Category: 4. Technology

This should have been a huge milestone for Microsoft, one years in the making. But instead it’s overshadowed by a critical new decision for 400 million of its users, the exact same number that seemed to ditch Windows in another surprising twist.

Windows 11 has finally caught Windows 10’s market share after years of frustration. Data to the end of June, courtesy of Statcounter, shows the two neck and neck — with less than a percentage point between them. Given this data is not exact, that’s within any statistical margin of error. We can say the milestone has been reached.

ForbesFBI Warning—Do Not Take These Calls On Your SmartphoneBy Zak Doffman

At least for now. Microsoft’s decision to let all Windows 10 users stick with the older OS for another year, even if their PC can be upgraded seems a mistake. That u-turn on Microsoft’s part made absolute sense when it comes to those with older, ineligible PCs. But given the acceleration in recent upgrades, it seems the wrong time to reverse.

Microsoft warns users eligible to upgrade that they should do so now, waiting is an unnecessary security risk. “New Windows 11 PCs have seen a reported 62% drop in security incidents,” it says, “and a 3x reported reduction in firmware attacks.”

Microsoft tells waivering users that “Windows 11 is secure by design and by default, with layers of defense enabled on day one to enhance your protection without the need to first configure settings.” It is, the company says, “the most secure operating system we’ve ever built, and offers advanced security like TPM 2.0.”

That’s the same hardware that separates PCs that can and can’t upgrade to Windows 11. “Security is at the heart of Windows 11,” after all, and so reducing pressure on users to upgrade by offering them a Windows 10 extension seems a backward step.

Conversely, allowing those with older, ineligible PCs to maintain Windows 10 security updates makes sense, and it should allay the feared PC landfill catastrophe feared by some. Although, this temporary solution is still being criticized as a part measure.

Of the 700 million Windows 10 users, it’s thought more than 400 million can upgrade, with at least 240 million PCs missing that security hardware hurdle. Those 400 million users should not be tempted into staying put and should upgrade now.

ForbesSamsung’s Android Upgrade Just Made Galaxy More Like iPhoneBy Zak Doffman

Notably, some reports suggested Microsoft may have actually lost 400 million Windows users in the last few years, which would make all this math wrong. But it turns out that was just an error in a company post that did say 1 billion instead of 1.4 billion users, but which now says “Windows powers over 1.4 billion monthly active devices.”

Whether it’s October 2025 or October 2026, I suspect Microsoft will be delighted to consign its Windows 10 to Windows 11 upgrade program to the company history books. This has been a roller coaster and it is now resulting in inevitable compromises. The sooner the user base hunkers down on the latest version of the OS the better.

Apple is reportedly developing smart glasses that could compete with the Meta Ray-Bans, but they are not expected to launch for a few more years.

Earlier this week, Apple supply chain analyst Ming-Chi Kuo said that he expects Apple’s smart glasses to enter mass production in the second quarter of 2027. Similar to the Meta Ray-Bans, he said that Apple’s glasses will allow users to take photos, record videos, and listen to music, with both touch and hands-free voice control. These type of smart glasses are intended to let you capture a moment without needing to take your phone out of your pocket.

Kuo said that Apple plans to offer multiple frame and material options for its smart glasses, but he did not indicate if it will partner with a major glasses brand, such as Ray-Ban or Oakley. Meta’s smart glasses are offered with three different Ray-Ban frames, including the iconic Wayfarer style that has been popular for decades.

Like the Meta Ray-Bans, Kuo said Apple’s first glasses will not have built-in augmented reality displays. However, next-generation Meta Ray-Bans with such displays are expected to launch later this year, so Apple will remain well behind.

Meta’s glasses are equipped with a 12-megapixel camera with 1080p video capture, dual speakers, five microphones, a touchpad on the right arm, and an LED that indicates when video recording is active. Meta says the glasses last up to four hours with a single charge, and up to 36 hours with a fully-charged carrying case.

Meta Ray-Bans were released in September 2023, with U.S. pricing starting at $299. In February, Ray-Ban owner EssilorLuxottica announced that it had sold more than two million pairs of the glasses, making them a relative hit in a growing device category.

For now, Apple’s only head-mounted device is the Vision Pro, which starts at a hefty $3,499. It is estimated that Apple has sold only 500,000 to 700,000 units of the Vision Pro, at best, since it launched in February 2024. Kuo believes that Apple’s smart glasses will be far more successful, with shipments reaching 3-5 million units or more in 2027.

The unfortunate part is that 2027 remains quite a while away, with Apple’s competitors in this space innovating at a much faster pace.

