What is memory in AI and how to make the most of it and use it correctly

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Let’s explain to you what is AI memory and how to use it well to get the most out of it. This is a feature that is already included in practically all the main chatbots with artificial intelligence, and it helps you give them context about you and apply it in their responses. If you often use bots like ChatGPT, Gemini or Claude, you may have noticed that there are things that are not remembered from one chat to the next. Or it may be the other way around, you may be surprised that he remembers a specific detail that you didn’t expect him to remember. This is precisely due to the way in which the two types of memory that AI has work. What is AI memory When we talk about memory in artificial intelligence, we are referring to an internal system that these chatbots have to save information about you between conversations. Not only within the conversations themselves where the context is maintained, but also between different conversations. For example, ChatGPT, Gemini or Claude may remember details such as the city where you live, your profession, or even your personal tastes. They can also remember if you have pets, what pets you have, your projects, the topics you usually write about, your style, and many other preferences. This memory is used to contextualize the answers that the AI ​​gives you based on your data that may make you prefer one type of response or another. So, even if you start a conversation from scratch in each chat, there will be some specific information that continues to be remembered so that you don’t have to write it over and over again. What types of memory does AI have? Artificial intelligence does not treat all the memories it has about you the same, and we can distinguish between two different types of memory. Each of them has its specific use. They are the following: Saved or explicit memory: This memory is like a kind of personal file about you that the AI ​​generates by saving specific data about you. It can do this automatically when you mention something from your context, like your job, but you can also ask it to remember something about you with a command like “remember that I’m a vegetarian.” History-based memory: Instead of saving specific data, some AIs can also consult the log of your previous conversations. This does not generate a profile of you, but rather they analyze the content of everything you have said before to give you more coherent answers. In practice, the difference is that the saved memory is that in which the AI ​​saves the elements that you ask for or those that it considers appropriate, and from them it generates a file about you that it updates. So, you then use that knowledge to generate the answer. Meanwhile, history-based memory is as if the AI ​​could reread its notes from previous conversations before responding to you. If you don’t want the AI ​​to remember a conversationAI systems also have an incognito mode, like in browsers. With it, chatbots will neither save data about you that you write in it nor will they remember the chat in the future. This conversation will not be saved in your history either, although the data you write does end up on the company’s servers. Why AI “forgets” by default An artificial intelligence language model like the one behind ChatGPT, Claude or Gemini, only processes what is within the active conversation. When you ask something, it will take into account everything you have asked it during this conversation, and once you close the conversation or open a new one, the previous one is forgotten or not taken into account. This is not a mistake, but a deliberate decision. On the one hand, it allows you to maintain the context of everything you are asking within the same conversation, while in each new one you can start from scratch without taking into account everything written in another chat. Beyond the practical reasons there are also the technical ones. Maintaining the context of millions of simultaneous chats permanently would require a large consumption of data and energy. Additionally, in the event of a security breach, if the AI ​​has already forgotten past conversations there will be fewer risks to users’ privacy. AI chatbots usually allow you to manage memory that they have stored. On the one hand, you will be able to edit or delete the explicit memory with the file they have made about your data, and on the other hand, you will also be able to configure how the history memory is managed. There is an important detail that should be remembered: Deleting a chat does not delete the saved memories that were created in it. They are two different types of memory. If you delete a conversation in which you have told ChatGPT that your hair is blue, the AI ​​will not refer to what you have talked about with it and will have forgotten it, but if it has saved in its explicit memory that your hair is blue, this is something it will always know in all the chats you start with it. Additionally, many AIs have a system to import memory from other competing AIs. so that you can pass from ChatGPT to Claude either from Claude to Gemini taking you from one to another explicit memory with everything an AI knows. How to make the most of AI memory AI memory can save you a lot of time when you use it well, but if you leave it to its own devices it can also accumulate incorrect, outdated or useless information, and cause frustration or incorrect answers. For example, I once asked ChatGPT to add a cumquat to an image, and until I removed it from memory, for months it was adding cumquats to all the images I asked it to. Therefore, it is important that periodically review … Read more

Microscopes had been dependent on human operators for almost a century. China wants to change that with AI

