Model

a 98″ model at a knockdown price

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Xiaomi continues without giving truce in the market televisions with an affordable price. The Chinese firm, which has been sneaking into living rooms around the world for years by offering televisions with an unbeatable quality-price ratio. Now it renews its QD-Mini LED range with the Xiaomi TV S Mini LED 2026a family that for the first time in this price segment, extends from 55 inches to an impressive 98-inch model. There are five screen sizes that do not waver in their technological commitment and do so at prices that, once again, invite us to ask the usual question: how do they do it? The answer, at least in part, lies in the technology inside them. All models mount QD-Mini LED panelsthe combination of Mini LED backlight with quantum dots that allow us to get closer to the chromatic purity of OLED without giving up a brightness that organic panels cannot yet match. The result is a panel that, according to Xiaomi, is capable of reaching 1,200 nits of peak brightness, reproducing 94% of the DCI-P3 color space and reaching 0.0001 nits of pure black. Something unthinkable in this price category just a couple of years ago. Xiaomi TV S Mini LED 2026 from 55″ to 75″ Xiaomi TV S Mini LED 2026 85″ and 98″ screen QD-Mini LED 4K (3,840 x 2,160 pixels) 60/120Hz 94% DCI-P3 (typ) 308, 384 and 512 dimming zones Peak brightness: 1,200 nits 178° (H)/178° (V) MEMC: 4K 60Hz HDR10+, HLG, Filmmaker QD-Mini LED 4K (3,840 x 2,160 pixels) 144/288Hz 94% DCI-P3 (typ) 532 dimming zones Peak brightness: 1,700 nits 178° (H)/178° (V) MEMC: 4K 120Hz Dolby Vision, HDR10+, HLG, Filmmaker Dimensions and weight with base 1,667 × 391 × 1,026 mm and 23 kilos 1,445 × 391 × 901 mm and 16.3 kilos 1,226 × 312 × 769 mm and 11.7 kilos 2179 × 84 × 1,246 mm and 50.8 kilos 1,890 × 413 × 1,154 mm and 33.4 kilos CPU Quad cortex A73 Quad cortex A73 GPU Mali-G52 (2EE) MC1 Mali-G52 (2EE) MC1 RAM MEMORY 2GB 3GB STORAGE 32GB 32GB wireless connectivity Wi-Fi 5 Dual band 2.4/5 GHz Bluetooth 5.0 Wi-Fi 6 Dual band 2.4/5 GHz Bluetooth 5.2 ports DVB-T2/C, DVB-S2 1x HDMI 2.1 (CEC ALLM VRR), eARC (HDMI 2) 2×HDMI 2.0 1x USB 2.0 Ethernet (LAN) CI+ 3.5mm jack Optical digital audio output DVB-T2/C, DVB-S2 3x HDMI 2.1 (CEC ALLM VRR), eARC (HDMI 2) 1x USB 2.0 1x USB 3.0 Ethernet (LAN) CI+ 3.5mm jack Optical digital audio output power 230W Standby consumption: ≤ 0.5W 500W Standby consumption: ≤ 0.5W operating system Google TV Google TV SOUND Speakers: 2 x 15W Dolby Audio, DTS:X Speakers: 2 x 15W Dolby Atmos others Google Cast Google Assistant Apple AirPlay Google Cast Google Assistant Apple AirPlay price From 549 euros From 1,399 euros Two families with important differences The Xiaomi TV S Mini LED 2026 range is divided into two blocks differentiated by the diagonal from your screen. The 55, 65 and 75 inch models share the same features, with special emphasis on the gaming performance thanks to the mode Game Boostwhich allows the native refresh rate to be scaled from 60 Hz to 120 Hz, although it limits the resolution to 1440p. Xiaomi did not want to skimp on dimming zones for this panel, offering 308, 384 and 512 zones attenuation local respectively for each of their sizes. This count allows you to better control the light that reaches the LCD panel and reduce defects inherent in LED technology like blooming and halos that wash out the intensity of the blacks around a very bright point in the image. In the sound section of this range, the audio system has Dolby Audio and DTS:X certification, which is not the same as Dolby Atmos. The models of 85 and 98 inches are another story. Here Xiaomi has tightened the screws on almost all fronts: the dimming zones jump to 640 and 880 respectively, the native refresh rate rises to 144Hz (with the possibility of reaching 288Hz in Game Boost), the three HDMI ports are 2.1 with support for VRR and full ALLM that support 4K signal at 144Hz. To top it all off, these models are compatible with Dolby Vision and Dolby Atmos, which makes them much more serious options for enjoying home theater or major sporting events like the World Cup. The processor of this large-inch range also improves, going from the Quad Cortex A55 to the Quad Cortex A73, with 3 GB of RAM compared to the 2 GB of the smallest models in the range. In all cases the operating system is Google TV, with Google Cast, Google Assistant and Apple AirPlay integrated. Xiaomi lands on large-inch televisions If there is something that differentiates this generation from the previous ones, it is precisely Xiaomi’s determined commitment to large inch formats taking its range beyond 85 inches. For years, a 85 or 98 inch television with MiniLED technology It was territory reserved for premium models from brands such as Samsung, LG or Sony, and with prices that easily exceeded 3,000 or 4,000 euros. However, today the scale of manufacturing large-inch panels and the increased production of panels MiniLEDs allow Xiaomi to put a 85-inch QD-Mini LED TV at half price that just a couple of years ago. With this proposal, the Chinese brand puts the finger on the sore from its competition, putting pressure on the mid-range and lower-middle segment with models with very good performance and technological equipment at an unbeatable price. Also, put your foot in the big inch segment which, until now, was reserved for the top televisions of each brand and does so by pouring salt into the wound that hurts the most: that of price. Versions and prices of the Xiaomi TV S Mini LED 2026 The new Xiaomi TV S Mini LED 2026 range is available in sizes ranging from 55 inches to 98 inches and its prices are: Xiaomi TV S Mini LED 2026 55″: 549 … Read more

