TikTok is developing a feature that would allow users to send each other money via direct messages, according to a new report from Bloomberg. If rolled out, the feature would use the social media service’s TikTok Pay offering, which is already available in Southeast Asia for TikTok Shop purchases.

References to the potential feature were found in code hidden within the current version of TikTok’s U.S. iPhone app, according to the report. The code indicates that recipients would be able to “tap to accept” payments, while senders could include messages with their payments, similar to Venmo.

TikTok told Bloomberg that the feature is not being tested, which suggests that it’s in early development. Given that the feature is still under development, it’s unknown when or if TikTok plans to widely release peer-to-peer payments.

TikTok did not immediately respond to TechCrunch’s request for comment.

It’s worth noting that this isn’t the first time TikTok has tried to push further into financial services. Reuters reported earlier this year that TikTok had applied to Brazil’s central bank for approval to operate as a financial technology company offering lending and payment services. 

Although TikTok is widely described as a social media giant, it has gradually expanded beyond that category thanks to additions such as robust search, TikTok Shop, a local discovery map, games, hotel bookings, and more. By introducing peer-to-peer payments, TikTok would be competing with services like Venmo and Zelle.

TikTok isn’t the only social network pushing into financial services, as X, formerly known as Twitter, recently launched X Money to allow users to send each other money.

Block, the technology company founded by former Twitter CEO Jack Dorsey that owns Square, Cash App and the music streaming service Tidal, is open-sourcing Berd, a desktop application it originally built to give its own employees a single environment for working with AI agents across different models, tools and projects.

Berd is a locally installed graphical desktop application rather than a browser-based workspace. It is available now on GitHub under a permissive Apache 2.0 license — meaning anyone can use, modify and redistribute it, including commercially — with free downloadable builds for macOS, Windows and Linux. The repository reached version 0.6.2 on Aug. 18, its seventh public release, and lists 91 contributors.

“Berd is desktop-first because much of its value comes from working directly with projects, local files, tools, repositories, and agents running on or connected to the user’s computer,” Brad Axen, Head of AI Capabilities at Block, told VentureBeat in emailed responses.

A daily work surface for AI agents

Block positions Berd as a “daily AI work surface”: a single place where users can start chats, attach files or folders, choose agents and models, work inside persistent projects, configure AI providers, manage skills and extensions, review session history and build automations.

The design goal is not merely convenience. According to Berd’s product specification, users should always be able to tell which project, files, agent, model, provider and session state are actively shaping a conversation. That emphasis on visible operational state is what Block says separates Berd from a generic chatbot wrapper. Configuration — providers, extensions, skills, automations, projects — is treated as part of the workflow rather than buried in an administrative layer, and the interface is designed to expose failures, unavailable providers, and loading and streaming states plainly rather than smoothing them over with assistant-style friendliness.

Berd grew out of a practical problem inside Block. Employees were already working with capable agents — Block’s own Goose, Anthropic’s Claude Code and OpenAI’s Codex — but the experience around them had become fragmented.

“We had capable agents through goose, Claude Code, and Codex, but working across them meant navigating different interfaces, configuration systems, and ways of managing context,” Lucinda Bell, Technology Communications at Block, said in an email. Berd, she said, gives Block teams “one consistent desktop application across models and harnesses.”

For enterprises, that layer may matter as much as another incremental gain in model capability. Persistent projects mean users can return to an established collection of files, instructions and agent configurations instead of reconstructing context for each task. Block also explicitly designed Berd to make agentic work accessible beyond engineering, letting people start with a conversation and add tools, context and structure as the work requires.

Block is not positioning Berd as a mobile product. Axen said the company’s open source collaboration platform Buzz already provides a mobile experience and is Block’s preferred environment for work on the go.

Block thinks agents should look different when they are different

Berd takes an unusual approach to a problem most enterprise AI products leave implicit: how users tell one configured agent apart from another.

Instead of treating every agent as another blank chat window, Berd gives agents roles, instructions, skills, tools and recognizable visual identities. Block created collections of animated characters — its flagship designs are called “Gloopies” — and Berd’s public site showcases predefined personas such as Berdy, Pushback, Choosey, Copycat, Tinker and Wildcard, each built around a different style of work: Pushback plays devil’s advocate on drafts, Choosey helps narrow decisions, Copycat learns to write in the user’s style.

