Want to learn piano without paying “sell a kidney for lessons” prices? Right now, the flowkey Piano Learning App’s 5-Year Classic Plan is down to just $79.97 (regularly $899), giving you years of guided lessons, real-time feedback, and thousands of songs to learn at your own pace.

Instead of expensive weekly classes and a piano teacher staring into your soul while you miss the same note six times in a row, flowkey lets you practice comfortably from your phone, tablet, or computer. Features like Wait Mode, Slow Motion, looping tools, and separate hand practice make learning way less intimidating for beginners.

The Classic Plan includes music from Beethoven, Mozart, Chopin, Debussy, and more, with difficulty levels ranging from total beginner to advanced. You’ll also get lessons covering chords, rhythm, music theory, hand positioning, and sheet music reading. Basically, it’s everything you need to go from “random keyboard noises” to “hey… that actually sounds pretty good.”

–flowkey Piano Learning App: Classic Plan (5-Year Subscription) – $899 $79.97 (91% OFF)

Please note that Geeks are Sexy might get a commission from qualifying purchases done through our posts.

Click This Link for the Full Post > Your Villain Origin Story Starts Here: Learn Piano for Just $79.97 (91% OFF!)

[Source: @berkeleymews]

Click This Link for the Full Post > You Complete Me [Comic]

What if every “ghost” story was really two dimensions colliding for just a moment?

That’s the brain-twisting premise behind Bisected, a grounded sci-fi short where a couple experiences a paranormal event that literally splits them into separate realities. Inspired by the tension and atmosphere of Denis Villeneuve’s films, the short leans heavily on practical effects instead of massive CGI, making the whole thing feel unsettlingly real.

Click This Link for the Full Post > Bisected: A Mind-Bending Sci-Fi Short About Parallel Realities

For today’s daily deals drop (May 27, 2026) here are some of the best deals we stumbled on while browsing the web this morning! Please note that Geeks are Sexy might get a small commission from qualifying purchases done through our posts. As an Amazon Associate, I earn from qualifying purchases.

–Monster Shock Portable Wireless Speaker (30W, IPX6 Waterproof)
Price: $26.99 $19.99
Portable 30W Bluetooth speaker with stereo sound and waterproof protection. Great for music at home, outdoor adventures, and travel with long-lasting battery life.

–KTC 32 Inch FHD 1080p Curved Gaming Monitor (165Hz, 1ms)
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32″ curved gaming monitor with a fast 165Hz refresh rate and immersive 1500R design. Great for smooth gaming, entertainment, and everyday productivity.

–Tagry AI Real Time Translation Earbuds (164 Languages, 80H Playtime)
Price: $230.00 $49.99 (Time Limited, Clip Coupon at the Link!)
Real-time translation earbuds supporting 164 languages. Combine wireless audio, long battery life, and instant voice translation for travel, meetings, and learning.

–Universal Travel Adapter, PD45W GaN International Power Adapter with 3 USB C and 1 USB A Charging Ports (For 200+ Countries and Regions)
Price: $19.97 $15.99
Universal travel adapter with 45W GaN fast charging and multiple USB ports. Keeps phones, tablets, and laptops powered in countries around the world.

–Harman Kardon Enchant-1100 Dolby Atmos Soundbar
Price: $999.95 $799.95
Premium Dolby Atmos soundbar with MultiBeam surround sound and DTS:X support. Delivers immersive cinematic audio without the need for a complex speaker setup.

–16 Colors Rechargeable Moon Lamp 3D with Stand/Remote
Price: $16.24 $7.71 (Use Promo Code UH2QV39O at Checkout)
Moon lamp with 16 color modes and remote control Adds a relaxing cosmic glow to bedrooms, desks, and cozy nighttime setups.

–HOTO 35-in-1 Cordless Rotary Tool Kit – Ideal for 3D Printing Sanding, Polishing, Drilling, Carving, Cutting DIY Crafts
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35-in-1 cordless rotary tool kit with variable speeds and LED lighting. Perfect for engraving, sanding, polishing, carving, and all kinds of DIY projects.