The next Mercedes-Benz model aims like a missile to fully enter the war

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In the middle of World War II, while Allied bombing destroyed German factories and consumed resources at an impossible rate, many plants that until then manufactured cars, engines or civil machinery began to transform hurriedly to produce military vehicles, aviation parts and weapons. Some of the most recognizable brands in the European automotive industry they then discovered something that decades later resonates strongly again: in times of geopolitical tension, an assembly line can change purpose much faster than it seems. The unexpected twist, or almost. For decades, the future of the European automobile seemed to come down to a single discussion: electric, hybrid or gasoline. However, the German industrial crisis and the accelerated rearmament of Europe are opening a possibility completely different. Mercedes-Benz, like before Volkswagenhas just made it clear that it is willing to enter the defense industry if the business makes economic sense. This has been confirmed through an interview in the Wall Street Journal of its CEO, Ola Källenius, and it is much more important than it seems because it reflects a profound change within the German automobile industry: the big brands are no longer only looking at the car of the future, they are also beginning to look at war as a new industrial opportunity. In a Europe increasingly obsessed with drones, missiles, air defense and military production, car factories are beginning to be seen not only as car plants, but as possible centers strategic manufacturing. The perfect storm. The context explains why this idea is beginning to seem reasonable even for companies historically far from the military business. The German automobile industry is going through one of its most delicate moments in decades: falling profits, pressure from Chinese manufacturers, high energy costs, lower European demand and tariff threats from the United States. Mercedes-Benz, for example, suffered a strong profit drop in 2025, while practically all major German manufacturers have announced cuts or adjustments labor. At the same time, the defense industry is experiencing exactly the opposite situation. European rearmament after the war in Ukraine has fired orders, investments and military contracts to historic levels. For many German industrial companies, the military sector is beginning to represent something very different from a marginal business: stability, growth and guaranteed public financing for years. From cars to artillery. The case of Mercedes is not isolated and we have been counting. Volkswagen is also exploring possible military collaborations as defense companies such as Rheinmetall study reuse factories of automobiles or absorb part of its industrial infrastructure. The message is clear: Europe is beginning to discover that many capabilities necessary to produce modern cars (advanced metallurgy, electronics, robotics, complex logistics chains or highly skilled workers) are also extremely useful to manufacture systems military. The border between both industries begins to fade little by little. It is no longer just about producing tanks or ammunition, we are talking about radars, drones, autonomous vehicles, electronic systems and air defense platforms that require technologies very similar to those of the modern automobile. The new European war economy. As we said, the ukrainian war It has caused an enormous psychological change within Europe. For years, much of continental industry assumed that globalization and stability made a large military capacity of its own unnecessary. Now the opposite happens: European governments are increasing defense budgets at speeds not seen since the Cold War. This transformation is pushing traditionally civil companies to reconsider their role within the new geopolitical context. The CEO of Mercedes himself insist that any military activity would remain dwarfed by its core business, but at the same time recognizes something revealing: can become a growing and profitable niche. That is to say, the German automobile industry is beginning to assume that part of future European growth could come directly from rearmament. The car of the future may not be a car. If you like, the most striking thing of all is the symbolism of change. For a long time, the automotive debate revolved around batteries, autonomous driving and sustainability. Now, some of Europe’s most iconic companies are beginning to speak openly on anti-drone defensemilitary production or collaboration with weapons manufacturers. The idea that the next big European industrial business could be closer to war than sustainable mobility would have seemed absurd just a few years ago. However, the combination of economic crisis, Chinese competition and continental rearmament is slowly pushing giants like Mercedes-Benz itself into completely new and unexpected terrain. And that reveals the extent to which Europe is entering a stage where the economy, industry and security are beginning to mix more and more. Image | Nara, RawPixel, Julian Herzog In Xataka | Europe wants to make more weapons and faster. Your biggest obstacle is not money: it is finding qualified welders and technicians In Xataka | In the midst of rearmament, Spain has just surprised Europe: 5,000 million for 34 warships and four submarines