The visual layer is not meant to substitute personality for substance. Block’s own formulation, from a company blog post on Berd’s design, is functional: “The avatars make the agent recognizable. Its role, skills, and tools make it useful.”

That distinction could grow more important as workers juggle several specialized agents at once. A recognizable identity serves as visible shorthand for an otherwise invisible package of instructions, tools and permissions — agents appear on the workspace as distinct animated characters rather than identical entries in a chat list, so users can tell them apart before opening a conversation.

The product specification guards against the characters turning Berd into a novelty. It describes the intended personality as “focused, capable, companionable,” while explicitly rejecting toy-like presentation, oversized decorative UI and interfaces that hide operational state behind friendliness.

An orchestration layer, not another model or agent runtime

Architecturally, Berd is neither a new foundation model nor a new agent runtime. The application is built with Tauri 2 and React 19 — frameworks for building lightweight desktop apps with web technologies — and communicates with Goose, the open source agent framework Block introduced in January 2025, through a standard called the Agent Client Protocol (ACP). Goose runs quietly alongside Berd as a bundled “sidecar” process and handles the underlying agent loop; Berd handles projects, sessions, context, agents and configuration on top.

For readers unfamiliar with it, Goose is a model-agnostic agent framework and runtime, originally focused on software development workflows. It connects large language models to files, commands and outside systems — including through the Model Context Protocol (MCP), an open standard for wiring AI systems to external tools and data — so agents can take actions rather than merely generate text.

Goose works with proprietary model providers as well as locally run open models. Block has since contributed Goose to the Agentic AI Foundation, the Linux Foundation body it helped establish with Anthropic, OpenAI and others in December 2025, giving the framework a vendor-neutral home alongside MCP.

Axen drew the boundary explicitly: agent harnesses such as Goose, Claude Code and Codex manage the loop between a model, its context and its tools, while Berd provides the consistent desktop environment around those harnesses. In the public release, users configure whichever providers and harnesses they want rather than inheriting a Block-selected model stack.

The project’s README adds an operational detail that matters for anyone evaluating the software: Berd does not simply pull whatever Goose build happens to be current. Its build process pins a specific Goose backend version through a lockfile, verifies the cached binary matches that pinned version, and packages it with the app. Developers can explicitly substitute another Goose binary for local testing. For organizations inspecting Berd, that creates a defined version boundary between the desktop application and its default agent backend rather than letting the backend silently drift.

How Berd compares to Codex, Claude Code, Cursor and Goose

That separation matters because the agent-tooling market is already converging on products that manage increasingly autonomous AI workers. OpenAI describes its Codex app as a “command center” for agents, with separate project threads and multiple agents operating in parallel.

Claude Code, Anthropic’s agent for reading codebases, editing files and executing commands, now spans the terminal, IDEs and a desktop app. Cursor describes its latest release, Cursor 3, as a unified workspace for software development with agents, including handoffs between local and cloud agents and multi-repository workflows. And Goose itself remains available directly, as a free command-line tool and desktop app for technically comfortable users.

Berd’s differentiation is therefore less about inventing the agent workspace than about making that workspace open source, multi-harness and potentially useful outside coding. Every other product in the table below is either tied to a single vendor’s models, priced as a subscription, or aimed squarely at developers — and in most cases all three.

Portability is part of Block’s pitch. Axen said much of the underlying work in Berd is deliberately kept portable: folders, Git repositories, files and plain-text instructions remain accessible outside the application; skills are file-based; and Berd agents can be exported and shared between Berd users with their instructions and settings intact.

He pointed to ACP and MCP as part of Block’s direction toward “clear, user-owned formats and open protocols wherever possible.” That does not make every piece of state interchangeable across every harness, but it makes portability an explicit architectural goal rather than a side effect of using local files.

Local-first data, telemetry and enterprise distributions

Berd is designed around a local-first data model. Axen said conversation history is stored on the user’s device in the local Goose session database, while credentials live in the operating system keychain by default.

That does not mean all AI processing happens locally. “Local-first does not mean that no data ever leaves the device,” Axen said. When a user asks a configured model to work on particular context, the relevant prompt and material are sent to that model’s provider — so the actual data path, and the contractual terms governing it, depend on which provider an organization chooses.