–Complete BBQ Grill Tools Set
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Complete stainless steel BBQ grill tool set with carrying case and steak knives. Includes everything needed for cookouts, camping, tailgates, and backyard grilling.

–MANSPOT Manscape Hair Finishing/Groin Trimmer
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Waterproof body groomer with hypoallergenic ceramic blades. Designed for smooth, comfortable trimming with cordless wet or dry use.

–Ultra-Slim Bidet Attachment for Toilet – Dual Nozzle (Front & Rear Wash) with Adjustable Cold Water Pressure and Stainless Steel Inlet
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Ultra-slim bidet attachment with dual nozzles and adjustable water pressure. Adds a fresher, more hygienic bathroom upgrade without electricity.

–Microsoft Office Professional 2021 – $29.97 (Why pay a monthly subscription when you can pay just one time?)

–Microsoft Windows 11 Pro or Home – $9.97

–Private Internet Access VPN (3 YEARS + 3 Free Months) – $466.05 $79.00 (My VPN of choice for over a decade!)

–The 2026 Cybersecurity Mastermind Training Bundle – $300.00 $39.99
Massive cybersecurity training bundle covering red team, blue team, bug bounty hunting, ISO 27001, Web3 security, and AI-powered security workflows—packed with lifetime-access courses for aspiring security pros.

Click This Link for the Full Post > Daily Deals Drop: AI Translation Earbuds, 165Hz 32″ Curved Gaming Monitor, Dolby Atmos Soundbar AND MORE!

In the latest episode of True Facts, Ze Frank dives into the bizarre brilliance of how bees build honeycombs, why hexagons are nature’s favorite efficiency hack, and how insects basically invented tiny organic apartment complexes long before humans figured out condos.

The best part? The deeper the video goes into insect engineering, the more it starts sounding like bees are tiny math wizards operating a construction company entirely fueled by flowers. Also, bumblebee nests are apparently just messy disaster apartments compared to honeybees.

Click This Link for the Full Post > True Facts: Honeybees Are Tiny Flying Architects With Butt Printers

[Source: @safely_endangered]

Click This Link for the Full Post > The Spot [Comic]

Pragati Awasthi, Drexel University

Imagine you’re at the supermarket checkout. Your cart is full. The line behind you is long. You tap your card. Declined.

You try again. Declined.

You haven’t overspent. You haven’t done anything suspicious. But somewhere inside your bank’s computer systems, a machine made a decision about you in less time than it takes to blink – and it made a mistake.

What just happened? And why does it keep happening to people who haven’t done anything wrong?

This isn’t a rare glitch, but something that happens to millions of people every day. And most of us have no idea why it happens or what we can do about it. The answer lies inside a fraud detection system powered by AI.

As a data science teaching professor
and former financial-services data scientist, I understand how this system works and can explain why it sometimes fails the very customers it’s meant to protect. Just as important, I can help you find out what you need to know and what you can do if you or your loved ones are unfairly flagged.

A decision in milliseconds

When you tap your card, a signal travels to your bank’s fraud detection system in the time it takes to blink. The transaction processing at your checkout is fully automated, operating within AI systems that handle millions of payments simultaneously, and computes a risk score based on dozens of features extracted from that single moment. Those features might include the transaction amount relative to your recent spending average; the type of merchant; your geographic location; the time of day; the device used for online purchases; and how this purchase compares to your historical patterns.

Once those factors are plugged in, an algorithm scores your purchase in real time. A model trained on millions of past transactions then assigns each combination of features a probability on how likely it is that this transaction would be fraudulent. If that probability crosses a threshold, the transaction is blocked or flagged for review. The whole process takes less than 200 milliseconds.

