The recent funding reportedly raises the start-ups’ value to around $30bn

In October of 2025, it was reported that Crusoe, a cloud computing company and data centre developer, was believed to be valued at more than $10bn, post the closure of a Series E funding round worth $1.375bn. 

The round was co-led by technology investors Valor Equity Partners and Mubadala Capital and included participation from investors such as 137 Ventures, Salesforce Ventures, Nvidia, Winklevoss Capital and Founders Fund.

Since then Bloomberg has reported that Crusoe has raised more than $3bn in a funding round. Unnamed sources close to the announcement have stated that the latest investment brings Crusoe’s valuation to roughly $30bn.  

The round was co-led by Atreides Management and Valor Equity Partners co-led the round. Mubadala Capital, the alternative asset manager owned by Abu Dhabi sovereign wealth fund Mubadala, is also participating, according to Bloomberg’s sources. 

Established in 2018 and headquartered in Denver, Colorado, by Chase Lochmiller and Cully Cavness, Crusoe aims to accelerate the merging of energy and intelligence. The company provides energy-first solutions for AI infrastructure, by harnessing large-scale energy resources, building AI-optimised data centres and delivering an AI cloud platform.

Reportedly, Crusoe also recently signed a major $13bn contract that would see the organisation supply quantitative trading firm Jane Street with GPUs and AI infrastructure over the course of the next five years.  

The last couple of years have been formative for Crusoe, as it extends its global reach. 

In February of last year it was announced that the company is establishing a European base of operations in Dublin, Ireland and will create 100 jobs for professionals in the areas of networking, site reliability engineering, customer success and support departments, over the course of three years. 

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What if the organization and storage benefits of tool shadowing could be had and improved with a modular, semi-automated process? Tracefinity attempts that by generating custom Gridfinity bins from photos of tools, and has quite a few nifty features that are worth a look.

The basic workflow is this: place one or more tools on a sheet of paper, take a photo, then upload the photo and have the system trace and save the outline and add it to a private tool library. When one is ready to create some bins, use the library of saved tool outlines to generate custom Gridfinity layouts.

If you’re unfamiliar, Gridfinity is a modular system of standardized bins and baseplates designed with 3D printing in mind, making it an ideal match for highly-customized organization tasks and a particularly natural fit for a tool-tracing system like this one.

The idea of taking a photo of a tool and generating a custom bin is a compelling one, and a couple years ago we covered a project that did just that. Tracefinity seems like a natural evolution of the idea, and includes handy features like easy design adjustments, optional magnet holes, and we really like the concept of a tool library from which individual tools are scanned once then later selected to create specific, project-based layouts.

Tracefinity takes advantage of new software capabilities like machine learning to improve and streamline the tracing process, but that doesn’t mean it relies on any external services. It can be entirely self-hosted and by default uses a local, CPU-friendly object detection model for tool tracing. There is an option to provide a API key to use Google Gemini instead, but it’s not required. It can come in handy for especially complex tool outlines or dealing with non-ideal source photos, however.

When it first happens to you, you think you’re crazy. You wander into a cafe and look at a menu with a variety of bagel sandwiches, but each illustration looks eerily flawless, precisely symmetrical, and oddly smooth, eliciting a visceral sensation that something isn’t right. You might think you’re paranoid, but you’re not losing your mind. Generative AI menus have hit the restaurant business courtesy of models trained on a narrow, “pleasing” aesthetic that feels wrong even when you can’t articulate why.

Sometimes, these illustrations are egregiously fake, like a burrito with cheese so bubbly and melty that it looks more like avant garde art than lunch. More often, they’re so ordinary looking that you only notice something is wrong when you take a second to look more closely.

“It’s almost like an alien trying to make a pizza without understanding its core principles,” Reality Defender CTO Alex Lisle told TechCrunch. (Reality Defender itself is part of a growing category of startups selling AI-detection and content-verification tools — a business that exists in part because of issues like this one.)