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A team of Chinese researchers has presented in Beijing which they claim is the first transmission electron microscopy system in the world capable of operating completely autonomously. Dubbed “Aeye-1”, the device has demonstrated in tests its ability to replace a human operator in all phases of the process thanks to AI. What exactly is it. A transmission electron microscope (TEM) is a tool that has been essential for decades to observe matter at the atomic scale. It is used to develop new materials, energy technologies, industrial chemistry, and has been a key instrument for evolution in science. For almost a century, these devices have always depended on manual handling by a technician, something that in the end ends up giving subjective results and entails certain difficulties in performing quantitative analyses. Why it is important. Aeye-1 makes the leap from “manual operation” to “AI-led autonomous operation”. According to they count its researchers, the system carries out the entire work chain by itself, from transferring the sample to capturing the images and analyzing the data without the intervention of any person. According to Deng Dehuiprofessor at the Dalian Institute of Chemical Physics (DICP) of the Chinese Academy of Sciences and leader of the project, the system works “like an ‘intelligent eye’ that visualizes the atomic world.” In detail. The development was carried out by the team of Deng Dehui and Professor Liu Wei, in collaboration with researchers from the Shenyang Institute of Automation. Together they have designed the algorithms that allow the microscope to perceive, analyze and control the process independently. To achieve this, they had to overcome many technical challenges, including the intelligent transfer of samples in high vacuum, the autonomous optical adjustment of the image, the precise localization of objects at the nanometer scale, the capture and analysis of images in real time and the coordination of all subsystems at the same time. The figures. According to Deng, image analysis It is more than 300 times faster than manual. To understand the magnitude, two weeks of Aeye-1 operation are equivalent to one year of work of a conventional microscope. In tests with molecular sieve catalysts, the system analyzed an average of 168 samples per day, captured more than 4,000 images per day and automatically generated professional reports with detailed statistics on particle size, dispersion or crystal structure. Who supports it. The system surpassed last Sunday an evaluation of scientific and technological achievements held in Beijing and organized by the Chinese Petroleum and Chemical Industry Federation. The evaluation committee unanimously concluded that it is a “highly innovative technology, the first of its kind in the world and an international leader.” And now what. Those responsible for it expect that Aeye-1 will be able to continuously provide large volumes of high-quality structural data in fields such as energy, industrial chemistry, advanced materials and life sciences. The long-term goal is for this new team to drive a paradigm shift in AI-supported scientific research. It really is a process in which automation through AI can be highly beneficial. We will have to wait to find out if it ends up setting a trend in the scientific world. Cover image | China Daily and National Cancer Institute In Xataka | South Korea has just entered the most exclusive club on the planet. And China and North Korea are not exactly calm

“People don’t appreciate how unique it is”

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Elon Musk is a polarizing figure opinions: for some he is a visionary genius; for others, a businessman with controversial practices. However, those who have worked closely with him describe a unique leadership style that challenges traditional norms of large corporations. Andrej Karpathyformer head of AI and computer vision at Tesla, spent five years working directly with him. “It’s hard to describe how unique it is”says Karpathy. But he tries: in his own words, Musk represents a leadership style that does not fit traditional molds, but is precisely what has allowed Tesla to redefine sectors such as automotive and aerospace. In some statements 2024, this former collaborator explained what makes Musk so different and how his personal style shapes the dynamics within Tesla. Against bureaucracy One of Karpathy’s main observations revolves around Musk’s emphasis on maintain small and highly specialized teamss. According to Karpathy, while many companies tend to grow in size and bureaucracy, Musk acts as a constant counterweight to that trend. “At Tesla, I practically had to beg him every time someone had to be hired“says Karpathy. In addition, Musk does not hesitate to fire employees who do not meet his standards, a policy that may seem harsh, but which in his opinion is essential to maintain agility and efficiency in a company that seeks to constantly innovate. The rejection of bureaucracy is also manifested in his aversion to non-technical middle managers: Musk prefers that engineers, and not managers, be the primary source of information and decision-making. This philosophy ensures that the company’s priorities remain aligned with its technical objectives. The importance of the work environment “He doesn’t like stagnation”. Another distinctive aspect of Musk’s leadership is his insistence on a dynamic work environment, in which employees are engaged in problem solving, which has led him to eliminate large and unnecessary meetings and to encourage employees to leave those in which they are not actively contributing. This contrasts with common practices in large Silicon Valley companies which, according to Karpathy, tend to “pamper” their employees with superficial comforts that do not always translate into greater productivity. An involved CEO Unlike many executives who delegate most decisions to senior managers, Musk has direct and constant contact with engineers and technical teams. According to Karpathy, Musk spends about 50% of his time interacting with engineering teamssomething unusual for a CEO. Your proximity allows you to deeply understand technical challenges and address bottlenecks immediately Karpathy goes so far as to give the example that if a team faces a lack of GPUs, Musk does not hesitate to contact key suppliers directly to solve the problem… even if that means calling the CEO of Nvidia directly. This level of involvement ensures that strategic decisions are based on accurate technical information, rather than going through multiple layers of management. Ambitious promises and complex realities The style of Musk also has his criticsclear. Above all because of his extreme optimism… or, rather, their tendency to promise overly ambitious goalsas fully autonomous vehicles capable of crossing the United States without human intervention, has generated skepticism. Despite having achieved significant advances, such as the arrival of robotaxis not without controversyTesla has not managed to meet these goals within the established deadlines. However, he claims that Musk’s obsession with continuous improvement is what allows Tesla to remain a leader in advanced assisted driving technologies. Image | Marcos Merino through AI In Xataka | Elon Musk’s word is not reliable: the failure of Tesla’s “solar roofs” exposes him again This topic was originally published on Genbeta in December 2024.