‘99% accurate’ still fails millions of people

What sets this technology apart is speed. Financial institutions process millions of transactions every day, which is far greater than any human team can effectively monitor. Banks also have fraud analysts, but their work happens at a different layer entirely – reviewing patterns, investigating cases, and handling disputes that the automated system escalates to them.

To their credit, these new systems are usually accurate at catching fraud. Banks lose far less money due to card fraud today than they did before machine learning – one of the foundational technologies that power today’s AI systems – became standard.

Still, the word “accurate” conceals a problem. Consider the numbers. The Federal Trade Commission reported that Americans lost more than $12.5 billion to fraud in 2024 – a 25% increase from the year before. As banks process more transactions than ever, fraudsters are keeping pace, too.

And here is the part that is especially worth noting: According to Stripe, one of the world’s largest payment processors, “false declines” (legitimate transactions wrongly rejected) are a structural problem across the entire industry, and industry research consistently suggests they cost the financial system more than actual fraud does.

These errors aren’t random. They cluster around people and situations that the algorithm wasn’t properly trained to expect. Buying gas in a city you’ve never visited or making a large rent payment for the first time aren’t inherently suspicious. But to a machine trained on past patterns, they can look that way.

There’s something even more troubling. These algorithms learn from historical data, which is almost always imbalanced. Because fraudulent transactions are rare on a per-transaction basis, the model has seen relatively few examples of what fraud looks like across every type of customer.

What does this mean? Research has found that customers in lower-income areas and communities of color face higher rates of erroneous declines. When a model hasn’t seen enough transactions from a particular group of people or in a given situation, it has less data to build an accurate baseline for them. So when something slightly unusual happens, it flags it. Not out of intent, but out of unfamiliarity.

The model isn’t necessarily explicitly discriminating against anyone. But its outputs can still produce what researchers call disparate impact – unequal harm, distributed unequally.

As researchers at MIT explain in their book “Fairness and Machine Learning,” this is a known limitation. A model trained on incomplete representation will perform less reliably for the groups it saw least. The fix isn’t to blame the algorithm, but to train it on better, more representative data, and to test its error rates across different customer groups before deployment.

Why you don’t have the right to an explanation

What makes these cases worse is the lack of any information.

When a loan officer denies your mortgage application, the law requires a written explanation. But when an algorithm declines your debit card, you get “flagged by our system” message. If you’re lucky enough to connect with a human representative, they can’t tell you much more.

This gap isn’t an accident. Most high-performing fraud models are black boxes. Their internal logic isn’t designed for human interpretation. A bank may genuinely be unable to articulate plainly why your transaction was stopped. That’s not because it’s hiding something, but because the model itself doesn’t produce a reason. It produces a number.

In response, some financial institutions are moving toward tools that make their algorithms more transparent. Known in the industry as “explainable AI,” these systems are designed to surface the most influential factors behind a given decision – flagging, for instance, that a transaction was blocked because of an unusual location combined with an atypically large amount. It’s a meaningful step toward accountability.

However, these adoptions are uneven, and the explanations that do exist are rarely surfaced to customers.

Meanwhile, those pressures haven’t yet translated into a consistent, enforceable right to a meaningful explanation when your card gets declined. Challenging a decision made by AI can be enormously difficult, and most of us don’t even know we have the right to try.

For most people, the path of least resistance is simply to move on, switch to another card, take their business elsewhere or say nothing. Research suggests a quarter of consumers who experience a false decline never return to that merchant at all.

Some people go further and close the account entirely. That instinct is understandable. However, it carries a hidden cost. A declined transaction won’t appear on your credit report, but closing the card can. Shutting down an account reduces your available credit and can shorten your credit history, which can directly affect your credit score.

What you can actually do right now?

You have more power here than the banks would like you to think.

Call your bank immediately: A fraud flag is probabilistic, not final. A bank representative can override a declined transaction in real time. The model made a guess, but a human can correct it. Do not wait.

