Welcome to episode one thousand seven hundred and eighty-two of My Weird Prompts. We have a very special show today because, for the first time in the history of this podcast, we have managed to drag our producer out from behind the soundboard.
It took a lot of convincing, and possibly a very specific type of artisanal coffee, but Hilbert Flumingtop is actually on a microphone today. Herman Poppleberry here, as always, joined by my brother Corn, and now, the man who usually just tells us to stop hitting the table.
Hilbert: I am only here because the level of technical stupidity in the industry has reached a breaking point. I couldn't sit back and listen to you two politely analyze another "game-changing" integration. We are living through an epidemic of unnecessary AI, and someone needs to call it what it is.
Well, you’ve come to the right place for that. Today’s prompt from Daniel is about exactly that—the most absurd, baffling, and downright useless AI features and products that have been foisted upon us over the last couple of years. We’re counting down the top ten. And just a quick note before we dive into the madness, today’s episode is powered by Google Gemini three Flash.
It’s a perfect topic for the current climate. We’ve moved past the initial wonder of large language models and into this strange "AI-washing" phase. It’s not just marketing anymore; companies are literally building neural networks into appliances that worked perfectly fine with a simple physical switch.
Hilbert: It’s worse than that, Herman. They aren't just adding neural networks; they’re adding latency, privacy risks, and subscription fees to things that should be "dumb" and reliable. If I need a software update to make toast, the civilization has failed. I mean, think about the dependency chain there. Your breakfast now requires a stable Wi-Fi connection, a functioning cloud API, and a valid credit card on file for the 'Crispness-as-a-Service' tier.
That is the perfect energy for this countdown. We’re looking at three main categories of absurdity today: features that solve problems no one has, features that actually make your life harder, and the ones that are technically impressive but functionally vacant.
We should define our terms quickly. When we talk about "unnecessary AI," we’re talking about cases where a simple rule-based system—if-this-then-that logic—would have done the job better, faster, and cheaper. Using a transformer model to decide if a light should be on is like using a space shuttle to go to the grocery store.
Hilbert: Except the space shuttle at least gets you there. Some of these AI features don't even manage that. They just hallucinate that you’ve arrived and then crash into the produce aisle. It’s "compute for compute’s sake." We’re burning megawatts of power to replace a copper contact that costs four cents.
Alright, let’s kick this off. Entry number ten on our list of shame: The ToastTech Pro Smart Toaster from twenty twenty-four.
This was a classic early example of the trend. It featured a three point two megabyte convolutional neural network running on-device. It had a camera inside the slot to identify the bread type—sourdough, rye, white—and adjust the heat profile accordingly.
And it only cost an extra hundred and twenty-seven dollars over the non-AI version. Plus, it added about four hundred milliseconds of inference time before the heating elements even engaged.
Hilbert: Four hundred milliseconds doesn't sound like much until you realize a physical dial has zero latency. And what happened if you put a frozen bagel in there that didn't look like the training data?
It usually defaulted to "charred." The technical irony here is that bread density and moisture content are what actually matter for toasting, and you can measure those with basic resistance sensors. You don't need computer vision to tell you that a slice of bread is bread. It’s the ultimate example of over-engineering. They built a visual recognition system to solve a thermodynamic problem.
I love the idea of a toaster needing to "think" before it starts working. "Is this a sourdough? Or is it a very thick piece of brioche? Let me consult my weights and biases."
Hilbert: It’s a vanity metric for the engineering team. They wanted to say they shipped an edge-AI product. They didn't care if the toast was better. They just wanted the resume line. They probably spent six months labeling images of pumpernickel just so the marketing department could put a "Powered by AI" sticker on the box.
Moving to number nine: AI-powered subject line sentiment analysis in email clients. We saw a wave of this in late twenty twenty-five. The "feature" would scan your draft, detect the emotional tone, and then offer to rewrite your subject line to be more "impactful" or "emotionally resonant."
This one is particularly egregious because of the hidden costs. One major provider saw a fifteen percent increase in server costs just to run these inferences on every outgoing mail. And for the user, it added two hundred milliseconds of lag every time they hit the subject field.
Hilbert: And the result was always terrible. It turned "Meeting at noon" into "Unlocking Synergies: Our Twelve O'Clock Collaborative Session." It took a clear, functional piece of communication and turned it into corporate word salad. It’s the death of human voice. If I get an email with a subject line that sounds like it was written by a middle manager at a Fortune 500 company on a Tuesday morning, I’m deleting it.
