from the good-deals-on-cool-stuff dept

The SunFounder Raspberry Pi Pico With the Ultimate Starter Kit offers a rich IoT learning experience for beginners aged 8 and up. With over 450 components, 117 projects, and expert-led tutorials, this kit makes learning microcontroller programming and IoT engaging and accessible. It also features 27 video lessons by renowned educator Paul McWhorter, simplifying microcontroller programming and IoT concepts. Packed with diverse hardware, including sensors, actuators, LEDs, and LCDs, it enables endless experimentation and creativity. Supporting three programming languages, MicroPython, C/C++, and Piper Make, the kit caters to varying skill levels while fostering coding versatility. With dedicated technical support and a vibrant online community, this all-in-one starter kit ensures a seamless, hands-on journey into the world of IoT. It’s on sale for $65 for a limited time.

Note: The Techdirt Deals Store is powered and curated by StackSocial. A portion of all sales from Techdirt Deals helps support Techdirt. The products featured do not reflect endorsements by our editorial team.

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Preorder deals are in effect on the just-announced M6 Mac mini at Amazon. Pick up the 2026 desktop for as low as $879.99.

Save $20 on Apple’s brand-new M6 Mac mini that was announced this week, bringing the price of the standard model down to $879.99. This configuration has the new M6 chip with a 12-core CPU and 12-core GPU, along with 16GB of memory and 256GB of storage.

Buy M6 Mac mini 256GB for $879.99

If you want the flexibility of additional storage, the 512GB Mac mini with the same 12-core M6 chip is $30 off, bringing the price down to $1,069.99.

Buy M6 Mac mini 512GB for $1,069.99

These are the lowest preorder prices available, according to our M6 Mac mini Price Guide, with the compact desktop officially launching on Sept. 22.

Google Health 5.07 is rolling out with a number of changes to how the app records and displays your workout and health data. Trail and incline runs will now count toward your Running Distance totals, manually logged workouts can include more information, and Google has fixed an issue that could cause Health Connect to disconnect for some Android users.

The update is rolling out on Android and iOS starting today and should reach more devices over the next week. It follows version 5.05, which added two-way Apple Health syncing and Smart Health Links, and version 5.06, which gave users the option to remove the Health Coach’s lengthy guidance from the Today tab.

More of your workout data should show up properly

For users who manually log their workouts, Google Health will now let you add and adjust more metrics. Google also says that any changes you make should show up in workout summaries more quickly.

Swimming workouts are getting a similar change. Users will be able to record more metrics when switching between pool and open-water swimming. Google is also fixing something that should have been counted already. Trail runs and incline runs will now be included in your Running Distance totals, giving you a more complete figure if either activity is part of your regular workouts.

Maps are getting some attention as well. Google says they should load faster and render more smoothly. An issue that could occasionally cause maps to disappear when using your phone to track a run, walk, or bike ride has also been fixed.

Health Connect gets a much-needed fix

Some Android users have been running into an issue where Health Connect would lose its connection with Google Health. Version 5.07 fixes the problem. If you were affected, Health Connect should now appear under Partner Apps in the Connections screen. Google has also recalibrated the dynamic minimum and maximum range of the weight graph. The change should make weight trends and changes easier to see at a glance.

Google Health has received several larger additions since replacing the Fitbit app in May. Version 5.07 is a much smaller update, but most of its changes deal with making sure the workout and health information already in the app is recorded and displayed properly.

Noise canceling is also top-tier. The buds have the standard noise cancellation, transparency, adaptive, and off modes, and you can fine-tune ANC strength (from one to five) using the case or Soundcore app. The only earbuds that noticeably canceled more noise were the Bose QuietComfort Ultra Earbuds (2nd Gen) and Sony WF-1000XM6.

Out of the box, the sound profile (called Soundcore Signature) isn’t an immediate slam dunk like call quality and ANC are. It’s bass-heavy, so much so that I would bet even an untrained ear would notice the bass boost, especially compared to other earbuds from Apple or Bose. There are five preset sound profiles (I preferred Soundcore Balance), an eight-band custom equalizer to play with, and a personalized HearID mode, which tunes the profile to your preferences based on an audio quiz where you select which clip you prefer.

