Whenever I wear a smartwatch, I find that my anxiety increases — specifically, my health anxiety. Also known as hypochondria or illness anxiety disorder, this type of anxiety makes me worry that I am or may become ill even when I’m healthy.
What’s ironic is that part of my job involves testing health-monitoring wearables, including fitness trackers and smart rings. While I love exploring this technology and do think it can help you learn more about your body, I have to be careful about how I use it so my anxiety isn’t triggered.
“Healthy adults and individuals with pre-existing medical conditions are increasingly using these devices to manage their health. Whether 24/7 access to health information from a wearable actually helps or potentially harms people is really unclear,” says Dr. Lindsey Rosman, assistant professor of medicine in the Division of Cardiology and co-director of the Cardiovascular Device and Data Science Lab at the University of North Carolina School of Medicine.
When you add in the ability to search your symptoms online or ask an AI chatbot in your wearable’s app about every anxiety-induced health question that pops into your head, it becomes even more difficult to discern between what’s helpful and harmful.
To help myself and others with health anxiety navigate the world of wearables so we can either enjoy using them or know when it’s time to stop, I reached out to experts for their advice.
Rosman has observed clinically that it can be beneficial to either scale back or turn off the features that make you anxious. This can be especially helpful for people with pre-existing conditions that are already being treated, such as atrial fibrillation (AFib, an irregular heartbeat), as your wearable’s irregular heart rhythm notifications will only make you anxious and can prompt you to see your doctor when it’s not medically necessary.
Plus, certain medications can affect the accuracy of wearable sensors, provoking false alarms.
“We published a case report on a patient who performed over 900 EKGs [electrocardiograms or ECGs, which measure the heart’s electrical activity] on her smartwatch in a single year,” says Rosman. While most of the EKGs were normal, inconclusive alerts fueled her anxiety, leading to multiple ER visits, spousal conflict and the need for therapy to reclaim her daily life. The patient had no psychiatric history prior to getting a smartwatch.
When you get an unexpected health alert on your device, it can understandably cause panic.
Dr. Karen Cassiday, author of Freedom from Health Anxiety and owner and managing director of the Anxiety Treatment Center of Greater Chicago, says that even patients who don’t have health anxiety can find wearables to be intrusive when they get too many alerts. “They discover they want to be less aware of every moment of their body’s functioning,” she says.
Thankfully, most wearable health features can be turned off completely or customized.
For instance, Shyamal Patel, SVP of science at Oura, maker of the Oura Ring, shares that the device’s Personalized Activity Goals allow you to choose to see steps instead of calories, adjust your daily activity goal or hide calories completely, which can be necessary for anyone who finds calorie counting triggering or overly rigid.
Referring to a 2024 study she worked on that examined the impact of wearables on the psychological well-being of patients with AFib, Rosman says that about half of the participants were checking their heart rate every day out of habit, not because they felt symptoms.
Cassidy explains that while people with health anxiety may initially find wearables helpful, compulsively checking to make sure their vitals are normal can accidentally become a form of negative reinforcement that further propels the anxiety.
“Often when I work with anxious people, we try to cut back or eliminate the need to compulsively check for reassurance on their wearables, as well as with ChapGPT or other digital ‘doctors,’” says Cassiday.
When people refrain from compulsively checking, wearables can provide useful feedback that counters the false belief that something terrible will happen to their health.
If checking your health metrics causes anxiety, try reducing how often you view them on your device or in its app. Setting an alert to check weekly, at a minimum, could help — especially since it’ll give you a broader picture, making you less likely to hyperfocus on a single data point that seems off.
You should also avoid checking your wearable’s health information right after you wake up or before you go to bed, as this can set the tone for an anxious day or make it harder to fall asleep.
If having a screen on your wrist makes it difficult for you to stop checking, a screenless smart ring or fitness tracker such as the Whoop 5.0 may be a better option, since they rely on apps instead of screens.
A screenless smart ring may help you stop compusively checking your device.
“You choose how much or how little you engage with the app, which gives those who might be anxious about their health the option to limit the amount of time they spend with their data,” says Patel.
When I asked both Patel and Dr. Jacqueline Shreibati, head of clinical for platforms and devices at Google, how people who wear their devices can reduce health anxiety, they emphasized the importance of tracking trends — not individual metrics.
“We focus on long-term trends (rather than isolated metrics) to help users maintain a balanced relationship with their data,” says Shreibati. “What being healthy means differs for everyone, and we encourage users to consult their physician if they have any concerns.”
Patel points to the Tags and Trends features in the Oura app. Tags lets you tag lifestyle factors such as travel, alcohol, meditation or late meals, which you can then view in Trends to see how your behavior affects your recovery and sleep over weeks, rather than looking at a single score that may one day seem abnormal.
Instead of viewing a single sleep or stress score, consider looking at that data weekly or monthly.
“Most consumer wearables were originally developed as personal wellness devices, which are not required to demonstrate safety and efficacy like traditional medical devices (e.g., a blood pressure cuff or pacemaker),” Rosman explains.
Yet we’ve begun using these wearables to monitor our health, using metrics such as heart rate and rhythm, blood oxygen, stress, sleep and physical activity. Now, some of these devices have medical-grade sensors, software and algorithms approved by the US Food and Drug Administration to detect irregular heart rhythms, hypertension and sleep apnea.
Despite FDA approval, wearables are simply not doctors, and they cannot provide medical diagnoses or treatment. That’s why it’s essential to understand what your device actually measures.
The ECG feature on many smartwatches is just one example of this. FDA-cleared as it may be, a single-lead ECG that only uses one electrode to record your heart’s electrical activity from your wrist is not the same as the 12-lead, hospital-grade ECG a cardiologist would use.
While your wearable’s ECG can surface a potential symptom worth investigating with your doctor, it can’t replace a professional or their medical-grade equipment.
Performing an ECG on your smartwatch is not the same as having that same measurement taken in a doctor’s office.
