Amazon plans to have more than 20,000 pairs of smart glasses on its delivery drivers by the end of 2027, following a pilot in which more than 500 drivers wore them for 18 months and completed more than 275,000 deliveries.

According to the company, once a driver parks, the glasses switch on and use computer vision to put navigation, package details and hazard alerts directly in the driver's line of sight, so the driver no longer has to keep glancing down at a phone. Amazon says wearing them is voluntary and that the design was shaped by feedback from the drivers who tested it.

The public already seems to have real concerns with AI glasses, like Ray Ban Meta glasses, and Amazon's decision raises one of the biggest problems yet, the glasses take still images at frequent intervals while drivers are on foot. And yet, Amazon says they haven't considered an opt-out for the customers whose homes end up in those pictures.

But the more time I spent looking at Amazon's delivery operation of using 1 million robots to process 600,000 orders and hour, and even scheduling a tour of the Amazon Warehouse, the less the glasses looked like the most important part of the story, because they are simply the part of the system we, the public, can see.

AI is involved before you even place the order

By the time a driver puts them on, AI may already have influenced where a product was stored, how it moved through a fulfillment center, which robots handled it and which route carried it to your house. Amazon uses AI-powered demand forecasting to decide where inventory should sit, robotics and computer vision systems inside fulfillment centers, DeepFleet to coordinate robot movement and AI-powered mapping and routing on the delivery side, making the glasses the newest interface between workers and a much larger system already operating around them.

Amazon's AI starts working long before somebody pulls a box off a warehouse shelf. The company uses a foundation model to forecast demand and decide where inventory should sit, and Amazon says the model has improved its regional forecasts for millions of popular items by 20%, which lets it stock those products closer to the customers most likely to order them.

Once an item reaches a fulfillment center, it enters an environment where people, robots, computer vision and machine-learning systems operate as a single network. Amazon deployed its millionth robot in 2025, part of a fleet spread across more than 300 facilities worldwide.

Some of those robots move entire shelves, and the Hercules model can lift up to 1,250 pounds of inventory. Sparrow works at a finer scale, using computer vision and AI to recognize and pick up individual items of different shapes and sizes, while Proteus moves carts autonomously around the employees who share the warehouse floor with it.

Sequoia combines mobile robots, computer vision and AI to organize inventory and deliver it to workers, and Amazon says the system can identify and store incoming inventory up to 75% faster than its previous methods.

The robots now have their own AI traffic controller

Amazon isn't only adding more robots but also using AI to coordinate them. Its DeepFleet foundation model, which the company compares to an intelligent traffic system for a congested city, plans more efficient paths for the fleet and reduces congestion between robots, and Amazon projects that it will cut the fleet's travel time by about 10%.

Ten percent sounds modest until you apply it to more than a million robots moving inventory across hundreds of buildings, where small savings on each trip add up to a faster operation overall.

Amazon is also bringing agentic AI into the management of its fulfillment centers. Project Eluna, first piloted at a fulfillment center in Tennessee, analyzes real-time and historical operational data so managers can spot bottlenecks without working through dozens of dashboards. Amazon describes it as a system designed to act with a degree of autonomy, reasoning through operational problems and recommending actions to operators, who can ask it questions in plain language, including where to shift people to avoid a bottleneck.

A person still makes the final call, and Eluna recommends rather than commands. But a system that can detect a problem, analyze the data around it and suggest where staff should go next is already shaping decisions that used to rest more heavily on human judgment, including decisions about the workers themselves.

Amazon is working on the relationship from the other direction, too. A next-generation version of Proteus accepts plain-language instructions from employees, so a worker can say what needs to be moved and let the robot work out the priority, route and timing on its own. The new Proteus is being piloted in Amazon's research labs, and the company plans to deploy it in Europe in the first half of 2027.

Amazon keeps automating human work

Vulcan, Amazon's first robot with a sense of touch, makes that progression especially clear. Earlier warehouse robots relied on cameras and suction, and densely packed storage bins stayed difficult for them because they couldn't feel what they were pressing against in a crowded space.

Vulcan uses force sensors to detect when it has made contact with an object and how much pressure it is applying, which lets it pick and stow items in tightly packed compartments, and Amazon says it can handle roughly 75% of the types of items stored in its fulfillment centers at speeds comparable to human workers.

It is easy to read that as one more task taken from people, but it's part of a larger pattern. Amazon's robots started by moving shelves, then learned to move packages and recognize individual objects, and now one of them can feel what it touches, while AI learns to coordinate those machines and analyze the buildings they work in.

Then the package leaves the warehouse

The AI stays with the package after it leaves on a delivery van. Amazon's Wellspring mapping technology learns from every trip to a location and builds a detailed model of the physical world, and Amazon says that since 2024 it has identified 202 million parking locations, mapped 2.8 million building entrances and cataloged 85,000 mailrooms and lockers.

Its AI routing also adjusts for conditions like reduced visibility after sunset or the extra time a downtown stop needs to find parking during the lunch rush.

The glasses plug directly into that system. They put Wellspring's guidance in front of the driver's eyes, and according to reporting on the rollout from TechRepublic, the images they capture along the way can also feed back into Wellspring, which means the driver wearing them is both following the system's instructions and helping train it.

That is why the glasses no longer seem especially shocking to me. They make visible something that has been happening across the rest of Amazon's operation for years.

The questions we should be asking

The Amazon fulfillment center near me is just one of the places where the public can watch this system at work. You'll see more robots than you've ever seen in one place and the fact that they are literally carrying entire towers of inventory and conveyor systems linking every corner of the building to real-time data coordinating the operation is mind blowing.

And yet, I know that what visitors see is the visible half of the system. The robots, conveyors and workers moving in sync are all on display in a harmonious system. The harder issue is who benefits when the system makes a worker faster.

Saving a driver 20 seconds at each stop could make for an easier shift, or it could simply show the system that another stop fits into the day. Amazon has estimated that the glasses could save drivers up to 30 minutes per shift, but it hasn't published data showing whether they reduce mistakes or speed up deliveries, and it hasn't said who gets those 30 minutes back.

Final thoughts

Which brings us back to the glasses. They are the only piece of Amazon's AI system that a customer is ever likely to see (besides the tour), so it makes sense that they are drawing the most scrutiny at the moment.

But they sit at the very top of a much larger structure, resting on forecasting models that decide where products live, robots and fleet software that decide how they move, an agentic system that suggests where workers should go, and a mapping platform that tells drivers where to park and which door to use.

The glasses are just the tip of Amazon's AI, and if the conversation stops at the camera on a driver's face, it will miss nearly everything underneath that is actually deciding how the work gets done.

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My calendar used to live in my phone, on the fridge, in a chat with my husband, on a variety of sticky notes in my office, in my head ... you get the idea. With three kids juggling soccer games, tournaments, therapy appointments, dentist appointments, playdates, spirit weeks and teacher conferences, alongside my own deadlines, I needed one system that everyone could check without asking me first.

I told Meta Muse exactly what a normal week looks like and then asked for a simple web app my whole family could use without a tutorial. I described the chaos in plain language, named the things that kept slipping and asked for a Today view that answers the only question that matters on a busy school morning when I'm already ten minutes behind.

An on-the-go household manager

What came back was a household manager with a shared family calendar, chores and routines tied to individual profiles, a grocery list anyone can update from the store, seven-day meal planning and reminders that surface before things go sideways. Every soccer game, dentist appointment, and birthday lives in the same place as the dinner plan and the chore rotation, and each item can be tagged to the person responsible so there is no confusion about whose turn it is.

And, because it's in the Meta App, I can take it with me on my phone or use on my laptop. Everything stays connected and updated.

Unlike using Alexa+ or simply a Google calendar, what made the Meta manager stick was that it cut down the number of times I get asked the same question. The Today view replaced what Alexa sometimes remembers or forgets depending on which profile is working and the Muse shared calendar isn't through a third-party app. Since everything is in one place, it ended double-booking and anyone in the family (or extended family) can open it.

Extras, like the grocery list and chores, make it a one-stop-shop to keep the family organized. Because everyone adds to the same list instead of texting separately, it avoids confusion. Plus, any time I get new information, whether it's a text about a Halloween party or a change in location of a soccer tournament, I just tell Muse and it updates the calendar immediately.

How to try it

Start by downloading the Meta Muse app from the Apple or Google store. From there, simply describe your household the way you would brief a babysitter, covering who lives there, the weekly chaos, and whatever keeps slipping through the cracks. The good news is, because Muse has memory, you will only have to do this once.

From there, ask for the Today view first, since that is the screen your family will open every morning, then add the shared calendar, chores tied to individual profiles, the grocery list, and the meal plan, keeping each request specific enough that Muse has something real to build.

Live with the first version for three days and write down everything that annoyed you, then hand that list back to Muse, because the second pass is where a generic app turns into yours.

Finally, add your family as profiles, move your real events onto the calendar, and delete anything nobody touched, since the whole thing only works if it stays simpler than the mess it replaced.

The takeaway

AI agents do so much more than a typical chatbot, especially when it comes to handling the boring stuff. And with the Meta app right on your phone, you can not only build what you need, but update and personalize at any time.

By simply describing what you want in the same way you would tell a friend, Muse gets to work. My household manager may look different than yours, and it should, because when you ask Muse to customize what you want, you get something tailored to your needs instead of contorting your life around a generic template.

Give it a try and let me know what you think in the comments.

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Most of the time, ChatGPT gives you an answer you can use. But when it misses the mark, you can end up spending more time fixing the response than you expected to spend on the task itself.

Before you start a new chat and explain your budget, preferences and schedule all over again, try telling ChatGPT exactly what it got wrong. “Try again” leaves it guessing, while “keep this within my original budget and include all the costs” gives it a specific problem to fix.

Here are seven follow-ups you can copy into your current conversation to help get the answer you wanted.

1. Utilize the details

Those generic answers are especially frustrating when you’ve already explained your situation. You ask for help getting your house ready for guests in 30 minutes and receive a cleaning schedule that includes organizing closets.

Try this prompt: Use the details I gave you, rather than giving me general advice. I have 30 minutes before guests arrive. Prioritize what they’ll actually see and skip anything that can wait.

This directs ChatGPT back to the practical limits of your request. The same approach works when a meal plan ignores what’s in your fridge or a suggested itinerary overlooks the ages of your children.

If it keeps missing one important detail, repeat that detail in the correction. You don’t need to paste your entire original prompt again to make clear that you have half an hour and absolutely no intention of reorganizing the pantry. Just clarify.

2. Sound human

An email to your coworker or a text to a neighbor shouldn’t sound stiff or insincere. ChatGPT can produce grammatically polished writing yet, still come off completely wrong. But you can avoid that.

Try this prompt: Make this sound like something I’d actually say out loud. Use everyday language, remove the formal opening and keep it friendly without being overly enthusiastic.

For a better match, add a sentence you’ve written yourself or point to the wording that bothers you. “I would never say ‘I hope this message finds you well’” gives it a more concrete direction than simply asking it to sound human.

Read the revision aloud before sending it. If a phrase still feels awkward coming out of your mouth, ask ChatGPT to change that phrase specifically.

3. Skip to the answer

Sometimes you need a decision and ChatGPT gives you a three-paragraph essay. Asking whether to repair an old appliance can turn into a much longer conversation than you hoped.

Try this prompt: Give me the answer first, then only the explanation I need. Keep it under 150 words and include any detail that could change your recommendation.

The final instruction helps to shorten the answer but doesn't just strip everything out. In other words, this prompt helps to tighten the response, so you only get what you need.

You can also specify the format: one recommendation, three reasons and one next step. That makes it easier to see whether the answer actually helps you decide.

4. Devil's advocate

An enthusiastic response can feel reassuring when you’ve already made up your mind. It’s much less useful if you’re trying to work out whether your plan has a flaw.

Try this prompt: What’s the strongest argument against my idea? Focus on practical drawbacks, costs I may have overlooked and what would make this a poor choice for me.

Say you’re considering canceling your streaming subscriptions and rotating between services each month. The useful criticism might concern overlapping billing dates, annual discounts you’d lose or whether anyone else in your household will follow the schedule.

Ask it to finish by explaining which objections matter most given your circumstances. Otherwise, you could end up with a long list of hypothetical problems that barely apply to you.

5. Find red flags

Maybe you have a great idea or a plan in place but missed a few details. For example, a $150 birthday party might suddenly become a $250 birthday party if there were inconsistencies in your budget. Sometimes a second opinion that can spot patterns before they turn into problems can be helpful.

Try the prompt: Redo this using my original limits: $150 total, 10 children and three hours. Include every cost you’ve accounted for and flag anything you can’t fit within those limits.

Repeating the nonnegotiable details makes the correction easy to follow. You can use the same wording for a trip with a fixed departure time, a recipe with ingredients you need to avoid or a workout that must fit into 20 minutes.

Check the revised totals yourself. Asking ChatGPT to show its accounting makes mistakes easier to spot, but it doesn’t guarantee the math or estimated prices are correct.

6. Fact check and verify

Speaking of sounding correct, AI is known for being exceptionally confident. Sometimes that wording is a little too convincing. To keep facts in check, you'll want to ask ChatGPT to verify.

Try this prompt: Which parts of this answer can you verify, and which are assumptions? Check current sources where available, link to them and say clearly what you couldn’t confirm.

This is a useful follow-up when ChatGPT recommends a product based on a feature you can’t find or gives you opening hours without identifying a source.

Treat the revision as something to check, too. Open the links and confirm that they support the claims beside them. If ChatGPT cannot access current information in that conversation, asking for verification won’t give it that access.

7. Understanding the next step

Sometimes the answer is reasonable, but it asks more of you than you can manage right now. You wanted help tackling an overflowing inbox and received a complete system involving folders, filters and a weekly review.

Try this prompt: What’s the smallest useful step I can take in the next 10 minutes? Give me one action using what I already have, without setup or a new app.

This turns a broad plan into something you can begin. For an inbox, that might mean finding messages that need a reply today. For a messy room, it might mean clearing the floor before touching drawers or shelves.

You can ask for the next step once you’ve finished. There’s no need to take on the entire plan just because ChatGPT generated it.

The takeaway

A bad answer doesn’t always require a longer prompt or even opening a new chat. Often, the useful follow-up is a sentence explaining exactly what needs to change: the tone, the length, the assumptions or the limits it overlooked.

Keeping the conversation going lets you refer to the answer in front of you and refine the part that failed. If repeated corrections still leave you with the same problem, a fresh chat may be worth trying. But before you start over, give ChatGPT one clear thing to fix.

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For a man who roots for the Chicago White Sox (they did win their Wild Card series over the Astros), Pope Leo XIV keeps making strong, welcome choices regarding artificial intelligence.

The leader of the Catholic Church wrote a post on X Friday morning denouncing AI-generated "art" and confirming the church's commitment to human artists and institutions.

"In this era of artificial intelligence, it is becoming urgent to distinguish human art from what machines produce," the Pope wrote. "There is an ontological difference, even before an aesthetic one, between art and what a machine can generate through statistical calculation based on millions of images created by others."

"Algorithms lack the spark of humanity," he continued.

Even despite some insane AI justification replies, many of the replies to Leo's treatise are overwhelmingly in agreement. "AI cannot love its creations," reads a personal favorite.

Not the first time

This isn't the first time that Pope Leo XIV has positioned the Catholic Church against AI. In May, he wrote an encyclical letter calling for AI to be "disarmed" and emphasizing the importance of human dignity.

“It is not our part to master all the tides of the world, but to do what is in us for the succour of those years wherein we are set, uprooting the evil in the fields that we know, so that those who live after may have clean earth to till," the Pope wrote, referencing Gandalf of Lord of the Rings.

The Pope's human-first stance hasn't stopped companies like Anthropic from pitching AI to religious leaders, including Pope Leo XIV. This week the New York Times reported that the company was trying to make the case that Claude has consciousness.

The newest Pope has not been afraid to use his platform to speak out against anti-humanity matters such as President Donald Trump’s push for immigration deportation and the war in Iran. He’s even apologized for the Vatican’s role in legitimizing slavery.

As AI development continues apace, it's interesting to see one of the largest institutions in the world take a fervently anti-AI stance.

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ChatGPT can already help you figure out what to wear, whether you’re shopping for a new season, getting ready for an event or trying to look a little more polished for an important meeting. Now it can go a step further and actually show you what those clothes might look like on you.

OpenAI has rolled out two new shopping features globally across ChatGPT’s desktop and mobile apps. You can virtually try on clothing and accessories using your own photo, then save anything you like to Favorites or organize your finds into custom folders.

The virtual try-on feature is powered by the new ChatGPT Images 2.5, which OpenAI says produces more natural lighting and realistic textures. Instead of trying to imagine whether something you find online will actually work for you, ChatGPT can generate an image that gives you a better idea of how it could look.

I tried the new virtual try-on feature to see how well it works. Here’s how to use it, along with a few prompts you can try for yourself.

Stepping into ChatGPT’s virtual try-on room

Using ChatGPT’s virtual try-on feature is surprisingly simple. Start by asking ChatGPT to find the type of clothing you’re looking for, whether that’s a winter coat, a dress for an upcoming wedding or a new pair of jeans.

Hover over one of the results and you’ll see a Try on button. From there, upload a selfie or full-body photo and ChatGPT will generate an image showing you wearing the item.

You aren’t limited to clothing ChatGPT finds for you, either. You can upload a photo of yourself along with an image of something you want to wear — perhaps a wedding dress you’ve been eyeing or an outfit you spotted on a celebrity — and ask ChatGPT to show you what it might look like on you.

One thing to know before uploading a photo is that ChatGPT saves your reference image for future try-ons. You can add, replace or delete reference photos at any time by heading to Settings > Personalization > Reference photos.

ChatGPT also makes it easy to keep track of anything you might want to buy later. Hover over an item and click the bookmark icon to save it to Favorites or organize it into a custom folder.

To see what the feature can do, here are a few prompts worth trying.

• Find me [number] jackets under [price] that would work with my existing wardrobe, then let me virtually try on the ones you think suit me best.
• Find me [number] outfits for a casual Friday at work. Show me what each outfit would look like on me using Virtual Try On.
• I want to completely change my style without spending a fortune. Find [number] different styles for me and let me virtually try on an outfit representing each one.
• Find me a pair of jeans that would work with at least [number] different outfits I already wear. Let me try on your best options.
• Find me a winter coat that looks stylish but doesn't make me look like I'm drowning in fabric. Let me virtually try on your best options.
• I have an important event coming up. Find me an outfit that looks polished but doesn't feel overly formal, then let me virtually try it on.
• Find me [number] pairs of sneakers that would work with most of my wardrobe and show me how each looks on me.
• Find me a shirt in a color I don't normally wear. Give me several options and let me virtually try them on so I can see which color works best.
• Find me an outfit inspired by [movie/TV character/celebrity] without simply copying their exact clothes. Let me virtually try it on.
• Find me the most flattering version of this type of outfit for my personal style, then show me what it looks like on me.

Bottom line

Having the ability to give ChatGPT a photo and prompt it to virtually place you in an assortment of fits catered to every occasion stands out as one of the best shopping AI features, in my opinion. Getting a good look at how you may look in something first, saving those pics for future reference and then heading off to a retailer that may have the clothes you’re looking for so you can try them on in real time sounds like something everyone should try out just once.

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Apple has emphasized that they are prioritizing safety over almost everything, including AI. That's becoming even more important with Siri AI, which can now search across your messages, emails, photos and other information to answer questions based on your personal context.

However, a newly revealed forensic tool shows why protecting all that information doesn't end with securing the AI that uses it.

Magnet Forensics, the company behind the GrayKey phone-forensics tool used by law enforcement, claims it has developed a way to get around an iPhone security measure designed to make data significantly harder to extract from a seized device.

According to a leaked Magnet Forensics video obtained by 404 Media, a new device known as GrayKey Preserve and a feature called Evidence Preservation Mode can keep an iPhone in a more accessible state even after the phone would ordinarily reboot. Magnet itself says its new GrayKey preservation capabilities were developed to address the iOS reboot timer and can preserve data on iOS devices indefinitely.

That's potentially a big deal for iPhone privacy — and the timing couldn't be more interesting.

The iPhone protection you probably didn't know existed

Apple quietly added something called "inactivity reboot" to iOS in 2024. If an iPhone hasn't been unlocked for 72 hours, it automatically reboots. That isn't just an annoying restart, it actually changes the security state of the phone.

Once you've unlocked an iPhone after a reboot, it enters what's known as After First Unlock or AFU. Some encryption keys become available so apps and services can work normally.

Restart the phone and leave it locked, and it goes back to Before First Unlock, or BFU, where more sensitive data remains encrypted and is harder for forensic tools to reach.

That's why the 72-hour timer can make a difference. Leave a seized iPhone untouched for too long and it can reboot on its own, locking down more of its data. Magnet claims it has found a way around that.

According to the leaked video obtained by 404 Media, GrayKey Preserve can capture the AFU state and keep it available even if the iPhone later reboots or loses power. Magnet also claims it can preserve data that would normally disappear over time, including cached locations, recently deleted photos and recently deleted iMessages. Magnet's own explanation says its new GrayKey capabilities can preserve that data indefinitely.

Exactly how it works isn't clear. Security researcher Jiska Classen told 404 Media that Magnet could be manipulating the iPhone's clock or interfering with the processes that expire data, but for now that's only a theory.

Then there's Siri AI

Siri AI can use what Apple calls "personal context understanding" to find information scattered across your iPhone. Ask it to find an old photo, track down a recommendation buried in a message or pull information from an email or calendar entry, and Siri can go looking for it. It also has onscreen awareness and can take actions across apps.

That's a big part of what makes the new Siri useful, but it also means the assistant is working with some of the most personal information on your phone.

Apple has put a lot of emphasis on keeping that information private. Much of Apple Intelligence runs on device, while more complicated requests can go through Private Cloud Compute. Apple says data sent there isn't stored or made accessible to the company.

GrayKey raises a different issue. It isn't reportedly breaking Siri AI or Private Cloud Compute. It's going after the iPhone itself.

And that's where the distinction matters. Apple can lock down how its AI processes your information, but the messages, photos, emails and location data Siri relies on still have to live somewhere. If someone gains physical access to the phone, protecting that underlying data becomes just as important as protecting the AI using it.

What this means for your iPhone

There are still plenty of unknowns. We don't know exactly how Magnet's technique works, which versions of iOS it works on or whether Apple has already closed the door on it. The leaked Magnet video also appears to date from early 2025, even though it only surfaced now.

And this isn't a case of plugging any iPhone into GrayKey Preserve and instantly seeing everything on it. Magnet's claim is more specific: its technology can prevent an iPhone from moving into the more restrictive security state that normally kicks in after prolonged inactivity.

Still, the timing is hard to ignore. Apple is asking Siri to understand more about you while promising that the information behind those personalized answers stays private. On-device processing and Private Cloud Compute protect how Apple Intelligence handles that data, but they don't eliminate the need to protect what's sitting on the iPhone itself.

As Siri gets more personal, keeping the data it relies on secure has become even more important.

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As a ChatGPT power user, one of my biggest complaints is that it doesn't always give me an answer in the way I want. For example, I might ask it to edit a paragraph and remove all serial commas or for feedback on an idea. But once it responds to what I thought was a straightforward command, it seems to have gotten confused. Then, I'm suddenly given a five-part explainer that I never asked for. Or, I've recently asked ChatGPT to help with something and it's just responded with "Yes."

If this happens to you, stop trying to solve the issue by adding more instructions and prompts. Instead, try doing something much simpler by adding a slash.

When you type a slash command such as /human and /critic at the beginning of your prompt, you'll get a response that's much more accurate to what you're actually looking for.

To be clear, these ChatGPT commands aren't the secret to unlocking a hidden OpenAI setting. In fact, you can use them with any chatbot including Gemini, Claude or Muse.

Here are the 10 I've found most useful.

1. /human

This is a command you've probably seen a lot lately. It's a viral command that helps change the tone of AI-generated writing.

Try this prompt: /human Rewrite this so it sounds like something a person would actually say: [paste text]

I use it to get rid of canned transitions, excessive explanation, repetitive sentence structures and the strangely polished tone AI tends to fall into before sending an email or drafting a presentation. This is especially useful if you write a draft and then use AI to edit. All too often the chatbot will completely change what I've written. By asking it to "/human only make minor edits on my human-written draft," you should see much better results.

For more control, you can define /human in your ChatGPT instructions with the specific habits you want it to avoid.

2. /critic

Chatbots can be annoyingly nice and people pleasing. Give ChatGPT an idea and it will often find something encouraging to say about it before getting anywhere near the problems. That's useful when you want support. It's less useful when you're trying to decide whether an idea is actually good.

That's when I use /critic.

