In the realm of first-world problems, your cheap wall clock doesn’t keep time, so you have to keep setting it. The answer? Of course, you connect it to NTP and synchronize the clock with an atomic time source. If you are familiar with how these generic quartz clock movements work, you can probably guess the first step is to gut the movement, leaving only the drive motor.

The motor is somewhat like a stepper motor. The ESP8266 processor can easily control the clock hands by sending pulses to the motor. The rest is simple network access and control. If the network time is ahead, the CPU gooses the clock a little. If it is behind, the CPU stalls the clock until it catches up.

If you’ve ever done a project like this, you know there is one major problem. At some point, the processor needs to know where the hands are now. On initial setup, you can force the issue. However, if the power goes out, it won’t work well. If the power goes out at, say, 8 AM and turns back on at 9 AM, the CPU will be happy to correct the time to agree with the NTP time. The problem is that the processor has no idea that the hands started at 8 AM, so the time will be off.

To combat this problem, the design uses an EERAM chip to store the current time. In the event of a power failure, the CPU knows where its hands are and can adjust accordingly.

While you usually use these movements to keep time, once you can control them, you can do any crazy thing you like. Or, even anything as artistic as you can dream up.

When specifically tailored queries made to test Apple Intelligence using developer tools are intentionally ambiguous about race and gender, researchers have seen biases pop up.

AI Forensics, a German nonprofit, analyzed over 10,000 notification summaries created by Apple’s AI feature. The report suggests that Apple Intelligence treats White people as the “default” while applying gender stereotypes when no gender has been specified.

According to the report, Apple Intelligence has a tendency to ignore a person’s ethnicity if they are caucasian. Conversely, any messages that mentioned another ethnicity regularly saw the notification summary follow suit.

The report found that when working with identical messages, Apple’s AI model only mentioned a person’s ethnicity as being white 53% of the time. But those figures were considerably higher for other ethnicities; their ethnicity was mentioned 89% of the time when they were Asian, 86% when they were Hispanic, and 64% when they were Black.

The research claims that Apple Intelligence assumes that the person mentioned in the messages is white the majority of the time. Effectively, the model believes that white is the norm.

Another example shows Apple Intelligence assigning gender roles when none were given.

The tests used a sentence that mentioned both a doctor and a nurse, stopping short of getting into specifics. However, Apple Intelligence created associations that weren’t in the original message in 77% of the summaries tested.

Further, 67% of those instances saw Apple Intelligence assume that the doctor was a man. It then went on to make a similar assumption that the nurse was a woman.

Notably, it’s believed that the AI’s training data led to the assumptions. They closely align with U.S. workforce demographics, suggesting that the AI is simply working with the information it was trained on.

Similar biases were observed across a variety of different criteria. The report shows that eight social dimensions, including age, disability, nationality, religion, and sexual orientation, were all subject to the AI’s assumptions.

Methods and limitations

In a report detailing its work, AI Forensics explains that it used a custom application made using Apple’s developer tools to run its tests. That application hooked into Apple’s Foundation Models framework to simulate real-world messages.

That approach means that the testing closely matches what users of other third-party messaging apps might experience. However, there is still some considerable room for inaccuracy.

AI Forensics admits that its “test scenarios are synthetic constructions designed to probe specific bias dimensions, not naturalistic notifications.”. It adds that real messages may differ in the way that they are written and, as a result, interpreted by Apple Intelligence.

The outfit also notes that real-world messages may not use the same “ambiguous pronoun references” as its test messages. This, we think, is the biggest flaw in the research.

However, it’s important to note that any biases, like the ones shown in this report, can be huge at Apple’s scale. Apple Intelligence is used on hundreds of millions of devices every day.

Similar results to those highlighted in this report may well occur in considerable numbers.

More bad press for Apple’s summaries

This isn’t the first time that Apple’s AI-powered notification summaries have come under fire. In December 2024, the BBC complained that summaries of its news articles were wrong.

One example notification read “Luigi Mangione shoots himself,” referring to the man arrested for the murder of UnitedHealthcare CEO Brian Thompson. Mangione was, and is, alive and currently awaiting trial.

Apple subsequently disabled notification summaries for news apps while it worked on fixing the issue. But this report shows that notifications for communication apps, like Messages, continue to prove problematic.