There are millions of squash players in the world. Unknowingly, they owe a debt to an 18th-century London prison

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If you are one of the 20 million of people who practice squash in the world you owe a debt to the bankrupt British of the 18th century. The reason is very simple: several centuries ago those held in the fleet prison (London) because of their debts, they devised a game to kill time that was played with a ball, racket and wall. Over time that hobby, “rackets”became popular and led to a somewhat more sophisticated (and prestigious) version among the students of Harrow School that laid the foundations for what we know today as squash. All thanks to bill-stifled Londoners. The pleasure of hitting a ball. Maybe they didn’t do it like Alcaraz, but our ancestors they already enjoyed of the pleasure of hitting balls with spin. In fact they did it even before the Dutch invented the racket. in the 15th century. We know that almost a millennium ago French children entertained themselves with he game of paumea game that consisted of throwing leather or cloth balls filled with sawdust against the walls, and the monks also entertained themselves in a similar way in the cloisters, sometimes hitting the ball with branches. Over time the game was refined until it became tennis, a sport that caught on in Great Britain and soon fascinated the Tudors. It is said that Henry VIII (1481-1547) had courts built in all his palaces. Also that around 1600 in Paris there could be at least 250 courts. The success of the game was not only measured by its popularity at court or the number of clues. The old one game of paume It also led to different games, with their own styles and rules, such as he fivesor much later racquetball. Athletes behind bars. At the beginning of the 18th century, the love for tennis took root even in fleeta former London prison. Perhaps fed up with seeing hours pass by between walls and bars, its inmates created their own version of the fivesa fairly simplified one that was played they had it on hand in prison: a small ball (similar to those used in golf) of rolled cloth and a racket. The game ended up being known as ‘racquets’ or “rackets” and its dynamics were simple. The players were dedicated to hitting the ball against a wall. A special prison. It may sound strange, but the truth is that Fleet was not any prison. And not only because of its age, which can date back to 12th century. Murderers, rapists and thieves did not sleep in their cells. Not at least in the 18th century, when prison was reserved for people convicted of debt or having committed contempt before certain courts. In the 1770s John Howarda philanthropist who wrote the treatise ‘State of Prisons in England and Wales’, visited Fleet and left us this snapshot about life within its walls: “The prisoners play bowling in the yard, the mississippihe fivestennis… And not just the prisoners. I saw among them several butchers and others from the market, who are admitted as in another tavern.” Why is it so important? Because the ‘racquet’the game that had worked so well in Fleet or King Bench, soon spread throughout Great Britain. Far from being seen as a stigmatized sport, typical of prisoners and ruined men, it began to be practiced in the courtyards of taverns and alleys. Special fields were even built. The hobby spread so much that we know that in 1830 The Royal Artillery built a covered track in Woolwich so that its soldiers could play games even on stormy days. And then came Harrow School. One of the places where the racquet and fives was Harrow Schoola prestigious boarding school founded in the 16th century in the London borough of Harrow, northwest of the city. It was there that what we know today as squash would come to fruition. His students used to play in the courtyard outside the main building, a corner with side walls and a front wall, although they soon adapted the rules to their tastes. For example, they replaced the rigid balls that were used until then with rubber ones. It was not a minor detail. The new balls, hollow and larger, influenced the dynamics of the game, its rhythm… and opened the doors to squash. A sport with hook. “At first squash was a sport exclusive to Harrow. Like other private schools with their particular sports, it only existed in their school,” they explain from the International Squash Federation. That didn’t take long to change. As they went on holiday, with their balls and rackets, or simply graduated and left boarding school, Harrow students spread their love of squash to the rest of the country. Over time, other British schools and organizations ended up adopting that game devised between the walls of Harrow and the courtyard of an old prison. What was Fleet’s actual role? Some authors, such as JR Atkins, consider that in reality racquets and tennis are so similar that “it is impossible to separate them historically”, which would reduce the weight of Feels’ role. In any case, most accounts agree that the British prison played a relevant role in the development of the game and helped it become popular in taverns and other venues in the country. The final development of the game (and its respectability) was the merit of Harrow School, but even so the contribution of the Feel convicts is recognized for example World Squashthe Oxford University or the IOC. “At some point in the early 19th century the obsession with rackets and balls gave rise to another variant of this sport in a place as unusual as Fleet Prison,” explains Ted Wallbutton of the World Squash Federation (WSF). “The Fleet prisoners, mostly debtors, exercised by hitting a ball against the walls, of which there were many, with rackets. Thus began the game of ‘Rackets’. By some strange path they led to Harrow and other select English schools around 1820, and it … Read more