The gap is even wider for features including stress and sleep scores, which haven’t been clinically validated because there’s no one single gold standard to validate against. These numerical scores are calculated from bodily signals such as heart rate, temperature, movement and heart rate variability, which tend to correlate with your stress and sleep states. But the translation from raw signal to “your stress score is 74” is more of an educated estimate.
“What you’re seeing is a rough indicator of how your nervous system is functioning, not a medical diagnosis,” Rosman emphasizes.
Patel adds that not all physiological stress is inherently negative. “Some forms of short-term physiological stress can be healthy and adaptive,” he says. “That’s why we aim to pair data with in-app context and insights, so members can better understand what they’re seeing rather than receiving that information in a vacuum.”
Nonetheless, when you don’t know exactly what your wearable is measuring, a “bad” stress or sleep score can seem scary when it isn’t necessarily a cause for alarm, but rather a sign that you may want to have a deeper conversation with your doctor.
Just like you should talk to your doctor before starting a new medication or diet, you should get their thoughts on whether you could benefit from using a wearable.
“Education is probably the most underused tool we have,” Rosman says.
When you don’t know what a healthy heart rate or ECG looks like, one seemingly atypical reading can send you into a panic. That’s why it’s essential to speak with your doctor so you understand your own baseline and if a wearable makes sense for your current health condition.
As a guide, Rosman provides the following questions you can ask your doctor:
“A fast heart rate after climbing stairs is not the same as a dangerous arrhythmia, but without that context, a notification can feel terrifying,” Rosman adds. “So much wearable-related anxiety comes not from the data itself, but from not knowing what to do with it.”
When asked when someone should consider parting with their wearable or seeing a professional for health anxiety, Cassiday says that it’s similar to what many notice when they keep checking their smartphone for the next text, TikTok or other digital data.
“If you find yourself interrupting pleasurable activities or your free time to check, or if you feel anxious about not checking, you have a problem,” Cassiday states.
For instance, if you only stop thinking that you’ll have a heart attack when you check your wearable and see your resting heart rate. Or, put simply, if you only feel at peace after someone or something, such as a wearable reassures you that you’re in good health, it’s time to get professional support.
If health anxiety is making it difficult for you to enjoy life, then it’s time to talk to a professional.
To find help, Cassiday recommends using the resources provided by the Anxiety and Depression Association of America or the International OCD Foundation, as health anxiety can be related to obsessive-compulsive disorder.
When you have health anxiety, the gold standard for care is cognitive behavioral therapy. It involves exposure to health-related worries without any form of reassurance and learning to accept the uncertainty that comes with not knowing our future health status, manner of death or time of death.
“People need to learn that all the vague symptoms that trigger their health anxiety are just normal variations of normal body functioning and aging,” Cassiday explains. “They have to reframe the symptoms they notice as nothing to examine, discuss or manage and instead trust the facts of their other evidence of good health.”
CBT can help you live in the present instead of spiraling into the anxiety-inducing “What if?” of the future.
Wearables can be great for people who like tracking their fitness to motivate them toward their goals, or for patients and their care teams when medically necessary. Though they usually cost hundreds of dollars, wearables can be less expensive than medical tests. Some are even HSA- or FSA-eligible.
“In AFib specifically, being able to correlate your symptoms with actual rhythm data can be genuinely empowering,” Rosman says. She’s observed that the patients who thrive with wearables are those who use the data as information — not as something to fear — and those who don’t participate in 24/7 surveillance.
In Rosman’s 2024 study, two-thirds of AFib patients said their wearable made them feel safer and more in control. Even so, there is still the risk of unintended consequences.
While they can be beneficial, wearables can also come with risks — especially since there isn’t enough research on the subject.
Just as doctors would never prescribe a medication without knowing the potential benefits, risks and how to manage them, wearables should be no different. “The technology has moved so much faster than the science, and we need the scientific evidence from clinical trials to catch up,” Rosman explains.
Since the evidence isn’t there yet, Rosman is hesitant to say anyone should categorically avoid wearables.
Despite that, people who are highly anxious about their heart or prone to obsessive symptom monitoring should approach with caution. The same goes for those with conditions involving unpredictable, abrupt symptoms, such as paroxysmal AFib and POTS, because the uncertainty of not knowing when the next episode will hit is stressful enough, and constant monitoring can make it worse.
Rosman has conducted research on the connection between wearables and anxiety, including a 2025 review describing the psychological effects of wearables on patients with cardiovascular disease and a 2024 study examining their impact on the psychological well-being of patients with AFib.
The 2025 review found that while wearables can help promote healthy behaviors and provide data for diagnosis and treatment, they also pose risks, such as adverse psychological reactions.
In the 2024 study, it was concluded that wearables were connected with higher rates of patients becoming preoccupied with their symptoms, being concerned about their treatments and using both formal and informal health care resources.
On the other hand, a 2021 study that analyzed the 2019 and 2020 US-based Health Information National Trends Survey found that using wearable devices for self-tracking can indirectly reduce psychological distress. Still, misinterpretation of wearable data may cause unnecessary panic and anxiety.
A 2020 qualitative interview study featuring patients with chronic heart disease also found that while wearables’ data may be a resource for self-care, it can create uncertainty, fear and anxiety.
Ultimately, more studies are needed.
“Honestly, we don’t have good scientific evidence in this area yet,” says Rosman. “Despite widespread use, there have been no clinical trials I’m aware of that have looked at the benefits and potential health risks of specific wearable health features.”
Rosman’s team plans to be the first to investigate this in patients with pre-existing heart conditions.
When wearables cause health anxiety, they can prompt healthy individuals to schedule unnecessary doctor’s appointments. This places a burden on our health care system, which is already experiencing shortages, making it difficult for people who actually require medical attention to access care.
Rosman’s 2024 study found that those using a wearable sent nearly twice as many patient portal messages to their doctors. Responding to these messages from patients takes time, isn’t reimbursed by insurance and can contribute to burnout.
When health anxiety caused by wearables prompts people to message their doctors, it can put a strain on the health care system.