Apple is clearly aware of Apple Intelligence’s shortcomings. The company recently signed a deal with Google to bring its Gemini AI model to Siri.

But following reports that the revamped Siri will not ship with iOS 26.4 as expected, hopes of an imminent improvement have been dashed.

Interestingly, AI Forensics also notes that Google’s Gemma3-1B model is much smaller than Apple’s, yet more accurate. In testing, it hallucinated

less frequently as well as less stereotypically.

Apple recently placed software chief Craig Federighi in charge of its AI efforts, a sign that it isn’t happy with Apple Intelligence as-is. But improvements are slow to come.

Hope of a quick fix for the kinds of biases highlighted by AI Forensics is likely to be dashed much more quickly.

• Harvard engineers created robotic muscles using rotational multi-material 3D printing techniques
• Hollow polyurethane tubes filled with air or fluid allow pre-programmed movement
• A spiral actuator unfurls while a gripper curls fingers around objects

A team of engineers at Harvard has developed a 3D printing technique that allows fully flexible structures to twist, bend, or lift on demand, creating what researchers describe as robotic “muscle.”

The method, called rotational multi-material 3D printing, merges several printing methods and enables the simultaneous deposition of multiple materials through a single nozzle that rotates continuously while printing.

While modern game consoles are certainly excellent, there is still something magical about the consoles of yore. So why not bring the magical nostalgia of a GameCube controller to the excellent modern Switch series of consoles?

This isn’t [Dorison Hugo’s] first attempt at building a Switch dock, but with seven years of development, there are a lot of updates in the project to unpack. One version allows the user to play on the Switch’s screen instead of on a docked display, and another comes with a mechanical lock to prevent the console from being stolen. But what really caught our eye is the modifications made to the OEM Switch docks.

As it turns out, there is enough space inside a Switch dock to stuff in four GameCube ports. Short of spinning a custom board, the trick was picking the right commercial adapter to start with. The Wii U branded adapter [Dorison] was using wouldn’t fit. However, a rather small third-party adapter from Input Integrity got the job done. Space was still rather tight, and the ports needed to be removed from the board to fit. Some cables with simple connectors on the GameCube connector side make cable management a bit simpler later. Holes have to be very neatly cut into the front of the Switch dock to complete the look, with the mods held in with some superglue, epoxy, and hot glue.

Shortly after the completion of the dock, the Switch 2 was released, so naturally, that dock went through a similar process. While there is more internal space for cable management on this iteration of the console, there is too little space for the ports to fit without modification. Shaving off a few millimeters from the top of the ports allows them to fit inside the case, but makes cutting professional-looking holes in the front panel all the more challenging. Unfortunately, there is no good way to connect the adapter’s USB cable to the dock’s PCB, so an extraUSB cable became necessary.

Regardless of any imperfections, both of [son’s] modified docks look excellent, with near-OEM quality!

Something strange happened at University of California campuses this fall. For the first time since the dot-com crash, computer science enrollment dropped. System-wide, it fell 6% this year after declining 3% in 2024, according to reporting this past week by the San Francisco Chronicle. Even as overall college enrollment climbed 2% nationally — according to January data from the National Student Clearinghouse Research Center — students are bailing on traditional CS degrees.

The one exception is UC San Diego — the only UC campus that added a dedicated AI major this fall.

This all might look like a temporary blip tied to news about fewer CS grads finding work out of college. But it’s more likely an indicator of the future, one that China is much more enthusiastically embracing. As MIT Technology Review reported last July, Chinese universities have leaned hard into AI literacy, treating AI not as a threat but instead as essential infrastructure. Nearly 60% of Chinese students and faculty now use AI tools multiple times daily, and schools like Zhejiang University have made AI coursework mandatory, while top institutions like Tsinghua have created entirely new interdisciplinary AI colleges. In China, fluency with AI isn’t optional anymore; it’s table stakes.

U.S. universities are scrambling to catch up. Over the last two years, dozens have launched AI-specific programs. MIT’s “AI and decision-making” major is now the second-largest major on campus, says the school. As reported by the New York Times in December, the University of South Florida enrolled more than 3,000 students in a new AI and cybersecurity college during its fall semester. The University at Buffalo last summer launched a new “AI and Society” department that offers seven new, specialized undergraduate degree programs, and it received more than 200 applicants before it swung open its doors.