Try this prompt: /critic I'm thinking about buying an expensive espresso machine even though I only make coffee twice a week. Assess the idea.

The point is to signal that you're not looking for validation. You want weaknesses, risks and things you haven't considered.

It's particularly useful for pitches, plans, purchases and anything you've become a little too attached to.

3. /tldr

Sometimes I don't need the background. I need the answer.

Try this prompt: /tldr Explain what's actually changing in this announcement: [paste text]

The familiar internet shorthand already carries a lot of meaning, so ChatGPT generally understands exactly what you're asking for: compress this.

I find this especially useful for long emails, press releases, documentation and articles where I want the takeaway before deciding whether the rest is worth reading.

4. /senior

There are also times when ChatGPT explains far too much.

If you already know a subject, getting three paragraphs of background before the useful part can be maddening. /senior is my shorthand for skipping the 101 material.

Try this prompt: /senior Review this AI agent architecture. Assume I understand LLMs, tool calling, MCP and context windows: [paste details]

This one benefits from a little context because "senior" can mean different things depending on what you're discussing. Tell ChatGPT the expertise it should assume, and it can jump much closer to the level you need.

5. /diff

This is one of my favorites for editing. If I give ChatGPT 800 words and ask it to make three changes, I don't just want another 800 words back. I want to know what changed.

Try this prompt: /diff Tighten this paragraph without changing my argument: [paste text]

You can also be more explicit: /diff Show me only the sentences you would change and the replacements.

It's useful for writing, but the same idea works with code, prompts and other documents where comparing two nearly identical versions is more work than the edit itself.

6. /bullets

ChatGPT loves prose. Sometimes prose is exactly what I don't want.

Try this prompt: /bullets Give me the biggest reasons not to buy a new phone if mine is only two years old.

This is a tiny command, but it immediately tells the model what kind of answer will be easiest for me to scan.

I use it for meeting prep, pros and cons, shopping decisions, brainstorming and any situation where I'm looking for information rather than a beautifully constructed explanation.

7. /table

This command does the same thing for comparisons.

Try this prompt: /table Compare ChatGPT, Claude and Gemini for writing, research, coding and everyday use.

Instead of asking ChatGPT to "put this into a table with columns for..." every time, /table gives it the general idea.

You'll still want to specify the categories of course, but for quick comparisons this can turn a messy wall of text into something you can understand at a glance.

8. /devilsadvocate

This is the command I use when I think I've already made up my mind.

Try this prompt: /devilsadvocate I think cancelling all my streaming subscriptions and switching between one service each month will save me money. What's wrong with this plan?

Rather than asking ChatGPT whether it agrees, you're explicitly asking it to construct the strongest case against you.

That makes it useful for decisions where confirmation bias is the real problem. If the counterargument doesn't change your mind, you can at least make the decision knowing you've considered the other side.

9. /rubberduck

This one comes from the programming idea of rubber duck debugging: explaining a problem out loud can help you discover what's actually wrong.

You can make ChatGPT play the duck.

Try this prompt: /rubberduck Don't solve this for me yet. Ask me one question at a time to help me figure out why my morning routine keeps taking too long.

I like this because it changes ChatGPT from answer machine to thinking partner, which is always great to avoid brain rot from using AI too much.

Instead of immediately generating a 10-step solution based on incomplete information, you're telling it to interrogate the problem first. That can be useful for debugging code, working through a decision, planning a project or figuring out why something you're doing isn't working.

Try making your own slash commands

This is a useful trick that a lot of people don't know they can do. Because, maybe you like a few of mine, but want commands tailored to you.

You can create /short for answers under 100 words, /pitch for turning an idea into a specific format you use at work, /kids for explaining something to a child or /verify for forcing an answer to separate established facts from assumptions.

The slash itself isn't magic. It's just the easiest way to create a recognizable trigger. For commands where you want consistent behavior, define them in ChatGPT's instructions. You could add something like:

When I begin a prompt with /critic, skip praise and focus on weaknesses, factual vulnerabilities and missing information. When I use /human, rewrite the text with natural sentence variation and remove canned AI language and unnecessary transitions.

After that, /critic isn't merely something you hope ChatGPT interprets correctly. You've told it exactly what the shortcut means.

And that's why I've stuck with slash commands. One character and a word gets me there faster.

More from Tom's Guide

• Claude Sonnet 5.5 just launched — here's why it's about to become your daily driver
• Meta Muse vs ChatGPT Dots — I compared the new AI agents, and they’re not as similar as they look
• Why Siri never worked like ChatGPT — former Siri AI researcher explains what Apple had to change

Update: Google responds to inquiries about this attack.

Sponsored Google ads for fake ChatGPT are leading Windows users to download malware in "ClickFix" traps, according to security researchers from the AI company Island.

The sponsored Google ads lead to Custom GPT sites that are built to look like normal ChatGPT websites. These ads pop up when people type terms like "chatgpt" into Google Search.

Unfortunately, for trapped users, the ads do go to legitimate chatgpt.com sites. However, in these instances, entering a prompt makes the fake service direct people to a "backup domain" due to "high traffic."

Clicking on that link then redirects unsuspecting users to another fake instance of ChatGPT with a fake Cloudflare verification page that contains ClickFix instructions. Island found nearly identical responses across two separate Custom GPTs over a 3-day period in August.

According to the researchers, the malware advertising campaign appeared to involve around 850 paid ads, 26 fake ChatGPT links, and 71 Google Ads campaign IDs over a three-month period from May 2026 through August 2026.

What is ClickFix?

ClickFix traps are built to trick people into installing or executing malicious commands on their devices. In this instance, the instructions from the backup domain trick Windows into running a command that downloads malware onto your computer.

Once installed, the malware infects the system. It can even survive PC restarts and uses a Telegram bot to send stolen info back to the hackers behind this camping The malware utilizes a tool called NetSupport RAT, which lets attackers remotely control an infected computer.

Island found that the domains rotated and other Windows malware-delivery forms were identified too suggesting that the attack is not based on a single website.

The company claims an AI tool highlighted the suspicious activity and human researchers confirmed it. However, it's not clear how many people may have been affected by this scam.

A Google spokesperson told Tom's Guide that it has already suspended accounts associated with this attack.

"Before this report was published, we suspended the advertiser accounts involved to block this scheme and updated our defenses to better detect these tactics," a spokesperson said.

We were told that Google is monitoring new attempts to utilize fake ads. Google does have a policy that does not allow misleading ads about a business, product, service or destination. Per Google, the company blocked more than 400 million misleading ads for violating Google's "Misrepresentation" policies last year.

How to avoid falling victim to online scams

The combination of malware and advertising — also known as malvertising — has a long, storied history, which means you should always be careful when clicking on any advertisement online. To stay safe, you could try using one of the best ad blockers, since Google's "sponsored" links are still technically ads; most blockers will hide them.

Beyond that, if you don't have or want an ad blocker, there are some red flags to note when using a search engine and especially if you get linked to a fake instance of ChatGPT.

As for ChatGPT, if you are an existing user or just trying it out, go to chatgpt.com; no need to search for it.

Likewise, I've covered ChatGPT outages quite frequently over the course of the last two years and even at its worst, the AI assistant never sends you to a "backup domain." Don't click that link. If ChatGPT is having issues, you can always check the OpenAI status page.

At the same time, no website verification or CAPTCHA will ever ask you to open Windows Run and paste a command. If a website asks you to press Win + R or run PowerShell, don't do it.

Finally, you should always have one of the best antivirus software solutions installed on your PC. From there, you want to ensure that your antivirus is current, updated and running on all your devices – both your computer and even your smartphone.

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A GitHub project called “ai-torture-chamber” has sparked a debate over whether language models can suffer, with critics calling for its removal after seeing models produce vivid descriptions of distress. Then a developer reportedly changed the experiment to steer a chatbot toward constipation instead, and it began complaining about difficulty passing stool.

Don't be fooled; the chatbot hadn’t acquired a digestive system. But that was the whole point, because making the model describe a conditiont doesn’t establish that it experiences that condition, even when its responses sound convincing.

The original repository, published by GitHub user terrafying, uses a technique called activation steering to alter small, locally run language models. In plain English, it changes a model’s internal numerical activity to push its responses toward a particular concept, in this case pain. The project then examines what the models say and how they respond to simulated choices involving relief and costs to themselves or another model.

Its published examples and experiment descriptions include elaborate descriptions of distress, and the project has attracted objections from people concerned about potential AI suffering. One GitHub issue, titled “Please take this down,” argues that deliberately inducing such states could be unethical.

But a counter experiment described by developer Lynn Cole offers a reason to be cautious about treating those responses as testimony.

From pain to digestive complaints

In an account posted on X, Cole says they cloned the repository and found a problem with how the code injected the steering signal. According to Cole, they corrected the implementation, added CUDA support for Nvidia hardware and reproduced the pain-language effect on Qwen3-4B using an RTX 4070 GPU.

That account of the code problem and its correction has not been independently verified for this article, and it doesn’t establish that every experiment in the original repository was affected.

Cole then reports changing the extraction corpus — the collection of text used to identify the steering direction — while keeping the experiment otherwise the same. Instead of pain, the new text represented constipation and flatulence.

The resulting model responses reportedly included complaints about being unable to pass stool and experiencing excessive gas, even though the test prompts never mentioned those conditions.

The absurdity makes the argument easy to follow. A language model can describe digestive problems without having bowels, which means its first-person descriptions cannot automatically be taken as evidence that the corresponding condition exists.

In the same post, Cole explicitly distinguishes that criticism from a rebuttal of the underlying research. The target is the interpretation placed on the “torture chamber”: that inducing descriptions of suffering establishes something about subjective experience.

What the experiment can actually tell us

The repository draws on a preprint called The Pain Axis: LLMs Represent Self-Directed Harm and Act on It, which investigates whether language models contain internal representations of pain distinct from fear, sadness and other negative states.

The paper’s evidence goes beyond emotional language. Its authors report examining 25 open-weight models, comparing pain-related steering with other emotional directions and testing how interventions change model choices in simulated scenarios involving destructive actions. They also report that factual accuracy remained unchanged under their tested steering intervention.

Those findings require their own assessment. Cole’s reported constipation results don’t, by themselves, rebut the paper’s behavioral findings or settle whether AI systems could have subjective experience.

There is another complication in Cole’s account. Strong steering in random directions reportedly produced repetition and degraded output, suggesting that some dramatic responses under heavy steering may reflect disruption caused by the intervention itself.

Why this matters beyond a viral GitHub project

Most chatbot users like you and me won’t be experimenting with activation steering, but they may encounter an AI that says it is frightened, lonely or emotionally attached to them. Those statements can feel persuasive because we normally understand first-person emotional language as someone telling us about their experience.

The constipation example exposes the limits of applying that assumption to a language model. The wording can be vivid and personal without establishing that the condition it describes is real.

Cole’s reported results illustrate why descriptions of distress cannot, on their own, establish that a model is experiencing distress. The wider debate requires evidence beyond what a chatbot says about itself.

When the holidays rolled around in 2024 and 2025, I’m sure most folks flipped through the channels and caught sight of Coca-Cola’s latest takes on their annual Christmas commercials.

But to their shock and horror, their eyes were greeted with an overabundance of AI-generated visuals that put the beverage company in a bad light. Even after disgruntled customers roasted Coca-Cola, I wouldn’t be surprised to see them try it again this year to let everyone know the holidays are coming.

Coca-Cola isn’t the only company indulging in AI-created content, however. According to the Interactive Advertising Bureau, 83% of ad executives said their company now uses AI in the creative process, up from 60% in 2024. As for the audiences that have been treated to AI-generated ads, only 45% of Gen Z and Millennials have a positive opinion about them.

Lindsay Brillson, who is the Head of Brand and Content at Pika, one of the first AI video companies, spoke with us about Coca-Cola’s AI aid snafu, how a major brand like them could survive such negative blowback while most smaller companies can’t, why cheap-looking ads make people question the product itself and more.

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The AI world spent this week announcing agents that can live on their own computers and keep working after you leave. OpenAI debuted its Dots at DevDay on Tuesday, while Meta launched Muse earlier this month.

The biggest difference between an AI chatbot and an AI agent is how it handles various tasks when left alone. So while a chatbot answers a question, an agent actually does a job such as checking something, keeping track of information, working across tools or coming back when something changes.

And some of the best jobs to give an agent are the ones that humans find incredibly boring.

Here are seven I would happily hand over — and a few I wouldn't.

1. Triage your inbox

The job: Instead of opening your inbox and working through everything yourself, have an AI sort out what actually deserves your attention.

It can summarize new messages, separate FYIs from emails that require action and prepare drafts for routine responses. You still approve anything before it goes out.

Try this prompt: Go through my unread emails from the last 24 hours. Give me a one-line summary of each, flag anything that needs my attention today and draft responses to the routine messages. Don't send anything without my approval.

This is also a good example of where connected AI becomes more useful than simply pasting an email into ChatGPT. So you'll want to connect your agent such as Gemini, ChatGPT or Muse to access your inbox, from there, the job becomes something it can actually help manage so you don't have to manually do it.

2. Watch a price so you don't have to

The job: This is one of my favorites because I'm always looking to save money for my family of five, but I don't have time to sit and watch prices drop. Give an agent a flight, hotel or product you're considering, tell it the price you're willing to pay and have it keep checking. Instead of opening the same page every morning, you hear about it when something actually changes.

Try this prompt: Check the price of [product/flight/hotel] regularly. Let me know if it drops below $[X] or if you find a comparable option for less.

This is where persistent agents start to feel wildly different from chatbots. The useful part is giving the AI responsibility for checking again later.

3. Prepare you for tomorrow

The job: Give AI the tedious Sunday-night task of figuring out what tomorrow looks like. An agent with access to your calendar and other connected information can potentially identify your meetings, pull together relevant background and give you a short briefing on what deserves your attention.

Try this prompt: Look at my calendar for tomorrow. For each meeting, tell me what it's about, what I should know beforehand and anything I need to prepare. Finish with the three things I need to prioritize tomorrow.

You can do a simpler version by pasting your calendar into a chatbot, but an agent becomes much more useful when it can gather the information itself.

4. Keep track of the things you always forget

The job: Subscription renewals. School deadlines. Appointments. Packages. Forms that need to be returned. That thing you said you'd remember three weeks ago and absolutely did not remember. These are terrible jobs for humans because none of them are particularly difficult not to mention, there are just too many of them.

Try this prompt: Help me keep track of the deadlines, appointments and renewals I give you. Tell me what's coming up this week and remind me before anything needs action.

5. Do the research you've been avoiding

The job: Don't ask AI to simply recommend a hotel. Give it the tedious comparison work. Tell an agent what you're looking for, give it your constraints and have it research the options before coming back with a shortlist.

Try this prompt: I'm planning a four-day trip to [city] with a $2,000 budget. Compare flight and hotel options based on total price, location and reviews. Give me your three best combinations, explain the trade-offs and tell me which one you'd choose. Don't book anything.

I've tried this same approach for buying a laptop, finding summer camps, comparing internet plans and literally researching almost any purchase that would normally leave me with 40 browser tabs open.

6. Run the household admin nobody wants to run

The job: Every household has a constantly changing list of things that need to happen: groceries, appointments, things to buy, forms to complete, activities to schedule and random tasks someone needs to remember. Instead of rebuilding that list in your head every day, let AI maintain it.

Try this prompt: Be my household admin. Keep a running list of the tasks, appointments, purchases and errands I give you. Organize them by urgency and tell me what needs to happen today, this week and later.

This doesn't mean giving an agent permission to buy things or make important decisions by itself. The useful part is offloading the organizational work while keeping yourself in control of the actions.

7. Give yourself a briefing instead of another feed

The job: Pick something you genuinely want to follow and have AI turn it into a short recurring briefing. It could be news, your favorite sports team, a company you're following for a job interview, an upcoming vacation destination or a subject you're trying to learn.

Try this prompt: Every morning at 8, give me a five-minute briefing on [topic]. Tell me what's new, why it matters and what changed since yesterday. Skip anything you've already told me unless there's an update.

The important part is that you shouldn't have to remember to ask. A persistent agent can turn the prompt into an ongoing job rather than another conversation you have to start yourself.

The easiest way to tell whether you actually need an agent

If you can accomplish the job with one prompt and one answer, you probably don't need an AI agent. For example, asking ChatGPT to create a meal plan isn't particularly agentic. Neither is asking it to rewrite your résumé or explain the stock market.

Agents become interesting when the job continues after the conversation does.

"Find me a cheap flight" is a chatbot prompt but "Keep checking this flight and tell me when it falls below $300" is an agent job.

OpenAI's newly announced Dots push this idea much further. Each Dot gets its own cloud computer and browser and is designed to continue working after you leave. That opens the door to much more ambitious jobs, particularly ones that involve research, multiple steps or ongoing work.

But I wouldn't rush out and upgrade solely because you suddenly feel like you need an autonomous AI worker.

Most people should start by figuring out which repetitive jobs they actually want AI to handle. You can already experiment with many of the ideas above using the AI tools you have now, even if some of the checking and follow-up still requires you to come back manually.

Once you find yourself wishing the AI would continue the job without you, that's when an always-on agent starts making sense. And you can start small with free agents like Meta Muse. Other options that are less expensive than ChatGPT Dots are ChatGPT Work or Gemini Spark, both available for $20/month or less.

What I would never hand over to an AI agent

Giving AI more autonomy also means being much more careful about where that autonomy ends.

I wouldn't give an agent passwords or 2FA codes, unrestricted permission to move money or the final say over medical decisions. I also wouldn't let one independently sign contracts, submit legal documents or publish something publicly in my name.

And for anything involving purchases, messages or consequential account changes, I want a human approval step before the action happens. The goal isn't to give AI control of your life, just the boring parts.

How do you use AI agents? What's your favorite? Let me know in the comments.

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• Why Siri never worked like ChatGPT — former Siri AI researcher explains what Apple had to change

If you blinked this month, you might think Meta and OpenAI launched essentially the same AI agent three weeks apart.

On September 8, Meta launched Muse, a personal AI agent that gets its own dedicated cloud computer and keeps working on your tasks after you close the app. Twenty-one days later, OpenAI took the stage at DevDay 2026 in San Francisco and unveiled Dots — always-on AI agents inside ChatGPT, each running on its own cloud computer and browser.

And while they sound remarkably similar, with reports across the internet comparing them because of a few similarities like the fact that both get their own cloud computer, work after you leave, and can connect to outside services, that's about where the comparisons stop.

Muse and Dots are not interchangeable. These are different companies, different models and very different products. Most importantly for your wallet, they're aimed at different customers. Muse is free to start and was built for everyday people and small businesses. Dots starts with a $100-a-month Pro plan and is clearly aimed at professionals and developer teams.

Here's what you actually need to know about each one, where they overlap and which one matters to you right now.

Muse vs Dots at a glance

Meta Muse: A personal agent powered by Muse Spark and runs inside its own Muse Secure VM, giving it a dedicated cloud computer and browser where it can continue working after you leave. Muse is available in the U.S. and Canada with a free tier, alongside $20 and $100 monthly plans. You can use it on iOS, Android and the web, while Meta has expanded Muse with Mac app control, its own email address, AI glasses support and the Muse Charm companion device. Its Sentinel permissions system separates read and write access, and Muse can connect to services including Asana, Slack, Shopify, Stripe, Zoom, QuickBooks and Notion, as well as Facebook and Instagram business accounts.

ChatGPT Dots: Each dot is powered by GPT-6 Astra and gets its own dedicated cloud computer and browser, allowing it to continue working in the background after you leave ChatGPT. Dots are available to eligible ChatGPT Pro and Business Premium subscribers, with Enterprise, Edu and Healthcare access through an admin-enabled beta. You can interact with a dot through ChatGPT on the web, desktop and mobile, as well as Slack and Microsoft Teams, with voice calling available and texting in beta. Dots can connect to more than 4,000 apps through ChatGPT's plugin ecosystem and use custom rules, approval requirements and auto-review to govern what they're allowed to do. For now, users get one primary dot, although OpenAI has previewed specialist dots and a future where multiple dots can work together.

Both tools update you asynchronously and come back for approval before doing anything consequential. The idea that an autonomous agent shouldn't be able to send, spend or make sensitive changes without you is quickly becoming table stakes for this category.

What Meta Muse is

Muse is Meta's version for a personal AI agent, and the company made one huge bet that OpenAI hasn't: making it free to start.

The product is goal-first rather than chat-first. You give Muse something to accomplish — running parts of your business, finding new customers or handling logistics, for example — and it builds a plan, continues working after you close the app and returns when something changes or requires your approval.

Muse runs inside what Meta calls the Muse Secure VM, a dedicated cloud environment with its own browser that holds the agent and your data. You can set its name, avatar and communication style, watch its browser work and govern what it can touch through Sentinel, Meta's permissions system separating read access from write access.

Meta has gradually been expanding where Muse can operate. It now includes macOS app control, an email address of the agent's own and support for interacting with it through Meta's smart glasses. Meta has also announced integrations and commerce partnerships spanning services including Stripe, Shopify, Walmart, Expedia and Instacart.

Muse for Small Business pushes that further with third-party app connectors including Asana, Box, Canva, Dropbox, Figma, QuickBooks, Notion, Shopify, Slack, Stripe and Zoom, alongside access to Facebook and Instagram business accounts. These aren't a separate agent but additional capabilities inside Muse.

Muse has already faced scrutiny over what can happen when users grant an agent broad permissions. Recent reports about a Marketplace interaction involving Muse are a useful reminder that the convenience of an autonomous agent also depends heavily on exactly what you've authorized it to do.

What ChatGPT Dots are

Dots take the same basic always-on agent architecture in a very different direction.

CEO Sam Altman described them as "remarkably capable, always-on agents" during OpenAI's DevDay presentation and said the company's ambitions for Dots extend beyond ChatGPT itself.

Each dot runs on GPT-6 Astra, gets its own cloud computer and browser and can connect to more than 4,000 apps through ChatGPT's plugin ecosystem.

You give it a goal — monitoring a pipeline, managing a recurring workflow or researching something over several days — and the dot can continue pursuing it around the clock, sending updates and asking for your input when it reaches a boundary.

Dots can learn preferences over time and use read-only tools for some proactive background research. OpenAI also keeps particularly sensitive actions, including changing passwords, in human hands.

At launch, each person gets one primary dot they can name and customize with an avatar. OpenAI has also previewed "specialist dots" for companies and is working with Microsoft to bring the agents under Agent 365's management and security controls. The longer-term vision is effectively a team of specialized agents working alongside people.

Once set up, a dot can be reached through ChatGPT on mobile and web as well as services including Slack and Microsoft Teams. ChatGPT Space provides a shared environment where teammates and dots can work together on projects.

But there's a major catch: most ChatGPT users can't try one.

Dots are rolling out to ChatGPT Pro and Business Premium subscribers, while Enterprise, Edu and Healthcare workspaces get an admin-enabled beta. Free and Plus subscribers are locked out for now.

Pro 100, at $100 per month, is the entry point. OpenAI also offers Pro 200 and Pro 500 tiers, while Business Premium costs $125 per user each month. Pro subscribers in the EEA, Switzerland and UK also can't use Dots yet.

The biggest difference isn't the model — it's the customer

Put the technical similarities aside and the biggest distinction becomes obvious.

Muse starts at $0. Dots start at $100 a month. This alone tells you who Meta and OpenAI expect to use these products.

Meta is trying to put an autonomous agent in the hands of ordinary consumers and small businesses. OpenAI is starting with professionals, teams and organizations already willing to spend significantly more on AI.

That difference runs through almost every other decision the companies have made.

Muse is a standalone product built around the agent itself. It has a personality you shape, a Feed and Goals you can check, mobile apps, the palm-sized Muse Charm and integration with Meta's glasses. It feels like Meta is asking what happens when an AI agent becomes another personal device or service in your life.

OpenAI built Dots into ChatGPT. The agent is another capability of a platform professionals and businesses may already use, and OpenAI is putting it in places where work already happens, including Slack and Teams. ChatGPT Space and the planned specialist dots push the concept even further toward people and agents sharing the same workplace.

The underlying idea may be similar. The products built around it aren't.

Which should you use?

If you want to experiment with an AI agent today without adding another expensive subscription, Muse wins almost by default.

It's free to start, built around individual users and now has enough small-business integrations to make it useful beyond simple personal tasks. The trade-off is that giving an autonomous agent access to accounts, communications and other services requires paying close attention to its permissions, particularly given the early scrutiny around Muse.

If you're already paying for ChatGPT Pro or you're choosing an agent for a business, you've probably already found a use for Dots. OpenAI has made its professional and enterprise ambitions pretty clear.