Lisle says that the way these models are built can help explain why illustrations seem to embrace such a specific aesthetic — one where every ice cream scoop is perfectly round, and where shrimp seem to have been genetically modified to eat their own tails, creating new “Lovecraftian food horrors.”

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Large language models (LLMs) and diffusion models — the kinds of AI models that make seemingly omniscient chatbots and image generators like ChatGPT and Midjourney possible — are trained on vast quantities of data. The models then identify patterns in the datasets to predict what a user is looking for when they ask something like, “Make me a menu for a burger restaurant.”

“A lot of this stuff looks like a Chili’s menu from 2015, and there’s a reason for that,” Lisle said. “That was the corpus of work from which [the models] drew their function.”

New training data is invaluable to the companies building AI models — Amazon has even been found to source rare books to scan and add to its training data, only to destroy those books once they’ve been uploaded. It’s inevitable that some AI-generated content will seep into these incomprehensibly large data sets. But when AI models train on too much of their own AI-generated content, they risk model collapse.

“Model collapse is almost like a mad cow disease… when you feed the outputs from one model back into itself, eventually the inbreeding becomes too much, and the whole thing collapses,” Lisle explained. “What we see here is convergence, which isn’t necessarily model collapse.”

Convergence is a bit less extreme, degrading the quality of an AI’s outputs without making it entirely useless.

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If someone asks an AI model to generate a menu for a fast food restaurant, the model will likely reference menus from Wendy’s, Burger King, McDonald’s, or another popular chain. These menus already share a similar style, which means that the AI-generated outputs will mimic that same style, only to further reinforce it further if the AI-generated menu ends up back in training data.

But menus and advertisements for food will always look better than the real thing, like a Big Mac in a McDonald’s commercial where each layer of the sandwich is arranged by a prop designer to look maximally appetizing. This effect can become even more pronounced in AI outputs.

“The optimization of the data sets is for pleasingness, or you know, not being offensive, and so there’s a way that turns into homogenization,” Lee Rainie, Director of the Imagining the Digital Future Center at Elon University, told TechCrunch. “What AI is known to do both in images and language is to shave off the edges.”

On a more localized scale, this smoothing of images seems to happen when you use an AI image generator to create a menu and apply edits to it. On X, a user named Labtec showed what happens when you make a menu in ChatGPT, then edit it 100 times to see how the food continues to look less and less like it should. (We replicated the experiment and found similar results.)

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“The end result actually makes me uncomfortable,” Labtec wrote.

I made a restaurant menu in ChatGPT then edited it 100 times to see how those hideous slop menus end up the way they are. The end result actually makes me uncomfortable pic.twitter.com/sDIUKlabdp

— Labtec (@labtec901) August 19, 2026

Restaurants are likely falling victim to this problem, revising their AI-generated menus to alter small details over and over, like prices or item names. It seems that with each edit, the food images become a tiny bit more round and smooth.

“People have an almost unexplainable sense about when they’re looking at something that’s AI-generated, compared with something that was real in the first place,” Rainie said. “There’s just a sensibility that people sometimes find hard to articulate, but they kind of know it when they see it and I think that’s one of the reasons why some of the early stories about the backlash [against restaurants using AI menus] is so pronounced.”

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There’s science behind our aversion to these AI menus. Researchers at the University of Duisburg-Essen in Germany found that AI-generated food images exhibited an “uncanny valley” effect, where images of food that looked almost real elicited more disgust and unease than images that were obviously fake. That squeamishness only intensifies in light of the cultural context around AI.

If people react to these images so negatively, then that’s probably reason enough for restaurants to stop trying to make AI menus work. But the issues that bring us perfectly browned hamburger buns extend beyond the dinner table.

“Seeing and hearing has always been believing, to the point where even our court systems are entirely tuned to the idea that the gold standard in evidence is taped confessions and videotaped evidence,” Lisle said. “That’s no longer the case. The world has fundamentally shifted, for good or for ill.”