As a result, Rosman believes we need better systems for managing wearable data in clinical settings before we scale it further: “Wearables are changing how we deliver care in ways we haven’t fully prepared for.”
Wearables can further widen health care inequity due to their cost.
“These devices are expensive, they were mostly designed and tested in young healthy people and they’re marketed toward higher-income consumers,” Rosman explains. “If we’re not thoughtful about access, wearables could actually widen health disparities rather than close them. That’s the opposite of what we want.”
While wearables have their benefits, there are also risks to consider, especially given the limited research on the subject.
If you purchase a wearable and it triggers health anxiety, you don’t have to use every available feature, wear it constantly or continue to wear it at all. Before you even buy that device, you can arm yourself with anxiety-reducing knowledge by getting your doctor’s expert opinion.
However, if health anxiety continues to take over your life, it may be time to remove your wearable and seek professional help.
As for me, writing this piece has been a necessary reminder that, while there’s a lot we can’t control in life, the power is in our hands (or on our wrists or fingers) when it comes to the technology we put on our bodies or invite into our homes. Just like an itchy sweater or a lumpy armchair, we can send the technology that doesn’t serve us packing.
The Green Chile pipeline that would fuel Oracle’s Project Jupiter data centre in New Mexico has been pushed from 15 August to 1 February 2027, according to a filing by Energy Transfer’s Transwestern unit. The state land commissioner has twice refused the route, citing water use and emissions.
The pipeline that would fuel Oracle’s largest planned data centre has slipped by nearly six months. Energy Transfer’s Transwestern unit told federal regulators on Friday that the Green Chile Project now has an in-service date of 1 February 2027, against a previous target of 15 August.
What it feeds is not a normal building. Project Jupiter in Doña Ana County is planned at 2.5 gigawatts, powered by Bloom Energy fuel cells, with Oracle as the tenant hosting AI infrastructure for OpenAI.
The numbers around it are large in every direction. Developers Stack and BorderPlex Digital Assets have talked of investing up to $165bn across 1,400 acres and four buildings, at a company already borrowing heavily to build data centres. The pipeline would carry up to 400 million cubic feet of gas a day, roughly 0.4% of everything produced in the lower 48 states.
Standing in the way is one elected official. New Mexico land commissioner Stephanie Garcia Richard has refused the route twice, first in March and again on 15 July, because it crosses state trust land.
Her reasoning was not procedural. The project would benefit investors and developers but bring “no significant benefits for state lands,” she wrote, calling the burden on New Mexico’s water and natural resources “extreme.”
She was blunter still on emissions, describing approval as “literally doubling down on dangerous emissions” at a moment when the state should be slowing climate change rather than accelerating it.
Oracle has made its own urgency clear. It told the Federal Energy Regulatory Commission in May that “time is of the essence” and that delay would jeopardise “the broader objectives of Project Jupiter.”
The fuel cells were already the compromise. Bloom’s solid oxide units replaced planned gas turbines and diesel generators, and Oracle has since expanded that order to 2.8GW, but they still burn gas, which is the pattern across an industry running on fossil fuel while promising otherwise.
Investors read it as a setback. Oracle shares fell about 4% on Friday, while Energy Transfer rose 1.4%.
The lesson is about land rather than capital. Money and chips can be bought at speed, but a 17-mile right of way cannot, and 500 US towns have already blocked data centres of their own.
It could soon become easier to identify AI-generated content, even if it’s not the usual “It’s Not X, it’s Y” type of post you’d come across on LinkedIn and other socials.
As you may be aware, the EU now requires AI companies serving its market to mark their AI-generated content so it’s easier to identify.
Anthropic and several other major AI providers have agreed to comply with the EU’s Code of Practice, with Anthropic becoming one of the first companies to share details about how it will implement watermarking across Claude.
Anthropic has also confirmed that a regular user won’t be able to see the watermark.
According to the company, it has no practical impact on the quality or content of Claude’s output, including creativity and readability.
For those unaware, invisible watermarking and provenance systems are already being used for some AI-generated images, and text-based output will now follow a similar concept, although the underlying implementation is different.
While the change is being introduced to comply with the EU AI Act, Anthropic says the watermark will initially be applied to Claude-generated text worldwide.
“We’re applying watermarking globally at launch because we don’t yet have a durable way to scope it by region,” Anthropic explained in a blog post.
Anthropic says future Claude models will generate watermarked text. Models launched before August 2, 2026, are covered by the EU’s transition period, and Anthropic says it is working to add watermarking to those models over the coming months.
Anthropic says its implementation is based on Google DeepMind’s SynthID-Text approach and explained that it works during generation, with certain exceptions.
As you may be aware, AI models generate text by repeatedly choosing which token could reasonably come next. Instead of adding characters or modifying the finished response afterward, Claude’s watermark changes the source of randomness used when making some of those choices.
“Watermarking uses low-stakes choices like these—which occur many times over a piece of generated text—to leave a pattern in Claude’s responses. That pattern is undetectable to the reader, but is detectable to anyone who has a key that encodes it,” Anthropic explained.
“When watermarking is used, choices are still made at random, but the source of the randomness is different. Instead of using an arbitrary random number generator to pick the next word, watermaking uses the key and a few words that come before to settle what word the model should pick.”
“That is, the words that Claude picks are still random, but now, one can check the sequence of words and see if it’s consistent with the choices Claude would make if it was using the key. If it is, one can assign a probability that the text was generated by Claude.”
I also read the research paper on the topic, and here’s an excerpt that explains how generative watermarking works:
Generative watermarking works by carefully modifying the next-token sampling procedure to inject subtle, context-specific modifications into the generated text distribution. Such modifications introduce a statistical signature into the generated text; during the watermark detection phase, the signature can be measured to determine whether the text was indeed generated by the watermarked LLM. A key benefit of the approach is that the detection process does not require performing computationally expensive operations or even access to the underlying LLM (which is often proprietary).
The paper goes in depth and has more examples, but the important part is that Anthropic is not adding a visible marker or hidden characters to Claude’s response.
