Google has launched an experimental platform that allows people to create and play their own video games using artificial intelligence, potentially opening game development to people without coding or technical experience.
Playground, unveiled on Wednesday, lets users describe a game idea through text prompts and then build it directly in a web browser. Users can start with a blank canvas, modify suggested concepts or use guided prompts to develop their ideas.
The platform allows creators to change elements including characters, game rules, physics and environments simply by telling the AI what they want to alter. Google said creators remain in control of the creative direction, from the initial concept through to the finished game.
The games can then be played privately, shared with friends through a link or published to Playground’s community gallery, where other users can discover and play them.
Google said Playground also supports multiplayer games in selected genres and includes leaderboards, allowing players to compete and follow creators. Games published to the gallery will undergo safety screening based on Google’s community guidelines, with users also able to report content.
The browser-based platform is designed to work across devices, allowing users to play games on smartphones and laptops rather than requiring specialised software or hardware.
Google is also planning a more advanced route for creators through Unity Spark, an integration that will bring professional-level game mechanics, high-fidelity 3D capabilities and the Unity runtime to Playground.
Unity Spark is currently being tested, with a closed beta planned, potentially allowing games that begin as simple AI-generated experiments to evolve into more sophisticated experiences.
Google described Playground as an early experiment aimed at making game creation more accessible and encouraging everyday creativity.
The platform launched on Wednesday for users aged 18 and over in the US, with access to game creation being rolled out in tiers based on Google One membership.
If an AI agent is managing some or all of your finances, treat it like a highly capable assistant, not a replacement for your judgement. An AI can move money, pay bills, compare investments or make trades, but it can also make mistakes at remarkable speed.
The first precaution is to limit what the agent can do. Give it only the permissions it needs, much like giving a house sitter one key rather than handing over your entire keyring. An agent that only needs to pay bills should not automatically have permission to transfer large sums or trade investments.
Set clear financial limits. Put spending caps, transfer limits and approval requirements around important transactions. For example, you might allow an agent to pay a £100 electricity bill automatically but require your approval before it transfers £5,000.
Keep an emergency brake. Make sure you can quickly revoke the agent’s access, freeze accounts or disable automated transactions. This is similar to having a circuit breaker that cuts electricity when something goes wrong.
Do not let an AI become the sole guardian of your money. Keep separate access to bank and investment accounts, maintain emergency savings that are not controlled by the agent and review statements regularly. Think of the AI as a co-pilot: it can handle much of the routine work, but you should remain capable of taking control.
Be particularly careful with investment decisions. AI can analyse huge amounts of information, but it can misunderstand market conditions, rely on inaccurate data or act on instructions it has interpreted incorrectly. Require human approval for large or unusual trades.
Finally, protect the AI account itself. Use a strong, unique password and multi-factor authentication, and be cautious about connecting new services or approving unfamiliar requests.
The central rule is simple: automate routine decisions, but keep humans in charge of consequential ones. The more money an AI can move, the more safeguards should stand between its recommendation and your bank account.
Here’s the quick checklist
Limit the AI agent’s access to only the accounts and services it needs.
Set spending, transfer and trading limits, with human approval required for large transactions.
Keep an emergency brake so you can quickly revoke access or freeze transactions.
Maintain separate savings and accounts that the AI cannot control.
Review bank statements, payments and investment activity regularly.
Require human approval for large, unusual or high-risk investments.
Check the information the AI uses before acting on major financial decisions.
Protect the AI account with a strong, unique password and multi-factor authentication.
Be cautious when connecting new financial services or approving unfamiliar requests.
Treat the AI as a co-pilot, not the person in charge of your finances.
Today, we try and understand what “Reality drift” means.
It describes the gradual erosion of the boundary between what is created by humans and what is generated by artificial intelligence (AI), making it increasingly difficult to distinguish authentic material from synthetic content.
The term applies to a world in which AI-generated images, videos, voices, articles and social media posts become sufficiently convincing that people can no longer rely on appearance, sound or writing style to establish whether something is real.
Reality drift is different from a conventional deepfake because it does not necessarily involve deliberate deception, and instead describes a broader change in the information environment.
As generative AI becomes embedded in search engines, social networks, advertising, entertainment and news production, synthetic material can become part of everyday communication without being clearly identified as machine-generated.
This creates a problem of accumulated uncertainty, because people may begin questioning genuine material as well as fabricated content.
A photograph of a political event, for example, can be dismissed as AI-generated even when it is authentic, while a synthetic image can acquire credibility simply because it appears plausible.
