It takes years to tape out a chip and bring it to market, with overall industry seismic shifts taking much less time. Nothing will demonstrate that better than the rumored six-month gap between the M6 processor debut and the AI-focused M7.

Apple’s chip lines have gradually become more AI-centric, and that will be the same in the future too. However, rather than sticking to an established release format, Apple’s intending to skip ahead to the bits it wants the public to use.

In Sunday’s “Power On” newsletter for Bloomberg, Mark Gurman revives a late June report that the M6 Pro and M6 Max chips won’t exist. Instead, it is putting the work into the development of AI-first chips for the M7 generation.

The fall cycle will include the usual base chip release, consisting of the M6. But there won’t be an M6 Pro, M6 Max, or even an M6 Ultra following months later.

Apple’s intended schedule is to instead bring the base M7 chip line a mere six months after the M6. That would make it a release in the first half of 2027.

The M7 Pro and M7 Max are thought to arrive at the end of 2027. That will then be followed by the M7 Ultra sometime in 2028.

Skipping to the good part

Gurman doesn’t really explain why Apple is moving to get M6 out of the way in favor of the M7 generation on Sunday. However, he did a better job doing so in June.

The M6 will improve the memory bandwidth from 153 gigabytes per second in the M5 to a massive 200 gigabytes per second. That will be by introducing a new memory architecture, as well as boosting the Neural Engine and using 12 GPU cores instead of ten.

While memory bandwidth is important, the M7 generation will have a much bigger focus on AI processing. This should help the prospective users of the M7 Pro, M7 Max, and M7 Ultra, who will have more complex workflows and could benefit from AI.

Even in the M7, memory bandwidth will also be increased, going up to around 240 gigabytes per second in the base chip.

The massive improvements in the M7 range are deemed by Apple to be sufficient enough to skip most of a chip generation.

Server Strategy

The usually massive performance of the Ultra chip is also at play here. To Gurman, the upgrades in the M7 Ultra allegedly bring the chip close in performance to dedicated AI accelerators, including Nvidia’s Blackwell.

That includes support for as much as 1.5 terabytes of memory. This may not necessarily be an amount presented to consumers or enterprise customers considering the current memory pricing crisis, but it could still be useful for servers.

Gurman proposes that the M7 Ultra could be the basis for Apple’s AI server strategy. While Apple is soon to introduce servers based on the M5 Ultra, engineers are said to be working on a new server chip built around the M7 Ultra.

That chip could see the light of day by 2029.

Old machines often end up sidelined once faster hardware arrives. Yet plenty of people still own working 386-era laptops and desktops that boot just fine. GentleOS steps in as a project built specifically for those systems. It delivers a graphical desktop experience without demanding modern resources or complex setup.

Luke 8086, the developer, worked on this operating system as a hobby. The goal is to provide hobbyists with a clean platform on which to work with vintage x86 hardware or run interactive graphical programs as near to the metal as possible. There is no online browser or app store, as the focus is on what the machine can accomplish directly in front of you. GentleOS/32, which targets 32-bit PCs starting with the i386 processor, was the first of two versions created. Depending on the boot image, you’ll need 2 to 4 MB of RAM, a mouse, and a VGA screen with a resolution of at least 640 by 480 and 16 colors, or higher VESA modes with 256 colors. Then there’s GentleOS/16, a more stripped-down version for actual 16-bit hardware such as 8086 or 80186 CPUs. That one uses less than 192 KB of RAM and rudimentary CGA graphics at 320 by 200 resolution in four colors.

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Both versions use the same design, with everything compiled down to a single binary that runs directly on the hardware, similar to microcontroller firmware rather than a standard operating system. The code is written in basic C with a little amount of assembly, allowing developers to easily follow the logic without becoming bogged down in layers of abstraction.

This approach lacks some of the capabilities that you would expect to see in newer systems. There is no virtual memory, no separate user and kernel zones, and no preemptive multitasking. Many modern systems provide file storage and networking capabilities, but this is not the case here. The entire system runs in memory and is single-threaded, with the main event loop managing graphics and dispatching actions to built-in programs.

Nonetheless, the finished design appears familiar, with a retro-style desktop with movable windows, an icon sidebar for quick access, and a simple color scheme. You get a retro clock with massive, segmented digits that update in real time, Klondike solitaire for some classic card game fun, a color palette tool for some quick creative work, and a about box to see what’s going on behind the scenes. The same concept is also used in programs for calendar functions, basic mathematics, art, and certain light games.

• SanDisk’s BiCS10 chip reaches 29Gb per square millimetre in density
• Bit density improved 59% compared to the previous BiCS8 generation
• Interface speeds now hit 4.8Gb/s, a 33% increase

SanDisk has confirmed it is now sampling BiCS10, its 10th-generation 3D NAND flash chip, built jointly with longtime manufacturing partner Kioxia.