There is a battle to have the AI ​​model that programs best. And a good, pretty and very cheap rival has appeared in it: Cursor

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Cursor has introduced Composer 2.5a generative AI model specifically intended for one thing: programming well. How good? Well, according to this startup, it does it as well as the best models of the moment, Claude Opus 4.7 and GPT 5.5, but it also does it for a lower cost. The challenge is striking not only because of what it means for Cursor, but because of how they have created that model: it turns out that it is based on a Chinese AI model. AI models specialized in one thing. While OpenAI and Anthropic try to develop general-purpose models—they do a lot of things really well— Cursor you have decided to focus on a specific task. The AI ​​startup has created an AI model specialized in programming, and has done so by arguing that a billion parameters are not necessary to compete with the best. Devoting yourself to a single thing allows you to not only gain efficiency, but also costs. This is not a decathlete, but a specialist in the 200 m event, so to speak. As good as GPT-5.5 or Claude Opus 4.7? That’s what they say in Cursor, because according to their tests with several specific programming benchmarks, the performance is on par with those two models that today are the great references both in programming and in other areas. And much cheaper. These results are also especially interesting when we add the cost factor. The average cost per task in the CursorBench 3.1 benchmark showed that Composer 2.5 managed to solve almost 65% of all tests for a cost of just $0.3. Opus 4.7 max and GPT-5.5 xhigh managed to reach that 65%, but at much higher costs: just over 4 dollars in the case of GPT, and 11 dollars in the case of Opus. The difference is abysmal. He API access price demonstrates the differences: 0.5 dollars per million input tokens 2.5 dollars per million output tokens, when Claude Opus 4.7 is 5/25 and that of GPT-5.5 is 5/30 respectively. Textual feedback. Unlike models that only learn from the final result, Composer 2.5 has been trained with a reinforcement learning technique (Reinforcement Learning) that allows us to offer clues about what is happening if errors are being made. This allows the model to recalibrate and act as a transparent teacher. One that also corrects word by word as it solves the exercise, not just when seeing the final result. 85% of the training budget has been dedicated exclusively to reinforcement learning, calibrating the model not for chat, but to execute code refactorings or fix bugs in real time. A model “born” in China. Those responsible for Cursor themselves have explained that Composer 2.5—like its predecessor, Composer 2launched at the end of March—is a model derived from Kimi K2.5, the AI ​​model of the Chinese startup Moonshot. Although that is the basis, already in Composer 2 the training and post-training tasks manage to improve the behavior in a very notable way in programming benchmarks and also in others such as Terminal Bench that evaluate the agentic behavior of these models. Cursor gets older. This startup became famous for creating a programming AI agent that was a pioneer in that fever we live for vibecoding. The user experience is no longer that of programming, as in traditional IDEs (Integrated Development Environments), but rather that of directing the machine to program it for you. Composer 2.5 doesn’t just program: it understands the structure and relationships between files, and turns Cursor into a much more competitive AI company, because it no longer depends on being able to work with Anthropic or OpenAI models, for example. Having both the AI ​​agent and the model processing everything makes it a much more competitive solution. Elon Musk has Cursor in his sights. Cursor’s good performance has led to growing interest in buying this company even before it becomes too big. Elon Musk knows this well and Grok, xAI’s model, is not so popular in the programming field. In April we learned that SpaceX had reached an agreement that gives you the option to buy Cursor for 60,000 million dollars. It would be a promising deal for both, because Composer 2.5 has already used Colossus’ infrastructure to train, and xAI could thus try to gain market share in the juicy enterprise sector. In Xataka | Elon Musk knows that TSMC is overwhelmed: Terafab is his idea to completely change the global chip industry

DeepSeek wants to raise its first round of financing and copies the last thing that remained to be copied from the US: the economic model