Chargers usually don’t have much personality. Plug something in, maybe glance at a tiny LED, and hope your phone or laptop is getting power as quickly as it should. Vention’s latest charger has a slightly more entertaining way of keeping you informed: It makes faces at you.

Unveiled at IFA 2026, the Vention Dash Pro is a 140W GaN charger with four ports and a small display that uses animated, color-coded faces to show what’s happening. Alongside it, Vention has introduced the H Series, a remarkably thin magnetic power bank that puts considerably more emphasis on design than the chunky battery packs we’re used to carrying around. Both are part of Vention’s bigger push into the US and European markets, but they tackle portable charging in very different ways.

Your charger looks happy for a reason

The Dash Pro’s display is easily its most unusual feature. Rather than expecting you to interpret charging figures, it translates the charger’s current status into one of five faces. You’ll see a sleepy white face when nothing is happening; for example, while fast charging produces a happy green face. Push the charger to its maximum output and the face turns an excited orange. The charger also shows a red warning face if it detects a problem.

If you’d rather have the numbers, Vention’s companion app provides information such as real-time charging power, current, temperature, and connected-device status. There’s plenty of power behind those expressions, too. The Dash Pro can deliver up to 140W and uses three USB-C ports alongside one USB-A port, allowing four devices to charge simultaneously. Vention says its EnergyCore system can identify connected hardware and adjust power delivery automatically, while support for standards including PD 3.1, PPS, QC, and UFCS should cover a wide range of phones, tablets, laptops, and other gadgets. The foldable plug and 100V-240V support also make it particularly suited to travel.

Vention also made a seriously thin power bank

The H Series takes a more minimalist approach. Its 5,000mAh battery is squeezed into a body measuring just 6.9mm thick, with an aluminum exterior and curved edges intended to make it easier to carry alongside a phone. Vention is offering it in Burgundy Dusk, Cloud White, and Midnight Black, with a two-tone finish that makes it look less like a traditional battery pack. Inside is a solid-liquid hybrid battery, which helps Vention fit that capacity into the slim enclosure.

The app also provides battery temperature, charging status, current, capacity, and system-health information rather than simply showing a remaining percentage. Vention hasn’t shared pricing or exact availability for either product yet. It also brought several other accessories to IFA, including a retractable charging cable, a 7-in-2 USB-C dock, and its SpotTag Pop Bluetooth tracker. But between a power bank barely thicker than some phones and a charger that gets visibly excited about fast charging, its newest power gear certainly has more character than most.

At an IFA event, Lenovo unveiled a range of new laptops aimed for everyone from developers and professionals to the more style-conscious among us.

Heading up the laptops is the 15-inch Yoga Pro 9N, as it’s the most powerful of the lot. Built for developers and creators in mind, the Pro 9N delivers up to 80W TDP and runs on Nvidia’s RTX Spark “super chip” that brings together Blackwell RTX GPU, Grace CPU and high capacity unified memory, all in one seriously mighty processor. 

Alongside the standard Yoga Pro 9N is the two-in-one iteration which also benefits from RTX Spark’s power. Unsurprisingly coined the Yoga Pro 9N 2-in-1, the laptop doubles as both a standard laptop and a tablet thanks to its folding keyboard. The two-in-one also supports the new Yoga Pen Gen 2, which promises low latency, palm rejection for easier drawing while resting your hand on the screen, smart gestures and customisable buttons too. 

That’s not all that Lenovo launched at its IFA-adjacent event. Let’s face it, not all of us really need all the power that RTX Spark promises and, realistically, just need a laptop for more casual or less intensive work uses. That’s where the Ideapad 5 line-up comes in.

