from the copyright-is-censorship dept

Techdirt has been around for almost 30 years now, and the one internet law issue that never seems to change is the bullshit use of copyright to take down speech. It’s run through basically our entire history. Back in 2020 we wrote about a copyright dispute involving an author of, well, “wolf-kink erotica” using the DMCA to remove works by a competitor writing books in the same genre. The YouTuber Lindsay Ellis did a (very good) explainer video about it all and then… faced a ridiculous copyright legal threat herself.

About a week or so ago, Ellis published a video on a very different topic: cruise lines. It was something like two and a half hours long. Ellis — who I would say is a skeptic of the entire concept of cruises — decided to go all in, taking a bunch of different cruises in a row to see what all the fuss was about and how the various lines compare. After seeing the video mentioned on Bluesky, I clicked on it and… ended up watching the whole thing (albeit at 2.5x speed). I’m not that interested in cruises, but Ellis is a great sardonic storyteller and the video is really well done, mixing details about the individual cruise lines and how they treat their workers (mostly not well) and the environment (potentially even worse) with her own experiences on those trips.

But, apparently that video is currently gone from YouTube (she has kept it up on the subscription service Nebula)… because of copyright, as Ellis explained in a YouTube short.

Yeah. In the middle of a two-plus hour video about cruise lines, there’s a clip — just a few seconds long — of a friend who joined Ellis on one of the trips singing karaoke. And that caused the entire video to come down while the “dispute” was adjudicated.

And, as Ellis notes, YouTube by default sides with the copyright holder, because the nature of copyright law is that it very, very, very strongly encourages intermediaries to side with the copyright claimant, even if that claim is obvious bullshit and clearly fair use (as is the case here). YouTube’s ContentID system is basically built with that in mind. Even as YouTube spent years fighting back against copyright maximalism and winning the massive DMCA case Viacom filed against it, in the end, to keep the big copyright holders happy, ContentID is really designed to strongly favor copyright claimants, even when their claims are bullshit.

The culprit in this case? Apparently our old friends at Warner/Chappell, which, let’s just say, have a history of this sort of absolute bullshit. Warner/Chappell is a music publisher, and publishers tend to be even more copyright maximalist than the labels themselves. It’s likely that Warner/Chappell holds the copyright on the underlying composition that is playing in the background, and which can barely be heard for the few seconds while Ellis’ friend is singing karaoke. That’s clearly fair use and similar to the dancing baby case from over a decade ago, where some music playing incidentally in the background shouldn’t lead to a video being taken down (the difference here being that it looks like this is a ContentID claim, rather than a full DMCA takedown which would require Warner/Chappell to take fair use into consideration).

On Bluesky, Ellis points out that this wasn’t an automated takedown. Her lawyers asked people at Warner/Chappell to pull the claim and they refused, claiming (incredibly) that because the song “was not the topic of the video” they wouldn’t pull the claim.

That is… not how any of this is supposed to work.

Ellis also notes that she uploaded the video weeks before publishing it (as YouTube encourages creators to do) precisely so ContentID could flag any potential issues — and nothing came up until the video passed a million views. Then suddenly Warner/Chappell put in a claim, as Ellis says, just “to be an asshole.”

This sort of nonsense happens all the time, but it’s pretty incredible that we’re now nearly three decades into the DMCA itself and we’re still dealing with this kind of blatant abuse of copyright, which is mostly just a shakedown from companies like Warner/Chappell which will then run to Congress and claim they need more copyright expansion to protect them from “piracy.”

Maybe what we actually need are laws that protect the people doing the creative work from parasites like Warner/Chappell.

Filed Under: contentid, copyfraud, copyright, dmca, fair use, karaoke, lindsay ellis

Companies: warner/chappell, youtube

The discerning modern gamer’s quest is to find a quieter computer without sacrificing performance. That proves challenging when modern components are highly efficient at producing monumental quantities of thermal energy. So the typical solution ends up moving enough air through the computer to create a rather tumultuous racket. So why have fans at all? Well, as [Billet Labs] demonstrates, that’s not necessarily a good idea. 