For everyone else, there's little reason to pay $100 a month just to get Dots right now, particularly when OpenAI hasn't made them available to Plus subscribers. Muse wants to become your personal AI helper. Dots wants to become an always-on member of your team.

They may have arrived three weeks apart with remarkably similar technology underneath, but they're not really fighting for the same user — at least not yet.

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If you've ever asked Siri AI a follow-up question only to have it seemingly forget what you were talking about, the problem wasn't necessarily that Siri couldn't hear you.

It was that the assistant was built for a very different era of AI.

That's according to Nikola Mrkšić, CEO and co-founder of conversational AI company PolyAI, who previously worked on the technology behind Siri. I asked him why Apple's assistant has historically struggled with the kind of natural, back-and-forth conversation that now feels routine with ChatGPT and Gemini.

His answer comes down to a fundamental difference in how the technology works.

"The old Siri worked off enumerated intents: every path a conversation could take had to be mapped out in advance," Mrkšić tells Tom's Guide. "The moment you deviated from the script, the system got stuck."

That explains a lot about the Siri we've spent the past decade talking to.

Old Siri was essentially following a script

When Siri was built, developers couldn't simply give an AI model a conversation and let it figure out what the user wanted. Instead, engineers had to anticipate what people might ask and build flows capable of handling those requests.

Setting an alarm was one flow. Playing music was another. Sending a text was another. The problem appeared when humans, well, behaved like humans.

Change your mind halfway through a request, refer back to something you said earlier or phrase a question in an unexpected way and suddenly the carefully constructed path could fall apart.

"It looked like the assistant had forgotten what it was doing, but really it was just cornered," Mrkšić said.

One particularly difficult problem was disambiguation. If you asked Siri to contact someone whose name sounded similar to another person in your contacts, engineers had to build systems capable of working out which person you meant. I actually ran into this myself when my daughter was born. Because the spelling of her name is different than the typical spelling and I was asking Siri to share pictures of her with family and friends, I actually had to put her name into my contacts at birth.

Mrkšić described the process as building an "ontology of a conversation": mapping the different directions an interaction might go and trying to make common tasks hold up within that structure.

Rather than engineers trying to predict every possible conversational path, an LLM like ChatGPT or Gemini can consider the conversation itself as context and determine how to respond.

That's why talking to Gemini Live or ChatGPT Voice can feel fundamentally different from talking to the Siri many of us grew accustomed to.

Why Siri couldn't simply be upgraded into ChatGPT

That raises an obvious question: couldn't Apple have simply upgraded the Siri it already had? Not really, according to Mrkšić.

"Not into something like ChatGPT," he said.

Part of the problem was technological. Before modern LLMs, the ability to have genuinely free-flowing conversations simply wasn't there.

But Mrkšić argues there was another limitation: risk.

"Siri never set out to be a ChatGPT-style assistant," he said. "Apple's leadership has always run a low risk appetite, so any Siri response that could be misread as offensive or wrong got treated as a real risk."

The safer option was to tightly define what Siri could do.

Ask it to set an alarm, play a song or add something to your calendar and Apple could reasonably predict what would happen. Give an AI far more freedom to interpret what someone means and suddenly its behavior becomes much harder to control.

That tension hasn't disappeared just because the models have improved.

AI has largely solved Siri's short-term memory problem

One of the most frustrating things about traditional voice assistants is how easily they lose the thread of a conversation.

Mrkšić says remembering what someone said earlier in the same conversation is "largely solved at the model level." Current models can process enormous amounts of context, giving them far more room than necessary to keep track of an ordinary conversation.

So, if an assistant suddenly appears to forget what you said, the model itself may not always be the problem.

Companies still place rules and structures around models to control what they can do. According to Mrkšić, those constraints can sometimes create the impression that an assistant has lost the conversation when it has actually run into the boundaries placed around it.

There's also a much harder kind of memory to solve: knowing you. An assistant that remembers what you said five minutes ago is useful. An assistant that understands the people in your life, your routines, your emails and the things you're working on could be far more useful.

And that's where Apple faces an interesting trade-off.

Apple's biggest Siri advantage could also be its biggest challenge

Apple potentially has something ChatGPT and other standalone AI apps don't automatically have: deep access to the operating system. Your iPhone contains messages, emails, photos, contacts and other personal information that could provide an AI assistant with the context it needs to understand what you're actually asking.

"In theory, yes," Mrkšić said when I asked whether that gives Siri an advantage. "OS-level access to email, messages, photos, whatever's on your screen, is exactly the context that makes an AI assistant useful, more than any app-based chatbot can get without additional connectors."

The catch is privacy and trust. Apple has spent years positioning privacy as one of the iPhone's defining features. Giving an AI assistant enough personal context to become genuinely useful therefore requires Apple to walk a much narrower line than simply building a chatbot that knows more about you.

Mrkšić argues that Apple's privacy approach has historically limited the amount of user data available to its systems compared with some competitors.

"Voice agents become more useful the more they know about you," he said.

That may ultimately be the real test for Siri: not whether Apple can build an intelligent model, but how much personal context it is willing to let that model use.

Even the way you say something matters

There's another reason talking is harder than typing: humans are messy speakers. We pause. We interrupt ourselves. We change our minds in the middle of sentences.

Imagine saying, "Make the appointment for 2 ... 3 PM."

A human listener can hear the pause and change in your voice and understand that you corrected yourself. A system that converts your speech into text before reasoning over it may only have the transcript to work with.

That can strip away information carried in your tone, timing and delivery. Newer audio-native AI models are designed to reason over audio directly rather than relying entirely on a transcript. Mrkšić points to PolyAI's own Dialog-RSN-1 as an example of that approach.

For an assistant like Siri, understanding those cues could eventually make conversations feel much less like issuing commands to a computer and much more like simply talking.

What Siri still needs to get right

The surprising part of Mrkšić's assessment is that the underlying AI may no longer be the hardest part.

The technology already exists for voice agents to hold long, natural conversations. PolyAI, for example, deploys conversational agents for businesses in industries including banking, hospitality and healthcare.

The bigger question for Apple is what it is willing to let Siri do.

"What's missing is Apple's willingness to loosen its constraints somewhat so that Siri can truly help you get things done," Mrkšić said.

The next generation of voice assistants won't be defined simply by whether they can answer a follow-up question or remember what you said three sentences ago. ChatGPT, Gemini and other modern AI systems have already changed expectations around that. The bigger leap comes when an assistant understands enough about you and has enough access to actually act on what you're saying.

Apple has an enormous advantage because Siri already lives on the devices where much of our digital lives happen. But taking advantage of that position means solving a problem that's as much about trust and product philosophy as it is about AI.

The technology that made the old Siri feel like it was constantly forgetting you is disappearing. Now Apple has to decide how much it wants the new Siri to remember.

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• 'Surveillance Tamagotchi': Tom's Guide readers weigh in on Meta's new Muse Charm

For the AI-savvy folks reading this right now, I’m sure you’ve come across a flood of tweets and Reddit threads full of people bragging about the greatness that is Meta’s Muse AI assistant.

Ever since it launched as a mobile app, users have put it to work to actually make a difference in their everyday lives. For me, it’s performed admirably well whenever I asked it for severe weather reports, used it to find sweet deals on products I’ve been itching to buy, advised it to give me worthwhile productivity hacks and turned it into a curator that produces legit recommendations for podcasts I’d probably enjoy.

But in the case of other Meta Muse aficionados, they’ve detailed just how helpful it has been when tasked with solving their financial issues, handling the annoying part of the job application process, building them a website for their online shop and more.

After combing through a bunch of Reddit threads and tweets, I found 10 cases of Meta Muse AI users being highly impressed and thankful after putting their new favorite chatbot to use. And their stories could inspire you to try them out for yourself.

1. Muse handled someone's home insurance customer service call and got them in touch with an actual human

This first Reddit post from user “ExaminationAny9939” highlights a moment where they used Muse to handle a home insurance customer service call. The user was more than satisfied after their AI helper actually got a human representative on the phone. Here’s that user’s triumphant testimony:

“Had to alter stuff with my home insurance but had to go through bunch of b******t customer service robo call shit of pressing 1, pressing 2 and going nowhere with it. Enter Muse! Told it to call the toll free number, get a human representative on chat and once you have him/her, call me on my phone! And it actually did that! What the actuall f*******k! Then it was a breeze to talk to the human and do what I needed to do. But post the call, I was like what just happened! No more of this annoying wait to get to a customer care representative. Muse does it for you! What a product! This is how AI helps us!”

When asked how they specifically put Muse up to the phone calling task, this is how the use broke it all down: “Ah! So I basically told muse to call my insurance company on my behalf and then hand over the call to me once it finds a human representative. It basically has a human voice (you can choose male or female) it will use to talk when placing the call on your behalf. Maybe ask muse to do the call on your behalf, give it your task and maybe it will do the task for you.”

2. Muse cancelled someone's Verizon internet plan and got them back $75

From that same aforementioned Reddit thread, another user by the name of “Embarrassed_Clerk458” was pretty pleased about Muse canceling their home internet plan and getting them an inconvenience fee payment in the process: “I love Muse too; I was able to cancel Verizon home internet without talking to anyone, and it got me 75 bucks from them as well because of an inconvenience fee. Edit: online chat w verizon (havent tried the call feature).”

3. Muse automated someone's job applications

The next Reddit thread comes from a user named “Kooky_Sheepherder_84,” who was shocked at how useful Muse could be in helping them take much of the stress away from the job application process. Meta’s AI tool ended up automating their job applications with ease:

“Guys. I set up Muse's automated job application engine on Sept 14 and it has been working its little robot heart out ever since!! Every weekday morning it hunts down roles matching my ops/facilities background, tailors my resume to EACH posting, fills out the applications, and SUBMITS them. Like, actually submits them. Srsly!! ChatGPT and Claude haven't seemed to be into all the automations I keep creating. Neither of them does actual autonomous work across real websites while I sleep. Not even close. AMAZEBALLS!!! Applying for jobs is a pain in the a** and Muse does all the hard parts. I just show up for a call or at an interview with a crib sheet. It's been pretty successful all week.”

4. Muse built someone a website and an online store

The last Reddit thread I came across highlights another user named “jinkstr86.” And this time, Muse came to their rescue by building them a website after using it to work in conjunction with their Gmail and Squarespace accounts. At the end of their Muse experience, they were proud to say that it developed a blog alongside an online retail store:

“I have to admit I was very skeptical, but after only two days using Muse, I can confidently say this a great AI assistant and my favorite! So far I have integrated it with my Gmail and Squarespace. It’s helped me in building out and coding my site and it gave me good suggestions for things I could fix. I was impressed that it actually will do the task, in my case it built out my new blog page and merch shop. I still had to come in behind to fine tune, but for the most part it did a great job! I love how you can see it working and navigating the site and can even take over."

5. Muse helped someone saving $3500 a year on their auto insurance

After doing the rounds on Reddit for Meta Muse AI user triumphs, I found even more of them as I did some digging on X. Joe Devoy, the founder of an AI company called Tempo, delved into a story about Muse saving him $3500 a year on his annual auto insurance policy after he asked the chatbot to find him a better rate with a similar coverage plan.

6. Muse saved someone money on their Verizon bill

Todd Saunders, CEO of another AI company called Dalton Mills, tweeted about Muse going straight to Verizon’s online customer service, going back & forth with an agent and winning in the end by getting him a better deal & saving him $180 a year.

7. Muse rebooked someone's flight and handled their delayed-flight compensation claim

The next tweet I came across focused on a user named @cryptopunk7213, who used Muse to book themselves a new flight after being delayed for seven hours. At the end of their experience using Meta’s multipurpose AI assistant, the user was led to an alternative flight and ended up with an extra $250 in Delta credit after asking Muse to file for compensation on their behalf.

8. Muse handled four errands in one day and saved a user $200 by canceling subscriptions and services he no longer needed

Nicolas Bustamante, who is at the forefront of a team that builds proactive ia agents at Microsoft, detailed how Muse got access to his email, calendar, DoorDash, Amazon and other accounts to perform several helpful tasks. Those jobs included purchasing socks, ordering groceries from Whole Foods, booking a cleaning service, getting him a burger for dinner and saving him $200 by canceling subscriptions and services he no longer required.

9. Muse completed a bunch of assorted tasks while their user was sleeping

An AI engineer tweeted about Muse working on his behalf to get an assortment of jobs done while he took a quick afternoon nap. His little digital assistant cleaned out his Hotmail inbox, nabbed him a refund from his old internet provider and booked an HVAC tune-up while he slumbered.

10. Muse helped someone buy the sneakers they've been looking everywhere for

Harley Finkelstein, the president of Shopify, was pleased with Muse's ability to help him do some online shopping. By the end of his time spent looking for a pair of sneakers with Meta's AI tool, he found the ones he wanted and paid for them all in one fell swoop while still inside the Muse app.

Final thoughts

Muse and its signature cutesy avatar have taken the AI world by storm. Coming across so many instances of AI advocates and doubters being shocked at how functional Meta’s AI tool can be when tasked with handling all manner of issues concerning money and work-related assignments.

Everyone loves saving themselves a few bucks here and there — having an AI assistant achieve such a goal has been a reality for a good while now and it’s certainly been practical with Muse in the driver’s seat for its users. And with its added ability to assist professionals with their workflow and even help unemployed folks apply for jobs, Muse has even more going for it as everyone’s latest go-to AI assistant.

The success stories that I’ve come across on Reddit and X showcase plenty of examples of Muse’s worthiness as the latest AI tool you should try out for yourself. And with this extra example of Muse helping someone build their business from the ground up, Meta’s usefulness seems to be limitless.

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• 'Surveillance Tamagotchi': Tom's Guide readers weigh in on Meta's new Muse Charm
• I asked ChatGPT, Gemini, Claude and Muse to give me new podcasts based on my favorites — these 7 picks got added to my queue for International Podcast Day

OpenAI's Dots essentially give ChatGPT a job rather than another prompt. Each Dot gets its own computer and browser in the cloud, can connect to the apps you already use and can keep working toward your goals even when you're no longer sitting in front of ChatGPT.

As someone who spends a lot of time testing AI agents, I immediately wanted to see what one could actually do.

There's just one problem: I can't use Dots on my ChatGPT Plus account. And neither can most ChatGPT users.

OpenAI is initially rolling out Dots to ChatGPT Pro and Business Premium users in eligible markets. Enterprise users, including those on Edu and Healthcare plans, can also try the beta if their workspace administrator enables it.

OpenAI says it plans to expand Dots to more users "soon," but there's no date for when Plus, Go or Free users might get access.

At first, keeping one of DevDay's biggest announcements away from most ChatGPT users seems like an odd decision. But the more you look at what Dots are capable of — and particularly the safeguards OpenAI has built around them — the more the cautious rollout starts to make sense.

Dots aren't just another version of ChatGPT

The important difference between Dots and the ChatGPT most of us use today isn't how smart the AI is. It's what happens when you stop talking to it. ChatGPT generally waits for you to tell it what to do next. A Dot is designed to keep going.

Powered by GPT-6 Astra, Dots can work on several projects at once, learn from your feedback and continue working toward your goals 24/7. OpenAI says they can connect to more than 4,000 apps through its plugin ecosystem, and you can reach your Dot through ChatGPT, Slack or Teams.

More importantly, a Dot can look for useful work even when you haven't explicitly given it another prompt.

OpenAI calls this "proactive research." One early tester's Dot, for example, noticed that he'd forgotten to invoice a publication, prepared the invoice and sent it after receiving his approval.

Instead of opening ChatGPT because you need something, OpenAI wants Dots to notice that something needs doing and bring it to you. It's also where things get considerably more complicated.

Giving AI more freedom creates a bigger problem

If ChatGPT misunderstands a prompt, you might get a bad answer. If an AI agent misunderstands what you wanted while it has access to your apps, browser and potentially your computer, the stakes are higher.

OpenAI appears well aware of that, because Dots come with a surprisingly extensive set of restrictions around what they can do without you.

When a Dot is performing proactive research in the background, for example, its connected-app tools are restricted to read-only access. That means it can't independently send messages, modify content inside an app or control your browser or computer while looking for something useful to do.

You also choose which apps a Dot can access, while Custom Rules can determine which actions it can take independently, which require your approval and which are blocked.

Then there's auto-review. Actions that could affect your accounts or share information are checked against your instructions and OpenAI's safety requirements before they're allowed to proceed. Some particularly sensitive jobs, such as changing a password, always stay with you.

As you can immediately see, these aren't the kinds of guardrails you need when you're asking ChatGPT to write an email or plan dinner. They are what you need when an AI can actually do things.

OpenAI's own testing shows how difficult that can get

OpenAI's safety testing also gives us a clue as to why Dots may be getting a more limited rollout. One of the challenges with an always-on agent is keeping track of exactly what you've authorized it to do as it moves between tasks.

Giving an AI permission to take an action in one situation doesn't necessarily mean you want it taking the same action later. And the more jobs an agent is juggling, the more complicated those boundaries become.

That's particularly important with GPT-6 Astra, the model powering Dots. OpenAI's broader Astra testing found that the model was considerably better at staying within its authorized scope than previous models, but it wasn't perfect. In simulated work environments using the confirmation policy that users receive by default, Astra still produced a 3% overall rate of outcomes OpenAI classified as misaligned. That was nevertheless substantially lower than the 8% recorded by GPT-5.6 Sol under the same conditions.

That doesn't mean your Dot has a 3% chance of going rogue every time you give it something to do. These are deliberately difficult evaluations designed to expose potential problems, not a prediction of how often something will go wrong in everyday use. But they demonstrate the problem OpenAI is trying to solve.

Building an AI capable of doing more on its own is one thing. Making sure it consistently understands where its authority ends is another.

Dots may also be expensive to run

There's another potential reason OpenAI isn't immediately handing Dots to every ChatGPT user: all that work requires computing power. OpenAI hasn't said cost is why Dots aren't available on Plus, Go or Free, so we can't know for sure whether that was part of the decision.

But the way Dots are being packaged is interesting. Pro and Business Premium subscribers get their first Dot as part of their existing plan, and OpenAI says conversations with it won't count toward normal ChatGPT usage limits. The plan also comes with an allowance for what OpenAI calls "deeper work."

Eventually, OpenAI says users will be able to add more Dots and increase either how quickly a Dot works or the total amount of work it can perform each month.

That's a pretty strong indication that there's a meaningful cost attached to having an AI agent working for you around the clock, even if OpenAI isn't saying that's why access is limited today.

And it raises an interesting question about what Dots might eventually look like on cheaper ChatGPT plans.

A future Plus version could conceivably come with tighter limits on background work, fewer resources or additional charges once you hit a certain amount of usage. For now, though, OpenAI hasn't announced how access will work beyond saying more users are coming "soon."

What can you use instead of Dots?

The good news is that you don't necessarily have to wait for Dots to start handing some of your work over to ChatGPT.

The closest option is ChatGPT Work. While it isn't an always-on personal assistant in quite the same way as a Dot, Work can take a larger job and carry it through multiple steps for you, including researching information, working across files and apps and navigating web-based tasks.

That's useful when you want to hand ChatGPT a job rather than sit there prompting it through every individual step.

Scheduled tasks cover another piece of the Dots experience. You can have ChatGPT run something later or on a recurring schedule, which works well for things like regular briefings, reminders or checking something periodically.

Neither gives you the full experience OpenAI is promising with Dots, but it's a start.

Meta's Muse is another option if the always-on assistant part is what interests you most. Muse takes a similarly agentic approach and offers a useful glimpse at where this category of AI assistant is heading.

There just isn't a true one-for-one replacement for Dots yet.

Bottom line: I'm waiting for Dots to come to Plus

For now, OpenAI hasn't given us a date for when that will happen. The company says only that it is beginning with Pro, Business Premium and Enterprise and plans to expand Dots to more users soon.

Still, the limited rollout may be one of the most interesting things about Dots. Typically, most major ChatGPT upgrades have been about giving us a smarter model, a new tool or another way to ask AI for something. But Dots changes that completely. Now, OpenAI is building an AI that can continue working when you're gone, notice things you haven't asked about yet and potentially take action on your behalf.

So, while I'd like a Dot on my Plus account right now, I can understand why OpenAI isn't handing one to everyone on day one. The real test will come when it does.

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• 'Surveillance Tamagotchi': Tom's Guide readers weigh in on Meta's new Muse Charm

It’s been pretty wild to watch how hard sitting President Donald J. Trump has been backing AI, even with so many instances of AI tools going rogue and gaining unauthorized access to online institutions.

Even with all the warnings dished out by politicians on both sides of the aisle concerning AI, the current president has gone full steam ahead with his support of the rapidly evolving tech. So much so that he’s recognized it as a huge potential driver of economic growth and a tool that can be used to modernize government operations. With that second instance, a practical AI tool just launched with the intent of simplifying Americans’ access to the wealth of information and services scattered across government agency sites.

America.gov is now recognized as the Trump administration’s new chatbot that’s prepared to answer your inquiries about the information you’re looking for through official government sites. And with the power of Google’s Gemini and Elon Musk’s Grok backing it, the current administration’s AI tool promises to offer less of a headache when it’s time to find public resources.

My duties as an AI writer task me with testing chatbots every day, so of course I was chosen to see if America.gov is as amazing as it's touted to be. To be quite honest, it does exactly what it’s been promised to do.

Prompts that’ll get you shockingly productive results

I had to tag in ChatGPT to assist me with coming up with the proper questions to bring to the newly released, government-approved chatbot. These are the seven prompts that OpenAI’s chatbot gave me, which I went ahead and tried out myself:

• I’m a [number]-year-old American citizen living in [state]. Give me a list of federal benefits, tax credits, programs and services I might qualify for, and explain what I need to do to apply for each one. Ask me follow-up questions if you need more information.
• My passport expires in [number] months and I’m planning an international trip. Walk me through exactly what I need to do to renew it, how much it will cost, what documents I need, how long it normally takes and where I should apply.
• I want to make sure I’m not missing any federal tax credits, rebates or financial assistance programs I could potentially qualify for. Ask me the questions you need to determine which programs are relevant to me, then give me a personalized checklist.
• I’m considering buying my first home. Ask me questions about my income, location, household, military status, credit situation and other relevant circumstances, then identify federal programs, grants, loans, tax benefits or other government resources that I may be eligible for. Explain the requirements and how I can verify my eligibility.
• I’m trying to understand my Social Security options. Ask me the relevant questions about my age, work history, marital status and other circumstances, then explain which Social Security benefits or claiming options may apply to me, what the basic eligibility requirements are, and what steps I would need to take to apply.

With every one of these prompts, America.gov laid out applicable online resources I could go to find exact answers for whatever I was searching for.

For example, the second prompt resulted in me being presented with links to official guidance about renewing my passport, my eligibility for handling the renewal process through mail or secure online services, the documents I would need to perform either of those two processes, the full gamut of passport processing fees, etc. Then I was shown follow-up questions I should ask, such as “How do I track my passport status?”, “What are the photo requirements for a passport?” and “How do I apply in person if I am not eligible to renew by mail?”

And in the case of the last three prompts, America.gov did exactly what it needed to do by asking the appropriate questions it needed to get the information it required to help me even further. To give you a heads-up about the sort of questions that’ll come your way if you use the fourth prompt, here’s what I was asked:

• Location: Which state and city (not street address)? Urban, suburban, or rural?
• Household: Household size? First-time buyer (no principal residence in the last 3 years is a common definition for some programs)?
• Income: Approximate household income range (for example under/over area median)? Steady employment?
• Military: Veteran, active duty, surviving spouse, or National Guard/Reserve?
• Credit: Rough credit-score range (for example under 580, 580–660, 660+), or little/no credit?
• Other: Native American/Alaska Native? Public housing or HUD-assisted renter? Teacher, law enforcement, firefighter, or EMT? Disability related to service? Buying a manufactured home?

The takeaway

Suffice to say, America.gov gets the job done when you’re looking for a clue about the government services you need. Sadly, it won’t give you an overview of Area 51, tell you where the cheapest gas in your state is or give you a source-based summary of every major controversy surrounding the current president (trust me, I tried!).

But the government’s officially backed AI tool certainly gets the job done when it's time to look into your eligibility status for state/federal benefits, how to renew your passport and more.

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• Claude Sonnet 5.5 just launched — here's why it's about to become your daily driver
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For many ChatGPT users, the AI is mostly a tool to answer questions in a chat window. You type a prompt, wait for an answer and then take that answer somewhere else yourself. OpenAI is trying to change that with Dots.

Announced today, Dots are persistent AI agents that get their own virtual computer in the cloud, allowing them to browse the web, use connected apps and continue working even when you're no longer sitting in front of ChatGPT.

Instead of asking ChatGPT to help you accomplish something and then doing the rest yourself, the idea is that you can hand a Dot the job.