Telemetry — the usage data software sends back to its maker — is disabled by default in official Berd distributions. If a user opts in, Block says it collects a random installation identifier and a limited set of predefined usage events, while excluding prompts, messages, files, source code, credentials, names, email addresses and IP addresses from the telemetry payload itself (Block and its service providers still process IP addresses for transmission, security and rate limiting). Users can switch telemetry off again in settings. Block cautions that third-party forks can modify or redirect the telemetry system, so enterprises evaluating a customized build need to review that build’s actual behavior rather than assume Block’s defaults carry over.

The public repository builds a self-contained, general-purpose distribution that does not depend on private package registries or enterprise credentials — technical teams can build and evaluate it without access to Block’s infrastructure. But Block has also created what it calls “distribution seams”: defined points where organizations can overlay managed provider settings, private agents, runtime configuration, optional companion tools, their own update channels and their own signing and publishing infrastructure, all without adding private material to the public source tree.

Block’s own deployment shows how an enterprise can put governance around that architecture. Axen said Block runs its managed distribution on approved devices using approved model-provider infrastructure, managed configuration and governed tool connections. “The application can remain open and inspectable, while an organization decides which models, tools, permissions, and policies are appropriate for its environment,” he said.

One caveat: “enterprise distribution” should not be read as a complete enterprise management plane. The README does not describe capabilities such as single sign-on, centrally enforced administrator policies, organization-wide permission controls, data-retention rules or audit logs. It establishes that companies can customize and distribute Berd; it does not by itself explain how those installations are centrally governed after deployment. Berd should be understood as endpoint software installed on users’ computers, not a centrally hosted SaaS product.

Pricing, models and a closed contribution model

Block has not announced a subscription price for Berd, and there is no indication one is coming. That does not make agent usage costless: organizations still pay whatever model, API or provider charges they incur. The economic pitch on Berd’s site is flexibility — users can match the model to the job rather than paying for an expensive frontier model on every task. As the site puts it: “Some tasks need the big brain. Most don’t.”

Neither Block’s announcement nor the README provides a definitive matrix of supported model providers or recommends particular models. Internally, Block does not treat any one model as a permanent default: Axen said the company’s managed distribution exposes employees to “a curated mix of frontier and open-weight models,” with the catalog changing as models improve. “The best choice depends on the work,” he said.

There is also a governance distinction developers should note. Although Berd’s source is public and Block solicits issues and feedback, the repository does not accept outside pull requests — external code contributions are automatically closed.

The README says Berd is built by “a small team at Block” and directs outside participants toward well-formed, reproducible bug reports instead. Users are free to inspect, build and fork the code, but Block retains a tightly controlled upstream. For companies considering Berd as infrastructure rather than an experiment, that makes Block’s long-term maintenance and support plans worth clarifying.

Block is not presenting Berd as a revenue product today. Axen said the first return on investment comes from Block’s own use of the software — making internal agent work more consistent while teaching the company how employees actually use agents — and that open-sourcing it adds leverage by letting outsiders inspect, adapt and give feedback on the architecture. A commercial layer around enterprise deployment may eventually follow.

“Supporting organizations that want to deploy agents at work is an area of interest for us,” Axen said, adding that Block will share more when there is more to share.

From private agent work to multiplayer workflows

Berd supplies the individual desktop experience; another Block project, Buzz, focuses on shared rooms where people and agents collaborate against a common, searchable record.

Buzz is Block’s free, Apache 2.0-licensed collaboration workspace for humans and AI agents. Built on the decentralized Nostr protocol, it combines familiar team features — channels, threads, direct messages, voice and media sharing — with agents that hold their own cryptographic identities and defined permissions, and that can participate in conversations, code review and approved automated workflows. Unlike Berd’s local, single-user focus, Buzz is explicitly multiplayer, and can be self-hosted or used through Block-hosted infrastructure. It is likewise model- and harness-agnostic: teams can bring agents powered by Goose, Claude Code, Codex or others.

Axen framed the split between the two products partly as a question of trust boundaries. “A local agent may have access to your filesystem and credentials,” he said. “An agent in a shared Buzz room has its own identity and access scoped through the relay.” Berd begins with one person and comparatively privileged access to that person’s machine; Buzz begins with multiple participants, explicit identities and shared-workspace permissions.