The 1Tb TLC chip packs 332 memory layers into a die that reaches an area bit density above 29Gb per square millimetre, which the company calls industry-leading.

from the the-anxious-generation-are-the-boomers dept

It’s no secret that I’m a fan of developmental psychologist Candice Odgers. I’ve mentioned her and her work on the site many times, and she was a guest on my podcast as well. She actually has expertise and has done the work to look at the impact of social media on kids. In many ways she’s the anti-Jonathan Haidt with actual facts, not made up nonsense (which is why when she debated Haidt, he came out of it looking pathetic).

Odgers gave a TED Talk recently, which is now online. It’s worth watching in its entirety (only about 12 minutes) as she details how the moral panic about kids and social media is bullshit, and how banning kids from social media will do way more damage to their mental health:

A few choice quotes (though, again, watch the whole thing). First, she points out that on many important metrics (including the metrics many teenagers were judged by in past generations), we’re doing incredibly well:

in the past 20 years, we’ve had some major wins.

Rates of teen violence, alcohol use, pregnancy have plummeted to historic lows.

You are looking at the most educated generation ever in terms of high school graduation. Young people are inventors. They’re activists. They’re leaders. They’re amazing singers. They’re Olympians. They’re amazing.

And, yes, in some cases they’re more anxious and sadder about the world. Though, some of that may just be the state of the world today. And while she doesn’t say this, I know I’ve heard her talk about it before: some of her work from way back started from the premise that the reason kids were stressed out and anxious was because of social media, but repeated studies failed to find any indication of that (some of which we’ve reported on).

As Odgers points out, the reality is that it’s the adults that are the problem. There’s a mental health crisis among adults, and much of it may be driven by issues like the opioid epidemic:

Now, since 2008, we’ve seen an uptick in youth suicide risk. But perhaps this shouldn’t be surprising, because adult suicide has been increasing dramatically in the United States since 1999.

Remember when I said that adult mental health and caregiver mental health is the most important predictor of child mental health? Well, with that in mind, I want to take a look at this slide.

This graph here shows you that between 2011 and 2021, the rate of overdoses due to drug use among parents more than doubled. People ask me all the time: what could have happened during this period other than social media coming online?

The answer is that adults were in distress and parents were dying.

And, she points out that the data suggests no significant impact for young boys, and for young girls the correlation is reversed. Those who are facing mental health problems and don’t have support go online more — not the other way around.

She also discusses how adults keep closing off spaces for kids to be kids, and banning them from social media just takes away yet another space for kids to be a part of a community.

We are punishing victims.

We’re kicking them out of the spaces they go to be with friends, to consume youth culture, and yes, sadly, many times to escape people that are harming them offline.

We’ve already kicked teenagers out of public spaces.

In the US, we’ve created a society where firearms are the number one killer of our children, and now we’re telling our kids that we’re going to take away the spaces that they’re going to virtually gather and create community, because adults broke that, too?

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Yeah, I’m saying adults broke the internet and they’re trying to fix it by kicking kids off.

So a social media ban might feel good for the adults in the room, but teens tell me, and I believe them — it’s not going to work.

It’ll push them into less safe and less regulated spaces, and it will prevent us from doing what we really need to help them be well.

And, no, contrary to some of the YouTube comments on the video, she’s not giving a talk in support of social media platforms. She admits that there are issues there, but notes that kicking kids off doesn’t solve them. It also makes it more difficult to fix the actual underlying societal problems. She comes up with a list of solutions which I won’t spoil, but it involves taxing some of the tech companies to pay for better support for children.

It’s a 12-minute TED talk, so it’s designed to be quick and straightforward, without going too deep into the data and the science, but given how those in favor of banning social media have taken over the narrative, it’s good to have the counter narrative out there.

As Odgers herself said about this talk when she posted it to Bluesky, the kids can be alright. “This isn’t an anxious generation, it’s a resilient one. Let’s start treating them that way.”

The real work, then, is making sure kids have the tools, spaces, community, and knowledge to be safe in the world — both online and off.

Filed Under: age verification, candice odgers, jonathan haidt, kids and social media, social media, social media bans

The vast majority of business data is tabular — living in data warehouses, CRMs, and financial ledgers — yet building a reliable model from it still means training a new one from scratch for every dataset, then maintaining hyperparameter tuning loops, feature engineering, and retraining pipelines to fight data drift. Google Research is proposing a way around that: a new foundation model called TabFM that treats tabular prediction as an in-context learning problem instead.

It can generate predictions for a new, unseen table in a single forward pass. For enterprise developers and AI engineers, this reduces the time-to-production from weeks of pipeline engineering to a single API call.

