democratize the climb to Everest

a drone DJI Mavic 3 Pro Equipped with a Hasselblad camera, he has managed to capture something that until recently seemed impossible: the complete ascent of Mount Everest from base camp to the summit in a single continuous flight. For 43 minutes, the aircraft traveled 3,500 meters of altitude, crossing the Khumbu icefall, the South Col and the final walls until reaching 8,848 meters of altitude. The images reveal the normal route of ascent in all its magnitude, including the characteristic queues of mountaineers who each season try to crown the roof of the world. The challenge. At that altitude, the air contains barely a third of the oxygen available at sea level, temperatures can drop to -30°C and winds reach speeds that would make the flight of any conventional drone impossible. The team used the Mavic 3 Pro with a four-thirds CMOS sensor, a combination that allowed stability and image quality to be maintained in extreme conditions. Beyond the visual spectacle, this flight is part of a more ambitious project by DJI: to demonstrate that drones can save lives on the highest mountain on the planet. Drones in high mountains. The tests of DJI on Everest respond to a clear commercial strategy: convert their drones into rescue and logistics tools in extreme environments. The Chinese company seeks to demonstrate that these aircraft can transport medicines, locate missing mountaineers and facilitate emergency operations at altitudes where thin air complicates any human intervention. The best-known precedent occurred in 2018, when Scottish mountaineer Rick Allen was located on Broad Peak after 36 hours lost at more than 7,000 meters of altitude. thanks to a DJI Mavic drone. That rescue, coordinated by Bartek Bargiel, brother of the skier Andrzej Bargiel, who we will now talk about again, marked a turning point in the perception of drones as high mountain safety instruments. Qualitative leap. In 2025, the Nepalese company Airlift Technology began providing drone logistics services between Everest Base Camp and Camp One, separated by approximately 2.9 kilometers in a straight line but by a 700-meter difference in altitude and the dangerous Khumbu Icefall. What takes the Sherpas between six and seven hours of crossing, a drone completes it in six or seven minutes. Milan Pandey, the company’s drone pilot, explains that during the 2025 climbing season they transported ladders, ropes and oxygen cylinders following radio instructions from the Sherpas who install the fixed routes. Safer. The impact on the job security of these high mountain workers is significant. The so-called “icefall doctors” (Sherpas specialized in preparing and maintaining the passage through the Khumbu glacier) traditionally had to go up and down dozens of times each season carrying heavy equipment through unstable terrain where Almost 50 people have died since 1953. They can now request additional material without having to descend to base camp, which dramatically reduces risk. The key case. On September 22, 2025, Polish mountain skier Andrzej Bargiel completed a feat that combines extreme mountaineering with technological innovation: Ascended Everest without supplemental oxygen and skied down to base camp without removing skis. After almost 16 hours climbing in the so-called “death zone” above 8,000 meters, Bargiel began the descent along the South Col route. What was innovative was the role of drones in this expedition: his brother Bartek piloted one from base camp to guide him through the Khumbu Icefall, the most dangerous section of the descent. All this is seen in the full 31 minute documentary which records the adventure using cameras mounted on Bargiel’s helmet and aerial shots captured by drones. The footage reveals a extremely technical descent: ice, almost vertical walls, traversing exposed ledges and, in the final stretch of the Khumbu waterfall, slow maneuvers avoiding deep cracks and blocks of ice the size of buildings. The assistance of the drone was critical precisely in this sector: Bartek flew in real time over the glacier, identifying stable snow bridges, marking dead ends and choosing safe slopes. Visual democratization. Videos like these are part of a broader phenomenon. YouTube hosts thousands of recordings documenting mountain climbs, cave explorations, glacier traverses, and cliff flyovers that until a decade ago could only be captured by helicopters or million-dollar productions. An example is that of the Chinese photographer Ma Chunlin, who spent five years obtaining the necessary permits and carrying out test flights before achieving a definitive recording of the ascent of Everest. in one shot. Technically possible. This type of content responds to a technological evolution that has made accessible tools previously reserved for professionals. Models like the DJI Mavic Mini, which weighs 249 grams, allow users without prior experience to capture stabilized aerial shots in resolutions higher than Full HD. Portability is key: Foldable drones that fit in a backpack during long hikes have removed the logistical barriers that previously limited aerial photography to specialized equipment. The doubts. The proliferation of drones in natural spaces has generated debates about their impact. Regulations vary significantly between countries and regions: some National Parks prohibit its use entirely, while others allow flights with prior authorization. The balance between visual access to nature and the preservation of these environments (including the protection of wildlife that may be disturbed by the noise and presence of these devices) remains an open question. In Xataka | China doesn’t know what to do with so many drones. Their solution: create lower airspace