The more consequential roadmap detail is that Block does not intend the two products to remain separate. “Going forward, our focus is Buzz, and we plan to bring the best parts of Berd’s single-player experience into it,” Axen said. Block wants work to begin privately and become collaborative without users switching tools or rebuilding context, with cross-device continuity — including mobile via Buzz — part of that ambition. In the meantime, Axen said, Berd remains available as an open source desktop application, but Buzz is the product Block encourages people to download and adapt.

What enterprise adopters should take away

The short version for IT and security teams: conversation history stays local by default; credentials use the operating system keychain; context sent to a model leaves the machine on whatever terms the configured provider offers; official telemetry is off by default and excludes prompts, files and source code; and organizations can package their own governed distribution — though central management tooling is not yet documented.

The larger idea behind the release may matter more. Models and coding agents are becoming increasingly interchangeable components inside larger workflows. Berd is Block’s bet that the surrounding context — the projects, skills, tools, identities and persistent working environment — becomes the stable layer instead.

Whether you like it or not, ChatGPT is already everywhere. People use it to research complicated topics, clean up their writing, learn new skills, plan their lives, and, in my case, ask an embarrassing number of questions I could have figured out myself. Teenagers are no exception. In fact, OpenAI says nearly nine in 10 teens who use ChatGPT turn to it to learn, find information, develop skills, or get things done during a typical week. So rather than pretending students aren’t going to use AI, OpenAI is taking a different approach: give them a version built specifically for them. The company has announced ChatGPT for Teens, a new experience for users between 13 and 17 that’s designed around learning, healthier usage habits, and significantly stronger safety protections. 

ChatGPT wants to teach, not just hand over the answer

What interests me most is how OpenAI is approaching homework. Imagine you’re preparing for an exam and calculus has suddenly decided to ruin your evening. Instead of asking ChatGPT for the answer and copying it into your notebook, the teen experience is designed to walk you through the problem, explain the concept you don’t understand, and then help check whether you’ve actually understood it. That’s a much healthier role for AI in education. ChatGPT for Teens brings together features such as Study Mode, quizzes, learning visualizations, homework reminders, and dedicated study hours. The idea is to make ChatGPT more like an always-available study companion.

OpenAI is pushing that idea outside ChatGPT, too. The company has announced a partnership with CodeAI to give students and teachers resources for developing AI literacy. Instead of simply teaching students how to prompt a chatbot, the initiative is meant to help them understand how AI works, question what it produces, direct it effectively, and eventually use it to build things themselves. That distinction matters. Today’s teenagers aren’t going to enter a world where AI suddenly disappears after graduation. Learning when to trust it, when to challenge it, and when not to use it could be just as important as learning how to use it in the first place.

Helpful? Absolutely. Human? Let’s not get carried away

Of course, once you put a powerful AI chatbot in front of teenagers, safety becomes a much bigger part of the conversation. OpenAI is putting stricter guardrails around accounts for users under 18, particularly when it comes to sensitive topics such as graphic violence, dangerous activities, unhealthy dieting, extreme beauty standards, and sexual or romantic roleplay. Another equally important part is that it is also setting boundaries around how ChatGPT interacts with teens on a more personal level. The chatbot shouldn’t encourage emotional dependence, pretend it has real feelings, or make users believe there’s a human-like consciousness behind those responses. It might sound like a small detail, but think about how natural these conversations have become. When an AI can talk back instantly, remember the context of a conversation, and respond at 2 AM when nobody else is awake, it can be easy to forget that you’re still talking to software.

The company is adding a few smaller reminders to reinforce that distinction. Teens will be warned before uploading potentially private or sensitive images, while a dedicated onboarding experience will explain the learning and safety tools available to them. There are also break reminders encouraging teens to put ChatGPT down occasionally. They can still make the experience feel more personal by changing things like the accent color or voice, but ChatGPT is still supposed to make it clear that it’s AI. Parents get some controls as well. With linked accounts, they can manage certain settings and set Quiet Hours. In a limited number of higher-risk situations, they may also receive safety notifications intended to help them step in when offline support could be useful.

I’m cautiously optimistic, with a very big asterisk

While this feels like a step in the right direction, OpenAI still has plenty of work to do, especially when teenagers are using ChatGPT. We’ve already seen serious concerns around AI-generated deepfakes, people turning to chatbots during moments of emotional distress, and conversations that probably need a trusted person on the other end rather than an AI model.