If you’re like me, you depend on a lot of systems and services, even within your home LAN. Because I work from home, that’s amplified to the point where I need certain applications available to me that aren’t hosted by a third party, for flexibility, ease of use, reliability and security.

Thankfully, Docker is there to make deploying those apps and services considerably easier; otherwise, I’d wind up having to first deploy a collection of virtual machines (VMs), keep them running and worry about upgrading/managing them efficiently.

Yeah, Docker makes this entire process easier. Even better, I can spin up those apps and services in seconds, instead of having to go the traditional route, which can often take quite a bit longer to deploy.

But what are the apps and services that I depend on for my LAN to keep me productive? Surprise, surprise: I have a list, and here it is.

Nextcloud

Nextcloud has essentially become my Google services for my home LAN. I began using Nextcloud in earnest on my LAN when I started fearing that Google would use my documents within Drive to train its AI. After that thought danced across the synapses of my mind, I pulled those documents and moved them to a Nextcloud deployment on my home network. Problem solved.

But Nextcloud isn’t just a document server; it’s much more. Nextcloud is an entire suite of applications that can be used for just about every need you have for a home office. There’s audio/video chat, calendars, email, whiteboard, AI assistant and agentic AI, file sharing, collaboration, file access control, versioning, machine learning (ML), tons of integrations, monitoring/auditing and so much more.

There’s even an app store, where you can extend the feature set to meet your exact needs.

Nextcloud is free to use and can be deployed with Docker from Docker Hub as simply as:

Grocy

If you need to manage things in your home, Grocy is the way to go. As you might have suspected from the name, Grocy is all about groceries and meal planning. If you’re as busy as I am, planning meals isn’t always the easiest thing to do, but this handy Docker app makes it considerably easier. Not only can you keep track of the items you have in your kitchen or pantry, but you can also categorize them by location (e.g., fridge, freezer, pantry, garage, basement, etc.) and even keep track of recipes. On top of all this, Grocy even lets you keep track of chores you need to take care of around the house. You can even keep track of batteries, charging cycles and warranties so you can take the guesswork out of when you replaced those batteries in your smoke detectors.

Grocy can be deployed with a docker-compose and a Dockerfile that looks like this:

Tududi

If you want a task manager that can be accessed from any machine on your network, consider Tududi. Tududi can help manage those tasks and even projects with a well-designed, user-friendly UI. The Tududi feature list includes comments, due dates, project names, status, priorities, hierarchical structure for tasks and projects, smart recurring tasks, areas, notes, tags and Telegram integration.

With the Telegram integration, you get the ability to create tasks directly through Telegram messages, receive daily digests of your tasks and quickly capture ideas and to-dos on the go. You also get smart parent-child relationships such that when a recurring task generates a new instance, each generated task maintains a link to the parent, those tasks are displayed as a Recurring Task Instance (with inherited settings), users can edit the parent recurrence pattern from the child task and changes to the parent settings affect all future instances within a series.

Tududi can be installed from Docker Hub with the command:

Bitwarden

Bitwarden is one of the finest password managers on the market. The app/service enjoys one of the best feature lists of all password managers and uses industry-standard encryption. Even so, there are certain highly sensitive bits of information that I would prefer to retain on my home LAN. For that, I make use of the Bitwarden server, which can be easily deployed via Docker. The Bitwarden server acts almost identically to the standard service, only it’s housed privately, so it doesn’t have to be available beyond your LAN. With that in mind, you could house highly sensitive information, and (as long as your network is secure), you shouldn’t have to worry about anyone stumbling upon your vault or the items contained within.

Bitwarden can be deployed with Docker with the command:

Portainer

If you want to manage all of your containers with the help of a powerful, web-based GUI tool, Portainer is hard to beat. Portainer allows you to see all running containers, view all container logs, get quick console access to containers, deploy code into containers using a simple form and turn your YAML into custom templates for easy reuse. Oh, and you can deploy, stop, run and remove containers. In fact, there’s very little you can’t do with Portainer.

Portainer is considered one of the most popular container management systems in the world and does require a bit of work to get up and running. You can check out the official Portainer documentation and get up to speed on the process.

Although this is a short list of containers I regularly use on my LAN, there’s always room for more. Make sure to check out Docker Hub to see if there’s another app/service you could benefit from.

Scientists are striving to discover new semiconductor materials that could boost the efficiency of solar cells and other electronics. But the pace of innovation is bottlenecked by the speed at which researchers can manually measure important material properties.

