Model

Mythos will be the most dangerous AI model, but companies are already taking note of its security tips

by

Top AI companies are in the race to create the best artificial intelligence model. That race has been won by Anthropic with Mythos. At least, That’s what they claim (of course)with phrases like it is so powerful that they cannot make it public. There is reasons to take Anthropic’s words with a grain of salt, but what is evident is that Mythos is already working. Although the company has not released it, has already given access to certain technology partners. The decision is based on the company’s fear that the model will be used maliciously. They themselves have described as a threat to cybersecurity based on the number of zero-day vulnerabilities that Mythos would have found in both the main operating systems on the market and in browsers. And, just when the model is arousing opinions from some and others, Mozilla arrives to affirm that the latest version of Firefox 150 It has security fixes for 271 vulnerabilities that have been discovered thanks to this preliminary version of Claude Mythos. For its part, OpenAI does not believe anything at all. “Just as capable as a human” Mozilla it details in one of the latest posts on his blog. The company had been collaborating with Anthropic for some time and using the Claude Opus 4.6 model to find errors. In January, it found 22 vulnerabilities in a couple of weeks, 14 of them rated very serious. Of those 22 found by Opos 4.6, which is already a powerful model, we move on to the 271 discovered by Mythos. It is a huge leap and Mozilla wanted to continue investigating to see to what extent the new model surpasses Opus. Analyzing Firefox 147, Mythos generated 181 functional exploits. Opus 4.6? Just two. 90 times less. Those results have led Mozilla to write that Mythos Preview is “just as capable as the best human cybersecurity researchers”adding that they have not found any categories that humans can detect that Mythos cannot. This has another reading since, as the company itself states, seeing that the model is capable of finding so many errors in such a short time makes them wonder if it is possible to stay up to date in cybersecurity work when alternatives to Mythos are developed that do fall into hands not controlled by those responsible. There is always the fact that Mythos has not found any errors that Mozilla’s human ‘watchmen’ have not detected and that a tool like this will help to have a more secure system. All of this, in the end, pushing that narrative that Mythos is practically a technological miracle. a nuclear bomb The other side of the coin is that Sam Altman, head of OpenAI, doesn’t believe anything. Taking advantage of his recent participation in a podcast, he has qualified The entire Anthropic movement as a fear-based marketing ploy. He accuses Dario Amodei’s company (Altman’s public enemy) of wanting to restrict AI to a small number of people in a strategy that he has compared to having an atomic bomb, threatening to release it and making a living by selling bunkers to protect themselves from that same bomb. “It is evident that this is an extraordinarily powerful marketing strategy. We have created a bomb and we are going to drop it. You can buy a bunker from us for 100 million dollars” It is one more point in that historical rivalry in which both companies (and managers) have been involved for some time, but it comes just when Anthropic is having a greater role and OpenAI is being forced to release ballast in the form of services like Sora. Altman is not the only one who thinks that Anthropic is repeatedly using this discourse of “We have something so powerful that we cannot make it public” because it is a good strategy to obtain financing. There are already voices that they point that Mythos is not that big of a deal and, in fact, other models have proven to be able to do the same, finding the same errors and problems detected by Anthropic. But, above all, we must remember that, in 2019, someone already said that a model was too dangerous for public release. Who? OpenAI itself with GPT-2. Obviously, it wasn’t that dangerous. In Xataka | OpenAI and Anthropic have proposed the impossible: lose $85 billion in one year and survive

A seven-dimensional black hole model proves that Stephen Hawking was right, to say the least.