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Chinese AI startups appear to have surrendered to Silicon Valley capitalism. Both DeepSeek such as Moonshot AI (Kimi) have begun to raise investment rounds or are preparing to do so. It is a turning point in a race that is now becoming especially interesting and that also raises a clear question: will these companies continue betting on open models? The valuation is multiplied by two. DeepSeek had always avoided making that decision and it seemed almost a personal project of its founder, billionaire Liang Wenfeng. However, the company is now in talks to raise its first round of external investment, they assure in Financial Times. According to company data, Wenfeng has 89.5% of the stake in the company. There is talk of a round that would increase DeepSeek’s valuation from the current $20 billion to around $45 billion. Who is the “Big Fund”. Behind this investment round is above all the China Integrated Circuit Industry Investment Fund, also known as the “Big Fund”. This consortium, the most important of its segment in the field of semiconductors, is supported by the Chinese state, and has a “cash” of 47 billion dollars contributed by the Chinese Ministry of Economy, the local government and several state banks thanks to a third round that was carried out in 2024. At the moment the “Big Fund” has not invested in other Chinese AI startups, but it has in companies like SMIC or Yangtze. The war for talent. The reason behind this decision is not only the need for capital to have access to more computing capacity. According to sources close to the operation, Liang Wenfeng has been forced to open that option to stop talent theft and thus be able to keep their best researchers on the payroll. In a market as competitive as this one, DeepSeek needs to offer shares to its employees to compete with the aggressive recruitment of talent by its local and Western rivals. A promising pairing. The relevance of this investment goes beyond the AI ​​model as such. DeepSeek has been significantly optimized for be able to run on Huawei hardwareallowing China to have a platform that works without the need for Nvidia chips. This symbiosis between this efficient AI model and the Chinese hardware giant is quite a bet by the Chinese government to try to win this race despite Washington’s blockades. The forced bet on “national” chips. Seeking that support in Huawei chips is not only a technical choice, but a political necessity for survive NVIDIA GPU crash. The problem is that Chinese hardware is still struggling to close the raw performance gap against architectures like Blackwell’s. If DeepSeek’s software hits a ceiling and chips created in China do not evolve at the necessary pace, the laboratory could find itself trapped: it would not matter to be very efficient when they cannot compete in raw power. Moonshot signs up for the rounds. DeepSeek is not alone in this race to achieve huge valuations. Moonshot AI just got up 2 billion dollars from investors such as Meituan, raising its value above 20 billion. Meanwhile, other rivals such as MiniMax and Zhipu AI (GLM) already surpass the 30,000 million valuation in their stock market debuts. This trend is therefore following what was already experienced (and continues to be experienced) in the US with AI startups, and the capital bubble that exists in the North American country now seems to have its eastern version in China. Moonshot AI and exceeds $200 million in annual recurring revenue (ARR). The paradox of copying the economic model. It’s ironic that DeepSeek, which became famous for challenging the “brute force” of American spending, ends up adopting its same funding structure. The company has shown that efficiency could offer an alternative to those almost unlimited resources of venture capital accessed by OpenAI or Anthropic. However, market reality dictates that a very solid capital structure is still what is needed to survive in the long term. Either you have it, or you can’t continue training models, reserving computing capacity and, of course, retaining talent. Open models? Until now DeepSeek had been one of the heroes of open weight AI models. Thanks to this, platforms like Hugging Face allow you to download it and allow everyone to take advantage of its achievements in terms of efficiency. The entry of venture capital and state funds could change the rules of the game: investors do not usually inject billions of dollars so that the product ends up being “given away” even for its competitors. The company will probably face the dilemma of closing its next models to protect its valuation and generate exclusive income, or keep its philosophy open at the risk that its investors no longer trust that strategy. In Xataka | If at some point NVIDIA has to choose between giving its best chips to the US or China, its choice is very clear.

Chrome has always liked to gobble up RAM. Now download a multi-gigabyte AI model without warning

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Chrome is part of the digital routine of millions of people to the point that we often stop wondering what exactly it does while we browse. We use it for almost everything, we trust it with sessions, extensions, passwords, searches and a good part of our life on the Internet. That is why it is so surprising to find a folder larger than 4 GB associated with an AI model downloaded by the browser itself. We are not talking about a minor update or a residual file, but rather a large component that many users probably did not expect to see there. The conversation began to take shape from a publication by Alexander Hanff in That Privacy Guy. Their finding, in essence, was simple to understand: according to its logs, Chrome had left a multi-gigabyte AI model on his computer without giving him a clear warning during the process. From that clue I did the checking on my own computer, used from Spain, and found the same folder that Hanff refers to: OptGuideOnDeviceModel, within Chrome’s internal files. In my case, macOS shows that folder as 4.27 GB in size, even though features like the Gemini sidebar are not yet available in this market. Gemini Nano downloaded to my computer Gemini Nano It does not work like a traditional download that we search for, accept and install manually. In the Chrome developer documentationthe company explains that the integrated AI capabilities are intended to be fluid and that model management is done automatically in the background. It also notes that the initial download can be triggered when an AI feature built into the browser needs to use the Gemini Nano for the first time. In other words: the model can reach the computer as part of Chrome’s internal workings, not necessarily through a clear and recognizable action for the user. An AI model that goes beyond an integrated chatbot The model is not limited to promoting a browser with a chatbot integrated within Chrome. Google has already described uses Gemini Nano on the device itself to detect technical support scams, a type of threat that often lasts a very short time online and can escape traditional tracking systems. In that scenario, Chrome can provide the model with content from the page the user is visiting to extract risk cues. AI, therefore, can also be part of the browser’s security layer. Gemini Nano also boosts security features in Chrome That’s where a good part of the discomfort lies. AI in the browser can have reasonable uses, from helping detect fraud to powering writing, translation or summarization functions, but the problem arises when the user does not fully understand what has been downloaded, why it is there and how they can manage it. Hanff sums it up with a very direct criticism: “Chrome didn’t ask. Chrome does not show it to the user. If the user deletes it, Chrome downloads it again.” There are also voices that reduce the seriousness of the case. On Reddit, a user defended that the model is only downloaded when someone tries to use an AI function that needs it and that it can also be disabled from the Chrome options. Hanff responded that his logs showed otherwise: the browser opened on schedule, stayed on a page for a few minutes without interaction, and still left a trace of the download. Beyond that specific discussion, Google’s own documentation points to a middle ground: the download can be triggered by built-in functions and continue in the background even if the tab that started it is closed. Chrome does offer controls to reduce the presence of some AI features, but it doesn’t concentrate everything in a single, easy-to-understand panel. From settings can be disabled or hide certain visible pieces, such as Gemini in the markets where it is available, typing assistance, search history or AI-powered search. To go deeper, however, We must enter more technical terrain, such as experimental options from chrome://flags. This jump changes the experience quite a bit: we are no longer talking about turning off a clear function, but rather touching internal parts that may also be linked to features that the user may want to keep. Firefox offers an easy way to disable AI features Firefox offers an interesting counterpoint because Mozilla has grouped its AI controls in its own section within the settings. Since Firefox 148, that section is now available as “AI controls” and allows you to block current and future improvements from a visible place, without having to chase options spread throughout the browser. It also separates specific sections, such as on-device AI, translations and chatbot providers in the sidebar. It is a more direct approach: the user not only sees that these functions exist, they also better understand what they can activate, block or leave available. The arrival of Gemini Nano to Chrome is part of a broader movement: browsers want to become more than just a window to the Internet and start executing AI tasks within the computer itself. That direction can have real advantages, especially if it serves to strengthen security or make some functions more agile. But the case also leaves a visible panorama. Some users won’t mind at all that Chrome downloads local models automatically; others, instead, they will want to knowunderstand what it is for and have room to decide. Images | Xataka with Grok | Screenshot In Xataka | It doesn’t speak, it doesn’t climb stairs and it doesn’t even always obey: this is the robot that the creator of the Roomba has been wanting to develop for 30 years