Using the Soundcore Balance or custom HearID profile, the Liberty 5 Pro sounds great. Again, it’s not quite as detailed as AirPods Pro 3 or QuietComfort Ultra Earbuds, but it’s close enough to be enjoyable for most people, especially given the significantly lower price.

Daily Versatility

Photograph: Harry Rabinowitz

It’s not often that a new pair of earbuds becomes my go-to. The convenience of the Apple ecosystem usually means AirPods Pro 3 are, begrudgingly, my earbuds of choice. But I’m still using the Liberty 5 Pro; I’m using them as I write this review. The Liberty 5 Pro is comfortable, sounds good, has great ANC, works well on iPhone and Android devices, and fits securely, making it well suited for almost any task. An IP55 rating means the earbuds should hold their own against dust and light rain. Most importantly, it offers such dramatically better call quality that I enjoy phone calls more, even with my mumbling and NYC street noise.

Sure, sound clarity isn’t perfect. Yes, the buds are a bit large. But when those are the only complaints I have about earbuds that cost nearly $100 less than most of my other top recommendations, that’s a big deal. In 2026, the Liberty 5 Pro might be the best value earbuds you can get.

OpenAI is ending its contract to supply models to Cursor following SpaceX’s $60B acquisition, with access shutting off on 12 November and its forthcoming Astra model withheld. Cursor and SpaceXAI shipped Grok 4.5 together in July, which is available in Cursor on every plan.

OpenAI is ending its contract to supply models to Cursor. The shutoff date is 12 November, the company said on Saturday.

The reason given is unusually direct. OpenAI said it cannot be confident SpaceX will use its technology within its terms of service, “based on our experience with Elon Musk’s companies violating contracts“.

Bloomberg reported it first. OpenAI published the same notice on X and on its own website.

Two grounds are named, both about earlier conduct. SpaceX’s acquisition of Twitter breached OpenAI’s terms, the company said, and xAI violated them in a way Musk admitted under oath this year.

The buyer completed its purchase this month. SpaceX closed the $60B all-stock acquisition of Cursor after agreeing it in June.

OpenAI says it gave the maximum notice its contract allows, about two and a half months. Astra, its next model, will not be supplied to Cursor at all. It added that it is ready to go above and beyond to support developers moving off.

The leverage in that is thinner than it looks. Cursor and SpaceXAI shipped Grok 4.5 together in July, a model Musk called Opus-class, available in Cursor on every plan.

So OpenAI is removing a supplier from a company that now owns a frontier lab. The gesture costs Cursor less than it would have in June, when the deal had only been agreed.

What it costs developers is real. Europe wrote rules for exactly this dependency, and since September 2025 cloud customers have been able to switch on two months’ notice and port their assets within 30 days. From January 2027 the provider cannot charge for any of it.

Whether those rules reach this is unresolved. The definition sweeps in as-a-service models broadly, and nobody has ruled on whether a model API counts as one.

There is a further gap in them. The switching rules protect a customer who wants to leave a provider, not a customer whose provider decides to leave. Nobody drafted for a supplier walking out.

Which is where European developers on Cursor now sit. Seventy-five days of notice, granted by a contract they never saw, with nothing statutory underneath it.

Apple’s latest Mac mini and Mac Studio models are reportedly set to ship with macOS Golden Gate, rather than macOS Tahoe.

The upcoming software appears to be mentioned on the product pages for both new Macs, giving us an early indication that Apple plans to have its next-generation operating system ready before the computers arrive.

According to Daring Fireball’s John Gruber, the new Mac mini and Mac Studio are “slated to ship” with macOS Golden Gate. Apple’s product pages already reference features coming with the next version of macOS, including Siri AI, further suggesting that the software will be installed on the machines from launch.

Apple itself also refers to the upcoming operating system in its announcement for the new Mac mini, saying that the computer will launch with “the upcoming macOS 27”. The company highlights Siri AI, Apple Intelligence features and improvements designed to make the Mac more responsive and reliable.