The build begins with a block of aluminum which, with a bit of hand-metal work, may become the base of the PC “case.” A dry-fit of the aesthetically modified parts taken from a previous attempt at a fanless PC reveals the steampunk aesthetic that defines the build. On final assembly, a large amount of thermal paste under the flex ATX power supply and 6mm of thermal pads under the motherboard provide passive cooling to the block of aluminum they are attached to

The cooling loop begins with three radiators: a big one, a small one, and a slightly smaller one. But raw metal PC radiators are difficult and expensive to come by these days. Some paint stripping and polishing will fix that problem. A few hand-made mounting brackets complete the aesthetic. All three radiators are stacked above the motherboard, and on top of each other, positioned horizontally to encourage passive airflow through them.

At this point, a normal PC would be plumbed up using standard flexible or, if you’re particularly ambitious, hard piping. But this is no normal PC. Instead, the plumbing uses household copper piping, soldered together. Two gauges sit near the front to show the water pressure and temperature. The reservoir is a larger diameter pipe with one end soldered onto a smaller pipe leading to the pump. The other end is a screw cap, with the corners machined off to match the pipe, to allow for filling of the loop. Soldering this took quite some effort because of the thermal mass in such a large pipe.

Once testing commenced, two issues became quickly apparent. The pressure gauge creates quite the racket as water flows past, and the stacked radiators are inadequate at cooling the PC. Rather show-stopping issues when the goal is gaming in silence. Was all this work for naught? No, the design proved the block of aluminum quite adept at cooling the motherboard and GPU. Moreover, adding a simple 120mm fan between the two smaller radiators and removing the pressure gauge effectively quelled the overheating and ticking issue. The result is a quiet (and quite pretty) living room PC with performance to match.

You can free up storage on a Mac without installing a cleaner app. Start with System Settings > General > Storage, identify the categories actually using space, and remove or move files you recognize before touching system-managed data.

On macOS Ventura 13 or later, choose Apple menu > System Settings > General > Storage. Earlier macOS versions show storage through Apple menu > About This Mac > Storage. The Storage view shows usage by category and updates as space is reclaimed.

Why You Usually Don’t Need a Cleaner App

macOS already covers the main jobs required for ordinary storage cleanup: it shows where space is going, exposes management options for several categories, lets you uninstall apps, supports moving files to external storage, and provides iCloud-based optimization. When storage is tight, macOS can also clear caches and logs that are safe to recreate.

A cleaner app can still make file discovery or maintenance more convenient by putting several functions into one interface. For example, a cleaner app can add convenience for reviewing large and old files. That convenience is different from necessity: you can perform the core storage-recovery workflow with macOS itself.

Manual cleanup also keeps the final decision with you. Software may identify a deletion candidate, but only you can determine whether a large video export is expendable, an older device backup is still valuable, or a photo library contains the only copy of important images. A category-first process keeps those judgments separate from file discovery.

What to Leave Alone: System Data, Caches, and Snapshots

macOS groups storage that does not belong to a more specific category under System Data. That label can therefore include material associated with both Apple and third-party software; it is not the name of one folder waiting to be emptied.

A cache is stored data that software can reuse instead of fetching or rebuilding it each time. When the Mac needs room, macOS already removes some caches, logs, interrupted downloads, staged updates, and other data that it considers safe to recreate. Clearing such files manually can produce only temporary savings because applications or macOS may build them again.

Time Machine local snapshots are recovery copies stored on the Mac’s startup disk. On disks using Apple File System (APFS), Time Machine can keep these snapshots locally so previous versions of files remain recoverable when the backup disk is unavailable. Their space is counted as available and snapshots are deleted automatically as space is needed, so manual removal is not a sensible first step in routine cleanup.

Warning
!Do not indiscriminately delete files from /System, /Library, ~/Library, cache folders, or snapshot structures just because the Storage graph is large. Remove recognizable personal files and application data first, then investigate System Data only if the ordinary categories still do not explain the shortage.

If Storage Still Looks Full

If you have removed obvious files but available storage is still lower than expected, narrow the problem before deleting anything more.

If the same user-controlled category remains large after these checks, return to that category rather than switching randomly between cleanup tricks. The goal is to identify the data responsible for the shortage, not to make every storage category as small as possible.