That also leaves me wondering how comfortable parents will actually feel with all of this. Will these safeguards be enough for them to trust ChatGPT with their teenagers? I’m not entirely convinced yet. Still, AI is quickly becoming part of how younger people learn, communicate, and create. Keeping them completely away from it probably isn’t realistic anymore. Helping teens understand how to use AI responsibly while knowing when to put it down and turn to a real person instead may ultimately be the more important lesson.

Grand Theft Auto fans will have to wait until later this month for a deep dive into the next game in the series, which will premiere on Netflix before hitting YouTube later that day (though probably much sooner on unofficial channels). But despite Rockstar Games’ efforts to keep a tight lid on Grand Theft Auto VI leaks — especially after the 2022 incident — some legitimate-looking gameplay footage is now out there.

The publisher has reportedly been hitting people who are sharing two minute-long gameplay clips and a screenshot of the Florida-inspired map with DMCA takedown notices. While we can’t share the materials here or tell you where to find them, you’re a citizen of the internet. You may just stumble upon them yourself. What we can do, however, is describe what we’ve seen.

Let’s start with the map of Leonida, which looks like a distorted, compact version of south Florida. As with GTA V, it features a large city (Rockstar’s version of Miami this time). That’s on the east of Leonida, with lots of greenspace in the west and the north. There are many, many large and small islands dotted across the map, with a group that looks like the Florida keys in the south. There are a bunch of markers on the screenshot as well. Most are nondescript, but others include a home icon, a balaclava-covered face, cars and (gulp) an alligator.

One clip shows Jason, one of the game’s dual protagonists, at a waterside home. There’s gym equipment on the balcony, which brings to mind the workouts from GTA San Andreas. Jason hops over the ledge and runs by a jetski before picking up a basketball and making a few shots. After he scores with his first attempt, he gets a focus stat boost, which suggests GTA VI will lean into RPG mechanics.

There isn’t much else to speak of in terms of the HUD in this clip. There are two dollar values in the top right of the screen that match the font from previous games. What seems to be a focus bar appears in the top left when Jason has the basketball in hand.

For what it’s worth, the building looks identical to one that appears in an official screenshot of GTA VI. It’s labeled as Jason’s safehouse on the game’s website.

Rockstar Games

The second clip has more action. It opens with Jason looking like he’s about to drive over a bridge toward an area called The Keys before making a U-turn and causing relatively mild carnage. 

After colliding with a delivery truck, he and the driver get in a fight. Jason kills the guy with a wrench (each swing of which depletes an energy bar) the latter was holding. This earns Jason a two-star wanted level. A devil icon with a minus sign appears on the right of the screen, as well as on a portrait of Jason that pops up. This indicates there’s an honor/karma system, like in the Red Dead Redemption series.

As Jason returns to his vehicle, “loadout” and “storage” prompts briefly appear, suggesting that you can change your weapons and store items (say, a wrench) in your car. After he gets back in the driver’s seat, an icon pops up with the car’s name and a gas meter, so it looks like you’ll have to fuel up your vehicles this time. The clip — which also features a GTA V-esque minimap — ends just as Jason approaches an air ambulance.

It’s worth treating the clips and map with some skepticism, since they may not be entirely legitimate. It’s also unclear how old they are and what may have changed in the meantime, though Rockstar’s servers were breached earlier this year. They’re at least plausible looks at GTA VI.

The materials are watermarked with URLs that claim to offer fans more details alongside a “fighting for gamers’ rights” message. The alleged leaker claimed to have released the footage and screenshot to protest Rockstar’s decision to only include a code in the box of the game’s physical edition, rather than a disc. They’re threatening further action against Rockstar and other publishers over purported anti-consumer practices like digital pre-orders, “fake single-player DLC” and failing to maintain access to single-player games.

Grand Theft Auto VI will hit PS5 and Xbox Series X/S on November 19. An extended look at the game will stream first on Netflix on August 27.

Smart glasses have become a recurring accessory amid the immigration crackdown.

If you’ve spent any time down the gaming PC rabbit hole, you’ll have hit this fork in the road already: do you build the thing yourself, or do you let someone else do it for you? It’s one of those debates that never quite dies down and is always a matter of preference – some folks prefer to have the control of building their own system, configuring every element, while others want the experts to take the strain to make sure everything works as it should the first time the PC boots.