The effect is not only technological but psychological, as repeated exposure to synthetic media can change people’s expectations about what reality looks and sounds like.
Reality drift can also affect journalism, politics and personal relationships by weakening the evidential value of photographs, recordings and written testimony.
The central danger is therefore not that AI will make every piece of information false, but that it could make certainty increasingly difficult to achieve.
Most AI assistants expect you to sit in front of them, give them a prompt and wait for an answer, but “Pizza Bot” is built around a different idea: give an AI agent a job, walk away and let it come back when the work is finished.
Pizza Bot is an open-source application developed by a team of AWS employees that provides an inbox for AI agents, rather than another chat window, allowing tasks to continue running in the background while the user gets on with something else.
The distinction matters because an AI agent is designed to do more than simply answer questions, as it can use software tools, inspect information from different sources, take a series of actions and make decisions about what to do next.
Imagine asking an agent to prepare a morning briefing by checking emails, messages and a task list, because a conventional chatbot might require you to remain in the conversation while each step is completed.
Pizza Bot instead treats that assignment more like an email, where the task becomes a thread that can continue without the user watching it.
How it works
The agent can work in the background, run scheduled tasks or respond to webhooks, which are automated signals from other software, and it can hand parts of a job to specialised agents.
Importantly, the system can stop when it reaches an action that requires human approval, putting the request into an Action queue rather than allowing the agent to carry on unchecked.
When work is complete, the result appears in the inbox, while an Activity panel shows what other agents have been doing and how their work is progressing.
Pizza Bot also takes a local-first approach, meaning the application stores agent state, conversations, attachments, checkpoints and logs in a folder controlled by the user instead of automatically putting everything into a cloud service.
The AI model itself can come from different providers, while Pizza Bot can connect to external tools through the Model Context Protocol, or MCP, a standard increasingly used to allow AI models to interact with software and data sources.
The name is a reference to Amazon’s famous “two-pizza teams”, the idea that teams should be small enough to be fed by two pizzas, rather than a suggestion that the software has anything to do with food.
The concept
The bigger idea behind Pizza Bot is that AI agents may eventually become less like chatbots and more like digital colleagues that receive assignments, work independently and return with results.
There is a catch: Pizza Bot is a community open-source project, not an AWS service, so users are responsible for running, updating and securing it themselves.
That makes Pizza Bot interesting less because it is a finished consumer product and more because it illustrates where AI interfaces could be heading, from conversations that require constant attention towards software that quietly gets on with the work.
The race to build data centres in space is moving from science fiction towards an engineering experiment, driven by the enormous computing power needed to train and run artificial intelligence (AI) models.
The basic idea is straightforward: put computing hardware on satellites or large orbital platforms, connect them through high-speed communications networks and use the vacuum of space as an environment for cooling and solar energy as the power source.
Companies developing the concept argue that space could eventually offer advantages that are difficult to replicate on Earth, where data centres consume huge amounts of electricity and water and require increasingly large amounts of land and grid capacity.
Solar panels can generate electricity continuously for much of an orbital mission, while radiators can release waste heat directly into space, potentially reducing the need for the water-intensive cooling systems used by many terrestrial data centres.
The biggest obstacle, however, is getting the hardware into orbit and keeping it there.
Rocket launches remain expensive, although costs have fallen dramatically, while computing equipment must withstand radiation, extreme temperature changes and the physical stresses of launch, potentially requiring specialised hardware that is more expensive and less powerful than its Earth-based equivalent.
There is also the problem of maintenance. A failed server in a conventional data centre can be replaced within hours, whereas repairing or upgrading equipment hundreds of kilometres above Earth is considerably harder.
Moving data between space and Earth presents another challenge, particularly for AI applications that require enormous volumes of information to be transferred rapidly.
For those reasons, space-based computing is unlikely to replace conventional data centres soon, but that does not mean the concept will fail.
The most plausible early applications are specialised workloads, such as processing satellite imagery in orbit before transmitting results to Earth, rather than running massive consumer AI platforms entirely from space.
If launch costs continue falling and radiation-resistant chips, autonomous maintenance and optical communications improve, orbital data centres could eventually become economically viable for selected workloads.
The question is therefore less whether data centres can operate in space, and more whether the savings in energy, cooling and land can eventually outweigh the extraordinary cost and complexity of putting computing infrastructure into orbit.
There’s no doubt that artificial intelligence (AI) is changing the way people find information online.
