis of a significant "detransitioner" malpractice ruling and its potential impact on transgender medical practices going forward.See omnystudio.com/listener for privacy information.

AOTR NOLA *Brought to you by Coin Trader Inc*Coin Trader Inc. - Visit www.goldpricesnow.comHosted by Victor Del Giorno "The King Of All Podcasting"Co-hosts Ted Semper- Nick VoebelIt's Season 8!-Somali Special-Willam Wallis from the Mardi Gras DC -Michael DelGiorno national talk show hostSupport the show (https://www.allovertheroadpod.com/)  https://linktr.ee/allovertheroadpodcastShare your story at the 24 hour listener comment line:  504-356-1062 ALL OVER THE ROAD - Originates in New Orleans, LA...Support the show

Boortz talks about the Somali fraud in Minneapolis and how afraid people are of being called racist for calling out the fraud and talking young black males being able to get away with crime.See omnystudio.com/listener for privacy information.

Rep. Jim Walsh of Washington State joins us with his concerns about Gov. Ferguson and AG Brown's comments about ICE, Somali fraud, and legislation they support that will foment violence, similar to Minneapolis. Tune in to find out how he says Thelma and Louise fit in!

In the two months Minnesota has been under siege by federal agents, immigration officers have shot and killed two U.S. citizens, poet and artist Renee Good and ICU nurse Alex Pretti. Local and state law enforcement say they've been blocked from properly investigating the shootings of Good and Pretti. “The federal government has blocked our state BCA, so that's the Bureau of Criminal Apprehension. They are the state law enforcement agency that has authority to investigate any kind of deadly use of force involving police,” says Hennepin County Attorney Mary Moriarty, who is leading local investigations into the killings of Good and Pretti. “We've not gotten anything from the federal government,” Moriarty says. “To tell you how odd this situation is, we are getting our information from the media ... we are not getting that from the federal government.” This week on The Intercept Briefing, host Akela Lacy speaks with Moriarty, whose office has jurisdiction over both killings. Moriarty says federal agents have blocked local and state law enforcement from properly investigating the killings. Even Moriarty, the top prosecutor in Minneapolis, does not know the identity of the agents who killed Pretti. In response, Moriarty says, “We set up a portal and asked the community to send any kind of videos or any other kind of evidence so that we could collect absolutely everything that we possibly could.” The BCA, she says, was even “blocked physically, actually, by federal agents from processing the scene where Alex Pretti was shot.”Meanwhile, attacks by the administration on Minnesota's Somali citizens persist. At her first town hall of the year in Minneapolis, an attendee sprayed Rep. Ilhan Omar with an unidentified substance on Tuesday. Trump has backtracked on some of his bluster and removed Border Patrol Gregory Bovino from Minnesota, replacing him with border czar Tom Homan. None of that has changed things on the ground yet in Minneapolis, says Moriarty. “Minnesotans care about their neighbors. They're delivering meals to people. They are there and they do not approve of the fact that their federal government is attacking them and their neighbors.“We hear a lot of people talking to us about how they understand the threat from the administration or from DHS on their neighbors and on their communities, and it's really much more rooted in an understanding that they think their freedoms are under threat, even if they are not an immigrant or even if they don't really have deep ties to immigrant communities, that this really matters to them and it really bothers them,” says Jill Garvey, co-director of States at the Core, an organization that leads and runs ICE Watch training programs. “So we hear a lot from folks who just haven't been engaged previously. But this for all those reasons is enough for them to step up.”Garvey says her organization is training community members in how to properly document ICE. “We also know that we can't stop all this aggression,” Garvey says. “The aggression is the point of these operations. So we can't guarantee that people aren't going to be targeted with violent actions from federal law enforcement. What we can say is, if you're doing this in community, other people are going to be watching.”Listen to the full conversation of The Intercept Briefing on Apple Podcasts, Spotify, or wherever you listen. If you want to support our work, you can go to theintercept.com/join. Hosted on Acast. See acast.com/privacy for more information.