Instead, when Claude has multiple reasonable choices for what to generate next, the watermarking system uses a secret key and some of the preceding words as part of the randomness used to make that choice.
Those individual choices should look completely normal to a reader, but across a sufficiently long piece of text, they leave behind a statistical pattern.
A detector that has Anthropic’s key can examine the sequence of words and determine how consistent it is with the choices Claude would have made while using the watermark, allowing it to estimate the likelihood that Claude was involved in writing the text.
According to Anthropic, internal testing found no impact on creativity, readability, or the content of Claude’s responses.
The company also says watermarking requires no additional tokens and has a negligible impact on generation speed.
“Nothing is added to the text and there are no hidden characters,” Anthropic noted. “Watermarking doesn’t require extra tokens, and will not be more expensive.”
As I mentioned, there are certain exceptions to watermarking, and they’re for good reasons.
For factual statements where only one answer is correct, Anthropic says the watermark does not interfere with the choice.
Likewise, the same principle applies to code, where replacing one term with another could break the output.
“Where an exact output is required—where there isn’t a choice, and something would be factually wrong or a piece of code would break if a different term was chosen—the watermark isn’t applied.”
“For example, once the model has written “2 + 2 =”, there is a very clear best choice for the next token (if the model is completing the sum, there isn’t an answer that’s equally as good as “4”; if it’s talking about George Orwell’s Nineteen Eighty-Four, there isn’t an answer that’s equally as good as “5”),” the company noted.
“The “nudge” of the watermark wouldn’t be applied here. For the same reason, code—which in very many cases has to be exact—has generally less watermarking than some other forms of text.”
Anthropic notes that watermarking can still be used in parts of code where arbitrary choices exist, such as comments, but says it should have a negligible effect on the actual code produced.
This aligns with Google’s SynthID-Text paper, which notes:
There are two primary factors that affect the detection performance of the scoring function. The first is the length of the text x: longer texts contain more watermarking evidence, and so we have more statistical certainty when making a decision. The second is the amount of entropy in the LLM distribution when it generates the watermarked text x. For example, if the LLM distribution is very low entropy, meaning it almost always returns the exact same response to the given prompt, then Tournament sampling cannot choose tokens that score more highly under the g functions. In short, like other generative watermarks, Tournament sampling performs better when there is more entropy in the LLM distribution, and is less effective when there is less entropy.
It is also worth noting that light proofreading of human-written text may leave too little Claude-generated material for reliable detection.
Anthropic says the watermark only applies to words Claude actually chooses, so a few grammar or punctuation changes might not provide enough evidence.
Anthropic says a translation produced by Claude carries a watermark because Claude chooses every word in the translated output.
It turns out that there’ll be an easier way to detect the watermarks, as Anthropic plans to offer a watermark detection API.
The API will be able to estimate the likelihood that Claude was involved in writing a piece of text, but Anthropic stresses that this is not the same as proving who wrote it.
A Claude watermark also cannot identify whether the text was written by another AI model, since other providers may use different watermarking methods and different keys.
“A watermark can only determine that Claude was likely involved with the content at some point. It cannot distinguish “Claude wrote this” from “Claude heavily edited this.”
“Light editing probably won’t remove the watermark completely; a complete rewrite where every word is replaced will.”
Detection also becomes less reliable with small samples because there are fewer word choices for the detector to analyze.
For generated PNG, JPG, and SVG files, Anthropic is taking a different approach.
Claude will attach cryptographically signed C2PA provenance metadata indicating that the file was created or processed with Claude, rather than modifying the file itself with an embedded watermark.
Overall prevention scores can hide what happens after initial access. Once attackers are using valid credentials, prevention drops sharply.
The Blue Report 2026 measures defenses technique by technique across 338 million simulations run in customer production environments.
Over the past year, one trend has become increasingly clear in generative AI: no single image or video model excels at everything. Some models produce more realistic motion, others generate stronger lighting and textures, while others perform better at maintaining character consistency across multiple generations. As a result, creators increasingly switch between different models depending on the task instead of relying on a single tool. The downside is that this often means managing multiple platforms, accounts, and subscriptions.
DaVinci AI aims to simplify that workflow by bringing multiple leading AI models into a single workspace. Instead of switching between different services, you can access a wide range of image and video models from one interface.
In this review, we take a close look at what DaVinci AI offers, which AI models are currently available, how its editing tools perform in practice, and whether its pricing represents good value.
DaVinci AI is a web-based generative media tool that brings together leading AI models for image and video generation on a single platform. You can switch between flagship models from different providers, including Sora, Veo, Kling, Seedance, Seedream, and Nano Banana, all from one interface, without needing to create separate accounts or make separate payments for each. Without needing to create separate accounts or make separate payments for each. The platform works on mobile, tablet, and desktop browsers, and it also has iOS and Android apps.
Rather than focusing on a single proprietary model, DaVinci AI acts as a central workspace for multiple AI image and video models. It’s multiple generative models brought together under one roof. When you want to generate video in Sora, an image in Nano Banana, and voiceover on an ElevenLabs based engine, they all appear on the same project screen.
Opening DaVinci AI’s model dropdown menu reveals dozens of image and video models. Some standouts include:
Bringing this range of popular image and video models together in one platform is one of DaVinci AI’s main differentiators; usually you’d need to visit a separate platform for each one. DaVinci AI’s value proposition here is clear: a user who wants to test the same prompt across multiple models can stay on a single screen instead of switching between browser tabs. With Higher-tier plans, you can also run multiple generations simultaneously and compare results side by side, which can make it easier to compare outputs before selecting the best result.
Generating an image is only part of the job; the real difference often shows up at the editing stage. The suite covers the essentials: an Object Remover / Erase tool for cleaning unwanted elements out of a scene, Background Remove & Swap for pulling out or fully replacing a background, Relight for reconstructing a product photo’s light source and shadows, Reshoot / Change Camera for regenerating a scene from a different angle, and Upscale for pushing a low-resolution image up to high resolution without losing detail.