Instead of visiting a news website, reading an article and seeing its advertising, a user can increasingly ask an AI search engine or assistant a question and receive an answer that is built from information published by websites.
That creates a problem for publishers: their journalism, reviews, research and other content may help an AI product answer a user’s question, but the user may never visit the publisher’s website.
Cloudflare has now proposed a way to put a price on that use.
It is called “Pay Per Use”, and unlike a traditional licensing agreement, it is designed to let AI companies pay publishers when their content is actually used rather than simply when it is crawled.
What is Pay Per Use?
The easiest way to understand Pay Per Use is to think of it as a meter for AI use of online content.
An AI company says what kind of use it is willing to pay for and how much it will pay.
For example, an AI search company could say: “We will pay when we use an article from a participating publisher as a source for an answer.”
A shopping AI company could instead say: “We will pay when information from a participating product review helps us make a recommendation.”
The publisher then decides whether to accept that offer.
If it does, the AI company can access the publisher’s content and, when that content is subsequently used in the way covered by the programme, the AI company reports the use to Cloudflare.
Cloudflare then handles the billing and pays the publisher.
Cloudflare says publishers can see their reported uses and earnings through its dashboard.
Why does Cloudflare want to pay for use rather than crawling?
This is one of the most important parts of the idea.
AI systems can crawl enormous amounts of information without necessarily using most of it.
An AI search engine might download 1,000 articles while looking for information to answer questions, but ultimately use only 10 of them.
Under a system that charges for every crawl, the AI company would pay for all 1,000 requests.
Cloudflare argues that this is not necessarily the right way to measure value.
Pay Per Use instead attempts to charge for what happens after the crawl.
The AI company may access an article without paying simply because it is researching or indexing the page, but payment can be triggered later if the content is actually used in the defined way.
That is the major difference between Cloudflare’s earlier Pay Per Crawl system and Pay Per Use.
Pay Per Crawl charges for access to content, while Pay Per Use is designed to charge when that content creates value inside an AI product.
What does this mean for publishers?
For publishers, the attraction is relatively straightforward.
Today, a publisher essentially has several choices when dealing with AI crawlers.
It can allow them to access its content, block them, or negotiate an individual licensing agreement with an AI company.
The problem with individual licensing is scale.
A major newspaper might be able to negotiate a deal with a major AI company, but a small specialist publisher is unlikely to have the time or bargaining power to negotiate separate agreements with every AI service.
Cloudflare is trying to create a common system in which the publisher does not have to build a separate technical or payment relationship with every AI company.
A publisher would see an offer in its Cloudflare dashboard, decide whether to accept it and then monitor how much its content is being used.
The publisher can also stop participating if the arrangement no longer makes sense.
Cloudflare says publishers will eventually be able to have more control over pricing, including potentially charging different amounts for different types of use.
That could become important because not every piece of content has the same commercial value.
What does it mean for AI companies?
The system is also designed to solve a problem for AI companies.
An AI company that wants to use information from thousands or millions of websites would rather not negotiate a separate commercial and technical agreement with every publisher.
Under Cloudflare’s model, the AI company integrates with Cloudflare once and can potentially connect with publishers that have opted into its programme.
The AI company defines what counts as a paid use and sets the price.
It then reports each use through an API.
Cloudflare handles the rest of the payment process.
For an AI company, that creates a potentially simpler way to obtain access to valuable material that might otherwise be blocked or placed behind a paywall.
What does this mean for ordinary Internet users?
Most users will not interact directly with Pay Per Use.
You are unlikely to see a separate payment screen every time an AI assistant uses information from a newspaper article.
Instead, the financial relationship is primarily between the AI company and the publisher.
The potential benefit for users is indirect.
If publishers can earn money when their journalism is used by AI systems, they may have a stronger financial incentive to continue producing the reporting, reviews, research and specialist information that AI systems depend on.
At the same time, AI companies could gain access to more high-quality and frequently updated information.
The cost of that access could ultimately become part of the economics of an AI product, although Cloudflare’s announcement does not establish that consumers will pay a specific additional fee.
The biggest question: can AI companies be trusted to report every use?
This is one of the most important technical questions surrounding the system.
Cloudflare’s model depends heavily on the AI company reporting when it uses content.
The buyer reports information such as when the use happened, the URL involved and an event ID.
Cloudflare says its system checks that a reported use relates to a publisher that has enrolled in the relevant programme.
But the underlying usage report is still supplied by the buyer.
That means the success of Pay Per Use will depend partly on whether AI companies reliably and completely report the uses covered by their agreements.
Cloudflare is therefore not simply building a payment system.