This is a free preview of a paid episode. To hear more, visit andrewsullivan.substack.comJon and I go way back to the early days of the marriage movement and before. He's currently a senior fellow at Brookings and a contributor editor at The Atlantic. He's written many landmark books, including Kindly Inquisitors, The Constitution of Knowledge (which we discussed on the pod in 2021), and Cross Purposes (which we covered last year). His new essay in The Atlantic, “Yes, It's Fascism,” is a must-read.And this episode is, if you don't mind me saying so, a must-listen. One of the best conversations I've yet had on the Dishcast. Jon is always lucid and fair and thereby chilling.For two clips of our convo — on the glorification of violence by Trump and his officials, and the cowardice of mainstream conservatives — head to our YouTube page.Other topics: Trump smashing norms; his vile indecency; his early rallies; reveling in war crimes; suing everyone; the “mean tweets” defense; cultural degeneracy in America; the need for party gatekeeping; blood-and-soil nationalism; Plato on tyrants; Stephen Miller's “iron laws”; the Zelensky meeting and “having no cards”; the assassination attempt on Trump; the reprehensible Randy Fine; ICE using white nationalist anthems to recruit; anonymous masked agents; the Pretti and Good killings; the racial element of ICE roundups; the Somali fraud scandal; the over-politicization of DoJ; the two legal systems under the Nazis; Carl Schmitt; the blanket pardon for all Jan 6-ers; Vance meeting with AfD; Heritage Americans; birthright citizenship; Greenland; Venezuela; Christian nationalism; evangelical loyalty to Trump; his Board of Peace; the vandalism of DOGE; Vought's evil genius; the East Wing demolition; violent threats against moderate Republicans; the woke playing right into Trump's hands; and fears that he will manipulate the midterms.Browse the Dishcast archive for an episode you might enjoy. Coming up: Jason Willick on trade and conservatism, Zaid Jilani on the Dems, Derek Thompson on abundance, Tiffany Jenkins on privacy, and Michael Pollan on consciousness. Please send any guest recs, dissents, and other comments to dish@andrewsullivan.com.

Steve reacts to the day's news: border czar Tom Homan speaks in Minnesota, the "bananas and rice" Somali woman gets arrested, and Tucker Carlson makes a puzzling statement in Saudi Arabia. In Hour Two, Katy Faust joins the program to talk about a new effort she is joining called "Greater Than" and to explain why the overturning of Obergefell is so needed (and possible). TODAY'S SPONSORS: JASE MEDICAL: https://jasemedical.com/ and enter code “DEACE” at checkout for a discount on your order COWBOY COLOSTRUM: https://cowboycolostrum.com/?discount=deace&utm_source=podcast&utm_medium=podcast&utm_campaign=deace CHIRP: https://gochirp.com/pages/steve-deace use promo code STEVE Learn more about your ad choices. Visit megaphone.fm/adchoices

Today On The Eric Metaxas Show, Eric talks with Jack Posobiec about the Minneapolis unrest, ICE operations, and whether Trump is escalating or pulling back after a high profile phone call. They break down sanctuary city defiance, claims of coordinated agitators and Antifa spotters, and the leverage angle involving Somali fraud rings and taxpayer money. And they dig into corporate power in the Twin Cities, GOP leadership that wants to wait Trump out, and why they frame this moment as an existential fight for America. Subscribe for clips from The Eric Metaxas Show to hear politics and culture from a Christian perspective.

Democrats are conducting a mass propaganda campaign against ICE and mass deportations. It may not be working on you, but it is working on weak Republicans. Jesse Kelly discusses this with Mike Cernovich. Plus, Dustin Grage joins the show with an update on Somali fraud and immigration crackdowns in Minnesota. I'm Right with Jesse Kelly on The First TV American Financing: Call American Financing today to find out how customers are saving an average of $800/mo. NMLS 182334, https://nmlsconsumeraccess.org APR for rates in the 5s start at 6.196% for well qualified borrowers. Call 866-891-2821 for details about credit costs and terms. Visit http://www.AmericanFinancing.net/Jesse. Choq: Visit https://choq.com/jessetv for a 17.76% discount on your CHOQ subscription for life PureTalk: Save on wireless with PureTalk—get unlimited talk, text, and data for just $25 a month, plus 50% off your first month at https://PureTalk.com/JESSETVFollow The Jesse Kelly Show on YouTube: https://www.youtube.com/@TheJesseKellyShowSee omnystudio.com/listener for privacy information.