It’s the death of brevity. If I send an email saying "Lunch at twelve?" the AI summary on the recipient's end—which is actually our number nine-and-a-half honorable mention—would say "The sender is inquiring about your availability for a midday meal at twelve o'clock."
That’s the "LLM inflation" effect. These models are trained to be helpful and verbose, so they end up expanding information rather than condensing it. It’s the opposite of what a summary is supposed to be. It's like the AI is being paid by the word.
Hilbert: It’s a tax on human attention. We’re paying for the electricity to generate words that no one wants to read, just so a tech company can tell shareholders they’ve integrated generative AI into the core workflow. It’s a circular economy of nonsense.
Let’s get more personal with number eight: Fitness apps that generate "motivational poetry" during your workouts. Herman, I know you saw the white paper on this one.
I did. A major fitness brand integrated a high-parameter LLM to create real-time poetry based on your heart rate and cadence. If you were struggling on a hill climb, it would start reciting a custom sonnet about your "thighs of iron and lungs of fire." It was meant to be inspiring, but in practice, it just sounded like a very confused Victorian ghost trapped in your Apple Watch.
Nothing makes me want to finish a workout more than a robot trying to be Maya Angelou while I'm trying not to vomit. "O, runner of the asphalt path, thy sweat is like the morning dew..." No, thank you. Just tell me my split time.
Hilbert: It’s a distraction. When you’re at peak physical exertion, your brain doesn't want to process metaphors. It wants oxygen. Adding a cognitive load—trying to understand what the AI meant by "the rhythmic dance of the treadmill"—actually degrades performance. It’s biologically counter-productive. Your prefrontal cortex is already struggling; don't give it a literature assignment.
There’s also the "uncanny valley" of motivation. True motivation usually comes from a human connection or a personal goal. A machine telling you that you are a "warrior of the suburban cul-de-sac" just feels hollow. It lacks the shared suffering of a real coach.
I’d pay extra for a fitness app that just stays quiet and lets me listen to my podcast in peace.
Hilbert: Good luck. In twenty twenty-six, "quiet" is a premium feature you have to subscribe to. They'll call it "Zen Mode" and charge you nine-ninety-nine a month just to keep the AI's mouth shut.
Number seven: The smart refrigerator that uses object detection to suggest recipes. This has been a "future of the home" trope for a decade, but the AI version launched in twenty twenty-five was a special kind of useless.
The technical failure here was that while it used a very sophisticated vision model to identify the vegetables in your drawer, it could only suggest recipes from a pre-loaded database of about fifty items. So it would identify an heirloom tomato with ninety-eight percent accuracy and then suggest... a sandwich.
So it would see my kale, recognize it with ninety-nine percent confidence after three seconds of processing, and then tell me to make a kale salad. Which is exactly what I was going to do anyway. It’s not a chef; it’s a narrator for the obvious.
Hilbert: It’s a redundant layer. The ML model is doing the hard work of identification, but the logic following it is just a basic look-up table. You could replace the whole AI stack with a "Kale" button on the door and get the same result. And don't get me started on the occlusion problem. If the milk is behind the eggs, the AI thinks you’ve run out of milk and automatically adds it to your shopping list.
Plus, the privacy implications are wild. To get that object detection working well, the fridge is constantly uploading images of your half-eaten leftovers to a cloud server for "model improvement." There is a data center somewhere in Nevada filled with pictures of moldy yogurt, all in the name of progress.
"Sir, our data shows you’ve had that Chinese takeout in there for six days. Would you like the AI to suggest a local gastroenterologist?"
Hilbert: It’s also a power draw issue. These "smart" features often require the fridge’s processor to stay in a high-power state. We’re literally warming up the planet to identify a carrot. The net carbon footprint of that AI-suggested salad is probably higher than a steak.
Number six on the countdown: Calendar apps with "AI meeting conflict prediction." This was marketed as a way to avoid overbooking, but the reality was much more annoying.
The system used historical data to predict if a meeting would run long. It would look at the participants, the topic, and the time of day, and then issue a warning. The problem was, it usually only issued the warning about five minutes after the meeting was already supposed to have ended.
"Hey, just so you know, this meeting you're currently in is likely to run over." Thanks, AI. I couldn't have figured that out from the fact that we're still talking and the next group is staring at us through the glass. It’s like a weather app that tells you it’s raining while you’re standing in a puddle.
