Podcasts about Gan

Ffilm 32 munud, 51 eiliad o hyd a wnaed gan José Alejandro Restrepo ym 1999 yw Paso del Quindío II. Mae'r ffilm yn ail-greu'r ‘carguero' olaf – pobl a oedd yn cario teithwyr Ewropeaidd drwy'r jyngl fynyddig anodd yn wreiddiol. Mae gwaith Restrepo yn ystyried y cysylltiadau pŵer cymhleth sy'n gysylltiedig â'r weithred hon o gario. Yn y ffilm, mae cymeriad gwrywaidd canolog yn dyfalbarhau'n dawel wrth iddo garo menyw ar ei gefn; mae hi'n eistedd ar gadair ‘guadua' (math o fambŵ) ac mae ei gefn ef ar ongl 60 gradd i'r ddaear. Mae'r gadair wedi'i chlymu wrtho â darn o ffabrig wedi'i lapio o dan y sedd ac am ei dalcen chwyslyd. Mae'r dyn canol oed yn berson o liw ac mae ganddo wallt byr du a llecyn moel. Mae'n droednoeth ac yn gwisgo trowsus byr gwyn a chrys T llwyd sy'n colli ei liw, gan ddal ffon bren denau hir er cydbwysedd. Mae'r fenyw sy'n eistedd hefyd yn berson o liw a chanddi wallt du byr ac mae'n gwisgo crys du â darnau patrymog amryliw, trowsus byr ac esgidiau glaw mwdlyd du. Meddai Restrepo, “Mae un o weithgareddau gorau hanes yn ymwneud â phwy sy'n llwyddo i osgoi deddfau cynrychioli a dehongli.” Yn 2007, cafodd arddangosfa Displaced, Contemporary Art from Colombia ei churadu gan María Clara Bernal a Karen MacKinnon a'i chyflwyno yn Oriel Gelf Glynn Vivian. Ochr yn ochr â darnau neilltuol eraill, roedd Paso del Quindío II yn ganolog, oherwydd pŵer ei ymagwedd ôl-drefedigaethol a'i ymgysylltiad â gwleidyddiaeth hunaniaeth ddiwylliannol. Cafodd y gwaith hwn ei brynu drwy gynllun caffael y Gymdeithas Celfyddyd Gyfoes ar ran Oriel Gelf Glynn Vivian yn 2010. Mae José Alejandro Restrepo yn artist o Golombia a aned ym 1959. Astudiodd y celfyddydau gweledol yn Universidad Nacional de Bogotá a'r École des Beaux-Arts ym Mharis ar ddechrau'r 1980au, gan gael ei ysbrydoli gan waith Bill Viola a Gary Hill. Dros y degawdau, mae wedi archwilio deinameg pŵer cynrychiolaeth weledol drwy gyfuno fideos, cerfluniau a'r archif. Mae ei waith yn archwilio hanesion gormes a gwrthsefyll sy'n llunio bywyd yn America Ladin, gan chwilio am ystyron newydd mewn naratifau gosod, a gofyn pa archifau gweledol a ddefnyddir i greu hanes – a pham. Roedd fersiwn gyntaf y gwaith hwn, Paso del Quindío I (Bwlch Quindío I) (1992), yn garreg filltir yn ei yrfa. Gan gynnwys 17 o sgriniau wedi'u trefnu ar lefelau amrywiol adeiledd pyramidaidd, dangosodd y gwaith ddeunydd o heic yr artist i fyny bwlch mynydd yn yr Andes ym 1991. Recordiodd y daith o uchderau gwahanol; mae rhai lluniau'n dangos cyflymder tawel, ond mae eraill yn cyfleu symudiad cynhyrfus rhywun ar y copa. Drwy'r esgyniad hwn, ail-greodd deithiau fforwyr Ewropeaidd y 19eg ganrif fel Alexander von Humboldt a Max von Thielman. Yn wahanol i'r fforwyr hynny, a gyflwynodd eu gwaith o safbwynt gwrthrychol, cydnabu Restrepo, er gwaethaf ei ymchwil hanesyddol a'i brofiad uniongyrchol, mai dim ond dehongliad arall mewn cadwyn hir, llawn “copïau o gopïau”, oedd ei brosiect.

Ffilm 3 munud ac 16 eiliad o hyd a wnaed gan Tim Davies ym 1992 yw Fflagiau Dros Solfach. Mae'r ffilm du a gwyn hon yn dogfennu digwyddiad penodol i safle'r bryniau uwchben Solfach, Sir Benfro, ar draws y cei o'r Gribin. Yng nghanol yr olygfa gymylog wyntog y mae pum polyn fflag metel a phren 15 troedfedd anhygoel o uchder wrth ymyl ei gilydd mewn grŵp. Mae pentref Solfach i'w weld yn y cefndir ar draws y cei. Mae'r fflagiau'n bwrw yn erbyn ei gilydd bob tro y mae'r gwynt yn chwythu. Mae pob fflag ar ffurf triongl cymesur tenau hir ac mae dwy fflag olau ganolog wedi'u hamgylchu gan fflagiau tywyllach ar y naill ochr a'r llall. Mae rhaff ategol yn clymu'r polion fflag yn sownd ychydig yn llai na hanner ffordd tuag at y ddaear, gan greu rhyw fath o dreipod wedi'i glymu yn y tir gan begiau gwyn. Wrth wneud Fflagiau Dros Solfach, roedd Davies wedi creu'r polion fflag fesul rhannau fel y byddent yn barod i'w cydosod yn ôl yr angen, a threuliodd wythnos yn eu rhoi yn nhirwedd Solfach mewn amrywiaeth o gyfluniadau. Ar ôl treulio rhan o'i blentyndod yn Solfach gyda'i dad-cu, mae'n hen gyfarwydd â'r lleoliad. Gan ddefnyddio fflagiau fel tirnod, archwiliodd y syniad fod perchnogaeth tir yn rhywbeth dros dro, gan hawlio safle am hyd pob ymyriad. O arwyddocâd diniwed fflagiau fel tegan i blant sy'n marcio cestyll tywod, sy'n cael eu llyncu gan y llanw, i oblygiadau glanio ar y lleuad, i edifarhau colli perchnogaeth leol ar hunaniaeth ddiwylliannol Cymru, a'r diymadferthedd sy'n deillio o hynny, mae'r gwaith yn cyfeirio at anghydfodau ynghylch perchnogaeth tir a'r gwrthrych fel dynodwr lleoliad. Mae craidd ei waith yn benodol i amser a lleoliad, gan ddefnyddio cyfryngau perfformio dau a thri dimensiwn. Mae ef hefyd yn archwilio'r gair ysgrifenedig, y gair llafar a'r gair ar ffurf delwedd. Ganed Davies yn Hwlffordd, sir Benfro ym 1960. Astudiodd Gelfyddyd Gain yn Ysgol Gelf Norwich a chwblhaodd MA mewn Materion Celfyddyd a Phensaernïaeth ym Mhrifysgol y Celfyddydau Creadigol yng Nghaergaint. Mae ef wedi gweithio mewn ystod o gyfryngau dros y 30 mlynedd diwethaf, gan arddangos a chreu gwaith yng Nghymru, y DU ac yn rhyngwladol. Mae ef wedi ennill llawer o wobrau, gan gynnwys gwobr cystadleuaeth agored Mostyn, y fedal aur mewn Celfyddyd Gain yn yr Eisteddfod Genedlaethol ac un o brif wobrau Cymru Greadigol. Ef oedd yr artist cyntaf o Ewrop i gyrraedd rhestr fer Gwobr Celfyddydau Gweledol Artes Mundi a chynrychiolodd Gymru mewn sioe unigol yn Biennale Fenis yn 2011. Cafodd y ffilm Fflagiau Dros Solfach ei chaffael yn 2025 drwy wobr Wakelin, a roddir bob blwyddyn i artist sy'n byw ac yn gweithio yng Nghymru ac y prynir ei waith ar gyfer casgliad parhaol Oriel Gelf Glynn Vivian.

Lorsqu'une fête juive est terminée, devient-elle un simple événement du passé ? Depuis la faute d'Adam, peut-on retourner au Gan 'Éden ? Qu'est-ce que la vraie joie ? Dans la vie, en quoi est-il important de "digérer" les bonnes choses, au lieu de simplement les accumuler l'une après l'autre ? Réponse à travers des propos du Zohar, de Rav Yérou'ham de Mir et du Gaon de Vilna.

Deportres 5 de Junio 2026 (1344) - www.deportres.comEn el Deportres de hoy: Ganó la selección mexicana, se acabaron los experimentos y el siguiente partido será la inauguración de la copa mundial de fútbol, además, todo el básquet,todo el beis, tu participación y como siempre ¡mucho más! https://www.patreon.com/c/Deportres

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

What does it take to bridge groundbreaking science, clinical medicine and global biotech innovation?Dr. Li-ming Gan is a physician scientist, professor and CEO of Ribocure Pharmaceuticals, where he leads the development of next-generation RNA therapeutics.With more than 20 years of experience from AstraZeneca, where he held senior leadership roles in cardiovascular, renal and metabolism R&D, Li-Ming has been at the forefront of translating scientific discoveries into clinical breakthroughs.Alongside his work in the pharmaceutical industry, he has continued his academic research work as supervisor and professor at the University of Gothenburg and clinical work at Ribocure clinic.Today, through Ribocure, he is helping shape the future of innovative RNA-based therapies on a global scale.In this episode we talk innovation, leadership and how to reach ikigai.Learn more about Ribocure.

Agradece a este podcast tantas horas de entretenimiento y disfruta de episodios exclusivos como éste. ¡Apóyale en iVoox! La historia de la técnica está sembrada de inventores olvidados cuyas creaciones seguimos usando, aunque nadie los recuerde con calles, placas ni estatuas. El caso más conocido es el del teléfono. Todos atribuyen su paternidad a Alexander Graham Bell, que patentó el aparato el 7 de marzo de 1876, pero Antonio Meucci ya había registrado un aviso previo en 1871 para su «telégrafo parlante», un prototipo que puso a funcionar con éxito en su casa de Staten Island y con el que se comunicaba con su esposa inválida. Meucci perdió la prioridad por una nimiedad, los 10 dólares que no pudo pagar para renovar el aviso y convertirlo en una patente. Algo parecido ocurrió con el alambre de espino. Dabb, Smith y Hunt patentaron una serie de diseños en 1867, pero fue Joseph Glidden quien, en 1874, logró fabricarlo en masa adaptando un molinillo de café, fundó la Barb Fence Co. y amasó una fortuna. Su ejemplo demuestra que no basta con tener la idea, también hace falta capital y capacidad industrial para triunfar. La máquina de coser repite el mismo patrón. Walter Hunt la concibió en 1833, pero no la patentó, en parte porque su hija, una ludita, temía por su empleo de costurera. Años después, Elias Howe patentó su propia versión que intentó vender en Inglaterra. No lo consiguió, pero al regresar a EEUU comprobó como le habían robado la patente. Ganó en los tribunales la llamada «Guerra de las máquinas de coser» contra Isaac Singer y el propio Hunt. Howe se hizo rico y le terminaron dedicando un estatua, sellos con su efigie y hasta calles con su nombre. El destornillador de estrella es otro de esos casos en los que el verdadero inventor se ha olvidado. John Thompson lo patentó en 1933, pero cedió los derechos a Henry Phillips, que se enriqueció con los royalties y prestó su nombre al invento. De Thompson apenas sabemos que era mecánico y que murió en 1940, condenado al anonimato pese a haber ideado algo que todos tenemos en casa. Escucha el episodio completo en la app de iVoox, o descubre todo el catálogo de iVoox Originals

Agradece a este podcast tantas horas de entretenimiento y disfruta de episodios exclusivos como éste. ¡Apóyale en iVoox! La historia de la técnica está sembrada de inventores olvidados cuyas creaciones seguimos usando, aunque nadie los recuerde con calles, placas ni estatuas. El caso más conocido es el del teléfono. Todos atribuyen su paternidad a Alexander Graham Bell, que patentó el aparato el 7 de marzo de 1876, pero Antonio Meucci ya había registrado un aviso previo en 1871 para su «telégrafo parlante», un prototipo que puso a funcionar con éxito en su casa de Staten Island y con el que se comunicaba con su esposa inválida. Meucci perdió la prioridad por una nimiedad, los 10 dólares que no pudo pagar para renovar el aviso y convertirlo en una patente. Algo parecido ocurrió con el alambre de espino. Dabb, Smith y Hunt patentaron una serie de diseños en 1867, pero fue Joseph Glidden quien, en 1874, logró fabricarlo en masa adaptando un molinillo de café, fundó la Barb Fence Co. y amasó una fortuna. Su ejemplo demuestra que no basta con tener la idea, también hace falta capital y capacidad industrial para triunfar. La máquina de coser repite el mismo patrón. Walter Hunt la concibió en 1833, pero no la patentó, en parte porque su hija, una ludita, temía por su empleo de costurera. Años después, Elias Howe patentó su propia versión que intentó vender en Inglaterra. No lo consiguió, pero al regresar a EEUU comprobó como le habían robado la patente. Ganó en los tribunales la llamada «Guerra de las máquinas de coser» contra Isaac Singer y el propio Hunt. Howe se hizo rico y le terminaron dedicando un estatua, sellos con su efigie y hasta calles con su nombre. El destornillador de estrella es otro de esos casos en los que el verdadero inventor se ha olvidado. John Thompson lo patentó en 1933, pero cedió los derechos a Henry Phillips, que se enriqueció con los royalties y prestó su nombre al invento. De Thompson apenas sabemos que era mecánico y que murió en 1940, condenado al anonimato pese a haber ideado algo que todos tenemos en casa. Escucha el episodio completo en la app de iVoox, o descubre todo el catálogo de iVoox Originals

Un soldado americano ayudó a planear la captura del presidente de Venezuela. Mientras la planeaba, apostó 34.000 dólares a cuándo iba a ocurrir. Ganó 400.000. Y según el Pentágono, él era el pez pequeño. Hoy en Lágrimas: tres miserias que comparten el mismo hilo. La inteligencia artificial está destruyendo millones de empleos en India y Filipinas. Los trabajadores de call center que durante veinte años grabaron sus conversaciones para entrenar a la IA que ahora hace su trabajo. Los mercados de predicción donde soldados e insiders apuestan sobre guerras que ellos mismos planean, con una tasa de acierto del 98%. Y un reloj de Swatch que provocó gases lacrimógenos en Francia, precios de 19.000 euros en eBay, y un comunicado de la marca diciendo que hay stock ilimitado. En ese orden. Lord Sartán está convaleciente tras una operación de tabique. Irónicamente, en el episodio que va de reemplazos, hay un hueco en la mesa. El Dr. Civeta cubre con sus interestings habituales: el origen del outsourcing en el bug del año 2000, los nuevos títulos de trabajo para desplazados por la IA ("prompt strategist", "AI enabled customer experience specialist"), y el organismo regulador que debería perseguir todo esto dirigido por un señor de 36 años que ha bajado las acciones de enforcement un 66%. Miseria destilada. Para miserieris exigentes. Si has llegado hasta aquí y eres de los/las que quieren miseria 24/7: Aquí tienes miseria en forma de links: https://linktr.ee/lagrimasenlalluvia ¿Quieres recibir los avisos de todos los programas? : https://t.me/lamiseriameencanta ¿Te sientes miserier y quieres conocer a otros carnales? https://t.me/lamiseriameencantabidireccional ¿Te apetece inmortalizar tus miserias? lamiseriameencanta@gmail.com twitter @lagrimas_lluvia twitch: https://www.twitch.tv/lagrimasenlalluvia

Muchos creen que el éxito es trabajar más…tener más dinero… lograr más cosas… Yo también lo creí. Trabajé durante décadas. Gané millones. Perdí millones. Construí empresas. Viví momentos increíbles. Pero hubo algo que entendí demasiado tarde: La paz, la familia, la salud y el tiempo… valen más de lo que imaginamos cuando somos jóvenes. En este video quiero compartirte las lecciones más importantes que aprendí después de los 70 años. Las cosas que hubiera querido entender antes. Y cómo cambié mi vida financiera y personal antes de que fuera demasiado tarde. Si hoy estás trabajando sin descanso… si sientes ansiedad constante… si estás persiguiendo dinero pero perdiendo tranquilidad… este video es para ti.

UGREEN continues to release new and updated accessories to help you stay charged on the move and keep track of all your items' locations. UGREEN Nexode Air 65W Charger The Nexode Air has an ultra-compact and palm-sized design. It is super light for a 65-watt charger as UGREEN has used their proprietary Airpyra stacked architecture to shrink down its size while still delivering high output numbers. Making the Nexode Air charger even more compact is the folding 3-pin plug. The 3 pins fold down completely flat into the base of the charger. The charger comes with one USB-C port on the front of the device. The Nexode Air utilises advanced GaN technology, enabling fast, efficient charging with lower heat and a smaller form factor. Like with all UGREEN charging products, there is a whole host of safety features built in to protect both the charger and the devices you are charging and to prolong the life of the batteries being charged. It is certainly the smallest charger we have come across to date that delivers this type of power, making it a great solution for on-the-go charging. The UGREEN Nexode Air 65W Charger delivers super-compact power on the go and will be available directly from the UGREEN website with an RRP of €34.99. UGREEN FineTrack Series The UGREEN FineTrack Series is a selection of smart trackers, which are an alternative to something like Apple's AirTags. We took a look at the first version of the FineTrack series here last year. UGREEN have updated the line now with some new trackers which focus on longer battery life than the original devices. UGREEN FineTrack Mini 2 Smart Finder The UGREEN FineTrack Mini 2 Smart Finder is a small device, ideal for connecting to something like a set of keys. The tracker comes with a silicone case and keyring connector to protect it from life's rough and tumble and help you attach it appropriately. The FineTrack Mini 2 comes with an inbuilt battery which can last between 5 and 7 years. The device is IP68 rated, which means it can withstand being fully submerged in 2 meters of water for up to 60 minutes. It also comes with a very loud 110dB speaker to help you audibly locate the device if needed. UGREEN FineTrack 2 Smart Finder The FineTrack 2 Smart Finder is a small ball-shaped device which comes with a short lanyard for attaching. The FineTrack 2 Smart Finder has similar specs to the FineTrack Mini 2 Smart Finder, also coming with an internal battery rated for 5 to 7 years of use and a 110dB speaker. The design lends itself towards being attached to a backpack or similar, but its solid design means it will be suitable for many applications. Both of these devices have high-visibility fluorescent accents, which make them stand out in low-light environments. They are both certified to work with Apple's "Find My" service, which means you can benefit from their global network for locating your devices and also get smart reminders on your phone when you leave a device behind. It is really straightforward to add the devices in the Find My App. You just head to "Items", click the "+" symbol and then press the button on the front of the tracker. Once you have given the device a name, you can then track its location from within the app, get directions to where it is located, play a sound to help find it and keep an eye on the battery level. More Info/ Where to Buy? To find out more about either the Nexode Air 65W Charger or the new FineTrack devices, visit the UGREEN website here: https://www.ugreen.com/en-eu The products will be available to pick up from the UGREEN website directly or Amazon this week.