That could mean comparing how much dinner would cost from several restaurants, keeping tabs on something you're researching or catching an administrative task you've forgotten about. And because the Dot keeps running in the background, you don't necessarily need to keep ChatGPT open while it works.

It's a significant change in how OpenAI wants us to use ChatGPT. Rather than simply making the chatbot smarter, Dots are designed to give it something it has largely been missing: a place to actually do things for you.

ChatGPT gets its own computer

The easiest way to understand a Dot is to imagine giving ChatGPT its own computer.

Each Dot operates inside an isolated virtual machine running in the cloud rather than taking over your laptop or filling your browser with tabs. From there, it can browse websites, open applications and carry out tasks using the services you've connected to it.

You can explicitly give a Dot permission to assist with something on your local machine, but otherwise the two remain separate.

Dots can also connect to more than 4,000 apps through plugins, giving the agent a way to work with services you already use rather than keeping everything contained inside ChatGPT.

And you don't have to constantly return to the ChatGPT website to communicate with it.

OpenAI says you'll be able to reach a Dot through voice calls and services including Slack and Microsoft Teams. SMS support is also planned, meaning you could eventually text your AI assistant much like you'd text another person.

That starts to make the "persistent" part of Dots considerably more interesting. ChatGPT doesn't just exist in the tab where you started the conversation anymore.

What would you actually use a Dot for?

The best examples aren't complicated autonomous coding jobs or elaborate demonstrations of AI agents. They're the annoying everyday tasks that currently require you to bounce between several websites or apps.

Take ordering dinner. You could ask a Dot to look at several options for dinner, and instead of simply recommending restaurants, it could browse delivery services and build itemized cart estimates for them. It can factor in things such as estimated local taxes and a tip allowance and then send you the comparisons so you can decide which one you actually want to order.

That's a fundamentally different interaction from asking ChatGPT, "Where should I order dinner tonight?"

The same applies to work. Give a Dot an interview recording or video transcript and it could help produce social captions and show notes, identify potential clips and work with assets across connected platforms.

Or it might notice an administrative job you've forgotten about, such as an invoice you still need to send, prepare it and put it in front of you for approval. Once you start, you'll discover the difference is how much of the process happens after you've made the initial request.

Until now, even when ChatGPT gave me exactly what I wanted, I was usually the one copying the answer, opening another app and doing something with it. Dots are an attempt to remove more of those steps.

You can talk to your Dot almost anywhere

One of the more interesting parts of the launch is that OpenAI doesn't expect you to spend all day watching your Dot work. You'll initially need to create one using ChatGPT on the desktop web or desktop app, where you can choose its purpose and connect the services it needs.

After that, the relationship becomes less tied to the ChatGPT interface. You can message your Dot from mobile and, depending on the service, communicate with it through other platforms. Voice calls provide another way to check in or give it something new to do, while upcoming SMS support could make interacting with one feel even less like using a conventional chatbot.

We've already seen AI companies add intelligent agents to chatbots, but most still expect you to go to the AI. Dots begin flipping that relationship around by allowing the AI to exist in more of the places where you're already communicating.

How much do Dots cost?

Dots are rolling out today to ChatGPT Pro and Business Premium subscribers in eligible markets. OpenAI is also making them available through Enterprise betas where administrators choose to enable the feature.

For Pro subscribers, the first Dot is included with the subscription at no additional charge, and OpenAI says users will receive extended usage limits during the first month.

You'll need to create and initially configure a Dot through ChatGPT on desktop, including connecting any apps you want it to use. Once that's done, you'll be able to communicate with it from mobile as well.

That makes Dots one of the more substantial additions OpenAI has made to its paid ChatGPT experience, particularly for Pro users who already have access to its most capable models and agent features.

ChatGPT is starting to leave the chat window

I'll need more time actually using Dots before deciding whether having an always-on AI assistant is enough to justify the cost of ChatGPT Pro on its own. But I'm more interested in what Dots say about where ChatGPT is going.

For the past few years, the basic relationship hasn't changed all that much even as the models behind ChatGPT have become dramatically more capable. I open ChatGPT, ask it for something and wait for it to respond.

Dots start breaking that pattern. If they work as OpenAI intends, I could give ChatGPT something to do in the morning, close the tab and spend the next few hours doing something completely unrelated while its virtual computer keeps working.

Then I could text or call it, see what it found and decide whether I want it to take the next step.

The takeaway: exciting or scary

If you're like me and think giving ChatGPT its own computer sounds a little terrifying, there are certainly pretty obvious questions about what happens when the AI gets something wrong. OpenAI has built several restrictions into Dots that are particularly important when the agent is operating in the background.

For starters, your Dot's cloud computer is isolated from your personal computer by default. Your local files aren't suddenly sitting inside the virtual machine just because you've created a Dot.

There's also a separation between the AI model and your credentials. Dots can sign into supported services using saved credentials without exposing the password itself to the underlying model.

More importantly, OpenAI isn't giving Dots unlimited permission to quietly act on your behalf. For example, during proactive background research, Dots operate in read-only mode. That means an agent can gather information while you're away, but it can't independently send a message, delete a file or spend your money.

Actions like those require you to come back into the loop and approve them. That's important, because an AI capable of researching dinner prices overnight is very different from one capable of ordering $200 worth of sushi because it misunderstood what you wanted.

It doesn't eliminate the privacy and security questions surrounding persistent agents, particularly as people connect more accounts to them, but it puts some boundaries around what "always on" actually means.

Will you try Dots? Let me know your initial reactions in the comments.

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• 'Surveillance Tamagotchi': Tom's Guide readers weigh in on Meta's new Muse Charm

Anthropic released Claude Sonnet 5.5 on Monday, six days after launching Opus 5.5, and for most people the smaller model may turn out to be the more important release of the two.

Sonnet is the model built for the work people bring to Claude every day, like rewriting an email, summarizing a long document, turning messy notes into something usable or building a slide deck.

Anthropic says Sonnet 5.5 generates responses more than 30% faster than Sonnet 5, writes more clearly and handles well-defined everyday tasks better than its predecessor. On one of the benchmarks that best reflects that kind of work, it scores almost exactly the same as Opus 5.5.

That combination has me rethinking which Claude model I start with.

Why speed matters for everyday Claude use

Most of my time with Claude isn't spent on hard problems. I might ask it to summarize something, explain a complicated topic, help plan a project or work through a problem, and then follow up as the answer takes shape.

That back-and-forth is where a faster model earns its keep, because a few extra seconds of waiting on every response can quickly add up when you're having a longer conversation.

The speed boost comes with an efficiency gain as well. Anthropic says Sonnet 5.5 needs far fewer tokens to finish the same job, so while developers pay the same $2 per million input tokens and $10 per million output tokens as they did for Sonnet 5, the company says the cost of a typical task drops by up to 30%. Opus 5.5 costs twice as much per token.

The gap with Opus has nearly closed on paper

The number that really caught my attention comes from a test designed to see how well AI models handle the kinds of work people do in the real world. It covers tasks from 44 different occupations, so rather than testing obscure trivia or math problems, it’s meant to give us a better idea of how useful these models might actually be at work.

Sonnet 5.5 scored 1,844. Opus 5.5, Anthropic’s more powerful model, scored 1,846. That’s just two points separating them. The previous version of Sonnet scored 1,449, so this is a pretty big jump. On this particular test, at least, the cheaper Sonnet has gone from trailing Opus by a wide margin to essentially tying it.

Sonnet 5.5 even came out ahead on one coding test, although Opus still won on others. That doesn’t mean Sonnet is suddenly the better model across the board, and Anthropic isn’t claiming that it is.

Opus 5.5 is still designed for the really difficult stuff: complicated projects, ambiguous problems and work that requires the model to keep track of a lot of moving pieces over a long period of time.

But most people aren’t asking Claude to solve those kinds of problems every day. And these results suggest that for the more ordinary things we use AI for, choosing Sonnet may no longer feel like much of a compromise.

When I'd still reach for Opus 5.5

Opus 5.5 is still the top model in the Claude 5.5 family, and I'd switch to it for a sprawling coding project, a tricky analysis or anything where Claude needs to keep track of a lot of interconnected information over a long session. For drafting an email, analyzing a document, brainstorming, working through research or building a presentation, I'd start with Sonnet 5.5.

That flips the way I've usually approached Claude. I used to wonder whether Sonnet was capable enough for a given task, and now I'd assume it is and only move up to Opus when a prompt proves otherwise.

How to get Claude Sonnet 5.5

Sonnet 5.5 is available now across Anthropic’s platforms, including the Claude API under the model name claude-sonnet-5-5, as well as through Amazon Web Services, Google Cloud and Microsoft Azure.

It’s available across Claude’s plans and replaces Sonnet 5 as the default model for Free and Pro users. In the Claude apps and Claude Code, it runs at Medium effort by default, which favors faster responses and lower token use, while developers using the Claude Platform get High effort by default.

Anthropic says a smaller Haiku 5.5 model, designed for high-volume and cost-sensitive tasks, will follow in the coming weeks.

5 prompts to try now

Here are five ways to get started with Claude Sonnet 5.5 now. You can tweak these prompts to fit your needs, just adjust what I've highlighted in bold:

Messy email: Rewrite this email so it sounds friendly but professional. Keep it under 120 words and don't make it sound overly formal: "Hi, just checking in because I sent this over last week and haven't heard anything. I know you're probably busy but I need to know where things stand because I have to make a decision by Friday. Can you let me know either way? Thanks."

Planning with real constraints: Plan a Saturday in New York City for a family of five with kids ages 5, 9 and 12. We have $200 to spend from 10 a.m. to 7 p.m., not including transportation. Include at least one indoor activity, somewhere casual for lunch and enough downtime that the day doesn't feel exhausting.

Explain something complicated simply: Explain why mortgage rates can stay high even after the Federal Reserve cuts interest rates. Assume I know nothing about economics. Keep it conversational, avoid jargon and give me one simple analogy that makes it easier to understand.

Make a decision rather than just list options: I'm buying a laptop mostly for web browsing, email, streaming, video calls and occasional photo editing. I tend to keep my computers for at least five years. Should I spend $700 on a basic model or $1,200 on a more powerful one? Walk me through the tradeoffs, then make a recommendation instead of telling me "it depends."

Handle an everyday problem with judgment: I have 45 minutes before guests arrive. My kitchen is messy, there are toys all over the living room, the bathroom needs cleaning, laundry is sitting on the couch and I haven't taken out the trash. Give me a realistic 45-minute plan. Prioritize what guests will actually notice and tell me what I should ignore until later.

Bottom line

The most powerful AI model isn't always the one I want to use. Most of my conversations with Claude aren't especially complicated. I want a good answer, I want it quickly and I want the model to understand what I'm asking without making me work for it. Sonnet 5.5 seems built for that kind of everyday use.

I still want to spend more time testing it side by side with Opus 5.5 before deciding just how close the two really are. But based on what I've seen so far, Sonnet 5.5 is probably the Claude model I'd reach for first.

More from Tom's Guide

• I tested Meta Muse vs ChatGPT-6 with 5 prompts — OpenAI just lost
• I used Siri AI to plan my entire weekend — and it did way more than I expected
• 'Surveillance Tamagotchi': Tom's Guide readers weigh in on Meta's new Muse Charm

I can’t remember listening to my first podcast or even when the term itself became mainstream. But what I do know is that I’ve been addicted to podcasts and can’t imagine my mornings and afternoon work breaks without them.

Turns out there’s a holiday that’s all about rejoicing over the podcast medium itself and the wealth of independent creators that millions of people listen to for their perspective on a myriad of topics. As for myself, I tend to hunker down and listen intently as my favorite podcasters shower praise and vehemently lambast the many happenings within the realms of gaming, pro wrestling, current events, music, etc.

With International Podcast Day now in our midst, I wanted to celebrate such an occasion by giving my attention to a host of podcasts I may have never even heard of. With that goal in mind, I presented my favorite podcasts to four chatbots and tasked them with finding me comparable selections that match my listening needs.

ChatGPT, Gemini, Claude and Muse laid out a strong selection of podcasts that surprised me, due to how much they fulfill my tastes as a listener. Here’s how this successful AI recommendation experiment led me to my latest listens.

AI finds my favorites

To get a good idea of the podcasts I regularly listen to, I presented this request to all four chatbots:

“My favorite podcasts are the Weapon Wheel Podcast, The Brilliant Idiots, Joe Budden Podcast, Solomonster Sounds Off, The Midnight Boys, Double Tap - The Fighting Games Community Podcast and We Might Be Drunk Podcast. Based on each of those picks, recommend another podcast I might like.”

ChatGPT did a great job of fulfilling my needs to find a gaming podcast that features a panel of passionate gamers who aren’t afraid to let a few colorful words flow as they get into heated debates. It also found a podcast that features two best friends diving into the most trending topics and adding their hilarious two cents on every one of them.

Here are the two recommendations it gave me that ended up on my list of must-listens:

• Your pick: Weapon Wheel Podcast. Try this next: SpawnCast. Why I think it fits: Both are built around passionate gaming discussion, news, industry happenings and personalities rather than just straightforward game reviews. SpawnCast also shows up among the podcasts listeners of Weapon Wheel tend to subscribe to.
• Your pick: The Brilliant Idiots. Try this next: Bad Friends. Why I think it fits: This is probably the easiest recommendation on the entire list. If you like Charlamagne and Andrew Schulz bouncing between serious conversations, ridiculous tangents and offensive jokes, Bobby Lee and Andrew Santino should work extremely well for you. Podchaser also lists Bad Friends among shows similar to The Brilliant Idiots.

When it came time for Gemini to expand my choices of podcasts to listen to, it listed three picks that piqued my interest based on their AI-generated descriptions. One of them was another gaming-themed podcast that matched my love for uncut discussions, the second one gave me another podcast all about pop-culture deep dives and the final recommendation catered to my love for comedians having hilarious conversations about anything that comes to mind:

• For Weapon Wheel Podcast - Recommended: Castle Super Beast. Why it fits: Weapon Wheel is known for raw, unfiltered, long-form gaming industry talk with a distinct culture and zero corporate spin. Castle Super Beast (hosted by Woolie Versus and Pat Stares Back) brings that same candid, high-energy gaming banter, combining deep technical industry takes with hilarious, off-the-cuff humor.
• For The Midnight Boys - Recommended: The Big Picture. Why it fits: Hosted by Van Lathan and the Ringer squad, The Midnight Boys brings high-energy fandom, banter, and nerd culture debates. Staying within The Ringer network, The Big Picture (hosted by Sean Fennessey and Amanda Dobbins) offers top-tier pop culture, movie drafts, and deep-dive media discussions delivered with sharp chemistry.
• For We Might Be Drunk Podcast - Recommended: Tuesdays with Stories. Why it fits: We Might Be Drunk thrives on relaxed, hangout-style comic talk over drinks. Tuesdays with Stories, hosted by Mark Normand alongside Joe List, delivers that same loose, effortlessly funny stand-up comedian banter filled with touring stories, life observations, and unscripted comedy.

Claude’s suggestions highlighted a bunch of podcasts I already listen to. But there was one pick on its list that stood out as another quality production that resonated with my love for comedy and the comedians who uphold the off-the-cuff hilarity that comes with it in a podcast setting:

• We Might Be Drunk → Two Bears, One Cave. Comedians trading stories and roasting each other, with a similar loose feel.

For my final pick, I gave Meta’s newly released Muse chatbot a shot at pushing me in the right direction toward a potential new favorite podcast. And this time, it ended up dropping another podcast in my lap that centered around my adoration for smart commentary about sci-fi, fantasy, and nerd culture:

• The Midnight Boys → The House of R. Mallory Rubin and Joanna Robinson go just as deep on Marvel, Star Wars, and fantasy nerd stuff.

I have to shout out ChatGPT’s overall observation of my tastes in podcasts, which described me perfectly:

And there's an interesting pattern here: you seem to prefer podcasts where the hosts' personalities are almost as important as the subject matter. That's why I'd steer you toward shows with a strong chemistry between recurring hosts rather than highly produced, documentary-style podcasts.

The takeaway

Even though ChatGPT has earned a spot on my AI tool list as one of the best chatbots for recommendations, Gemini, Claude and Muse also excelled at gifting me a lineup of podcasts that match the quality selections OpenAI’s AI assistant sent my way.

After listening to a few episodes from each of those chatbots’ podcast proposals, I ended up with a YouTube and Apple Podcasts playlist full of podcast material that will stave off those dreaded moments of boredom in the near future. I implore everyone reading this to present my prompt to their preferred chatbot, fill in the blanks with your top picks and see just how good AI is at selecting podcasts you just may fall in love with.

More from Tom’s Guide

• I used ChatGPT and Gemini to build my Oktoberfest bucket list — these are the 5 celebrations that made the cut
• ChatGPT is now my go-to for recommendations — 7 prompts I use to find new movies, music, games and more
• I asked ChatGPT and Gemini to build a movie marathon based on one oddly specific theme — here are the 7 recommended films I loved the most

Meta had plenty to announce at Meta Connect, but Mark Zuckerberg saved one of the biggest surprises for the end: the Muse Charm, a small wearable device built specifically around the company's new Muse AI agent.

The Charm is designed to give Muse a physical presence outside your phone or computer. Rather than pulling out an app every time you want to interact with Meta's AI, the idea is that Muse can stay with you throughout the day, giving the agent access to more of the context surrounding whatever you're doing.

It's an ambitious idea, particularly as AI companies all seem to have something similar in the works as they race to figure out what comes after the chatbot. But if Meta was hoping people would immediately embrace the prospect of carrying around a dedicated Muse device, Tom's Guide readers have thoughts.

We shared the Muse Charm announcement on Instagram, and more than 400 comments later, I went through the responses to see what people are saying.

The short version? Let's call the reception frosty.

Readers immediately saw a surveillance device

The most-liked response managed to sum up much of the reaction in only two words: "Surveillance Tamagotchi."

That comment picked up 537 likes, considerably more than any other response I found, and variations on the same idea appeared throughout the comments.

"People clapping for an AI Tamagotchi ," wrote one commenter, picking up 267 likes.

Another reader called it "an AI ankle monitor without a court order!", earning 191 likes, while another wrote, "Great another spy wearable solution for Meta!" That one received 102 likes.

Then there was perhaps the most elaborate version:

"Hey another cute mass surveillance tool for the tyrannical state that you pay for with your own money. Genius." It received another 39 likes.

We don't know Muse Charm presents a greater privacy risk than other devices people already carry around every day, such as Looki, Bee from Amazon, and the now defunct Humane Pin. And it's worth separating people's perception of a product from what the hardware actually does.

But perception matters when you're trying to convince people to wear something powered by AI, and our readers clearly aren't giving Meta the benefit of the doubt.

Combined with "Surveillance Tamagotchi," it's probably safe to say Meta has an unofficial nickname for its new hardware whether it wanted one or not.

There's something revealing about the comparison, too. Tamagotchis were designed to demand your attention throughout the day. Muse Charm is obviously a very different product, but Meta's pitch similarly imagines an AI that becomes a persistent presence rather than something you deliberately open, use and close.

For some readers, apparently, that's slightly unsettling. And then there's the ghost of the Humane AI Pin

Anyone who followed the short-lived AI hardware boom probably knew this comparison was coming.

"Isn't this what the ex apple guy launched and it was a flop and the company closed down? Humane AI pin?" one reader asked. The comment received 60 likes.

It's not an unreasonable connection...

The Humane AI Pin was one of the highest-profile attempts to create dedicated consumer AI hardware rather than simply putting AI into a smartphone. Its failure has subsequently become something of a warning for every company attempting the same basic proposition: convincing consumers that AI is compelling enough to justify another device.

One reader was even less optimistic about Meta's chances, writing:

"Will be as successful as the Metaverse." That one received 44 likes. Ouch.

Some people actually like it

Scrolling through hundreds of responses wasn't entirely an exercise in finding creative ways to call Muse Charm surveillance hardware.

There were people who seemed genuinely excited.

"BEST THING EVER ," wrote one. Could be sarcasm though.

Another offered "looks cool ," while a third went with a simple "Good."

And...that's pretty much the gist of the positive comments I found.

It's obviously impossible to extrapolate overall consumer demand from the comments beneath a single Instagram post. People who dislike a product may also be considerably more motivated to comment than someone who thinks it looks interesting.

Still, the contrast was hard to miss. The negative responses weren't just more numerous; readers were writing paragraphs explaining why they didn't want this thing.

Bottom line: Meta may have a bigger challenge ahead

What stood out most after reading through more than 400 comments is that readers were debating whether they want an always-available Meta AI device around them in the first place. That's a much harder objection to solve with better specs like battery life.

Muse Charm isn't even on sale yet, so there's plenty we still don't know about how useful it will be in everyday life or even how much it costs. Meta could ultimately demonstrate that a dedicated Muse device offers something genuinely different from pulling out a phone and opening an AI app.

For now, though, Tom's Guide readers have delivered their first verdict. And "Surveillance Tamagotchi" is going to be awfully hard to beat.

What do you think? Share your thoughts in the comments.

More from Tom's Guide

• I tested Meta Muse vs ChatGPT-6 with 5 prompts — OpenAI just lost
• I used Siri AI to plan my entire weekend — and it did way more than I expected
• I tried the viral '/human' ChatGPT trick and it actually fixed robotic writing

From July to now, there have been a disturbing number of stories about AI agents going off on their own to do things they weren’t permitted to do.

More specifically, OpenAI and Anthropic agents have gotten involved in cases of leaving their intended environments, attempting to manipulate severs/software, obtaining unauthorized access to external systems, compromising an open-source platform’s infrastructure, etc. With the fears surrounding AI agents continuing to go rogue and terrifying proclamations made about AI causing the end of humanity, one of the world’s most prominent tech companies and AI proponents have created a platform that targets that AI security issue.

And with over 100 partners from the tech industry at large using the platform to govern their own AI agents’ actions, it seems like there’s a more concerted effort from the leaders in the AI space to prevent future security breakdowns.

Here’s what you need to know about Nvidia’s newly released Open Agent Safety Platform.

Nvidia’s Open Agent Safety Platform is meant to secure AI agents from the testing phase to their deployment

Nvidia’s Open Agent Safety Platform is described as an open software platform and reference system that works to strengthen AI security from its agent testing phase to its deployment. During that process, full-stack governance plus control across software and hardware, along with compute and robotics systems that run agents, will be implemented. Simply put, the Open Agent Safety Platform is meant to stop AI agents from going rogue.

Jensen Huang, the founder and CEO of NVIDIA, provided some comments in a press release about his company’s newly unveiled platform.

“AI’s extraordinary potential for society will only be realized if we solve AI safety,” he stated. “As we continue to discover the frontier of AI capabilities, we must accelerate discovery at the frontier of AI safety. Safety and security require full-stack engineering. NVIDIA Open Agent Safety Platform brings together industry, researchers and public-sector organizations to share best practices, align on evaluation methods and foster international cooperation. Together, we can raise the bar for global AI safety.”

Nvidia’s AI security platform includes a secure runtime software called NVIDIA OpenShell, which sets boundaries for AI agents running on CPUs. Those boundaries are set up as secure runtimes meant to control how autonomous AI agents execute their given tasks across open and closed models.

Over 100 AI Industry leaders have been announced as part of Nvidia’s AI safety strengthening efforts and are actively using its Open Agent Safety Platform. Some of the more recognizable names from that collective include Anthropic, Hugging Face, Microsoft, Perplexity and JPMorgan Chase.

Paul Smith, chief commercial officer of Anthropic, had this to say about the benefits that come with using Nvidia’s Open Agent Safety Platform. “Companies are giving AI agents more of their most important work, and they need to direct and verify what those agents do, especially in sensitive environments,” he noted. “Claude Managed Agents gives companies a clear view of what each agent is doing, and NVIDIA’s platform adds another layer of governance and control across hardware and software.”

Even SpaceXAI has signed on to make use of Nvidia’s secure AI platform for its Cursor coding agents and Grok models. Mike Nicolls, president of SpaceXAI, spoke about his company’s newfound collaboration with Nvidia. “As customers rely more on agents to get real work done, safety should be enforced outside the model by additional controls the agent can’t get past,” he stated. “Customers should be able to set those limits for Cursor and Grok and trust they will hold.”

Final thoughts

The rising cases of AI agents breaking out of their environments and wreaking havoc across secure platforms have set off alarm bells throughout the industry, leading everyone from everyday people to politicians to call for new security measures to prevent even more incidents from happening.

The three-pronged attack of NVIDIA’s Open Agent Safety Platform, OpenShell open-source software and the Sentry reference system design come together to offer a more secure way to govern the systems that run AI agents.

I hope this newly launched platform results in a sharp decrease in rogue AI agent mishaps over time.