China can’t buy the best Nvidia chips. So Alibaba has decided to connect theirs and sell them as if they were one

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Alibaba does not want its infrastructure artificial intelligence (AI) continues to depend on Nvidia technologies. Little by little, the largest technology companies in China are assuming the request that Xi Jinping’s government made them at the beginning of October 2024: as far as possible They had to use chips produced in China. Ten months later this recommendation became a requirement. And the data centers that belong to the State throughout the country had to use at least 50% Chinese integrated circuits on their servers. This scenario especially favors Huawei, Moore Threads and Cambricon Technologies because they are Top AI GPU Manufacturers from China, but it also works great for Alibaba. In fact, Alibaba Cloud, its cloud computing subsidiary, has taken a very important step forward. A few days ago it presented a new chip for AI, the Zhenwu M890, and made official a very ambitious itinerary that describes what solutions it will develop over the next three years. This GPU has been designed by T-Head, the semiconductor division that Alibaba founded in 2018. It incorporates 144 GB of HBM3 memory and achieves an interconnection transfer speed between chips of up to 800 GB/s. As we are about to discover, this last feature is essential in the strategy that Alibaba has developed to compete in the AI ​​hardware market. Alibaba is going to spend $53 billion on its infrastructure According to Alibaba, the performance of its Zhenwu M890 chip is triple that of its predecessor. Additionally, it has been designed to perform well both during training of cutting-edge AI models and during inference. An important note: inference is broadly the computational process carried out by language models with the purpose of generating responses that correspond to the requests they receive. Alibaba wants to compete face to face with Nvidia in the deployment of infrastructure for data centers However, there is another relevant fact that is worth not overlooking: in medium precision operations (FP16) the Zhenwu M890 chip reaches 0.6 petaflops, a performance comparable to that of Nvidia’s A100 GPU and three times higher than that of the H20 chip. On the other hand, the ICN Switch interconnection chip allows link up to 128 GPUs M890 so that they work in unison. Alibaba assures that this architecture makes these GPUs work as a single chip, which, on paper, will allow it to compete head-to-head with Nvidia in the deployment of infrastructure for data centers. Regarding the itinerary that will follow until 2028, this Chinese company has anticipated that it plans to launch the Zhenwu V900 during the third quarter of 2027. According to Alibaba, it will implement its own significantly improved parallel computing architecture, will have three times the performance of the M890 chip, will be supported by 216 GB of memory and will reach an interconnection transfer speed of 1,200 GB/s. The Zhenwu J900 will arrive during the third quarter of 2028 with another major architectural leap. This roadmap It reflects that Alibaba goes all out. In fact, it has also announced that it will support this plan with an investment in 380 billion yuan (about $53 billion) over the next three years. Is the largest engagement of its kind in history of the company. Additionally, T-Head is planning its IPO to fund a more aggressive infrastructure investment program, which would put it in direct competition with Cambricon Technologies and Huawei’s Ascend line in the domestic AI chip market. Image | Alibaba More information | Alibaba | ChinaDaily In Xataka | Nvidia has to deal with the absolute distrust of several US legislators. Your plan in China is in danger In Xataka | The US wants to end Chinese AI chips sold abroad. And China knows how to defend itself

