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Últimas Noticias de IA
Nvidia confirms it will buy Hugging Face for $12.9 billion
After weeks ofswirling rumors, Nvidia confirmed today that it has acquired Hugging Face for $12.93 billion. Hugging Face’s platform hosts three million models, one million applications used by over 18 million developers, and half a million datasets. Ina blog post,Nvidia’s CEO Jensen Huang said that Hugging Face will continue to support open source and open-weight models and will work on expanding developer access. “Hugging Face will remain an open platform for the entire AI ecosystem. Developers will choose the models they want, the frameworks they want, the clouds and inference service providers they want and the computing platforms they want. Nvidia compute will not be required to build on or deploy through Hugging Face,” said Huang. Huang touted Nvidia’s contributions and said that the chip company has released more than 500 models and 250 open datasets on Hugging Face. He argued that the company builds open models to get developers across the world to use them. For a company like Nvidia, which is a dominant hardware platform for AI development and inference, an open ecosystem that it controls is beneficial as it can create a platform suited for its chips. Plus, as TechCrunch wrote earlier, Nvidia will be able tosell its unused capacityto enterprise customers packaged with Hugging Face’s offering. Hugging Face was founded in 2016 and has raised over $395 million in funding to date, according to Crunchbase. The company’s last round was in 2023,when it raised $235 millionled by Salesforce Ventures, with investments from Google, Amazon, IBM, and Nvidia. Ina post on X, Hugging Face CEO Clem Delangue thanked the community for showing the company could be an alternative to closed-source APIs. “But for it to happen at [a] larger scale, it needs more compute, more support, more collaboration, and more visibility. That’s why we went to talk to Jensen, who offered to do exactly that with us,” said Delangue. Hugging Face has risen in prominence, with more models being released every day. Last year, the company rejected a $500 million deal from Nvidia, according toFinancial Times. Last month,The Informationreported that Hugging Face is clocking $150 million in annualized revenue. In an interview with TechCrunch in July, Delangue said that itsgrowth rate is helping the platform to get “close to profitability.” Nvidia’s Huang has been a strong proponent of open models. He wrote a letter, co-signed by several other organizations, to advocate foropen-weight modelsto strengthen the U.S.’s position against rivals like China in the AI sector. The company is heavily investing in model development. Last month, The Wall Street Journal reported that it strucka $6 billion deal with coding startup Poolsideto develop open models. During its recent earnings call, the company said it has infused over $50 billion into AI frontier labs. Huang pitched open models while answering one of the analysts and said that almost all open models run on Nvidia hardware. He also signaled the importance of these models in cybersecurity. “One of the areas where frontier models are vital is cybersecurity. You see the number of cybersecurity companies that are enabled by frontier models so that they could have massively distributed, continuously running autonomous cybersecurity systems to defend. Those companies are emerging. There are some amazing companies. They couldn’t do it without open models. And so open models is both incredibly successful and have finally reached the frontier, but they’re also vital to the American economy. It’s vital to the world economy,” he said. In July, Delangue said that Nvidia’s open model helpedHugging Face defend against cyberattacksafter proprietary models failed to protect the platform. Days before that, OpenAI admitted that itsunreleased model breached Hugging Face.
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Google’s latest AI weather model gives you no excuse to forget your umbrella
Scientists at Google DeepMind and Google Research released a new artificial intelligence model for weather forecasting today that sees our changing atmosphere more clearly and predicts its behavior more often. WeatherNext 3 is the latest wave of a sea change in meteorology brought out by deep learning techniques, and Google says it will start feeding into weather information users see in search, Google Maps, and Gemini, as well as being available to users and researchers on Google’s cloud platforms. “This is going to be the first time that some of the core variables feed and power a lot of the Google products,” Samier Merchant, a Google senior staff engineer, told TechCrunch. The new model has already proven to be the most accurate among leading contenders tested onOperational WeatherBench, a utility for comparing AI forecasts built by the startup Brightband. It looks at metrics like temperature, windspeed, and humidity. As well as beating out other deep-learning models built by Google, Microsoft, Nvidia, and the European Center for Medium-Range Weather Forecasting (ECMWF), it also beats traditional forecasts from the U.S. National Weather service and the ECMWF. Most weather forecasts come from government-owned supercomputers laboriously churning through mathematical equations written to describe the physics of weather; while these systems have become remarkably accurate, they are expensive and comparatively slow. After the ECMWF released more than half a century of weather data produced by these systems in 2018, deep learning researchers began training models that could make predictions far more quickly and with comparable accuracy to government tools. “Weather is chaotic, and so small differences really start to perturb massively…Machine learning targets the problem we are really solving, which is approximate noisy physics from incomplete information and finite compute, and