by

For a long time it was thought that black holes could only grow, since nothing escapes from them. Later, Stephen Hawking dismantled this theory, pointing out that radiation can come out of its interior and that, in fact, with this process the black hole it is fading away little by little. This hypothesis generated a new paradox; since, according to quantum mechanics, information cannot be created or destroyed in a quantum system. If the information cannot be destroyed, when the black hole disappears, where does all the information it stored go? This question has been a mystery until a team of scientists from the Slovak Academy of Sciences It occurred to him to do simulations in a 7-dimensional system. A reminder about black holes. a black hole It is an astronomical object so massive that its gravitational pull does not allow anything to escape from it. Not even the light. At a certain distance from the black hole is the event horizon, which is that point of no return from which everything is attracted towards its interior. Hawking radiation. In the 1970s, Stephen Hawking launched a hypothesis which destroyed the idea that nothing can escape from a black hole. According to him, if we take quantum physics into account, there is something that can do it. Heisenberg’s uncertainty principle states that a vacuum is not empty as such. Particle-antiparticle pairs continually form and appear and disappear. If this occurs in the vicinity of the event horizon, it could be that one of these particles is attracted towards the black hole, while another manages to escape from it, being slightly beyond the point of no return. That exhaust extracts energy from the black hole. This is what was called Hawking radiation. Disappearing black holes. We have all heard the famous formula from Einstein’s theory of relativity: E = mc². Since c is a constant, if there is energy, there must also be mass and, therefore, if energy is lost, for the constant to be maintained, mass must also be lost. That means that every time a black hole loses energy it is also losing mass. They are very massive objects, they would take a long time to turn off, but they finally do. The paradox arrives. Initially, many colleagues saw Hawking’s hypothesis as nonsense. However, today it is much more accepted. However, it is undeniable that it poses problems, such as the black hole information paradox. Where does the information go? Twisting space-time. The solution to the mystery has been possible by putting aside the theory of general relativity and analyze the problem with a somewhat more complex one: the Einstein-Cartan theory. The first points out that mass and energy can curve space-time. On the other hand, the second points out that it can also twist. For scales that are not excessively small there is no difference. However, when moving to tiny scales and therefore very high densities, This torsion plays an important role. A 7D model. Quantum physics models are often made in 4 dimensions: the three we all know and time. However, the authors of the recently published study took three more into account, so that the effects of the Einstein-Cartan torsion could be analyzed. Thus, they saw that when the matter of a black hole collapses its density increases greatly and, therefore, the twisting of space-time is detected. This gives rise to a repulsive effect, which counteracts the gravitational attraction that would normally take place in the engrossing hole. As a result, the evaporation of the black hole stops, which remains in a stable state, generating a remnant with a mass of 9×10⁻⁴¹ kg. A remnant with a lot of information. This tiny remnant is capable of storing all the information of the matter that the black hole contained. Specifically, these scientists’ models suggest that the remnant of a black hole the size of the Sun could store up to 1,515 × 10⁷⁷ qubits of information. Therefore, Hawking’s hypotheses are still valid and there is not even a paradox that dismantles them. At least this is not the lost information. Image | ESO (Wikimedia Commons) | ASA/Paul Alers (Wikimedia Commons) In Xataka | In 2009 Stephen Hawking hosted “the party of the century.” No one came precisely because Stephen Hawking organized it

Claude Mythos is an AI model so powerful it’s scary. So Anthropic has decided that you won’t be able to use it

by

Claude Mythos Preview it’s already here and it’s so good it’s scary. Literally. Anthropic has just introduced it to the public, but it has been done so cautiously that we won’t even be able to test it and it will only be available for certain technology partners. That’s frustrating and disturbing at the same time, but also reasonable. So powerful that it scares. On February 24, 2026, Anthropic engineers were able to test their new artificial intelligence model for the first time, which they called Claude Mythos Preview. As soon as they did they realized one thing: “demonstrated a dramatic leap in its cyber capabilities over previous models, including the ability to autonomously discover and exploit vulnerabilities zero-day in the main operating systems and web browsers on the market. Threat to global cybersecurity. This finding made it clear to Anthropic officials that although this capability makes it very valuable for defensive purposes, it also poses clear risks if the model were offered globally. Thus, a cybercriminal could take advantage of it to find vulnerabilities in all types of systems and exploit them. A few hours ago the company developed this analysis of Mythos as a threat to cybersecurity in a post on his blogand for example highlighted how Mythos found a vulnerability (now corrected) that had been present in OpenBSD for 27 years, an operating system precisely recognized for its very strong security. There were more examples, and all of them made the conclusion clear: Mythos is too powerful for ordinary mortals to use. Superior in all benchmarks, and in some cases such as USAMO (mathematics), the jump is simply incredible. Source: Anthropic. The best in history according to benchmarks. Anthropic has published a very in-depth report about this model with its “system card”. Among the data present is, for example, its performance in benchmarks, where it has swept GPT 5.4, Gemini 3.1 Pro and also Claude Ous 4.6, which until now was the best model in the world in almost all performance tests. Although in some cases the jump is not spectacular, in others such as USAMO —mathematical problem solving—Mythos practically achieves perfection. He barely hallucinates… That system card also talks in detail about how Claude Mythos Preview has a drastically lower hallucination rate than Claude Opus 4.6 and earlier models. He is also capable of saying “I don’t know” if he does not have enough information to answer, something that reduces hallucinations due to overconfidence. …but when it does, be careful. The paper warns of a new phenomenon: when the model fails in some complex tasks, the “hallucinations” are not obvious errors, but rather extremely subtle and well-argued technical failures. This is dangerous because the answer seems totally correct to experts, requiring very deep verification. Glasswing Project. That power and capacity has meant that the model will only be available through a “defensive” program that they have called Glasswing Project and which will be exclusive to some Anthropic technology partners. Specifically AWS, Apple, Broadcom, Cisco, CrowdStrike, Google, JPMorganChase, the Linux Foundation, Microsoft, NVIDIA and Palo Alto Networks. All of them will have the privilege (and responsibility) of having access to Claude Mythos Preview to identify vulnerabilities and exploits and correct them before bad actors can do so. Mythos Preview “it’s just the beginning”. Although this model is the most capable that has been seen so far, at least according to the benchmarks and data presented by Anthropic, the company assures that “we see no reason to think that Mythos Preview is the point at which the cybersecurity capabilities of language models reach their peak.” They assure that they expect the models to continue improving in the coming months and years, although this new model is certainly on another level. In Xataka | OpenAI and Anthropic have proposed the impossible: lose $85 billion in one year and survive