Mistral has a new AI model. The good news is that it is absolutely European; the bad one, which is absolutely mediocre

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The French startup Mistral has just launched Mistral Medium 3.5an open-weight AI model that is the great European exponent in an industry absolutely dominated by China—which competes directly with this type of projects—and by the US. And if this is the best they can do, it seems Europe has a problem. Mediocre. This is a “dense” model with 128 billion parameters and a context window of 256,000 tokens. While models with Mixture-of-Experts (MoE) architecture only activate a subset of the total parameters to achieve enviable efficiency and capacity, Mistral activates them all. That makes it much less efficient, but theoretically it should make its performance promising. And that’s the problem. Which it is not. Benchmarks. Pedro Domingos, professor of deep learning at the University of Washington, he expressed it very well: “Mainstream AI companies brag about how their model is much better in benchmarks. Soo Mistral brags about how their model is much worse.” It is true that the models with which it is compared are larger in total number of parameters, but as we will see later, even taking that into account, they are cheaper and theoretically more efficient thanks to the use of that MoE architecture in many of them. The model, however, unifies the previous catalog and follows the market trend of being able to establish the desired level of reasoning (reasoning_effort) as a parameter. Bad results. And he is somewhat right: Mistral does not seem to have problems showing the results of various benchmarks in which it performs poorly, but it also performs poorly with models that are by no means the most recent or powerful on the market. Thus, it is compared with Claude Sonnet 4.5/4.6, with Kimi K2.5, with GLM-5.1 or with Qwen 3.5 397B. In almost all cases (except GLM 5.1) there are already more recent and powerful versions of all of them. Not so far from local models. In fact Medium 3.5 scored 77.6% in SWE-Bench Verified, a programming test in which Qwen3.6-27b It reaches 72.4% with a fundamental difference: you can run it “for free” (with the appropriate hardware, and you paying the electricity bill) with a relatively affordable machine. More expensive (and somewhat more restrictive). If we use it via API, Mistral Medium 3.5 costs $1.50 per million input tokens and $7.5 per million output tokens. GLM-5.1 costs 1.4/4.4 respectively, and Kimi K2.5 costs 0.5/2.8 respectively. Its recent successor, Kimi K2.6, costs 0.95/4, and it is significantly better than Mistral being cheaper. There is a curious fact: Mistral uses a “modified MIT license” instead of the traditional Apache 2.0, and indicates that this model can be used commercially or non-commercially except for “high-income” companies. Chasing Anthropic. In addition to the model itself, the company has presented the so-called remote scheduling agents using Mistral Vibe CLI to, for example, send pull requests to GitHub in an automated way. It also has the so-called “Work Mode” in LeChat, allowing multi-step tasks to be managed autonomously. These are tools clearly intended to strengthen Mistral’s role as a base for scheduling agents, which is the path that has worked fantastically for Anthropic. Your advantage: being European. The only great strength of this model is that it has been developed by a European startup, and that gives it clear visibility at a time when many EU countries they talk about digital sovereignty. It is the only Western model that seems to want to compete with China in the field of open weight models, which is good news, but the truth is that in terms of performance it does not seem that the Mistral Medium 3.5 is going to perform competitively. The geopolitical security network. That, together with the fact that it costs more than its competitors, makes the decision to use it difficult unless for those who prioritize clearly that European origin. That is Mistral’s ace in the hole, and they are taking advantage of perfectly. The company has recently obtained financing to create data centers in Europe, and is nourished and fed by this new obsession with minimize dependency of North American Big Tech. In Xataka | The CEO of Mistral sends a message to Europe: the end of being the technological vassal of the United States