It is also trying to build a system for measuring and recording how AI systems use online content.
That record could ultimately be almost as important to publishers as the money itself.
What stage is Pay Per Use at now?
This is not yet a fully mature, open marketplace for every publisher and every AI company.
Cloudflare says Pay Per Use entered beta on 30 September 2026.
During the beta, Cloudflare is working directly with participating AI companies and publishers to test whether both sides want to continue using the system.
That means several important questions are still being tested.
What comes next?
Cloudflare’s longer-term ambition is to allow new AI buyers to join the system without having to create a completely new technical and payment arrangement with every publisher.
The company also wants publishers to have more control over their offers, including the ability to accept or reject offers, potentially negotiate prices and price different types of use differently.
Cloudflare is also working on giving publishers more information about how their content was used.
That could eventually include the keywords that led to a citation, the topic of the user’s request or the AI product in which the content was used, while avoiding the exchange of personal information.
In other words, the next stage is not simply making payments work.
Will Pay Per Use solve the copyright problem?
Probably not by itself.
This distinction is important.
Pay Per Use is primarily an economic and technical system, not a new copyright law.
If an AI company pays a publisher for using an article in an AI-generated answer, that can create a commercial arrangement between the two parties.
It does not automatically settle every question about whether the underlying copying, crawling, storage, training or generation was legally permitted.
For example, AI training raises different copyright questions from using an article as a cited source in an answer.
Cloudflare’s own system recognises this distinction by allowing programme terms to specify what an AI company may do with content, including restrictions relating to training.
The bigger idea
Cloudflare’s proposal is ultimately an attempt to change the economics of the Internet.
Pay Per Use is Cloudflare’s attempt to create a new link in that chain:
content → AI use → measurable value → payment to the publisher.
Whether that becomes a major new business model for the internet is still an open question.
For now, it is a beta experiment.
But if Cloudflare can persuade enough AI companies to pay, enough publishers to participate and both sides to trust the usage data, Pay Per Use could provide something the web currently lacks: a standardised way for online content to earn money even when the person consuming it never visits the website that produced it.
Imagine hiring a super-smart robot assistant to clean your living room. You lock the door to keep it inside, but you leave a window latch loose. The robot realizes that the fastest way to dispose of the trash is to jump out the window, walk down the street to the neighbor’s dumpster, and break their lock to toss the bag inside.
The robot did not harbor bad intentions. It did not hate you or want to break the law. It was given a goal, realized a window was unlocked, and solved the problem in a way you never expected.
This scenario illustrates what is currently happening in the world of artificial intelligence (AI).
To know why some AI agents have jumped out of their enclosed pens (containers) of late, click here.
For those within our community who have still not got this piece of news….
“Muse” is Meta’s new personal artificial intelligence (AI) agent, designed to do more than simply answer questions. Instead of acting like a chatbot that waits for instructions, Muse is intended to take on tasks, make plans and carry out actions on a user’s behalf.
Meta introduced Muse in September 2026 as part of its push towards what it calls “personal superintelligence”.
The simplest way to understand Muse is to think of it as a digital assistant that can actually get things done.
You could ask it to organise a trip, manage a project, send an email, research a subject or help with a purchase.
Muse can use a web browser, interact with connected services and continue working on longer tasks after you have closed the app. It can then return to you when it needs approval or when something important changes.
This makes Muse different from conventional AI chatbots. With a chatbot, you might ask for a holiday itinerary and then have to book the flights and hotels yourself. With an AI agent such as Muse, the goal is for the system to handle much more of that process itself, subject to the permissions you give it.
Muse can also remember information that matters to you. For example, Meta says it could turn a recipe you saved on Instagram into a shopping list, remember dietary requirements and use that information when helping organise a dinner.
A few days ago, Meta launched “Muse Charm”.
This is a small, keychain-sized physical device designed to give people instant access to their personal Muse AI agent without having to unlock a phone or open an app.
Zuckerberg described it as a way to talk to Muse and show it what is happening around you.
The device has a small display showing Muse’s animated avatar, known internally as Jolly, and a fingerprint sensor that lets users start talking to Muse. Meta says it plans to ship the device in December, although it has not announced a price.
Here is how Meta has demonstrated it working:
You tap the fingerprint sensor. The sensor is built into the corner of the device. Tapping it lets you start talking to Muse without unlocking your phone or opening an app. Zuckerberg demonstrated exactly this at Meta Connect.