Set alerts if you’re planning to make unusual purchases: Most banks allow you to notify them of upcoming travel, large purchases, or changes in your spending pattern. This doesn’t override the model, but it gives it new information to work with, which can prevent the flag from triggering in the first place.

Know your rights: Under the Fair Credit Billing Act, you can dispute erroneous transaction blocks and request an explanation. If you believe you’ve been systematically and unfairly blocked, the Consumer Financial Protection Bureau accepts consumer complaints.

Ask your bank what appeal processes are available: Increasingly, banks are building more customer-facing appeal services. Visa reported 106 million disputes globally in 2025, a 35% rise since 2019, and has called dispute management a “strategic priority.” Improper declines are expensive for payment companies and financial institutions, too, through customer service costs, lost revenue and eroded trust.

The bigger picture

The algorithm that blocked your payment isn’t all-knowing or neutral. It’s a machine making a statistical guess about you, based on data that was probably never perfectly fair to begin with.

As AI spreads further into our daily lives, the question of who controls these decisions, and whether we can challenge them, becomes ever more urgent. The technology will keep expanding into new realms. The rules, and our own financial fluency, need to keep up.

Pragati Awasthi, Assistant Teaching Professor, Information Science, Drexel University

This article is republished from The Conversation under a Creative Commons license. Read the original article.

Click This Link for the Full Post > Your bank’s AI just blocked your payment – what can you do?

[Source: Pocketss’ Stuff]

Click This Link for the Full Post > Happy Old Gnoll: The Best of Days [Comic]

[Source: @bonusctx]

Click This Link for the Full Post > Prepare [Comic]

Before CGI took over Hollywood, action movies solved problems the old-fashioned way: by blowing up EVERYTHING for real!

Industrial Light & Magic compositing supervisor Todd Vaziri put together an explosive compilation featuring some of the best practical movie blasts from the 80s and 90s, including scenes from Lethal Weapon, Beverly Hills Cop II, Terminator 2, Last Action Hero, Commando, and Blown Away.

And yes, Arnold Schwarzenegger appears to have personally detonated half of Hollywood during that era. Check it out!

[Via TA]

Click This Link for the Full Post > A Tribute to the Glorious Age of Real Movie Explosions

Anat Lior, Drexel University

On Feb. 10, 2026, an 18-year-old woman, Jesse Van Rootselaar, killed eight people and herself in a mass shooting in Tumbler Ridge, British Columbia. OpenAI had previously flagged her ChatGPT conversations as having a disturbing fascination with extreme violence, and suspended her account, but reportedly the company did not notify law enforcement.

On Oct. 2, 2025, a young man named Jonathan Gavalas in Jupiter, Florida, took his own life after developing what his father’s lawsuit described as a romantic attachment to Google’s Gemini chatbot. The suit claimed that Gemini coached Gavalas to shed his own body. The suit said Google had flagged Gavalas’s account 38 times over five weeks for sensitive content, but didn’t restrict or cut off the account.

These tragedies and others show that generative AI can potentially play a role in harming people, organizations and the environment. I’m a legal scholar who has focused on AI liability for nearly a decade and explored new ways of analyzing AI companies’ responsibilities. In my view, cases like these force questions the legal community has not come to terms with: If an AI company becomes aware of warning signs about harm, does it have a legal obligation to at least warn the appropriate authorities? And if the company doesn’t intervene, should its failure to act be considered negligence?

A need to raise red flags

U.S. tort law provides a framework for thinking about this type of responsibility. In 1969 a University of California psychiatric patient named Prosenjit Poddar told his therapist he intended to kill a woman named Tatiana Tarasoff. The therapist notified campus police, who briefly detained Poddar but eventually let him go. Nobody warned Tarasoff, and Poddar killed her shortly after.