A fully autonomous robotic system developed by MIT researchers could speed things up.

Their system utilizes a robotic probe to measure an important electrical property known as photoconductance, which is how electrically responsive a material is to the presence of light.

The researchers inject materials-science-domain knowledge from human experts into the machine-learning model that guides the robot’s decision making. This enables the robot to identify the best places to contact a material with the probe to gain the most information about its photoconductance, while a specialized planning procedure finds the fastest way to move between contact points.

During a 24-hour test, the fully autonomous robotic probe took more than 125 unique measurements per hour, with more precision and reliability than other artificial intelligence-based methods.

By dramatically increasing the speed at which scientists can characterize important properties of new semiconductor materials, this method could spur the development of solar panels that produce more electricity.

“I find this paper to be incredibly exciting because it provides a pathway for autonomous, contact-based characterization methods. Not every important property of a material can be measured in a contactless way. If you need to make contact with your sample, you want it to be fast and you want to maximize the amount of information that you gain,” says Tonio Buonassisi, professor of mechanical engineering and senior author of a paper on the autonomous system.

His co-authors include lead author Alexander (Aleks) Siemenn, a graduate student; postdocs Basita Das and Kangyu Ji; and graduate student Fang Sheng. The work appears today in Science Advances.

Making contact

Since 2018, researchers in Buonassisi’s laboratory have been working toward a fully autonomous materials discovery laboratory. They’ve recently focused on discovering new perovskites, which are a class of semiconductor materials used in photovoltaics like solar panels.

In prior work, they developed techniques to rapidly synthesize and print unique combinations of perovskite material. They also designed imaging-based methods to determine some important material properties.

But photoconductance is most accurately characterized by placing a probe onto the material, shining a light, and measuring the electrical response.

“To allow our experimental laboratory to operate as quickly and accurately as possible, we had to come up with a solution that would produce the best measurements while minimizing the time it takes to run the whole procedure,” says Siemenn.

Doing so required the integration of machine learning, robotics, and material science into one autonomous system.

To begin, the robotic system uses its onboard camera to take an image of a slide with perovskite material printed on it.

Then it uses computer vision to cut that image into segments, which are fed into a neural network model that has been specially designed to incorporate domain expertise from chemists and materials scientists.

“These robots can improve the repeatability and precision of our operations, but it is important to still have a human in the loop. If we don’t have a good way to implement the rich knowledge from these chemical experts into our robots, we are not going to be able to discover new materials,” Siemenn adds.

The model uses this domain knowledge to determine the optimal points for the probe to contact based on the shape of the sample and its material composition. These contact points are fed into a path planner that finds the most efficient way for the probe to reach all points.

The adaptability of this machine-learning approach is especially important because the printed samples have unique shapes, from circular drops to jellybean-like structures.

“It is almost like measuring snowflakes — it is difficult to get two that are identical,” Buonassisi says.

Once the path planner finds the shortest path, it sends signals to the robot’s motors, which manipulate the probe and take measurements at each contact point in rapid succession.

Key to the speed of this approach is the self-supervised nature of the neural network model. The model determines optimal contact points directly on a sample image — without the need for labeled training data.

The researchers also accelerated the system by enhancing the path planning procedure. They found that adding a small amount of noise, or randomness, to the algorithm helped it find the shortest path.

“As we progress in this age of autonomous labs, you really do need all three of these expertise — hardware building, software, and an understanding of materials science — coming together into the same team to be able to innovate quickly. And that is part of the secret sauce here,” Buonassisi says.

Rich data, rapid results

Once they had built the system from the ground up, the researchers tested each component. Their results showed that the neural network model found better contact points with less computation time than seven other AI-based methods. In addition, the path planning algorithm consistently found shorter path plans than other methods.

When they put all the pieces together to conduct a 24-hour fully autonomous experiment, the robotic system conducted more than 3,000 unique photoconductance measurements at a rate exceeding 125 per hour.

In addition, the level of detail provided by this precise measurement approach enabled the researchers to identify hotspots with higher photoconductance as well as areas of material degradation.

“Being able to gather such rich data that can be captured at such fast rates, without the need for human guidance, starts to open up doors to be able to discover and develop new high-performance semiconductors, especially for sustainability applications like solar panels,” Siemenn says.

The researchers want to continue building on this robotic system as they strive to create a fully autonomous lab for materials discovery.

This work is supported, in part, by First Solar, Eni through the MIT Energy Initiative, MathWorks, the University of Toronto’s Acceleration Consortium, the U.S. Department of Energy, and the U.S. National Science Foundation.