When Moving Files Is Better Than Deleting Them

Deletion is the wrong answer when a file is valuable but does not need to remain on the Mac’s internal storage. Photos, raw video, completed project archives, virtual machines, sample libraries, and other large data can often be moved to an appropriate external drive instead.

A Photos library needs extra care because its new location has specific requirements. The external device should be formatted as APFS or Mac OS Extended (Journaled). It should not be the disk used for Time Machine backups, an SD card, a USB flash drive, a network share, or internet-based storage. These storage requirements apply when moving a Photos library.

Quit Photos, move the library to the supported external device, then double-click the library in its new location to open it. If you use iCloud Photos or need the library through the Photo Picker in other apps, designate the moved library as the System Photo Library, meaning the library macOS treats as the primary Photos library for those integrations. Only after the moved library opens correctly should you remove the original from the Mac. Keep the external device connected and available when you open Photos; if Photos cannot find the library there, it can create a new empty library in the default Pictures folder.

iCloud Photos offers a different trade-off. With Optimise Mac Storage enabled, Photos can use smaller local versions when storage is limited while iCloud retains the full-size originals. This requires iCloud Photos and enough iCloud capacity for the library. It is synchronization and storage optimization, not an independent offline backup.

The same principle applies beyond Photos: delete data that is genuinely expendable, but move or optimize data that still has value. Once the Mac has enough free storage for normal work, an update, or the task that prompted the cleanup, stop there rather than chasing a smaller storage graph.

If human-written content is flagged as AI, do not immediately rewrite it to chase a lower score. Preserve the exact document and detector result, collect genuine records of how the work was created, check the rule that applies, and ask for a human review based on the full evidence.

An AI detector can be useful as a signal, but a result by itself does not record who actually wrote a document. The safest response is therefore evidence-first rather than score-first.

What an AI Flag Actually Means

An AI detector looks at patterns in text and estimates whether those patterns resemble generated writing. A false positive happens when human-written text is classified as AI-generated.

Do not assume that a percentage means the same thing across different detectors. For example, Turnitin says its current AI Writing Report does not show a numerical percentage for results from 1% to 19% because that range has a higher incidence of false positives. Instead, those results are shown as *%. For displayed results of 20% or more, its percentage refers to qualifying text that the system identifies as likely AI-generated or likely AI-generated and then modified with an AI paraphrasing tool.

That matters because a detector percentage should not automatically be read as “the percentage of this document that a machine wrote.” The meaning depends on the particular detector and how it defines its result, so even a high score still needs context.

When deciding whether content was written by AI, a detector result is only one signal. Draft history, source records, factual accuracy, the writing process, and other evidence can provide information the detector cannot see.

AI detector accuracy depends on the specific tool, text, language, and testing conditions rather than one universal accuracy figure.

Before You Respond: Preserve the Original Record

Before changing the disputed document, preserve what actually existed when it was flagged. If you rewrite the only copy first, you may make it harder for a reviewer to compare the detector result with your drafts and editing history.

If you cannot see the detector report yourself, ask the reviewer which text was flagged and what result they received. Knowing what was actually assessed is more useful than arguing against a percentage you have not seen.

How to Build an Authorship Evidence Pack

Version history is useful, but it is only one part of the record. A stronger response combines several genuine pieces of evidence that show how the document developed.

Check Google Docs version history

On a computer, open the Google document and click Last edit, the Version history control near the top right. Choose an earlier version in the panel to inspect changes and, where available, who updated the file.

Google notes that you need edit permission to browse earlier versions. It also warns that revisions may occasionally be merged, so an incomplete revision trail does not necessarily mean earlier editing never occurred.

If an earlier state is important, you can make a copy of that version instead of replacing the current document. This lets you keep both states available for comparison.

Check Word or Microsoft 365 version history

For a Word file stored in OneDrive or SharePoint in Microsoft 365, open the file, select the file title, and choose Version history. You can then select an earlier version and open it separately for comparison.

Microsoft makes an important limitation explicit: Microsoft 365 Version History works for files stored in OneDrive or SharePoint. A document that existed only as a local file should not be expected to have the same Microsoft 365 cloud revision trail.