Stāsta Latvijas Mākslas akadēmijas Mākslas vēstures institūta vadošā pētniece un direktore Kristiāna Ābele; pārraides producente – Inta Zēgnere Pirms simt gadiem grāmatā “Latvju rakstniecība portrejās” (sast. Alberts Prande, Rīga: Leta, 1926) iekļauta arī nodaļa “Literatūra par tēlotājām mākslām un celtniecību”, kurā mākslas vēsturnieks Jānis Siliņš (1896–1991) – viens no tolaik nedaudzajiem šīs nozares profesionāļiem Latvijā – uzsvēra: “Parādoties mūsu mākslai kā tautas gara dzīves izteicējai, palēnām mostas ari latviešu mākslas kritikas pirmie pasākumi. Kritiķu speciālistu toreiz nebij un viņu lomu pa lielākai daļai bija jāuzņemas pašiem māksliniekiem. Vēl tagad, kad mākslas kritika un vēsturiski pētījumi jau sāk ievērojami kuplināties, kritiķu-teorētiķu vairums ir mākslinieki.” Apzīmējums “tolaik” te attiecas uz 19. gadsimta beigām un 20. gadsimta pirmajiem gadiem. Studentu pulciņa “Rūķis” lokā izveidojušos nacionālās mākslas celmlaužu paaudzē Janis Rozentāls (1866–1916) bija viens no tiem autoriem, kuru raksti ne tikai ir nozīmīgi kā individuālas radošo uzskatu liecības, bet arī mērķtiecīgi kalpoja sabiedrības izglītošanai par sava laika latviešu, Baltijas un ārzemju mākslas parādībām un personībām. Rozentāla rakstu mantojuma iepazīšanu pirms publikāciju meklējumiem pie vēsturisko žurnālu plauktiem Latvijas Nacionālās bibliotēkas un Misiņa bibliotēkas lasītavās vai tagad visbiežāk Latvijas Nacionālās bibliotēkas veidotajā resursā Periodika.lv interesentiem ieteicams sākt ar monogrāfiskā albuma “Janis Rozentāls” (sast. Aija Brasliņa un Laima Slava, Rīga: Neputns, 2017) bibliogrāfijā iekļauto rādītāju. Ceļvedis vajadzīgs, jo liela daļa viņa rakstu parakstīti tikai ar iniciāli R. un pirmais zināmais 1897. gada beigās Rīgas vācu avīzei Düna-Zeitung iesūtīts bez autora norādes. Šajā apskatā “Divi Baltijas mākslinieki” Rozentāls no Pēterburgas vēstīja par savu draugu Vilhelma Purvīša (1872–1945) un Johana Valtera (1869–1932) diplomdarbiem Ķeizariskās mākslas akadēmijas konkursa izstādē. Toreiz godalgoto Purvīša gleznu “Pēdējie stari” pazīstam tikai pēc reprodukcijām un aprakstiem, savukārt pie Johana Valtera “Tirgus Jelgavā” Latvijas Nacionālajā mākslas muzejā arvien nāk prātā Rozentāla trāpīgie vārdi, piesakot svaigas, modernas un vienlaikus dzimtenes vidē sakņotas mākslas dzimšanu: “Viss tur ir gaisma un dzīvība. Karstā pusdienas saule žilbinoši apstaro gan kustīgo ļaužu drūzmu gleznas vidū, gan ēkas pie tirgus laukuma un slīd pāri divām jaunām elegantām dāmām, kuras gaišos vasaras tērpos priekšplānā soļo pa trotuāru. Kopumā asprātīgi risināts, tas ir gabals no īstas un, proti, Jelgavas dzīves.” Kā lasāms vēstulēs, Rozentālam rūpēja, lai arī latviešu prese rosīgāk pievērstos jaunās tautiešu mākslas popularizēšanai. Iespēja daudz paveikt šī mērķa labā viņam radās Jāņa Veismaņa (1867–1913) vadītajā žurnālā “Vērotājs”, kas iznāca Jelgavā 1903.–1905. gadā kā “mēnešraksts sirds un prāta izglītošanai”. Šajā izdevumā pilnā mērā izpaudās Rozentāla daudzpusība, strādājot gan par žurnāla mākslinieku, kas darina tā vizuālo ietērpu un izvēlas reproducēšanai citu autoru mākslas darbus, gan redakcionāli vadot mākslas nodaļu, ko viņš lielā mērā piepildīja ar saviem rakstiem. Šajā īsajā posmā latviešu lasītāji saņēma ar iniciāli R. parakstītas Rozentāla apceres par viņa laikabiedriem – gan jau mirušajiem Ādamu Alksni (1864–1897) un Arturu Baumani (1867–1904), gan mākslas dzīvē vērienīgi darbīgajiem Vilhelmu Purvīti un Rihardu Zariņu (1869–1939). Ņemot talkā citzemju izdevumus, Rozentāls plaši rakstīja par savai un citu laikabiedru jaunradei būtiskiem ārzemju māksliniekiem – šveiciešu simbolistu Arnoldu Beklīnu (Arnold Böcklin, 1827–1901), amerikāni Džeimsu Abotu Maknīlu Vistleru (James Abbott McNeill Whistler, 1834–1903) un somu Albertu Edelfeltu (Albert Edelfelt, 1854–1905). Vairākos turpinājumos Rozentāla skatījumā atklājās somu un krievu mākslas kopaina. Atsevišķos apskatos viņš komentēja Rīgas Mākslas biedrības rīkotās izstādes tās mākslas salonā un visbeidzot 1905. gada rudenī atklātajā Rīgas pilsētas mākslas muzejā. Šie apcerējumi nebija atturīgi un distancēti parādību vērojumi, bet ļoti līdzdalīgi un dedzīgi vēstījumi, apvienojot informatīvu bagātību ar spilgti subjektīviem vērtējumiem un retoriskiem pārspīlējumiem. Tā, piemēram, draudzīgi zobgalīgu klātbūtnes sajūtu rada apgalvojums, ka pulciņā “Rūķis” “gandrīz vienīgais rīkotājs un vadītājs” pastāvīgi bijis Rihards Zariņš – jaunāko biedru stingrais “vagars”, kas vēlējies “visus sabāzt zem vienas cepures”. Rozentāla mērķis bija uzveikt tautiešu sabiedrības kūtrumu attieksmē pret mākslu. Tūdaļ pēc “Vērotāja” gadiem, kas Rozentālam pie rakstāmgalda bija paši intensīvākie, ļoti nozīmīgs viņa teorētiskajā mantojumā ir 1906.–1907. gadā Ata Ķeniņa (1874–1961), Augusta Saulieša (1869–1933) un Rozentāla kopīgi rediģētajā žurnālā “Zalktis” divās daļās publicētais raksts “Māksla un tehnika”. Tas 2016. gadā kļuva par atslēgu Aijas Brasliņas īstenotajai mākslinieka 150 gadu jubilejas izstādes iecerei Latvijas Nacionālajā mākslas muzejā un pilnā apjomā lasāms arī nākamajā gadā izdotajā monogrāfiskajā albumā. Formulējot savu radošo kredo, Rozentāls apliecināja, ka “būt jaunam ar katru darbu ir mākslinieka ideāls”. Turpat viņš rakstīja: “Katrai mākslai jābūt pirmā vietā iekšēja prieka un kairinājuma sajūtas izteiksmei. Katris darbs, kuram nav par pirmavotu šī stiprā prieka dziņa, var būt viss kas cits, tikai ne māksla.” Gan publicētajos rakstos, kuru klāstam turpmākajos gados latviešu un vācu laikrakstos pievienojās daži papildinājumi, gan Rozentāla piezīmēs, kas saglabātas Latvijas Nacionālā rakstniecības un mūzikas muzeja krājumā, spraigi atklājas vēl kāds viņa paaudzei būtisks un papildu izpēti mūsdienās pelnījis uzdevums – mākslas terminoloģijas un leksikas veidošana un iedzīvināšana latviešu valodā. Mūžam apraujoties piecdesmit gadu vecumā, Rozentāls atšķirībā no Riharda Zariņa nepieredzēja iespēju kļūt par memuāristu, taču viņš bija atstājis būtiskas rakstu liecības un atziņas par sava laika mākslu un māksliniekiem. Desmit gadus pēc gleznotāja nāves Jānis Siliņš sprieda: “Viņa rakstiem stils vietām pasmags, tiem nav veiklas uzbūves, bet te runā mākslinieka dvēseles siltums, te vērojama nopietna, R[ozentāla] un viņa laikabiedru centienus izteicoša doma.” Šī daļa Rozentāla veikumā ir pašsaprotami svarīga mākslas un estētisko ideju vēstures pētniekiem. Oriģināltekstu sniegto pieredzi iespējams papildināt, piemēram, ar Intas Pujātes (1957–2025) pētījumu par jūgendstila estētiku žurnālā “Vērotājs” no rakstu krājuma “Latvijas māksla tuvplānos” (Rīga: Neputns, 2003; pieejams LNB digitālajā kolekcijā https://gramatas.lndb.lv/) un Rozentāla uzskatiem veltīto nodaļu Stellas Pelšes grāmatā “Latviešu mākslas teorijas vēsture: Mākslas definīcijas valdošo laikmeta ideju kontekstā (1900–1940)” (Rīga: Latvijas Mākslas akadēmijas Mākslas vēstures institūts, 2007). Viņa publikācijas varētu saistīt arī plašāku 19.–20. gadsimta mijas kultūras interesentu loku, tāpēc bagātīgajam “Rozentāla plauktam” Latvijas mākslas grāmatniecībā līdzās mākslinieka sarakstes izdevumam “Dzīves palete” (sast. Inta Pujāte un Anita Putniņa-Niedra, Rīga: Pils, 1997) droši vien būtu vērts pievienot arī rakstu antoloģiju ar plašiem komentāriem.

Ja iepriekšējie stāsti vairāk bija par sugām, kas atbilst nosaukumam "pavasara vēstnesis", šoreiz stāsts par vasaras vēstnesi – svīri. Svīre ir putns, kurš Latvijā ierodas no dienvidiem viens no pēdējiem un uzturas te vien dažus mēnešus. Stāsta Latvijas ornitoloģijas biedrības pārstāve Ance Priedniece. "Svīre ir tālās distances gājputns, kas pārziemo Āfrikā uz dienvidiem no ekvatora. Latvijā svīre atgriežas maija sākumā vai vidū, bet Latviju pamet aptuveni trīs mēnešus vēlāk – augustā. Tātad kārtīgs vasaras putns," iepazīstina Ance Priedniece. "Svīres ir putni, kas lielāko daļu dzīves pavada gaisā, bet uz kādas virsmas nolaist tikai, lai ligzdotu. Tās var gan gulēt gaisā, gan baroties. Un tas galvenokārt barojas ar dažādiem kukaiņiem vai zirnekļiem, ko tās noķer gaisā. Pat lai padzertos, svīres mēdz lidot. Tas var pielidot ļoti tuvu ūdens virsmai, lai padzertos," iepazīstina Ance Priedniece. Kā svīri atšķirt no bezdelīgām un čurkstēm, kas varētu šķist līdzīgas pēc izskata? Svīre gandrīz pilnībā melni brūna ar nelielu gaišu laukumu pie rīkles pazodē. Gan bezdelīgām, gan čurkstēm mugurpuse zilganmelna, diezgan zaigojoša, vēders balts un bezdelīgai ir arī arī rūsgana rīkle.  Būtiskākā atšķirība no bezdelīgām un čurkstēm ir spārni un aste. Svīres spārni lidojumā ir izvietoti sirpjveida formā. Tie ir šauri un ļoti gari. Svīres tos nekad lidojuma laikā nepiekļauj ķermenim.  Svīres nekad neredzēsim, tupot uz vadiem vai televīzijas antenām, visticamāk, arī ne uz mājas jumta. Svīru kājas ir īsas, ar ļoti asiem nagiem un pirkstiem, kas visi ir vērsti vienā virzienā, lai var viegli pieķerties dažādām virsmām, kā sienām. Taču svīres nevar paiet, un, ja tās kaut kādu apstākļu dēļ nokļūst uz zemes, tās ir diezgan bezpalīdzīgas, un tās nevar pacelties gaisā. Šādā gadījumā būtu vienkārši jāpaņem svīre un jāpamet gaisā, un, visticamāk, tā aizlidos. 

 jQuery(document).ready(function(){ cab.clickify(); }); Original Podcast with clickable words https://tinyurl.com/27mnh3du Contact: irishlingos@gmail.com No news yet from activists captured by Israeli forces. Gan aon scéala fós ó ghníomhaithe atá tógtha ag fórsaí Iosrael. It is understood that no one in this country has received any news from any of the activists from a humanitarian aid flotilla detained by Israeli military forces in the Mediterranean. Tuigtear nach bhfuil scéala faighte ag aon duine sa tír seo ó aon duine de na gníomhaithe ó loingeas cabhrach daonnúla atá faoi choinneáil ag fórsaí míleata Iosrael sa Mheánmhuir. The Israelis intercepted over twenty boats from the aid fleet in the eastern Mediterranean yesterday and have taken the approximately 400 people who were on board those boats. Tháinig na hIosraelaigh roimh os cionn dhá scór bád ón loingeas cabhrach in oirthear na Meánmhara inné agus tá an tuairim is 400 duine a bhí ar bord na mbád sin tógtha acu. It seems that ten more boats are still heading towards Gaza. Is cosúil go bhfuil deich mbád eile ag tabhairt aghaidh ar Gaza i gcónaí. A number of Irish citizens are part of the fleet and Dr. Margaret Connolly, sister of the late President Catherine Connolly, is one of those captured by the Israelis. Tá roinnt saoránach de chuid na hÉireann páirteach sa loingeas agus tá an Dr Margaret Connolly, deirfiúr leis an Uacharán Catherine Connolly, ar dhuine den dream a ghabh na hIosraelaigh. Taoiseach Micheál Martin has condemned the Israelis’ actions in international waters and has called for the immediate release of the hostages. Tá a bhfui déanta ag na hIosraelaigh in uiscí idirnáisiúnta cáinte ag an Taoiseach Micheál Martin agus d’éiligh sé na géibheannaigh a scaoileadh saor láithreach. The vessel closest to Gaza, the Sirius, was 145 nautical miles from its destination, according to the group in Ireland to which the activists are affiliated. An soitheach ba ghaire do Gaza, an Sirius, bhí sí 145 muirmhíle ó cheann scríbe, dar leis an ngrúpa in Éirinn a bhfuil na gníomhaithe bainteach leis. In a statement, Israel’s foreign ministry said that no one would (not) be allowed to breach the naval blockade they are imposing on Gaza, which the Israelis say is a “legal” blockade. I ráiteas, dúirt roinn gnóthaí eachtracha Iosrael nach gceadófaí d’aon dream an léigear mara atá siad a dhéanamh ar Gaza a shárú, ar léigear “dleathach” é, dar leis na hIosraelaigh. The Israeli navy has a habit of taking people they capture to the port of Ashdod in Israel and deporting them from the country shortly thereafter. Tá sé de nós ag cabhlach Iosrael daoine a thógann siad a thabhairt go calafort Aisdeod in Iosrael agus iad a dhíbirt as an tír go gairid ina dhiaidh sin. RTÉ News and Current Affairs Nuacht agus Cúrsaí Reatha RTÉ

Bill Evans no aparece en ningún tracklist de esta semana. Y sin embargo, está presente. Makoto Ozone graba For Someone con su trío TRiNFiNiTY en los estudios alemanes donde trabaja ECM. Una declaración personal por la paz que incluye un contrafacto de «Peace Piece» como homenaje silencioso. En la misma semana, el pianista romano Enrico Pieranunzi y el guitarrista sardo Bebo Ferra publican Evanscape —palabra que ellos mismos inventaron fundiendo «Evans» y «escape»—, un disco de dúo donde el tercer músico está presente en cada silencio. Gabrielle Cavassa debuta en Blue Note con Diavola, producido por Joshua Redman y Don Was. Ganó los Sassy Awards en 2021 —el mismo concurso que lanzó a Cyrille Aimée, Jazzmeia Horn y Samara Joy— y el disco confirma que la apuesta de Blue Note no fue solo estrategia de márquetin. También esta semana: Ron Carter, a sus 87 años y con más de 2.500 grabaciones a sus espaldas, avanza en dúo con el guitarrista israelí Yotam Silberstein una bossa nova contemporánea que anticipa un álbum por llegar. El guitarrista y cantante Djamal B rescata la elegancia de la chanson con una versión del clásico de Sacha Distel, «La Belle Vie», adelanto de su álbum Les Dernières. E Ibrahim Maalouf toca una trompeta de cuatro válvulas que le permite ir donde la mayoría no puede: entre el jazz y los cuartos de tono de la música árabe. Tracklist: – Rolling Tales, Makoto Ozone; – Chasing the Horizon, Makoto Ozone; – Evanscape, Enrico Pieranunzi & Bebo Ferra; – Song for Helen, Enrico Pieranunzi, Bebo Ferra & Diego Imbert; – Nova Ilusão, Ron Carter & Yotam Silberstein; – La Belle Vie, Djamal B; – Las Trompetas de Nael, Ibrahim Maalouf; - Prisoner of Love, Gabrielle Cavassa.