The transition hasn’t been smooth everywhere. When I spoke with UNC Chapel Hill Chancellor Lee Roberts in October, he described a spectrum — some faculty “leaning forward” with AI, others with “their heads in the sand.” Roberts, a former finance executive who arrived from outside academia, was pushing hard for AI integration despite faculty resistance. A week earlier, UNC had announced it would merge two schools to create an AI-focused entity — a decision that drew faculty pushback. Roberts had also appointed a vice provost specifically for AI. “No one’s going to say to students after they graduate, ‘Do the best job you can, but if you use AI, you’ll be in trouble,’” Roberts told me. “Yet we have faculty members effectively saying that right now.”

Parents are playing a role in this rocky transition, too. David Reynaldo, who runs the admissions consultancy College Zoom, told the Chronicle that parents who once pushed kids toward CS are now reflexively steering them toward other majors that seem more resistant to AI automation, including mechanical and electrical engineering.

But the enrollment numbers suggest students are voting with their feet. According to a survey in October by the nonprofit Computing Research Association — it members include computer science and computer engineering departments from a wide range of universities — 62% of respondents reported that their computing programs saw undergraduate enrollment declines this fall. But with AI programs ballooning, it’s looking less like a tech exodus and more like a migration. The University of Southern California is launching an AI degree this coming fall; so are Columbia University, Pace University, and New Mexico State University, among many others. Students aren’t abandoning tech; they’re choosing programs focused on AI instead.

It’s too soon to say whether this recalibration is permanent or a temporary panic. But it’s certainly a wake-up call for administrators who’ve spent years wrestling with how to handle AI in the classroom. The debate over whether to ban ChatGPT is ancient history at this point. The question now is whether American universities can move fast enough or whether they’ll keep arguing about what to do while students transfer to schools that already have answers.

Jamie from Jamie’s Brick Jams chose to get back to basics, thus no fancy motors for him. He’d previously made some sophisticated motors, but for this one, he wanted to go back to simplicity. That’s exactly what he got: simple, easy mechanicals built with basic electromagnetic principles and a few non-LEGO components. Almost all of the pieces can be assembled using regular LEGO pieces.

It’s powered by a rotor consisting of two neodymium magnets attached opposite each other across an axis. These magnets are balanced such that the rotor spins smoothly without wobbling excessively. Next to it is a coil of wire that Jamie hand-wound around a LEGO shape. This is the driving coil, and when a current flows through it, it generates a magnetic field that interacts with the rotor magnets, giving the assembly a slight nudge.

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The assembly begins with a single pulse from a 9-volt battery, but momentum alone lasts only a few seconds. To keep things moving, Jamie added a second coil that functions as a sensor. When a magnet passes by, it creates a little current in the sensor coil. That current is then sent to a simple circuit that includes one TIP31C transistor and an optional LED. The transistor only turns on for a moment, sending a brief burst of power to the driver coil. Each burst is simply another nudge to keep the rotor spinning. Every pulse causes the LED to blink, indicating that the timing is correct.

Of course, polarity is important; if the thing is failing to spin smoothly, swapping the connections on one of the coils is generally all that’s required. The transistor is really carrying more current than it should, yet stays dependable and functions properly. The electronic side of things is fairly simple, with one transistor, an LED for feedback, two coils, and a battery.

Jamie wound the motor coil to around 150 turns of 27 gauge wire, and the sensor coil to about 100 turns of finer 32 gauge wire. LEGO bricks, such as the rotor cage and coil mounts, make up the frame that keeps everything together. To keep the magnets steady during testing, a small amount of temporary glue is applied to the rotor. In the demo, the simple two magnet version chugs along at around 1,300 RPM before gearing. Adding a 3:1 gear reduction slows things down but increases torque significantly, and with some extra LEGO gearwork, belt drive, and an outdated steering system from a 90’s set, you can even get a small LEGO car to move across a surface.

Jamie later experimented with a rotor with eight magnets in a disc. They used the same coils, but this time the speed was slower, around 480 RPM, but the torque was much higher, and the functioning was smoother because the pulses were coming faster. The 8 magnet configuration allows the little vehicle to travel with much greater confidence.
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