The distinction between Google Docs and Word version history matters when you are trying to reconstruct how a file changed over time.

Add drafts, notes, sources and prior work

Do not stop at revision history. Earlier drafts, outlines, research notes, saved source material, comments from collaborators, emails about the work, and earlier writing on similar subjects may all help a reviewer understand the process behind the final document.

In academic disputes, for example, Cornell recommends looking beyond initial suspicions and considering tangible evidence. Relevant evidence can include outlines, drafts, prior discussions, previous submitted work, and whether the student can explain the submission. The exact standard will differ outside education, but the broader point is useful: several consistent pieces of evidence are more informative than a detector score alone.

No single item below should automatically be treated as conclusive proof.

If you did not use generative AI

Keep the response simple and evidence-based. State that the disputed writing was produced by you, explain the basic process you followed, and provide the records that best support that account.

You do not need to prove that every sentence looks unlike machine-generated prose. The goal is to give the reviewer better evidence than a style-based classification alone.

If you used permitted editing, translation or AI assistance

“Human-written” does not always mean “produced without any software assistance.” A person may write the original material and later use spelling, grammar, translation, rewriting, or generative features.

The important question is whether the actual assistance complied with the rule governing the work. If the policy permitted a tool or required disclosure, state what you used and provide the required disclosure. Do not turn a legitimate detector dispute into an inaccurate claim that no automated assistance was involved.

If the policy is unclear

Ask which rule is being applied and which kind of assistance the reviewer believes violated it. A detector result and a policy violation are separate questions.

This is particularly important in education because rules can vary by instructor and assignment. Cornell’s current guidance says generative-AI rules may be set on an assignment-by-assignment basis and advises students to ask when the policy or tool classification is unclear. Other organizations may define acceptable assistance differently.

The procedure has reached a useful stop point when the responsible reviewer has the exact disputed material, the applicable rule, your truthful description of any assistance, and the relevant process evidence. A particular detector percentage is not the stop condition.

What to Do If You Have No Version History

No version history does not automatically mean you have no evidence. A document may have been written offline, imported from another application, saved as separate local files, copied between systems, or created somewhere that does not preserve a detailed revision trail.

When revision history is missing, other ways to show how a document was written can include:

• earlier local copies of the file;
• handwritten or digital outlines;
• research notes and saved source material;
• citation-manager records;
• emails or messages discussing the work;
• comments from editors, teachers, colleagues, or collaborators;
• earlier writing on the same subject; and
• your ability to explain why particular sources, examples, arguments, or revisions were used.

Even detector vendors acknowledge this limitation. Originality.ai’s current review guidance says that absence of document history is not proof of AI use and recommends using other evidence when history is unavailable.

File dates and metadata can add context, but they should not be presented as unquestionable proof. Files can be copied, exported, restored, or moved between devices and services. Use metadata as one supporting clue alongside stronger process records.

What Not to Do After a Flag

A disputed detector result can make it tempting to keep changing the text until a different tool gives the answer you want. That approach solves the wrong problem.

• Do not fabricate drafts, notes, timestamps, or screenshots. False evidence creates a separate credibility problem and can be more damaging than the original detector dispute.
• Do not delete the disputed version. Keep the material that was actually assessed before creating revised copies.
• Do not treat a second detector as automatic proof. Different systems can produce different results, so disagreement adds uncertainty rather than establishing authorship.
• Do not repeatedly rewrite legitimate prose solely to lower a score. Rewriting changes the evidence and may also make the prose less natural.
• Do not hide assistance that was actually used. If editing, translation, paraphrasing, or generative tools were involved, describe them accurately and compare that use with the applicable rule.
• Do not assume a detector percentage is a plagiarism percentage. AI detection and source-matching or plagiarism systems answer different questions.
• Do not upload confidential work everywhere just to collect more scores. Before sending sensitive academic, client, employer, or unpublished material to another service, check whether you are authorized to share it and how that service handles submitted content.

A controlled rescan can help investigate an inconsistent result, but it should compare like with like. When possible, use the same text, detection model, language settings, and citation treatment. A different result under different conditions does not automatically invalidate the first scan.