Hour 2 for 5/7/26 Drew and Nick pray the Chaplet of Divine Mercy (1:00). Then, Dr. Eugene Gan from Franciscan University from Steubenville joins Drew to discuss media's effect on us (30:44), screentime and AI (45:11), and what Dr. Gan is seeing at his University (48:32). Link: https://eugenegan.weebly.com/

The subject of whether you should top your pepper plants can bring on a pretty strong debate among gardeners. That's because this is one of those topics where the answer genuinely is: it depends. And I mean that in a very specific, evidence-based way that comes down to two things: your climate and your pepper type. I'll be straight with you, I do not top my peppers. We are in a zone 6b in west central Missouri and our season is just short enough that for our large sweet peppers, by the time a topped plant recovered and loaded up with new fruit, I'd be in a race with the first frost, so I don't love my odds of winning. And for our smaller peppers, both hot and sweet, they branch naturally. They've never needed my help getting bushy and they generally end up so loaded with fruit there's no need for me to create new growing points. But that does NOT mean topping is wrong. In fact, if your growing season is long enough and you are growing the right type of pepper, there is a solid, research-grounded argument for it and I want to make that argument fairly today. Let's dig in! References: Illinois Extension (University of Illinois) — Frillman, N. (2021). “Pruning tomatoes and peppers for healthier plants and a stronger harvest.” Flowers, Fruits, and Frass Blog. https://extension.illinois.edu/blogs/flowers-fruits-and-frass/2021-05-17-pruning-tomatoes-and-peppers-healthier-plants-and Nebraska Extension — “Garden Peppers.” University of Nebraska–Lincoln Extension Publications. https://extensionpubs.unl.edu/publication/967/html/view University of Minnesota Extension — Ask Extension response on topping pepper plants (2021). https://ask.extension.org/kb/faq.php?id=740168 University of Minnesota Extension — Weisenhorn, J. Ask Extension response on topping for yield (2016). https://ask.extension.org/kb/faq.php?id=333053 University of Maryland Extension — Home and Garden Information Center. Ask Extension response on topping chile plants (2024). https://ask.extension.org/kb/faq.php?id=869966 University of Minnesota Extension — “Growing Peppers in Home Gardens.” https://extension.umn.edu/vegetables/growing-peppers-home-gardens Peer-Reviewed Research: Humadi, F. (1980). “Effects of plant growth retardants and mechanical topping on growth and yield of pimiento pepper (Capsicum annuum L.).” Dissertation, University of Tennessee. Tennessee Research and Creative Exchange. https://trace.tennessee.edu/utk_graddiss/7869/ Buczkowska, H., & Najda, A. (2001). “Impact of plant topping on chemical composition of sweet pepper fruit.” Zeszyty Naukowe Akademii Techniczno-Rolniczej w Bydgoszczy. Rolnictwo, 46, 33–37. Cao, D., Chabikwa, T., Barbier, F., Dun, E. A., Fichtner, F., Dong, L., Kerr, S. C., & Beveridge, C. A. (2023). “Auxin-independent effects of apical dominance induce changes in phytohormones correlated with bud outgrowth.” Plant Physiology, 192(2), 1420–1434. https://doi.org/10.1093/plphys/kiad034 Avent, A. R., & Armitage, A. M. (2015). “Effects of Paclobutrazol and Pinching on Ornamental Pepper.” HortScience / Journal of the American Society for Horticultural Science. ResearchGate: DOI 10.21273/HORTSCI. Hu, Q., Wei, Y., Gan, X., Zhang, O., Huangpu, J., Hu, B., & Wu, L. (2016). “Effects of pruning methods and harvest time on yield and benefit of pepper in greenhouse.” Jiangsu Agricultural Sciences, 44, 182–185. Resources: Just Grow Something: https://justgrowsomething.com Gardening Courses: https://justgrowsomething.com/courses Just Grow Something Merch and Downloads: https://justgrowsomething.com/shop Just Grow Something Gardening Friends Facebook Group: https://www.facebook.com/share/g/18YgHveF5P/ Check out how you can become a patron on Patreon: https://www.patreon.com/JustGrowSomething Feed my coffee habit: https://buymeacoffee.com/justgrowsomething Amazon storefront: https://www.amazon.com/shop/justgrowsomething Get 10% off and FREE shipping on my favorite raised planters at Planter Box Direct using code JUSTGROW10: https://planterboxdirect.com/?ref=593 Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Stāsta komponiste, literāte, Jāzepa Vītola Latvijas mūzikas akadēmijas docētāja Gundega Šmite. Pārraides producente: Rūta Paula. Iedomāsimies – Latvijas Radio 3 „Klasika” translē liegas, klusinātas skaņas, kuras iedzīvina jūtīgi pianista pirksti. Kā piemēru aicinām jūs iztēloties Bēthovena slaveno Bagateli Nr. 25 la minorā, kas pazīstama ar nosaukumu "Elīzei". Šis skaņdarbs izraisa gaiši skumju, vienlaikus mierpilnu noskaņojumu. Taču vai šāds iespaids būs visiem klausītājiem? Vēl jo vairāk – vai visi klausītāji šo skaņdarbu uztvers kā gaišās pasteļkrāsās gleznotas skumjas? Vairāki mūzikas psiholoģijas pētījumi uzrāda, ka tādas mūzikas klausīšanās, kurai raksturīgs lēns temps, legato artikulācija, klusa dinamika, ierosina tādas psihofizioloģiskas reakcijas kā sirdsdarbības palēnināšanos, ādas elektrodermālās aktivitātes jeb mitruma līmeņa un asinsspiediena samazināšanos, kā arī elpošanas palēnināšanos. Tomēr nē, reakcijas varam paredzēt visdažādākās. Kāds mazulis varbūt rimsies raudāt, kāds pusaudzis savukārt nopūtīsies: "Ak, garlaicīgā klasiskā mūzika…" un pametīs istabu. Kāds cits apstāsies veikt mājas soli un aizsapņosies, veroties tālēs, vēl kāds cits sajutīs pakrūtē nepatīkamu kņudoņu un steigšus nomainīs radioviļņus, tikai vēlāk atceroties savu stingro klavierskolotāju, kuras stundas bija salīdzināmas ar zobu sāpēm. Kas ietekmē veidu, kā mēs uztveram mūziku? Emocionālais stāvoklis mūzikas klausīšanās brīdī un autobiogrāfiskā atmiņa ietekmē mūzikas uztveri saistībā ar limbiskās sistēmas aktivizēšanos mūsu smadzenēs. Šī svarīgā smadzeņu daļa atbild par emocijām un uzglabā atmiņas, kuras viegli var ierosināt muzikāls stimuls. Ja dzirdam skaņdarbu, kas mums saistās ar kādu pagātnes situāciju, mūzika var mūs teju teleportēt uz pagātnes notikumiem un sajūtām. Gan tīkamām, gan nepatīkamām. Otrkārt, mūzikas uztveri ietekmē kulturālie konteksti. Vai Rietumeiropas klasiskās mūzikas skaņdarbu līdzīgi uztvers Āfrikas safari vai, piemēram, Indonēzijas iedzīvotājs? Vai viņiem nepietrūks ritmiskas aktivitātes vai arī mūzika liksies pārāk vienkārša? Ir pierādīts, ka klausītāji, kas uzauguši dažādās muzikālās tradīcijās, var izjust atšķirīgas gaidas attiecībā uz mūzikas labskanību jeb konsonējošām harmonijām. Arī katrai paaudzei ir savas muzikālās vērtības un sava piederības sajūta noteiktam stilam. Protams, arī muzikālā izglītība ietekmē uztveri – klausītājs ar vismaz 3–4 gadu muzikālo izglītību parasti izrāda paaugstinātu jutību pret toņu augstumu, laika struktūru, harmonisko funkciju, kas atspoguļojas neiroplastiskās izmaiņās, saistītās ar dzirdes centru un pieres daivas, kas atbild par informācijas analīzi, sadarbību. Savukārt klausītājs bez muzikālās izglītības visbiežāk uztvers mūzikas melodiju, tempu un kopējo emocionālo iespaidu. Mūziķiem un cilvēkiem bez muzikālās izglītības atšķirīgi ir arī prognozēšanas mehānismi. Smadzenes nepārtraukti rada gaidas par mūzikas virzību, un atšķirības iepriekšējā pieredzē rada atšķirīgus prognozēšanas modeļus, mainot uztveramo spriedzi un pārsteigumu, kuriem jābūt balansā, lai mūzika mūs nedz pārsātinātu, nedz garlaikotu. Visbeidzot, individuālās atšķirības dzirdes asumā, personības iezīmēs un kognitīvajā jeb domāšanas veidā veicina estētiskā vērtējuma un emocionālo reakcijas atšķirību. Ko izjuta pats Bēthovens, komponējot Bagateli Nr. 25 la minorā? Kāds ir komponista dotais emocionālais kods, to neuzzināsim, un varbūt tas nemaz nav tik būtiski. Šo mūziku ir iespējams saklausīt un izjust neskaitāmos veidos.  

Ganó Kimi

Fevzi Turkalp, the Gadget Detective, joins Clive Bull to discuss the latest tech news and reviews. On this week's show; Finally a good story about AI as six residential care homes in Dorset trial using AI monitoring with some extremely encouraging results.Gadget of the Week goes to;Anker's Prime Charger. This mains powered compact desktop GAN charger features 4 USB-C and 2 USB-A sockets allowing you to charge up to six devices simultaneously and is capable of supplying up to 100 watts on the USB-C sockets, with a total of 200 watts output, making it ideal for those charging a lot of power hungry devices or who prefer a tidy solution to their charging needs. Scoring 4 out of 5, listen in for more details.You can hear the Gadget Detective on LBC every Friday morning around 3.40am and can follow and contact him on X @gadgetdetective and BlueSky @GadgetDetective.com#Fevzi#Turkalp#Gadget#Detective#Tech#Technology#News#Reviews#Help#Advice#Clive#Bull#LBC#Radio#Artificial#Intelligence#AI#Care#Home#Dorset#Monitoring#Sensor#Apple#Watch#Boston#Dynamics#GadgetoftheWeek#Week#Anker#Prime#Desktop#Charger#Mains#Powered#GAN#Six#6#Socket#USBC#USBA#200watt#200w#Laptop#Tablet#Phone

Klimata pārmaiņas Latvijā galvenokārt saistītas ar ilgstošiem sausuma periodiem, stiprām vētrām un, protams, plūdiem. Tieši par intensīviem nokrišņiem dzirdam vislielākās bažas, jo ar tiem grūti tikt galā gan lauku reģionos, gan pilsētās. Klimatam mainoties, Latvijā biežāk novērojam ekstrēmus nokrišņus - dažkārt dažu dienu laikā var nolīt vairāku mēnešu norma. Daba prasmīgi prot šo ūdeni uzņemt, bet, kā ir ar pilsētām? Cik gatavi esam plūdu riskam un kā šobrīd tiek pieskatīta ir šī klimata pārmaiņu joma Latvijā? Raidījumā Zināmais nezināmajā analizē Latvijas Vides, ģeoloģijas un meteoroloģijas centra Prognožu un klimata daļas vadītājs Andris Vīksna un Jelgavas valstspilsētas pašvaldības iestādes "Pilsētsaimniecība" vadītāja vietniece Sandra Liepiņa. Bet vispirms stāsts par to, kā Rīgā pārcieta vienu no smagākajām applūšanas epizodēm. Cik sena ir mūsu galvaspilsēta, tik arī te Daugavas un tās daudzo atteku ūdeņi ir vairakkārt nodarījuši postu pilsētai un tās apkaimei. Šajā sižetā lūkojam uz 18. gadsimtu, kad upe "ienāca" pilsētā un apkārtnes laukos. Ņemot vērā, ka tolaik Rīga atradās krietni zemāk nekā šodien, Daugavas krastmala pilsētā nav uzbērta pietiekami augstu un ziemas bija daudz bargākas, kas savukārt pavasarī radīja milzīgus ledus sablīvējumus Daugavā. Šie priekšnoteikumi arī kalpoja par iemeslu tam, ka mājas pa upi peldēja un ielās zivis tika ķertas. Plūdi ir daudzas reizes postījuši Rīgu, bet Rīgas vēstures un kuģniecības muzeja vēstures nodaļas vadītāja Margarita Barzdeviča stāsta par plūdiem Rīgā 1709. gadā, par kuriem saglabājušās liecības par to, kā rīdzeniekiem klājies, gan kādas bijušas sekas. Vairāk par pilsētniekiem cieš apkārtņu iedzīvotāji, jo tur nebija, kur patverties. No tiem laikiem ir arī saglabājusies Rīgas mērnieka un kartogrāfa Eberharda Tolksa dienasgrāmata, kur viņš raksta: "1709. gada pavasara plūdos 6. aprīlī vēlu vakarā, pēc tam, kad jau 11 dienas laiks bija tapis siltāks un kusa sniegs, pie Rīgas sāk iet ledus. Daugavā sākas plūdi, kas aizskaloja mājas, gan salās, gan Pārdaugavā."  Gan  šie, gan arī  citi  17. un 18. gadsimta. plūdi atstāja postošas sekas  gan pašā pilsētā, gan apkaimes laukos. -- Latvijas Ornitoloģijas biedrības pārstāve Ance Priedniece stāsta par tītiņu – savdabīgo galvas grozītāju.

Stāsta Tallinas Universitātes jaunākā pētniece, doktorante, vēsturniece, Mg. hist. Rūta Bruževica. Pārraides producente – Dina Dūdiņa-Kurmiņa. Mūsdienās mums karnevāla nosaukums saistās ar Venēciju, Brazīliju un katoliskām zemēm tepat Eiropā. Reti iedomājamies, ka pirms vairāk kā 500 gadiem, karnevāls bija ļoti ierasta tradīcija tepat Rīgā. Gan mūsdienās, gan viduslaikos karnevāls notika tieši pirms lielā gavēņa, kas sākās 40 dienas pirms Lieldienām. Lai gavēni godam izturētu, nedēļu pirms tā pieņemts pieēsties, dzīrot, dejot un ālēties, maskējoties jeb darīt gandrīz visu, kas pretējs gavēņa laika atturībai. Viduslaikos to, ko mūsdienās saucam par karnevālu, Rīgā sauca par vastlāvi, kas viduslejasvācu valodā nozīmēja gavēņa priekšvakaru. Par vastlāvja svinībām viduslaiku Rīgā visvairāk zinām no Rīgas Melngalvju brālības dokumentos pierakstītā – viņu šrāgām, rēķinu grāmatām, kā arī detalizētajiem karnevāla noteikumiem no 16. gadsimta sākuma. Kaut arī visplašāk vastlāvi viduslaiku Rīgā svinēja tieši turīgi tirgotāji no Melngalvju brālības un Lielās Ģildes, kā arī pilsētas rātskungi, domājams, ka mazāka apjoma svinības noturēja arī citi pilsētas iedzīvotāji un brālības. Viduslaiku beigās Melngalvji Rīgā svinēja vastlāvi divu nedēļu garumā, iekļaujot programmā dažādus pasākumus savā lokā, kā arī ielūdzot viesus un radot svētku atmosfēru visā pilsētā. Oficiālā svētku atklāšana notika trešdienā nedēļu pirms pelnu trešdienas, ar mūziku, svinīgām runām un mielošanos ar ēdieniem un dzērieniem zālē, kas bija izdekorēta svētkiem. Ceturtdienā Melngalvji svinības izveda ārpus sava nama, kur tie ar simbolisku parādi caur vārtiem ieveda karnevāla svētkus pilsētā. Atklājot vastlāvja svinības pilsētā, šajā un citās dienās notika dejas gan Melngalvju nama zālē, gan tirgus laukumā, gan deju parāde uz pilsētas Rāti un Lielo Ģildi, lai aizvestu karnevālu pie pārējiem pilsētas tirgotājiem. Turpmākajās dienās norisinājās alus vakari, kā arī mielasti ar dejām, uz kurām tika aicināti viesi, tostarp dāmas. Tā kā Melngalvji bija jauni un neprecēti tirgotāji, kas vēl apguva savu tirgotāja amatu, šādi pasākumi varēja būt laba iespēja iepazīties un padejot ar pilsētas jaunavām, kuras arī bija no labām tirgotāju ģimenēm. Melngalvju šrāgas arī iekļauj noteikumus, kā būtu jāapietas ar dāmām, kas uzaicinātas ciemos, un, galvenais, – cik dejas viņām jāvelta vakara gaitā un kā jāielūdz uz nākamo deju vakaru. Mielasti turpinājās katru vakaru, līdz pelnu trešdienas priekšvakarā galdā jau tika celta siļķe, lai atzīmētu gavēņa sākumu. Ceturtdienā pēc pelnu trešdienas Melngalvji pulcējās agrā dievkalpojumā Svētā Pētera baznīcā, bet vakarā ar dejojošo parādi cauri pilsētai atkal izveda svētkus pa Smilšu vārtiem. Melngalvji svinēšanu gan turpināja gandrīz vēl nedēļu un tikai nākamajā otrdienā naksnīgajā tirgus laukumā sadedzināja bluķi, kas noslēdza vastlāvi pavisam. Vēlākajos gadsimtos Reformācijas ietekmē daudzas svētku tradīcijas, tostarp vastlāvis, izzuda. Taču paražas pirms gavēņa kārtīgi pieēsties, dejot, pārģērbties un iet pie kaimiņiem, kā arī sadedzināt bluķi, joprojām pastāv Meteņa svētku tradīcijā. Pēdējo gadu Meteņu svinību atgriešanās Rīgas rātslaukumā nav tikai folkloras tradīciju adaptēšana pilsētā – tā ir arī viduslaiku svētku atgriešanās Rīgas vecajā centrā.

GP, Senior Director of Aerospace & Defense Strategy at Infineon Hi-Rel, talks with Pat Hindle about the benefits of GaN power conversion technology for space applications and the implications on satellite architecture. Sponsored by Infineon Hi-Rel.

Microwave Journal editors Pat Hindle and new addition Tim Rainear discuss the April Amplifiers and Oscillators themed issue product articles, talk with Infineon Hi-Rel about Space-qualified GaN, and review industry news and events. Sponsored by RFMW and Infineon Hi-Rel.

Para precio y disponibilidad, vaya a este vínculo: https://amzn.to/3OESlJR En este episodio Otto nos presenta un innovador cubo cargador diseñado para cargar hasta cinco dispositivos simultáneamente. Revisa sus características principales: dos cables retráctiles de 2.6 pies con cierre magnético, pantalla integrada que muestra el wattage por puerto, dos puertos USB-A de carga rápida (marcados en verde), un puerto USB-C y una base adhesiva removible. Explica la tecnología GaN que permite reducir el tamaño del cargador y la capacidad de 140 W que puede llevar una computadora al 50% en 25 minutos. WWW.OTTOTECNOLOGIA.COM 

17-4-26: Mary Oliver: prestar atención, sorprenderse, contarlo Mary Oliver (1935-2019) nació en Maple Heights, Ohio, en el seno de una familia disfuncional. Por esa razón, desde muy pronto la escritura, la lectura y las escapadas a los bosques cercanos se con­virtieron en tempranas herramientas de huida o defensa. Estudió en la Universidad Estatal de Ohio y en el Vassar College, aunque no llegó a obtener ningún título ni tuvo mayor interés en ello. A los veintiocho años publicó su primer poemario, y desde entonces su trabajo siempre se inspiró más en la naturaleza que en el mundo humano, y provino de su inexpugnable y constante pasión por los paseos solitarios por territorios salvajes. Ganó tanto el National Book Award como el Premio Pulitzer, impartió clases en la Universidad Case Western Reserve, ocupó la cátedra Catharine Osgood Foster en el Bennington College y fue doctora honoris cau­sa por cuatro universidades distintas. Fue autora de más de una treintena de libros, la mayoría poemarios y unos pocos ensayos, entre los que cabe destacar La escritura indómita, Horas de invierno (ambos publicados por Errata naturae), Why I Wake Early (2004) o Blue Horses (2015).