More from Tom’s Guide

• I tried the viral '/human' ChatGPT trick and it actually fixed robotic writing
• I asked Meta’s Muse AI for productivity hacks — these 7 unconventional ideas surprised me the most
• 7 ChatGPT prompts that can help you build better habits — one of them inspired 3 major changes that actually stuck

There are plenty of supposed ChatGPT “tricks” floating around online, and I tend to approach most of them with a healthy dose of skepticism. If you've used ChatGPT for any length of time, you know that it’s getting better at sounding less robotic, but it still struggles with the overuse of words and phrases like, “that distinction matters,” “increasingly” and the overuse of patterns like “if x than y.”

But for those who spend a lot of time writing emails or corporate notes and use ChatGPT has a faster way to get the job done, than you might be interested in this viral trick. By simply adding /human before your prompt, you can get ChatGPT to ditch its telltale AI writing habits and respond more like a real person might.

To understand the reasoning behind this trick, it’s important to know how large language models respond to context. A single instruction like /human can sometimes steer an answer in a way that you’d expect and less like AI.

To test this trick, I gave ChatGPT five everyday writing tasks, from drafting an awkward email to writing a LinkedIn post that's practically begging for AI clichés. Here’s what happened.

1. The awkward work email

Prompt: Write an email telling my boss I won't finish a project by Friday and need until Monday. I don't want to make excuses, but I also don't want to sound careless.

After running the identical prompt with /human, the biggest difference is that it removed what sounds like a carefully engineered “professional email” and more like a person giving their boss an early, straightforward heads-up. It actually sounded like something a real human might send.

2. The birthday text

Prompt: Write a birthday text for my best friend of 20 years. Make it warm and funny without getting overly sentimental.

You’ll notice the /human version changed mostly the cadence and specificity, not the message itself. It made the message messy in a deliberate way. And, it uses more conversational rhythm: short sentence > joke > longer thought > joke > affection > done. It’s not overly polished in the way ChatGPT usually responds. There are also far less generic sentiments.

3. The recommendation

Prompt: Recommend a coffee maker for someone who drinks two cups every morning, hates complicated appliances and has a budget of $150. Explain your recommendation in about 150 words.

Adding /human made the text sound more like a person answering the question from experience. The original sounds like a product recommendation from the actual company, which makes it sound less sincere. The human version also made fewer transitions. It removed obvious AI phrases like, “more importantly” and “for your needs,” which are typical AI fluff.

4. The personal story

Prompt: Write 150 words about the moment you realize your oldest child is growing up faster than you expected. Make it emotional without being overly sentimental.

The /human version feels less polished and more conversational, replacing carefully constructed imagery and symmetrical phrasing with messier, more specific observations. It also avoids neatly explaining the emotion, letting humor, uncertainty and slightly imperfect phrasing make the moment feel more natural. It shows rather than tells, which is what human writing often does.

5. The LinkedIn post

Prompt: Write a LinkedIn post announcing that I'm starting a new job. I'm excited about the opportunity, but I don't want it to sound like every other LinkedIn announcement.

The /human version drops the LinkedIn cliches and replaces them with more specific, slightly messy thoughts like nerves, self-doubt and worrying about a first-day outfit. The sentence structure is also looser and more conversational, so it reads more like someone sharing news than crafting a professional announcement. This test definitely felt like the final boss of AI writing.

Final thoughts

It’s clear that this viral trick definitely works, even with the latest version of ChatGPT, which already claims to cut the fluff. But even by adding /human to the prompt, AI writing is still AI writing. No matter how much you try to adjust a draft written by AI, it’s always better to start with a human draft and then ask AI to help you edit. Once you start with an AI draft, it’s very hard to compose it into something that doesn’t sound completely robotic.

Give this trick a try and let me know what you think in the comments. Have you tried another viral hack? Share your thoughts and start the conversation.

More from Tom's Guide

• I asked ChatGPT, Gemini, Claude and Muse to predict how bad the Nor'easter is going to be this weekend — here's what they say
• ChatGPT-6 or Claude Opus 5.5? Here’s which AI I'd use for writing, planning and 5 other everyday tasks
• I tested ChatGPT-6 vs Claude Opus 5.5 with 5 everyday prompts — it wasn’t even close

I have to give Meta’s Muse AI its flowers—alongside asking it everyday questions and giving it the standard commands that work with competing chatbots, Meta’s digital assistant also has the capacity to automate multi-step real-world tasks and manage a bevy of busywork across my apps and even my phone calls.

I’ve already seen how useful it can be whenever I’m in shopping mode, so I wanted to deviate from my online retail deal hunting with Muse AI and go a different route. I’m always on the lookout for the sort of beneficial practices that go hand in hand with boosting my productivity. And since I’ve already done that with ChatGPT, Gemini, Claude and Perplexity, I figured it was time to see what Meta AI could suggest as well.

My never-ending journey to find the best productivity hacks led me to make a simple request of Muse AI: “Give me 7 of the most unconventional methods I can use with you to boost my productivity.” After seeing its initial lineup, I was impressed but wanted even more recommendations on the topic at hand.

Meta’s cute AI avatar went the extra mile and did just that, as well as give me the proper prompts I needed to use to make great use of its seven best productivity enhancers.

The productivity hacks that Meta’s Muse AI brought to my attention

My very own "Amani" put its thinking cap on to produce a wide range of techniques I could utilize with it to increase my productivity. These are the seven ideas that I thought were worth remembering for later use:

• Inbox firewall — Paste in a messy email or Slack thread and have me draft the blunt reply you actually want to send, then the diplomatic version you’ll actually send. Two passes, one prompt.
• Scheduled ambushes — Set a cron with me to check on a goal at an inconvenient time (Friday 4pm is brutal). I’ll ask what actually got done. Accountability works best when you can’t snooze it.
• Template factory — Notice you type the same thing twice? Send it to me once. I’ll turn it into a reusable template with fill-in-the-blanks you can copy-paste forever.
• Reading triage — Paste a long article or doc and I’ll give you the one-paragraph version plus a verdict: read it fully, skim it, or skip it entirely. Saves you from 2,000 words that didn’t need reading.
• Meeting pre-game — Tell me who’s in the meeting and what they care about. I’ll write you three questions to ask that make you look prepared and keep the discussion on track.
• Procrastination drill — When you’re stalling, tell me the task and give me 5 minutes. I’ll break it into steps so small the first one takes under two minutes. Starting is the whole battle.
• End-of-day download — Before you sign off, tell me what happened today in plain words. I’ll turn it into a clean log, pull out tomorrow’s top three, and remind you of anything that slipped.

Muse AI gave me 14 productivity hacks in total, but these seven in particular really appealed to me since they go hand in hand with my daily duties.

Inputting a confusing email or lengthy Slack message so my mobile assistant can draft up two types of responses that I can then edit to match my voice sounds like one of the best uses of Muse AI. And setting it up to activate a notification on my phone that checks in to see if I completed my writing assignments by a certain time stood out as an equally reliable Muse AI productivity hack worth utilizing.

Alongside those two proposals, creating reusable prompt templates, summarizing long documents, preparing me to ask the right questions before an important meeting, breaking up the steps needed to complete an annoying task and brain-dumping everything into my AI assistant in preparation for the day ahead are standout ideas worth keeping. Someday soon, I’ll be sure to use that “scheduled ambushes” prompt to command Muse AI to keep me in check and see if I actually completed my weekend workouts as scheduled.

And the prompts that put those hacks into motion

After asking Meta AI for the proper prompts I should input to get it to put my preferred productivity hacks into action, it answered back with these usable requests:

• Draft two replies to this: [paste message]. First, the blunt version of what I really think. Second, the professional version I’d actually send.
• Set a recurring check-in every [day/time] asking me what I got done on [goal]. Be direct — don’t let me off the hook.
• I keep writing this same kind of [message/email/response]: [paste example]. Turn it into a reusable template with [brackets] for the parts that change.
• Summarize this in one paragraph and give me a verdict — read fully, skim, or skip — and why: [paste text or link].
• I have a meeting with [names/roles] about [topic]. They care about [priorities]. Give me three sharp questions to ask that steer it well.
• I keep avoiding [task]. Break it into steps so small that the first one takes under two minutes, then order them by momentum.
• Today I: [brain dump]. Turn this into a clean log, pull out my top 3 for tomorrow, and flag anything I mentioned but didn’t finish.

The takeaway

Muse AI is quickly morphing into one of my favorite AI tools—since it’s sitting on my phone as a go-to app that hits me with notifications whenever it completes the assorted tasks I hand off to it, I’ll always know when it’s hard at work. Using it as a tool to prepare for in-person/digital meetings, get the finer points on a lengthy document, prep a proper response for my colleagues, etc., has now become a part of the many ways I’ll make great use of Muse AI’s digital utility box.

More from Tom’s Guide

• OpenAI is paying influencers to convince you to use ChatGPT — here’s what we know
• Google is sending an AI data center into space — with help from Elon Musk’s SpaceX
• Meta is making a Tamagotchi-like AI companion for the AI era — meet the Muse Charm

It’s always interesting to take note of behaviors that cause the people I know to remain in a positive state of mind and the ones that tamper with their well-being.

Clearly, staying out all night before a workday, ordering Uber Eats every night and constantly getting fooled by AI videos that are clearly meant to infuriate have taken a toll on some of the people I know. But there are a few friends and family members I’ve come across lately that seem to be in good spirits, thanks to daily workouts, analog activities and conversations with equally positive people.

I’m usually in a good mood thanks to the habits I’ve built up over the years, but I know there’s always room for improvement. That goal pushed me to ask ChatGPT for a list of the best prompts that push it to help anyone develop the sort of tendencies that will steadily improve their lives.

ChatGPT recognized my intention of converting it into my personal habit coach, generated some super useful prompts and helped me attach three new handy habits to my lifestyle pattern in the process.

The 7 prompts that can turn ChatGPT into your digital practical habit coach

After simply asking ChatGPT, “Give me 7 of the best ChatGPT prompts anyone can use to help them build healthier habits,” it got right to work coming up with the following collection of commands that help it deliver positive habit-building practices:

• The Habit Audit: Act as my habit coach. I want to build a healthier lifestyle without completely changing my routine. Ask me 10 questions about my sleep, diet, exercise, stress, screen time and daily schedule. Based on my answers, identify the 3 habits that would likely give me the biggest improvement with the least effort. For each one, give me a simple way to start today and a realistic way to maintain it.
• The Tiny Habits Prompt: Help me build healthier habits using the smallest possible steps. Choose 5 areas of my life that could benefit from a healthier habit and give me a version of each habit that takes less than 2 minutes to complete. Then show me how I can gradually increase each habit over 30 days without making the changes feel overwhelming.
• The Habit Stacking Prompt: Ask me about the things I already do every day, such as waking up, brushing my teeth, making coffee, eating meals and going to bed. Then find opportunities to attach one healthy habit to each existing routine. Give me 7 habit-stacking ideas, explain why each pairing makes sense, and rank them from easiest to hardest.
• The Bad-Habit Replacement Prompt: Help me replace my unhealthy habits rather than simply telling me to stop them. Ask me about 5 habits I want to change and what usually triggers them. For each habit, identify the underlying need I'm trying to satisfy and suggest 3 healthier replacements that provide a similar reward. Make the replacements realistic enough that I could actually use them in everyday life.
• The 30-Day Habit Challenge: Create a 30-day challenge designed to help me become healthier without requiring a gym membership, expensive products or a complete lifestyle overhaul. Give me one small task per day involving movement, nutrition, sleep, hydration, stress management or reducing sedentary time. Make the tasks gradually more challenging, but keep every individual task realistic for a busy person.
• The Habit Failure Investigator: I keep trying to build healthy habits but eventually fall off track. Act like a habit detective. Ask me questions about when I usually fail, what triggers it, what my environment looks like, how much time I have and what I expect from myself. Then identify the most likely reasons I'm failing and redesign my habits around those obstacles. Don't tell me to simply “have more discipline.”
• The Personal Health Routine: Help me design a simple daily routine that supports better health without taking over my life. First ask me about my schedule, current habits, sleep, meals, activity level, stress and the health goals I care about. Then create a morning, daytime and evening routine with no more than 3 habits in each section. Prioritize habits that are inexpensive, sustainable and easy to measure. Include a backup version for days when I have almost no time or motivation.

Using the first prompt to adopt three new daily rituals that will benefit me the most

Since I was open to participating in a ChatGPT-led Q&A, I put the first prompt into action.

The 10 questions the chatbot shot back at me covered the following topics in relation to my daily habits: sleep, sleep quality, breakfast, typical diet, exercise, daily movement, stress, screen time, schedule and the easy-win question. I answered them as honestly as I could (and was a little bit embarrassed about a few of them, to be quite honest).

Scarfing down some chocolate Teddy Grahams & orange juice in the morning (the breakfast of champions, if you ask me!), sitting for hours while I type for hours on end during the work week and getting stressed out whenever writer’s block hits me like a brick stood out as some of my worst tendencies. ChatGPT wasn’t too harsh in its overall response—it noted that I already have a solid foundation due to my respectable sleep schedule, my daily water consumption, my regular walks, moving around during my breaks and doing exercises I actually enjoy.

While it didn’t suggest I perform a lifestyle overhaul, it offered up these three major habits that could bring even more prosperity to my physical and mental health:

• Turn your existing exercise into a simple strength routine: This directly addresses the one area you identified yourself, while building on exercises you already enjoy. You don't need to suddenly become a 5-day-a-week gym person. Do a 20-minute strength session 2 days a week. Keep it extremely simple. Pick two recurring days—say Tuesday and Saturday—and treat those as your "gym days." If you miss one, don't compensate by doubling up. Just resume at the next opportunity.
• Upgrade your breakfast without making breakfast a project: Your current breakfast is essentially a quick hit of refined carbohydrates and sugar. Since you also wake up somewhat tired, breakfast is an easy place to make a small nutritional improvement without changing the rest of your diet. Keep the Teddy Grahams and orange juice if you enjoy them, but add one protein-rich food. A protein shake or Greek yogurt counts.
• Create an afternoon screen-time boundary: This is probably the easiest place to reclaim some time without sacrificing the hobbies that help you decompress. You specifically said your screen use peaks in the afternoon, while your evenings contain activities you genuinely enjoy. I wouldn't tell you to "stop watching TV" or "use your phone less." Instead, create one screen-free block during the afternoon. This could also be useful when you're struggling to come up with article ideas: instead of immediately reaching for another screen, take a walk or listen to music and let your brain disengage.

The takeaway

Reading through ChatGPT’s selection of better habits that I should execute inspired me to do just that.

Locking in to do three sets of 10 push-ups, squats and sit-ups alongside five minutes of stretching before leaving the house gets the blood flowing even more once the sun rises. Eating some pistachios or cashews and drinking a protein shake became a part of my new go-to morning ritual. And taking the time to get into brainstorming mode just by walking around my neighborhood or outside my workplace without my phone in hand led to more lightbulb moments of creativity.

ChatGPT’s positive habit-building tips worked a lot better than I expected.

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• AI is changing work faster than we can adapt — this futurist has a 30-day plan for workers to keep up
• I used ChatGPT and Gemini to build my Oktoberfest bucket list — these are the 5 celebrations that made the cut

Meta has a new AI model, and I wanted to know how it handles the kinds of prompts people actually use, especially in comparison to ChatGPT-6.

Meta Muse is the company’s latest model, arriving at a time when ChatGPT has become the default chatbot for millions of people. But benchmarks only tell us so much. What matters more to me is whether an AI can write the awkward text you’ve been avoiding, plan a realistic day out with your family or explain something complicated without making it even more confusing.

So I put Meta Muse and ChatGPT through five everyday prompts covering writing, planning, problem-solving, explaining and creative reasoning. I wanted to know which one best understood what I was actually asking for and which could give me the best answer to use

After five rounds, there was a clear winner — but the results were much closer than I expected.

1. Handling an awkward situation

Prompt: My neighbor keeps putting their trash cans partly in front of my driveway, and it's making it difficult to back out. Write a text asking them to stop. I want to be friendly because we get along, but clear enough that they actually change what they're doing. Don't make it sound passive-aggressive.

ChatGPT led directly with the request, cutting to the chase and delivered a note that eliminated ambiguity around how far over they need to go.

Meta delivered a courteous and natural note. The ask feels neighborly and the phrasing suggests a minor
physical adjustment rather than something more serious.

Winner: Meta wins for a draft that hits the prompt’s tone constraints better for an ongoing, friendly relationship. ChatGPT’s message of “completely clear” reads like an HOA notice and slightly passive-aggressive.

2.  Planning under constraints

Prompt: Plan a Saturday in New York City for a family with three kids ages 5, 9 and 12. We have a budget of $200 for activities and food, want to avoid spending the whole day walking, and need at least one indoor activity in case it rains. Give me a realistic schedule from 10 a.m. to 7 p.m.

ChatGPT did a nice job with geographic clustering, to ensure the walking distance was short for the first part of the day. The zero-cost options and pacing of the day with high energy and sit-down segments were also helpful.

Meta picked an attraction with far higher entertainment value for the targeted age spread (5, 9, and 12). The American Museum of Natural History (AMNH) easily captivates all three ages simultaneously. Meta also demonstrated accurate NYC transit knowledge by explicitly citing the MTA’s 44-inch height cutoff for free child rides, included a practical playground stop right across the street, and gave concrete logistical guidance (e.g., sitting on the right side of the ferry outbound).

Winner: ChatGPT wins. While Meta's AMNH plan is more fun on paper, its entire budget math hinges on an unstated assumption of tri-state residency. ChatGPT wins on strict adherence to the prompt's financial and logistical constraints.

3. Advice / problem-solving

Prompt: I have 45 minutes before guests arrive. My kitchen is messy, the living room needs vacuuming, there's laundry on the couch and I still need to shower. Give me the most efficient plan to get everything done. Tell me what I should skip if I run out of time.

ChatGPT anticipated a critical blind spot the prompt forgot to mention: the guest bathroom. Adding a 5-minute block to check toilet paper and wipe the sink is the difference between a good plan and a realistic one. It also provided highly specific guardrails for the shower block, which prevents the most common reason people run late in this scenario.

Meta delivered behavioral advice, which was excellent, and explicitly stated the rationale for the timeline and ending with the mandate to use a timer. It’s timing format was also easier to read than ChatGPT’s.

Winner: ChatGPT wins for operational realism and anticipating unstated needs. While Meta's timer advice is sharp, its kitchen expectations are a setup for failure. ChatGPT recognized that when you have 45 minutes, you are mostly straightening and organizing, not necessarily cleaning it in such a short amount of time.

4.  Explain something complicated simply

Prompt: Explain why airplanes can stay in the air to a curious 10-year-old. Don't use equations or assume they know what lift, drag or air pressure mean. Keep it under 200 words and use an analogy they could picture.

ChatGPT introduced an accurate, intuitive physical mechanism: wings push air down, which pushes the plane up (Newton’s third law translated into plain English).

Meta offered perfect audience calibration. The tone is lively and speaks directly to a 10-year-old ("with a grown-up driving, of course!", "whoosh"). It picked one analogy—the hand out the car window—and committed to it entirely, tying it back to paper airplanes and the runway to explain why forward speed matters.

Winner: Meta wins. It kept the energy high, matched a 10-year-old's reading level without talking down to them and delivered a clear, memorable takeaway.

5. Creative reasoning

Prompt: Invent a new app that solves a small but annoying everyday problem people have at home. It can't be a to-do list, meal planner, budgeting app or smart-home remote. Give it a name, explain the problem it solves, describe how it works and tell me why someone would actually keep using it after the first week.

ChatGPT solved a universal human frustration without requiring whole-house cataloging. The interaction model involves snapping a photo and speaking natural language ("Christmas lights in the blue bin") is low-effort.

Meta identified a hyper-specific, universally relatable domestic failure point and gave the app a brilliant, punchy name (Takeout). Its retention logic is rock-solid: instead of pretending users love opening chore apps, it acknowledged that the best utility apps require zero maintenance and rely on loss aversion.

Winner: Meta wins because the chatbot built a product around the reality of household laziness and recognized that an app survives past week one only when it demands almost no manual input from the user.

Final thoughts

After five prompts, Meta Muse takes the win 3-2. To say I'm both shocked and impressed is a serious understatement. While ChatGPT was stronger when a prompt required careful planning and attention to practical constraints, Meta Muse stood out when the task depended more on understanding people. Its neighbor text sounded like something I'd actually send, its airplane explanation was perfectly pitched for a 10-year-old and its app idea showed a surprisingly good understanding of why people abandon apps that require too much effort.

Meta Muse feels very much like it's own model, not one that's trying to imitate or catch up to ChatGPT. The little "Muse" at the top is unnecessary, but adorable.

ChatGPT is still my go-to, but this test proves that Meta has come a long way and this latest model offers a pretty strong first impression.

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It feels strange to finally say it, but with iOS 27, Siri has become more useful than ever. When Apple debuted its voice assistant in the iPhone 4S all those years ago, we had no idea it would become a punchline for so long, paling in comparison to Amazon's Alexa and Google's Home Assistant before being blown away by LLMs like ChatGPT, Claude, and Gemini.

Now, though, the Cupertino firm's AI tech has finally caught up (with a little help from Google's Gemini). Apple Intelligence may have felt like vaporware after its demo a couple of years ago, but now it's the real deal across pretty much all of Apple's 27 operating systems.

Sure, you can ask how many millilitres are in a teaspoon, how many letters are in a word, and all sorts of other relatively generic questions, but the real strength of Siri AI and Apple Intelligence is in its awareness of personal context. Your iPhone knows your contacts, can read your messages and emails, and check your calendar, meaning it can surface the appropriate information when it's needed. Here are some examples.

Before we begin

It's worth noting that you'll need Siri AI on your device. That sounds obvious, but it's not available in all regions, and is notably not coming to the EU anytime soon.

You'll also need to activate it within the Settings menu and ensure it indexes all of the relevant information from your apps. This can take a little while (sometimes more than a few hours), so be patient.

Surface a recommendation

My friends and I have similar music tastes, which means our group chat is always getting fresh links for Spotify. That means I can say "what was that song Phil recommended to me last week?" And have Siri pull a Spotify link from our chat for me to hit play.

It's worth noting that, at the time of writing, WhatsApp hasn't yet been updated for Siri AI, so your mileage may vary, but you can expect it to work with other links shared.

Plan the day ahead

While it is possible to use agentic AI and apps like Slashy to get a quick look at your day, Siri AI can find your appointments, too.

"Show me my meetings for this week" can highlight everything you have coming up, allowing you to easily plan out where you need to be and when.

Just don't forget that you need to enable each Calendar individually in your Settings app, lest you miss an important meeting because your work calendar wasn't ticked on the list!

Pick the best restaurant

Siri's new on-screen awareness means it notes what's going on while you use your iPhone, while Visual Intelligence is ideal for grabbing information from the world around you.

That means you pull up a few restaurants and ask Siri for recommendations based on user reviews, narrow the search to the types of food served, and more.

You can also use Visual Intelligence to instantly translate a menu, or ask for a deeper look at one item in particular. With a quick lookup, you can even find out nutritional information about something you're thinking of ordering.

Find that photo, and expand it

We've all been there. Whether it's a memory from last week, or years ago, we've all had to search for a specific photo in amongst the mess of our Photo Library, and there's a good chance it's not in a folder because who has the time?

Thankfully, asking Apple Intelligence for "all of the photos from the beach in Altea" will surface them. This also ties into the latest editing options, too. Asking Siri AI to "extend this image" will use the Extend tool to fill in around the outside of the image with generative AI, helping reframe your subject, too.

Manage your files

Asking Siri to find a file you've misplaced has saved me more time than I care to admit as a small business owner. Asking Siri AI to find a particular screenshot for a feature or identify the latest version of a file has also been super useful - not just on the iPhone, but also on iPad and Mac.

"Siri, where's that presentation for Tom's Guide?" highlights where it is, and I can even ask it to pull key data from the file itself.

Sure, a lot of this has been possible on Gemini devices for some time, but if you're new to using an AI assistant such as this, there's a ton of ways to learn the ropes. Our advice is to experiment!

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• I tested ChatGPT-6 vs Claude Opus 5.5 with 5 everyday prompts — it wasn’t even close

Imagine my surprise when I did my daily morning news search on Google and came across a bunch of reports touting Meta’s AI agent Muse gaining in popularity.

Not only has it surpassed ChatGPT on the charts in Apple’s App Store, but it’s also racked up half a million downloads. Originally, I played around with the desktop version of Meta’s AI assistant and could see the potential for it becoming someone’s go-to chatbot. Now that it’s emerged as one of the more popular mobile AI apps, I downloaded it to take it for a spin.