so it learns patterns from a lot of data,” said Ferran Alet, a staff research scientist manager at DeepMind. Since then, model-makers have pushed on the key weaknesses of AI forecasting models: They tend to forecast over a wider area — 15 to 25 square km — than is truly useful, they’re not always great with rain, and they still depend on the formatted datasets produced by government agencies. WeatherNext 3 takes on all three challenges. On key variables, researchers told TechCrunch, it can predict down to a resolution of 5 km. Its evaluations on rain are 60% improved over WeatherNext 2, and it can now produce hourly forecasts, instead of the standard prediction every six hours. Those improvements are the result of specific choices made by the designers. WeatherNext 3 is a larger model, with 2.4 times more parameters than its predecessor, tailoring the targets for the decoder heads to give more useful answers. While most weather forecasts output as metrics averaged across a 3D grid, DeepMind researchers have already won plaudits by tuning their model to also visualize cyclone paths. This time around, the designers also trained the model to target its forecasts to specific weather data stations. This is important not only for offering more granular predictions, but also for being able to evaluate its work against specific, ground-truth data. “The idea, with a lot of AI applications, is to try to run tasks as end-to-end as possible,” Daniel Rothenberg, an atmospheric scientist at Brightband, said. “Adding a capability where this model is now also predicting, say, what Denver’s airport’s weather station is going to measure on an hourly basis, just connects that forecasting task closer to the core.” The model is able to forecast more frequently because it can ingest weather satellite data collected in real time on an hourly basis. Feeding AI models on raw empirical observations, rather than the analysis produced by weather supercomputers, promises a more accurate forecast, but it is still technically challenging to get models to work with unformatted data. Google says WeatherNext 3 is the “first” AI model to directly incorporate raw observations for a high-resolution global forecast, but the AI weather startup WindBorne says its model, WeatherMesh 6, has beenincorporating raw observationsfrom its fleet of weather balloons and other sources since late 2025. Asked about that, Google pointed out that its forecasts are higher resolution across the globe. Regardless, both models still rely on national weather datasets to perform forecasts, so more work will be required for true direct data assimilation. While LLMs get the bulk of the attention, the transformer revolution in meteorology has been just as important. European and U.S. weather agencies are already using AI models in their forecast products, and their speed and low cost promise to bring economic impact to poorer regions where the expense of high-quality sensors and supercomputers has put accurate forecasts out of reach. Bill Gatesrecently citedAI-powered weather forecasting as a crucial benefit of the technology, with better forecasts improving crop yields in developing countries. Alet, the DeepMind researcher, said that higher-resolution forecasts of wind, rain, and cloud cover will be useful to make renewable energy projects more dependable. “At the end of the day, I think Google is about providing useful information to the user, and a lot of what users are looking for has to do with the weather in some way or another,” Alet said.
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Ollie is betting its focus on privacy can help it win the AI assistant race
For an AI assistant to become truly useful, it first has to know a lot about you.Ollie, a personal assistant for everyday life, is betting that doesn’t mean you have to hand over all your data and sacrifice your privacy in the process. While some enterprise-focused AI assistants have data privacy protections in place, Ollie is among the first mainstream, family-focused AI to achieve SOC 2 compliance — a framework that allows a company to demonstrate, through an independent audit, that it has formal controls in place to protect customer data and operate its systems securely. Achieving this was an important milestone for Ollie’s team, as it signals to potential users that the assistant isn’t trying to scoop up your personal data for other purposes, like AI training. “We fundamentally think that trust and privacy are absolutely imperative, and that’s why our business model is a subscription, because we want our users to know that Ollie works for you,” explained Ollie co-founder and CEOBill Lennonin an interview with TechCrunch. “We’re not sharing your data with anyone,” he stressed. There are many personal AI assistants positioning themselves as consumer-friendly solutions focused on integrating with users’ daily lives via text messages, which is why Ollie’s privacy angle could prove a significant differentiator. Today,Olliecompetes with other existing text-based assistants likePoke(acquired by Cognition),Fambot,Ohai,Folk,Saner.ai, andTomo. There are also a number of assistants focused more on work, calendar management and email, likeTown,LindyandReclaim.ai. And of course, there’sInstinct, the AI assistant thatjust landed $350 million in fundingat a $2.5 billion valuation before it even launched. To say the space is heating up is putting it mildly, in other words. By comparison, San Diego-based Ollie has raised a much more modest $7.5 million seed round, largely fromKhosla Ventures, along withAI House. Despite the utility these assistants provide, consumers who turn to these tools are wrestling with how much privacy and personal data they want to give up in the process. Instinct, for example,came under fire for its overly broad Terms of Service and privacy policythat granted it a “perpetual and irrevocable” license to “access, use, host, cache, store, reproduce, transmit, display, publish, distribute, and