I am nothing if not a huge movie buff. I watch way too many of them, and I’m always on the hunt for a new format in which to experience them. So when XREAL, the smart glasses company, sent me an a01 — one of its newer models, which it released in May of this year — I was eager to give them a spin as the newest vector by which to satisfy my media fanaticism.

The a01 isn’t a particularly sophisticated smart glasses model. Unlike more software-heavy AR glasses like, say, the Meta Orion or Snap’s Specs, it’s basically just an external monitor. It also doesn’t have a battery or an internal power mechanism. Instead, a simple USB-C cable plugs the glasses into a device of your choosing, which then becomes the headset’s power source. It’s also not so expensive, at an accessible price point of around $300.

The a01 is actually optimized for gaming, in that it can be plugged into a Steam Deck or other handheld gaming device. However, XREAL also advertises them as a way to watch movies and TV — and since that’s more my speed, once I had the a01 in hand, I plugged it into my personal laptop and booted up the Criterion Channel. I then sat watching David Lynch’s film “Wild at Heart” for a while, enjoying a sequence where Nicolas Cage, dressed in a snakeskin jacket, beats up a guy in a bar and then sings an Elvis song.

I’ll say this: The images look quite good. The glasses, which come outfitted with dual mini OLED panels, provide quite a nice image (those panels offer a 1080p resolution with up to 1,600 nits of brightness), with very vibrant colors. If you aim the glasses at a wall, it feels vaguely like you’re using a really vivid home projector — or perhaps are at a drive-in movie. In a dark room, you’re one step closer to the in-theater experience.

However, the overall experience also brought some questions to mind. Namely, why would I sit next to my computer with glasses on my head watching a thing that is also playing on my laptop only 14 inches away? The reason, XREAL offers, is that the glasses are more immersive (they claim the device’s projections are equivalent to viewing content on a 147-inch screen). Still, the redundancy of watching a movie while it plays right next to you makes you question what the actual purpose of the device is.

XREAL has suggested that the glasses can function as a “second monitor” (indeed, they’ve actually been referred to as a “wearable display”) but the functionality of this is, again, questionable. It’s rather difficult to see anything other than what the glasses are projecting — which would make it quite difficult to, say, work on a laptop while also wearing them.

The glasses can also be connected to your phone. Unfortunately, I have an older iPhone, which means that the a01’s cable is not compatible with the phone’s port. An adapter would have been necessary to link the two.

The user experience is easy enough to imagine, however. Connecting the glasses to a phone means that you’re not captive to an indoor experience anymore as you would be with a heftier device like a laptop. You can watch a movie while you’re traveling on a plane or a train (or, hell, while you’re walking down the street — although I would say this last option is generally ill-advised unless you want to accidentally walk into traffic).

However, there are still inconvenient limitations with using the device this way. For one thing, the glasses still only function as a screen-mirroring device — meaning that the screen of your phone needs to remain active while you’re using the glasses. This brings us back to the redundancy problem. You’re watching a video on a screen attached to your face while the same video plays on a different screen that is located less than a foot away. You could partially solve this issue by putting the phone in your pocket, but the chances seem high that any jostling might upset the device’s playback functionality.

It’s worth noting that the device can also be paired with a separate device, dubbed the Beam Pro, which is essentially a mini-tablet and can act as an isolated streaming hub. Users download shows and movies onto the Beam, connect it to the glasses, and watch. However, this device will cost you another $200.

Then there’s the heat. It doesn’t take long for the a01 to start warming up — producing an odd tingling sensation on the bridge of your nose and over your eyes. This is, of course, not an experience unique to XREAL’s products — it’s a well-known defect of most XR glasses. You can only cram so much computing into a small plastic device before all the electrical processing begins to warm everything up. Still, it’s a tad disconcerting, and not exactly what you would want from an accessory that you’re wearing on your face.

