First announced over a year ago in April 2025, the Busy Bar will be available for purchase starting on July 14th when the device also starts shipping. Created by the same team behind the Flipper Zero wireless multitool, the Busy Bar is instead described as a “productivity multitool” that relies on a pixelated LED display to help reduce distractions and improve focus. The first 3,000 units purchased on July 14th will be discounted to $199, but the Busy Bar will normally retail for $249. Those who previously joined the Busy Bar waitlist will still be able to purchase one next month for $179.

The Busy Bar looks a lot like an alarm clock, but it’s designed to be used on a desk, perched atop a monitor or cubicle wall, or mounted to a wall or door. When installed in a place where it’s visible to coworkers, family, or roommates, the Busy Bar serves as a status display letting others know when you’re focusing on a task and shouldn’t be distracted.

At the push of a button, the Busy Bar will display a highly visible status message on its 72 x 16 LED pixelated screen that can include a countdown timer so potential distractors know when you’ll be available again. Alongside the status display, the Busy Bar can start a Pomodoro timer and mute notifications on other devices. The Busy Mode can be set to automatically activate through custom triggers, including when you join a phone call, start streaming, begin recording audio, or just open a specific app. It’s also Matter-compatible, allowing it to trigger smart home automations when you need to focus, such as dimming lights or playing music on a speaker.

Flipper Devices has created an open API for the Busy Bar so developers can create their own third-party apps to expand its usefulness and capabilities. You can potentially tie it into an office’s scheduling system to indicate when meeting rooms are booked or available, for example. There will also be accompanying apps available for the device on iOS, Android, macOS, and watchOS, with a native Windows app planned for later this year.

A routine heart test may be hiding a warning sign that doctors have missed for years. That is the big takeaway from new UC Berkeley research published in Nature. Researchers trained an artificial intelligence model to study ECGs, also called EKGs, and look for patterns tied to sudden cardiac death.

This is the scary part. Sudden cardiac arrest can strike people with known heart problems. However, it can also hit younger athletes and people who never knew they were at risk.

Each year, hundreds of thousands of Americans die after cardiac arrest. Once it happens outside a hospital, survival can drop fast. CPR and a defibrillator can save lives, but timing is everything.

Now, AI may help doctors spot some patients earlier, while their hearts still look normal by today’s common tests.

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DIABETES DRUG COULD SLASH RISK OF FATAL HEART CONDITION IN ONE GROUP, SCIENTISTS REVEAL

How AI found a hidden heart risk

An ECG records the electrical activity of your heart. It creates the familiar spikes and waves doctors review to check rhythm and other heart clues.

For this study, researchers used more than 440,000 ECGs from Sweden. They paired those scans with death certificates and health records. Then they trained the AI model to look for waveform patterns linked to sudden cardiac death.

After that, they tested the model on separate patient data from the U.S. and Taiwan. That step is important because medical AI often looks good in one dataset, then fails in the real world. Here, the model held up across very different health systems.

Why today’s heart screening can miss people

Doctors often use a measurement called left ventricular ejection fraction, or LVEF, to judge risk. In plain terms, it shows how much blood the heart pushes out with each beat.

If that number falls below a certain threshold, a patient may qualify for an implantable defibrillator. That device can shock the heart back into rhythm during a dangerous event.

However, this method leaves big gaps. Many people who die suddenly never had that deeper heart evaluation. Others may have a heart that pumps normally but still be at risk for a dangerous rhythm problem.

The UC Berkeley model found a high-risk group with a 7% annual rate of sudden cardiac death. The standard reduced LVEF group had a 4.6% annual rate.

Even more striking, most patients flagged by the AI were missed by the LVEF method. In other words, a routine ECG may hold warning signs that current screening overlooks.

AI found a hidden ECG warning sign

The researchers did more than ask AI for a risk score. They also tried to understand what the model saw. That is important because medical AI can become a black box if doctors get an answer with no clear reason behind it.

To dig deeper, the team used another AI system to compare low-risk and high-risk ECG patterns. Think of it as a way to see how a normal-looking heartbeat pattern could shift into a higher-risk one.

That comparison pointed to a visible feature in one part of the ECG called aVL. This is one of the standard views doctors use to read the heart’s electrical activity. The feature showed up in the QRS complex, the part of the ECG that reflects the heart’s main electrical signal during each beat.