Definitivamente, Paul Seixas asusta y ya calienta la Liega-Bastogne-Lieja. Ganó en la Flecha Valona con una gran autoridad en el Muro de Huy. Contamos lo que está pasando con este chico de 19 años. Además, es jueves, y como cada jueves viene @ADescenso para dejarnos su sección semanal sobre Movistar Team. ️ Presenta: Alberto Marcos. ¿Quieres ser el mejor oyente de La Bicicleta Podcast? Retomamos nuestra actividad en Patreon. Allí puedes apoyarnos desde tan solo 1,5€ al mes. Puedes disfrutar de contenidos exclusivos: https://patreon.com/LaBicicletaPodcast?utm_medium=unknown&utm_source=join_link&utm_campaign=creatorshare_creator&utm_content=copyLink Síguenos en redes sociales: Twiter: @podcastLBC Instagram: @LaBicicletaPodcast TikTok: @LaBicicletaPodcast Instagram: @amarcosgallego TikTok: @amarcosgallego YouTube: https://www.youtube.com/channel/UCZPuzTB6PXX363rL2CRju3w Escúchanos en Spotify: https://open.spotify.com/show/4FgUyioG97fwjEh5yXJETh?si=a0090831798c4d0d ¿Te gustaría anunciarte en este podcast?: https://advoices.com/la-bicicleta-podcast ⚠️ ¿Quieres ayudarnos? Puedes contribuir y convertirte en mecenas de La Bicicleta Podcast en Patreon. Desde 1,50€ al mes puedes ayudar a que sigamos aquí contigo cada día: https://patreon.com/LaBicicletaPodcast?utm_medium=unknown&utm_source=join_link&utm_campaign=creatorshare_creator&utm_content=copyLink Únete al canal de Telegram: https://t.me/boost/LaBicicletaPodcast

Garajās Lieldienu brīvdienās paspējām piedzīvot gandrīz visus iespējamos pavasara laikapstākļus – gan siltu sauli, gan lietu, sniegu un stipru vēju. Beidzot ieradās arī pagājušajā nedēļā apspriestais cīruļputenis. Bija gan cīruļputenis, gan daļā Vidzemes izveidojusies sniega sega, kā tas pavasaros pie mums notiek gandrīz vienmēr. Pērn 5. aprīlī Vidzemē un Latgalē cīruļputenis atnesa pat 10–12 centimetru biezu sniegu, no Cēsu puses iedzīvotāji sūtīja mērījumus ar 18 cm biezu sniega kārtu, tā ka šogad cīruļputenis vēl tāds ļoti maigs un rāms. Kad cilvēki redz ligzdā stāvam apsnigušu stārķi vai dzērves staigā pa apsnigušu lauku, tāpat mēdz satraukties, vai putniem tas nekaitē. Patīkami noteikti nav, bet viņiem tā sanāk katru gadu. Ja vien sniegs nav ļoti dziļš uz daudzām dienām, tad nekādas lielas problēmas putniem tas nesagādā, turklāt – ja pie mums, piemēram, dzērvēm šāds cīruļputenis uznāk vienu vai divas reizes pavasarī, tad Igaunijā un vēl jo vairāk Somijā pat vēl maijā tās bieži brien pa sniegu, bet dzēvju populācija turpina pieaugt un par šo interesants pētījums aprakstīts Igaunijas sabiedriskā medija ERR ziņā. Gan pie viņiem, gan Latvijā, gan citur Eiropā dzērvju populācija pēdējās desmitgadēs ir stabili un diezgan strauji palielinājies. Igaunijas ornitologi ir apkopojuši visplašākās pieeajmās ziņas par Igaunijas dzērvēm, to dzīvesveidu un migrāciju un ir sanākuši interesanti secinājumi. Protams, tas, ka tuvējie migranti, kas ziemo relatīvi tuvu Baltijai, siltāku ziemu un pavasaru dēļ atgriežas arvien agrāk, bet igauņu pētnieki ir secinājuši, ka dzērves arī ziemo arvien tuvāk. Piemēram, pagājušā gadsimta 60.–70. gados, dzērvju galvenās ziemotnes bija Ziemeļāfrikā, visvairāk Marokā. Astoņdesmitajos un deviņdesmitajos gados tās sāka ziemot Spānijā, bet pēdējos 15–20 gadu laikā dzērves ziemo Francijā un Vācijā. Tām ir vienkāršāk atgriezties, ja pavasaris sākas agrāk, nav vairs tik tālu jālido. Vienlaikus pētnieki atklājuši, ka pēdējās desmitgadēs ir ievērojami palielinājusies dzērvju mazuļu mirstība un tas savukārt kaut kā nelīmējas kopā ar stabili pieaugošo populāciju. Izrādās, ka dzērvju skaits Eiropā ir tuvu maksimālajām iespēju robežām. Daļai dzērvju vairs nepietiek vietas tām vislabāk piemērotajās dzīvotnēs, tādēļ tās sāk ligzdot tur, kur mazuļu izaudzināšana ir izaicinošāka. Apkārtnē ir mazāk barības vai vairāk plēsēju. Ja 80tajos gados Igaunijā 96–97 procentu mazuļu izdzīvoja, tad šobrīd tie ir tikai ap 60 procentiem, turklāt daļa no izdzīvojušajiem ir vājākā stāvoklī un palielinās risks, ka tie pēc tam iet bojā migrācijā vai ziemošanas vietās. Tāpat pētnieki secinājuši, ka dzērvju populācija ir augusi ne tikai skaitā, bet izpletusies teritoriāli, piemēram, tās sākušas ligzdot Vācijas dienvidos un Ungāriju, kur agrāk to nedarīja. No 1. aprīļa, lai sargātu mazo zīriņu ligzdošanas vietas, cilvēkiem ir slēgtas trīs upju grīvas, ziņo Dabas aizsardzības pārvalde. Tās visas ir Ventspils novadā. Zināmākā ir Irbe, vēl arī divas mazākas – Ķikans un Lūžņa. No 1. aprīļa līdz 1. augustam cilvēkiem liegts pastaigāties vai veikt jebkādas citas darbības šo upju grīvās un ieteicams arī nekur tuvumā nepastaigāties ar suņiem bez pavadas. Tur arī dabā izvietotas informatīvas zīmes tā, ka nejauši ieklīst šajās zonas nevar, par to nevajag satraukties. Zīriņi un tārtiņi ligzdas ierīko liedaga smiltīs, izveidojot mazu bedrīti. Bieži vien ligzdas un dējums tajās pilnībā saplūst ar apkārtējo vidi, tādēļ cilvēka acij paslīd garām nepamanīts. Pludmalē ligzdojošo putnu mazuļi ir ligzdbēgļi. Proti, drīz pēc izšķilšanās tie pamet ligzdu un seko saviem vecākiem, kas tos baro un pieskata. Putnu mazuļi šajā laikā nelido, tie veikli pārvietojas pa zemi. Pētījumi parāda, ka putni cilvēku savā tuvumā uztver kā draudu. Pieaugušie putni pamet ligzdas, savukārt mazuļi bēgot noklīst no vecākiem. Ar laiku putni pamet agrāk izvēlētās teritorijas, vairs nedēj olas un neaudzina mazuļus, kā rezultātā sarūk sugas populācija. Vēl jāpiemin, ka no 1. maija tādi paši ierobežojumi būs pie Gaujas ietekas RĪgas līcī, tur pie Carnikavas, kas gan ir cilvēku ļoti iecienīta pastaigu vieta. Mazie zīriņi Vidzemes pusē ligzdošanu sāk mazliet vēlāk, tādēļ ierobežojumi ir no 1. maija. Putnu atgriešanās Latvijā turpinās, brīvdienās redzēti kārtējie dzērvju bari lidināmies un sasaucamies debesīs, bet vēl pavasarī notiek viena cita migrācija. Cilvēki – tādi, kas pamatā dzīvo pilsētās un kuriem ir mazdārziņi, sāk arvien biežāk uz tiem doties, rušināt, sakopt, lai būtu gatavi jaunajai sēšanas un stādīšanas sezonai. Rīgā aizvadīto dažu gadu laikā jauna dzīvība un arvien lielāka aktivitāte ir Lucavsalas mazdārziņos, tāpēc tikāmies ar vienu dārza entuziasti Andru Čudari, kura arī jau sākusi rosīties un vispār, pagājušajā nedēļā, kad es tur ciemojos, tur aktivitāte tiešām augsta – cilvēki zāģē, grābj, cērt, rok. Pilsētnieki ir sākuši pavasara darbus.  Pavasaris vēl vēss un šur tur pat ar cīruļputeni, tā, ka lielie dārza darbi darbi vēl priekšā. Nākamnedēļ es sarunāšos ar ainavu arhitekti, turklāt tādu, kas labi pārzina šīs profesijas vēsturi. Pat ne profesijas, bet ainavu arhitektūras vēsturi, kad varbūt pat apzināti to nesauca par ainavu iekārtošanu. Ar Ilzi Locāni no Turaidas muzejrezervāta runājām, jo lai nu kas, bet ainava – gan dabiska, gan cilvēku veidota – Turaidas pusē ir neatņemama un ļoti spilgta visa kompleksa sastāvdaļa.

Los mejores días (Páginas de espuma) de Magalí Etchebarne fue ganador del Premio Ribera del Duero de Narrativa Breve 2024 en una contienda por seguro muy reñida contra otras de escritoras como Fernanda Trías, Katya Adaui, Nuria Labari y Dahlia de la Cerda. Ganó seguramente por el talento de la escritora, por la frase contundente, por los inicios de esos 8 cuentos que nos orillan al precipicio de historias que nos ausculta y que urgan en esos dolores y centros sensibles que han en torno de la muerte, la fragilidad y la pérdida de las certezas. Hoy lo reseñamos aquí.

Miembro fantasma - Fernanda Trías Fernanda Trias (Montevideo, 1976) es escritora, traductora y docente de creación literaria uruguaya. Vivió en Francia, Berlín, Buenos Aires, Nueva York, Valparaíso, España y desde hace más de diez años reside en Colombia. Esa experiencia migrante y el cruce de culturas atraviesan su obra. Ganó el Premio Sor Juana Inés de la Cruz por Mugre rosa (2020) y El monte de las furias (2025). Curiosamente, Mugre rosa, escrita antes del COVID-19, imagina una ciudad paralizada por una extraña enfermedad y el aislamiento social. Una inquietante premonición de lo que vino después.   ++++++++++++++++++++++++++++++++++ Pre producción y voz: CECILIA BONA Editó este episodio: DANY FERNÁNDEZ @danyrap.f para @activandoproducciones.proyecto ⚙️ Producción: XIMENA GONZALEZ @ximegonzal3z Edición de video: LUZ FERNÁNDEZ @luzma.fz ¡Ayudanos a crecer! Patrociná POR QUÉ LEER: https://porqueleer.com/patrocina Nuestras redes sociales: ⚡https://instagram.com/porqueleerok ⚡https://twitter.com/porqueleerok ⚡https://www.facebook.com/porqueleerok/

 jQuery(document).ready(function(){ cab.clickify(); }); Original Podcast with clickable words https://tinyurl.com/27q6mz22 Contact: irishlingos@gmail.com Iran is being crushed again by Israeli forces. An Iaráin á slacairt an athuair ag fórsaí Iosrael. Israeli military forces are clearly not resting on their laurels, as they are carrying out another massive raid this morning on the Iranian capital, Tehran, after suffocating the place overnight. Níl suí ná foras ar fhórsaí míleata Iosrael, is léir, agus iad ag tabhairt ollruathair arís ar maidin ar phríomhchathair na hIaráine, Tehran, tar éis dóibh an áit a shlacairt i gcaitheamh na hoíche. There is no word yet on the dead but it is assumed that scores of them are there. Níl aon chaint fós ar na mairbh ach glactar leis go bhfuil na scórtha acu ann. Two other cities – Shiraz in southern Iran and Tabriz in the northwest – have also been bombarded by the Israelis since morning. Dhá chathair eile – Shiraz i ndeisceart na hIaráine agus Tabriz san iarthuaisceart – tá siad á dtuairgneáil ag na hIosraelaigh ó mhaidin chomh maith. It is not a time of neglect or delay for the Israelis in Lebanon either, and it is reported that they are once again bombing villages in the south of that country. Ní tráth faillí ná moille ag na hIosraelaigh é sa Liobáin ach an oiread, agus tuairiscítear go bhfuil sráidbhailte i ndeisceart na tíre sin á mbuamáil acu arís. The Israeli army says it is also fighting Hezbollah on the ground. Deir arm Iosrael go bhfuil siad ag dul i ngleic le Hezbollah ar an talamh freisin. It appears that Iranian military forces fired an explosive drone at Dubai airport this morning, hitting a large fuel tank. Is cosúil gur scaoil fórsaí míleata na hIaráine drón pléascach le haerfort Dubai ar maidin agus gur buaileadh mórumar breosla ann. Smoke is rising from the tank but it doesn’t appear to be affecting flights at the airport much. Tá deatach ag éirí as an umar ach dealraíonn sé nach bhfuil sé ag cur isteach mórán ar eitiltí san aerfort. Military authorities in the United Arab Emirates say they have been diverting or shooting down dozens of Iranian drones since morning. Deir na húdaráis mhíleata in Aontas na nÉimíríochtaí Arabacha go bhfuil na scórtha drón ón Iaráin á gcur malairt riain acu nó á séideadh san aer acu ó mhaidin. Not to mention the destruction and killing, the economic toll of unrest is affecting countries around the world, including, what else, the countries that are waging war on Iran. Gan trácht ar an scrios agus an marú, tá deasca eacnamaíochta na corraíola ag cur isteach ar thíortha an domhain, lena n-áirítear, cad eile, na tíortha atá ag cur an chogaidh ar an Iaráin. The most pressing issue for them at the moment is the unhindered passage of oil and gas tankers through the Strait of Hamuz in southern Iran, while the Iranians still have some control over that neck of water. Tancaeir ola agus gáis a sheoladh saor ó bhuaireamh trí Chaolas Hamuz i ndeisceart na hIaráine is mó atá ag déanamh scime dóibh i láthair na huaire agus smacht áirithe go fóill ag na hIaránaigh ar an muinceann uisce sin. The European Union’s foreign affairs and security chief, Kaja Kallas of Estonia, indicated that EU naval ships might be sent to the region to pressure the Iranians to allow fuel tankers through. Ceannasaí gnóthaí eachtracha agus slándála an Aontais Eorpaigh, Kaja Kallas as an Eastóin, thug sí le fios go mb’fhéidir go seolfaí longa cabhlaigh ón Aontas chuig an réigiún chun brú a chur ar na hIaránaigh tancaeir bhreosla a ligean tríd. It seems that Denmark is in favor of that. Dealraíonn sé go bhfuil an Danmhairg ina fhabhar sin. Yes, and Britain – a former member state – but Germany is adamant about it. Tá, agus an Bhreatain – iar–bhallstát – ach tá an Ghearmáin patuar ina leith. The foreign ministers of the member states will discuss the matter today, Kallas said. Pléifidh airí gnóthaí eachtracha na mballstát an scéal inniu, a dúirt Kallas. It is unclear what exactly the European naval ships would do in that part of the Persian Gulf, but British Prime Minister Keir Starmer suggested that a threatening nod to the Iranians might be enough. Níl sé soiléir cad go baileach a dhéanfadh na longa cabhlaigh ón Eoraip sa chuid sin de Mhurascaill na Peirse ach thug Príomh-Aire na Breataine Keir Starmer le tuiscint go mb’fhéidir go mba leor nod bagrach do na heolaigh san Iaráin. All this in light of demands from US President Donald Trump that other countries come to the aid of the Americans in the Strait of Hormuz to ensure that tankers are allowed to enter and exit again. É sin ar fad i bhfianaise éileamh ó Uachtarán na Stát Aontaithe Donald Trump go dtiocfadh tíortha eile i gcabhair ar na Meiriceánaigh i gCaolas Hormuz chun a chinntiú go mbeadh cead isteach ann agus cead amach as an athuair ag tancaeir. It is through this strait that a fifth of the world’s oil supplies and a fifth of the world’s liquefied gas are delivered every day. Is tríd an gcaolas sin a sheachadtar an cúigiú cuid de sholáthairtí ola an domhain agus an cúigiú cuid de ghás leachtaithe an domhain gach aon lá. RTÉ News and Current Affairs Traces of bombings in a residential area of the Iranian capital, Tehran, 15 March 2026 Nuacht agus Cúrsaí Reatha RTÉ Lorg na buamála i gceantar cónaithe i bpríomhchathair na hIaráine, Tehran, 15 Márta 2026

Joan Laporta es uno de los grandes protagonistas deportivos de las últimas horas. Ganó las elecciones y será presidente del Barça hasta 2031. Sus retos de cara al futuro, razones especiales de un lunes que mira a Europa, a la Champions y a Manchester, a donde viaja el Real Madrid con Bellingham y Mbappé. En los británicos, el equipo de Pep Guardiola ha tenido día de descanso en la previa del choque de octavos de Champions. Además, el resumen de la jornada de Liga, la previa de los cierres de la jornada, una destitución en Segunda y otro fin de semana de decepción española en el Mundial de Fórmula 1, esta vez en China. Además, la Barra Libre con Antonio Romero, Miguel Martín Talavera, Jordi Martí y Antón Meana.

Melnā dzilna ir lielākais no Eiropas dzeņiem. Arī Latvijā tā ir lielāka dzeņu suga. "Šim putnam ir raksturīgi divi saucieni – vienu var dzirdēt lidojumā, otru – kad dzilna sēž kokā. Tas ir vairāk melanholisks sauciens, ko var dzirdēt tālu. Vēl tālāk var dziedāt bungošanu," stāsta Latvijas Ornitoloģijas biedrības pārstāvis Madars Bergmanis. 'Dzeņiem atšķirībā no dziedātājputniem ir raksturīga bungošana. Tie ir ļoti ātri sitieni pa sausu substrātu, vai nu tas būtu nolūzis koka zars vai koka galotne. Vidēji tie ir kādi 20 sitieni sekundē ļoti lielā tempā. Un funkcionāli tā bumbošana atbilst dziedātājputnu dziesmām. Bet dzilnām atšķirībā no pārējiem dzeņiem vēl ir raksturīgi, ka viņām ir arī saucienu sērija, ko parasti mēdz saukt par dziesmu," skaidro Madars Bergmanis. Gan melnās dzilnas dziesma, gan bungošana ir dzirdama pat 2-3 kilometrus tālu. Putns ir arī iespaidīga izmēra, gandrīz vārnas izmērā, tikai mazliet slaidāks un tievāku kaklu. "Melnās dzilnas nosaukums ļoti labi raksturo to, kā viņa izskatās. Viņa ir pilnīgi melna. Dzimumi atšķiras tikai ar ar galvas krāsojumu – tēviņiem visa galvas virs sarkana un mātītei tikai pakauša daļa ir sarkana. Bet citādi dzimumi nav atšķirami, un cilvēki nespēj atšķirt pēc balsīm, vai tā ir mātīte vai tēviņš," norāda Madars Bergmanis. "Dzilnas, kas sāk ligzdot diezgan agri, ir dzirdamas visvairāk martā. Ligzdo, tāpat kā visi dzeņi, dzilnas koku dobumos, ko paši arī izkaļ. Lielos, resnos kokos, jo dzilnas izmēri ir iespaidīgi un kokam jābūt vismaz 35-40 centimetru diametrā pie pamata, lai tur varētu dzilna mēģināt ligzdot. Tas rada zināmas grūtības ligzdu vietu atrašanā, jo vajadzīgs tiešām vecs mežs, nevis vidēja vecuma," turpina Madars Bergmanis. Melnajai dzilnai ir garš un spēcīgs knābis. Viņa var izkalt pamatīgus caurumus kokos. "Bungošana un vokālā aktivitāte vairāk notiek koku galotnēs, kalšana bieži vien notiek uz zemes, uz kritalām vai koku stumbros zemu. Melnās dzilnas kalumus var viegli atpazīt pēc izmēriem, jo skaidu garums un biezums ir tik liels, ka citiem dzeņiem tas nav pa spēkam," atzīst Madars Bergmanis.