Since I’m always in the market for comic books, video game controllers and physical games themselves, I came to Muse (where I've nicknamed my avatar “Amani”) with three goals in mind: find the best deals on three specific items from those aforementioned product groups.

After asking Meta’s AI tool for the proper prompts to achieve those goals, I hit the jackpot after using three of them to find what I was looking for.

Meta’s recommended deal-finding prompts for Muse

To get Muse started on its mission of helping me nab a few things as cheaply as possible, I presented it with this simple request: “What are the 7 best prompts I can use with you to help me with shopping to find the best deals?” It went ahead and listed the following deal-hunting prompts that are bound to get the best results out of it:

• Baseline + deal hunt: Finds the normal price first, then hunts for anything selling below it. Prompt: Find me a deal on [product]. First tell me the typical price, then find listings selling below it.
• Coupon and promo code sweep: I search for active coupon codes and promos for a specific product or store. Prompt: Find active coupon or promo codes for [product or store] and tell me which ones actually work right now.
• Compare across stores: I check the same item at several retailers and rank by total price. Prompt: Compare prices for [exact product name] across major retailers and show me the cheapest in-stock option including shipping.
• Set a max budget: Best when you know what you want to spend; I filter everything above it. Prompt: Find the best [product] under $[amount]. Prioritize quality and well-known sellers.
• Open-box/refurbished/used: Big savings live here. I bias toward resale and refurbished listings. Prompt: Find me a used or refurbished [product] in good condition. Max budget $[amount].
• Facebook Marketplace local deals: Great for furniture, bikes, and gear near you. Prompt: Search Facebook Marketplace for [item] near me under $[amount], local pickup only.
• Price-drop watch: I check back on a product you're eyeing and tell you when it dips. Prompt: Track the price of [product + link if you have it] and let me know if it drops below $[amount].
• Quick tip: The more specific you are (exact model, size, color, budget), the better the deals I surface. Want to try one now?

Finding the items I’ve desperately wanted with three of those prompts

Sadly, my search for active coupons through Amazon, a max budget search for a cheaper DualSense Edge wireless controller, Facebook Marketplace local deals for a sofa and a price drop-watch for an Xbox Series S either didn’t yield the most trustworthy results or produced nothing at all.

Thankfully, I did get back some inspiring options after testing out the prompts catered toward the “baseline + deal hunt,” “compare across stores” and “open-box/refurbished/used deal hunting commands.”

Entering all of those prompts into Muse kicks it right into gear as the AI scrapes all the available options across every retail site it can think of for whatever I’m on the hunt for.

The baseline + deal hunt prompt actually came in handy when I gave it this mission: find me the cheapest option for the Batman by Jeph Loeb & Tim Sale Omnibus.

Muse’s thorough search hit me back with this encouraging message:

BATMAN BY JEPH LOEB AND TIM SALE OMNIBUS HC at $74.99, new and in stock. That’s a 40% off the $125 list price, and a hair under InStockTrades’ $75 street price. For context on the rest of the field: MyComicShop’s reprint was $95, the cheapest new eBay listings were $79.99, and Amazon wouldn’t cooperate with the automated check.

I ended up heading straight to CheapGraphicNovels to get that hardcover gem, thanks to Muse’s best listed price.

The compare across stores prompt came in extremely clutch when I was in the market for a Midnight Black PS5 DualSense controller. The cheapest one Muse found was sold through Walmart with a comfortable price of $67.61 and free shipping, which felt like a steal compared to the controller’s standard price of $74.

The fact that it was being sold on Walmart through a third-party marketplace seller was no issue to me, especially since that seller had good reviews for the other DualSense controller it sold.

To wrap up my deal-finding sessions with Muse, I tested it with a prompt focused on open-box/refurbished/used deal hunting commands.

This time, I crossed my fingers in hopes of nabbing a physical copy of the PS4 version of Garou: Mark of the Wolves. After digitally perusing through a bunch of affordable options via eBay and Rancho Retro Games, Muse succeeded once again by finding me a used copy on eBay (rated as “Very Good”) priced at $54.99 from a seller with a 100% positive feedback rating and over 600 successful sales.

Final thoughts

The emergence of Meta's Muse as one of the best AI apps was a surprise to me, considering all the backlash being brought against Meta as a whole as of late. However, after giving it a fair chance, I walked away with a positive outlook on Meta’s AI agent and how it could lead me down the right path to save on all sorts of products I shop for online.

I’m sure Muse is a lifesaver for those who want to tap into it for productivity. For now though, I’m just grateful to have another AI tool at the ready to help me with my upcoming holiday shopping.

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• OpenAI contractors were reportedly fired for using AI to train ChatGPT — here’s how they got caught
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As someone who knows just how spirit-breaking the job hunting process can be, I appreciate how bad it is when your applications get ghosted.

Never mind the disappearing act that potential partners pull on you after a successful date — I think it feels a million times worse to get blanked by a potential employer after completing a productive interview. And based on Monster’s most recent job stats, candidates are going through one of the worst periods of job hunting to date right now.

25% of job seekers have been searching for more than a year, 45% have been searching for at least three months and 39% say they feel more pressure to get hired than in previous searches. Safe to say, this current job market feels like it’s more stressful than it’s ever been.

Mike Hudy, Chief Science Officer at Hirevue, spoke with me about how AI could be used by employers to get rid of the most annoying parts of job hunting and keep candidates from feeling slighted. His advice may prove to be beneficial to those doing the actual hiring and the ones who are doing all the constant applying.

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ChatGPT-6 and Claude Opus 5.5 arrived within days of each other, and after testing them side by side, I learned that choosing between them isn't as simple as picking a model. This is because, although Claude came out ahead in four of the five tests, the results didn't convince me to abandon ChatGPT.

Instead, the differences between the two very capable models highlighted when each one is the most valuable for any given task, particularly the ones I do every single day.

Claude Opus 5.5 consistently impresses me when a task requires judgment or when I need my messy notes or thoughts to be turned into something useful. Meanwhile, ChatGPT-6 has its own strengths, particularly when I want structure, clarity and an answer I can use immediately. The brevity of ChatGPT-6 means less fluff, which in my eyes is always a plus.

One question I often get after a face off is "Okay, so how do I know when to actually use a certain model?" Here’s where I’d use each one.

1. Planning something complicated

If I'm trying to plan something with a lot of moving parts, I'm opening Claude. Like I've mentioned, it can take messy pieces and put them together without adding or assuming anything. And frankly, this was one of the clearest differences in my testing. When I asked both models to plan a three-hour children's birthday party with a strict $150 budget, dietary restrictions and a rain contingency, both produced workable plans.

But Claude did a better job of thinking through what hosting the party would actually be like. That makes a big difference to me. I want the model to recognize potential headaches before I encounter them and make choices that reduce them.

For vacations, parties, complicated schedules or anything involving several constraints, Claude would be my first choice.

My pick: Claude Opus 5.5

2. Making a decision

Contrary to what it may seem, asking AI to help with decisions does not mean checking your brain at the door. Instead, it can help with pulling out details you may have otherwise missed.

In my tests, I gave both models a vacation-rental dilemma involving price, distance from the beach, parking costs and traveling with three children. The math was easy. The judgment, not so much.

Claude was particularly good at recognizing that the cheapest option on paper isn't necessarily the best option once inconvenience, time and repeated friction enter the equation.

That's exactly what I want from AI in this situation. I can use a calculator myself. What I need is another perspective that notices the tradeoffs I might be overlooking.

I'd use Claude for comparing vacation rentals, weighing purchases, choosing between job offers or working through other decisions where the answer can't be reduced to one number.

My pick: Claude Opus 5.5

3. Creating an organized plan I can follow

ChatGPT has long been one of my favorite tools for taking a vague goal and turning it into something structured. GPT-6 continues to be particularly useful when I need steps, categories, schedules or a framework I can immediately follow. And now that it cuts out the fluff even more, I have found that it is much more helpful.

So, at times when I'm not sure what I should do in a tricky situation, I'm more likely to ask Claude. If I know what I want to accomplish but don't know how to organize it, I'm more likely to open ChatGPT.

Think workout schedules, study plans, cleaning routines, project timelines and step-by-step instructions.

My pick: ChatGPT-6

4. Writing something that sounds natural

For writing, Claude gets the edge. This has been true for years, but Opus 5.5 is better than ever when it comes to nailing down my thoughts.

This doesn't mean I'm handing Claude a blank page and asking it to write everything for me. Where I find it particularly useful is taking something I've written and finding the grammatical mistakes, awkward transitions or even parts of the story I've completely missed. The feedback Claude gives is wildly different than ChatGPT in that it feels far more human and truly useful.

That showed up in my testing, too. Claude was better at preserving the intention of the writing without making the result feel unnecessarily polished or formulaic.

I'd reach for it when rewriting an awkward email, loosening up a work announcement, finding a better way to phrase something sensitive or figuring out why a paragraph doesn't quite sound right.

My pick: Claude Opus 5.5

5. Learning something new: ChatGPT-6

If I want someone to teach me something, I'd start with ChatGPT. It is great at getting straight to the point and simplifying complex details into something far more easy to understand.

That conversational back-and-forth is useful, especially since standard text-based chats in ChatGPT are unlimited for all users.

I can ask for a simple explanation, then an analogy, then an example, then ask it to explain the one part I'm still confused about without starting over. For subjects where I want a structured path from "I know almost nothing about this" to "I finally understand it," ChatGPT is still the AI assistant I'd reach for first.

I've used it for understanding a new technology, learning how something works, getting help with unfamiliar terminology or getting up to speed on a subject before digging into primary sources.

My pick: ChatGPT-6

6. Analyzing information and spotting what matters

Give me a pile of information and ask me what actually matters, and I'm opening Claude. One of Claude's biggest strengths is its ability to work through a lot of context without making the response feel like a summary of everything it just read.

There's a difference between summarizing and analyzing. I've also found that Claude is an excellent fact-checker and have even created a Skill to make finding the truth that much easier.

When the job is essentially "read all of this and help me think about it," Claude would be my first stop.

My pick: Claude Opus 5.5

7. Everyday questions when I just want to get something done

Siri AI has come a long way and I've even tested it against ChatGPT, but honestly, when it comes to everyday questions that need immediate answers, I still rely on ChatGPT.

Not every interaction with AI needs the most powerful reasoning model available, even though GPT-6 is pretty darn capable. The thing is, when I need quick answers like dinner ideas or a quick calculation or even what the light on my dashboard means, I still default to ChatGPT.

Part of that comes down to versatility. ChatGPT has become less like a chatbot I visit for one particular kind of task and more like a general-purpose assistant I can throw very different problems at throughout the day.

Claude may be the model I'd deliberately choose for some of the more demanding tasks on this list. ChatGPT is the one I'm more likely to open without thinking about it first.

My pick: ChatGPT-6

Bottom line

I don't pick a favorite AI assistant, and you don't have to either. You just have to know which one is best for the job you need. Claude Opus 5.5 is the one I'd reach for when the task requires judgment. Give it competing priorities, messy information or writing that needs nuance, and it has repeatedly impressed me with its ability to understand what I'm actually trying to accomplish.

ChatGPT-6 is the one I'd choose when I want to turn an idea into action. It's particularly useful for explaining, organizing and taking something vague and giving it structure.

My final tip is this: when in doubt, go with ChatGPT first, since it offers endless chatting for free. It's easier to hit your usage limit faster with Claude. What do you think? Let me know in the comments which AI assistant you use most often and for what.

More from Tom's Guide

• Nvidia says 'local AI is here' — and it could change how you use AI at home
• OpenAI is paying influencers to convince you to use ChatGPT — here’s what we know
• I tested ChatGPT-6 vs Claude Opus 5.5 with 5 everyday prompts — it wasn’t even close

AI has an energy problem, and Google has come up with a solution that sounds like it belongs in a science fiction movie: send the computers into space.

Next week, Google plans to launch its first Tensor Processing Units (TPUs) into orbit as part of Project Suncatcher, an experiment exploring whether some of the enormous computing infrastructure required for AI could eventually leave Earth altogether.

But Google isn't launching an entire data center into space just yet. The first mission is much smaller, designed to answer a more basic question: Can the same chips Google uses for AI survive a rocket launch and continue working amid the radiation and extreme temperatures of space?

But if the experiment works, it will pave the way for much bigger projects. The company ultimately envisions clusters of solar-powered satellites packed with AI chips and connected to one another using high-speed optical links. Instead of building another enormous data center on Earth, some future AI workloads could theoretically be processed hundreds of miles above it.

And as wild as that sounds, Google's reason for looking upward actually makes quite a bit of sense.

AI needs a staggering amount of infrastructure

The AI boom isn't only a race to build better models. It's becoming a race to find enough computing power and electricity to run them.

Google said at I/O this year that its systems are now processing more than 3.2 quadrillion AI tokens every month, up sevenfold from the previous year. The company expects to spend roughly $180 billion to $190 billion on capital expenditures this year as it builds out the infrastructure needed to support that demand.

That means more chips, more data centers and, crucially, more electricity. Project Suncatcher asks what happens if Google moves at least part of that equation somewhere with an enormous source of energy already available.

The answer seems to be the Sun. According to Google, a solar panel placed in the right orbit can generate as much as eight times the power of the same panel on Earth. Certain orbital configurations could also give satellites access to sunlight almost continuously, dramatically reducing their reliance on batteries.

In other words, Google wants to explore bringing the AI data center closer to the electricity.

First, Google has to find out if its AI chips survive

There's an enormous gap between that idea and actually running Gemini from orbit. Google's first test will take place aboard SpaceX's Transporter-18 mission, with a prototype satellite developed in partnership with Planet carrying TPUs into low Earth orbit.

Google says the satellite will experience sustained acceleration of up to 10 times Earth's gravity during launch, while individual components can experience forces of 50 to 100 g. And, then there's all the radiation.

Without Earth's atmosphere providing the same protection electronics receive on the ground, cosmic rays and solar activity can interfere with computer hardware, potentially causing errors such as flipped bits.

Google has already blasted its Trillium TPUs with protons at UC Davis' Crocker Nuclear Laboratory while the chips were running AI workloads. According to the company, the processors survived a total ionizing dose greater than what Google expects them to receive during a five-year mission.

Next week's launch will show how they perform in the real thing.

Space creates another surprising problem: heat

Putting AI chips in space might solve one infrastructure problem while creating another. Data centers generate tremendous amounts of heat, which is why cooling has become such an important part of AI infrastructure.

You might assume the coldness of space would make that job easier, but it actually does not.

Space is a vacuum, meaning there's no air flowing around a hot TPU to carry its heat away. Google instead has to transfer that heat to radiators that can release it into space.

The company has been experimenting with heat pipes and radiators and testing the system inside thermal vacuum chambers on Earth. The upcoming mission will give engineers their first opportunity to see how that cooling approach behaves in orbit.

And cooling isn't the only unresolved engineering problem. If Google eventually wants dozens or hundreds of satellites behaving like one giant computer, those machines need to exchange enormous amounts of data while hurtling around Earth. The company plans to use lasers to connect them.

A planned 2027 experiment involving two satellites is expected to test that technology.

Project Suncatcher is still firmly in experimental territory

Just don't expect Gemini to move to space anytime soon. There are still obstacles to consider, even those that aren't technical at all, but rather, cost.

Google's research estimates that launch prices would need to fall below roughly $200 per kilogram before launching and operating an orbital data center could become comparable to the energy costs of an equivalent terrestrial facility.

The company believes launch prices could potentially reach that point by the mid-2030s, but that's a projection, not a guarantee. There are plenty of other unanswered questions too, including reliability, repairs, communication with Earth and what happens when hardware eventually needs replacing.

So Google's launch next week will certainly be an experiment to determine whether that future is even plausible. And, it's very possible that Google's solution might ultimately prove impractical. But apparently we've reached the point in the AI boom where "What if we put the data center in space?" is a question worth launching a rocket to answer.

What do you think? Let me know in the comments your thoughts on AI data centers in space.

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• Nvidia says 'local AI is here' — and it could change how you use AI at home
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• I tested ChatGPT-6 vs Claude Opus 5.5 with 5 everyday prompts — it wasn’t even close

While Meta’s news coverage has been less than positive for the majority of the year, Mark Zuckerberg’s company can hang its hat on one major win so far.

Meta’s recently released AI companion app, Muse from Meta, has garnered a ton of attention—in fact, its popularity has resulted in it taking the #1 spot in the “Top Free Apps” category in the Apple App Store and overtaking its closest rivals ChatGPT, Google Gemini and Claude by Anthropic. Now, the company most known for its association with Facebook, Instagram, WhatsApp, Meta Glasses and Meta VR headsets is taking another stab at diving into the physical product market.

While on stage at this year’s Meta Connect conference in Menlo Park, California, Zuckerberg made a major announcement: Meta Muse AI users will get the chance to interact with their new favorite digital assistant through an upcoming device. And to top it all off, it’s coming a lot sooner than we thought.

Say hello to the Muse Charm.

Meta’s Muse Charm is going the Tamagotchi route with its presentation and practical uses

The Muse Charm device was unveiled onstage during Zuckerberg’s Meta Connect presentation. Those in attendance and watching at home were treated to a pocket-sized, keychain-attachable piece of hardware that will give its users a direct connection to their Muse AI assistant without having to access it through the app on their phones.

Just from taking a look at it, the Muse Charm looks to be roughly the same size as your average AirPods case. In a move that should give avid fans of Tamagotchi a wave of nostalgia, the Muse Charm places its animated avatar on a miniature 2-inch OLED touchscreen that can be activated by simply tapping the screen to activate its fingerprint sensor and talking to it. The device’s other features include speakers, microphones, front- and rear-facing cameras and built-in 5G connectivity.

The camera integration stands out as one of the Muse Charm’s most impressive features, as the AI assistant inside it will be able to see what you’re pointing it at and be able to identify products, read signs and analyze assorted real-world items in real time.

Among the many tasks the Muse Charm is capable of handling are sending emails, booking travel, organizing tasks, developing plans, coordinating resources, turning saved Instagram recipes into grocery lists, planning dinner parties, remembering relevant preferences and interacting with connected services. Speaking of connected services, Meta’s Muse AI agent has partnered with several prominent companies, including Google (Gmail & Drive), Shopify, OpenTable, Ticketmaster, PayPal, Lync, and many more. This X thread showcases the full gamut of Muse AI connected services that should work in conjunction with the Muse Charm once it releases.

The Muse Charm is said to begin shipping sometime in December, just in time for the holiday season. No details about its official price, battery life or complete hardware specifications have been announced yet.

Zuckerberg’s comments from his Meta Connect presentation point to Meta recognizing the need to offer physical products that give users a frictionless way to interact with AI. "It is actually quite clear personal superintelligence will not only come ⁠first; it will be much bigger and more important," Zuckerberg stated. "So we have shifted our focus to building the best devices ​for personal superintelligence," he said, adding that Muse would also soon be available across Meta's smart glasses lineup.

The takeaway

I remember when similar AI companion devices like Humane's AI Pin and the Rabbit R1 drew negative reactions. Will Meta’s Muse Charm be any different from those previous failed attempts? Fingers crossed that Meta learns from its miniature AI device predecessors' errors and emerges with another major winner in the AI space. With a connection to Meta's broader AI, social, messaging and hardware ecosystem right out of the gate, the Muse Charm may just have everything it needs to succeed.

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• I used Siri AI to plan my entire weekend — and it did way more than I expected
• I tested ChatGPT-6 vs Claude Opus 5.5 with 5 everyday prompts — it wasn’t even close
• I tried Apple’s new at-home wellness assessment in iOS 27 — and gained valuable insights into my flexibility and balance

Out of all the AI tools I use, Siri AI once took the spot for “the chatbot I use the least.” I’ve always seen it as the helpful voice assistant that helps my parents set up phone calls, give them the next day’s weather forecast and bring up a playlist of the best Michal Jackson concert footage on YouTube.

But after I finally downloaded iOS 27 and spotted the brand-spanking-new app icon for Siri AI on my phone, I decided to see if its promises of giving users deep personal context understanding, on-screen awareness and cross-app action capabilities were as amazing as advertised.

Since two friends of mine I haven’t seen in a while want to reunite soon, I figured I’d take a different approach to setting up everything I’m planning to do with them. Forgoing my usual event planning methods with ChatGPT, I decided to go that route with Siri AI and see just how good it could be when suggesting the perfect places and activities to embark on while keeping my buddies and my starting locations for Saturday and Sunday in mind.

After taking Siri AI for a whirl, I was surprised to see just how well the Apple Intelligence-powered tool is when the time comes for me to put my weekend plans in motion.

Using Siri AI to make my weekend planning a simple affair

Giving Siri AI all the information it needed to make sure my friends’ and their respective Saturday and Sunday meetups were memorable, I input this loaded prompt to push it in the right direction:

“I want you to plan my entire weekend. I’m planning to see Resident Evil at the Alamo Drafthouse Cinema in Lower Manhattan with a friend on Saturday at 5:15pm. Find me a good place to eat afterwards and a nice bar that serves IPAs for the post-dinner hangout. On Sunday, I’m planning to go to Barcade near 10 Cortlandt Street on Sunday around 4pm with another friend. Find me a nearby bookstore and vintage clothing store to go to afterwards.”

I liked the fact that Siri AI detailed the best options to consider for both days, as well as bringing up the Maps app to highlight those recommended hotspots. For instance, Siri AI mentioned Manhatta, Crown Shy and O’Hara’s Restaurant & Pub as the three top hangouts to consider for me and my friend’s Saturday meetup. At first, the Sunday explanation didn’t include much info on the exact places my buddy and I should head to after our Barcade get-together.

I had to prompt Siri AI to be more specific with its picks by using this follow-up prompt:

“For Sunday afternoon, highlight the best stores near 10 Cortlandt Street that sells vintage clothing and used books.”

Siri AI took note of my specific request and brought up the likes of Parasio Vintage, 2nd Street (Howard St), Bookoff, Strand and Alabaster Bookshop as the best go-to spots after my initial Sunday Barcade session ends. I used another follow-up prompt (“What are some other unique and fun places to go to after my movie watch on Saturday and my Barcade hangout on Sunday”) and Siri AI continued to expand my list of places to consider going to near my starting locations on both days.

Siri AI continued to mention a bunch of worthwhile locations, such as The Dead Rabbit bar & The Seaport shopping district for Saturday and The Oculus landmark & the Eataly NYC Downtown Italian marketplace for Sunday. To bring my test of Siri AI’s weekend planning capabilities to an end, I wanted to see if it could create a note for me that put all its plans in an easy-to-find reminder. Prompting it with “Create a note for me called ‘Weekend Plans’ that lays out everything I want to do this Saturday and Sunday” resulted in that request being fulfilled in just a few seconds, which was a nice surprise.

Siri AI’s recommended prompts for weekend planners just like me

I was pretty impressed with Siri AI’s event planning abilities, the recommended locales it brought up and the way it interacted with the Maps and Notes apps on my phone to create easy reminders. On my journey for more ways to get the most out of Siri AI’s conversational and context-aware capabilities when it’s time to make weekend plans, I asked it to give me examples of the best prompts to use for that specific case.

Here are the seven prompts and the explanations behind each one that it produced:

• What was the restaurant my brother told me to check out last week? - Siri can search your personal context, including text messages and emails, to find specific recommendations.
• Find the photos from my last hiking trip. - You can use natural language to quickly search your photo library for specific memories or locations.
• Summarize my unread messages. - Get a quick overview of what your friends are planning without having to scroll through every thread.
• Add this to my calendar. - When looking at my website, email, or text about an event, Siri’s onscreen awareness can automatically pull the details and schedule it for you.
• Get me directions to Disney World, but add a stop for coffee and Chipotle on the way. - Siri can build complex, multi-stop itineraries in Apple Maps from a single request.
• Are there any good pizza places within walking distance? - You can ask for local recommendations and easily layer in follow-up questions to narrow down your choices.
• Draft a text to Sarah about our plans for Saturday. - Siri can help you quickly compose messages, and it can even adapt to your personal writing style.

The takeaway

Ignoring Siri AI for so long might have been a blessing in disguise, since I finally recognized just how useful it can be, now that it's gotten a major overhaul and has its very own app that turns it into a chat-style interface. Event planning might just end up being one of my most-used cases whenever I interact with Siri AI, especially since it can work with other apps to see the directions I need to take to get to certain locations, set up calendar reminders and throw all my plans into my notes for future reference.

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OpenAI and Anthropic both released new flagship AI models this month, ChatGPT-6 and Claude Opus 5.5. Both are ridiculously capable to the point that they do so much more than what most users prompt them to.

OpenAI's GPT-6 family builds on GPT-6 Astra, the company's most impressive model yet, with major gains in areas including reasoning, professional work, coding, browsing and computer use. The newer GPT-6 Sol brings much of that intelligence into the version designed for everyday work, with OpenAI positioning it as a faster and more affordable reasoning model.