I will say that — heat aside — the a01 is a relatively lightweight and comfortable device — and it isn’t overly cumbersome like other smart glasses that I’ve worn. (Having given Snap’s Specs a try at CES earlier this year, I promise you those are significantly heavier — although it’s also a very different kind of device than the a01.)

XREAL continues to iterate its product line, with each new device seeming to improve upon the previous one. Indeed, some of the existential dilemmas present in the a01 and previous XREAL headsets seem to have been ironed out in the company’s newest (and yet to be released) device: Project Aura — which I caught a glimpse of during my visit to Google I/O earlier this year — promises a significantly more immersive and convenient experience.

The Aura is powered by Android XR, an extended reality operating system developed by Google and Samsung. The Aura comes with native hand tracking (which is absent in the a01), as well as access to the Google Play Store, giving the glasses significantly more interactive abilities and AR potential. It also comes with a puck, tethered to the glasses, that acts as both the charging source and a compute node. The puck, which can be easily placed in your pocket, means that — unlike the a01 — you have substantially more mobility and you don’t have to keep it plugged into a separate device.

Let’s return to the a01, though. Unfortunately, from a cinephile’s perspective, watching movies on a pair of sunglasses just isn’t ideal. In general, movie fans like a big screen — the bigger, the better, really. In a world of 4K OLEDs of varyingly gargantuan sizes, consumers have a lot of options. My TV — a 55-inch TCL S-series — isn’t even a particularly powerful device, but it provides a home-viewing experience that is more comfortable and satisfying than what the a01 can provide. To my mind, watching a movie at home on a large flat screen is second only to actually going to a theater. Watching a film on tiny screens less than an inch from your eyes, meanwhile, is an interesting experience for its distinct sense of immersion but not what I’d call optimal.

The a01 is an interesting glimpse into a hardware industry that continues to evolve and that is still finding its footing with consumers. I’m curious to see how the user experience shifts with XREAL’s upcoming Aura, and I’m game to reevaluate my movie-watching preferences when that time comes.

The big picture: The fight over data centers is increasingly centered on what these facilities could mean for nearby residents’ power bills, water supplies, and quality of life. New research suggests that opposition is driven less by fears about AI itself than by concerns that communities will be left to absorb the infrastructure costs required to support it.

Researcher Andy Masley reviewed polling data on the backlash and concluded that its main drivers are not simply distrust of Big Tech, skepticism about AI, or concerns about China. Instead, he found that people are responding to “object-level local environmental and economic harms” associated with proposed projects.

Power is often the first concern. Large data centers can require significant grid upgrades, including new substations, transmission lines, and other equipment. In the PJM Interconnection region, those costs have often been spread across all customers, even when the upgrades are driven by data center demand. Some customers have seen their utility bills rise by as much as 76%.

The issue has become significant enough to draw attention from the Trump administration. President Trump brought together technology companies, AI hyperscalers, utilities, and governors to sign a “ratepayer protection pledge” intended to ensure that AI companies “pay their own way” for the energy infrastructure they require.

Some states are beginning to address the issue directly. Oregon has adopted a law requiring facilities that use at least 20 megawatts of electricity to cover their share of grid-related costs. Portland General Electric, the state’s largest utility, raised rates for data centers by 30% while cutting residential rates by 1.3%.

Water use has also become a flashpoint. In Georgia, reports linked data center operations to low water pressure and, in one case, muddy water coming from household taps. A separate dispute in Wyoming involved a Meta contractor that allegedly contaminated Cheyenne’s reclaimed-water system. City officials later suspended certain fill-and-flush and closed-loop discharges associated with the project.

Other communities have raised concerns about noise and emissions. Residents near a Michigan data center said the facility produced a constant sound, comparing it to having “someone set up a vacuum, like in your living room.” The company was fined for violating a local industrial noise ordinance and offered to buy homes from nearby residents.

Some projects have also faced criticism over the use of mobile turbine generators or plans to build dedicated gas-fired power plants. A proposed Amazon-related facility in Texas includes a 7.65-gigawatt natural gas plant authorized to emit up to 33 million tons of greenhouse gases annually.