O número de "obituários do BTC", artigos e publicações em redes sociais por personalidades e doutores do mundo das finanças cantando a morte do Bitcoin, cresceu muito nos últimos meses e especialmente no mês de fevereiro de 2026. Vamos conversar sobre isso para entender o que isso significa para o futuro do Bitcoin.BTC moggado pela IBOVhttps://x.com/bitdov/status/2025951856183177596https://x.com/bitdov/status/2026062010379804700https://x.com/bitdov/status/2026599304027070829https://beta.predyx.com/market/vencedor-btc-vs-ibov-mises-ou-cerize-1771840961Índice de Medo e Ganânciahttps://alternative.me/crypto/fear-and-greed-index/Obituários do Bitcoinhttps://bitcoindeaths.com/https://99bitcoins.com/bitcoin-obituaries/https://bitbo.io/dead/Gravado no bloco 938550________________APOIE O CANALhttps://bitcoinheiros.com/apoie/⚡ln@pay.bitcoinheiros.comPara agendar uma CONSULTA PRIVADA com o Dov: https://consultorio.bitcoinheiros.com/Consulta pública: https://ask.arata.se/bitdov00:00 Introdução00:48 Situação do Bitcoin em fevereiro de 202604:29 Momentos de medo extremo no Bitcoin11:27 O Bitcoin morreu?14:57 Artigo "Deveríamos comprar Bitcoin?" de Bernardo Guimarães20:14 Início das transações de Bitcoin21:36 Formas de comprar coisas com Bitcoin22:47 O Bitcoin é utilizado por criminosos?23:44 Concentração de bitcoin em corretoras e empresas26:04 A mentira dos índices de inflação27:09 Valorização de ações e Bitcoin34:56 Aposta Mises vs. Cerize na Predyx é sinal de depressão41:24 Baja más, compramos másEscute no Fountain Podcasts (https://fountain.fm/join-fountain)para receber e enviar satoshinhos no modelo Value4ValueSIGA OS BITCOINHEIROS:Site: https://www.bitcoinheiros.comTwitter: https://www.x.com/bitcoinheirosAllan - https://www.x.com/allanraicherDov - https://x.com/bitdovBecas - https://x.com/bksbk6Instagram: https://www.instagram.com/bitcoinheirosFacebook: https://www.fb.com/bitcoinheirosPodcast: https://anchor.fm/bitcoinheirosMedium: https://medium.com/@bitcoinheirosCOMO GUARDAR SEUS BITCOINS?Bitcoinheiros recomendam o uso de carteiras Multisig com Hardware Wallets de diferentes fabricantes ou próprias.Para ver as carteiras de hardware que recomendamos, acesse https://www.bitcoinheiros.com/carteirasVeja os descontos e clique nos links de afiliados para ajudar o canalPor exemplo, para a COLDCARD - https://store.coinkite.com/promo/bitcoinheirosCom o código "bitcoinheiros" você ganha 5% de desconto na ColdCardPlaylist "Canivete Suíço Bitcoinheiro"https://www.youtube.com/playlist?list=PLgcVYwONyxmg-KH5bwzMU4sdyMbVMPqwbPlaylist "Carteiras Multisig de Bitcoin"https://www.youtube.com/playlist?list=PLgcVYwONyxmi74PiIUSnGieNIPqmtmdjWISENÇÃO DE RESPONSABILIDADE:Este conteúdo foi preparado para fins meramente informativos.NÃO é uma recomendação financeira nem de investimento.As opiniões apresentadas são apenas opiniões.Faça sua própria pesquisa.Não nos responsabilizamos por qualquer decisão de investimento que você tomar ou ação que você executar inspirada em nossos vídeos.P.S. para os buscadoresSomos bitcoinheiros, não bitconheiros, nem bitconheros, bitcoinheros, biticonheiros, biticonheros ou biticoinheros.O Dov é bitcoinheiro, não bitconheiro, nem bitconhero, bitcoinhero, biticonheiro, biticonhero ou biticoinhero.É Bitcoin, não Bitcon e nem Biticoin :)

Los ciudadanos periodistas deberían cambiar la letra de su canción. Durante más de una década nos han machacado con el mismo estribillo. Daba igual que el Madrid ganase más de la mitad de las Champions disputadas. El Madrid no se sabía a qué jugaba y todo lo basaba en sus individualidades. ¿Ganó tres Champions seguidas? Sí, pero sólo para tapar sus carencias. Min. 01 Seg. 56 – Intro Min. 07 Seg. 54 - Desordenados y sin rumbo Min. 15 Seg. 31 - Los canteranos ayer también mal Min. 23 Seg. 12 – Ni cortan ni cosen Min. 29 Seg. 52 - No le da ni jugando medianamente bien Min. 35 Seg. 54 - Un equipo no diseñado para el juego Min. 40 Seg. 15 - Sin esquema de juego ni fiabilidad Min. 45 Seg. 11 - Jugar al contragolpe es el mal Min. 49 Seg. 25 - Un partido que debería ser impugnado Min. 57 Seg. 07 - Las gracietas de Gerard Piqué Min. 62 Seg. 22 - Despedida Dinosaurs (Corte Madera, CA 22/02/1983) Highway 61 Revisited Jesse James Minglewood Blues Twelve Gates To The City Route 66 St. Louis Blues Dancin' Fool S.O.S. Crooked Judge Del McCoury Band & Friends - For What It's Worth (Hendersonville, TN 11/05/2017)

VOV1 - Các nhà nghiên cứu Trung Quốc đã đạt được thành công đầu tiên trên thế giới trong điều trị bệnh nhân suy gan bằng phương pháp truyền dịch ngoài cơ thể với gan lợn chỉnh sửa gen.Bước đầu xác nhận tính an toàn và hiệu quả của kỹ thuật này và cung cấp một chiến lược mới cho điều trị lâm sàng bệnh gan giai đoạn cuối.Bước đột phá này đạt được nhờ nhóm nghiên cứu do Viện sĩ Đậu Khoa Phong của Viện Hàn lâm Khoa học Trung Quốc kiêm Trưởng khoa Phẫu thuật Gan mật Bệnh viện Tây Kinh thuộc Đại học Y khoa Không quân dẫn đầu.Nhóm nghiên cứu đã thu được gan từ một con lợn được chỉnh sửa 6 gen, kết nối nó với thiết bị truyền máu cơ học ở nhiệt độ phòng và thiết lập hệ thống tuần hoàn chéo liên kết gan lợn dị loài với hệ thống tuần hoàn máu của bệnh nhân.Trong quá trình kết nối giữa hệ thống và bệnh nhân bị suy gan cấp tính trên nền mạn tính, gan lợn tạm thời đảm nhiệm các chức năng giải độc, tổng hợp và chuyển hóa của cơ thể, trong khi gan của bệnh nhân vẫn được giữ nguyên. Không giống như các ca ghép gan thông thường, quy trình này sử dụng phương pháp hỗ trợ sự sống ngoài cơ thể, theo Báo Khoa học Trung Quốc.Hình minh họa quá trình điều trị bệnh nhân suy gan bằng phương pháp truyền dịch ngoài cơ thể với gan lợn đã được chỉnh sửa 6 gen. (Nguồn ảnh: Bệnh viện Tây Kinh)

Over 1.5million adults in the UK tried weight loss drugs in 2024-25. Many swear by them, but they have been associated with side effects including nausea and, in some cases, extremely painful gallstones. But what does the evidence actually tell us, and what is the wider impact on the way we view our bodies in society?James Gallagher is joined by Professor of Cardiometabolic Medicine at the University of Glasgow Naveed Sattar, Dr Beverley O'Hara, Lecturer in Public Health Nutrition at Leeds Beckett University, and Dr Margaret McCartney, resident Inside Health GP. They discuss what the evidence tells us about the potential known side effects of these weight loss drugs, and the potential impact their use has on our view of obesity as a society. We also hear from Sarah Le Brocq, who has struggled with obesity all her adult life and has been on these drugs for the past 2-3 years about her experiences. Margaret McCartney has no conflicts of interest to declare.Beverley O'Hara has no conflicts of interest to declare. She has 2 roles with the Association for the Study of Obesity (voluntary academic positions).Naveed Sattar has consulted for and/or received speaker honoraria from AbbVie, Amgen, AstraZeneca, Boehringer Ingelheim, Carmot Therapeutics, Eli Lilly, Gan & Lee, GlaxoSmithKline, Hanmi Pharmaceuticals, Kailera, Mass Medicines, Menarini-Ricerche, Metsera, Novo Nordisk, Pfizer, Regeneron, Roche, UCB Pharma, and Verdiva Bio; and received grant support paid to his University from AstraZeneca, Boehringer Ingelheim, Novartis, and Roche.Presenter: James Gallagher Producer: Hannah Fisher Researcher: Tom Hunt Production coordinator: Stuart Laws Content Editor: Ilan Goodman

Make a Logo on Fiverr If you're upgrading your setup in 2026—whether it's for content creation, streaming, or just building a smarter workspace—this gear haul hits all the right notes. From blazing-fast storage to a surprisingly versatile charging hub, these are the tools that stood out in real-world use and deserve a spot on your radar. Kingston Fury G5 SSD: PCIe 5 Speed Enters the Chat Starting strong, the Kingston Fury G5 SSD delivers next-gen PCIe 5.0 performance, pushing speeds up to a theoretical 14,000 MB/s. Even when running in a PCIe 4.0 system, it still delivers a noticeable performance bump, especially in heavy workloads like video editing and large file transfers. Installation is straightforward—drop it into your NVMe slot, secure it, and you're off. The real benefit shows up in sustained speeds and reduced bottlenecks, making it a solid upgrade for creators who need reliability and speed. Qwiizlab TB5 NVMe Enclosure: Portable Powerhouse Pair that SSD with the Qwiizlab TB5 NVMe enclosure and you've got a portable storage solution that doesn't slow you down. With Thunderbolt 5 support and up to 80Gbps bandwidth, this enclosure is built for speed across multiple systems. It's fanless, aluminum-built for heat dissipation, and designed for creators who jump between machines. Bring your own NVMe, snap it in, and you've got a flexible, high-speed external drive that works across Mac, PC, and USB-C environments. Ivanky FusionDock Pro 3: Desktop Command Center The Ivanky FusionDock Pro 3 is a Thunderbolt 5 docking station that turns your desk into a connectivity hub. With 11 ports, including 2.5Gb Ethernet, multiple USB-A and USB-C connections, and up to 140W power delivery, it's built to handle everything from laptops to full production rigs. It's especially useful for anyone juggling multiple devices—plug in once and everything connects. No HDMI here, but USB-C display support keeps things modern and flexible. Joyroom Podix 140W Charger: The Star of the Show The highlight of this haul is the Joyroom Podix 140W charger. This compact GaN charger packs serious functionality into a small cube design. It features retractable USB-C cables, multiple ports, and a smart display that shows real-time power distribution across devices. Whether you're charging a laptop, phone, tablet, or accessories, it dynamically balances up to 140W total output. What really sets it apart is usability—those retractable cables reduce clutter, and the display gives you instant feedback on what's drawing power. It's ideal for desks, studios, or even travel setups where you want everything powered without a mess of cables. *Joyroom sponsored this post Get the Charger here Lumary Rope Light: Smart Lighting with Personality For ambiance, the Lumary Rope Light brings customizable RGB IC lighting into your space. With segment-based color control, app integration, and voice assistant compatibility, it's more than just a light strip. You can design patterns, sync with music, or set scenes for different moods. The diffused rope design keeps the light looking smooth rather than dotted, making it perfect for studio backdrops or creative spaces. Dingbox D2: Flexible Android TV Box The Dingbox D2 runs Android 12 and offers a customizable TV experience with features like app sideloading, Wi-Fi 6, and extended recording capabilities. It supports up to 6K output and includes generous storage and RAM for smooth performance. While the security patch is dated, the flexibility makes it appealing for developers or users who want more control than typical streaming boxes allow. Logitech G522 Lightspeed Headset: Gaming Meets Broadcast Audio The Logitech G522 Lightspeed headset blends gaming performance with broadcast-style audio controls. With tri-connectivity (Lightspeed wireless, Bluetooth, USB), RGB lighting, and up to 60 hours of battery life, it's built for long sessions. The inclusion of Blue VO!CE tech allows for deep microphone tuning—EQ, noise reduction, compression—all customizable through Logitech's software. It's a strong option for gamers who also stream or record content. XVive U35 Wireless Microphone System: Cut the Cord Rounding out the haul is the XVive U35 wireless microphone system. Operating on 5.8GHz, it avoids the crowded 2.4GHz spectrum and provides stable wireless audio for dynamic microphones. Setup is simple—plug transmitter and receiver into your mic and mixer, and you're good to go. It's ideal for live performances, studio movement, or any situation where cables get in the way. The range and reliability make it a practical upgrade for musicians and presenters alike. Check out the Geekazine Merch, including "I AM AI " T-Shirt.  Thanks for reading! Don't forget to subscribe to Geekazine: RSS Feed - YouTubeTwitter - Facebook Tip Me via Paypal.me Send a Tip via Venmo RSS Bandwidth by Cachefly Get a 14 Day Trial Be a Patreon: Part of the Sconnie Geek Nation! Reviews: Geekazine gets products in to review. Opinions are of Geekazine.com. Sponsored content will be labeled as such. Read all policies on the Geekazine review page.  Reviews: Geekazine is also an affiliate of Amazon Last Updated on April 14, 2026 4:10 pm by Jeffrey PowersThe post New Gear Haul for 2026 Featuring the Joyroom Podix appeared first on Geekazine.