The superapp model that dominates in China never caught on in the West. something is changing

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Superapps are mobile applications that offer many unified services, from messaging to mobile payments and much more. In Asia, especially China, They are the default formula that has been successful for years with apps like WeChat, Meituan or AliPay. In the West we are more into specialized apps, but the market is beginning to show clear signs of approaching the Chinese model. The Uber case. Uber just announced the integration of hotel reservations in your app through its alliance with Expedia. In this way, in the same app we have car reservations, food delivery and hotel reservations, a solution that is quite similar to the model of a Chinese super app like WeChat, which integrates all types of services under one umbrella. Uber’s goal is that, by offering more services, the Uber One subscription will be more attractive to consumers and thus increase its income. An important detail: Uber CEO Dara Khosrowshahi was previously CEO of Expedia, so this alliance does not seem coincidental. TikTok Shop. Uber is not the only one that is following this strategy, there are other proposals that also point in the direction of consolidation. We have the clearest example with TikTok and the integration of the marketplace. ByteDance has managed to export a very Asian model: see a product in a video and buy it without leaving the app. TikTok Shop has been in Spain since the end of 2024 and, at the end of 2025, there is already a TikTok account more than 12,000 stores operating on its platform. The adoption data is positive, but the model is still very far from the penetration it has in China. There have been attempts. The creation of a super app that succeeds in the West was Elon Musk’s obsession when he bought Twitter. The bet did not work out and today X continues to be what Twitter was: a microblogging social network. PayPal also tried its superapp version integrating hotel reservations with little travel. Years ago there was talk that WhatsApp could be the WeChat of the West, but despite having been adding functions, it is still a messaging app. Looking to the future, we have the case of ChatGPT and its path to a super app that integrates the chatbot with the Atlas and Codex browser. Why in China yes and here no. It is not a question of simple preferences, but has a structural explanation: Internet penetration in China was much slower and, in some ways, skipped the era of the personal computer. While Western consumers came to the smartphone with already formed habits (a browser to search, an email program, an online store), the Chinese did so directly from the mobile phone. By not having already created habits, this made the creation of these “everything apps” much easier. Likewise, the penetration of credit and debit cards was also slow and many consumers switched from cash to mobile payments, hence apps like WeChat or AliPay have become the default standard for paying everywhere. Another factor that plays in favor of the adoption of these apps is that they had no competitors. With the entire Google and Facebook suite blocked by the Chinese government, these apps did not have to compete, but rather filled a void. And of course there is the regulatory issue and institutional support. in China you can pay taxes from WeChatapply for a business license or pay a traffic fine without leaving the app, because the Chinese government actively integrated its public services into these platforms. In the West, the merger between a private company and the State would generate immediate political and regulatory scrutiny. something is changing. On the one hand, the perception we have of China from the rest of the world has been changing in recent years. The success of TikTok, the Labubu, the popularity of electric cars… are symptoms that China has become a cultural reference and technological. This opens a new opportunity for success. On the other hand, there is a new variable: AI. The arrival of AI tools is already changing our information-seeking habits and has the potential to function as a layer on top of everything we already use, connecting services that previously lived separately. Image | IlgmyzinUnsplash In Xataka | The US has made an almost total commitment to enormous AI models. China is showing another way

Now you have an omnipotent model that reads, sees and listens. Everything at once