Each new AI model is the best ever until the next one arrives. Anthropic and OpenAI have turned that into a business

by

It doesn’t matter what technological product we are talking about, because both the product and how it is sold to you matters. And here making promises and generating expectations is the classic strategy. The next processor is going to be more powerful, the next smartphone is going to take better photos… and of course, the next AI model is going to be (much) better. We are seeing that message constantly in the AI ​​segment, but now it is going further. Anthropic and a curious leak. A group of security researchers they detected a few days ago 3,000 unpublished documents in an accessible Anthropic database. They included a draft of the blog entry that corresponded to the theoretical launch of their next AI model. The striking thing is not so much the filtration itself (whether intentional or not), but what those documents reveal. Mythos goes beyond mere evolution. Or at least that’s what that leaked draft seems to reveal. It describes a model called Claude Mythos—also called Capybara—which would not be a simple improvement on Claude Opus, but would be a level above it. The document says that this model is “bigger and smarter than our Opus models, which until now were the most powerful.” Anthropic signs up for hype. According to this leak, the benchmark scores would be notably higher than those of Opus 4.6 in programming, reasoning and cybersecurity. At Anthropic have ended up confirming the existence of this development, and have described it as “a level change” and “the most capable model we have created to date.” It’s not too surprising a phrase, because it’s basically the same thing they’ve been saying about every new model they’ve released. And even they are scared. In fact, what is surprising in that draft is not the message that it is better, but the warnings that accompany that future presentation. Thus, Anthropic describes Mythos as “currently far ahead of any other AI model in cybersecurity capabilities.” In fact, they warn that this may be the beginning of “an imminent wave of models that can exploit vulnerabilities in ways that far exceed the efforts of the defenders.” Or what is the same: Mythos could be a extraordinary tool for cyber attackers. The actual launch plan is to first offer Mythos to cybersecurity organizations to prepare. We will see if that gives an advantage, if Mythos meets expectations. OpenAI also makes a move. Both Anthropic and OpenAI have been moving in parallel for some time, and now they have done so again. At OpenAI they are preparing their new AI model, codenamed “Spud” (“potato”). Hardly anything is known about him beyond the fact that his pre-training phase has been completed. More relevant is that this model appears just when At OpenAI they have decided to be less OpenAI and more Anthropic. They have abandoned Sora and they are redirecting resources to regain ground where they are losing it. That is, in companies. But the count is not infinite.. These days, users of Claude’s $100 and $200 per month plans began to notice how they used up their limits and token quotas in less than an hour during their work hours. What is happening is that Anthropic is training more powerful but much more expensive models to use and that makes it difficult to serve them. Demand is growing faster than the efficiency improvements that are coming, so according to some analysts, AI companies are adjusting those quotas and in a sense making Their models behave as if they were “dumber” to save. It’s something we’ve seen in the past. hedonic adaptation. The psychologists called hedonic adaptation to the phenomenon by which humans quickly become accustomed to any level of experience, good or bad, and return to our starting emotional state. When applied to AI, this phenomenon explains that this model that seemed miraculous to us six months ago today seems slow and limited, and what six months ago seemed like science fiction is today the minimum we ask of companies. Anthropic and OpenAI have not invented the concept, but they have integrated it into their roadmaps like other technology companies in the past. We mentioned it before: they not only sell what they have today, but (more importantly) what they will have tomorrow. Mythos will be brutal and very expensive. Anthropic’s draft warns that Mythos will be “very expensive to serve and will be very expensive for our customers.” That points to two possibilities. The first is that only users of the Max plans can access some consultations with this model. The second, that a subscription appears even more expensive than that 200 dollars a month so we can leverage Mythos with more leeway. We already had a free AI, a basic paid AI and a high-end paid AI. Now we will also have super high-end AI. In Xataka | The hard landing of OpenAI: after years at the forefront, it is discovering that AI is not won only with memes and hype