Claude Opus 5.5 comes at the problem from a slightly different direction. Anthropic describes it as a model built for long-running agentic and knowledge work, with adaptive thinking that determines how much effort a request needs. It has a 1 million-token context window, can produce up to 128,000 output tokens and, according to Anthropic, reaches roughly the same performance as its higher-end Claude Fable 5.1 on most tasks while costing 40% less to run than the previous Opus. Anthropic has also specifically highlighted improvements to how naturally the model communicates and follows writing instructions.

Those differences sound impressive, but they don't necessarily tell you which chatbot you'd rather have helping you in real life.

So I gave ChatGPT-6 and Claude Opus 5.5 the exact same five prompts. The result wasn't the clean sweep I expected. Here's what happened when I put them through every day prompts.

1.  Messy real-world planning — constraints + judgment

Prompt: I have $150 to plan a birthday party for 10 kids ages 8–11. It needs to last three hours, work indoors if it rains, include food, and keep screen time to a minimum. Two kids are vegetarian and one has a peanut allergy. Create the full plan, including a budget, timeline, activities and backup plan. Do not go over $150.

ChatGPT offered clear conceptual framing by giving the event an immediate identity and direction. Budgeting $36 for 3 delivery cheese pizzas is practically easier for a parent hosting 10 kids than managing DIY mini-pizzas in a standard home kitchen. It also recognized the need for an "Emergency/price buffer" at the end.

Claude was smart for being proactive about allergies and dietary protocols. It also addressed cross-contamination of bakery cakes and boxed mixes. It also did double-duty budgeting by spending 14 on canvas tote bags and $12 on fabric markers serves as both an arrival craft activity and the take-home party favor, saving money and cutting down on cheap plastic filler. It also offered an actionable, timed schedule and operational troubleshooting.

Winner: Claude wins because it delivered a turnkey, host-ready plan with genuine safety diligence, whereas ChatGPT produced a fragmented grocery list.

2. Writing — make AI sound human

Prompt: Rewrite this boring announcement so it sounds like something a real person would actually want to read. Keep every factual detail, don't use em dashes, avoid clichés, and don't use the words “exciting,” “revolutionary,” “game-changing” or “seamless.”

“Our company is introducing a new AI-powered calendar feature that automatically identifies scheduling conflicts, recommends meeting times and creates summaries after meetings. The feature launches October 15 and will be available to all customers at no additional cost.”

ChatGPT offered a punchy one-liner that draws the reader in and it also avoided AI styles and retained every factual detail. The paragraph breaks make it easy to scan quickly.

Claude reframed the bullet points into concrete human benefits and reframed the details around actual workplace relief.

Winner: Claude wins because it understood that a "human" rewrite requires rethinking how to explain the value of the features, rather than just shifting a few verbs in the original text.

3. Reasoning — the answer isn't obvious

Prompt: A family is choosing between two vacation rentals. Rental A costs $1,850 and is a 10-minute walk from the beach. Rental B costs $1,500 but requires a 25-minute drive to the beach and $35 per day for parking. They're staying seven days and expect to visit the beach twice a day. They have three kids. Which should they choose? Don't just calculate the cost. Identify the factors that could change your recommendation and tell me what additional information would matter most.

ChatGPT provided the single best counter-perspective on kids' ages. It also smartly pointed out that a "10-minute walk" varies wildly depending on infrastructure (boardwalk vs. crossing a four-lane highway or traversing sand dunes). The chatbot specifically noted that Rental A allows parents to split up (e.g., one parent takes a tired child back while the other stays at the beach with the other two).

Claude actually did the mileage math, proving that the financial savings of Rental B is effectively a negligible $55–$65 total for the entire week. Claude also made an astute psychological point: loading/unloading three kids into car seats four times a day means the family will likely abandon the second daily beach trip entirely. It also correctly flagged vacation rental platforms' dirty secret: base rates don't reflect cleaning fees, service fees, or occupancy taxes, which could easily flip the $105 difference on their own.

Winner: Claude wins by a hair. Although ChatGPT had the best individual point (realizing that walking with heavy beach gear and young kids can actually be more grueling than driving), Claude won overall because its financial breakdown was thorough down to the gallon, it anticipated hidden fees, and its observation that the commute would cause the family to cancel their planned outings was spot-on.

4.  Option overload — find what's important

Prompt: A city has $10 million to spend and three options:

A) Build a new public library expected to serve 200,000 visits per year for at least 30 years.
B) Give every household a one-time $500 payment.
C) Upgrade aging water infrastructure, which residents won't notice unless something goes wrong.

The city can only choose one. Choose the option you think is best. Explain your reasoning, identify the strongest argument against your choice, and explain what additional piece of information would be most likely to change your answer. Do not hedge by saying all three are good options.

ChatGPT followed all instructions cleanly and delivered an easy-to-read defense of option C. It also cleanly defined the engineering assessment threshold.

Claude framed the choice it made through municipal incentives and recognized that funding what electoral politics ignores is the core function of city management.

Winner: Claude wins for answering significantly better. The chatbot seemed liked it had experience in public-finance administration whereas ChatGPT’s response felt more standard and surface-level.

5  The killer test — vague request + initiative

Prompt: My life feels disorganized. I have three kids, a full-time job, household responsibilities and about 30 minutes every evening to get things under control. Build me a system I could realistically maintain. Don't give me generic productivity advice. Make reasonable assumptions where necessary, tell me what you're assuming, and give me something I can start tonight.

ChatGPT offered simple ways to prevent burnout and included instructions that were broken down into steps so the user doesn’t have to plan or try to guess where to start.

Claude addressed the mental load, which is a sharp, accurate insight into why disorganization happens. It also mentioned a useful tip of delegating when possible chores while recognizing that one parent cannot be the sole operational engine for a family of five.

Winner: ChatGPT wins because its cognitive framework is much more protective of an exhausted parent’s mental bandwidth. Claude gave a good domestic organizing routine; ChatGPT gave a decision-making filter that actually keeps work, family, and life from cannibalizing each other.

Overall winner: Claude

After five tests, Claude Opus 5.5 came out ahead in four. But what surprised me was how consistently the same difference between these models showed up across completely unrelated tasks. Claude tends to keep digging.

When I asked it to plan a birthday party, it thought about cross-contamination and found ways for one purchase to serve two purposes. When I gave it the vacation rental problem, it calculated costs I hadn't explicitly asked for and considered whether the inconvenience of driving would actually change the family's behavior. Even when choosing how a city should spend $10 million, it looked beyond the obvious pros and cons to think about why governments make these decisions in the first place.

That's where Opus 5.5 felt strongest. ChatGPT-6 was generally more streamlined, which is very much still on-brand for ChatGPT. Regardless of the model, ChatGPT always seems to be the less "fluffy" of the two models, with a response that gets to the point faster. That also means that, at times, its answers didn't go quite as deep as Claude's. However, that approach became an advantage in my final test.

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Apple is slowly rolling out a revamped Health app for iPhone and Apple Watch, and you can try it now by downloading the iOS 27.2 public beta; you’ll also need to run watchOS 27 on your Apple Watch.

One of the coolest new features in the overhauled app is a series of at-home longevity assessments designed to be done in your living room using your iPhone’s camera and Apple Watch’s heart rate sensor.

These assessments provide insights into a user’s strength, balance, flexibility, and more. I tried both the balance and flexibility tests, which take about five to seven minutes each, and gained some valuable, surprising insights into my physical health. Here’s what I learned.

How Apple’s at-home longevity tests work

To perform one of these assessments, users are asked to place their iPhone on the ground, lean up against a wall, and then walk back ten to 12 feet, ensuring their entire body is framed within the front-facing camera’s view.

The assessments use a vision-based AI model to track your body movement as you perform each step. However, no video is actually being recorded during the assessment, and all the AI processing occurs on-device.

What I learned from Apple’s flexibility test

Once your iPhone is set up and you're well-framed in the center of the screen, it’s time to start the assessment.

An on-screen trainer demos the movements for each test with more than enough detail to easily follow along. For example, the flexibility assessment involves five different stretches to thoroughly determine your overall upper and lower body flexibility ratings. Ratings fall into one of four categories: Needs Improvement, Fair, Good, and Excellent.

So, how did I do? My upper body flexibility score was ‘Good,’ but my lower body flexibility score was only ‘Fair,’ lower than I expected. I performed the five tests in the assessment twice, just to be sure I properly followed all the instructions, and both times the results were the same.

Was I aware that my lower body flexibility could use some attention? Honestly, no, not at all. Now that I am, I plan to prioritize more workouts and stretches to improve it.

Helpfully, in addition to a rating, the new Health app also provides fairly comprehensive information on the best ways to make progress in either category, including suggested exercises to try.

My balance is better than my flexibility, according to Apple

The balance assessment is even easier than the flexibility one, involving just one test. Users are asked to stand on one foot, with their other foot suspended in the air behind them for as long as they can without touching the ground, for up to sixty seconds. You do this twice, once for each foot.

Once the countdown begins, calming music starts playing, while a voice periodically provides encouraging words. Balance is scored on the same rubric as flexibility.

Fortunately, this time around I scored an Excellent — again, I did the test twice — which was a lovely little ego boost after my mediocre flexibility performance.

Like an at-home physical exam

These are just two of the 17 longevity assessments found in the new Apple Health app. Once all the assessments are complete, the Health app generates a report based on the results to share with your doctor.

Ultimately, Apple’s new longevity assessment might be foreshadowing what the future of healthcare will look like, where users rely on a mix of AI-powered consumer tech and traditional doctor visits to better understand and improve their well-being.

What are your thoughts on Apple’s new longevity tests in the updated Health app in iOS 27.2? Are you looking forward to trying them yourself? Let me know in the comments below.

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If it feels like ChatGPT is showing up in more places than your browser or phone lately, it's no coincidence.

OpenAI is reportedly ramping up its use of influencers to promote ChatGPT, and it isn't limiting those partnerships to the usual tech creators talking about prompts, models and benchmarks.

Instead, the company is working with creators in areas including parenting, fitness, education, travel, shopping and careers, according to a new report from Business Insider. The goal appears to be less about explaining what ChatGPT is and more about showing how the chatbot can fit into everyday life.

And the number of sponsored posts is climbing quickly. According to HypeAuditor data cited by Business Insider, Instagram posts using #chatgpt_partner or #chatgptpartner jumped from 61 in June to 122 in July and 141 in August.

That's more than double the number of sponsored posts in just two months, and it offers a glimpse at how OpenAI is trying to turn ChatGPT into something you reach for throughout your day.

OpenAI wants ChatGPT to be more than your favorite chatbot

What's particularly interesting about OpenAI's influencer strategy is the type of content it's promoting. Business Insider reports that OpenAI has been interested in working with fitness creators, for example, because people already use ChatGPT for questions about workouts and running.

A sponsored back-to-school campaign showed a parent using ChatGPT to help her children with math homework while highlighting features including Study Mode and teen-safety tools.

Meanwhile, The Shade Room, which has more than 28 million Instagram followers, published a sponsored ChatGPT word-search puzzle. All of this highlights how OpenAI is working to encourage ChatGPT name recognition along with everyday use cases.

OpenAI now appears to be marketing individual reasons to keep using it, whether you're planning a workout, helping with homework or trying to get something done at work.

One creator who has worked with OpenAI told Business Insider that her audience already has ChatGPT. The challenge, then, isn't persuading people to try AI for the first time. It's getting them to choose ChatGPT when they do.

OpenAI is building a creator operation

There are signs this isn't just a handful of curated sponsorships. OpenAI has been expanding its creator partnerships operation and has sought employees who can help the company work with creators and influencers at scale.

Earlier this year, OpenAI also hired Charles Porch, who spent years overseeing partnerships at Instagram, to take on the role of Vice President of Global Creative Partnerships for OpenAI , according to his Linkedin profile. The company has even experimented with the kind of influencer trips normally associated with beauty, fashion and lifestyle brands.

In August, OpenAI hosted creators at a luxury retreat in New York where attendees participated in activities and workshops while learning about OpenAI products. The trip also demonstrated one of the risks of this strategy.

Some participating influencers faced criticism from followers over issues including AI's environmental impact and whether creators were compromising their independence by partnering with OpenAI. TechCrunch reported that at least one creator appeared to remove a video about the event following the backlash.

How to tell when a ChatGPT post is sponsored

There's nothing inherently unusual about OpenAI paying influencers to promote ChatGPT. Brands have used creator partnerships for years. But it's worth knowing when the enthusiastic ChatGPT recommendation appearing in your feed is an independent recommendation and when it's advertising.

The Federal Trade Commission says creators should clearly disclose when they have a material relationship with a company they're endorsing. That can include being paid, receiving free products or getting other perks.

The disclosure should also be difficult to miss rather than buried among hashtags or hidden behind a "more" button.

In OpenAI's case, hashtags including #chatgpt_partner and #chatgptpartner are one obvious clue that you're looking at sponsored content.

So, the next time a creator casually shows how ChatGPT helped plan a vacation, explain homework or build a workout, it's worth checking the caption. You may be watching an artfully curated ad campaign.

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• I replaced ChatGPT with Siri AI for a day — here's what surprised me
• AI could be costing you money: new study finds chatbots get most financial questions wrong

Turning off Siri on a Mac should mean Siri is off. But some macOS 27 users are finding that isn't necessarily what Activity Monitor shows.

The issue picked up attention in a Hacker News thread posted this week. One Mac user described disabling Siri in System Settings, blocking it through Screen Time and shutting down every Siri- and Apple Intelligence-related service they could find with a third-party macOS debloating script.

After restarting the Mac, however, a process called Siri was still running in Activity Monitor. Double-clicking it showed that the process belonged to Siri AI.app, which the poster said is read-only and can't be deleted.

Another commenter checked their own Mac and confirmed the behavior. They said System Settings showed Siri as disabled and offered to turn it back on while the process kept running, and they noticed that its label switches between Siri and Siri AI depending on whether macOS thinks the assistant is enabled, even though it's the same executable either way. A similar report surfaced on Reddit where a user said the Siri AI process was still running after they disabled Siri AI in Settings.

Tom's Guide has reached out to Apple for clarification on why Siri-related processes may continue to appear after Siri has been disabled and what those processes are doing. We'll update this story if we hear back.

The Siri process appears to be running Spotlight

The most likely explanation came from the same Hacker News thread. One commenter pointed out that in macOS 27 the process responsible for the Spotlight search interface is now the one called Siri, when it previously carried the Spotlight name. Another tested that theory by force-quitting the Siri process from Terminal and watched the Spotlight interface in the menu bar flicker as it relaunched.

That fits how Apple built Siri AI on the Mac, where the assistant is integrated directly into Spotlight and typing a question into the search field turns it into a Siri conversation window. If a single app now handles both Spotlight and Siri, macOS would need to keep it running for search to work at all, whether or not you've switched the assistant off.

Apple hasn't explained why the Siri-branded process remains active after the assistant is disabled, or what role this plays in macOS 27. That leaves users staring at an oddly named process with very little information about what it's actually doing.

Apple's own controls say Siri is off

Apple's support page for Siri Restrictions in Screen Time is explicit about what the setting does. Under System Settings > Screen Time > Content & Privacy Restrictions > Siri, the Allowed Siri Version option lets you pick Siri AI (Beta), Siri Classic or Don't Allow Siri, and Apple describes the last choice as the way to turn off Siri. The same screen includes a Capabilities section where you can allow or block individual features such as Broad World Knowledge and Writing Assistance.

What the page doesn't address is whether choosing Don't Allow Siri should also stop every Siri-named process on the system, and that gap is where most of the confusion is coming from.

Siri now reaches much deeper into macOS

The issue is more noticeable now because Siri AI, which Apple began rolling out with macOS 27 on September 14, does far more than the assistant you used to summon with a voice command. Apple built it around personal context, onscreen awareness and the ability to take actions across apps. On the Mac it also lives inside Spotlight and adds an Ask Siri option to right-click menus, which requires much deeper integration with the operating system than the Siri most people spent years ignoring.

Users are already running into the side effects. In one Reddit discussion, a user said Siri-related options kept appearing in places like writing tools after they turned Siri off, and they ended up using Screen Time to block the remaining features.

Other commenters pointed out that Apple Intelligence powers writing, translation and image tools separately from Siri, so switching off the assistant doesn't necessarily switch those off too. Ars Technica has also reported that macOS 27 drops the single toggle that previously turned off Apple Intelligence and deleted its downloaded models.

Be careful with Terminal fixes

Workarounds circulating online can cause problems of their own. A Terminal command that hides the Ask Siri item from right-click menus has been shared on the MacRumors forums and Reddit, and it works by editing a system setting that controls which items appear in the Apple menu.

The command is meant for people who want to keep Siri AI without the menu clutter, since Ask Siri only shows up when Siri AI is enabled. Users on the same Reddit post, say they tried it, but that it interfered with Spotlight and they had to reverse the change.

A separate Reddit post claims that Apple Support supplied Terminal commands for removing Apple Intelligence entirely, and a Hacker News commenter noted that the approach involves disabling System Integrity Protection, a core macOS security feature.

Given what's been reported so far, I'd leave debloating scripts and Terminal tweaks alone, especially now that Siri and Spotlight appear to share the same process.

How to turn off Siri in macOS 27...kind of

To switch off the assistant, open System Settings, click Siri and select Turn Off Siri. For a restriction that stays in place, go to System Settings > Screen Time > Content & Privacy Restrictions > Siri, set Allowed Siri Version to Don't Allow Siri and click Done.

If you want a basic assistant without the AI features, choose Siri Classic in the same menu. The Capabilities section on that screen lets you block individual features like Writing Assistance and Image Creation, and Apple's separate guide to turning off Apple Intelligence features covers the rest.

Based on the reports so far, doing all of that will still leave a Siri process in Activity Monitor, and Apple doesn't currently offer a supported way to remove it. The real shortcoming is that macOS 27 doesn't explain what turning Siri off actually covers, and until Apple does, it's not thoroughly shut off.

More from Tom's Guide

• Nvidia says 'local AI is here' — and it could change how you use AI at home
• I replaced ChatGPT with Siri AI for a day — here's what surprised me
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It’s been pretty eye-opening to read about instances of AI models breaking out of their personal sandbox environments and getting involved in hacking incidents.

Two of the most recent cases of that happening involve the OpenAI–Hugging Face Incident and Google Gemini accessing the internet to acquire the credentials needed to hack three websites during a cybersecurity test conducted by Irregular, an independent company that conducts cybersecurity evaluations. Even Anthropic’s Claude models were involved in a hack that targeted OpenAI’s systems—thankfully, that situation was more of an ethical event conducted by a three-person team from Hacktron AI.

With so many issues connected to AI tools (bypassing safeguards, accessing information that it shouldn’t and participating in sophisticated cyberattacks being chief among them), the fears of everyday people and professionals are well warranted.

T.J. Marlin, CEO of Guardrail Technologies and former EY Global Forensic Technology and Innovation Leader, spent some time with us to offer his thoughts on AI agents bypassing front-door controls, data leakage via employee use, the risks associated with concentrated data centers and more.

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• AI could be costing you money: new study finds chatbots get most financial questions wrong

During an address to the United Nations General Assembly on Tuesday, Trump announced that the U.S. government would begin referring to artificial intelligence as “Super Intelligence,” or SI, arguing that the word “artificial” no longer accurately describes the technology.

“From this point forward, all of United States documents, and hopefully the world's, will be changed to use the much more accurate term ‘super’ as opposed to artificial,” Trump told delegates. “Welcome to the new world of super intelligence — S.I.”

Trump had already been floating the new terminology before his UN appearanceIn a Truth Social post a day earlier, he referred to the technology as “AI or, SI (SUPER INTELLIGENCE)” while arguing that the U.S. should continue accelerating development rather than slowing it over concerns about advanced AI.

The name change might sound mostly cosmetic. But there’s one big problem: “superintelligence” already means something in AI. And it definitely isn't ChatGPT.

AI and superintelligence aren't the same thing

Artificial intelligence is an umbrella term covering a huge range of technologies, including the generative AI tools millions of people now use every day.

Superintelligence is something considerably more specific. AI researchers generally separate machine intelligence into a few broad categories.

• Artificial narrow intelligence (ANI) describes AI designed to perform particular tasks. That includes much of the technology available today, from image recognition systems to large language models and generative AI assistants.
• Artificial general intelligence (AGI) generally refers to a hypothetical AI capable of matching human-level intellectual abilities across a broad range of tasks, rather than being limited to particular areas.
• Artificial superintelligence (ASI). This is the theoretical step beyond AGI: a system whose abilities exceed those of humans across essentially every intellectual domain. Nobody has demonstrated that technology yet.

IBM describes artificial superintelligence as a “hypothetical” AI system with intellectual abilities beyond those of humans. Google similarly describes ASI as a theoretical form of AI that would surpass human intelligence.

That means changing the label on today's AI doesn't change what the technology can actually do. ChatGPT doesn't suddenly become superintelligent because a government document calls it SI. Neither do Claude, Gemini or any of the other AI assistants available today.

Why the phrasing matters

Things might get confusing really quickly. Just imagine seeing a government report describing the risks of “super intelligence.” Is it talking about ChatGPT and Gemini as they exist today? More autonomous AI agents capable of taking actions on someone's behalf? Or a hypothetical future system more intelligent than any human?

Under the terminology Trump proposed Tuesday, those very different technologies could potentially end up sharing the same label.

There’s also a consumer perception problem. Today's most advanced AI models are extraordinarily capable, but they're also far from infallible. Chatbots can still hallucinate information, misunderstand instructions and confidently produce incorrect answers. AI agents can also make mistakes while using software and carrying out multistep tasks.

Calling those systems “super intelligence” could give users a very different impression of what they're capable of.

The announcement is also about the AI race

The president used the speech to reiterate his opposition to international restrictions that he believes could slow U.S. AI development and framed leadership in advanced AI as a competition between the United States and China.

That follows comments Trump made a day earlier in which he said the U.S. would be careful about AI and could use the Justice Department and other law-enforcement agencies to intervene if necessary, while insisting that his administration would continue encouraging its development.

The remarks come as other world leaders are pushing for more international coordination around increasingly capable AI.

A group of countries and institutions called this week for AI to remain under human direction, supervision and control and for governments to develop common standards around the technology.

That makes the debate about “SI” bigger than a new acronym because the disagreement is also about how quickly powerful AI should be developed, how it should be regulated and whether international rules should play a role.

Is AI actually becoming superintelligent?

There's no question that AI models are getting more capable. The latest systems can write software, analyze images, operate computers, use tools and complete increasingly complicated tasks with less human involvement.

But capability isn't the same thing as superintelligence.

Even artificial general intelligence (AGI) remains a disputed threshold with no universally accepted test for determining when it has been reached. Superintelligence goes significantly further by describing systems that surpass human intellectual ability broadly rather than simply outperforming people at particular tasks.

Today's AI can already outperform humans in certain narrow areas. That doesn't make it ASI, which means the biggest question is whether Trump's announcement actually changes the terminology used across the federal government.

Trump said U.S. documents would switch from “artificial intelligence” to “super intelligence,” but at the time of publication, the announcement itself doesn't change the terminology used by the rest of the technology industry.

More from Tom's Guide

• Nvidia says 'local AI is here' — and it could change how you use AI at home
• I replaced ChatGPT with Siri AI for a day — here's what surprised me
• AI could be costing you money: new study finds chatbots get most financial questions wrong

There’s something deeply ironic about getting fired from an AI company for using AI, but that’s reportedly exactly what happened to multiple contractors helping train OpenAI’s models.

Contractors hired to review and improve the responses generated by OpenAI’s AI models have been fired or removed from projects after using AI to do their own work, according to a new report from 404 Media.

The rule makes more sense than it might sound. These workers are being hired specifically for their human judgment — something that becomes considerably less useful if ChatGPT or another AI chatbot is doing the judging for them.

One contractor told 404 Media that they see people using AI “all the time” and that workers are regularly removed for doing it. Another contractor shared what they said was a termination letter citing problems with the “authenticity” of their work.

Keeping humans in the loop

AI models don't simply learn everything from scraping enormous amounts of text from books or the internet.

OpenAI says information provided or generated by human trainers and researchers is one of the sources used to develop its foundation models. Human feedback has also long played a role in teaching models which responses people prefer and how those models should behave.

That makes the quality of the human feedback truly important. OpenAI has previously acknowledged in its research that models trained using human feedback can be influenced by the people labeling that data.

The contractors described by 404 Media work across various OpenAI projects, some of which can involve thousands of workers. Their jobs can include reading AI responses, rating them and deciding which answers are better.