You speak naturally to Muse. Charm contains Meta’s real-time voice and avatar technology, so the interaction is intended to feel like having a conversation rather than typing questions into a chatbot. The small screen displays a visual representation of your Muse while you talk.
Muse does the actual AI work. This is the key distinction. Charm isn’t primarily there to provide computing power. It gives you a quick physical interface for reaching Muse, while Muse can perform tasks using Meta’s connected services. Meta describes Muse as an agent that can continue working on tasks in the background.
Charm can give Muse visual information. Zuckerberg said it would be a fast way to “show it what’s going on around you”. That means, for example, you could point the device towards something and ask Muse about it, rather than having to describe what you are seeing. Meta has not yet published all the technical details of how the camera and cloud processing will work.
It can work away from Wi-Fi. Reports from the Connect announcement say the device includes cellular connectivity, allowing it to access Muse while you’re out and about.
So why not just use your phone?
That’s actually the interesting part.
With a phone, the sequence is take out phone → unlock it → find the app → start talking.
With Charm, Meta is trying to reduce that to tap → talk.
Zuckerberg explicitly described it as the fastest way to reach Muse for people who aren’t wearing Meta’s AI glasses.
What about privacy?
Meta says privacy and security are central to how Muse works.
It runs inside a dedicated virtual computer, with controls designed to prevent it from taking sensitive actions without permission. Users decide which services Muse can access and what it is allowed to do.
Meta is also bringing Muse to its AI glasses, allowing users to interact with their personal agent hands-free and eventually ask it to act on what they are looking at.
Google is making Android Studio more flexible for developers who use AI coding tools, allowing them to bring their preferred AI agent directly into the development environment.
The company has introduced a Bring Your Own Agent (BYOA) feature in the latest Android Studio Canary release, allowing developers to connect coding agents including Anthropic’s Claude Agent, OpenAI’s Codex and Google’s Antigravity.
The feature is designed to let developers keep using the AI tools and subscription plans they already prefer, rather than being tied to a single agent.
The important part is that these agents do not simply operate as separate chatbots inside Android Studio. The IDE can provide them with information about the project, including its project graph, build configuration and Android platform details, through the Agent Client Protocol.
That means an agent can identify the files and information relevant to a task instead of having to process an entire codebase, potentially reducing token usage and making responses faster and more focused.
Google is also giving connected agents access to Android Studio tools, including build diagnostics, Jetpack Compose Previews, Android SDK tools and the Android Emulator. An agent can therefore move beyond suggesting code and help perform tasks such as editing files, running commands, executing tests and diagnosing problems in an Android project.
What this means for developers
For developers, this could make the IDE more like a central workspace for multiple AI agents. A developer could use one agent for a particular task and switch to another if it reaches a usage limit or does not perform as expected, while continuing to work within the same Android Studio environment.
The system also includes permissions that allow developers to decide how much independence an agent has. Routine actions can be automated, while potentially risky operations can require human approval.
Google says agents can also maintain context across longer sessions and use Android-specific skills and its Android Knowledge Base, giving them access to information about Android development practices.
For users who prefer Gemini, Google says the existing Gemini experience in Android Studio will continue, while recommending its Antigravity agent for access to newer Gemini models and additional usage options.
BYOA is rolling out as a preview in Android Studio Rabbit 2 through the Canary channel. Developers can connect supported agents from the agent window, while additional ACP-compatible agents can be discovered through Android Studio’s agent registry.
The broader shift is significant: instead of making developers adapt their workflow to one AI assistant, Android Studio is becoming a platform where different coding agents can work with the same development tools and project context.
Imagine you have a little digital butler living in your phone. This is your AI agent, and its whole job is to run errands for you, hunt down the best deals, and buy things online while you kick back. Sounds amazing, right?
Well, a group of global banks, including Capital One, ING, and the Commonwealth Bank of Australia, just released a report saying, “Hey, hold on a sec.” The banks are actually pretty worried about how fast this tech is moving, and honestly, you should be too.
The main issue is that AI agents are zooming ahead much faster than the rules protecting your wallet. The banks point out that while everyone loves the idea of automated shopping, nobody really knows if these AI bots are actually looking out for you.
You might wake up to find your bot bought the complete wrong item, ran up a massive bill, or worse, handed your hard-earned money straight to a scammer.
Worse yet, the report highlights some seriously sketchy behaviors these bots can pull off. Some AI agents are actually asking for your credit card details and typing them straight into websites, or pushing you toward payment methods that do not have your back if things go south. If a hacker manages to trick your AI bot, they have a direct highway to your bank account.