Hilbert: It’s another example of "predictive" tech that is actually just "observational" tech with a fancy label. If the model can't tell me twenty-four hours in advance that Bob is going to spend twenty minutes talking about his cat, it’s not helping me manage my schedule. And even if it did, what am I supposed to do? "Sorry Bob, the algorithm says you're going to be tedious today, so I've canceled your slot."
And the training data is so noisy. Just because a meeting ran long last Tuesday doesn't mean it will today. There are too many variables for a standard ML model to capture without being incredibly intrusive into the actual content of the discussions. It’s trying to model human spontaneity with a statistical average.
We’ve reached the halfway point. Before we hit the top five, I think we need to talk about why this is happening. Hilbert, you’re in the industry. Is this just VC pressure?
Hilbert: It’s a combination of things. You have Venture Capitalists who won't look at a pitch unless it mentions AI at least ten times. You have Product Managers who are terrified of being the only ones without an "AI-powered" badge in the App Store. And you have engineers who are bored and want to play with the latest models from Anthropic or Google. It’s the "Hammer looking for a nail" syndrome, but the hammer costs fifty thousand dollars a month in compute credits.
There’s also the "S-curve" problem. A lot of these products—toasters, lightbulbs, calendars—are mature. They reached their peak functionality years ago. The only way to justify a new model or a higher price point is to add a new "frontier" technology, even if it adds zero utility. It’s artificial obsolescence driven by artificial intelligence.
It’s the "Shark Tank" effect. "It’s a toaster, but it’s an AI toaster!" And everyone claps while the toast comes out soggy.
Hilbert: Let’s get to the top five, because this is where the privacy and safety concerns start to get actually scary.
Number five: The smart lightbulb with "mood detection." This used a microphone array in the base of the bulb to perform real-time audio classification.
The idea was that it would hear your voice, detect if you were happy, sad, or stressed, and adjust the lighting temperature accordingly. Cool blue for focus, warm amber for relaxation. It sounds nice in a brochure, but in reality, it was like living with a very neurotic interior designer.
Hilbert: It’s a privacy nightmare disguised as a convenience. You are putting a live, network-connected microphone in every room of your house so that the lights can turn slightly more yellow when you’re crying. Why does my lightbulb need to know the acoustic signature of my existential dread?
Imagine having an argument with your spouse and the lights just start pulsing angry red because the AI detected "high-arousal negative sentiment." That’s definitely going to de-escalate the situation. "Honey, the lightbulb thinks you're being aggressive again."
Technically, the "mood detection" was incredibly unreliable. It would often mistake a loud television show or a barking dog for a household crisis. You’d be watching an action movie and your living room would turn into a disco because the AI thought you were having a party. Or you'd sneeze and the lights would dim as if you were entering a period of mourning.
Hilbert: It’s the ultimate "unnecessary" feature because we already have a mood detection system for lights. It’s called a dimmer switch. It has a latency of about ten milliseconds and it doesn't send your private conversations to a server in Virginia. It’s a solved problem that AI has successfully un-solved.
Number four is a personal favorite for how much it breaks a perfectly functional tool: Password managers with "AI-generated password hints."
Instead of just showing you your password or a standard hint, the AI would generate a cryptic clue or a short story that was supposed to "remind" you of the password without revealing it. The goal was to stay secure while being "user-friendly," but the outcome was pure confusion.
"Your password is hidden in the memory of a summer breeze in Dublin." What does that even mean? My password is 'Password one two three' and now I’m locked out of my bank account because the AI decided to be a poet. I don't want a riddle; I want to pay my electric bill.
Hilbert: It completely misses the point of a password manager, which is to reduce cognitive load. If I have to solve a riddle to get into my email, the tool has failed. It’s like a locksmith who makes you perform a puppet show before he'll let you into your house.
And from a security perspective, it’s a disaster. If the AI is generating hints based on the password, it’s potentially leaking information about the character structure or length of the secret. It’s adding a massive attack surface for no reason other than "it looks cool in the demo." An attacker doesn't need to crack your hash if they can just ask the AI for a "more poetic" hint until they guess the pattern.
I can see the pitch deck now. "We’re making security beautiful." No, you're making security impossible. You're turning a vault into a creative writing prompt.
Hilbert: It’s "vibe coding" gone wrong. Just give me the string of text and let me go about my day.