Masley argues that these are concrete concerns, not vague fears. They stem from issues that residents can see, hear, or pay for. However, he also noted that coverage of individual disputes can sometimes leave out important details about proposed projects or fail to compare them with other data center sites. That can lead people to assume that problems reported elsewhere will automatically occur in their own communities.

For developers, this makes transparency more important. Residents want to know how much power a site will use, who will pay for grid upgrades, where the water will come from, and how it will be handled after use. They also want clear information about backup generators, emissions, and noise controls.

Instead, some developers have taken a more confrontational approach. Several have sued local governments that imposed data center bans or moratoriums, arguing that officials exceeded their authority or violated due process and equal protection rules.

In one Pennsylvania town, officials presented a developer with 43 requirements before approving a project. The developer considered the demands too onerous, withdrew from the talks, and later reapplied. The council described the move as an attempt at “approval by tantrum.”

The dispute over data centers is unlikely to disappear as demand for AI computing capacity grows. However, the resistance is not only about the technology itself. It is also about whether the costs of building that infrastructure will be borne by the companies using it or by the communities living next to it.

Image credit: Andy Masley

Presented by Nutanix

Autonomous systems that can reason, make their own decisions, and execute actions across an environment introduce a category of risk that application-level controls were never built to contain. Treating that risk as a single problem produces incomplete architectures, says Oscar Wahlberg, senior director of product management at Nutanix.

“The guardrails to catch a malicious prompt won’t stop an agent from hallucinating and doing something it never should have done, like accidentally deleting databases or leaking sensitive data with a credential it was granted but then uses for something entirely different,” Wahlberg says. “That’s the central problem as enterprises move autonomous agents out of experimentation and into production.”

Once an agentic system is granted execution privileges across the data center, the security posture has to scale into a defense-in-depth architecture spanning infrastructure, storage, compute, networking, and a governing control plane. Each layer addresses a distinct category of risk, rather than duplicating the same controls across the stack. No single security control or vendor can provide that protection on its own. Defense-in-depth depends on those layers working together.

By dividing the responsibilities across the stack and adhering to zero trust segmentation, organizations can create a secure framework that improves their overall posture. Understanding which risks belong in each layer is what turns the principle of defense-in-depth into a practical security framework, with three layers that each have a distinct responsibility.

Infrastructure layer: Establishing trust where AI agents run

The infrastructure layer’s foundational responsibility is establishing a root of trust that answers a simple question: who is operating in the environment? That trusted identity becomes the prerequisite for every security control above it. Before an organization can trust what an agent does, it first has to trust the integrity of the environment where the agent runs. When an agent requests permission to execute an operation, the system must be able to verify that the request came from the legitimate agent — not something impersonating it.

Delivering that kind of assurance depends on technologies that root trust in the hardware itself, including platform attestation, confidential computing, and secure boot, alongside controls that prevent unauthorized access both within a server and beyond it. For regulated industries such as financial services, this layer provides the ability to isolate AI production workloads so that neither the agent nor the environment can operate outside its assigned scope. That mitigates risks including model and runtime tampering, supply chain compromise, and unauthorized access to sensitive AI workloads.

Network layer: Governing how AI agents communicate

Once agents begin communicating with other agents, APIs, applications, and enterprise systems, they generate a level of concurrency and dynamic communication that traditional static network configurations were never designed to handle. An agent configured to call APIs, query data sources, and spin up additional agents without constraint creates a sprawling web of east-west traffic that becomes very difficult to reason about, and that complexity can easily mask lateral movement or data exfiltration when the right network security layers are not in place.

“We should treat AI agents as a new class of network identity, and make sure that an agent can only talk to other agents or data sources where it’s explicitly allowed to do so,” Wahlberg says. “That means moving away from rigid static rules toward dynamic policy enforcement.”

Nutanix’s solution is Agent Gateway, part of the Nutanix Agentic AI solution. It’s a unified, governed layer that is designed to provide cost control and governance capabilities to help manage autonomous agent users. Coupled with agents grounded in zero trust segmentation and using capabilities like Nutanix Flow for micro segmentation and integrating with networking vendors, including its integration into the Cisco Secure AI Factory, Agent Gateway helps enterprises govern interactions across agents, models, data sources, and enterprise applications.