How to Protect Future Work

The simplest protection is to keep ordinary records while you work instead of trying to reconstruct them only after a dispute. You do not need an elaborate surveillance system. A normal revision trail, retained drafts, research notes, and accurate disclosure records are usually more useful.

For important work, consider drafting in a system that preserves revision history, keeping meaningful intermediate copies, and retaining the notes and sources that shaped the document. Google Docs also lets you name important versions, which can make major milestones easier to find and helps prevent those named versions from being merged.

If a school, employer, client, or publisher permits some use of AI, translation, grammar, or rewriting tools but requires disclosure, keep a simple record of what was used and for what purpose. That is easier than trying to remember the exact workflow months later.

The aim is not to make human writing “pass” every detector. It is to keep enough genuine context that an important authorship question can be reviewed using more than one automated score.

I’ve never loved ads turning up where I go to think, so this one makes me wince. 

OpenAI is testing visual ads in ChatGPT image generation. The software will decide whether you should actually see one.

How doChatGPT’ss new Image ads work?

OpenAI says the ads show products in use or the experiences they unlock, carry clear labels, and sit apart from the imageyou’ree creating. 

The more interesting part is thatOpenAI’s guardrails determine whether a chat is emotionally vulnerable or sensitive before placing ads. In technical terms, automated guardrails analyze the chat context before triggering a personalized ad. In simpler terms, OpenAI wants to be safe. 

DoubleVerify and Integral Ad Science (third-party firms) will check how brand safety rules get applied, in a controlled setup without real conversations. I’d say an outside check matters, since I’d want any system that seems fragile to be audited first before it starts showing me targeted advertisements.

The business logic is quite simple as well. Ads let OpenAI monetize beyond subscribers among 1.2 billion weekly users.

Why isEurope’ss rulebook part of this story?

Ads have run across 31 European countries since late August, but only for free and Go users, while Plus, Pro, and Enterprise subscribers see none. That same week, the European Commission labeled ChatGPT a very large online search engine (focus on the term) because its 159.1 million monthly EU users dwarf the 45 million threshold. 

The consequence: by January, OpenAI must publish a year of ads, with advertisers, run dates, targeting, and reach. That matters because Europe’s rulebook forces OpenAI to leave a paper trail for its growing ad business. At least in the EU, ChatGPT must disclose who paid, when the ads ran, whom they targeted, and how many people they reached. 

This will give users and regulators a clearer view of howChatGPT’ss advertising business operates. ChatGPT already shows text-based ads, and visual ads take that further. 

RELATED COVERAGE:

South Korea’s Financial Services Commission (FSC) held an emergency meeting following a series of cyberattacks targeting financial institutions in the country.

During the meeting, officials confirmed a data breach at Shinhan Bank and said other cybersecurity incidents affected other South Korean banks, including Kookmin Bank.

Shinhan Bank and KB Kookmin Bank are large private South Korean commercial banks, each holding more than $400 billion in assets.

Authorities said they launched on-site investigations after receiving incident reports and shared all actionable information with relevant agencies, including KISA (Korea’s data protection agency).

Financial companies in the country are now instructed to:

• Inspect all externally accessible IT systems and services, including those that are not customer-facing.
• Reduce unnecessary information exposure and check for missing or inadequate authentication and access controls.
• Quickly share threat information and coordinate their responses.
• Submit their internal security inspection results as soon as possible.

Authorities also pledged to oversee consumer protection and compensation, and analyze the incidents to identify necessary regulatory improvements.

Yesterday, local media outlets reported that South Korea’s President Lee ordered a thorough investigation into personal data leaks at financial and public institutions.

At the same time, Hana Bank was also found to have suffered a limited-scope breach after its sales-support system was compromised.

According to the same reports, Shinhan Bank leaked the details of 25,000 customers, while Kookmin Bank leaked credit card information of 119,000 clients.

AI-powered attacks suspected

While official channels provided no details about the perpetrators, Korean news agency Yonhap reported that a server used in the attacks had an HTML page title containing a Chinese-language string associated with ARTEX AI.