Of these, the lighting and camera-angle tools are particularly notable — they let you turn a single shot of a product photo into multiple usable variations. Some supported models also provide character consistency features, allowing recurring characters to remain visually similar across multiple scenes.
DaVinci AI‘s “Artwork” section offers dozens of pre-built artistic styles and moodboards for users without needing advanced prompt-writing skills. Picking a style and applying it to your own image is much faster than building a prompt from scratch.
Templates follow a similar logic: categories like business portraits, video thumbnails, logo design, and product/ad visuals come as ready made formats. This can save considerable time for small business owners and marketers who need to produce content regularly.
One thing that sets DaVinci AI apart from many other visual focused tools is its audio side. The platform integrates voice generation technologies such as ElevenLabs and MiniMax Speech to generate natural sounding text to speech, allowing you to choose from different languages, accents, and delivery styles. On the music side, you can create instrumental tracks or full songs with vocals by entering your own lyrics, with control over parameters like genre, mood, and tempo.
For creators producing complete marketing assets, having image generation, voice synthesis, and music creation within the same workspace can reduce the need to move between multiple AI platforms.
When you create a new account, you can generate your first image without entering credit card information, making it easy to explore the platform before committing to a paid plan. If you decide to continue, DaVinci AI offers several subscription options with different credit allowances and model access. Since pricing and promotions may change over time, it’s best to check the latest details directly on the official website.
Credit-based system, your monthly credit allowance and access to certain AI models and priority queue access vary by plan. We recommend checking current pricing and any promotional campaigns directly on davinci.ai, since these kinds of promotional prices can change over time.
Pros:
Things to watch out for:
A few user profiles stand to benefit most. AI enthusiasts and creators who regularly compare different models will find the dropdown alone worth the subscription. Marketers and e-commerce sellers get the most practical mileage — product visuals, ad creatives, and UGC-style videos are exactly the volume-heavy work this platform is built for. Designers and freelancers can lean on it for concept art, moodboards, and client mockups, content creators for quick clips, thumbnails, and transition effects for Reels, Shorts, and TikTok, and small business owners for everyday social media visuals and brand content.
Yes. DaVinci AI provides access to these models and more through a single subscription and a single interface.
No. Rather than replacing dedicated AI generators, DaVinci AI brings together multiple leading image and video models within one workspace, making it easier to compare different tools without managing separate subscriptions.
No credit card information is required for your first image generation after creating an account. Moving to a paid plan is required for full feature access.
Yes, the platform is accessible from mobile browsers and also has iOS/Android apps.
Commercial use depends on your subscription terms and compliance with applicable copyright and trademark laws. Users should review DaVinci AI’s current licensing policy before publishing commercial work.
It wouldn’t be accurate to judge DaVinci AI by a single feature. Its real strength lies in bringing together functions that are typically spread across five separate tools, image generation, video generation, editing, audio, and music, under one subscription and one interface. It offers a practical consolidation especially for content creators, marketers, and designers who are tired of switching between multiple generative AI tools.
If you want to see whether DaVinci AI fits your workflow, we recommend starting with the free trial. It provides a practical way to evaluate the platform before committing to a subscription. Testing a few real-world projects is often the best way to determine whether it meets your creative needs.
Lamborghini just raised the ceiling on what a production V12 hybrid can do. Unveiled during Monterey Car Week, the Revuelto SV arrives as the most powerful road car the company has ever built, pairing a familiar 6.5-liter naturally aspirated engine with a more capable electric system and a fixed rear wing that transforms how the car sticks to the road.
Power increases to a combined 1050 horsepower and a staggering 1051 lb-ft of torque. That is due to the V12 still producing a decent 814 horsepower at 9250 rpm, revving just as far as the normal Revuelto, all the way up to 9500. A more dense 7.3 kilowatt-hour battery provides additional power to the three electric motors. What was the result? These motors can provide a little more assistance and maintain a faster pace for longer periods of time, with Lamborghini claiming up to four times better consistency when pushing the car to its limits repeatedly.
The numbers speak for themselves: according to official claims, the 0-62 mph dash now takes 2.4 seconds and the 0-124 mph run takes 6.7 seconds. If you’re wondering how fast it’ll travel, the official top speed is more than 214 mph. Despite the larger battery and more aero features, the dry weight only adds a few hundred pounds, thanks to the significant usage of carbon fiber components and some optional lightweight pieces.
One of the most noticeable modifications is the work done on aerodynamics, and it’s a game changer. The standard car’s dynamic rear wing has been replaced by a fixed device that packs a powerful punch. Not only is it a noticeable cosmetic improvement, but it also represents an 80% increase in downforce over the standard Revuelto and an incredible 10% increase over the previous Aventador SVJ. The car’s front end is also impressive, with a deeper splitter, angled fins, and a Gurney flap all working together to generate enormous downforce increases. The good news is that front-end downforce has doubled, which may come in handy while cornering at high speeds.
Lamborghini also managed to enhance the balance of downforce and drag by 60%, making life much easier for the driver. There have also been changes to the chassis. The ordinary car’s adaptive dampers have been replaced with manually adjustable ones from the company’s GT3 racing program. The results are impressive: a 17% boost in agility and a 10% increase in lateral grip, making the car a lot more enjoyable to drive. Wheels have also been updated, with center-lock units measuring 20 inches up front and 21 inches in the back, and they are wrapped in Bridgestone’s top-of-the-line Potenza Race R tires, which were developed exclusively for the SV. These beauties provide more grip and traction when you’re laying down the power.
Finally, the brakes have been given some TLC. New CCM-R Plus carbon-ceramic discs measuring 16.5 inches up front and 16.1 inches at the rear have been fitted, complete with a more efficient carbon construction and increased ventilation. The result is an 11% greater peak deceleration rate and a 23% better heat rejection, which should keep the brakes from overheating under load. Braking temperatures drop by about 12% under load, which can only be good for the driver. To top it all off, an updated inertial sensor system sends real-time motion data to the brake controller, allowing it to modulate pressure over curbs or during trail braking with greater accuracy.