We talk to young Spaniards who reject consciously using AI

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While the AI is increasingly integrated into studies, work and daily life, a parallel and still minority phenomenon is brewing in the subsoil of public opinion and professional environments: that of a current of young people who view this technology with skepticism, fatigue or rejection. Some try to limit its use; others directly reject it. Although young generations have quickly embraced and integrated these tools into their daily lives, there are studies that point to the growth of a certain reluctance. A survey conducted in 2026 by the Walton Family Foundation, GSV Ventures and Gallup reveals how despite the fact that 51% of American Generation Z say they use AI weekly, “negative emotions towards it have intensified in the last year.” The study reflects concern about the “cost” that the continued use of this technology may have on “creativity or critical thinking.” Diego Castilla, member of the History Student Association of the Carlos III University of Madrid, is one of them. In his opinion, “AI stupidifies the mind.” Understand that the use of this technology is driven by increasingly academic and work rhythms. harder to hold. He tries to stay out of it and assures that he only uses it in a “very specific and specific” way, because he is convinced that “it creates bad habits.” For him, in addition, there is something easily recognizable in the content generated by AI: “It is noticeable. What is made by AI lacks soul.” Along these lines, Marcos, a 26-year-old graphic designer, believes that young people lead the “resistance” or “rejection” of AI. While he observes how the older generations feel a genuine fascination with this technology – “they love making songs, videos and images” – and accept its use without questioning it, he perceives a much more critical view among young people. Faced with the “devotion” that he detects in some older people, Marcos observes in youth a growing need to “escape from AI.” In fact, he considers that interest in “the physical” is gaining more and more strength: “I see more young people interested in having books, attending craft workshops or dancing…”. Activities that, in his opinion, respond to the desire to get away from digital, “rest” and “connect” again. “There are many valid reasons to reject AI” The ecological impact, the possible loss of autonomy, the potential risk for certain professionals, the power accumulated by large technology companies behind these tools… The reasons for distancing ourselves from AI are multiple. Marcos Escudero-Viñolo, professor at the Higher Polytechnic School of the Autonomous University of Madrid, knows several profiles that show a total rejection of AI: “Some for neo-Luddite reasons, that is, they reject AI for its social impacts; others for degrowth reasons, that is, they reject it based on its enormous ecological impacts; others practice resistance or active boycott of this technology, for example, as a criticism of heteronomy “Some combine these and other factors.” Although these positions seem to be a minority, they are present especially among young profiles linked to groups environmentalists either degrowth —as Ecologists in Action, beyondGrowth either Your cloud dries up my river—, but, according to Escudero-Viñolo, also among students, researchers or some professionals. (Unsplash) For Francisco José Estupiñá Puig, a contract professor at the Faculty of Psychology of the Complutense University of Madrid and co-director of the addictive behavior research group Controlab, “there are many valid reasons to reject AI,” and these can be framed in “ethical, political or ecological positions.” In some sectors, skepticism—which often does not reach rejection— is perceived with more intensity. “It is more common that from the artistic field they can feel threatened and even generate very strong rejection,” says César Poyatos Dorado, professor of educational technology at the UAM. This is corroborated by Marcos, a graphic designer, who finds in his professional environment a growing reluctance towards works generated entirely with AI.ç Paula Jimenez, content creator in a 27-year-old communications agency, he feels that “AI is making us idiots.” She is concerned about the widespread use of these tools to carry out “creative and human tasks,” and believes that this concern is becoming more and more evident among young people: “In fact, I consider myself one of those young people who claim not to do things with artificial intelligence.” Along these lines, Marcos, a 19-year-old History and Politics student, observes among his group of friends “a great rejection of AI,” and although he believes that this position is not the majority among young people, he does consider it to be increasingly common. Between rejection and critical use “It’s the same as when a smoker admits that tobacco is bad but continues smoking. Young people use AI because it is a very practical resource but they are afraid that AI can replace people in their jobs, they criticize that what is created by AI is not as creative or interesting…” This is how María Ángeles Gutiérrez García, teacher, explains the ambivalent relationship that many of her students have with this technology; They are “capable of making many arguments against artificial intelligence despite the fact that they use it.” Manuel Armayones, professor of Behavioral Design at the Open University of Catalonia, believes that this tension between use and rejection responds to a growing sense of discomfort. “They use AI, but at the same time they are not clear to what extent doing so is legitimate or harms them in the long term (…) We are facing a technology that not only changes how we do things, but also how we think, decide and perceive ourselves as professionals,” he explains. (Unslpash) According to Armayones, many young people feel that integrating AI is almost mandatory in order not to be left behind, but at the same time they fear being the ones who stop making decisions and taking on a supervisory role: “For this reason, rather than frontal rejection, many times what we see is a need to set limits and understand what role we want to have in that system.” This … Read more

In Zambia, gas bubbles in hot springs point to an unusual birth: a new tectonic plate