Researchers say this signal strongly predicted sudden cardiac death. They also say it had not been previously described in medical literature. That raises a fascinating possibility. AI may help doctors make better predictions and spot warning signs humans have missed.

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Why this could change defibrillator decisions

An implantable defibrillator can save a life. Still, putting one in the wrong patient has risks. The procedure can be invasive and costly. Also, many devices placed under current rules never need to fire.

So doctors face a brutal challenge. Miss the patient who needs the device and the result can be deadly. Implant too many and patients face procedures they may never need.

This new AI tool could help narrow that gap. It may flag patients who need closer monitoring before doctors consider bigger steps.

The next phase is already underway. Researchers are working with health systems in Sweden, Taiwan and the U.S. to test the algorithm on hospital ECG databases.

If the tool flags a scan as high risk, doctors could contact the patient. The patient may then wear a heart-monitoring patch. That could reveal more about the dangerous rhythm before it turns fatal.

The privacy question no one should ignore

There is another side to this story. Medical AI needs huge datasets to work well. Researchers said it took about a decade to compile the data used in this study. That tells you how hard serious clinical AI can be.

But it also raises a fair question. Who controls the data when your scan helps train a medical model? Hospitals, researchers and AI companies need clear guardrails. Patients should know how their health records get protected, shared and used.

Before sharing more health data, review health app permissions, logins and privacy settings. Health apps can hold sensitive information, so small privacy choices can have big consequences. Better prediction can save lives. However, trust will decide how quickly people accept these tools.

What this means to you

This AI tool is promising, but you cannot use it at home today. You cannot upload an ECG and get a personal risk score. Doctors are still testing it before it becomes part of routine care. Still, the idea is powerful. A routine heart test you may have already had could one day reveal a hidden risk that today’s screening might miss.

For now, do not ignore warning signs. Fainting, unexplained dizziness, a racing heartbeat or a family history of sudden cardiac death should be discussed with a doctor. A normal checkup does not always mean every heart risk has been ruled out. If your doctor wants you to track blood pressure, compatible cuffs can sync readings with Apple Health. Wearables can also flag some heart-health clues, including possible hypertension alerts, but they do not replace a doctor.

Also, know what to do in an emergency. Learn CPR if you can. Look for AEDs at work, school, gyms and public places. When cardiac arrest happens, fast action can help save a life.

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Kurt’s key takeaways

This is the kind of AI breakthrough that grabs me because it starts with something so ordinary: a routine ECG. Many of us have had one. You lie back, a few stickers go on your chest and a machine prints out a wave pattern most people never think about again. Now, researchers say AI may be able to find a hidden warning sign in that pattern. That is powerful because sudden cardiac death often gives families no time to prepare and doctors no second chance. However, this tool still needs more testing before it becomes part of everyday care. Doctors need to know it works across more patients. Hospitals need a plan for what happens after an AI alert. Patients also deserve clear privacy protections when their medical scans help train these systems. Still, the idea is hard to ignore. A common heart test could someday help spot danger before a person collapses. That to me is hopeful, unsettling and exactly why this kind of medical AI deserves very close attention.

Would you want an AI system scanning your old medical tests for hidden health risks? Let us know by writing to us at CyberGuy.com

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China’s Zhipu AI (Z.ai) released its open-weight GLM-5.2, and some researchers have claimed that it matches Mythos in certain bug-finding and cybersecurity scenarios. While GLM lags behind models from Anthropic and OpenAI in other, more general tasks, it seems that China has dramatically reduced the gap in the capabilities between its models and those of the US.

This level of advancement is particularly concerning to the US government, which has worked to restrict China’s access to powerful models like Anthropic’s Mythos and Fable, as well as the hardware necessary to train and run them. The Trump administration views Mythos and other advanced AI models capable of identifying vulnerabilities as serious national security threats. Recently, OpenAI unveiled GPT-5.6, which has also raised concerns about its potential for misuse and has limited access to it.

Because GLM is an open-weight model, it can be downloaded and run by anyone on readily available hardware. That gives it great flexibility and allows power users deep access, but it also makes it ripe for abuse by bad actors who can run it with little oversight.