ARTEX AI is an open-source penetration-testing system that uses agents to automate information gathering, vulnerability discovery, attack-path planning, security-tool execution, and vulnerability verification.

The bank and financial authorities have not confirmed its use in the Shinhan breach, and the Chinese-language string doesn’t link the attacks to any particular threat actor.

However, Moon Jong-hyun, the head of the Genian Security Center, posted on LinkedIn that several threat analysts believe that the breaches involved AI-based attack automation tools.

Join Mikko Hyppönen and security leaders from the NFL, CHANEL, and Atlassian for a two-hour digital summit on what AI-speed attacks change, what defenders should stop doing, and how to validate, decide, fix, and re-validate at machine speed.

Save your seat

Zachary Tong had never designed a chip. He had never programmed an FPGA either, and he will tell you his electrical skills were rough. With about a month left before the first wafer.space deadline, he still decided to write a microprocessor, send the file to a real foundry, and wait to see if silicon came back alive.

After researching old processor ideas, he eventually settled on Transport Triggered Architecture, an unusual choice. A normal chip is told to add one register to another and store the result. BreakingTTAPs mostly moves data. You enter a value into a port on a functional unit, and the operation occurs when the data arrives. He has two 32-bit busses side by side, so he can complete two of those moves in a single clock cycle, which is effectively enough to qualify as superscalar in today’s terms.

Apple 2026 Mac mini Desktop Computer M6 chip

• LITTLE DO-IT-ALL — Mac mini packs pure power into a small, five-by-five-inch desktop as the M6 chip delivers next-level AI capabilities. Mac mini…
• M6 CHIP — Everything you do on Mac mini feels more responsive with the M6 chip and its next-generation CPU. Fly through AI workflows with up to 4.8x…
• CONNECT IT ALL — Features three Thunderbolt 4 ports, an HDMI port, and a 2.5Gb Ethernet port in the back, and two USB-C ports and a headphone jack…

The clock speed is in the 18-20 megahertz range. Program memory featured 1,024 slots, each with two instructions, plus 256 words of stack and 4 kilobytes of general purpose RAM. There are approximately 80 operations spread among 20-30 functional units, including branches, comparisons, math, bitwise operations, and a random number generator for good measure. The pin headers provide sixteen general-purpose inputs and sixteen outputs, as well as an 8-bit parallel boot channel for loading programs. Unfortunately, his SPI is dead on the die, and the UART is a write-off; nevertheless, it may still be recovered.

One specific section of code is a huge pain in the neck since a multiply-and-accumulate unit took up a significant amount of space on the die and fouled up the timing. Tong was still new to this type of stuff, so he didn’t feel comfortable digging into the failure all the way to the bottom of it, thus the clock speed target remained a fight until the file had to be pushed out the door. He developed the logic in Spade, a hardware language he is also learning on the side, and then used open-source tools to arrange the transistors and draw the cables. The handoff process is also rather stringent, as the foundry expects a clean GDS file, and any minor errors in it become permanent as soon as the wafer begins rolling.

Wafer.space, run by Tim out of Singapore, is the shuttle that made the order possible. The designs are printed onto a single reticle on a GlobalFoundries 180 nm wafer using the GF180MCU mixed-signal process. Every time it prints, we receive a wafer with approximately a thousand dies on it after 3-4 months in the oven. Older geometry implies that the silicon is quite large by today’s standards. Unlike a Tiny Tapeout tile, this die contains only one circuit. The bundled parts we received revealed the real chip underneath, which was viewable via clear epoxy and ready to be inserted into the socket.

Bring-up was the short path, not the careful one. A carrier is slapped into a breadboard, a Raspberry Pi Pico starts up and loads a program, and the chip just counts up to 100 before shutting down. That was the proof in a single loop, with no bother. SPI failed just as expected, according to the debugging. The code for the Spade version, as well as the produced Verilog, are all publicly available, and Tong has even been distributing finished BreakingTTAPs boards to anyone who wants to experiment with one on their bench.

The second design is named BTX. That one is already in the foundry line. It’s a special processor designed specifically for ray tracing, with a completely new graphics system planned for it. Spaces on the shuttle are still available for anyone wishing to create their own chip, as you can purchase a slice of the wafer for a few thousand dollars for a thousand dies, with files expected in December 2026.