Allen, Rosemary, and Yolanda discuss Ming Yang’s proposed $1.5 billion factory in Scotland and why the UK government is hesitating. Plus the challenges of reviving wind turbine manufacturing in Australia, how quickly a blade factory can be stood up, and whether advanced manufacturing methods could give Australia a competitive edge in the next generation of wind energy. Sign up now for Uptime Tech News, our weekly newsletter on all things wind technology. This episode is sponsored by Weather Guard Lightning Tech. Learn more about Weather Guard’s StrikeTape Wind Turbine LPS retrofit. Follow the show on YouTube, Linkedin and visit Weather Guard on the web. And subscribe to Rosemary’s “Engineering with Rosie” YouTube channel here. Have a question we can answer on the show? Email us! The Uptime Wind Energy Podcast brought to you by Strike Tape, protecting thousands of wind turbines from lightning damage worldwide. Visit strike tape.com And now your hosts.  Allen Hall: Welcome to the Uptime Wind Energy Podcast. I’m your host Allen Hall, and I’m here with Yolanda Padron and Rosemary Barnes, and we’re all in Australia at the same time. We’re getting ready for Woma 2026, which is going to happen when this release is, will be through the first day. Uh, it’ll, it’s gonna be a big conference and right now. We’re so close to, to selling it out within a couple of people, so it’ll be a great event. So those of you listening to this podcast, hopefully you’re at Wilma 2026 and we’ll see, see you there. Uh, the news for this week, there’s a number of, of big, uh, country versus country situations going on. Uh, the one at the moment is [00:01:00] ING Yang in Scotland, and as we know, uh, Scotland. It has been offered by Ming Yang, uh, to build a factory there. They’re put about one and a half billion pounds into Scotland, uh, that is not going so well. So, so they’re talking about 3000 jobs, 1.5 billion in investment and then. Building, uh, offshore turbines for Britain and the larger Europe, but the UK government is hesitating and they have not approved it yet. And Scotland’s kind of caught in the middle. Ming Yang is supposedly looking elsewhere that they’re tired of waiting and figure they can probably get another factory somewhere in Europe. I don’t think this is gonna end well. Everyone. I think Bing Yang is obviously being pushed by the Chinese, uh, government to, to explore Scotland and try to get into Scotland and the Scottish government and leaders in the Scottish government have been meeting with, uh, [00:02:00] Chinese officials for a year or two. From what I can tell, if this doesn’t end with the factory in Scotland. Is China gonna take it out on the uk? And are they gonna build, is is me gonna be able to build a factory in Europe? Europe at the minute is looking into the Chinese investments into their wind turbine infrastructure in, in terms of basically tax support and, and funding and grants of that, uh, uh, aspect to, to see if China is undercutting prices artificially. Uh, which I think the answer is gonna be. Yes. So where does this go? It seems like a real impasse. At a moment when the UK in particular, and Europe, uh, the greater Europe are talking about more than a hundred gigawatts of offshore wind,  Yolanda Padron: I mean, just with the, the business that you mentioned that’s coming into to the uk, right? Will they have without Min Yang the ability to, to reach their goals?  Allen Hall: So you have the Siemens [00:03:00] factory in hall. They have a Vestus factory in Hollow White on the sort of the bottom of the country. Right. Then Vestus has had a facility there for a long time and the UK just threw about 20 million pounds into reopening the onshore blade portion of that factory ’cause it had been mothballed several months ago. It does seem like maybe there’s an alternative plan within the UK to stand up its own blade manufacturing and turbine manufacturing facilities, uh, to do a lot of things in country. Who I don’t think we know. Is it Siemens? Is it ge? Is it Vestus or is it something completely British? Maybe all the above. Rosemary. You know, being inside of a Blade factory for a long time with lm, it’s pretty hard to stand up a Blade factory quickly. How many years would it take you if you wanted to start today? Before you would actually produce a a hundred meter long offshore blade,  Rosemary Barnes: I reckon you could do it in a year if you had like real, real strong motivation [00:04:00] Allen Hall: really. Rosemary Barnes: I think so. I mean, it’s a big shed and like, it, it would be, most of the delays would be like regulatory and, you know, hiring, getting enough people hired and trained and that sort of thing. But, um, if you had good. Support from the, the government and not too much red tape to deal with. Then, uh, you know, if you’ve got lots of manufacturing capability elsewhere, then you can move people. Like usually when, um, when I worked at LM there were a few new factories opened while I was working there, and I’m sure that they took longer than, than a year in terms of like when it was first thought of. But, um, you know, once the decision was made, I, I actually dunno how long, how long it took. So it is a guess, but it didn’t, it didn’t take. As long as you would think it wasn’t. It wasn’t years and years, that’s for sure. Um, and what they would do is they don’t, you know, hire a whole new workforce and train them up right from the start. And then once they’re ready to go, then they start operating. What they’ll do to start with is they’ve got, you know, like a bunch [00:05:00] of really good people from the global factories, like all around, um, who will go, um, you know, from all roles. And I’m not talking just management at all, like it will include technicians, um, you know, every, every role in the factory, they’ll get people from another factory to go over. And, um, you know, they do some of the work. They’re training up local people so you know, there’s more of a gradual handover. And also so that you know, the best practices, um, get spread from factory to factory and make a good global culture. ’cause obviously like you’ve got the same design everywhere. You want the same quality coming out everywhere. Um, there is, as much as you try and document everything should be documented in work instructions. That should make it, you know, impossible to do things wrong. However, you never quite get to that standard and, um. There is a lot, a lot to be said for just the know-how and the culture of the people doing the um, yeah, doing the work.  Allen Hall: So the infrastructure would take about a year to build, but the people would have to come from the broader Europe then at [00:06:00] least temporarily.  Rosemary Barnes: That, that would be the fastest and safest way to do it. Like if it’s a brand new company that has never made a wind turbine before and someone just got a few, you know, I don’t know, a billion dollars, and um, said, let’s start a wind turbine factory, then I think it’s gonna be a few years and there’s gonna be some learning curve before it starts making blades fast enough. And. With the correct quality. Um, yeah. But if you’re just talking about one more factory from a company that already has half a dozen or a dozen wind turbine blade factories elsewhere in the world, then that’s where I think it can be done fast.  Allen Hall: This, uh, type of situation actually pops up a lot in aerospace, uh, power plants, engines. The jet engines on a lot of aircraft are kind of a combined effort from. Big multinational companies. So if they want to build something in country, they’ll hook up with a GE or a, a Honeywell or somebody who makes Jet engines and they’ll create this division and they’ll [00:07:00] stand this, this, uh, plant up. Maybe it’s gonna be something like that where GB energy is in the middle, uh, providing the funding and some of the resources, but they bring in another company, like a Siemens, like a Vestas, like a GE or a Nordex even to come in and to. Do the operational aspects and maybe some of the training pieces. But, uh, there’s a, there’s a funding arm and a technical arm, and they create a standalone, uh, British company to go manufacture towers to go manufacture in the cells to manufacture blades. Is that where you think this goes?  Rosemary Barnes: It depends also what kind of, um, component you’re talking about. Like if you’re talking about, I, I was talking a specific example of wind turbine blades, which are a mediumly complex thing to make, I would say, um. Yeah. And then if you go on the simpler side, when turbine towers, most countries would have the. Rough expertise needed, um, to, to do that. Nearly all towers at the moment come out of [00:08:00] China, um, or out of Asia. And with China being the, the vast bulk of those. Um, and it’s because they’ve got, aside from having very, very cheap steel, um, they also have just got huge factories that are set up with assembly lines so that, you know, there’s not very much moving of things back and forth. So they have the exact right bit of equipment to do. The exact right kind of, you know, like rolling and welding and they’re not moving tower sections around a lot. That makes it really hard for, um, for other countries to compete. But it’s not because they couldn’t make towers, it’s because they would struggle to make them cheap enough. Um, so yeah, if you set up a factory, you know, say you set up a wind turbine, um, factory in, uh, wind turbine tower factory in Australia, you, you could buy the equipment that you needed for, you know, a few hundred million dollars and, um. You could make it, but unless you have enough orders to keep that factory busy, you know, with the, the volume that you need to keep all of that [00:09:00] modern equipment, uh, operating just absolutely around the clock, your towers are gonna be expensive out of that facility. So that’s kind of the, that it’s cost is the main barrier when it comes to towers  Allen Hall: with Vestus in Mitsubishi recently having a partnership and then ending that partnership. It would seem like Vestus has the most experience in putting large corporations together to work on a, an advanced wind turbine project is they would, it would make sense to me if, if, if Vestus was involved because Vestus also has facilities in the uk. Are they the leading choice you think just because they have that experience with Mitsubishi and they have something in country or you think it’s somebody else? Is it a ge  Rosemary Barnes: My instinct is saying Vestas. Yes,  Allen Hall: me too. Okay.  Rosemary Barnes: Ge. It’s wind turbine Manufacturing seems to be in a bit of a, more of an ebb rather than a flow right now, so I [00:10:00] mean that’s, that’s probably as much as what it’s based on. Um, and then yes, like the location of, of factories, there are already some vest, uh, factories, vest people in the uk so that would make it easier. : Delamination and bottomline failures and blades are difficult problems to detect early. These hidden issues can cost you millions in repairs and lost energy production. C-I-C-N-D-T are specialists to detect these critical flaws before they become expensive burdens. Their non-destructive test technology penetrates deep into blade materials to find voids and cracks. Traditional inspections completely miss. C-I-C-N-D-T Maps. Every critical defect delivers actionable reports and provides support to get your blades back in service. So visit cic ndt.com because catching blade problems early will save you millions.[00:11:00] Allen Hall: Can you build a renewable energy future on someone else’s supply chain? Well, in Australia, the last domestic wind tower manufacturers are down. Last year, after losing a 15 year battle against cheaper imports from China, now the Albanese government wants to try again, launching a consultation to revive local manufacturing. Meanwhile, giant turbines are rising in Western Australia’s. Largest wind farms soon to power 164,000 homes. Uh, the steel towers, blades and the cells, they all arrive on ships. And the question is whether that’s going to change anytime soon. Rosemary?  Rosemary Barnes: Yeah, it’s, uh, it’s a topic I’ve thought about a lot and done a fair bit of work on as well, local manufacturing and whether you should or shouldn’t, the Australian government does try to support local manufacturing in. General, um, and in particular for renewables, but they focused much more on solar and [00:12:00] batteries. Um, with their manufacturing support, Australian government and agencies like a uh, arena, Australian Renewable Energy Agency have not traditionally supported wind like at all. It bothers me because actually Australia is a fantastic place to be developing some of these supporting technologies for wind energy and even the next generation of wind energy. Um, technologies, we, not any manufacturing. There are heaps of, um, things that would make it more suitable Australia, like just actually a really natural place to develop that. The thing about Australian projects is that they are. Big. Right. That makes it really attractive to developers because like in Europe where they’re, you know, still building wind, but you know, an onshore wind farm in Europe is like a couple of turbines here or there, maybe five, like a big wind farm would be 10, 10 turbines over there. Um, in Australia it’s like a hundred, 200 turbines at a time. Um, for onshore also choosing. Really big turbines. Australians, for some reason, Australian developers really like to [00:13:00] choose the latest technologies. And then if we think about some of the, um, you know, like new supporting technologies for existing wind turbines, like, you know, let’s, um, talk about. O and m there’s a whole lot of, um, o and m technologies, and Australia’s a great place for that too because as Australia wind farms spend so much on o and m compared to other countries. So a technology provider that can improve some of those pain points can much quicker get like a positive, um, return on investment in Australia than they would be able to in somewhere like America or, or Europe. So I think it makes sense to develop here  Allen Hall: with the number of wind farms. Rosie, I, I completely agree with you and. When we were talking about the war Dge wind Farm, which is the Western Australian wind farm that’s gonna expand, they’re adding 30 turbines to provide 283 megawatts. That’s like a nine and a half megawatt machine. Those are big turbines. Those are new turbines, right? That’s not something that’s been around for a couple years. They’ve been around for a couple of months in, in terms of the lifespan of, of wind [00:14:00] turbines. So if Australia’s gonna go down the pathway of larger turbines, the, the most advanced turbines. It has to make sense that some of this has, has to be developed in country just because you need to have the knowledge to go repair, modify, improve, adjust, figure out what the next generation is, right? I don’t know how you, this happens.  Rosemary Barnes: We see some examples of that. Right. And I think that Fortescue is the best example of, um, companies that are trying to think forward to what they’re going to need to make their, you know, they’ve got ambitious plans for putting in some big wind farms with. Big wind turbines in really remote locations. So they’ve got a lot of, um, it’s a lot of obvious challenges there. Um, and I know that they’re thinking ahead and working through that. And so, you know, we saw their investment in, um, nbra wind, the Spanish company and in particular their nbra lift. The bit of the tower that attaches to the rotor. It looks [00:15:00] pretty normal. Um, but then they make it taller by, um, slotting in like a lattice framework. Um, and then they jack it up and slot in another one underneath and jack it up and slot in another one underneath. So they don’t need a gigantic crane and they don’t need, um, I mean, it’s still a huge crane, but they don’t, they don’t, it doesn’t need to be as, as big because, you know, the rotor starts, starts off already on there by the time that the tower gets su to its full height. So, um, yeah, it’s a lot. That’s an innovative solution, I think, and it would, I would be very surprised if they weren’t also looking at every other technology that they’re gonna need in these turbines.  Allen Hall: If Australia’s gonna go down the pathway of large turbines on shore, then the manufacturing needs to happen in country. There’s no other way to do it. And you could have manufacturing facilities in Western Australia or Victoria and still get massive turbine blades shipped or trucked either way. To [00:16:00] wherever they needed it to go. In country, it would, it’s not that hard to get around Australia and unlike other countries like, like Germany was a lot of mountains and you had bridges and narrow roads and all that, and it, it’s, it’s much more expansive in Australia where you can move big projects around. And obviously with all the, the mining that happens in Australia, it’s pretty much normal. So I, I just trying to get over the hurdle of where the Albanese government is having an issue of sort of pushing this forward. It seems like it’s a simple thing because the Australian infrastructure is already ready. Someone need to flip the switch and say go.  Rosemary Barnes: I don’t know if I’d say that we’re we’re ready. ’cause Australia doesn’t have a whole lot of manufacturing of anything at the moment. It’s not true that we have no manufacturing. That’s what Australians like to say. We don’t manufacture anything and that’s not true. We do manufacture. We have some pretty good advanced manufacturing. If you just look at the hard economics of wind turbine manufacturing in Australia of solar panel manufacturing, battery manufacturing. Any of that, it is cheaper to just get it from China, not least [00:17:00] because some of the, um, those components are subsidized by the, the Chinese government. If you start saying, okay, we’re gonna have local manufacturing, like, you can either, you can achieve that either by supporting the local manufacturing industry, you know, like giving subsidies to our manufacturing. Or you could, um, make a local content requirement. Um, say things, you know, if you want project approval for this, then it has to have so much local content. You have to do it really carefully because if you get the settings wrong, then you just end up with very, very expensive, um, renewable energy. And at the moment, especially wind is. Expensive, and I think it’s still getting more expensive in Australia. It has been since, basically since the pandemic. If you then said, we’ve gotta also make it in Australia, then you add a bunch more costs and we would just probably not have wind energy then, so, uh, or new, new wind energy. So there needs to be that balance. But I think that like, even though you can say, okay, cheapest is best, it is also not good to rely on. [00:18:00] Exclusively on other countries, and especially not on just one other country to give you all of your energy infrastructure. If it was up to me, I would be much more supporting the next wave of, um, technologies. I would really love to see, you know, a new Australian. Wind turbine blade manufacturing method. Like at some point in the next decade, we’re going to start getting, uh, advanced manufacturing is gonna make it into wind turbine blades. It’s already there in some of the other components.  Allen Hall: Wait, so you just said if we were gonna build a factory in Scotland, it would take about a year. Why would it take 10 years to do it in Australia? Australia’s a nice place to live.  Rosemary Barnes: No, I didn’t say that. It would, it would take teens. I said in, sometime in the next decade around the world, wind turbine blades are basically handmade, right? They, you know, there are some, um, machines that are helping people, but you know, you have a look at a picture of a wind turbine blade factor and there’s, you know, there’s 20 people walking over, walking over a blade, smoothing down glass. And at some point we’re gonna start using advanced manufacturing methods. I [00:19:00] mean, there are really advanced composite manufacturing methods. Um, you know, with, um, individual fiber placement and 3D printing with, um, continuous fibers. And that’s being used for like aerospace components a lot. It’s early days for that technology and there is no barrier to the technologies to being able to put them, you know, like say on a GaN gantry that just, you know, like ran down the length of a whole blade like that, that could be done. If it was economic, that’s the kind of technology that Australia should be supporting before that’s the mainstream, and everybody else has already done it, right? You need to find the next thing, and ideally not just one next thing, but several next things because you’re not gonna, you don’t know ahead of time, um, which is gonna be the winner. Allen Hall: That hasn’t been the tack that China has taken, that the latest technology in batteries is not something that China is producing today. They’re producing a generation prior, but they’re doing it at scale. At some point they, the Chinese just said, we’re stopping here and we’re gonna do this, this kind of [00:20:00] battery, and that’s it. And away we go. If we keep waiting until the next generation of blade techniques come out, I think we’re gonna be waiting forever.  Rosemary Barnes: I don’t think why I think. Do, you know, make the next generation of, of blade bio technologies?  Yolanda Padron: I think it makes sense for someplace like Australia, right? Because we, we’ve talked about the fact that like here, you, you have to consider a lot of factors in operation that you don’t have to consider in other places, especially for blades, right? So if you can eliminate all of those issues, for the most part that are happening in the factory at manufacturing, then that can really help boost. The next operational projects.  Allen Hall: So then what you’re saying is that. There are new technologies, but what stage are they at? Are they TRL two, TRL five, TRL seven. How close is this technology because I’d hate for Australia to miss out on this big opportunity.  Rosemary Barnes: Frown Hoffer has actually just published an article recently, uh, [00:21:00] about some, I can’t remember if it was fiber, um, tape placement or if it was printed, small wind turbine blades. Small wind is a nice, like, it’s a, a nice bite-sized kind of thing that you can master a lot quicker than you can, you know, you can make a thousand small wind turbines and learn a lot more than making 100 meter long blade. That would probably be bad because it’s your first one and you didn’t realize all of the downsides to the new technology yet. Um, so I, I think it is kind of promising, but. In terms of, yeah, like a major, like in terms of let’s say a hundred meter long blade that was made with 3D printing, that would be terra, L one. Like it’s an idea now. Nobody has actually made one or, um, done, done too much. Um, as far as I know. I think you could get, could get to nine over the next year. Like I said, like I think sometime in the next decade will be when that, when that comes.  Allen Hall: Okay. If you, you didn’t get to a nine that quickly. No, it is possible. Yeah. You gotta put some money into it.  Rosemary Barnes: If someone wants to give me, [00:22:00] you know, enough money, then I’ll make it. I’ll make it happen. I’ll, I would, I would absolutely be able to make that happen, but I don’t know when it’s gonna be cheap enough.  Allen Hall: I would just love to see it. If, if, if you’ve got a, if you’ve got a, a factory, you got squirreled away somewhere in the. Inland of Australia that is making blades at quantity or has the technology to do that. I would love to see it because that would be amazing.  Rosemary Barnes: Technologies don’t just fall out of the sky, you know, like they, you, you, you force them into existence. That’s what you, that’s what you do. You know what this comes down to? Have you ever done the, is it Myers-Briggs where you get the, like letters of your personality? You and I are in opposite corners inside some ways.  Allen Hall: That wraps up another episode of the Uptime Wind Energy Podcast. If today’s discussion sparked any questions or ideas, and it surely should, we’d love to hear from you. Reach out to us on LinkedIn, particularly Rosie, so it’s Rosemary Barnes on LinkedIn. Don’t forget to subscribe to who you never miss an episode. And if you found value in today’s conversation, please leave us a review. It really helps other wind [00:23:00] energy professionals discover the show. For Rosie and Yolanda, I am Alan Hall, and we’ll see here next week on the Uptime Wind Energy Podcast.

Sección: Noticias - OVNI sobre la Isla de Pascua (foto Néstor Berlanda, 1986, Mar del Plata). - Preparando el desembarco en Marte. - Tritón: ¿satélite de Neptuno con atmósfera? - Oleada de Apariciones Marianas. - El monstruo del Lago Ness reaparece. - Medicina: láser para todo. - Cursos de Parapsicología: ¿son confiables? - Profesorado en Parapsicología Aplicada reconocido oficialmente en Paraná. Sección: Cartas de oyentes - Críticas a un Pai. Vasografía. ¿Perciben los fantasmas su entorno? El "tercer ojo". "Yo visité Ganímedes". Los ángeles de la Biblia. San Martín de Porres. - Crítica al Suplemento de "Ciencia para todos" de Clarín a propósito de una encuesta de Gallup en EE.UU. y una teoría científica explicativa sobre Fantasmas. - Libro: "La Rama Dorada" (James George Frazer). - Soñar en colores. Sección: Revolviendo la Biblioteca - Libro sobre Ocultismo: "Formulario de Alta Magia" (Pierre Piobb) - Gemoterapia: La energía de las piedras preciosas, semipreciosas y cristales aplicadas a la salud. La "energía de las formas". Las leyes del Universo. La velocidad de las galaxias. Grabado el 30/05/1993. Aclaración: Este episodio se elaboró a partir de diferentes grabaciones de Gustavo Fernández en su programa de radio AM, en LT14 Radio General Urquiza de Paraná (Entre Ríos, Argentina), en algún momento entre agosto de 1988 y junio de 1994. Hemos quitado la música original por cuestiones de derechos de autor. No contiene publicidad. Relacionados: Más texto, audio y video sobre los temas del Misterio en nuestro portal: https://alfilodelarealidad.com/ Utiliza el buscador o busca por categorías y etiquetas. Plataforma de cursos: https://miscursosvirtuales.net * * * Programa de Afiliados * * * iVoox comparte con AFR un pequeño porcentaje si usas uno de estos enlaces: * Disfruta de la experiencia iVoox sin publicidad, con toda la potencia de volumen, sincronización de dispositivos y listas inteligentes ilimitadas: Premium anual https://www.ivoox.vip/premium?affiliate-code=68e3ae6b7ef213805d8afeeea434a491 Premium mensual https://www.ivoox.vip/premium?affiliate-code=7b7cf4c4707a5032e0c9cd0040e23919 * La mejor selección de podcasts en exclusiva con iVoox Plus Más de 50.000 episodios exclusivos y nuevos contenidos cada día. ¡Suscríbete y apoya a tus podcasters favoritos! Plus https://www.ivoox.vip/plus?affiliate-code=258b8436556f5fabae31df4e91558f48 Más sobre el mundo del Misterio en alfilodelarealidad.com

Ganó títulos, cambió el boxeo y aun así fue olvidado. Pocos recuerdan que El Maromero Páez fue uno de los mejores peso pluma de la historia. Desde su origen circense hasta su caída física y emocional, este video revela por qué México aún le debe una deuda histórica. Hosted by Simplecast, an AdsWizz company. See https://pcm.adswizz.com for information about our collection and use of personal data for advertising.