It should be impossible for an iPhone 17 Pro to run a gigantic 400B AI model. Ought

by

The iPhone 17 Pro has 12 GB of unified memory. It is a very decent figure for a mobile phone, but in theory absolutely insufficient to run large AI models locally. And therein lies the surprise: a new project has made it possible for this mobile phone to run locally a model with 400,000 million parameters (400B). And that opens the doors to a promising horizon. Giant AI model, dwarf memory. A developer named Daniel Woods (@dandeveloper) has created, thanks to AI, a new inference engine called Flash-MoE whose code has been published as Open Source on GitHub accompanied by a study about his behavior. woods managed to run locally the Qwen 3.5 397B model (the full version, without distillation or quantization) on your MacBook Pro with 48 GB of RAM. Downloaded the model (209 GB on disk) and developed that inference engine to achieve something that seemed almost impossible. Other developers have gone even further and have managed to run models like DeepSeek-V3 (671B) or even Kimi K2.5 (1.026B!!) on their MacBooks. The speed is slow, no doubt, but they work, they work. It’s amazing. iPhone 17 Pro is capable of running a 400B model. Another developer called Anemll wanted to go a little further and try to run this model with almost 400,000 million parameters on his iPhone 17 Pro with 12 GB of RAM… and he succeeded. It is true that the model is very slow in responses (0.6 tokens per second, very unusable), but achieving something like this opens the doors to a future in which video or unified memory is no longer so critical to be able to use huge AI models locally. a few hours ago doubled the speed at 1.1 tokens per second, reducing the number of experts to four (2.5% quality loss in responses). It is still not entirely usable, but the technical demonstration is evident. Another user has preferred to use a somewhat smaller model (Qwen 3.5 35B) but still huge for the iPhone, and has already managed to get it to run locally at about more than acceptable 13.1 tokens per second. Why it matters. The AI ​​models we use in the cloud (ChatGPT, Gemini, Claude) are gigantic and run in data centers with thousands of chips and enormous amounts of memory and storage. They are the most powerful because they run on the most powerful machines. Although it is possible to use AI models locally, the models that we can run are much smaller and that makes it difficult for them to behave equally well both in quality of responses and in their speed or precision. This method opens the door to a future in which even on “modest” machines it is possible to run giant AI models that give better answers and allow us to avoid using models in the cloud. Apple already warned. Three years ago a group of Apple researchers published the study ‘LLM in a flash‘ which precisely pointed to that: to run AI models locally it would be possible not only to take advantage of the unified memory of Macs, but also their storage units. The speed would be slow, yes, but this would open up the possibility of running gigantic models locally on machines with much smaller amounts of unified memory. Woods used Claude Code with Claude Opus 4.6 and applied the new methodology “autoresearch” by Andrej Karpathy to implement Flash-MoE based on that research. The result is really promising. Video memory was everything. On my Mac mini M4, for example, I have 16 GB of unified memory. This means that with tools like Ollama you can install and run models like Qwen 3.5 4B locally with some fluidity, but 7B models or others like gpt-oss 20B would be much slower in responding (or would get stuck altogether). Video memory (or unified on Apple devices) is the most important parameter when running local models, both in terms of quantity and bandwidth. If you want to use them fluidly, that’s the limiting factor. It is possible to use “regular” RAM, but the speeds when using it are reduced so drastically that it is often better not to use that option at all. If you have a fast SSD, you have a treasure. Now the limiting factor is our SSD drive, since the model uses it as if it were a kind of substitute for video memory. And the faster the SSD drive on our computer, the better. There is good news here, because lately we are seeing how PCIe 5.0 drives they achieve about 15 GB/s without too many problems, and that speed already gives enough room for maneuver to use much larger AI models locally than we could use before. A promising future for local (and more private) AI. This discovery is really striking for everyone who wants to use AI locally, because it allows you to use huge models without having to make a huge investment in the latest generation graphics cards or, for example, in a Mac with a lot of unified memory: a Mac Studio M3 Ultra with 512 GB of memory, for example, costs more than 10,000 euros. With this new method we could opt for a much cheaper machine that, with a good SSD unit, would allow us to use giant models in a fairly decent way. Not as fast as those other options, sure, but still very decent. It’s a notable step forward in enjoying the benefits of running local AI models, including the biggest of them all: privacy. With this type of local execution, our conversations and everything we tell the chatbot stays on our machine, it does not end up on the servers of companies like Google, OpenAI, Meta or Anthropic. In Xataka | Jensen Huang believes we have reached the “coming of the AI ​​wolf.” It is perfect for feeding a Tamagotchi