Welcome to Impact Theory with Tom Bilyeu. In today's episode, Tom and co-host Drew dive headfirst into a rapidly shifting global landscape that feels like "everything, everywhere, all at once." Against a backdrop of escalating unrest in Minneapolis following the ICE shooting of Alex Pretti, Tom and Drew unpack the fragile state of world order—from National Guard deployments and violent protests in the U.S., to economic instability in Japan and rumors of a failed coup in China's military. You'll hear sharp analysis on how intertwined global economics, politics, and social forces are driving chaos both at home and abroad. The discussion explores not only the tragic events in Minnesota, but also the deep-rooted ideological battles playing out across America, the shifting power dynamics in China, and the potential for dramatic military action in Iran. Tom and Drew break down complex issues like the ethics of resistance, the role of local government in federal conflicts, and the underlying economic moves shaping the future. If you're seeking clarity in tumultuous times and want to understand the mechanisms powering today's most urgent news stories, this episode of Impact Theory is essential listening. Tune in for thoughtful conversation that encourages critical thinking, first principles analysis, and a focus on actionable solutions for navigating uncertainty. Quince: Free shipping and 365-day returns at ⁠https://quince.com/impactpod⁠ HomeServe: Help protect your home systems – and your wallet – with HomeServe against covered repairs. Plans start at just $4.99 a month at ⁠https://homeserve.com⁠ Shopify: Sign up for your one-dollar-per-month trial period at ⁠https://shopify.com/impact⁠ Incogni: Take your personal data back with Incogni! Use code IMPACT at the link below and get 60% off an annual plan: ⁠https://incogni.com/impact⁠ Sintra AI: 72% off with code IMPACT at ⁠https://sintra.ai/impact⁠ Huel: High-Protein Starter Kit 20% off for new customers at ⁠https://huel.com/impact⁠ code impact Bevel Health: Visit ⁠https://bevel.health/impact⁠ and use code IMPACT to get your first month free. Ketone IQ: Visit ⁠https://ketone.com/IMPACT⁠ for 30% OFF your subscription order Cape: 33% off your first 6 months with code IMPACT at ⁠https://cape.co/impact⁠ Plaud: Get 10% off with code TOM10 at ⁠https://plaud.ai/tom⁠ Pique: 20% off at ⁠https://piquelife.com/impact⁠ What's up, everybody? It's Tom Bilyeu here: If you want my help... STARTING a business:⁠ join me here at ZERO TO FOUNDER⁠:  ⁠https://tombilyeu.com/zero-to-founder?utm_campaign=Podcast%20Offer&utm_source=podca[%E2%80%A6]d%20end%20of%20show&utm_content=podcast%20ad%20end%20of%20show⁠ SCALING a business:⁠ see if you qualify here.⁠:  ⁠https://tombilyeu.com/call⁠ Get my battle-tested strategies and insights delivered weekly to your inbox:⁠ sign up here.⁠: ⁠https://tombilyeu.com/⁠ ********************************************************************** If you're serious about leveling up your life, I urge you to check out my new podcast,⁠ Tom Bilyeu's Mindset Playbook⁠ —a goldmine of my most impactful episodes on mindset, business, and health. Trust me, your future self will thank you. ********************************************************************** FOLLOW TOM: Instagram:⁠ https://www.instagram.com/tombilyeu/⁠ Tik Tok:⁠ https://www.tiktok.com/@tombilyeu?lang=en⁠ Twitter:⁠ https://twitter.com/tombilyeu⁠ YouTube:⁠ https://www.youtube.com/@TomBilyeu⁠ Minneapolis ICE shooting, Alex Pretti, National Guard deployment, violent mobs, global trade war, devaluing the dollar, Chinese military purge, Xi Jinping coup rumors, illegal immigrants, law and order, economic instability, Japanese yen, yen carry trade, US treasuries, Minnesota fraud, Somali community organization, voter registration fraud, coordinated resistance, NGOs tactics, Iran military strike group, protests in Iran, regime change, international oil supply Learn more about your ad choices. Visit megaphone.fm/adchoices