The Neoclouds promised to democratize the AI. Right now are the most fragile and indebted link in the entire sector

Coreweave, Lambda Labs, Crusoe and Nebius They represent the most booming and also more fragile link in the AI ​​value chain: Neoclouds. These companies have raised tens of billions in capital and debt to build Data centers full of NVIDIA GPUSbut its business model rests on an increasingly questionable premise: that the demand for computing capacity to continue to grow exponentially. Why is it important. The problem is not just that these companies lose money. Is that its financial structure depends on a vicious circle: They raise debt to buy GPUS. They use those GPUS as a guarantee for more debt. And the money they enter comes mostly from the same companies that sell them the chips and lend them the money. The model. The Neoclouds They came promising GPU infrastructure in months, no years, already prices up to 66% cheaper than AWS, Azure or Google Cloud. The proposal sounded well: companies needed GPUS and Hyperscalers (AWS, Azure and Company) did not supply. The market responded with enthusiasm: Coreweave went from billing 16 million dollars in 2022 to 5,350 million in the last year. Nebius (which has exploded in the stock market and whose germ is Yandex) grew from 5 million quarterly to 105 million. The segment Neocloud As a whole, 82% per year has grown in the last four years. The problem of the single client. Coreweave generated 60% of its 2024 revenues by renting capacity to Microsoft for Openai. Only Microsoft. Nvidia represents another 15%. If you eliminate a The magnificent seven already openai of the accounts of the main Neocloudsthere are hardly 1,000 million dollars of combined income, As calculated by analyst Ed Zitron. Lambda Labs has half of his income at Amazon and Microsoft, plus 1.5 billion in a contract with Nvidia. Almost all Nebius’s growth projection comes from A 19,000 million agreement with Microsoft. There is no diversified market of business clients. There are a handful of technological giants using these suppliers as an exhaust valve or as a vehicle to move money without inflating their own capital expenses Aka Capex. The money trail. Coreweave owes 25,000 million with annual revenues of 5,350 million. Its debt-active ratio reaches 85.4%. It is like two times your annual salary. And unlike the property that supports a mortgage, the GPUS depreciate quickly. Nebius He has just closed a 4,200 million round to build the infrastructure that allows you to fulfill your contract with Microsoft. Lambda Labs and Crusoe have raised hundreds of millions in risk and debt capital. The model is always the same: You get a large contract. You use that contract as a guarantee to raise debt. Purchases Gpus to Nvidia. Rrena more data centers. Repeat. The problem arises when the Ancla client decides that he no longer needs so much capacity, or when you cannot build the infrastructure quick enough. Between the lines. Nvidia has invested directly into several Neoclouds And it is also its largest supplier and, in many cases, its largest client. Coreweave signed a 6,300 million agreement with Nvidia a few days ago For the manufacturer to buy any capacity that cannot be sold to other customers until 2032. In the end we see an elaborate mechanism of Circular financing: Nvidia needs to sell GPUS to maintain its growth. The Neoclouds They need to buy GPUS to fulfill their contracts. The Hyperscalers They need additional capacity but do not want to inflate their capex. And the Private Equity You need to place tens of billions in something that seems the future. In figures. Building a Data Center Capacity Gigavatio costs between 32,500 and 50,000 million dollars. Oracle and Crusoe took 2.5 years to complete a gigavatio for Openai. Nebius has promised to build multiple gigawatts in increasingly unrealistic terms. The alarm signal. Coreweave has reported important operating losses in its last quarter despite explosive growth in income. Nebius plans to reach 1,100 million in annual recurring revenues by the end of 2025, almost exclusively driven by the contract with Microsoft. What happens if Microsoft decides that you can build your own cheaper capacity? Or if Openai, the final customer of much of that capacity, collapses under the weight of their own losses? The decisive moment. The consolidation has already begun. Coreweave has just bought Core Scientific for 9,000 million in shares. Only great will survive, and probably not many. It is a matter of time when the adjustment will arrive. The doubt is how much damage will cause when billions in debt collide against the reality that the real demand for GPU capacity is a fraction of what is assumed. In Xataka | The PC is mutating: the future is filled with AI work stations so you can have your chatgpt at home Outstanding image | Nebius

For years, “fecal doping” is a problem in elite sport. Now science wants to democratize it