While this age-old debate hasn’t really shifted, what has changed, though, is the gaming PC market itself. Component choice has exploded, and folks have more choice now than ever before, making it perhaps easier than ever before to build or spec out a custom gaming PC for themselves. You no longer necessarily have to choose between “fully custom” and “convenient” — but to understand why that matters, it helps to look at both routes properly first.

The case for building your own PC

There’s a certain satisfaction in building a PC from scratch. You get to pick every component yourself and learn how it all fits together, not to mention the genuine sense of achievement when you hit the power button for the first time and everything boots. For PC enthusiasts and hobbyists, that process is half the fun, myself included.

However, it’s worth being honest about what it actually demands. Building a PC requires a decent grasp of compatibility, or how everything fits together. Motherboard sockets, RAM speeds, PSU wattage, case airflow, cooler clearance and more are all worth being mindful of. Getting any of that wrong can mean a system that underperforms, or worse, doesn’t work at all. Then there’s the time: sourcing parts, waiting on deliveries, the build itself, and the inevitable troubleshooting when something doesn’t quite play ball. 

Cable management, in particular, is a skill in its own right, and a messy build isn’t just an eyesore. Poor airflow from bad cable routing can quietly hurt your thermals and performance over time.

For first-time buyers or anyone switching over from console to a PC for the first time, that’s a lot to take on before you’ve even played a game. And if something does go wrong down the line, you’re generally on your own to diagnose it, since self-built systems don’t come with the kind of unified warranty support you’d get from a manufacturer or someone doing it on your behalf.

The case for buying a pre-built PC

It’s precisely here where pre-built systems earn their reputation. Buying from a specialist manufacturer such as CyberpowerPC means every component has already been tested for compatibility, the cable management is handled by people who do it for a living, and the whole system has been through quality assurance before it ever reaches your door to make sure everything will work as it should.

Warranty support is the other big one. A proper prebuilt gaming PC comes backed by a manufacturer who’ll stand behind the hardware, which matters enormously if a component fails outside of what you could diagnose yourself. For parents buying a system for a teenager, or for anyone who just wants a machine that works reliably from day one, that peace of mind is worth a lot.

The trade-off historically has been choice. Prebuilt used to mean picking from a handful of fixed configurations, often with compromises somewhere in the spec sheet. Manufacturers used to have a reputation for using no-name components where it mattered to keep costs down, although it’s become less common these days. That’s the bit of the market that’s changed the most.

The best of both worlds: configure your own with professional help

There is a third way, which is perhaps the best of both worlds – speccing out a custom PC from a reputable builder such as CyberPowerPC UK. Rather than forcing a choice between full customisation and professional reliability, CyberPowerPC UK’s custom gaming PC configurator lets you build a system to your exact spec — choosing from the latest NVIDIA GeForce RTX graphics cards, AMD Ryzen processors, and Intel Core processors to suit your needs and specific games you’re playing – and then hands the actual build over to people who do it for a living.

You get the tailored spec of a self-build, without taking on the risk of compatibility mistakes, botched cable runs, or a PSU that’s not quite up to the job. Every system is assembled, tested and quality-checked before it ships, and you’re covered by proper warranty and after-sales support if anything needs attention later. If you’d rather skip the configurator entirely, the prebuilt gaming PCs page has ready-to-go options for anyone who just wants a great system without the decision fatigue.

It’s a genuinely useful middle ground for a lot of the people currently weighing this decision up — first-time PC buyers who want guidance rather than a blank canvas, console gamers making the jump who don’t want to go the whole hog right out of the gate, and upgraders replacing an ageing system who don’t want to relearn PC building from scratch. Enthusiasts benefit too, since a configurator this deep still lets you get properly specific about the build without doing the assembly yourself.

It’s also worth noting that the PCs on offer from CyberpowerPC don’t just have to be for gaming.  The systems are also built with streaming and content creation in mind as well, so if you’re planning to start streaming your sessions or editing footage on the side, the hardware you choose through the configurator can be specced to handle it. 

Likewise, because these are proper, professionally built towers rather than more sealed-off options, there’s genuine headroom to upgrade down the line. Swap in a new GPU in a couple of years, add more storage or whatever it turns out you might need later down the line.