It's Hump Day on the Majority Report On today's program: Donald Trump continues his assault on Rep. Ilhan Omar, posting almost daily about her on Truth Social and going on a racist tirade aimed at her and the entire Somali community at a rally in Iowa. Just a few hours after Trump's speech, a man attacks Ilhan Omar at a town hall, spraying her with an unidentified liquid. Publisher of the Labor Politics Newsletter, Eric Blanc joins Sam to discuss how everyday people are uniting to resist ICE's fascist invasion of Minnesota. Rep. Ro Khanna (D-CA) joins Sam to discuss what Congress is doing to stop ICE, his thoughts on the proposed wealth tax in California and more. In the Fun Half: The Prime Minister of Slovakia, a self-proclaimed Trump fan, expressed concern about Trump's cognitive decline after speaking with him in Davos, Switzerland. Trump said that learning the parents of Renee Good — the woman killed by an ICE agent — were "big Trump fans" made him feel even worse about her death. San Antonio Spurs star Victor Wembanyama speaks out against the recent ICE murders but admits he is holding back given he is a foreign worker and is afraid of retribution. Conrad Blackburn announces his DSA endorsed campaign for New York state assembly district 70 in Harlem. all that and more To connect and organize with your local ICE rapid response team visit ICERRT.com The Congress switchboard number is (202) 224-3121. You can use this number to connect with either the U.S. Senate or the House of Representatives. Follow us on TikTok here: https://www.tiktok.com/@majorityreportfm Check us out on Twitch here: https://www.twitch.tv/themajorityreport Find our Rumble stream here: https://rumble.com/user/majorityreport Check out our alt YouTube channel here: https://www.youtube.com/majorityreportlive Gift a Majority Report subscription here: https://fans.fm/majority/gift Subscribe to the AMQuickie newsletter here: https://am-quickie.ghost.io/ Join the Majority Report Discord! https://majoritydiscord.com/ Get all your MR merch at our store: https://shop.majorityreportradio.com/ Get the free Majority Report App!: https://majority.fm/app Go to https://JustCoffee.coop and use coupon code majority to get 10% off your purchase Check out today's sponsors: NUTRAFOL: Get $10 off your first month's subscription + free shipping at Nutrafol.com when you use promo code TMR10 COZY EARTH: Go to cozyearth.com/MAJORITYREPORTBOGO for an exclusive deal only available Jan 25th - Feb 8th! SUNSET LAKE: Use coupon code "Left Is Best" (all one word) for 20% off of your entire order at SunsetLakeCBD.com  Follow the Majority Report crew on Twitter: @SamSeder @EmmaVigeland @MattLech On Instagram: @MrBryanVokey Check out Matt's show, Left Reckoning, on YouTube, and subscribe on Patreon! https://www.patreon.com/leftreckoning Check out Matt Binder's YouTube channel: https://www.youtube.com/mattbinder Subscribe to Brandon's show The Discourse on Patreon! https://www.patreon.com/ExpandTheDiscourse Check out Ava Raiza's music here! https://avaraiza.bandcamp.com

President Trump is making big changes. What should we make of them? Sean Spicer joins Jesse Kelly to discuss, but not before Jesse has an uncomfortable conversation about the future of the Democrat Party. Jesse is also joined by Nick Shirley to discuss what he's learned about Somali fraud in Minnesota. Plus, Priya Patel sounds off on a variety of issues happening across the country. I'm Right with Jesse Kelly on The First TV PureTalk: Save on wireless with PureTalk—get unlimited talk, text, and data for just $25 a month, plus 50% off your first month at https://PureTalk.com/JESSETV Cowboy Colostrum: Get 25% Off Cowboy Colostrum with code JESSETV at https://www.cowboycolostrum.com/JESSETV Choq: Visit https://choq.com/jessetv for a 17.76% discount on your CHOQ subscription for lifeFollow The Jesse Kelly Show on YouTube: https://www.youtube.com/@TheJesseKellyShowSee omnystudio.com/listener for privacy information.