suffocate your business model

by

If we limit ourselves to the average gasoline prices in Spain, the measures imposed by the Government have eased the pockets of the Spanish people a little. That, at least, is what the portal says. dieselgasolina.com which collects the prices of all the gas stations in our country. However, the full photograph does not tell us this. There seem to be nuances. And one of them has the low-cost gas stations at odds with the big oil companies. A meeting with the CNMC. It is the information that brings Populi Voicewho assure that the National Association of Automatic Service Stations (AESAE) will meet with the CNMC to express their complaints about the policies of Repsol and Moeve when applying aggressive discounts on the purchase of fuel. According to the newspaper, this association is in favor of filing a formal complaint because they understand that their discounts are only intended to harm their business. In Xataka We have contacted this association but at the time of writing this article we have not received a response. What has happened? On March 20, 2026, The Government reduced VAT on fuel from 21% to 10%. This has caused an immediate drop in the price of about 30 cents on average, according to figures collected by the portal. dieselgasoline. All in all, both diesel and gasoline remain well above the prices we found on March 1, when the Iran War had just begun. Then, the price of gasoline was 1.495 euros/liter and today it is 1.584 euros/liter. Diesel is the most affected fuel. From the 1,447 euros/liter registered at the beginning of the month, it is today at 1,783 euros/liter, even above 98 gasoline. Repsol and Moeve tighten. In this context, Repsol and Moeve have taken the opportunity to launch aggressive discount campaigns that, of course, are not available to everyone since they rely on their loyalty cards and multi-energy programs to catch the consumer with more attractive prices. Repsol relies on Waylet to put on the table discounts of up to 40 cents/liter. With the card, the discounts are 10 cents/liter but these are doubled if we have the electricity contracted with Repsol. And they reach 40 cents/liter if we have also taken out home or car insurance. Moeve uses a very similar strategy. Using its alliance with Naturgythe company offers discounts of 20 cents/liter with each report if we have also contracted electricity or gas. And we will also have six cents/kWh refueled with each electric car recharge. These figures grow if we have also contracted other services and even the consumption of our home with plates. In that case, the discount reaches 60 cents/liter and 15 cents/kWh with each electric recharge. Low cost companies complain. These discounts are not being seen well by low-cost companies. These types of companies He already pointed out to the Government in the early days of the Ukrainian War that subsidy of 20 cents/liter of the State put its business model at risk. But, also, they pointed out to the big oil companies as architects of a staging with its discounts that compromised its economic viability. Therefore, according to Populi Voicethese companies will file a formal complaint with the CNMC against a pricing policy that they consider abusive. They are not the only ones, FACUA also denounces that service stations are absorbing state aid with the VAT reduction. Already in 2022 The CNMC verified that the discounts applied were quickly absorbed by the oil companies. In Xataka We have contacted the Spanish Fuel Industry Association, who defend that their members, including Moeve and Repsol, “have always taken the side of consumers in times of crisis such as the years of Covid-19, the War in Ukraine or in this case.” Same story (more or less). Those 2022 complaints seemed founded, as time has shown. It’s only been a few months since The CNMC fined more than 20 million euros in penalties to Repsol for applying discounts during fuel purchase subsidies four years ago. According to Competition, the company launched a two-direction strategy. It offered extra discounts of five cents/liter to professionals and, at the same time, raised the sales price of its fuel to independent service stations. The objective was to narrow their profit margin while offering itself as a company with more attractive prices than the competition. A question of margins. Big oil companies have an obvious advantage in times of crisis. As has been seen with the 2022 discounts and is seen right now, they are companies that can play with their profit margins with greater ease than low-cost companies. First because they have a dominant position with more establishments in the market, second because they buy a greater amount of fuel. Low cost companies can offer more attractive prices in normal circumstances but they are more sensitive to crises if we are talking about an increase in the price of the product. This is because their purchases are smaller and more frequent, so they live adrift from the price of oil, reflecting the rise in price before large companies. And, obviously, they suffer much more when these companies apply aggressive discounts since their profit margins are narrower but their room for maneuver is also smaller since, as we say, they cannot make large purchases that lower the price for a few days, no matter how few. Photo | Repsol and Ballenoil In Xataka | Finding the cheapest gas station in your area is very simple thanks to this very powerful tool