The challenge with traditional ML

To extract reliable predictions from a gradient-boosted tree, data scientists must build and maintain complex data pipelines. They have to clean messy inputs, impute missing values, encode categorical variables into numerical formats, and engineer custom feature crosses.

Once the data is ready, they must run repetitive hyperparameter optimization loops, searching across learning rates, tree depths, subsampling ratios, and regularization grids to find the best configuration. 

Once deployed, these traditional models “incur ongoing operational debt through data drift monitoring and retraining pipelines to stay accurate,” Weihao Kong, Research Scientist at Google Research, told VentureBeat.

Meanwhile, the rest of the AI industry has moved on. Generative AI models for text and computer vision have seamlessly shifted to zero-shot inference, where a model can perform a completely new task simply by being prompted with context. 

Large language models (LLMs) already excel at in-context learning, so why can’t we just feed tables into an off-the-shelf LLM?

Because LLMs are trained on natural language rather than structured data, they struggle to process tables directly. First, their context limits are exhausted quickly by medium-sized tables containing just a few thousand rows and hundreds of columns. Second, LLMs suffer from tokenization inefficiency, awkwardly splitting numerical values and destroying mathematical precision. Finally, they suffer from structural blindness. When a 2D table is serialized as a 1D text string, LLMs lose track of which value belongs to which row and column as the table grows. 

“That’s why, today, it is far more effective to use an LLM to write the code that handles feature engineering and calls XGBoost than to ask the LLM to read the table itself,” Kong said.

What is TabFM?

To run inference with TabFM, you do not update any model weights. Instead, you take your historical examples (the training rows with their known labels) and your target rows (the new data you want to predict) and pass them to the model as a single, unified prompt. The model learns to interpret the relationships between columns and rows directly from this context at runtime.

For example, consider an enterprise analyst trying to predict customer churn. Instead of building a bespoke data pipeline and training an XGBoost model, they can simply pass a sample of historical user session data alongside a new, active session into TabFM. In one forward pass, the model returns an instant churn probability. 

TabFM overcomes the limitations of LLMs by treating the data as a grid, preserving its structural integrity without forcing it into a single-dimensional text string.

To effectively process diverse tabular structures while enabling scalable zero-shot prediction, TabFM synthesizes the strengths of earlier experimental architectures, TabPFN and TabICL. TabPFN, developed by Prior Labs, first proved that a transformer architecture could perform zero-shot classification on small tables, though it struggled to scale computationally to larger datasets. 

Later, TabICL, developed by France’s National Research Institute for Digital Science and Technology, addressed this bottleneck by introducing row compression, allowing in-context learning to efficiently process much larger tables. 

TabFM combines TabPFN’s deep feature contextualization with TabICL’s efficient compression into a novel hybrid design built on three key mechanisms:

1. Alternating row and column attention: The raw table is first processed through a multilayer attention module that alternates across both columns (features) and rows (examples). By continuously attending across these two dimensions, the model natively captures complex feature interactions. This deep contextualization does the heavy lifting that would usually require tedious manual feature crafting by data scientists.

2. Row compression: Following this contextualization, the cross-attended information for each row is compressed into a single, dense vector representation. TabICL pioneered this by using CLS tokens to compress a row’s rich information into one vector, “in contrast to TabPFN v2, v2.5, and v2.6, which attend over the full cell grid throughout the network,” Kong explained. This drastically shrinks the computational footprint.

3. In-context learning (ICL): A causal Transformer then operates on this sequence of compressed embeddings. This Transformer model uses the attention mechanism of TabICL to attend over these dense row vectors, drastically reducing the computation cost and allowing the model to process large datasets efficiently.

A major selling point of TabFM is its pretraining recipe. The model was trained entirely on hundreds of millions of synthetic datasets. These datasets were dynamically generated using structural causal models (SCMs) that incorporate a wide variety of random functions. By training exclusively on synthetic SCMs, TabFM learned the fundamental mathematical priors of how tabular features interact without ingesting real-world, confidential CSV files.

TabFM in action

To test the model’s capabilities, Google researchers benchmarked TabFM on TabArena, a comprehensive evaluation suite spanning 51 diverse tabular datasets across 38 classification and 13 regression tasks.

On these public benchmarks, TabFM’s zero-shot predictions already match or beat heavily tuned supervised baselines. However, Google is careful to note that this does not automatically mean TabFM will universally dethrone bespoke, hyper-optimized production models on every enterprise workload.

“Instead of replacing hyper-optimized production models, the true practical business value it unlocks for lean engineering teams is velocity,” Kong said. “It allows data analysts and backend engineers to instantly spin up high-quality baseline models without a dedicated data science team managing a complex lifecycle.”