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In 2005, the floor of the Afar Desert in Ethiopia suddenly opens up along more than 50 kilometers in just a few days after an intense seismic and volcanic sequence. For many geologists, that image was like observing in real time the type of fracture that, in millions of years, could end. creating a new ocean. Zambia has just given the most serious warning. Bubbles as an almost unequivocal sign. In Zambia, simple bubbles emerging from hot springs have begun to reveal something much bigger than a local geothermal phenomenon. Scientists at the University of Oxford believe have found signs that the southern African subsoil could be entering an early phase continental fracturea geological process so slow that it is imperceptible for human life, but so gigantic that it can end up rdrawing entire maps. The key is in the helium detected in the thermal springs of the Kafue Rift: Its isotopic composition contains too much helium-3, a chemical marker directly associated with the Earth’s mantle. Translated into less technical language, it means that fluids from dozens of kilometers beneath the crust are finding ways to ascend to the surface. And that, for geologists, is an extremely serious sign that the African crust could be starting to break down from within. A silent crack beneath the continent. Rifts are not simple faults or isolated earthquakes. They are areas where the lithosphere begins to stretch and weaken until, in some cases, it ends separating into tectonic plates different. Most never make it that far and remain an unfinished geological scar, but the Kafue Rift presents something that changes the scene: a active connection between the mantle and the surface. The researchers analyzed gases from eight wells and hot springs, six within the suspected area and two outside it to compare results. Only within the rift did they appear associated chemical signatures to the deep interior of the Earth. In addition to helium, they also detected carbon dioxide with characteristics typical of mantle fluids. For scientists, this suggests that the fracture is no longer solely superficial and that the system could be entering into a tectonic phase more advanced than previously thought. Location map of the extensional zone within the Central African Plateau of Zambia. The Kafue Rift is connected to the Luano and Luangwa rifts to the northeast, and to the western branch of the EARS in the Rukwa rift (RRB) and the Rungwe Volcanic Province (RVP) The possible birth of a new plate. The hypothesis is especially relevant because the Kafue Rift is part of a huge strip of geological weakness about 2,500 kilometersone that crosses Africa from Tanzania to Namibia. For years, many researchers had considered that the great candidate to divide the continent was the East African Rift, in Kenya and Ethiopia, where volcanic and tectonic activity is much more visible. However, the new study of Oxford researchers suggests that the southwest African system could have important structural advantages. According to Mike Dalythe natural crustal weaknesses in that region are better aligned with the tectonic forces acting around Africa, which would reduce the resistance needed for future continental breakup. In other words, the Zambian bubbles could be signaling the extremely slow birth of a new African tectonic plate. The continent moves, even if you don’t notice it. The investigation It also serves as a reminder that Earth is still a planet geologically alive. Hundreds of millions of years ago, all continents were part of Pangea before slowly breaking up into their current shape. That process never stopped. Beneath our feet, tectonic plates continue to shift, recycling minerals, raising mountain ranges and opening new oceans. Africa is today one of the places where this dynamic can best be observed. From the Afar Depression to the East African Rift, the continent already presents huge tectonic scars visible from space. What is happening in Zambia could be an additional piece of that continental puzzle, although scientists insist that we are talking about time scales of millions of years and not immediate changes. A geological fracture… and economic opportunity. Beyond scientific fascination, the discovery It has very real economic implications. Early rift systems typically offer relatively clean access to geothermal energy and gases valuable substances such as helium and hydrogen, increasingly important for the technology and energy industry. Unlike mature volcanic zones, where fluids appear mixed with more aggressive and difficult to handle gases, in Kafue the material from the mantle still arrives relatively “pure”. In fact, that is precisely the reason why several energy companies already They are funding research in the region. The problem is that the authors of the study themselves they ask for caution: The samples come from only a specific part of the system and it remains to be seen whether these signals are repeated throughout the entire fracture. But even with caution, the idea is so powerful that it is already on the table: in Zambia, the bubbles that silently emerge from a hot spring could be announcing the beginning of a continental separation that will one day change Africa forever. Image | PexelsDaly et al., 2020 7 Legg, 1974; Tamburello et al., 2022 / R. Karolytė et al. 2026 In Xataka | We thought we were clear about how the continents were formed, until researchers found a stone in Australia In Xataka | More than 5 million earthquakes spread throughout the Earth, gathered in a very complete map

The advanced chip business is growing so fast that it cannot keep up

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ASML, the Netherlands-based company that makes the most advanced integrated circuit production machines, had planned to hire 600 new employees in Taiwan this year. Finally she was forced to revise upwards your hiring plan. In 2026 they will arrive at their facilities on this Asian island 1,000 new additions. Grace Wang, the vice president and general manager of ASML in Taiwan, has declared that this change has been brought about by the insatiable demand for chips for artificial intelligence (AI). ASML does not manufacture semiconductors, but its equipment extreme ultraviolet photolithography (UVE) are being used by TSMC, SK Hynix, Samsung, Intel and Micron to produce the advanced integrated circuits that data centers demand. Especially CPU, GPU and HBM type DRAM memories (High Bandwidth Memory or high bandwidth memory). In fact, this company alone occupies the first link in the global chip manufacturing chain because it is the only one that produces EUV lithography machines. Be that as it may, Grace Wang’s declaration of intent responds to an unappealable reality: Taiwan is the industrial heart of this Dutch company. ASML manufactures components on this island and assembles UVE lithography equipment which it subsequently delivers to its local customers. These operations are also carried out in the Netherlands, but there are two compelling reasons why Taiwan is enormously relevant to ASML’s business: its best customer and its biggest focus on its global customers reside there. TSMC is ASML’s largest customer A determining factor that is promoting ASML’s expansion in Taiwan is its close relationship with TSMC, the largest manufacturer of integrated circuits of the planet. The operations of this company on the island currently generate 8.3 billion eurosa quarter of ASML’s global revenue. And much of that money comes from the coffers of TSMC, which is building new advanced semiconductor production plants in Taiwan, Japan, Germany and the US. ASML is building a facility in New Taipei that costs about $954 million. However, ASML’s Taiwan branch is not just hiring more staff (it currently has 4,500 employees on this island); is also building a new facility in New Taipei that costs about 954 million dollars. Their plan is for this plant to begin operating before the end of 2026 and to house about 2,000 employees in its initial phase. We still don’t know for sure what this factory will do, but it will probably combine component production, EUV machine assembly, and technical support to customers, primarily TSMC. ASML’s infrastructure in Taiwan is distributed between two cities with very specialized functions. Linkou is responsible for reconditioning chip manufacturing equipment, producing grating manipulators for deep ultraviolet (DVP) machines, and cleaning UVE collectors. Tainan, however, serves as a large global customer service center. And in a few months, as we have just seen, the New Taipei plant will be ready. The future of ASML is promising even though US sanctions They prevent it from selling its most sophisticated machines to its Chinese customers. Image | ASML More information | DigiTimes Asia In Xataka | The chip of the future comes from Japan: it is 1,000 times faster than current semiconductors and does not heat up