Inside the cabin, a small red knob on the steering wheel that you spin clockwise will activate Pilota mode, which sits perfectly alongside the more known settings, Strada, Sport, and Corsa, which will almost certainly remain the go-to selections for most people. Pilota, on the other hand, enables a snappy 5-stage traction-control system derived from Lamborghini’s GT3 cars. You can choose how much of it you want; do you want the system to correct everything that is wrong, or do you want to have fun and see where the car goes? The stability and ABS systems have also been improved, allowing for a more precise reaction to bumps and curbs while still letting you to have fun and slide the car around when you want.
The Revuelto SV’s production is limited to 1963 units, which coincides with the company’s founding year. The first deliveries are scheduled to arrive in early 2027. One thing we do know is that the price will be exorbitant, perhaps far higher than the standard Revuelto, which was already a costly prospect.
Waymo has announced that it’s received permission from the California Public Utilities Commission (CPUC) to expand its robotaxi service across more counties in California. The company says the decision will allow it to offer rides in Sacramento and San Diego for the first time, and expand service in the San Francisco Bay Area and Los Angeles.
As spotted by Electrek, the CPUC’s approval appears to be in response to a letter Waymo filed in January asking for approval on a safety plan so it can offer rides in the expanded service area it announced in November 2025. Waymo received feedback and even at one point had its request suspended, according to the CPUC’s page, but the company’s announcement suggests those issues have since been resolved and just haven’t been publicly recorded yet.
Big news for the Golden State — we have received the CPUC’s approval to expand our autonomous ride-hailing service across the SF Bay Area and LA, and bring our service to Sacramento and San Diego.
Expansion will be gradual and guided by our safety framework. We look forward to…
— Waymo (@Waymo) August 14, 2026
Engadget has contacted the CPUC to confirm the details of its Waymo decision. We’ll update this article if we hear back.
The approval should allow Waymo to offer rides in 18 counties across northern and southern California: Alameda, Contra Costa, Marin, Napa, Sacramento, San Francisco, San Mateo, Santa Clara, Santa Cruz, Solano, Sonoma, Yolo, Los Angeles, Orange, Riverside, San Bernardino, San Diego, and Ventura. The company likely won’t have robotaxis in all of them immediately, though. Waymo’s announcement specifically notes that “expansion will be gradual and guided by [its] safety framework.”
Waymo announced its intention to expand into San Diego in November 2025 and Sacramento in February 2026. In July, the company shared that it would soon test fully autonomous rides without a safety driver in San Diego, Denver, Tampa and Las Vegas. Based on the original letter Waymo filed, CPUC’s permission should allow the company to offer rides in California in its existing Jaguar I-Pace vehicles and its new Ojai robotaxis, custom minivans built by Chinese car maker Zeekr.
A federal judge in California ruled this week that Google must stop making it difficult for consumers to install third-party Android app stores, saying the corporate giant was using “anticompetitive friction” to prevent people from doing so.
It was the latest chapter in the years-long legal battle between Google and Epic Games, which originally accused the corporate giant in 2020 of restricting easy access to third-party app stores and non-Google payment methods, and behaving as a monopoly.
Installing a third-party Android app store such as Aptoide Games hasn’t been a smooth process. For example, if you go to the Play Store on an Android phone and search for “Aptoide,” you’ll see a pop-up section at the top that asks, “Looking for Aptoide Games?” You then have to click on a “Go to page” link. On that next page, it shows the Aptoide Games app, but instead of an Install button, there is a View button. Selecting the View button finally takes you to the Install link.
Also, if you search for “app stores” in the Play Store, a pop-up appears with the question, “Looking for app stores?” with a Go to page link that takes you to the app stores menu.
Northern District Judge James Donato was not thrilled when Epic Games attorneys illustrated all of this in court, The Verge reports. He called those extra steps “anticompetitive friction” and “not acceptable” and ordered Google to remove them from the installation process by next week. In his ruling, Donato said that if someone searches “app stores” or “store for apps” — or any similar combination of search terms — the Play Store must show a list of such stores.
He also ruled that Google must replace the “view” button with “install” on the screen that shows the third-party app and that Google must remove the “Looking for an app store?” screen and instead must show the list of app stores.
Jason Howell, co-host of the Android Faithful podcast, said replacing the View button with an Install button was puzzling, since most apps in the Play Store search results don’t show it either, not until you click on the app itself. “It seems Google was likely replicating how it presents its normal apps in listings for third-party app stores, not immediately doing anything punitive,” Howell told CNET.
Howell did say it was “concerning” that consumers were being directed to a separate page of third-party app stores. “I’m happy to know they will address that.”
Google customers have been able to install third-party app stores on Android phones throughout the operating system’s 18-year history. But prior to July, you could not install those apps from the Play Store. Instead, you had to use the more cumbersome method of sideloading, in which you install the app from an unofficial source or computer. It often required changing the security settings on the phone.
That changed in July, when Donato ruled that Google must allow people to download third-party apps in the Play Store.
But Google added speed bumps to that process, Epic Games complained to the court last month. Its lawyers argued that “Google will not include any app stores in the results,” and instead will direct them to a separate page showing a list of app stores. The lawyers argued that “will confuse users and will make app stores harder to find and less likely to be used” and that they “will just give up” if they don’t see those stores on the initial search results page.
Representatives for Google and Epic Games did not immediately respond to requests for comment.
OpenAI’s ChatGPT chatbot and Codex coding agents have had access to memories, the company’s term for context (background information) derived from previous chat activity, since April 2024. A new Computer History feature builds on that to include (if you opt in) logging your interactions with websites and applications, so it can essentially follow your footsteps to extrapolate where you want to go.
The feature launched this week on MacOS for subscribers to its Pro, Business and Enterprise plans (but not yet for users in the European Economic Area, Switzerland or the United Kingdom). According to the documentation, admins for the latter two plans can control whether users have the ability to enable the feature, in addition to individual user opt-in.