For advanced practitioners looking to squeeze out maximum accuracy, the research team also introduced a “TabFM-Ensemble” configuration. By running the model through 32 distinct variations and blending the results, TabFM pushes the performance even further. 

Getting started, trade-offs, and the cloud future

The shift to in-context learning for tables introduces a new economic trade-off that engineering teams must consider. 

With traditional algorithms, training is slow and expensive, but inference is lightning-fast and cheap. TabFM flips this dynamic. While training time drops to zero, inference becomes significantly heavier. Because the model must process the entire historical dataset as context during every single prediction, it requires more compute and memory at runtime. 

In this new paradigm, “traditional machine learning training becomes the ‘prefill’ phase (KV caching) in the context window,” Kong said. While this prefill cost is steep, it is paid only once per table, and the cache is reused across subsequent queries. “The catch is prediction latency, which no amount of caching removes,” Kong added. Every new prediction requires a pass through a large transformer. “Any production API requiring single-digit-millisecond response times cannot tolerate TabFM’s forward-pass overhead.”

For developers looking to evaluate the model today, the barrier to entry is low. Google designed TabFM as a drop-in replacement for traditional ML workflows, offering a scikit-learn compatible API (TabFMClassifier and TabFMRegressor). It natively handles mixed numerical and categorical columns, works directly with pandas DataFrames, and requires no manual ordinal encoders or numerical scalers. The library supports both JAX and PyTorch backends.

However, enterprise teams need to be aware of current limitations and licensing restrictions. The model architecture has a hard limit of 10 output classes for classification tasks, and it is optimized for tables with up to 500 features. More importantly, while Google released the underlying codebase under the permissive Apache 2.0 license, the pre-trained model weights are published on Hugging Face under a strict tabfm-non-commercial-v1.0 license. Developers can evaluate the model internally, but it cannot be deployed in commercial products yet.

Looking ahead, Google is addressing the commercial deployment friction through its cloud ecosystem. TabFM is being integrated directly into Google BigQuery, allowing analysts to run zero-shot predictions natively via an “AI.PREDICT” command. By putting foundation model inference right next to the data warehouse, TabFM could soon make complex tabular machine learning as accessible as a basic database query.

In practice, TabFM shines in rapid prototyping, high data drift environments, and small to medium-sized datasets under 100,000 rows. Conversely, teams should stick to traditional models for strict, ultra-low latency APIs, or massive tables exceeding one million rows, which currently require aggressive row sampling that degrades the foundation model’s competitive advantage.

Scientists have successfully shown a quantum computer can improve the accuracy and reach of generative artificial intelligence drug discovery models. And they did it using their spare time and money leftover from other projects.

The Technical University of Denmark team ran their generative AI model for predicting proteins in conjunction with a printer-sized quantum computer built by British startup ORCA Computing, which sped up AI by linking quantum machines with traditional processors. The researchers used the hybrid technique to generate novel peptides—short chains of amino acids—capable of binding to specific proteins in the body. Doing so is a crucial step in vaccine development.

The team of researchers worked weekends and pooled unspent money from other projects because “most innovative science is too scary for foundations,” according to DTU professor Timothy Patrick Jenkins, who led the project.

Making the peptides in the laboratory and testing whether these would bind to the particular proteins showed the model produced more successful peptides than its classical counterpart, with the strongest improvements where training data was rare.

The team believe the machine could accelerate the development of personalized immunotherapies and vaccines, as well as improve drugs’ efficacy in understudied groups.

“We needed to really prove it to convince skeptics that our predictions connect to the real world,” Patrick Jenkins tells WIRED. Quantum computing remains a nascent field and faces intense scrutiny due to the technical challenges of building these machines and successfully applying them to solve problems.

Even Patrick Jenkins was initially reluctant to explore the technology: “I was a huge quantum skeptic” he says with a laugh, believing any application to his work would be “decades away.”

He and his team use big data and AI to discover proteins which could unlock new immunotherapies cheaper and faster, often funded by the Novo Nordisk Foundation. While most biological model makers are desperate for more data, a particular challenge for his team has been the lack of data on the full variety of genetic information across the human race, since most medical research has focused on Western populations. This can make it difficult to develop peptides that will work on understudied populations, such as those in Asia and Africa, he says.

His team hypothesized that embedding a quantum computer into their workflow could make it generate a more diverse set of peptides, especially for targets where they had less data, after learning that the machines had a similar effect in generating images.

The newly discovered process won’t revolutionize research yet as quantum computers are still too small to run full-scale, cutting-edge AI models, meaning better results could be achieved on a classical computer.