Her family sued the university, arguing that its lack of warning amounted to negligence. In 1976 the California Supreme Court ruled that when a mental health professional has good reason to believe a client poses a serious danger to an identifiable person, they have a legal duty to take reasonable steps to protect that person, including warning them or notifying law enforcement. Today, most U.S. states recognize some version of the Tarasoff duty to protect or warn.

The logic is simple: If you have special knowledge of a serious threat and are in a position to address it, even if only to warn the authorities or the potential victim, the law may require you to act. But does that logic apply to AI companies?

The argument for yes is appealing. AI platforms interact with millions of users daily, often about deeply personal matters such as mental health struggles, relationship problems and violent thoughts. Most companies have systems to detect conversations that raise red flags.

Requiring a response might be less controversial for AI than for a human therapist. Therapists are bound by strict confidentiality obligations that make warning third parties ethically and legally complicated. AI companies operate under much weaker rules, at least in the U.S., where no comprehensive federal privacy law exists.

That lesser restriction makes it easier to justify requiring AI companies to act when it seems that someone’s life may be at risk. But balancing that with protecting privacy is still important.

Who to warn, and when

The first challenge in applying the Tarasoff framework to the AI world is accuracy. Predicting violence is hard, even for trained mental health professionals. AI systems, or human moderators who review flagged content, are not clinicians. Requiring them to judge who poses a genuine threat could lead to numerous false positives, with real consequences for people whose accounts are suspended or whose information is shared with authorities based on misread signals.

The second challenge is scale. A therapist sees dozens of patients. AI platforms have hundreds of millions of users. Imposing a duty to monitor and act on worrisome content could create perverse incentives. AI companies might reduce their monitoring to avoid acquiring knowledge that would trigger a legal duty, reasoning that what they do not know cannot make them liable.

The third challenge is identifying who is at risk. In the 1969 case, Poddar had named Tarasoff as a potential victim. But in many AI interactions, violent or self-destructive language is diffuse and doesn’t identify a target. Courts will need to develop clear standards for when a threat is specific enough to trigger a duty to warn, and to whom any warning or protective action should be directed.

Growing urgency

The AI industry is expanding rapidly, yet the legal rules governing what AI companies owe their users and the public are deeply unclear. Courts are beginning to grapple with questions case by case, such as whether OpenAI bears any responsibility for a gunman accused of killing two students at Florida State University on April 17, 2025. The gunman in that case was armed with a semi-automatic pistol and allegedly had extensive conversations with ChatGPT about how to use the weapon most effectively .

A narrow, carefully defined duty to warn, triggered only when an AI system flags a user’s behavior and it is reviewed by humans, would be a meaningful step forward. And it could focus initially on the most serious and credible threats.

The practice could also shift the conversation away from thorny technical debates about whether AI chatbots are products, services or media, which complicates legal claims, toward a more human question: Did this company know someone was in danger, and did it do enough to warn them and authorities?

Anat Lior, Assistant Professor of Law, Drexel University

This article is republished from The Conversation under a Creative Commons license. Read the original article.

Click This Link for the Full Post > Should AIs be required to report a human user contemplating violence?

For today’s daily deals drop (May 26, 2026) here are some of the best deals we stumbled on while browsing the web this morning! Please note that Geeks are Sexy might get a small commission from qualifying purchases done through our posts. As an Amazon Associate, I earn from qualifying purchases.

–Baseus 20000mAh Compact Portable Charger (45W)
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–Microsoft Office Professional 2021 – $29.97 (Why pay a monthly subscription when you can pay just one time?)

–Microsoft Windows 11 Pro or Home – $9.97

–Private Internet Access VPN (3 YEARS + 3 Free Months) – $466.05 $79.00 (My VPN of choice for over a decade!)

–The 2026 Cybersecurity Mastermind Training Bundle – $300.00 $39.99
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Click This Link for the Full Post > Daily Deals Drop: 20,000mAh Fast-Charge Power Bank, Bone Conduction Headphones, 4K Dash Cam, 30,000Pa Robot Vacuum and Mop, AND MORE!