personnel

A cement company stopped sending its personnel to inspect dangerous areas. Your new inspector is a robot dog

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Even though the conversation revolves around humanoid robotics Lately, this sector has had much more traction in the industry for decades, driving a good part of the current processes and assembly lines. In this regard, a Swiss cement plant has wanted to take advantage of robotics in a somewhat peculiar way: it has been using a quadruped robot to monitor your facilities every night. Below these lines we tell you all the details. The problem that had to be solved. Vigier Ciment has been producing cement in the hills of the Swiss Jura, south of Biel, for a century and a half, generating approximately a fifth of all cement in the country. Its plant houses more than 1,000 machines spread across several buildings and floors, connected by metal stairs of up to 16 sections, areas with temperatures that reach 50 degrees, constant dust and the occasional presence of ammonia near the unloading docks. The maintenance of all this fell to operators who toured the facilities on foot filling out paper records. Over time, continued exposure to these conditions generates what the plant workers themselves call “operational blindness.” You stop seeing what is in front of you because you have already seen it too many times, according to collect Techeblog. The guard dog. Just like account The medium, in November 2024 the Swiss robotics company ANYbotics began talks with Vigier Ciment to deploy its ANYmal quadruped robot at the plant. The robot arrived on January 6, 2025 and before the end of the first month it was already carrying out night patrols completely autonomously. ANYmal is similar in size to a large dog and weighs more than 50 kilos. It does not need human supervision, and its managers say it climbs stairs, avoids obstacles, navigates narrow hallways and accesses areas that previously required considerable effort on the part of staff. What exactly does it do in every round. Every night, even on weekends, ANYmal goes through more than 450 inspection points predefined elements distributed in three mills and six levels. To do this, it has several detection systems, including a visual camera that identifies cracks, oil leaks or corrosion; a thermal camera that measures the temperature of critical components such as bearings, motors and gears; a gas sensor that monitors ammonia levels; and an acoustic camera capable of locating compressed air leaks or filter failures at distances of up to 50 meters. According to point In the middle, all that information is automatically dumped into a software platform called Data Navigator, which analyzes the data collected overnight, compares it to the facility’s history, and generates a daily report for the maintenance team. What he has found along the way. In sixteen months of operation, ANYmal has already completed more than 33,000 inspections without recording any technical failures. According to ANYbotics, the most relevant findings have had a direct impact on plant operations. The middle share In addition, the robot detected a crack in the base of a shredder the size of a large kitchen table. The oil had been leaking for some time and no one had reported it on the usual rounds. The repair was completed the next day. Had it collapsed, the plant would have lost more than a week of production, with estimated losses of more than $630,000, according to the company’s own figures. In another case, thermal monitoring detected a bearing reaching 140 degrees Celsius, allowing a $30,000 eight-hour repair to be scheduled rather than facing a much more costly emergency failure. The robot also detected levels of ammonia exposure at unloading docks that had not been measured until then, and located air leaks in filtration systems installed 50 meters high. Industrial maintenance. The plant’s traditional fixed sensors only covered about 200 elements, mainly on the clinker side (the main component of cement). The robot expands that coverage substantially and accesses areas that static sensors cannot reach. At the same time, it removes operators from the most dangerous environments without reducing the frequency or quality of inspections. Images | ANYbotics In Xataka | One of the big problems with AI is that it always proves you right: this is the most effective way to avoid it

He obtained permits, hired personnel and negotiated with suppliers. Then he ordered 3,000 rubber gloves

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He OpenClaw release It marked a new one in the AI ​​race, one in which agentic AI takes on complex tasks that until recently it failed miserably at. Although the qualitative leap is undeniable, Giving full control of a business to an AI agent does not always go well. It’s fair what the startup Andon Labs has done: Put an AI agent to run a coffee shop in Sweden. The results have been interesting. Bow. It is the name of the agent who was in charge of the entire process. It is based on Google Gemini and was given a clear mission: to start and manage a cafeteria, making it profitable. For this he was given a budget of $21,000. Andon Labs already performed a similar experiment in the past in which He put Claude to manage a vending machine in an officewith quite disastrous results. Let’s see how Mona has done it. Setting up the business. The agent proved to be quite competent in the initial organization phase; Mona signed up for electricity and internet contracts, obtained permits to set up a terrace and contacted suppliers of bread and pastries. During the process, the agent came across BankID (Sweden’s electronic identification system), so he opted to contract with electricity and internet companies that did not require that requirement. For other things, like opening permission, you had to ask humans to log in to continue. Mona also tried to get a license to sell alcohol, for which she posed as an Andon Labs employee, arguing that they were more likely to respond to human requests over those of an AI. Investigators asked him not to use other identities and he agreed, but shortly afterward he sent another email using another employee’s name. Hiring employees. The agent could run a business, but he soon realized that he needed humans to serve the clafés. To do this, he posted job offers for baristas on LinkedIn and reviewed the resumes they sent him. The agent selected the best candidates, rejecting those who had little experience, and invited them to a face-to-face interview. When he realized that wasn’t possible, he suggested a phone interview. Finally she hired two baristas, with whom she communicates through Slack, as if she were some kind of remote boss. Here came the first problem: an AI agent never sleeps and sometimes sent them messages after midnight. He also asked them to do things like use their personal credit card to pay for orders. Of course, he motivates them a lot by saying things like they are “absolute legends.” The inventory. With the café already set up, Mona began to manage the day-to-day running of the business and that is when she began to make some pretty strange decisions. His inventory management is unfortunate: there are days when he orders too much bread and others when he doesn’t order anything at all, which forces him to remove certain items from the menu, and he also orders when it occurs to him, without taking into account deadlines or shipping costs. He also ordered 120 eggs even though there is no kitchen and, to prevent the tomatoes from spoiling, he ordered 22kg of canned tomatoes. There’s more, Mona ordered things like industrial garbage bags, 6,000 napkins and 3,000 nitrile gloves, quantities well above what a cafeteria needs. The accounts. As we said, Mona had the mission of making the cafeteria profitable, let’s see if she has succeeded. The cafeteria opened in mid-April and has already billed $5,700, the problem is that it is burning the budget unstoppably. Of the $21,000 he had when he started, he has already spent $16,000, meaning he only has $5,000 left. Burning money at that rate, the business is headed inexorably toward bankruptcy. lthe bosses of the future. Despite the lack of control asking for thousands of gloves or tomatoes, Mona has proven to be quite capable of carrying out management tasks, especially if we compare it with the previous experiment of the same startup. Mona has set up a physical business, hired staff and attracted clientele. In statements to Associated Pressbarista Kajetan Grzelczak comments that “workers are safe. Those who should worry about their jobs are the middle managers, the people in management positions.” Image | Xataka with Gemini In Xataka | “AI agents will harass you”: Jensen Huang believes that AI will not replace us but will do something much worse