Beyond having the ability to opt in, you can independently select which applications and websites are fair game, as well as pause tracking; you can also view or delete the history at will.
If Computer History sounds familiar, it’s because integrating your every on-device activity in the service of ultrapersonalized agenting is one of the foundations of every whizzy AI feature trotted out to sell tech in recent years. Notably, it hearkens back to Windows Recall, which Microsoft announced in May 2024 — and then quickly rolled back because of controversy over its security and privacy flaws. About a year later, a refined version began to appear in the operating system.
Unlike Recall and OpenAI’s Chronicle preview, which Computer History replaces, this particular implementation doesn’t capture screens, record voice input or use other similar types of monitoring. Instead, it uses accessibility application programming interface in MacOS to capture everything that API exposes: “clicks, typing, keyboard shortcuts, app switches, and context,” according to the company’s description.
It then periodically synthesizes that activity into a summary of what you’ve done rather than what you saw, heard or said. So, as far as I can tell, it can track and show you where to find files you looked at but not the content of the files. However, if you use that content as part of a future chat, it can link your activity to that content.
Even if only a series of keyboard events gets recorded, that doesn’t necessarily mean the content can’t be inferred or exploited. These types of features fall into my privacy-slash-security complacency zone, where browser fingerprinting, deanonymization and similar schemes dwell.
The summaries are generated in the cloud but stored locally on your system. The company says it doesn’t retain the activity files unless legally mandated and doesn’t use them for training.
OpenAI does issue an explicit warning about the feature in its description: “Computer History files can contain sensitive information. They are not encrypted by Computer History, and other programs running as your macOS user may be able to access them.” The built-in disk encryption on MacOS probably offers a layer of protection in some cases, though.
The feature creates tokens for summarizing and memory creation, so your plan’s token budget and context window size may have an impact.
There’s no indication at the moment when or if a Windows or mobile app will be available.
Privacy advocates and musicians rallied outside Madison Square Garden on Friday, pushing for new legislation to prevent facial-recognition technology at the venue and other public spaces in New York City.
“MSG and its CEO, James Dolan, have been flagrantly abusing biotechnology,” City Council member Shahana Hanif said at the event on Friday, after leading a chant of “ban the scan.” In January, Hanif proposed a bill that would prohibit public venues from using biometric recognition tech such as Madison Square Garden’s facial-recognition systems.
Madison Square Garden is well-known for its biometric data collection practices. As WIRED has previously reported, fans who are critical of Dolan or the New York Knicks can end up on watch lists, their faces filed in MSG’s extensive database. In one instance, MSG security tracked the movements of a specific fan at a Knicks game down to the minute, cataloging their movements, who they spoke with, when they ate, and where they sat. Hundreds of lawyers involved in disputes with Dolan or Dolan-owned properties have been banned from Dolan’s venues, including a mother who was blocked from taking her daughter to a Radio City Music Hall show.
In July, WIRED reported that MSG also maintains a database that assigns risk scores to around 400 celebrities and VIPs. Some of the dossiers also include the race and sexual orientation of those who attend the arena. MSG filed a defamation lawsuit against WIRED later that month.
At the rally on Friday, Hanif was joined by Brooklyn-based country musician Paisley Fields, a representative from the democratic-socialist working group NYC DSA Tech Action, and members of the digital rights advocacy group Fight for the Future.
Hanif’s “Ban the Scan” bill now has 27 endorsements, more than half of the City Council. Members are in talks with speaker Julie Menin about advancing the legislation, according to Hanif.
“People have been recognizing each other’s faces since the beginning of time—we just do it electronically,” an MSG spokesperson said in an emailed statement.
Hanif tells WIRED that, as a Muslim who grew up in post-9/11 New York, surveillance isn’t new. However, she says, many may not understand the extent of the surveillance that they’re exposed to simply by attending a Knicks game or a concert at Madison Square Garden or one of its associated venues. The fact that Dolan, who has a long relationship with President Donald Trump, is the one collecting the data worries Hanif even more.
“New Yorkers need to understand, right now, between AI and all these tools that are being sold to us as being for … our city’s safety, are actually making us less safe,” Hanif says.
Bede Liu, a digital signal processing pioneer, died on 7 May. He was 91.
Liu was widely regarded as one of the founders of modern digital signal processing, a field that applies mathematical algorithms to analyze, modify, and transmit signals including sound, images, and video.
The IEEE Life Fellow taught electrical engineering at Princeton for more than 50 years. From 1994 to 1997, he chaired the university’s electrical and computer engineering department.
Liu’s research aided the transition from analog to digital processing of sound, images, and video. His work helped establish many of the mathematical and engineering techniques that underpin modern communications, multimedia systems, and consumer electronics.
Although little known outside engineering circles, his work is embedded in technologies used by billions of people. The low-power digital signal processors that make cellphone calls, streaming video, and Internet communications possible can be traced to research he conducted in the 1970s and ‘80s.
Liu received the 2018 IEEE Jack S. Kilby Signal Processing Medal for “sustained contributions to the analysis and the development of low-complexity realizations of digital signal processing algorithms.”
“We stream music and video. We take photos with our phones, and we send them around. We don’t even think about it,” IEEE Life Fellow H. Vincent Poor said in an obituary for Liu. “But it’s all because of the signal processing, image processing, and video processing that’s been developed over the years, as well as other technologies that have grown up beside it and enabled it, like semiconductors. The development of these processing advances was exactly what Bede was a major part of.” Poor is a professor of electrical and computer engineering at Princeton.
Liu was born in Shanghai in 1934. During his childhood, his family relocated to Taiwan amid the upheaval of the Chinese Civil War. His father, Henry Liu Sr., was an electrical engineer.
Liu earned his bachelor’s degree in electrical engineering in 1954 from the National Taiwan University, in Taipei. After graduating, he and his family moved to the United States. Liu and his father attended the Polytechnic Institute of Brooklyn (now the New York University Tandon School of Engineering) together. They earned their master’s degrees in electrical engineering in 1956. Liu continued his studies at the school, earning a doctoral degree in electrical engineering four years later.