modify” any of the user’s materials, including for training its AI models. Yikes. Lennon is taking a different approach with Ollie. Currently, this family assistant connects to calendars and email to organize family schedules and keep its users updated about their days. It also offers tools that can help you plan meals, shop for groceries, track to-dos, book appointments, and pay bills via group chats. Later, it may help manage household budgets, too. Yet, all this access requires trust, which Ollie claims, is its value-add compared to its rivals. “We’re not sharing your data with anyone. This is super sensitive, and that is necessary to win the trust of the users,” Lennon said. In addition to the data protections provided by SOC 2 compliance, Ollie doesn’t request usernames or passwords to complete its tasks. When Ollie needs to log in to a website on the user’s behalf, it does so using its own browser in the cloud, and sends the user a link to a remote session in the browser. The process is similar when the assistant needs to make a payment or purchase on the user’s behalf. While this method offers security, the downside is that users always have to log in to complete certain tasks. Still, that’s something Lennon believes technology will improve upon in time. “This is… new territory. I think in the future, we will do some form of hard tokenization, in a secure way, so you’re not going to have to re-enter [your information] every time,” he said. “That’s frontier stuff… we want to find the right user experience that balances convenience and trust.” Building that trust will be important before users trust Ollie to access more sensitive materials, like their bank accounts, even if that’s through a connector like Plaid. Lennon, who has a Ph.D in AI, also has a background in fintech, having previously sold his startupGroundwork, a neobank for nonprofits, in 2021. That experience could help as Ollie moves further into money management. Still, it’s not yet clear if people even want any of this assistance. Lennon is hopeful, though, saying that Ollie’s retention curves are consistent with the leading AI subscriptions in terms of paid subscribers. (He didn’t share how many users or paid customers are now using Ollie, saying the space is too competitive.) These systems can also be hit-or-miss at times. Despite the hype around Instinct, one of my first tests with it was to price out hotels then book one, and it quoted me all the wrong rates. Ollie, meanwhile, experienced an infrastructure outage with its text provider when I was testing it, and it stopped responding. We asked the founder how Ollie is handling such problems, given that most consumers only give a new app or technology one shot. If it’s broken, they simply move on. Lennon agreed this is an issue with AI overall. “This is the challenge with LLMs, in general — because they’re stochastic [i.e., involve probability], they’re inherently unreliable,” he said. “We have to essentially build the harness — the agent harness — in a defensive way to catch and prevent those things… It’s almost like there’s just 1,000 cuts that you’ve got to solve first.”
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India’s Data Centre Boom Is Creating a Jobs Paradox
The data centre industry is attracting billions of dollars in investment. But the number of permanent jobs created at these facilities is likely to remain relatively small.
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NVIDIA Confirms $12.9 Bn Deal to Acquire Hugging Face
The AI platform will continue supporting open-source and open-weight models across different hardware, cloud and inference providers.
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IBM Ventures Invests in BQP as Physics Platform Enters Production
The investment takes BQP’s total funding to $8 million as it expands its platform beyond aerospace and defence.
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India has the Highest share of AI ‘Frontier Professionals’, Reveals Microsoft
“We’re not going after prompting, speed, and more efficiency. We’re going after judgment, taste, critical thinking, quality control,” said Puneet Chandok, Microsoft India president.
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YC Backs India’s Legal Tech Nonprofit Adalat AI As It Takes Courtroom AI Global
Adalat AI says it now works across 11 states and has reached more than 6,000 judges, covering roughly 20–25% of courtrooms nationwide.
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Bengaluru’s Makr Microsystems Raises ₹10.2 Cr Led by Bluehill VC
The funding will support Makr’s technology development, customer validation and path towards commercial deployment.
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Adobe Acquires Indian AI Startup Rilo for Marketing Automation
The acquisition gives Adobe access to Rilo’s AI workflow automation technology as businesses adopt agentic AI tools.
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Anthropic Claude Fable 5.1, Claude Mythos 5.1 Models Announced for Advanced Coding, Scientific Research
Anthropic has introduced Claude Fable 5.1 and Claude Mythos 5.1 as its latest models designed for advanced coding and knowledge work. Fable 5.1 is confirmed to be generally available, while Mythos 5.1 is accessible through trusted programmes. The two models work on the same system, but have different safety safeguards. As per Anthropic's benchmarks, Claude Fable 5.1 surpassed Fable 5 and Opus 5 in science and agentic coding tasks by scoring 52.6 percent on Terminal-Bench-Science and 73.4 percent on CursorBench.
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Google Launches Gemini 3.8 Flash Alongside New Cybersecurity Model
The model is priced at $0.75 per million input tokens and $3.75 per million output tokens, while its cyber variant is available to trusted defenders through Google’s Fairwind Program.
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