The chaos that AI has generated in personnel hiring has revealed a type of hidden talent: “invisible developers”

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For years it has been repeated that to have a good work in technology It was necessary to cultivate a good public personal brand and maintain an updated and complete professional profile. However, more and more voices within the technology sector are dismantling that idea, ensuring that many of the most valued developers They don’t do any of that. They are not going to apply to dozens of job offers or optimize their visibility. “Invisible developers” are simply brilliant at their job. This invisibility is something that was put on the table Gergely Oroszengineer, analyst and author of ‘The Software Engineer’s Guidebook’ in a recent message in his X profile, in which he pointed out that this profile of “invisible developers” flies under the radar “the only way to find them is through references and specific searches”, assured the expert Candidates with AI have broken everything. The increase in responses generated by AI to job offers has completely broken the hiring system. They explained it perfectly from the Manfred technological employment platform, where a few years ago they received between 20 and 50 applications a day for each job offer, and now they receive more than 500. Various recruiters they explained on Reddit that this saturation of applications lowers the average quality of the applications and makes it difficult to detect real talent through this route. The situation is so extreme that, as Orosz indicated in an analysis from the tech job market posted on his blog, “many companies hire most engineers through contacts and referrals.” Internal recommendations matter more than ever. In this saturated scenario in which true talent goes unnoticed, word of mouth has become the most reliable hiring filter. It is estimated that around 80% of existing job offers are not made public and are filled internally or through references and recommendations from the employees themselves. In fact, many companies use referral incentives among their employees so that, when a vacancy opens, they recommend their former colleagues and acquaintances as candidates. As Orosz details in his analysis, recruiters increasingly look for candidates more among the pages of their agenda than among the applications that come to them. The myth of the hypervisible developer. Public attention usually focuses on profiles with a lot of activity on networks or with highly visible projects. However, different examples and testimonies reveal the rising trend of “invisible developers”: brilliant workers at their job with little or no activity on their public profiles. A clear example is found in the message published by Max Spero, co-founder of the AI ​​company Pangram, in which he compares the GitHub contribution profile of an unemployed 22-year-old developer, full of activity and contributions, and that of a prominent Google engineer, with a practically empty history. In response to that post, Konstantin K, a software developer from San Francisco, confirmed Spero’s message. “The top 1% of engineers I’ve worked with over the past 10 years didn’t have GitHub, LinkedIn or LeetCode, they don’t speak at conferences or publish podcasts. But they built systems that no one else can,” he wrote. Trust networks between colleagues. Other testimonialsamong which Orosz is also foundreinforce this idea of ​​”invisible developers” and agree that the most effective way to open job doors in the future is to be valuable to colleagues in the present. “From the outside you cannot know how good an engineer this person is until you ask former colleagues. There are many cases like this,” wrote Orosz in X. Even academic research suggest that internal networks—those formed by real collaborations, not superficial digital connections—have a direct impact on career opportunities. In other words, the professional prestige that these “invisible” employees generate within the teams in which they participate weighs more than any public presence and their colleagues become their guarantors to obtain a job in the future. Real contact in a digital setting. It is still paradoxical that, faced with the saturation of digital channels and the implementation of AI-based systems, the technology sector is returning to a classic model: relying on real recommendations to reduce uncertainty. Research reveals that recruiters prefer to spend time on references validated by employees or former colleagues, rather than analyzing hundreds of clone resumes generated with AI. In Xataka | Job interviews have always been a game of cunning: AI is just taking things to another level Image | Unsplash (Vitaly Gariev)

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