“Quantum is still not very powerful, so the level of complexity that we could encode wasn’t a normal-sized antibody, which is what we usually work with,” says DTU PhD student Jonathan Funk. Furthermore, finding a peptide that can bind to a specific gene is just one step in vaccine development, and wouldn’t alone yield successful drugs.

“I think it’s no surprise that lots of industrial companies think quantum is hazy and far away,” ORCA Computing chief executive officer Richard Murray tells WIRED, partly because the technology “has not ever had really clear near-term examples of usefulness.”

He says this study is novel in that it shows a near-term commercial application for quantum. His company is also applying the technology through projects with oil major BP on chemistry and carmaker Toyota on making its design process more efficient.

The DTU team will now see if it can use the workflow with more cutting-edge models and larger proteins. “We needed this as an easy way to validate that now we actually have a shot at moving the needle substantially,” says Patrick Jenkins, noting that generative AI workflows are particularly valuable in neglected diseases that receive little research money. He’s also looking at using a quantum computer to enhance his generative AI method for designing synthetic antidotes for snakebite venom.

Gardner also asked why Apple never removed Grok, which still hosted sexualized deepfakes of celebrities as recently as June, from its App Store.

“When someone points out that these apps are deepfaking teenagers or creating child sexual abuse materials, they sort of quietly remove them from the App Store without making any announcement about it, so their inconsistency in terms of the App Store is really pronounced,” Gardner says.

Apple says nudification apps are against its guidelines, and it has proactively rejected many and removed others, including those which people have flagged through the App Store’s reporting tools. The company did not address why Grok is still available on the platform.

As for whether Apple is still looking into deploying CSAM detection tech, Apple pointed to its Communication Safety feature, which blocks images and videos containing nudity, violent content, or gore in various apps. (It’s turned on automatically for users under 18.) The company will also make a new function available for reporting these types of content. Users in Australia, Brazil, the US, and the UK will get it first, with expanded availability coming to other regions over time.

“We have a long-standing commitment to building a safe and trusted platform for kids, and provide many industry-leading tools that help keep them safe while also safeguarding their privacy,” Apple says.

Anunay Kulshrestha, an applied cryptographer and information security consultant at Infosec Clinic, says Apple’s CSAM implementation had no accountability guarantees. He doesn’t think Apple implementing it today would be any better than if the company had charged ahead three years ago. “A government can pressure Apple into adding something to the set that isn’t CSAM, and Apple is known to defer to governments,” Kulshrestha says.

What Apple Is Changing

So what can you expect later this year in iOS 27, iPadOS 27, and macOS 27? Let’s break down some of the new improvements and capabilities.

Courtesy of Apple

Updates to Child Accounts

The onboarding process for creating a child account has been revamped, with Apple saying it should take around six minutes to set up. It’s required for children under 13 and available for kids up to 18. This process includes limiting adult websites, setting age-appropriate media, and implementing age-based restrictions in the App Store.

Parents can also choose what apps kids can access on the device—there’s an option to start with a few essential apps, a curated set, or to manually choose apps. (You can add more apps over time.)

Ask to Browse

Courtesy of Apple

Ask to Browse is a new experience in Safari; if it’s enabled, kids must ask parents for permission to visit a new website in the browser. It works similarly to the Ask to Buy function in the App Store (where kids must ask permission to purchase or install new apps). When they ask permission, a message is sent to the parent’s device via Messages.

Approve New Contacts

Courtesy of Apple

By default, kids need to request permission before saving or communicating with a new contact on the Phone, FaceTime, or Messages app. Parents will receive a message asking for approval, which they can decline or approve right there.

Communication Safety Updates

Apple’s existing Communication Safety feature automatically detects and blurs nudity in Messages, FaceTime, and AirDrop for users under 18. This is now expanded to include gore or graphic violent content, and Apple says it also works in Shared Photo Albums, Contact Posters, and the Contacts app.

Time Allowances and Custom Schedules

Apple says it’s working with the American Academy of Pediatrics and its Family Media Plan as a reference point for parents and their children’s digital wellbeing. That’s why Time Allowances has suggestions on how much time kids should spend in specific app categories based on their age, like Entertainment, Games, or Social Media.

Parents can customize these allowances, and there are daily schedules let you allow groups of apps at certain times of the day or week. For example, parents can block gaming apps during school hours. And if you want your kid off their phone during dinner, parents can pause device access through their own device. In general, the Screen Time interface has been redesigned for an at-a-glance view that shows a kid’s average device usage and most-used apps.

Other Small Improvements

• Screen Time Passcode Notifications: You can set up a notification every time a Screen Time passcode is entered on the kid’s device.
• User Reporting Tools: Apple says a new reporting tool is available in Australia, Brazil, the US, and the UK (more regions to come), making it easier for users to report CSAM or other inappropriate material.
• Child Safety Website: Apple has a dedicated website that runs through the new tools with helpful resources and answers to common questions.