The model challenges benchmarks in a key area

by

When we think of Xiaomi, it is normal that its mobile phones come to mind or, at most, its foray into electric cars with models like the SU7. However, what we have seen now points to a much more ambitious move: the company also wants to compete in the artificial intelligence race. It has done so with the launch of MiMo-V2-Proa model that, according to the data shared by the company itself, seeks to position itself close to the most advanced systems on the market, but with a very different focus on costs. And that changes the conversation quite a bit. What Xiaomi proposes. The company presents its model as the “brain” of systems capable of executing complete tasks, not just responding to specific requests, which in the sector is known as agent-oriented models. According to official information, we are looking at an architecture that exceeds one trillion total parameters, although it only activates 42 billion in each execution, and that can work with contexts of up to one million tokens. On paper, this allows you to maintain long, complex processes without fragmenting them, something designed for large tasks and more demanding workflows. Performance against the greats. If we look at the data, Xiaomi does not present its model as the best on the market, but as one that can compete in certain scenarios. In the GDPval-AA benchmark, oriented to real agent-type tasks, it reaches an Elo of 1426, surpassing Chinese models such as GLM-5 (1412) and Kimi K2.5 (1309), although it falls short of proposals such as Claude Sonnet 4.6, which marks 1633. The external reading is provided by Artificial Analysis, which assigns it a score of 49 on its intelligence index, which places it in the group of most competitive models on the market. The key is in that closeness in some benchmarks, not in general leadership. The key to the price. This is where Xiaomi’s proposal changes the board. According to data collected by Artificial Analysis, running your IQ with this model costs approximately $348, compared to $2,304 for GPT-5.2 or the 2,486 of Claude Opus 4.6. It is not exactly the same comparison as the price per API use, but on both levels Xiaomi appears clearly below several Western rivals. In its own API, the company sets prices of $1 per million tokens for entry and $3 for exit in the range up to 256K, a lower rate than models such as Claude Sonnet 4.6 and Claude Opus 4.6 at the same level of use. Beyond chat. What Xiaomi is proposing with this model is not only to improve the quality of the responses, but to change the type of work it can do. The company insists on moving from conversation to action, with a system capable of using tools, interacting with environments and completing chained tasks. In this context, it presents it as a model optimized for agentic scenarios and links it to frameworks such as OpenClawin addition to mentioning collaborations with OpenCode, KiloCode, Blackbox and Cline. On paper, this reinforces the idea of ​​an AI designed to execute workflows and not just answer questions. behind the scenes. Xiaomi enters the race with a model that, according to available data, is close to the major benchmarks in some scenarios, although without generally surpassing them. Where there does seem to be a clear bet is on the price, and that is where it tries to differentiate itself. The question is whether this balance between cost and performance is maintained outside of benchmarks and in real environments. We will have to wait to know if what the data shows is also projected in the real world. Images | Xiaomi In Xataka | China has immediately understood the future of the technology industry: “one-person companies” powered by AI

Your bet in the AI ​​race is to bring together several functions in a single model