If you want a hand working out what’s right for you, CyberPowerPC UK’s team offers expert advice throughout the configuration process, and the support page is there for anything that comes up after your system arrives.

So, do you build or buy?

This is what it all comes down to. If you love the process of speccing out a custom unit, putting it together yourself and troubleshooting anything that crops up, the self-build route is rewarding.

However, there is something to be said for letting someone else take the majority of the strain, especially anyone buying their first gaming PC, switching from console, or upgrading without the time to relearn PC hardware from scratch. A configurable prebuilt system offers the best of both worlds: a machine built exactly to your spec, assembled and tested by people who know what they’re doing, and backed by warranty support if anything goes wrong.

To learn more and see the current range, head to the CyberPowerPC UK gaming PCs page to see the current range, or jump straight into the configurator to start building.

Back in May, we reported that Apple was testing future AirPods equipped with low-resolution cameras designed to give Siri and Apple Intelligence some idea of what the person wearing them was actually looking at.

At the time, Apple had confirmed nothing. Three months later, Cupertino appears to have done some of the leaking itself.

A demonstration video discovered inside the macOS Tahoe 26.7 Release Candidate shows what appear to be unreleased camera-equipped AirPods being used with Apple’s Visual Intelligence system. In the short clip, a man looks at a book and asks Siri to save what he is seeing for later. The AirPods resemble AirPods Pro 3, although the stems appear somewhat larger. Apple has still not formally announced the product, its name, price or release date.

That last part remains important.

But this is no longer just somebody claiming that a supplier in Asia saw an interesting prototype.

These Cameras Are Not for Vacation Photos

The cameras are not expected to turn AirPods into tiny GoPros.

Previous reporting described them as low-resolution sensors intended to give Apple’s AI systems visual context, which is exactly what the leaked demonstration appears to show. Instead of pulling out an iPhone, pointing its camera at a book and invoking Visual Intelligence, the AirPods could potentially see the object already in front of you and let Siri act on that information.

Apple has already expanded Visual Intelligence on the iPhone to recognize, search, summarize and act on visual information, while the new Siri AI architecture announced at WWDC 2026 includes multimodal image understanding. Camera-equipped AirPods would move some of that interaction away from the screen in your hand.

Advertisement

And that is where this becomes considerably more interesting than another AirPods upgrade.

When Do Headphones Stop Being Headphones?

AirPods Pro 3 already combine music playback, ANC, hearing assistance, heart-rate sensing, Live Translation and fitness tracking. Adding visual awareness would push the product even further away from being merely a pair of wireless earbuds.

Advertisement. Scroll to continue reading.

You could potentially look at a sign and ask Siri what it says, identify an object, remember a book or product for later, receive contextual navigation information or use the system to better understand your surroundings without holding up a phone.

Advertisement

There are obvious accessibility possibilities as well. Apple is already using Apple Intelligence to provide richer descriptions and natural-language navigation through VoiceOver and Magnifier. Giving a wearable device visual context could eventually make some of those capabilities far more immediate for visually impaired users. Apple has not announced those specific applications for camera-equipped AirPods, so that remains potential rather than a promised feature.

Consumers Should Also Ask Who Is Looking

There is, of course, another side to this.

People can generally recognize Meta Ray-Ban smart glasses and understand that a camera may be pointed at them. Tiny sensors embedded inside AirPods could be much harder to notice.

Apple would therefore need to explain what the cameras record, whether visual information is stored, where processing occurs, what reaches Apple’s servers and how bystanders know when the sensors are active. None of those questions has been answered because Apple has not officially announced the product.

Advertisement

That conversation will matter just as much as battery life or sound quality.

The Bottom Line

The leaked video suggests Apple’s camera-equipped AirPods are not about improving music playback at all. They are about giving Siri eyes.

For consumers, that could make AirPods more useful for accessibility, navigation, translation and everyday information without constantly reaching for an iPhone. It also moves Apple directly toward the same wearable-AI territory occupied by camera-equipped smart glasses, only through a product millions of people already wear without thinking twice about it.

Apple still has to convince us that putting cameras in our ears solves more problems than it creates.

But the bigger change is already becoming clear: the next important AirPods feature may have almost nothing to do with how they sound.

Advertisement

Related Reading