Ripple effect: As Sony, and likely Microsoft, prepare to take their gaming businesses fully digital, more users may find themselves at risk of losing their purchases to account hijacking. In one case that could be a sign of challenges to come, a user only got their account back after taking Microsoft to court.

Redditor “Ordo_Liberal” recently claimed to have won back their compromised Microsoft account by taking the tech giant to court. The episode offers a useful playbook for dealing with the company’s customer support, and serves as a warning as gaming companies phase out physical media.

Ordo’s troubles began in April, when their Microsoft account was compromised despite having two-factor authentication enabled. It’s unclear whether the 2FA code arrived via email or the less secure SMS. Microsoft has been pushing users toward passkeys and away from traditional passwords for exactly this kind of risk.

Update on my last post “Microsoft deleted my acc and told me to buy my games again”. I sued their asses and won! They have to restore my acc with all my games + pay me 400$USD. Translation in comment.
by u/Ordo_Liberal in xbox

Microsoft’s customer support told Ordo that it had verified the account was stolen, but said it couldn’t restore it because the thief had already changed its security details. The company’s only offered solution was to close the account entirely, forcing the original owner to buy his games again, including Minecraft.

After Ordo contacted a lawyer, a follow-up message from Microsoft’s support team in Brazil, where the user lives, revealed that the earlier response had been automated and that the case remained under investigation. One commenter on the Reddit thread claimed to have received an identical message, signed by a support representative with the same name.

Interestingly, Ubisoft’s customer service handled the situation far more effectively. When the thief tried to hijack Ordo’s Ubisoft account by logging in through the compromised Microsoft account and changing its security details, Ubisoft simply asked for ID verification and restored the account within an hour of the support ticket being filed.

Ordo claimed Microsoft fought the lawsuit with 12 lawyers and a 300-page filing, and still lost. Under a ruling filed with case number 0811207-44.2026.8.19.0002 in Rio de Janeiro’s court system (TJRJ), the company has 15 days to restore the Redditor’s account and purchases or face mounting fines that could reach hundreds of dollars. Microsoft also owes Ordo roughly $400 in moral damages.

Filing the lawsuit cost Ordo nothing, thanks to Brazilian consumer protection laws that let public defenders take on cases like this at no charge. He joked that the only expense was $10 for a pair of pants, since he couldn’t file the claim while wearing shorts.

Consumer protection regulations could soon carry even more weight in the gaming industry: Sony has confirmed that starting in January 2028, new PlayStation games will be sold only in digital form. Xbox appears headed the same way – Microsoft’s next-gen console, reportedly codenamed Project Helix, is rumored to ship without a disc drive, though the company is said to be testing a disc-to-digital feature that would convert legacy physical discs into digital licenses.

Anyone who still thinks Them! is just a 1950s giant bug movie has clearly never had ants invade a kitchen, a picnic, or the inside of a wall. Ants are not peaceful. They are organized, relentless, and disturbingly good at turning a small problem into a full-scale occupation. Fortunately, they do not have nuclear death rays. Were that to happen, Raid could go sit back down.

Gordon Douglas’ 1954 science-fiction classic takes that very real anxiety and gives it an atomic-age upgrade. Much like Godzilla, released the same year in Japan, Them! uses nuclear weapons testing as more than a convenient monster-movie excuse. The bomb does not merely create a creature; it creates a consequence. Godzilla turned nuclear trauma into a walking national nightmare. Them! takes American desert testing, mutates one of nature’s most efficient predators, and follows the premise to its logical extreme: if radiation made ants gigantic, humanity would have a very serious problem under its floorboards.

That is one reason the film has always remained one of my favorite childhood discoveries. Yes, the title is blunt. Yes, the ants are enormous. Yes, there is a giant insect shriek that can make your dog leave the room. But Them! is far smarter and more disciplined than its reputation as a creature feature suggests. It works because Douglas does not treat the material like disposable drive-in nonsense. He stages the film like a police procedural that slowly mutates into a military thriller, letting the dread build long before the monsters take center stage.

James Whitmore stars as New Mexico State Police Sergeant Ben Peterson, who discovers a traumatized young girl wandering alone in the desert near a destroyed trailer. A nearby general store has been ripped apart, its owner killed, and sugar scattered across the floor. FBI agent Robert Graham, played by James Arness, joins the investigation, and the evidence soon brings in entomologists Dr. Harold Medford and Dr. Patricia Medford, played by Edmund Gwenn and Joan Weldon. Their conclusion is not exactly the kind of thing one wants to hear before lunch: atomic testing has mutated ordinary ants into enormous predators capable of spreading new colonies.