by

The artificial intelligence race is often told as a competition to see who builds the most powerful model or the one that dominates the most benchmarks. In the middle of that board, the French startup Mistral AI has just presented Mistral Small 4a proposal that tries to occupy a different place in that conversation. It is not presented as a model limited to a single function, but as one that, according to the company, seeks to bring together several advanced capabilities within the same tool. What exactly is Small 4. The company presents it as the new great iteration of its Mistral Small family and, above all, as the first model of the house that brings together capabilities that were previously distributed among several lines. Specifically, it integrates functions associated with Magistral, Pixtral and Devstral along with those of the Small series itself. Fewer models, more features. One of the central ideas of the announcement is to concentrate tasks that are normally solved with different tools in a single system. According to Mistral, the goal is that the same model can be used to converse, analyze complex information, work with images or assist in programming without having to switch between several specialized systems. The numbers behind Small 4. The model is based on a Mixture of Experts architecture, a design that distributes processing between different specialized submodels and that today appears in several artificial intelligence systems. In the case of Small 4, Mistral indicates that the system has 128 experts and that only four participate in each generated token. According to the company, the model reaches 119B total parameters, with 6B assets per token, and offers a context window of up to 256k. Who is this model intended for?. Beyond its architecture, Mistral also describes quite clearly the scenarios in which it imagines the use of Small 4. Let’s see. Developers: Automate programming tasks, explore code bases, and code agent workflows Businesses: conversational assistants, document understanding and multimodal analysis Research: mathematics, complex analysis and reasoning tasks The underlying idea is that the model can move between quite different needs without forcing you to change the system depending on the type of work. The graphics. In the material accompanying the announcement, Mistral includes several graphs where it compares Small 4 with other models in different benchmarks. These comparisons are not limited to the score obtained in each test. They also show the average length of the responses each system generates, a data the company uses to illustrate how much text each model needs to produce to achieve certain results. One of the graphs in the advertisement corresponds to the AA LCR benchmark, where Mistral compares the scores of various models and the average length of the responses they generate to solve the same tasks. The data published by the company are the following: • Mistral Small 4: 0.72 score with 1,600 characters• GPT-OSS 120B: 0.51 with 2,500 characters• Claude Haiku: 0.80 with 2,700 characters• Qwen3-next 80B: 0.75 with 5,800 characters• Qwen3.5 122B: 0.84 with 5,700 characters The comparison. Small 4 is not the highest scoring model. Both Claude Haiku and the Qwen models appear higher in that indicator. However, Mistral highlights another aspect of the comparison: the length of the responses. According to the company, its model achieves this combination of score and output length by generating significantly less text than several of its competitors, something it relates to lower latency and lower inference cost. The short answer trick. A shorter answer is not better simply because it takes up less space. It is only if it manages to solve the task with a level of quality comparable to that of a longer answer. This is where Mistral tries to put the focus: if a model achieves a competitive result by generating less text, it can respond faster, consume fewer resources and reduce the cost of inference. In other words, the advantage is not in being more concise, but in needing less output to reach a useful result. How to access the new model. Small 4 can not only be used via API and AI Studio. Being published under license Apache 2.0is also proposed as an open model that can be downloaded, adjusted and deployed in your own environments. The company adds that it can be tried for free at build.nvidia.com, in addition to offering it for production as NVIDIA NIM. Images | Mistral In Xataka | OpenAI has been wanting to be the bride at the wedding and the dead man at the funeral for years: now it has finally defined its priority

The AI ​​race is no longer about who has the most powerful model. Who launches the easiest and safest OpenClaw

by

2026 began with an earthquake in the world of AI, and it did not come from any of the big technology companies, but from an unknown programmer and his open source project OpenClaw (formerly Clawdbot and Moltbot). Not even two months have passed and we can say that the boom of this AI agent is reconfiguring the AI ​​career, causing more and more companies to jump on the bandwagon. The last one was Perplexity. Personal Computer. a month ago, Perplexity announced Computerwhich was a cloud-based tool capable of orchestrating agents using various models. The next step is Personal Computeryour own OpenClaw. can be left running on a Mac Mini and control it from another device, such as a mobile phone, exactly the same as OpenClaw, but with a simpler interface that does not require technical knowledge. Further user-friendly. Another key aspect is that they focus on security, one of the delicate points of OpenClaw. Perplexity claims that with Personal Computer, “Every sensitive action requires your approval. Every action is logged. There’s an off switch.” At the moment Personal Computer is not available yet, but if you want to try it before anyone else you can sign up for the waiting list. NVIDIA NemoClaw. Which is the most valuable company in the world has taken good note of the success of OpenClaw and a couple of days ago they announced that they will launch their own open source platform for enterprise AI agents, they will call it NemoClaw. This announcement is also important because it places NVIDIA in a position of direct competition against companies like Anthropic, OpenAI or Perplexity. This changes its position from a hardware supplier to a software competitor. and OpenAI…The project had not even been three months old when OpenAI, not only bought it, but also hired its creator Peter Steinberger. It was not the only one who bid to achieve the viral success of the moment, Meta also tried, but OpenAI was the one that won the bid. Stenberger said the project would continue to remain “open and independent.” This case is a good example of two things: how far a person can go with a good AI idea and how difficult, if not impossible, it is to compete in an ecosystem in which the competition is some of the largest and most valuable companies in the world. David against Goliath. The agentic AI race. We spent a good part of 2025 watching AI agents take their first steps, many times with quite mediocre results. It was clear that agentic AI was getting a lot betterbut I don’t think anyone expected that the first viral hit would be carried out by an independent and open source project. OpenClaw not only succeeded, it has launched a new race in AI, one that seeks the ultimate custom AI agents. OpenClaw has two barriers to entry, on the one hand requiring certain technical knowledge and on the other security. It is a very powerful agent, but sometimes unpredictable. Hence, Perplexity is appealing precisely to improve these two aspects. We’ll see who will be next. In Xataka | Social networks were born for humans: Meta has just bought one designed for AI agents Image | Pexels