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Available in a choice of six eye-catching colours including Spicy Gold, Zen Green, Dreamy Violet, Radiant Coral, Jewel Blue and Lowkey Grey, Lenovo explained at the launch event that the colour was one of the things they were “most excited about” as the brand partnered with the Pantone Colour Institute and analysed emerging trends to come up with the colour palette. 

Design aside, powering the IdeaPad 5 is powered by Intel – although at the time of writing we don’t know the exact processor series behind it.

Overall, Lenovo’s new launch seemingly offers something for everyone, whether it’s for developers who need extra oomph, or for those who don’t need too much power and would rather a stylish machine. I mentioned when covering Acer’s relaunch of Packard Bell, that I found the bright orange DotBook a welcome relief from the stale and boring colours usually adorned by laptop manufacturers. 

With this in mind, it’s great to see Lenovo embracing more bold colours too.

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A team of astronomers discovered a strange polygonal atmospheric structure on Saturn near the planet’s south pole. This 10-sided shape, known as a decagon, joins one of the gas giant’s most famous features: a hexagon near the north pole that was first observed 44 years ago. Images of the new decagon were captured by the Hubble Space Telescope, and the discovery was recently published in the journal Science Advances.

The formations at both the north and south poles aren’t solid structures, like a wall or a rock formation. Saturn doesn’t even have a defined solid surface like Earth’s crust. If you were to descend through the atmosphere of the ringed planet, the pressure and density would progressively increase, but you would never reach a “ground” on which to land. Instead, what astronomers have witnessed are patterns formed by clouds, albeit very unusual ones.

Each side of the decagon measures approximately 16,800 kilometers, which is a few thousand miles longer than the diameter of Earth. Near the feature is a jet stream reaching speeds of 116 meters per second, or nearly 260 miles per hour. By way of comparison, a Category 5 hurricane—the most intense storm level possible on Earth—is defined by winds above 157 miles per hour. Interestingly, although the jet stream circulates at tremendous speed, the decagon pattern shifts at a mere 2.5 meters per second—less than 6 miles per hour.

Photograph of the new decagon at Saturn’s south pole

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Photograph: Courtesy of Space Telescope Science Institute Office of Public Outreach

The south pole feature appears to have formed recently. For decades, scientists have been searching for a counterpart to the famous northern hexagon. However, major missions that observed the planet have previously not been able to find an equivalent, long-lasting structure in the south. The search was further complicated because, due to Saturn’s tilt, its southern hemisphere was not clearly visible from Earth for many years.

The first clues of the decagon came from ground-based observations, some by amateur astronomers. As the images accumulated, it became evident that something strange was happening in Saturn’s southern cloud bands. Data from the Hubble Space Telescope made it possible to trace the pattern back to images taken in 2023. Monitoring continued, and by 2025 its 10 vertices had become much more clearly defined, confirming the presence of a new polygonal feature in the ringed planet’s atmosphere. Science, however, hasn’t settled on an explanation for how these structures came to be.

Various hypotheses have been proposed since the discovery of the north polar hexagon. One is that the polygons are the visible manifestations of enormous atmospheric waves confined by powerful jet streams. You can think of these as similar to ocean waves, which are not simply objects traveling across the ocean but rather disturbances propagating through the water.

A wave’s behavior changes depending on the space in which it moves and the boundaries it encounters. On Saturn, waves of atmospheric gases may be encountering jet streams that confine them, in a sense, like the edge of a beach. Except in this case, the “beach” wraps all the way around the planet.

JLab has unveiled its full JBuds Mini ANC range of headphones, adding Apple Find My compatibility to its JBuds Mini ANC earbuds, the first pair under $100 to offer the feature.

Only Apple’s AirPods and Beats models have offered native Find My tracking before now, so JLab’s move brings that reassurance to a sub-100-dollar pair of earbuds. Users can clip the charging case to a bag and locate it inside the Find My app alongside their other Apple devices.

Active Noise Cancellation and Spatial Audio carry over from that CES debut, and the updated range is available now across Europe, with wider US retail availability following from 1 October.