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Eight years ago, when Nvidia was still a company that made graphics for video games, the company pointed out to something that is starting to enter the conversation: physical robotics. They are the robots with artificial intelligence integrated to behave autonomously. Like a ChatGPT with arms, ears and eyes. It has rained a lot since then and It’s now when we’re starting to enter that future. However, Nvidia has continued to experiment with that way of making the physical and digital worlds converge, and its latest product is the Nemotron 3 Nano Omni. An AI model that sees, hears and reads the physical world. Omni Models. These models are multimodalbut in a much stricter sense. While the models we use every day require separate channels to process and generate audio, text, image and video, an omni model is designed to be inherently multimodal. This implies that they use a unique neural network architecture trained end-to-end so that the interaction between models and stimuli is more natural, faster and capable of recognizing more nuances. An example is an AI that can “see” what a camera captures, analyze the entire situation and give feedback to the user more quickly than one that can do the same, but whose text model has to ask the video model what it has seen and then generate the content. In even fewer words: it better imitates the way humans perceive and respond to the stimuli of the world. Integration. And that’s what Nvidia affirms What Nemotron 3 Nano Omni can do. In the same architecture, it is a model that integrates vision, audio and language capabilities to eliminate the fragmented workflow of current AI agents. According to the company, it is built on a hybrid architecture of mixing experts (AIs trained in various subjects) with 30 billion parameters, of which 3 billion are for inference. It has been designed as a model that is nine times faster than separate models and has three times the performance of other open omni models, consuming 2.75 times less computing power in tasks such as reasoning from a video. Okay, but why?. That is the key question, beyond the numbers and the raw capabilities of this technology. The use cases detailed by the company are the following: Agents: power those agents that navigate graphical user interfaces, reasoning based on the content on the screen and understanding what they are seeing in real time and persistently. The native input resolution is 1920 x 1080 for that HD visual understanding. Documents– Interprets graphs, tables, documents, screenshots, and mixed media inputs. Comprehension audio and video: is able to understand what he sees and hears to maintain consistency in his interpretation instead of reasoning based on disconnected models. For professionals. What is clear is that Nemotron 3 Nano Omni is not something that is launched with the goal of being something for the masses like other AI models that we see every day. Nvidia focuses it on something business, a tool that can be accessed through platforms like Hugging Face and to be deployed on local systems such as DGX Spack or Jetson. That is, it is not something available to everyone. The interesting thing is that it is a technology that is strongly pushing the narrative of agents as omnipotent entities, and it fits with the speech latest from Jensen Huang, CEO of the company, that AI will not come to take away our jobs, but to ‘micromanage’ us. Image | Nvidia In Xataka | There is a company that has grown 3,000% in the stock market, even beating the performance of Nvidia: Sandisk

For generations, we Spaniards embraced the three-course menu. Now that model has entered into crisis

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Christianity has its holy trinity. The theater has its classic structure in three acts, just like the traditional novel. Even life itself can be divided into three blocks: youth, adulthood and old age. For a while (centuries, actually) food also participated in this obsession with triads. When you sat down to eat, whether in your own home, that of a family member or in a bar, you expected to be served three courses: something light to start, like a soup or a salad, a heartier second and dessert to finish the job. Now that model has gone into a spin. Goodbye to three dishes? That is the reflection that left bouncing a few days ago The Country in its section on food: after generations and generations settled in homes and hospitality, meals structured in three courses (first, second and dessert) are in decline. He is not the first to point it out. More than a decade ago it already launched a similar warning Adam Liaw, a chef, presenter and author of gastronomic books who in 2015 warned in Guardian about the gradual “disappearance” of three-course menus. Even Dr. Nicolás Romero issued a warning in 2019, in an interview with The Basque Journal: “We should start by recovering a custom that we are abandoning in Spain, that of three dishes on the menu.” He was so convinced of this that he even encouraged transferring the same formula to dinner, “as the Mediterranean diet dictates”, opening the menu with vegetables and closing it with fruit. Is it really in crisis? It is difficult to find studies that confirm this, but, as Liaw signalif we do not look at our surroundings we will realize that the meal in ‘three acts’ seems to have “fallen from favor”. And that is something that can be transferred both to our homes and to restaurants. In fact there are those who now slide that menus with starters, main courses and desserts risk becoming something extraordinary, a luxury reserved for weddings, New Year’s Eve or other special occasions. Just like silverware or old wine. And why this change? The explanation varies a little depending on whether we are talking about what we do at home or what happens in the hospitality industry, although in both cases a common denominator can be seen: a change in consumer habits. In an increasingly busy society we are less willing to spend hours between the stoves, selecting fresh food…or even sitting in front of a plate, which explains the growing success of snacks. Cooked less? It seems so. In 2003, experts were already warning that, in a matter of a few years, we Spaniards had reduced three hours a week the time we spend cooking. Other surveys most recent show that 48% spend about 90 minutes cooking and 41% barely spend more than 60. There are still the majority of those who prepare their own food, but the Spaniards who barely set foot in the kitchen They are counted in millions. With less (or no) time between pots and pans, it is difficult to prepare meals divided into several dishes. Does everyone lose? “Households are spending less and less time cooking, reducing processes and complexity to optimize the time spent cooking. This implies that people are increasingly opting for single-course occasions, which are 71.3% of the time at dinner and 55.7% at lunchtime,” commented recently Eduardo Vieira, from Worldpanel by Numerator (Kantar), who pointed out that this represents an “opportunity” for the industry. Our tendency to spend fewer hours in the kitchen is giving wings to a business that has been growing for years: that of pre-cooked and ready-to-eat foods. The Spanish Association of Prepared Meal Manufacturers (Asefapre) estimates that in 2025 the consumption of precooked meals in the country’s homes grew by 3.8% and that sales exceeded 4.3 billion, with a growth of 5%. What happens in restaurants? There another extra factor comes into play: the economy. Although the menu of the day has been implemented for decades in Spain, where it is quite an ‘institution’, the formula is in crisis. And not only because of cultural changes or the snackficationa trend that leads us to spend less and less time on food. In recent years it has come under cost pressure. The rising cost of raw materials, energy, labor… has forced hoteliers to review their rates, increasing them by 19.5% between 2016 and 2024. The problem is that the sector assures that this increase is lower than the CPI, which makes it difficult for them to make their menus profitable. “It is in danger, fortunately because it is not a sustainable model,” recognize to The Country Paco Cruz, The Food Manager. Given this situation, it is necessary ‘reinvent’ the menucutting costs. As? Exactly: putting the scissors in and leaving it on a single plate. Do more factors influence? Yes. As if the above were not enough, the hoteliers have to deal with a new rival: the merchantssupermarkets that, like Mercadona, have a wide range of ready-to-eat dishes and spaces in which to consume them. Customers can often choose dishes and devour them in just a few minutes, putting pressure on traditional menus where a waiter serves starters, mains and dessert. Images Michael Clarke Stuff (Flickr), Diogo Brandao (Unsplash)F.arhad Ibrahimzade (Unsplash) In Xataka More and more Spanish bars refuse to let you pay at the table. Its objective is very simple: greater rotation