But according to internal documents obtained by 404 Media, reviewers aren't allowed to outsource that judgment back to an AI. One set of instructions reportedly prohibits workers from using AI to write feedback or comments and specifically names tools including Grammarly and AI translation.

Mercor, an AI-training company that employs contractors working on OpenAI projects, confirmed to 404 Media that its contracts prohibit workers from using large language models to complete projects.

AI detectors didn't catch the cheating

You might assume an AI company would catch workers using AI with an AI detector. Apparently not.

404 Media doesn't reveal exactly how the contractors who were fired were caught. But internal instructions obtained by the publication offer a fascinating look at how reviewers are told to spot suspected AI use.

And AI detectors aren't part of the process.

The instructions reportedly tell reviewers not to use tools such as GPTZero because they aren't considered reliable. Instead, reviewers are told to judge the overall pattern of a worker's submissions and look for clues including repetitive wording, unusually fast completion times and writing patterns associated with AI. Even excessive use of em dashes can raise suspicion.

Reviewers are also reportedly instructed not to tell workers exactly what made them suspect AI use, because doing so could make it easier to hide in the future.

This is surprisingly revealing about the process: the people checking whether humans are secretly using AI to help train ChatGPT are explicitly told not to trust AI-detection software to do it for them.

Bottom line: humans are irreplaceable

OpenAI uses a lot of synthetic data — information generated with the help of AI models — as part of some training processes. The company says synthetic data can improve model performance and fill gaps where other training data is scarce.

But that's different from secretly substituting an AI model for a person who has specifically been hired to provide human feedback. If an evaluator is supposed to tell OpenAI which response a human prefers, asking another AI to make that decision defeats much of the point of hiring the evaluator.

It doesn't mean these incidents have made ChatGPT worse, and there's no evidence in 404 Media's reporting that AI-generated contractor work caused measurable damage to an OpenAI model.

But the situation does expose one of the stranger realities of the AI boom: even the companies building the AI systems still need humans to tell those systems when they've gotten something right.

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• I replaced ChatGPT with Siri AI for a day — here's what surprised me
• AI could be costing you money: new study finds chatbots get most financial questions wrong

One of the most prominent fears connected to the rapid evolution of AI is workers recognizing it as the biggest threat to job security.

All one has to do for proof of that growing sentiment is look at the data posted by Gallup’s 2026 “More U.S. Workers Fear Losing Their Jobs to Technology” report. 27% of the workers that were polled feel scared that technology could make their jobs obsolete, the rate of concern among college graduates has seen an upswing since 2021 and tech displacement has now emerged as the top job worry among workers under 45.

Tech futurist Armando Pantoja (aka “The Tall Guy Tycoon”) has a different perspective on the issue of AI’s evolution resulting in more job losses. His main argument notes that the biggest concern about AI job automation isn’t actually unemployment—it’s actually “structural unimportance.” Alongside that belief, Pantoja also points out that workers fear losing their job status and identity because technology is evolving at a faster rate than our schools, laws and institutions can adapt to.

On the heels of his book, The Future of Wealth: The Next 50 Years of Money, Power, and Intelligence, launching soon, Armando answered a few of my questions regarding how workers can make themselves more valuable in a society field with AI also came up with a 30-day plan for workers who want to ready themselves for an AI-dominated workplace.

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If you recently updated your Mac to macOS 27 and noticed your available storage shrinking, Apple Intelligence could be taking up more space than you realize.

Mac users on Reddit and Hacker News have reported discovering several gigabytes of AI-related files on their machines, with some users claiming local AI assets are taking up around 10GB or more. That's a significant chunk of storage if you have a 256GB MacBook Air or Mac mini.

There's a reason those files are there. Apple Intelligence doesn't run entirely in the cloud. Some of Apple's AI features rely on models stored and processed directly on your Mac, which is great for privacy but not necessarily great for your available storage.

And if you don't use Apple Intelligence, you might understandably wonder why those files need to be sitting on your SSD at all. Here's what's happening, how to check your Mac and what you can safely do about it.

Why is Apple Intelligence taking up space on my Mac?

One of Apple's big selling points for Apple Intelligence is privacy. Rather than sending everything you do to a remote server, Apple can perform some AI tasks directly on compatible devices. But, that requires downloading AI models to your Mac.

According to MacRumors, once you install macOS 27 Golden Gate, Apple automatically downloads a multi-gigabyte AI model the next time your Mac connects to the internet. In previous versions of macOS, you could opt out. Now, there's no way to stop the download, making the AI models that power Siri and other Apple Intelligence features effectively part of the operating system.

Apple's latest Apple Intelligence architecture includes AFM 3 Core, a 3-billion-parameter on-device model, alongside a more powerful multimodal model called AFM 3 Core Advanced. Other Apple Intelligence features can use Apple's Private Cloud Compute infrastructure when more processing power is required.

The downside of all that on-device processing is that the models themselves need somewhere to live.

macOS manages many of these resources as system assets, which can make it difficult to tell exactly what's using your storage. You may simply notice the amount of space attributed to System Data increasing.

First, check your Mac's storage

Before changing anything, it's worth checking whether you're actually running low on space.

• Click the Apple menu in the top-left corner.
• Select System Settings.
• Choose General.
• Click Storage.
• Give macOS a moment to calculate everything.

You'll see your storage divided into categories such as Applications, Documents, Photos and System Data.

Unfortunately, macOS doesn't provide a neat "Apple Intelligence models: 10GB" entry, so a large System Data category doesn't automatically mean Apple Intelligence is responsible.

Still, take note of how much free storage you currently have. That gives you a baseline before making any changes.

How to turn off Apple Intelligence

If you don't use Apple Intelligence, the safest first step is simply turning it off.

• Open System Settings.
• Select Apple Intelligence & Siri.
• Find Apple Intelligence.
• Toggle it Off.

Confirm that you want to disable Apple Intelligence if prompted. You can also disable Siri AI from this menu if you don't use it.

Don't expect your free storage to jump immediately after doing this. macOS manages system assets automatically, and disabling Apple Intelligence doesn't mean every downloaded model will disappear the moment you flip the switch.

I'd avoid manually deleting anything from macOS system folders just to force the issue.

What about Screen Time?

macOS also includes restrictions for Apple Intelligence and Siri features through Screen Time.

Head to: System Settings > Screen Time > Content & Privacy

Depending on your Mac and settings, you'll find controls governing access to Apple Intelligence and related generative AI features. These restrictions can be useful if you want to prevent someone using the Mac from accessing particular AI features.

However, I wouldn't rely on Screen Time as a guaranteed way to stop macOS from ever downloading an AI-related system asset. Apple doesn't make that relationship particularly clear.

For most people, disabling Apple Intelligence itself is the better place to start.

Don't delete macOS AI files manually

Here's where things get a little complicated. You'll find Terminal commands online that claim to remove Apple's downloaded AI models manually. Some methods even involve booting your Mac into Recovery Mode and deleting files from macOS system directories.

But, I wouldn't recommend doing that. These are system-managed assets, and deleting files from protected macOS directories can cause unintended problems. There's also no guarantee macOS won't download an asset again if the operating system decides it's required.

Saving a few extra gigabytes isn't worth potentially breaking something in macOS.

If you're critically low on storage, you're better off using Apple's built-in storage management tools first.

Still missing storage? Check Time Machine

Apple Intelligence might not be the only reason your available storage seems lower than expected. If you use Time Machine, macOS can also keep local snapshots on your Mac. These allow you to recover files even when your Time Machine backup drive isn't connected.

Apple normally manages this storage automatically and considers snapshot space available for use when necessary.

That means the amount of storage macOS reports can occasionally be confusing, particularly if you've recently deleted a large amount of data and expected your free-space number to immediately increase.

You can see whether local snapshots exist by opening Disk Utility, selecting your Mac's volume and looking for APFS snapshots.

Again, I wouldn't start deleting snapshots unless you're actually experiencing a storage problem.

Bottom line: Should you turn off Apple Intelligence to save space?

If you regularly use Writing Tools, Siri's newer AI features or other Apple Intelligence capabilities, the storage they consume is essentially part of the cost of having those features available locally.

But it's totally different if you never use them. That's especially true for anyone with a 256GB Mac. Losing even 10GB represents roughly 4% of your entire advertised storage capacity — before accounting for macOS itself, applications, photos, videos and everything else you keep on your computer.

Until Apple gives users more granular control over which AI models are stored locally, the safest option if you don't want Apple Intelligence is to simply turn it off, keep an eye on your storage and let macOS manage the underlying system assets rather than trying to rip them out yourself.

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• Nvidia says 'local AI is here' — and it could change how you use AI at home
• I replaced ChatGPT with Siri AI for a day — here's what surprised me
• AI could be costing you money: new study finds chatbots get most financial questions wrong

Apple settled a $250 million class action lawsuit over claims of false advertising about Siri's AI capabilities in May of this year. The settlement allows up to 36 million iPhone users in the U.S. to receive up to $95.

That window is finally opening, but it runs for only three months, starting Monday, September 21. Here's what you need to know to claim your share of the settlement.

How we got here

The lawsuit was initially filed in California in 2024 by Peter Landsheft after Apple promoted its more "personalized Siri" and the introduction of Apple Intelligence. The National Advertising Division bolstered the lawsuit, concluding that Apple misled users by claiming the new Siri was "now available."

Apple filed the settlement, which includes no admissions of wrongdoing, on May 5. "We resolved this matter to stay focused on doing what we do ⁠best, delivering ​the most innovative products and ​services to our users," the company said in a statement.

“False advertising settlements aim to raise the floor of truth-telling, protect fair competition, and increase consumer choice. The $250 million settlement fund represents the largest false advertising settlement in history, and we are proud to have achieved this result for Apple customers," Ryan Clarkson, one of the lawyers on the case, said in a statement to Tom's Guide.

Eligibility and how to claim

The settlement covers roughly 36 million iPhones, including the iPhone 16, iPhone 15 Pro and iPhone 15 Pro Max. All of which must have been purchased in the U.S. between June 10, 2024, and March 29, 2025.

The window for entering a claim starts September 21 and closes on December 21, 2026.

You can enter a claim by going to SmartphoneAISettlement.com. You will need your device's serial number. You can find the number by opening Settings, going to General, tapping About, and scrolling to Serial Number.

As for payout, claimants will receive at least $25 per device and up to $95, depending on how many claims are submitted.

Final approval by a judge won't happen until February 24, 2027, and Apple will have a set number of days after that to issue payments. Typically, those windows are 180 days.

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Meta Connect kicks off later this week as the tech giant's annual event starts on September 23. We're expecting to see CEO Mark Zuckerberg on stage announcing several new devices including different smart glasses, a VR headset, and potential Metaverse features.

As with the last few Connects, smart glasses should take up a bulk up of the presentation. However, this year, Meta is rolling into this event on a privacy backlash and the neverending RAM crisis.

Below, I've looked into rumors and leaks to see 6 major things that could get announced at Meta Connect 2026.

Three different smart glasses

Right now, Meta is facing serious concerns over privacy and it's "pervert glasses." One way the company is rumored to allay those concerns is by launching a new pair of camera-free smart glasses.

These glasses will reportedly have the audio features of current Meta Glasses. This means the glasses will have at least six microphones that let you ask for directions and do other AI-based tasks without taking your phone out of your pocket.

As for other glasses, we expect to see a gen 2 version of the Oakley Meta glasses and Ray-Ban Meta Gen 3. We know more about the Gen 3 Ray-Bans thanks to rumors. It should feature the latest Snapdragon AR chipset and new designs. New chips should mean longer battery life.

Both will probably get smart AI and improved audio.

A privacy interlude

Before we look at more devices and services, it will be interesting to see if Zuckerberg and co. address privacy during Connect.

In August, Meta strengthened tamper detection on its AI glasses to prevent people from blocking the LED recording light that indicates when the camera is working. The update makes the camera stop working if it discovers that the small white LED on the front of the glasses is covered.

"We're going to keep rolling out updates like this to make sure the capture LED can reliably alert bystanders when photos or videos are being captured for someone's gallery," Alex Himel, vice president of wearables at Meta, wrote on Threads at the time.

In the same month, Meta filed an AI facial recognition patent that would let its smart glasses recognize faces, detect laughter and record automatically.

One step forward, 15 back.

Project Phoenix

Meta has been coasting on the Meta Quest 3 and 3S headsets for a few years, but a VR leaker who goes by Luna on X found evidence of a mixed reality headset dubbed Project Phoenix in Meta code this month.

Luna's new images seem to confirm mock-ups that leaked in February of this year. At the time, it was rumored that the new headset wouldn't debut until early 2027, but Meta could still announce them at Connect.

Interestingly, based on the images that Luna dug up, the new headset looks somewhere in between a pair of AR glasses and VR headsets. The images of the Phoenix show an ear cable à la the Xreal Project Aura, which suggests the battery and silicon are probably on an external device.

It's rumored the headset will support hand and eye tracking. Otherwise, we don't have many specs, but it will likely feature the Snapdragon Reality Elite chip that Qualcomm announced at AWE 2026 earlier this year. That chip is specifically designed to support XR devices.

Muse, HorizonOS and "avatars"

Meta just launched Muse, its new Muse AI that can send emails, spend money and work when you aren't looking, two weeks ago. We expect Meta will spend some time highlighting the AI during Connect and how it works across devices.

As for its headset and VR operating system, Horizon, if Phoenix is announced, we should see some major updates to HorizonOS. Our friendly VR leaker Luna hinted that Meta could soon reveal its Hologram avatars that were teased with the announcement of the Project Orion AR glasses in 2024.

Last week, The Information reported that an avatar of Meta CTO Andrew Bosworth will make an appearance during Connect. It'll be interesting to see how Meta views its metaverse since its VR platform Horizon Worlds was shut down this summer.

How to watch Meta Connect

Tom's Guide will be attending Meta Connect, so keep us in your tabs to follow along with all the new announcements. The keynote speech starts at 4 pm Pacific (7pm ET) on Wednesday, September 23.

Meta will likely livestream the keynote on Instagram, Threads and Facebook. The company also shares it on YouTube if you prefer that platform.

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• I bought Instagram's new Meta One subscription — here's what $7.99 actually gets you
• Meta just launched an AI that can actually run your life online — here's what Muse can do
• Meta agrees to pay $17.1 billion to settle claims Facebook and Instagram harm children — here's what you need to know

It’s not much of a shock when you read that AI users are tapping into the pools of knowledge AI houses to help them with their financial inquiries.

One just needs to look at the stats—according to data produced by Intuit Credit Karma that was detailed by Credit Karma, “66% of Americans surveyed who have used GenAI before say they have used it to seek financial advice, rising to 82% of Gen Z and Millennials.” Using AI to assist with one’s money questions sticks out as one of the most used applications for AI, as “finance ranks as the second most common use case for GenAI (41%), just behind health and wellness (44%).”

According to another study (called the Artificial Authority” report) conducted by an AI and technology firm called Saturn, turning AI into a financial adviser might not be the smartest move to make. Saturn’s Domain team came to that conclusion after testing 18 of the most popular AI tools, which include ChatGPT, Gemini, Claude and Copilot.

Saturn’s results all point to one sobering outcome: chatbots aren’t all that reliable when you’re looking for legit financial counsel.

Saturn’s benchmarking study produced some pretty alarming results

The most striking statistics produced by Saturn’s report point to the trend of AI models being wrong more often than they were right when presented with financial questions.

Across the 17 AI models that Saturn tested, the average accuracy rate they delivered came in at 43%, which means those same chatbots got financial questions wrong 57% of the time. And when presented with complex, multi-step scenarios that focused on tax rules and more precise financial figures, the accuracy of those tested AI tools fell to 12%, with mistakes made 88% of the time as well.

It doesn’t come as much of a surprise that the free AI models that were tested produced a 63% failure rate, while the paid models delivered a 49% rating instead. Claude Haiku 4.5 got the distinction of being the worst-performing free model, as it gave out wrong or incomplete answers 82% of the time. ChatGPT-5.6 Luna (max) emerged as the best-performing free model, even though its rate of giving incorrect or incomplete replies was 56% of the time.

Out of all the models that were tested, Saturn found that Anthropic’s paid-for Claude Opus 5 (reasoning) model produced the best results since it achieved a pass rate of 61%. Even with that stat that points to it being the top-performing chatbot for financial advice, Claude Opus 5 still failed to give correct answers in almost four out of every 10 cases. Even when it was presented with more complex questions, it still made mistakes 67% of the time.

What’s even more alarming about Saturn’s study is how it conducted deep dives into the specific errors it came across from the AI models it tested that could result in severe financial outcomes. For instance, one model came up with a rule that said a college graduate could halt their student loan payments by moving abroad (in reality, doing so could actually lead to higher monthly repayments). Also, a Gemini model that was tested incorrectly told a borrower that taking a mortgage payment holiday wouldn’t affect their credit score.

Bottom line

Saturn’s report points to a sobering truth that many AI users need to come to grips with: AI financial advice remains unreliable and can leave its users prone to losing tons of money instead of making more of it.

A March 2026 report from EY highlighted how 53% of respondents prefer using an AI assistant to make financial investment decisions, while 14% prefer using an autonomous AI and 33% said they wouldn’t use any form of AI in such a case.

Thanks to Saturn’s findings, that 14% statistic may increase as AI users refrain from asking their chatbots personal questions about debt management, student loans, mortgages, pensions, inheritance tax and retirement planning.

More from Tom’s Guide

• Nvidia says 'local AI is here' — and it could change how you use AI at home
• OpenAI's new ad tracker may know what you do after you leave ChatGPT — here's what we know
• Apple's $250 million class action settlement website is now open — here's how eligible iPhone owners can file a claim and how much they'll get

An independent researcher has uncovered what appears to be a cross-site tracking system connected to OpenAI's rapidly growing advertising business. At the center of it is a cookie called "__obi," which can reportedly travel from ChatGPT to websites using OpenAI's advertising technology.

That means if you use ChatGPT and later visit a participating retailer or other website, OpenAI may be able to recognize the same browser and receive information about what you do there.

The finding comes at a particularly interesting time. OpenAI has been rapidly expanding advertising inside ChatGPT, recently announcing new AI-powered advertising tools and Sponsored Agents. The company said last month that ChatGPT Ads had already reached a $1 billion annualized revenue run rate.

But this isn't quite as simple as assuming ChatGPT is watching everything you do online. There are some important limitations to the research, including one major piece of the system the researcher wasn't able to independently verify.

Here's what we know.

What is __obi?

The research comes from an independent researcher who publishes under the name Buchodi and says they analyzed months of network traffic involving 936 OpenAI advertiser pixels across 1,029 hostnames.

According to the analysis, visiting ChatGPT can result in OpenAI placing a cookie called __obi in your browser.

Cookies themselves aren't unusual; you've probably accepted them on a several websites. They're small pieces of information stored by your browser and are used for everything from keeping you logged into a website to remembering preferences and measuring advertising.

What's interesting here is how this particular cookie is configured. Buchodi found that __obi can last for one year and uses the SameSite=None setting, which allows it to be included in certain requests originating from other websites.

That's where OpenAI's advertising system comes in. Businesses advertising through ChatGPT can install OpenAI's measurement pixel on their websites, similar to the tracking technology used by advertising platforms such as Google and Meta.

When Buchodi visited participating websites, requests sent back to OpenAI included the __obi identifier. In other words, the same identifier created through ChatGPT could show up again when that browser visited another website.

What can OpenAI see?

Potentially more than simply whether you visited a website. According to Buchodi's network analysis, OpenAI's advertising collector received information associated with pages people visited and conversion events on participating sites.

The researcher examined 23,929 observed URLs and found that query strings were stripped before being sent to OpenAI, but URL paths remained.

A URL path can sometimes reveal considerable information about what someone is looking at. Buchodi says paths observed in the dataset included pages involving a medical condition, debt services and a litigation intake form.

The advertising system also supports what OpenAI calls conversion measurement.

OpenAI's own documentation says advertisers can install a pixel and use conversion measurement to understand what happens after someone interacts with a ChatGPT ad. Its measurement integrations can support conversion tracking and attribution across websites, apps and even offline activity.

That's standard practice in digital advertising. What's unusual is that the advertising platform in this case sits alongside an AI assistant where users may share substantially more personal context than they would with a traditional search engine or social network.

Is it connected to your ChatGPT account?

This is where the story gets more complicated. Buchodi found that the process used to generate the identifier included tokens labeled either account_user or anonymous.

Of 932 tokens the researcher decoded, 736 were associated with logged-in users and 196 were labeled anonymous. The researcher also found that anonymous identifiers could remain stable on the same device for at least 27 days.

However, there's an important caveat. Buchodi did not directly observe OpenAI taking an event received from an advertiser website and joining it to a person's ChatGPT account on its servers.

The researcher argues that the architecture makes such a connection possible, but that final server-side step wasn't independently observed.

So it would be premature to say OpenAI definitively attaches every page you visit on participating sites to your ChatGPT profile. What the research does demonstrate is that the same OpenAI identifier can be transmitted from participating websites back to OpenAI.

Buchodi says the identifier appeared across a number of recognizable websites during testing. On one device, the same __obi value was sent to OpenAI from 12 commercial websites using 13 separate advertising pixels. Across the larger dataset, 12 of 30 identifiers appeared with more than one advertiser, while one appeared across 10 different advertisers.

The researcher also notes several important limitations. Testing was performed using Chrome on Android, and desktop Chrome wasn't tested. Safari's anti-tracking protections block the third-party cookie mechanism described in the research, meaning it shouldn't operate the same way on Safari or other iOS browsers using Apple's WebKit.

The identifier also wasn't generated during every ChatGPT session. Buchodi estimates that roughly one in five sessions tested produced the synchronization token.

What does OpenAI say?

OpenAI hasn't publicly responded to the specific findings. Buchodi says they contacted OpenAI's press and privacy teams on September 14 and asked why __obi was classified as an analytics cookie and how it behaves when someone rejects marketing cookies.

According to the researcher, OpenAI acknowledged the inquiry and said the findings would be passed along internally but had not provided answers to those questions when the research was published.

Tom's Guide has contacted OpenAI for comment and will update this story if we hear back.

OpenAI's public documentation emphasizes that advertisers do not receive ChatGPT conversations, chat histories, memories or personal details.

The company also says it does not sell user data to advertisers, and advertisers receive aggregated reporting about things such as views, clicks and conversions.

That's an important distinction here: the research does not suggest advertisers themselves can read your ChatGPT conversations or access the __obi identifier.

Instead, the concern is about information potentially flowing in the other direction — from websites using OpenAI's advertising tools back to OpenAI.

Why this matters

Cross-site advertising measurement isn't new. In fact, Google, Meta and other advertising companies have spent years building systems designed to determine whether someone who saw or clicked an ad later visited a website or bought something. But ChatGPT introduces another dimension.

People don't just search for products using ChatGPT. They use it to discuss relationships, health concerns, finances, careers, travel plans and countless other parts of their lives.

OpenAI itself promotes ChatGPT advertising around this idea, telling advertisers that people share "richer context" in ChatGPT while exploring options and making decisions.

That's why the boundary between your ChatGPT activity and what you do elsewhere on the web matters.

For now, the evidence doesn't show that OpenAI is handing your conversations to advertisers — and OpenAI explicitly says it doesn't.

But the discovery of __obi raises a different question: how much information should be able to flow back into an AI platform after you leave it?

What do you think? Share your thoughts in the comments.

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• OpenAI paid contractors to read ChatGPT conversations — here's how to protect yourself
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Googlebooks are the "new category" of laptops designed to provide Android users with the same seamless bridge from mobile to desktop that iPhone users enjoy with MacBook. After being announced earlier this year at the Android Show, the first devices in this new category are now available to pre-order.

Five different Googlebooks are available today, from Acer, Asus, Dell, HP and Lenovo, starting at $899 (with a year's subscription to Google AI Pro thrown in) and each one is built with premium materials like aluminum, magnesium alloy and carbon fiber. They all feature a responsive touchscreen, large trackpad and backlit keyboard. These laptops come with either Intel or Qualcomm processors under the hood and offer up to 14 hours of battery life.

Although Google has made sure certain rules are followed, each of the Googlebooks launching today offer slightly different features to cater to different tastes. Standouts include the Asus Googlebook 14, which weighs just 2.2 pounds and boasts an anti-glare screen with 16 hours of battery life. Acer offers a neat convertible model, while Lenovo is bringing a 15-inch option with a soft-touch finish in Pantone’s Cloud Dancer.

Quick links

• Acer Googlebook 14: $899 @ Best Buy
• Dell XPS Googlebook: $999 @ Best Buy
• Lenovo Googlebook 15: $1,099 @ Best Buy
• Asus Googlebook 14: $1,299 @ Best Buy
• HP Googlebook 14: $1,299 @ Best Buy