Would you let them clean your house for free in exchange for filming it from top to bottom? This startup thinks you’ll say yes

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Your floor like the jets of gold down your face. In principle, this is how good the proposal sounds. shifta service launched in New York that offers comprehensive home cleaning services. Is it perhaps an NGO? Well no, the company does not charge in currency: an operator enters your house to the kitchen (literally) wearing a recording device that allows him to record his movements on video during the entire cleaning session. That video is then converted into training data for robotics and AI. In other words, the user does not pay with money, they pay with data. This exchange is not new by any means, but the saying “if something is free it is because the product is you” has gone from the screens to the most intimate part: your home. Clean your house and pay with your privacy. The mechanism is direct: a service in exchange for data. According to says Harry KilbergShift’s US CEO on his X/Twitter profile, upon your request, the company sends a “verified” operator to clean up and leave. In exchange, it records the cleaning so that robotics companies have access to those movements and, through training, their units can replicate it. In other words, there is a camera monitoring the movements of the operator and in the background, your dirt, the rooms of your house and each and every one of your things that are visible and can be cleaned. The Service FAQ They detail that the recordings are anonymized before being processed and that they blur any information that could identify you. But of course, “anonymized” is not the same as private: There is research that shows that anonymized data is not so anonymized: it can be re-identified quite often when crossed with other sources. And in a house it is even easier: the distribution of space, objects and your routines They make up a unique image of you, your tastes and your habits.. Anonymizing the video does not eliminate that trace, it only hides it in plain sight. How does it work? shift Why is it important. Because the home has historically been the last stronghold of privacy. You may post photos of yourself having brunch on a terrace in Malasaña, but you might think twice before sharing your breakfast muffin in a cup of Mr. Wonderful with a cosque while wearing a threadbare robe with cheese stains from last night’s pizza. It is true that the fever of connected devices and wearables had reduced that redoubt, but Shift goes one step further: it is an active recording of the interior of private homes made by an outsider and that is expressly dedicated to a market. The company accumulates a huge amount of information about you: how you live, what you have, how you behave in private. In return, you have a vague idea of ​​what he does with your data and you don’t know who he sells it to or how he uses it. It is, in short, an imbalance of information from which there is no turning back. On the other hand and as Shift explains, home cleaning and its automation towards an eventual service carried out by robots is just the beginning: there will be an expansion towards home maintenance, repairs and errands. If the model scales, the volume of private indoor data that would be generated would be enormous, an asset as valuable as it is sensitive. Context. The closest examples of the digital attention economy are well known: Google and Facebook have built their respective empires by offering free services in exchange for behavioral data, only Shift takes it to the physical world, one step further, more intimate and more complex to revoke. Its business model is part of the trend of training robots by knowing how humans move and how we perform in real spaces, something that companies such as Figure AI either Physical Intelligence (Pi) because in reality, we are living in a race to obtain this information. How they do it. Its operation consists of three steps: verifying the operators, recording during service and anonymization before processing. The Shift project begins in New York and on its website it announces its presence in 15 countries (although it seems that it is more of a promise of deployment than a reality). Its beginnings are common in these times of social networks and virality: respond to the publication with “Shift” to receive early access and gain visibility. Of course, what is not publicly explained is the technical architecture behind data anonymization, which third parties receive the data, the security standards applied to the devices carried by the operators or the audit mechanisms (if they use them). Yes, but. In fact, as explained it would not meet the standards of the GDPR European (article 5 refers to the fact that any processing of personal data must be transparent, limited and justified). One of Shift’s slogans is: “You get a spotless apartment. We get training data. Everyone wins.” One thing must be given to the startup: it is honest from the beginning when it comes to making it clear that the recorded data is going to be commercialized. How many conditions of use of applications that we use daily are less clear when it comes to talking about the destination of the data. Of course, informed consent is weak precisely because of the opaqueness behind it and because of an obvious reality: a recording of your home is not a tweet and the consequences of sharing it are much more serious. In Xataka | Have I been Trained: how to know if your data and work has been used to train an artificial intelligence In Xataka | AI has become the best example that if you don’t pay for the product, you are the product Cover | shift with Gemini