From there, Them! moves from the New Mexico desert to the storm drains of Los Angeles, where the surviving queens threaten to reproduce beneath the city. Flamethrowers, soldiers, missing children, and one of the great “what the hell is making that sound?” monster noises in cinema follow. The effects may look mechanical by modern standards, but the film’s sense of escalation still works because the threat is treated seriously.

That seriousness is why Them! remains both popular and underrated. Fans of classic science fiction and horror know exactly why it matters, but the broader audience still tends to file it under “old movie with big ants.” That undersells it badly. The film is tense, well-acted, sharply paced, and rooted in the same nuclear unease that gave the 1950s its most durable monsters. Childhood nostalgia brought me back to Them! more than once; the craftsmanship, atmosphere, and stubbornly effective nightmare logic are why it has never left the shelf.

The Best Them! Has Ever Looked

Scream Factory’s two-disc Collector’s Edition gives Them! the 4K UHD and Blu-ray treatment, with both discs housed in a standard case and slipcover. The new transfer comes from a 4K restoration of the original camera negative, and the UHD is presented in native 2160p with Dolby Vision and HDR10 support. The disc is framed at 1.67:1, which keeps it very close to the film’s original 1.66:1 theatrical presentation.

I have owned Them! in just about every home video format one could reasonably defend without requiring an intervention, and this UHD is easily the best the film has ever looked in my collection. The old Warner Bros. Blu-ray was perfectly watchable for its time, but putting it on after the Scream Factory 4K is not especially kind to it. The UHD offers a more stable grayscale, deeper blacks, better dimensionality, and a far more convincing layer of film grain. On the Warner disc, grain could look clumpy and somewhat flat. Even Scream Factory’s included Blu-ray does not fully escape that flatter appearance, but the 4K disc finally gives the image some real texture and depth.

The Dolby Vision presentation is not about turning Them! into some glossy modern spectacle, which would be a crime against common sense and giant ants alike. The HDR gives the black-and-white photography more separation and snap, especially in darker interiors, desert exteriors, and the Los Angeles storm-drain sequences. Fine detail is also stronger, with facial textures, uniforms, sand, concrete, and practical effects all looking more resolved without making the film appear scrubbed or artificially sharpened.

Does the Mono Track Still Have Bite?

Scream Factory includes a DTS-HD Master Audio 2.0 Mono track, and it is a meaningful upgrade over the earlier Warner Blu-ray. The 2015 disc was listenable, but dialogue could sit too low in the mix, and the presentation lacked the presence needed to make the film’s sound design fully register.

The new track sounds cleaner, better balanced, and more authoritative without pretending to be something it is not. Dialogue is easier to follow, Bronislau Kaper’s score has welcome warmth, and the effects have more weight than expected from a 1954 mono presentation. The ant shrieks remain sharp and unsettling, while bazooka shots, explosions, aircraft, and the storm-drain climax land with more impact.

Through the Focal Mu-so Hekla currently under review, the track had surprising scale and body without feeling artificially inflated. It also helps that Joan Weldon’s Dr. Patricia Medford could explain ant behavior for hours and still hold one’s attention, even if her delivery is so composed that the giant radioactive insects almost sound like a scheduling issue.

“THEM! Memories” is a short Richard Bellis interview, with the former child actor recalling his small role, Sandy Descher, and Gordon Douglas’ very 1950s method of producing tears on command.

“Nameless Terror” gives Courtney Joyner time to explain why Gordon Douglas and Warner Bros. turned Them! into something far better than just another bug problem with a budget.

“Entering the Atomic Age” focuses on James Arness, his John Wayne connection, and why his understated performance works alongside James Whitmore, Joan Weldon, Edmund Gwenn, and Fess Parker.

The archival extras include the brief 2003 behind-the-scenes footage of the giant ants and the original theatrical trailer; neither is pristine, but both are worth having unless one is allergic to context.

Scream Factory’s Them! Collector’s Edition is the best home video presentation this atomic-age classic has received, with a superb 4K image, stronger mono audio, and enough supplemental material to remind viewers that giant radioactive ants were never the silly part. The silly part was assuming humanity could keep detonating nuclear weapons in the desert and not eventually create something that wanted sugar, darkness, and possibly Los Angeles.