Europe has reached the end of winter with depleted gas reserves. A country has a model to save it: Spain

by

This winter, which is coming to an end, is being colder than expected, something that as we have seen has caused havoc. Without going any further, there have been planes that have not been able to fly due to lack of antifreeze. If we talk about gas for heating, storage has also reached red numbers: the Netherlands has a reserve of approximately 12%, Germany and France are around 21%, according to AGSI data. In this low-minimum scenario, there are two countries that deviate from the norm: Spain and Portugal, with reserves of 56.87% and 76.7%, respectively. Of course, the difference in capacity is abysmal: 3.57 TWh for the first and 35.9 TWh for the second. It is not a coincidence: it is that the Spanish state has a particular infrastructure that has led it to this point. The context. The conflict between Ukraine and Russia that began in 2022 accelerated the independence of the old continent from Russian gas. Among the measures from Brussels, an emergency rule by which all EU member states had to start the winter with their gas reserves at 90% to ensure supply. However, in 2025 the EU decided to maintain that 90% target. but relaxing the norm to optimize costs. This greater flexibility together with a harsher than expected winter has brought an end to winter with reserves that are at their lowest in the last five years. The harsh European winter. In mid-January, deposits fell below 50%. If the winter ends with a capacity of 30%, Europe will have to inject 60 billion cubic meters of gas. To get an idea, approximately the annual gas consumption of all of Germany. In short, Europe has to refill its tanks in the summer and it will need a lot of imported gas to do so, which means go out into the market and face other competitors and the logistics of bringing it here in an increasingly complicated geopolitical scenario. The Spanish strategy. The Spanish gas storage system is based on two pillars: underground storage and LNG regasification. The second leg is providential, insofar as it is where Spain makes the difference and, furthermore, It is a powerhouse. In fact, Spain owns 35% of all LNG storage capacity in the EU, how Sedigas collects. Its enormous regasification capacity enables diversification of origin, with USA as first supplier with 44.4% of the total gas and another 15 different countries later, according to Enagás data. Spain has an infrastructure of seven plants that makes it possible to receive LNG ships from different sources, thus ensuring supply in case any mishap (technical problems, conflicts, political decisions) fails. Spain started the winter making decisions. Although the previous strategy gives it an advantage over other member states, Spain adopted a conservative strategy When facing this winter 25/26, adjusting to concentrate reserves in January and February, the coldest and with the most demand. A management decision to not waste that cushion prematurely. He was absolutely right: in January gas consumption rose 10.2% compared to the previous year, with a 30% increase in that destined to generate electricity because renewables contributed less than expected. Spain plays in another league. Thanks to its infrastructure, Spain no longer only consumes gas: it re-exports it. It has become a hub for redistributing gas to Europe as a kind of energy logistics platform, providing geopolitical and economic value to a state that, due to its geographical location, is isolated (which, for example, in the electrical field plays tricks on him) Is there real risk? While it is true that widespread shortages are not expected, there are localized risks in Europe. As summarizes El Economista, Spain has precedents of similar levels, such as 2016, 2017, 2019, 2022, where supply was not compromised. Of course, we will have to see what happens with the demand for LNG in summer globally, because it could make European replenishment significantly more expensive. In any case, Spain will get to that moment better than most. The scenario is not very rosy at the moment, precisely, with the Strait of Hormuz closed and the diplomatic crisis between Spain and the US, its main supplier. In Xataka | Europe believed it had won the gas war against Russia. Now it faces a much more uncomfortable reality: its dependence on the United States. In Xataka | The gas market becomes unpredictable: we have tanks full and ships on the way, but the price remains an enigma Cover | Pronor

Log In

Forgot password?

Forgot password?

Enter your account data and we will send you a link to reset your password.

Your password reset link appears to be invalid or expired.

Log in

Privacy Policy

Add to Collection

No Collections

Here you'll find all collections you've created before.