The network layer governs lateral movement, data exfiltration, and gates the agent’s network interactions. A zero trust framework with access blocked by default and scalable interaction monitoring is important for agents since they can exhibit unreliable behavior. The Nutanix software integration with Cisco UCS servers and Cisco AI PODs delivers the turnkey physical infrastructure (compute, storage, and networking) that the AI factory runs on.

Control plane layer: Governing what AI agents are permitted to do

The control plane is the brains of the operation, providing a central point for managing agent permissions, tool access, resource consumption, and runtime visibility. What matters most is having a single place where policies can be enforced consistently rather than reinvented for every agent, Wahlberg says.

“Agent Gateway acts as a universal endpoint for different models and tools, so an IT team can configure their agents to talk to this single control point,” he explains.

The centralized AI gateway enables the admin to observe, audit, and control access to models as well as MCP tools protecting data and gating privileged access. This layer is designed to help mitigate risks such as privilege misuse, runaway agents, unauthorized tool usage, data leakage, and the excessive model consumption that can lead to increased token consumption when agents get stuck in runtime loops. And it depends on treating governance as a runtime control system rather than a compliance afterthought.

Why one-size-fits-all security fails agentic AI environments

The biggest architectural mistake enterprises make is assuming a single security model can be stretched across every layer of an AI stack. When an organization tries to solve for hardware-level trust with application-level software, or leans on static legacy network rules to manage dynamic agents, it builds an architecture that either blocks the agentic system from doing its job or leaves critical doors wide open. One-size-fits-all thinking tends to produce significant performance penalties and operational friction.

“By failing to assign specific responsibilities to the appropriate layers, enterprises end up with blind spots in governance,” Wahlberg says. “They might secure the model output but miss that there’s data leakage between agents, or they might secure the network but lack the control plane visibility to understand that they’re wildly burning tokens because the agents are stuck in some kind of runtime loop.”

Focusing exclusively on the model leaves the largest gaps of all, because a guardrail that catches a malicious prompt does nothing to stop a hallucinating agent from misusing a legitimate credential. Embedding security across the full stack helps ensure that even when a model level threat slips past the initial filters, the agent remains constrained by hardware rooted trust, network isolation, and access controls at the agent layer.

How Intel, Cisco, and Nutanix build defense-in-depth together

The three-way partnership from the three companies demonstrates how the layered architecture comes together in practice as a well-governed, enterprise-grade AI Cloud. Intel supplies the computer to run agentic workloads and secures the execution environment through hardware-rooted trust and confidential computing, while also driving costs down through their accelerators. Intel Xeon 6 processors with built-in AMX accelerate AI inference efficiently without relying exclusively on expensive GPUs.

Cisco wraps the environment in a secure fabric that governs communication between agents and enterprise tools, while Nutanix provides the software platform, minimizing architectural silos, and the central control plane that enforces permissions, delivers visibility and cost governance, and ties the architecture together into a defense-in-depth solution that lets enterprises scale agentic AI.

Of the three layers, enterprises currently underestimate the control plane the most, Wahlberg says. A true control plane extends far beyond initial deployment to simplify Day 2 operations, he explains, giving IT teams the continuous observability, and strict token governance required to keep autonomous agents secure and cost-effective in production.

“Apart from model and tool selection, governing the agent deployments and their access to models and business tools in a tightly integrated full stack platform will be important for the success of AI projects,” he says, pointing to a near future in which organizations move from a handful of AI use cases to thousands of agents working autonomously to drive the business.

Technology leaders should prioritize building a centralized governance layer today that can manage agent identities, tool permissions, and token budgets in real time, because that control point is what builds the operational muscle to scale safely.

“You can’t build an AI system without getting into a lot of complex decisions,” he explains. “And you need a control plane that talks across multiple vendors and infrastructures to help you solve for those defense-in-depth strategies.”

Learn more about the Nutanix Agentic AI solution here.

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