I was in the audience at Meta’s Connect conference last week when Mark Zuckerberg held up a pendant in his hands and surprise announced the Muse Charm, a little novelty-like device with Meta’s disarmingly adorable Muse AI agent onboard. Muse Charm is coming as soon as December this year.

But the funny thing is, while most people called it an AI Tamagotchi, all I saw was a strapless Apple Watch. Is that what the Muse Charm could be, eventually? A watch as well as a pendant? Is this Meta’s entry to wrist wearables in disguise? I’m still thinking about it…because smartwatches are also on their way to being AI assistants soon, too.

Reports of Meta working on its own watch wearable have been cooking for several years now. At Connect, there were smart glasses galore and even a new glasses-like VR headset coming next year. But things were suspiciously quiet on the wrist wearables front.

Meta already has one wrist wearable, the neural band that comes with the Ray-Ban Display glasses. But it received no hardware updates this year, which surprised me. The sensor-studded band uses electromyography, or EMG, to measure skin-contact electrical signals for a set of gesture controls to navigate the heads-up glasses display. Meta’s already ambitiously promised that EMG could open up all sorts of neural input possibilities, even testing the tech in a smart car dashboard prototype.

As far back as last year, Meta CTO Andrew Bosworth told me that some sort of display or watch-like evolution of the neural band could be in the works next. And Meta already introduced a partnership with Garmin to link fitness tracking to smart glasses last year.

AI agents as Meta’s new priority

The Muse Charm, however, brings a different piece of the puzzle to the table. Muse AI is Meta’s biggest priority now, clearly, based on its widespread availability on desktops and smartphones and on the company’s intentions to put the new agentic AI on all its smart glasses and, eventually, Meta’s VR Glasses. And where else could Muse work well? Enter the Charm.

The Charm has another watch-like tell. It’s powered by Qualcomm’s Snapdragon Wear Elite chipset, which already runs on Samsung’s latest smartwatches. The Wear Elite, as I learned from Qualcomm earlier this year, is optimized for a range of AI-ready wearables, ranging from watches to pendants to even glasses. There’s no reason why Meta’s Charm couldn’t easily get a wrist strap…and a watch OS to go with it.

Many reasons for a Meta smartwatch

A smartwatch would benefit Meta on several fronts. Clearly, the company wants to keep breaking into lifestyle wearables, and watches could offer another way onto people besides faces. Fitness and health tracking would give Meta data that Muse and its smart glasses could use. And watches are a key part of the glasses interface’s future. Google’s glasses will work with Wear OS watches soon. Apple’s rumored glasses will likely work with Apple Watches.

And Meta has to find a better way to make its neural band more wearable. I find the first-gen neural band fascinating in theory, but an annoying extra thing to charge in practice. If it doesn’t become a watch, or at least a fitness tracker, why wear it at all? The neural band doesn’t even control anything other than Meta’s one line of display glasses right now, but chances are Meta’s going to try to figure out ways it could work with the rest of its smart glasses lineup, and VR too.

Google and Apple are already showing how quickly smartwatches are becoming more like AI companions. Gemini lives on Wear OS watches. Apple’s adding memory-assisting audio intelligence modes to its new watches later this fall. Both can tap into Gemini and the new Siri, respectively.

Meta has Muse now. And a charm by the end of the year. Is the watch after that? Meta didn’t have any comment on the topic when I reached out to ask, but I think we’re seeing the beta test in action right in front of our eyes. Or is this the pivot to a modular idea that could be a pendant and a wristworn device, without committing to either? 

One thing’s clear, no matter what: Meta’s looking for ways for that disarmingly adorable Muse mascot to be with us all the time, even without glasses. And the Charm looks like the experiment.

Scott Stein

Editor at Large

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I started with CNET reviewing laptops in 2009. Now I explore wearable tech, VR/AR, tablets, gaming and future/emerging trends in our changing world. Other obsessions include magic, immersive theater, puzzles, board games, cooking, improv and the New York Jets. My background includes an MFA in theater which I apply to thinking about immersive experiences of the future.

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