Last time we spoke about the battle of Nanchang. After securing Hainan and targeting Zhejiang–Jiangxi Railway corridors, Japan's 11th Army, backed by armor, air power, and riverine operations, sought a rapid, surgical seizure of Nanchang to sever eastern Chinese logistics and coerce Chongqing. China, reorganizing under Chiang Kai-shek, concentrated over 200,000 troops across 52 divisions in the Ninth and Third War Zones, with Xue Yue commanding the 9th War Zone in defense of Wuhan-Nanchang corridors. The fighting began with German-style, combined-arms river operations along the Xiushui and Gan rivers, including feints, river crossings, and heavy artillery, sometimes using poison gas. From March 20–23, Japanese forces established a beachhead and advanced into Fengxin, Shengmi, and later Nanchang, despite stiff Chinese resistance and bridges being destroyed. Chiang's strategic shift toward attrition pushed for broader offensives to disrupt railways and rear areas, though Chinese plans for a counteroffensive repeatedly stalled due to logistics and coordination issues. By early May, Japanese forces encircled and captured Nanchang, albeit at heavy cost, with Chinese casualties surpassing 43,000 dead and Japanese losses over 2,200 dead.    #187 The Battle of Suixian–Zaoyang-Shatow Welcome to the Fall and Rise of China Podcast, I am your dutiful host Craig Watson. But, before we start I want to also remind you this podcast is only made possible through the efforts of Kings and Generals over at Youtube. Perhaps you want to learn more about the history of Asia? Kings and Generals have an assortment of episodes on history of asia and much more  so go give them a look over on Youtube. So please subscribe to Kings and Generals over at Youtube and to continue helping us produce this content please check out www.patreon.com/kingsandgenerals. If you are still hungry for some more history related content, over on my channel, the Pacific War Channel where I cover the history of China and Japan from the 19th century until the end of the Pacific War. Having seized Wuhan in a brutal offensive the previous year, the Japanese sought not just to hold their ground but to solidify their grip on this vital hub. Wuhan, a bustling metropolis at the confluence of the Yangtze and Han Rivers, had become a linchpin in their strategy, a base from which they could project power across central China. Yet, the city was far from secure, Chinese troops in northern Hubei and southern Henan, perched above the mighty Yangtze, posed an unrelenting threat. To relieve the mounting pressure on their newfound stronghold, the Japanese high command orchestrated a bold offensive against the towns of Suixian and Zaoyang. They aimed to annihilate the main force of the Chinese 5th War Zone, a move that would crush the Nationalist resistance in the region and secure their flanks. This theater of war, freshly designated as the 5th War Zone after the grueling Battle of Wuhan, encompassed a vast expanse west of Shashi in the upper Yangtze basin. It stretched across northern Hubei, southern Henan, and the rugged Dabie Mountains in eastern Anhui, forming a strategic bulwark that guarded the eastern approaches to Sichuan, the very heartland of the Nationalist government's central institutions. Historian Rana Mitter in Forgotten Ally described this zone as "a gateway of immense importance, a natural fortress that could either serve as a launchpad for offensives against Japanese-held territories or a defensive redoubt protecting the rear areas of Sichuan and Shaanxi". The terrain itself was a defender's dream and an attacker's nightmare: to the east rose the imposing Dabie Mountains, their peaks cloaked in mist and folklore; the Tongbai Mountains sliced across the north like a jagged spine; the Jing Mountains guarded the west; the Yangtze River snaked southward, its waters a formidable barrier; the Dahong Mountains dominated the center, offering hidden valleys for ambushes; and the Han River (also known as the Xiang River) carved a north-south path through it all. Two critical transport arteries—the Hanyi Road linking Hankou to Yichang in Hubei, and the Xianghua Road connecting Xiangyang to Huayuan near Hankou—crisscrossed this landscape, integrating the war zone into a web of mobility. From here, Chinese forces could menace the vital Pinghan Railway, that iron lifeline running from Beiping (modern Beijing) to Hankou, while also threatening the Wuhan region itself. In retreat, it provided a sanctuary to shield the Nationalist heartlands. As military strategist Sun Tzu might have appreciated, this area had long been a magnet for generals, its contours shaping the fates of empires since ancient times. Despite the 5th War Zone's intricate troop deployments, marked by units of varying combat prowess and a glaring shortage of heavy weapons, the Chinese forces made masterful use of the terrain to harass their invaders. Drawing from accounts in Li Zongren's memoirs, he noted how these defenders, often outgunned but never outmaneuvered, turned hills into fortresses and rivers into moats. In early April 1939, as spring rains turned paths to mud, Chinese troops ramped up their disruptions along the southern stretches of the Pinghan Railway, striking from both eastern and western flanks with guerrilla precision. What truly rattled the Japanese garrison in Wuhan was the arrival of reinforcements: six full divisions redeployed to Zaoyang, bolstering the Chinese capacity to launch flanking assaults that could unravel Japanese supply lines. Alarmed by this buildup, the Japanese 11th Army, ensconced in the Wuhan area under the command of General Yasuji Okamura, a figure whose tactical acumen would later earn him notoriety in the Pacific War, devised a daring plan. They intended to plunge deep into the 5th War Zone, smashing the core of the Chinese forces and rendering them impotent, thereby neutralizing the northwestern threat to Wuhan once and for all. From April onward, the Japanese mobilized with meticulous preparation, amassing troops equipped with formidable artillery, rumbling tanks, and squadrons of aircraft that darkened the skies. Historians estimate they committed roughly three and a half divisions to this endeavor, as detailed in Edward J. Drea's In the Service of the Emperor: Essays on the Imperial Japanese Army. Employing a classic pincer movement, a two-flank encirclement coupled with a central breakthrough, they aimed for a swift, decisive strike to obliterate the main Chinese force in the narrow Suixian-Zaoyang corridor, squeezed between the Tongbai and Dahong Mountains. The offensive erupted in full fury on May 1, 1939, as Japanese columns surged forward like a tidal wave, their engines roaring and banners fluttering in the dust-choked air. General Li Zongren, the commander of the 5th War Zone, a man whose leadership had already shone in earlier campaigns like the defense of Tai'erzhuang in 1938, issued urgent orders to cease offensive actions against the Japanese and pivot to a defensive stance. Based on intelligence about the enemy's dispositions, Li orchestrated a comprehensive campaign structure, assigning precise defensive roles and battle plans to each unit. This was no haphazard scramble; it was a symphony of strategy, as Li himself recounted in his memoirs, emphasizing the need to exploit the terrain's natural advantages. While various Chinese war zones executed the "April Offensive" from late April to mid-May, actively harrying and containing Japanese forces, the 5th War Zone focused its energies on the southern segment of the Pinghan Railway, assaulting it from both sides in a bid to disrupt logistics. The main force of the 31st Army Group, under the command of Tang Enbo, a general known for his aggressive tactics and later criticized for corruption, shifted from elsewhere in Hubei to Zaoyang, fortifying the zone and posing a dire threat to the Japanese flanks and rear areas. To counter this peril and safeguard transportation along the Wuhan-Pinghan Railway, the Japanese, led by the formidable Okamura, unleashed their assault from the line stretching through Xinyang, Yingshan, and Zhongxiang. Mobilizing the 3rd, 13th, and 16th Divisions alongside the 2nd and 4th Cavalry Brigades, they charged toward the Suixian-Zaoyang region in western Hubei, intent on eradicating the Chinese main force and alleviating the siege-like pressure on Wuhan. In a masterful reorganization, Li Zongren divided his forces into two army groups, the left and right, plus a dedicated river defense army. His strategy was a blend of attrition and opportunism: harnessing the Tongbai and Dahong Mountains, clinging to key towns like lifelines, and grinding down the Japanese through prolonged warfare while biding time for a counterstroke. This approach echoed the Fabian tactics of ancient Rome, wearing the enemy thin before delivering the coup de grâce. The storm broke at dawn on May 1, when the main contingents of the Japanese 16th and 13th Divisions, bolstered by the 4th Cavalry Brigade from their bases in Zhongxiang and Jingshan, hurled themselves against the Chinese 37th and 180th Divisions of the Right Army Group. Supported by droning aircraft that strafed from above and tanks that churned the earth below, the Japanese advanced with mechanical precision. By May 4, they had shattered the defensive lines flanking Changshoudian, then surged along the east bank of the Xiang River toward Zaoyang in a massive offensive. Fierce combat raged through May 5, as described in Japanese war diaries compiled in Senshi Sōsho (the official Japanese war history series), where soldiers recounted the relentless Chinese resistance amid the smoke and clamor. The Japanese finally breached the defenses, turning their fury on the 122nd Division of the 41st Army. In a heroic stand, the 180th Division clung to Changshoudian, providing cover for the main force's retreat along the east-west Huangqi'an line. The 37th Division fell back to the Yaojiahe line, while elements of the 38th Division repositioned into Liushuigou. On May 6, the Japanese seized Changshoudian, punched through Huangqi'an, and drove northward, unleashing a devastating assault on the 122nd Division's positions near Wenjiamiao. Undeterred, Chinese defenders executed daring flanking maneuvers in the Fenglehe, Yaojiahe, Liushuihe, Shuanghe, and Zhangjiaji areas, turning the landscape into a labyrinth of ambushes. May 7 saw the Japanese pressing on, capturing Zhangjiaji and Shuanghe. By May 8, they assaulted Maozifan and Xinji, where ferocious battles erupted, soldiers clashing in hand-to-hand combat amid the ruins. By May 10, the Japanese had overrun Huyang Town and Xinye, advancing toward Tanghe and the northeastern fringes of Zaoyang. Yet, the Tanghe River front witnessed partial Chinese recoveries: remnants of the Right Army Group, alongside troops from east of the Xianghe, reclaimed Xinye. The 122nd and 180th Divisions withdrew north of Tanghe and Fancheng, while the 37th, 38th, and 132nd Divisions steadfastly held the east bank of the Xianghe River. Concurrently, the main force of the Japanese 3rd Division launched from Yingshan against the 84th and 13th Armies of the 11th Group Army in the Suixian sector. After a whirlwind of combat, the Chinese 84th Army retreated to the Taerwan position. On May 2, the 3rd Division targeted the Gaocheng position of the 13th Army within the 31st Group Army; the ensuing clashes in Taerwan and Gaocheng were a maelstrom of fire, with the Taerwan position exchanging hands multiple times like a deadly game of tug-of-war. By May 4, in a grim escalation, Japanese forces deployed poison gas, a violation of international norms that drew condemnation and is documented in Allied reports from the era, inflicting horrific casualties and compelling the Chinese to relinquish Gaocheng, which fell into enemy hands. On May 5, backed by aerial bombardments, tank charges, and artillery barrages, the Japanese renewed their onslaught along the Gaocheng River and the Lishan-Jiangjiahe line. By May 6, the beleaguered Chinese were forced back to the Tianhekou and Gaocheng line. Suixian succumbed on May 7. On May 8, the Japanese shattered the second line of the 84th Army, capturing Zaoyang and advancing on the Jiangtoudian position of the 85th Army. To evade encirclement, the defenders mounted a valiant resistance before withdrawing from Jiangtoudian; the 84th Army relocated to the Tanghe and Baihe areas, while the 39th Army embedded itself in the Dahongshan for guerrilla operations—a tactic that would bleed the Japanese through hit-and-run warfare, as noted in guerrilla warfare studies by Mao Zedong himself. By May 10, the bulk of the 31st Army Group maneuvered toward Tanghe, reaching north of Biyang by May 15. From Xinyang, Japanese forces struck at Tongbai on May 8; by May 10, elements from Zaoyang advanced to Zhangdian Town and Shangtun Town. In response, the 68th Army of the 1st War Zone dispatched the 143rd Division to defend Queshan and Minggang, and the 119th Division to hold Tongbai. After staunchly blocking the Japanese, they withdrew on May 11 to positions northwest and southwest of Tongbai, shielding the retreat of 5th War Zone units. The Japanese 4th Cavalry Brigade drove toward Tanghe, seizing Tanghe County on May 12. But the tide was turning. In a brilliant reversal, the Fifth War Zone commanded the 31st Army Group, in concert with the 2nd Army Group from the 1st War Zone, to advance from southwestern Henan. Their mission: encircle the bulk of Japanese forces on the Xiangdong Plain and deliver a crushing blow. The main force of the 33rd Army Group targeted Zaoyang, while other units pinned down Japanese rear guards in Zhongxiang. The Chinese counteroffensive erupted with swift successes, Tanghe County was recaptured on May 14, and Tongbai liberated on May 16, shattering the Japanese encirclement scheme. On May 19, after four grueling days of combat, Chinese forces mauled the retreating Japanese, reclaiming Zaoyang and leaving the fields strewn with enemy dead. The 39th Army of the Left Army Group dispersed into the mountains for guerrilla warfare, a shadowy campaign of sabotage and surprise. Forces of the Right Army Group east of the river, along with river defense units, conducted relentless raids on Japanese rears and supply lines over multiple days, sowing chaos before withdrawing to the west bank of the Xiang River on May 21. On May 22, they pressed toward Suixian, recapturing it on May 23. The Japanese, battered and depleted, retreated to their original garrisons in Zhongxiang and Yingshan, restoring the pre-war lines as the battle drew to a close. Throughout this clash, the Chinese held a marked superiority in manpower and coordination, though their deployments lacked full flexibility, briefly placing them on the defensive. After protracted, blood-soaked fighting, they restored the original equilibrium. Despite grievous losses, the Chinese thwarted the Japanese encirclement and exacted a heavy toll, reports from the time, corroborated by Japanese records in Senshi Sōsho, indicate over 13,000 Japanese killed or wounded, with more than 5,000 corpses abandoned on the battlefield. This fulfilled the strategic goal of containing and eroding Japanese strength. Chinese casualties surpassed 25,000, a testament to the ferocity of the struggle. The 5th War Zone seized the initiative in advances and retreats, deftly shifting to outer lines and maintaining positional advantages. As Japanese forces withdrew, Chinese pursuers harried and obstructed them, yielding substantial victories. The Battle of Suizao spanned less than three weeks. The Japanese main force pierced defenses on the east bank of the Han River, advancing to encircle one flank as planned. However, the other two formations met fierce opposition near Suixian and northward, stalling their progress. Adapting to the battlefield's ebb and flow, the Fifth War Zone transformed its tactics: the main force escaped encirclement, maneuvered to outer lines for offensives, and exploited terrain to hammer the Japanese. The pivotal order to flip from defense to offense doomed the encirclement; with the counterattack triumphant, the Japanese declined to hold and retreated. The Chinese pursued with unyielding vigor. By May 24, they had reclaimed Zaoyang, Tongbai, and other locales. Save for Suixian County, the Japanese had fallen back to pre-war positions, reinstating the regional status quo. Thus, the battle concluded, a chapter of resilience etched into the chronicles of China's defiance. In the sweltering heat of southern China, where the humid air clung to every breath like a persistent fog, the Japanese General Staff basked in what they called a triumphant offensive and defensive campaign in Guangdong. But victory, as history so often teaches, is a double-edged sword. By early 1939, the strain was palpable. Their secret supply line snaking from the British colony of Hong Kong to the Chinese mainland was under constant disruption, raids by shadowy guerrilla bands, opportunistic smugglers, and the sheer unpredictability of wartime logistics turning what should have been a lifeline into a leaky sieve. Blockading the entire coastline? A pipe dream, given the vast, jagged shores of Guangdong, dotted with hidden coves and fishing villages that had evaded imperial edicts for centuries. Yet, the General Staff's priorities were unyielding, laser-focused on strangling the Nationalist capital of Chongqing through a relentless blockade. This meant the 21st Army, that workhorse of the Japanese invasion force, had to stay in the fight—no rest for the weary. Drawing from historical records like the Senshi Sōsho (War History Series) compiled by Japan's National Institute for Defense Studies, we know that after the 21st Army reported severing what they dubbed the "secret transport line" at Xinhui, a gritty, hard-fought skirmish that left the local landscape scarred with craters and abandoned supply crates, the General Staff circled back to the idea of a full coastal blockade. It was a classic case of military opportunism: staff officers, poring over maps in dimly lit war rooms in Tokyo, suddenly "discovered" Shantou as a major port. Not just any port, mind you, but a bustling hub tied to the heartstrings of Guangdong's overseas Chinese communities. Shantou and nearby Chao'an weren't mere dots on a map; they were the ancestral hometowns of countless Chaoshan people who had ventured abroad to Southeast Asia, sending back remittances that flowed like lifeblood into the region. Historical economic studies, such as those in The Overseas Chinese in the People's Republic of China by Stephen Fitzgerald, highlight how these funds from the Chaoshan diaspora, often funneled through family networks in places like Singapore and Thailand, were substantial, indirectly fueling China's war effort by sustaining local economies and even purchasing arms on the black market. The Chao-Shao Highway, that dusty artery running near Shantou, was pinpointed as a critical vein connecting Hong Kong's ports to the mainland's interior. So, in early June 1939, the die was cast: Army Order No. 310 thundered from headquarters, commanding the 21st Army to seize Shantou. The Chief of the General Staff himself provided the strategic blueprint, a personal touch that underscored the operation's gravity. The Army Department christened the Chaoshan push "Operation Hua," a nod perhaps to the flowery illusions of easy conquest, while instructing the Navy Department to tag along for the ride. In naval parlance, it became "Operation J," a cryptic label that masked the sheer scale unfolding. Under the Headquarters' watchful eye, what started as a modest blockade morphed into a massive amphibious assault, conjured seemingly out of thin air like a magician's trick, but one with deadly props. The 5th Fleet's orders mobilized an impressive lineup: the 9th Squadron for heavy hitting, the 5th Mine Boat Squadron to clear watery hazards, the 12th and 21st Sweeper Squadrons sweeping for mines like diligent janitors of the sea, the 45th Destroyer Squadron adding destroyer muscle, and air power from the 3rd Combined Air Group (boasting 24 land-based attack aircraft and 9 reconnaissance planes that could spot a fishing boat from miles away). Then there was the Chiyoda Air Group with its 9 reconnaissance aircraft, the Guangdong Air Group contributing a quirky airship and one more recon plane, the 9th Special Landing Squadron from Sasebo trained for beach assaults, and a flotilla of special ships for logistics. On the ground, the 21st Army threw in the 132nd Brigade from the 104th Division, beefed up with the 76th Infantry Battalion, two mountain artillery battalions for lobbing shells over rugged terrain, two engineer battalions to bridge rivers and clear paths, a light armored vehicle platoon rumbling with mechanized menace, and a river-crossing supplies company to keep the troops fed and armed. All under the command of Brigade Commander Juro Goto, a stern officer whose tactical acumen was forged in earlier Manchurian campaigns. The convoy's size demanded rehearsals; the 132nd Brigade trained for boat transfers at Magong in the Penghu Islands, practicing the precarious dance of loading men and gear onto rocking vessels under simulated fire. Secrecy shrouded the whole affair, many officers and soldiers, boarding ships in the dead of night, whispered among themselves that they were finally heading home to Japan, a cruel ruse to maintain operational security. For extra punch, the 21st Army tacked on the 31st Air Squadron for air support, their planes droning like angry hornets ready to sting. This overkill didn't sit well with everyone. Lieutenant General Ando Rikichi, the pragmatic commander overseeing Japanese forces in the region, must have fumed in his Guangzhou headquarters. His intelligence staff, drawing from intercepted radio chatter and local spies as noted in postwar analyses like The Japanese Army in World War II by Gordon L. Rottman, reported that the Chongqing forces in Chaozhou were laughably thin: just the 9th Independent Brigade, a couple of security regiments, and ragtag "self-defense groups" of armed civilians. Why unleash such a sledgehammer on a fly? The mobilization's magnitude even forced a reshuffling of defenses around Guangzhou, pulling resources from the 12th Army's front lines and overburdening the already stretched 18th Division. It was bureaucratic overreach at its finest, a testament to the Imperial Staff's penchant for grand gestures over tactical efficiency. Meanwhile, on the Nationalist side, the winds of war carried whispers of impending doom. The National Revolutionary Army's war histories, such as those compiled in the Zhongguo Kangri Zhanzheng Shi (History of China's War of Resistance Against Japan), note that Chiang Kai-shek's Military Commission had snagged intelligence as early as February 1939 about Japan's plans for a large-scale invasion of Shantou. The efficiency of the Military Command's Second Bureau and the Military Intelligence Bureau was nothing short of astonishing, networks of agents, double agents, and radio intercepts piercing the veil of Japanese secrecy. Even as the convoy slipped out of Penghu, a detailed report outlining operational orders landed on Commander Zhang Fakui's desk, the ink still fresh. Zhang, a battle-hardened strategist whose career spanned the Northern Expedition and beyond , had four months to prepare for what would be dubbed the decisive battle of Chaoshan. Yet, in a move that baffled some contemporaries, he chose not to fortify and defend it tooth and nail. After the Fourth War Zone submitted its opinions, likely heated debates in smoke-filled command posts, Chiang Kai-shek greenlit the plan. By March, the Military Commission issued its strategic policy: when the enemy hit Chaoshan, a sliver of regular troops would team up with civilian armed forces for mobile and guerrilla warfare, grinding down the invaders like sandpaper on steel. The orders specified guerrilla zones in Chaozhou, Jiaxing, and Huizhou, unifying local militias under a banner of "extensive guerrilla warfare" to coordinate with regular army maneuvers, gradually eroding the Japanese thrust. In essence, the 4th War Zone wasn't tasked with holding Chao'an and Shantou at all costs; instead, they'd strike hard during the landing, then let guerrillas harry the occupiers post-capture. It was a doctrine of attrition in a "confined battlefield," honing skills through maneuver and ambush. Remarkably, the fall of these cities was preordained by the Military Commission three months before the Japanese even issued their orders, a strategic feint that echoed ancient Sun Tzu tactics of yielding ground to preserve strength. To execute this, the 4th War Zone birthed the Chao-Jia-Hui Guerrilla Command after meticulous preparation, with General Zou Hong, head of Guangdong's Security Bureau and a no-nonsense administrator known for his anti-smuggling campaigns, taking the helm. In just three months, Zhang Fakui scraped together the Independent 9th Brigade, the 2nd, 4th, and 5th Guangdong Provincial Security Regiments, and the Security Training Regiment. Even with the 9th Army Group lurking nearby, he handed the reins of the Chao-Shan operation to the 12th Army Group's planners. Their March guidelines sketched three lines of resistance from the coast to the mountains, a staged withdrawal that allowed frontline defenders to melt away like ghosts. This blueprint mirrored Chiang Kai-shek's post-Wuhan reassessment, where the loss of that key city in 1938 prompted a shift to protracted warfare. A Xinhua News Agency columnist later summed it up scathingly: "The Chongqing government, having lost its will to resist, colludes with the Japanese and seeks to eliminate the Communists, adopting a policy of passive resistance." This narrative, propagated by Communist sources, dogged Chiang and the National Revolutionary Army for decades, painting them as defeatists even as they bled the Japanese dry through attrition. February 1939 saw Commander Zhang kicking off a reorganization of the 12th Army Group, transforming it from a patchwork force into something resembling a modern army. He could have hunkered down, assigning troops to a desperate defense of Chaoshan, but that would have handed the initiative to the overcautious Japanese General Staff, whose activism often bordered on paranoia. Zhang, with the wisdom of a seasoned general who had navigated the treacherous politics of pre-war China, weighed the scales carefully. His vision? Forge the 12th Army Group into a nimble field army, not squander tens of thousands on a secondary port. Japan's naval and air dominance—evident in the devastation of Shanghai in 1937, meant Guangdong's forces could be pulverized in Shantou just as easily. Losing Chaozhou and Shantou? Acceptable, if it preserved core strength for the long haul. Post-Xinhui, Zhang doubled down on resistance, channeling efforts into live-fire exercises for the 12th Army, turning green recruits into battle-ready soldiers amid the Guangdong hills. The war's trajectory after 1939 would vindicate him: his forces became pivotal in later counteroffensives, proving that a living army trumped dead cities. Opting out of a static defense, Zhang pivoted to guerrilla warfare to bleed the Japanese while clutching strategic initiative. He ordered local governments to whip up coastal guerrilla forces from Chao'an to Huizhou—melding militias, national guards, police, and private armed groups into official folds. These weren't elite shock troops, but in wartime's chaos, they controlled locales effectively, disrupting supply lines and gathering intel. For surprises, he unleashed two mobile units: the 9th Independent Brigade and the 20th Independent Brigade. Formed fresh after the War of Resistance erupted, these brigades shone for their efficiency within the cumbersome Guangdong Army structure. Division-level units were too bulky for spotty communications, so Yu Hanmou's command birthed these independent outfits, staffed with crack officers. The 9th, packing direct-fire artillery for punch, and the 20th, dubbed semi-mechanized for its truck-borne speed, prowled the Chaoshan–Huizhou coast from 1939. Zhang retained their three-regiment setup, naming Hua Zhenzhong and Zhang Shou as commanders, granting them autonomy to command in the field like roving wolves. As the 9th Independent Brigade shifted to Shantou, its 627th Regiment was still reorganizing in Heyuan, a logistical hiccup amid the scramble. Hua Zhenzhong, a commander noted for his tactical flexibility in regional annals, deployed the 625th Regiment and 5th Security Regiment along the coast, with the 626th as reserve in Chao'an. Though the Fourth War Zone had written off Chaoshan, Zhang yearned to showcase Guangdong grit before the pullback. Dawn broke on June 21, 1939, at 4:30 a.m., with Japanese reconnaissance planes slicing through the fog over Shantou, Anbu, and Nanbeigang, ghostly silhouettes against the gray sky. By 5:30, the mist lifted, revealing a nightmare armada: over 40 destroyers and 70–80 landing craft churning toward the coast on multiple vectors, their hulls cutting the waves like knives. The 626th Regiment's 3rd Battalion at Donghushan met the first wave with a hail of fire from six light machine guns, repelling the initial boats in a frenzy of splashes and shouts. But the brigade's long-range guns couldn't stem the tide; Hua focused on key chokepoints, aiming to bloody the invaders rather than obliterate them. By morning, the 3rd Battalion of the 625th Regiment charged into Shantou City, joined by the local police corps digging in amid urban sprawl. Combat raged at Xinjin Port and the airport's fringes, where Nationalist troops traded shots with advancing Japanese under the absent shadow of a Chinese navy. Japanese naval guns, massed offshore, pounded the outskirts like thunder gods in fury. By 2:00 a.m. on the 22nd, Shantou crumpled as defenders' ammo ran dry, the city falling in a haze of smoke and echoes. Before the loss, Hua had positioned the 1st Battalion of the 5th Security Regiment at Anbu, guarding the road to Chao'an. Local lore, preserved in oral histories collected by the Chaozhou Historical Society, recalls Battalion Commander Du Ruo leading from the front, rifle in hand, but Japanese barrages, bolstered by superior firepower—forced a retreat. Post-capture, Tokyo's forces paused to consolidate, unleashing massacres on fleeing civilians in the outskirts. A flotilla of civilian boats, intercepted at sea, became a grim training ground for bayonet drills, a barbarity echoed in survivor testimonies compiled in The Rape of Nanking and Beyond extensions to Guangdong atrocities. With Shantou gone, Hua pivoted to flank defense, orchestrating night raids on Japanese positions around Anbu and Meixi. On June 24th, Major Du Ruo spearheaded an assault into Anbu but fell gravely wounded amid the chaos. Later, the 2nd Battalion of the 626th overran spots near Meixi. A Japanese sea-flanking maneuver targeted Anbu, but Nationalists held at Liulong, sparking nocturnal clashes, grenade volleys, bayonet charges, and hand-to-hand brawls that drained both sides like a slow bleed. June 26th saw the 132nd Brigade lumber toward Chao'an. Hua weighed options: all-out assault or guerrilla fade? He chose to dig in on the outskirts, reserving two companies of the 625th and a special ops battalion in the city. The 27th brought a day-long Japanese onslaught, culminating in Chao'an's fall after fierce rear-guard actions by the 9th Independent Brigade. Evacuations preceded the collapse, with Japanese propaganda banners fluttering falsely, claiming Nationalists had abandoned defense. Yet Hua's call preserved his brigade for future fights; the Japanese claimed an empty prize. I would like to take this time to remind you all that this podcast is only made possible through the efforts of Kings and Generals over at Youtube. Please go subscribe to Kings and Generals over at Youtube and to continue helping us produce this content please check out www.patreon.com/kingsandgenerals. If you are still hungry after that, give my personal channel a look over at The Pacific War Channel at Youtube, it would mean a lot to me. The Japanese operations had yet again plugged up supply leaks into Nationalist China. The fall of Suixian, Zaoyang and Shantou were heavy losses for the Chinese war effort. However the Chinese were also able to exact heavy casualties on the invaders and thwarted their encirclement attempts. China was still in the fight for her life.