In 2019, a team of scientists from Harvard University monitored bacterial flora of 15 Boston Marathon runners during the previous and the posterior week. They made many discoveries, but one especially interesting: after the competition everyone suffered a significant increase in bacteria of the genus Veillonella. It was already known that exercise Altera the microbiota And, in fact, it was not especially surprising that these bacteria (which break the lactic acid and, therefore, reduce fatigue) were in there. What they wanted to discover was something else. Therefore, they took samples of that flora and introduced them into mice. The result was an increase in very significant physical resistance. Since then, there are people trying to take advantage of this. The treasure that hides the intestine. Now a France team has studied the intestinal microbiota elite athletes with high aerobic capacity (soccer players and cyclists). The central idea was to see if there were differences in the composition of the flora and in its functionality with respect to non -athletes. The first surprise is that the more sport the subjects were doing, the lower the diversity of their microbiota. And I say it is surprising because, as Rosa del Campo tells us Through SMC Spain“This is associated with an unhealthy condition.” However, in this case it seems that “it is justified with the specialization of these bacteria in the intestine.” That is, by submitting the microbiota to more exExigent environments, it is self -appointed to optimize. However, As he says The Ramón y Cajal hospital researcher is not the most interesting. “The most striking thing is when they evaluate the ability to reproduce this in mice.” What have they done? They have taken very sedentary and very athletes and “have transplanted their feces to mice for several days.” The result shows that “aerobic effort capacity in mice is conditioned by the microbiota.” Because? Although research is still preliminary, everything seems to indicate that “it is mainly due to glycogen consumption, good sugar control and production of short chain fatty acids.” What implications does this have? Well, it seems that enough. Remember that for years the anti -doping agencies work to fight against microbiological doping. In fact, everything seems to indicate that “Fecal transplant“s a usual practice in certain elite sport environments. But the question, as always, is whether this can be climbed. If we can begin to intervene in the microbiota in a massive way to improve the health of large layers of the population. For years, the boom of probiotics has caught Als great pharmaceuticals with the changed foot and has flooded the market with pseudoscience. However, the possibilities (as we see) are on the table. It is increasingly silent than health will be conquered with the stomach. Image | Julien Tromeur | Miguel to Amutio In Xataka | This pill is a peanked but can save your life

Meteorological forecasts are a private preserve. A new AI model wants to democratize the prediction of time

A group of researchers has just raised a most striking option: democratize meteorology. Your new weather prediction system challenges traditional systems, very expensive in computational resourcesand make use of AI so that (almost) any of us can become a meteorologist who performs his own personalized predictions. Aardvark Weather. This is the name of a new system of weather predictions that according to those responsible will make any researcher with a desktop PC in a full -fledged meteorologist. The system makes use of AI algorithms and raises an alternative to the conventional systems they use thousands of times more computing capacity. Homemade predictions. The normal thing is that a weather forecast platform takes several hours to process a prognosis. For this, it also needs supercomputers and a team of experts who develop, maintain and display those forecast systems. Aardvark Weather allows you to train an AI model with data from Meteorological stationssatellites, ships or airplanes worldwide and then make predictions based on that data. The investigation. The study Published in Nature this week comes from a group of researchers from the University of Cambridge, the Alan Turing Institute, Microsoft Research and the European Center for Medium-Russian Weather Forecasts (ECMWF). In it they explain how the numerical weather prediction (NWP) is being replaced For automatic learning and neuronal networks that allow “improving the speed and precision” of the prediction. Hyperlocalized forecasts. Among other things the system would allow to offer hyperlocalized forecasts and adapted to specific industries. Richard Turner, automatic learning professor at the University of Cambridge, explained In The Guardian how this model could be used to predict temperatures for agricultural crops in areas of Africa or The wind speeds For a renewable energy company in Europe. The time in the next eight days. Turner adds that the model could be able to generate precise forecasts for a range of up to eight days in the future, when it is normal for precise forecasts to only be guaranteed to five days. Rapid. This system is capable of generating a complete forecast from observational data in a second when processing it in four NVIDIA A100 GPUS, when 1,000 hours-nodo is normally taken in the HRES model of the ECMWF. Ideal for developing countries. There are regions in which these types of forecasts are especially important, and having a “personalized” system would be very useful. Aardvark Weather offers that option according to its creators, because both its implementation and its use is much more accessible. Previous attempts. At the end of 2023 Deepmind precisely Graphcast announceda meteorological prediction system based on AI that had an operation up to 1,000 times cheaper in energy consumption. Its precision was in fact greater than the best of current systems, but it does not seem that development has been implemented in practice. A few months ago Deepmind researchers presented their evolution, called Gencastanother prediction based on automatic learning that improved its predecessor and that of course competes with Aardvark Weather. Everything therefore points to this type of systems are gaining ground and interest, but remains to be seen if they apply massively. Image | Brian McGowan In Xataka | Meteorological prediction will improve a lot in Spain: Aemet has invested 25 million euros in it

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