This is how Mario Rodríguez, CPO of GitHub, sees the future of programming

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Almost five years ago we asked ourselves Why program when a machine could do it for you?. It was July 2021 and GitHub Copilot was launched, the first major AI assistant that also boasted of being powered by GPT-3. That was quite a turning point for the world of developers, and since then we have experienced the explosion of a segment that has been the first to test the honeys of generative artificial intelligence. Among those who were at the forefront of that development is Mario Rodriguezan engineer born in Cuba but who emigrated to the US when he was 14 years old. After studying at the University of Miami, Rodríguez began working at Microsoft, and has developed his entire professional career there. In 2018, following the acquisition of GitHub by Microsoftjoined the management team as vice president of product. Since August 2024, he has been its Chief Product Officer, and therefore he is the one who decides where GitHub goes as a platform. It is an enormous responsibility considering that we are dealing with the collaborative platform that has become the social network for programmers on its own merits. A few days ago we had the opportunity to sit down to talk with him precisely to talk (“in Spanish, I prefer it, that’s how I practice it”) about the present and especially the future of GitHub, now totally involved in the generative AI revolution. The competition tightens Github Copilot was an absolute pioneer in normalizing that code generation support between 2021 and 2023, but the absolute dominance that seemed to have with the appearance of Cursor and, later, in mid-2025, with the release of Claude Code by Anthropic. In the last year and a half, Cursor’s popularity surpasses that of GitHub Copilot, at least if we take into account visits to their respective websites. Source: Sherwood News. Both AI agents have not stopped growing since then, and the popularity is moving apparently to these new platforms although GitHub Copilot still has an exceptional market share in this segment. If we talk about Claude Code, things are even more striking, because his success is such that even Microsoft engineers themselves they have been using it instead of using the company’s own alternative. The situation was so unique that Microsoft has ended canceling your Claude Code licenses to force their engineers to use Github Copilot, although there is a strong financial argument here: heavy use of Claude Code was becoming too expensive. Microsoft executives recently stated in The Informationwere very concerned about the erosion of their leadership. Rodríguez is clear that now there is more competition, but clarifies that “we knew that was going to happen“. Not only that, because he added that “competition is good. “It’s exciting for me to wake up every day and see what we have to do to continue leading.” GitHub Copilot App, currently in Technical Preview, is the company’s answer to Cursor or Claude Code. Source: GitHub. But GitHub, as he explained, is much more than GitHub Copilot, “it is a platform in itself.” That doesn’t mean they don’t continue to push that part, and in fact in May GitHub announced the launch of the preliminary version of GitHub Copilot App, which, as Rodríguez explains, solves a gap because Cursor or Claude Code (among others) offered “the Integrated Desktop Environment (IDE), which is what we didn’t have. Beyond the model: why GitHub’s strategy is not to compete in pure AI At the moment the situation is what it is: OpenAI has its AI agent for programming, called Codexbut it also develops one of the best frontier models in the world, GPT-5.5. Google, the same: it has Antigravity as an IDE, but it also has models like the recent one Gemini 3.5 Flash. Anthropic is not short, of course: it has Claude Code as an AI agent, but it also has its Claude Opus 4.7 model as a very clear reference in the field of programming and agentic software engineering. Even Cursor, which initially only had its AI agent to program, has ended up launching a surprisingly good model in programming tasks, Composer 2.5. GitHub has the tool, but not own model. For Rodríguez this is not a problem at all, because he sees GitHub as something that goes beyond the modelas a native platform for collaboration in development tasks. “For me the code repository is like a garden that is alive and there are always AI agents collaborating with the human in that repository. So, when you change one thing, people say, ‘Oh, you changed it, this has to change.’” In fact, although GitHub Copilot appeared with OpenAI models as the main protagonists, today it is a multi-model platform that works with cloud models but also with local models. Actually Microsoft does have own models like MAI“but our strategy is not the model. Where we believe the value is in the systems themselves, not in the models.” In fact, he pointed out, in the model segment things change too quickly. “Tomorrow the best will be OpenAI, the next day Anthropic, then it may be an Open Source model… what’s the difference? Every day it changes, and differentiating at that layer is very complicated, so where we are going to differentiate ourselves is in the platform itself, in our AI agent platform.” For him, GitHub’s role is differentiating because it is not an IDE or a model, but a platform. One that not only provides tools to share code and work with it, but also focuses on what he calls “macrodelegation and microsteering“(“macrodelegation and microdirection”). Macrodelegation is high-level autonomy, which makes the developer focus not on looking at each line of code, but on the results. Microsteering is the constant control to correct course, having a human being in the loop (human-in-the-loop) so that errors can be avoided and micro adjustments made. These are the options that GitHub proposes for the future, and they also focus it on two crucial tasks: “For all this to … Read more

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