(00:00:00) INTRO (00:10:37) La noticia no es que hay una red social donde las inteligencias artificiales comentan sin que los dueños sepan… si no que ya crearon su religión (00:43:56) TikTok accidente o de verdad ya les están metiendo censura (00:54:38) El menú (00:57:53) PATREON (01:10:43) Si todos estos dulces son peligrosos por qué no hay medios grandes hablando de esto (01:19:33) Ganó la continuidad del Cambio en Costa Rica, sí eso lo dijo ella no yo (01:27:09) España le lleva la contraria a Europa en esto de lo migrantes y legaliza un camión (01:31:53) Todas las redes sociales a juicio (01:46:08) Este no es el periodista preso al que le debemos prestar atención (01:49:07) Australia se prepara para vivir a los 50 grados a la sombra (01:52:37) Líder de la iglesia de Inglaterra (01:55:39) Apostar en política es rentable cuando nadie sabe quién eres (02:10:57) Los futurólogos dicen que la cosa va a ser híbrida, vida eterna y sueldo básico universal (02:20:15) Para entender la política hay que entender la lucha libre (02:29:41) Y mientras tanto el mapa del Cáucaso va cambiando (02:33:56) Cuando eres el jefe de seguridad y subes cualquier cosa a ChatGPT (02:35:08) Ok esta semana ¿qué toca Irán? (02:40:16) Epstein temporada 3 (02:44:29) Digan lo que quieran de Trump pero Europa tuvo que moverse finalmente (02:48:20) Le dieron un negocio a una IA y se volvió comunista (02:50:22) Por qué me debe importar el presidente de esta banco central (02:53:39) Deutsche bank bajo sospecha (02:55:15) EXTRA - Por qué la generación Harry Potter debe crecer COMO DIJIMOS EN EL EPISODIO LA MERCH ESTÁ AQUÍ 🤾👉👉👉https://quesevayantodos-shop.fourthwall.com/collections/all LE PUEDES COMPRAR A UN PANA LA SUSCRIPCIÓN CON TARJETA DE REGALO 🤾👉👉👉 https://www.patreon.com/profesorbriceno/gift O COMPRAR UNA GIFT CARD DE PATREON EN 🤾👉👉👉 https://rewarble.com/brands/patreon 🔹 EPISODIO COMPLETO Y PARTICIPACION EN VIVO EN 💻https://www.patreon.com/profesorbriceno 🔸 Las Grabaciones pueden verse en vivo en TWITCH 🖥️https://www.twitch.tv/profesorbriceno SUSCRÍBETE AL PODCAST POR AUDIO EN CUALQUIER PLATAFORMA ⬇️  AQUÍ LAS ENCUENTRAS TODAS: ➡️➡️➡️ https://pod.link/676871115 los más populares 🎧 SPOTIFY ⬇️   https://open.spotify.com/show/3rFE3ZP8OXMLUEN448Ne5i?si=1cec891caf6c4e03 🎧 APPLE PODCASTS ⬇️   https://podcasts.apple.com/es/podcast/que-se-vayan-todos/id676871115 🎧 GOOGLE PODCASTS ⬇️   https://www.ivoox.com/en/podcast-que-se-vayan-todos_sq_f11549_1.html 🎧 FEED PARA CUALQUIER APP DE PODCASTS ⬇️   https://www.ivoox.com/en/podcast-que-se-vayan-todos_sq_f11549_1.html Si te gustó, activa la campanita 🔔 🎭  FECHAS DE PRESENTACIONES ⬇ ️ http://www.profesorbriceno.com/tour Redes sociales: ✏️Web https://www.profesorbriceno.com ✏️Instagram https://www.instagram.com/profesorbriceno/ ✏️X https://x.com/profesorbriceno ✏️Facebook https://www.facebook.com/profesorbricenoOficial/ #profesorbriceño #aburrido #podcast #noticias #trump #IA

1. Una gran noche para Puerto Rico y para los latinos en los Grammy: Bad Bunny hace historia para el país. Ganó 3 premios: “Álbum del Año”, “Mejor Álbum de Música Urbana” y “Mejor Interpretación de Música Global” con “Debí tirar más fotos”2. Lloró y se emocionó con el Album del Año, su mensaje casi todo fue en español. Pero antes había dicho: “ICE out "Fuera ICE, no somos salvajes, no somos aliens. Somos humanos": Bad Bunny al recibir Grammy al Mejor Álbum Música Urbana por Debí Tirar Más Foto3. “Hay que cerrar el espectáculo como un campeón”: Xander Zayas. El boricua es el nuevo doble campeón de las 154 libras de la OMB y AMB.4. El turismo puertorriqueño cerró el 2025 con cifras históricas 5. Ucrania está en guerra hace 4 años y hoy están con apagón masivo y sin agua. En Puerto Rico no hay guerra sino politiquería y estamos igual6. Atrévete a Coser Telas Elásticas, una guía paso a paso la confección de ropa en telas de punto con la gran diseñadora Tommie HernándezEste es un programa independiente y sindicalizado. Esto significa que este programa se produce de manera independiente, pero se transmite de manera sindicalizada, o sea, por las emisoras y cadenas de radio que son más fuertes en sus respectivas regiones. También se transmite por sus plataformas digitales, aplicaciones para dispositivos móviles y redes sociales. Estas emisoras de radio son:1. Cadena WIAC - WYAC 930 AM Cabo Rojo- Mayagüez2. Cadena WIAC – WISA 1390 AM Isabela3. Cadena WIAC – WIAC 740 AM Área norte y zona metropolitana4. WLRP 1460 AM Radio Raíces La voz del Pepino en San Sebastián5. X61 – 610 AM en Patillas6. X61 – 94.3 FM Patillas y todo el sureste7. WPAB 550 AM - Ponce8. ECO 93.1 FM – En todo Puerto Rico9. WOQI 1020 AM – Radio Casa Pueblo desde Adjuntas 10. Mundo Latino PR.com, la emisora web de música tropical y comentarioUna vez sale del aire, el programa queda grabado y está disponible en las plataformas de podcasts tales como Spotify, Soundcloud, Apple Podcasts, Google Podcasts y otras plataformas https://anchor.fm/sandrarodriguezcottoTambién nos pueden seguir en:REDES SOCIALES: Facebook, X (Twitter), Instagram, Threads, LinkedIn, Tumblr, TikTokBLOG: En Blanco y Negro con Sandra http://enblancoynegromedia.blogspot.comSUSCRIPCIÓN: Substack, plataforma de suscripción de prensa independientehttps://substack.com/@sandrarodriguezcottoOTROS MEDIOS DIGITALES: ¡Ey! Boricua, Revista Seguros. Revista Crónicas y otrosEstas son algunas de las noticias que tenemos hoy En Blanco y Negro con Sandra.

Merriam-Webster's Word of the Day for February 1, 2026 is: gargantuan • gahr-GAN-chuh-wun • adjective Gargantuan describes something that is very large in size or amount; something gargantuan is, in other words, gigantic. // Bigfoot is said to be a creature of gargantuan proportions. See the entry > Examples: “By the late 1870s, he was asked to take part in the gargantuan task of evaluating and cataloguing the results of the five-year Challenger expedition—an ambitious British global research voyage, the first ever dedicated purely to science. [Ernst] Haeckel's contribution to the final 50-volume Report of the Voyage of H.M.S. Challenger took a decade to complete and spanned three volumes, 2,750 pages, and 130 plates.” — Michael Benson, Nanocosmos: Journeys in Electron Space, 2025 Did you know? Gargantua is the name of a giant king in François Rabelais's 16th-century satiric novel Gargantua, the second part of a five-volume series about the giant and his son Pantagruel. All of the details of Gargantua's life befit a giant. He rides a colossal mare whose tail switches so violently that it fells the entire forest of Orleans. He has an enormous appetite, such that in one incident he inadvertently swallows five pilgrims while eating a salad. The scale of everything connected with Gargantua led to the adjective gargantuan, which since William Shakespeare's time has been used for anything of tremendous size or volume.

¿Ustedes cómo entienden la ganancia de las chicas de los esquites?, los leemos. Ganó el Puma, perdió el Ave, es quincena... ¡Todo bien! Eduardo Iniesta nos habla de los pedidos de juguetes sexuales que llegan al Pentágono y las complicaciones que esto tiene. Y, ¿qué onda con la pregunta que le hacen a Gabriel Milito? 

En este episodio exploramos cómo la triple crisis ambiental - el cambio climático, la pérdida de biodiversidad y la contaminación - impacta directamente nuestra salud. Conversamos sobre la importancia de entender que cuidar el planeta es también cuidar nuestro cuerpo, nuestra comunidad y nuestro futuro. Muchos niños crecen siendo conscientes de los problemas ambientales, pero con el tiempo dejamos de hablar de ellos. Hoy queremos recuperar esa conversación, desde la mirada médica y humana, para reconectar la salud del planeta con la salud de las personas. Hemos invitado a una experta en la materia, la Química Carol Perelman, quien es Química Farmacéutica Bióloga egresada con Mención Honorífica de la Facultad de Química de la Universidad Autónoma de México y divulgadora de la ciencia. Ganó la Medalla Oro en las Olimpiadas de Química Nacionales y la Medalla de Bronce en la 1era edición de las Olimpiadas de Química Iberoamericanas. Carol es co-creadora y directora del Jardín Weizmann de Ciencias, el primer museo de ciencias completamente al aire libre en México; es promotora de los jardines de ciencia como espacios democratizadores de la ciencia, ha desarrollado guiones y contenidos museográficos para distintos espacios de ciencia del país, así como para exposiciones temporales e itinerantes sobre conservación y medio ambiente. En el 2019 fue Premiada con Tercer Lugar en Periodismo de Ciencia e Innovación por COMECYT,  es autora del cuento infantil y juvenil "Coronesio, Covidín y los Secretos de lo Invisible" con el que obtuvo el Segundo Lugar en el Premio Jorge Flores Valdés al mejor producto de divulgación del 2020 en torno a la pandemia de COVID-19 por la Sociedad Mexicana para la Divulgación de la Ciencia y la Técnica, A.C. (SOMEDICyT). Instagram: @‌carol.perelman Recursos: https://www.sciencedirect.com/science/article/pii/S1538544221000821. Click or tap if you trust this link." style="color:blue;text-decoration:underline;">A pediatrician's guide to climate change-informed primary care - ScienceDirect The Lancet Planetary Health Home Page . Click or tap if you trust this link." style="color:blue;text-decoration:underline;">The Lancet Planetary Health Home Page https://hsph.harvard.edu/wp-content/uploads/2024/11/CLiME_Final-Report.pdf. Click or tap if you trust this link." style="color:blue;text-decoration:underline;">Climate Change and Children's Health and Well-Being in the United States ¿Tienes algún comentario sobre este episodio o sugerencias de temas para un futuro podcast?  Escríbenos a pediatrasenlinea@childrenscolorado.org.  

Qualcomm unveils another high-end chip, foldable iPhone rumors persist, and the holiday shopping season has arrived. Holiday Gift Ideas: iPad for $279 MacBook Air M4 for $749 or $650 for the M2 MacBook Air Mac Mini for $479 Apple Watch Ultra 2 for $600 or OG Ultra for $360 Anker Power bank for $75 3-port GAN charger for $42 3-in-1 MagSafe Charger for $95 Qi 2.2 Car Mount Car Play Screen How to Contact us:www.thecellphonejunkie.com questions@thecellphonejunkie.com Twitter How to Listen:Subscribe iTunes Download the show directly