We already know what happens to the GPU hourly price when OpenAI or Anthropic launch a new model: it doubles

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This week, an analyst named Tomasz Tunguz published in X two revealing graphs. They show the evolution of what it costs AI startups to access cloud computing, and there is bad news. The cost of renting the NVIDIA B200 GPUs with Blackwell architecture has gone from $2.31 per hour in early March to $4.95 per hour this week. It is an increase of 114% in just six weeks and it has a clear cause: the arrival of new models from Anthropic and OpenAI. What the graphs show clearly. Those charts focus on the price index of Ornna cloud computing trading marketplace. The first of them covers the price of renting the B200 chips from the end of 2025 until today, and there are vertical lines showing each release of the latest models from OpenAI and Anthropic. The correlation is almost perfect: GPT-5 Codex, Claude 4.5, GPT-5.3 Codex, Claude Opus 4.7 and GPT-5.5 coincide with a jump in price indices. Every time these companies announce a new version of their frontier models, demand skyrockets, and so does the cost. If you want the best, pay (much more). The second graph shows the price difference between renting the previous generation of chips, H200 with Hopper architecture, and the new B200. The historical average of that “spread” is $1.06, but now it stands at $2.09, practically double. That means buyers—startups and AI companies—are paying a record premium for the extra memory and superior computing power of Blackwell architecture chips. Accessing the latest of the latest was already expensive. Now it is even more so. This also makes the H200 in a second class option for the most demanding models of 2026. Action and reaction. There is overwhelming logic here. When OpenAI or Anthropic release a new model, there is an explosion in inference. Developers and companies want to test them as soon as possible and integrate these models into their products (or compete with them). To do this, they need computing quickly, and a simultaneous demand is caused that unbalances the available inventory in the market for renting AI chips by the hour. The problem is that the supply of B200 does not grow at the same rate. Some companies have wanted to anticipate, and we have the perfect example in Google. He has bought all the B200s he can, and that has made these GPUs around now the 500,000 dollars on the secondary market according to analyst Jack Minor. The irony of efficiency. The curious thing is that the more efficient these chips are – and the B200s are – the more companies want to rent them at the same time to take advantage of those efficiency advantages that should lead to cost savings. What actually happens is that the scarcity of these advanced chips cancels out any theoretical savings. Long term contracts. Startups and companies that think in the short term are especially harmed in this area, because they face price jumps that are increasingly difficult to assume. Companies that signed computer rental contracts at the price then can now operate at less than half the cost of their competitors. Thinking in the medium or long term seems reasonable, although once again those who win are the hyperscalers and those companies that have managed to get hold of many B200s. And who wins even more is of course NVIDIA, which cannot cope. Few alternatives. In other markets such as energy or metals there is usually room for maneuver, Tunguz points out, but the same is not happening at the moment in the AI ​​segment. In the oil market, for example, if the price rises 114% in six weeks, companies can buy futures, options or fixed-price supply contracts to protect their margins. In cloud computing rental, those options are much more limited. And the result is a much more volatile segment. This will go further. We are probably facing a peak in demand that will be followed by a correction: the new batch of B200 chips that arrive in the second half of 2026 are expected to cause a drop in current prices. However, that $4.95 is now the new floor, not a peak, because demand for AI computing will continue to grow faster than TSMC’s production capacity. In the absence of the supply of AI chips growing significantly – and there are certainly movements that are trying to achieve this, such as those of Google with its TPUsAmazon with its Trainium or Huawei with its Ascend—, the problem will still be there. In Xataka | Europe is taking its technological independence so seriously that it is aiming for the most ambitious goal: NVIDIA

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