In 1959 he was awarded a Bell Labs fellowship and worked at the company’s Murray Hill, N.J., location until he joined Princeton in 1962.
“Liu was a highly impactful scholar and teacher—always thinking ahead of future needs and changing technologies,” said IEEE Life Fellow Peter J. Ramadge, a Princeton professor emeritus of engineering.
Cellphones make use of a considerable amount of digital signal processing, Liu once noted. Many of the field’s advances, he added, involved making sophisticated processing practical on devices with limited computing power—which is the challenge that confronted generations of engineers designing portable electronics.
Liu’s research contributions helped shape both the theory and practice of digital signal processing. With Abe Peled, a former graduate student, he authored the 1976 textbook Digital Signal Processing: Theory, Design, and Implementation, which is a standard reference for engineers. Published before digital signal processing had fully emerged as a distinct discipline, it helped define the subject for practitioners and students around the world.
Liu also published 250 technical papers and was granted 12 U.S. patents. His papers are available to read on the IEEE Xplore Digital Library.
The first patent granted to him and Peled was in 1976 for a hardware design that processed bits in parallel, rather than in sequence. The innovation greatly increased computing efficiency for data including sound and communication signals.
Peled says Liu “demonstrated an openness to new ideas and a willingness to challenge the orthodoxy of the EE department at that time—which leaned heavily toward more theoretical information theory.”
Liu’s influence extended beyond his own research. He advised 53 doctoral students, many of whom went on to distinguished careers in academia and industry, including leadership positions at Google and IBM. One former student, computer scientist Robert Kahn, helped create the architecture of the modern Internet. Kahn, an IEEE Life Fellow, received the 2024 IEEE Medal of Honor.
“His former students were very successful,” Poor said of Liu, “and I think that’s a testament to his skill as a mentor.”
“Liu was a highly impactful scholar and teacher—always thinking ahead of future needs and changing technologies.”—Peter J. Ramadge
Together with several Ph.D. students, Liu developed methods of filtering and compressing digital signals to mitigate errors and dramatically reduce the computation needed for signal processing.
As digital signal processing moved from laboratories into commercial products, the impact of Liu’s ideas spread across industries. His research helped spawn the development of lower-cost and lower-power electronics and contributed to advances in mobile communications, multimedia technology, industrial automation, and biomedical imaging.
In the 2000s, Liu turned his attention to media integrity and copyright issues.
“With the increasing accessibility of digital media source material, the protection of ownership and the prevention of unauthorized alteration has become an important concern,” he wrote in his 2002 book, Multimedia Data Hiding. The book, which he co-wrote with his former doctoral student IEEE Fellow Min Wu, discussed the theory, techniques, applications, and security of digital watermarking—hidden signals that could identify a genuine copy of a song, image or video to prevent unauthorized distribution or tampering.
A Princeton team that included Liu, Wu, and another of his doctoral students uncovered serious vulnerabilities in watermarking technologies being considered by an industry consortium. They found that the standardization efforts were immature and would not protect against digital piracy.
“Now nearly every copy of a Hollywood film given to a critic or theater carries a unique digital forensic watermark to prevent unauthorized redistribution,” said Wu.
Liu, an active IEEE volunteer, served on the IEEE Board of Directors in 1984 and 1985. He was the 1982 president of the IEEE Circuits and Systems Society.
He was a member of the U.S. National Academy of Engineering, an academician of China’s Academia Sinica, and a foreign member of the Chinese Academy of Sciences.
Outside the classroom, he was recognized for his humility, humor, enthusiasm, and generosity. When thinking of Liu, IEEE Life Fellow Kenneth Steiglitz says, cheer is the first word that comes to mind.
Liu was “always ready with a positive remark, a quick smile or, maybe, some tips on the right way to cook a duck,” says Steiglitz, professor emeritus of computer science at Princeton.
Liu encouraged his students to take on ambitious, unconventional projects, and he inspired students and colleagues with his adventurous spirit.
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The average US electric vehicle sold for $56,126 in July, up 1.6% on a year earlier and the first rise of 2026, according to Kelley Blue Book. Automakers cut EV incentives by 24.3% year on year after pulling models and shrinking supply.
The average electric car in the United States sold for $56,126 in July. That is 1.6% more than a year earlier and 1.2% more than June, and it is the first increase of 2026 after prices had fallen every month since December, according to Kelley Blue Book.
The sticker is not what moved. Manufacturer discounts on EVs fell to $6,626 in July, down 24.3% on the same month last year, so buyers simply paid closer to the asking price.
Electric cars are still discounted far harder than anything else on the forecourt. Incentives run at 11.8% of the transaction price against 6.4% across the industry, with Tesla at 10.4%.
Automakers can pull back because they cut supply first. A dozen EV models have been discontinued in the US this year as tariffs, the lost tax credit and import costs reshaped the market.
“Automakers have less excess inventory of EVs,” Sam Abuelsamid, vice president of market research at Telemetry, told Business Insider. “They don’t have to spend as much on incentives, and they don’t have to negotiate on price as much. It gives car companies more pricing power.”
The whole market is drifting upwards. The average new vehicle went for $49,855 in July, up 1.9% year on year and the highest figure of 2026.
One caveat sits inside the EV average. Luxury buyers are disproportionately likely to buy electric, according to K.C. Boyce of research firm Escalent, which pulls the number up regardless of what is happening to prices.
Cheaper cars are coming to push it back down. Slate’s stripped-out pickup, priced at $24,950, is due to start deliveries later this year.
Ford’s Fathom pickup starts at $28,350 before destination, reaching $29,945 on the road. Escalent found buyers define an affordable new car as $32,000 to $48,000, so both land underneath it.
The firm’s most useful finding is not about price at all. Bundling a home charger and its installation with a new EV shifted how affordable buyers judged it by as much as taking $8,000 off the sticker.
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