Movie Details:

• STUDIO: Scream Factory / Shout! Studios
• FORMAT: Ultra HD 4K Blu-ray (June 16, 2026)
• THEATRICAL RELEASE YEAR: 1954
• ASPECT RATIO: 1.67:1
• ORIGINAL ASPECT RATIO: 1.66:1
• HDR FORMATS: Dolby Vision, HDR10
• AUDIO FORMAT: DTS-HD Master Audio 2.0 Mono
• LENGTH: 94 mins.
• MPAA RATING: Not Rated
• DIRECTOR: Gordon Douglas
• STARRING: James Whitmore, James Arness, Joan Weldon, Edmund Gwenn, Fess Parker, Sandy Descher

Our Ratings

★★★★★★★★★★ Movie

★★★★★★★★★★ Picture

★★★★★★★★★★ Sound

★★★★★★★★★★ Extras

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Old electronics have a talent for making themselves invisible. The laptop that got replaced two years ago is still in the office, the printer that stopped cooperating is in the garage and the desktop tower is somewhere in a closet behind things you haven’t touched since you moved in. None of it is being used, none of it is going away on its own, and the vague intention to deal with it keeps getting deferred because dealing with it feels complicated. It isn’t. Most major retailers and manufacturers run free take-back programs that handle old tech responsibly, and using them typically requires nothing more than a short drive and a few minutes of prep.

Major retailers such as Best Buy and Staples have become drop-off hubs for digital junk. You can walk into a store with a dead PC or a clunky old scanner and hand it over for free, regardless of where you bought it. Some of these places will even give you a discount on new gear or a trade-in credit just for helping them reclaim the heavy metals and plastics that don’t belong in a landfill. It’s the easiest way to recover your storage space without feeling like a jerk for tossing electronics in the trash.

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The only real “work” on your end is making sure you aren’t handing over your entire life history along with the hardware. Before you dump a device, you need to do a legitimate data wipe — not just drag files to the trash can. A 10-minute factory reset or a dedicated drive-scrubbing tool ensures your old tax returns and saved passwords don’t become someone else’s property. Stop acting like you’re going to “fix” that laptop from 2015 and let a professional recycler break it down for parts instead.

What to do before you recycle your old computer

Wherever you take or mail in your items to be recycled, you’ll want to protect your data by removing it as best you can. One way to do this is to perform a factory reset on your computer. Our guide walks you through the process.

Where to recycle your old printers and computers

Some retail stores will accept computers and printers for recycling, but it’s not always a free service. Policies vary by company.

Apple

You can recycle your old Apple computers, monitors and peripherals, such as printers, for free at an Apple store, but there’s a costly catch. According to the Apple Free Recycling program, you must purchase a qualifying Apple computer or monitor to receive this service. Need another option? A third-party company called Gazelle buys old MacBooks to recycle them. After accepting Gazelle’s offer, you print a prepaid label or request a prepaid box and ship the machine to them.

Read more: Phone and Laptop Repair Goes Mainstream With Push From iFixit

Best Buy

Best Buy generally accepts up to three household items per household per day to be recycled for free, including desktop computers and printers, as well as other items ranging from e-readers to vacuum cleaners. While three is the limit for most items, there’s a higher limit for laptops — Best Buy will take five of those per household per day. Note that rules for dropping off monitors vary by state, and it’s not always free to do so. Best Buy also offers a mail-in recycling service for select items, but that’s also not free. A small box that holds up to 6 pounds costs $23, while a large box (up to 15 pounds) costs $30. One CNET editor recently lugged in an old, nonworking tube TV-VCR combo for e-cycling, and was happy to pay $30 to be rid of it.

Office Depot 

Office Depot and OfficeMax merged in 2013. The retailers offer a tech trade-in program both in-store and online, where you may be able to get a store gift card in exchange for your old computers and printers. If the device has no trade-in value, the company will recycle it for free. Office Depot also sells e-waste recycling boxes that you can fill with electronics to be recycled and then drop off at the stores, but they aren’t free. The small boxes cost $8.39 and hold up to 20 pounds, the medium ones cost $18.29 and hold up to 40 pounds, and the large boxes cost $28 and hold up to 60 pounds.

Staples 

You can bring your old desktop computers, laptops, printers and more to the Staples checkout counter to be recycled for free, even if they weren’t purchased there. According to a Staples rep, the retailer also has a free at-home battery recycling box, which has led customers to recycle thousands of batteries per week, up from an earlier average of 50 per week. Here’s a list of everything that can be recycled at Staples.

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Where to find electronics recycling centers

If you don’t live near a major retailer or would rather take your computers and printers to a recycling center, you can locate places near you by using search tools provided by Earth911 and the Consumer Technology Association.

Earth911

Use the recycling center search function on Earth911 to find recycling centers near your ZIP code that accept laptops, desktops and printers. Note that the results may also turn up places that accept mobile phones and not computers or printers, so you may have to do a little filtering.

Greener Gadgets

Consult the Consumer Technology Association’s Greener Gadgets Recycle Locator to find local recycling centers in your area that will take old items. The search function also allows you to filter the results to separately hunt for places that take computers versus printers.