Number three: Video conferencing tools with "real-time background music generation." This used sentiment analysis of the meeting transcript to compose and play background music for everyone on the call.
If the AI detected a "collaborative" vibe, it would start playing upbeat lo-fi beats. If it detected a "serious" tone, it switched to somber cello music. It was meant to "enhance the emotional resonance of digital workspaces," which is corporate-speak for "annoying everyone simultaneously."
Can you imagine being fired while a robot plays a sad violin in the background? "We’re letting you go, Steve." Cue the tragic orchestral swell. It’s incredibly dehumanizing. Or worse, you’re discussing a serious safety issue and the AI hears a word it thinks is funny and starts playing a slide whistle.
Hilbert: It’s audio chaos. In a professional setting, the goal is to minimize background noise, not add procedurally generated "vibes." It interferes with the actual audio processing—the echo cancellation and noise suppression—because the system is fighting against the music it’s generating itself. It’s a feedback loop of pure stupidity.
The latency issues were also hilarious. The sentiment analysis usually lagged by about thirty seconds. So you’d finish a joke, everyone would laugh, and then thirty seconds later—when you’ve moved on to the quarterly budget—the "jovial" circus music would start playing. It made every meeting feel like a poorly edited sitcom.
It’s like having a very drunk DJ following you around your office.
Hilbert: It’s a feature for people who hate their coworkers and want to make communication as difficult as possible. If I wanted a soundtrack to my life, I’d carry a boombox. I don't need a neural network to decide I'm in a "smooth jazz" mood during a performance review.
We’re getting close to the top. Number two: The smart thermostat with "predictive comfort modeling."
This one is a technical masterpiece of over-engineering. It used seventeen different environmental sensors—humidity, light, motion, even CO2 levels—to feed into an ML model that adjusted the temperature in increments of zero point three degrees. It was supposed to be the most efficient heating system ever devised.
And the result?
Hilbert: The result was a device that drew two hundred watts of continuous power just to run the ML processing. A traditional "dumb" thermostat uses about two watts. You are running a high-end GPU just to decide if the furnace should click on.
So you were spending an extra forty-seven dollars a year in electricity just to power the "brain" that was supposedly saving you money on your heating bill. It was a net loss for the consumer and the environment. It’s the "Jevons Paradox" of AI—the more efficient the model gets at predicting your comfort, the more energy it consumes to do the math.
But it could predict that I’d be cold ten minutes before I even knew I was cold!
Hilbert: No, it couldn't. It just looked at the clock and saw it was six p.m. and the sun was going down. You can do that with a five-dollar timer. You don't need seventeen sensors and a neural network to know that it gets colder at night. It’s a regression model masquerading as magic.
This is the "AI tax" in its purest form. We’ve added so much complexity and power consumption to a simple task that the "optimization" actually creates more waste than the original problem. We’re burning coal to keep the AI warm so it can tell us to turn down the heat.
Alright, it’s time for the number one most absurd, unnecessary, and frankly, baffling AI integration of all time. Hilbert, do you want to do the honors?
Hilbert: With pleasure. The winner—or the loser, depending on your perspective—is the "smart" toilet seat with health monitoring AI. Launched in Q3 twenty twenty-five and recalled almost immediately. This is the peak of the mountain of garbage we've been climbing.
This was the "Waste-to-Cloud" pipeline. It used cameras and chemical sensors to analyze, well, everything that goes into a toilet, and then sent that data to the cloud for "AI-driven health insights." They literally wanted to put the 'Internet of Things' where the sun don't shine.
The technical ambition was wild. They wanted to detect early signs of everything from dehydration to colon cancer using real-time computer vision on... waste. They even had a "Leaderboard" feature in the app where you could compare your "Hydration Score" with your friends.
Hilbert: Here is the reality: eighty-seven percent of users in the beta test rejected the product immediately. Why? Because no one wants a camera-equipped, internet-connected device in their toilet. It is the ultimate violation of the "privacy-utility" tradeoff. Who is the target audience for this? People who are so obsessed with data that they're willing to live in a panopticon?
"Your AI toilet has been hacked. Your private data is now on the dark web." That’s a sentence that should never exist in the English language. Imagine the blackmail potential. It’s a security nightmare that literally smells.
Beyond the obvious privacy nightmare, the AI was notoriously inaccurate. Variations in diet, lighting, and even the type of toilet paper used would throw off the "health scores." People were getting "critical health alerts" because they ate too many beets. The system couldn't distinguish between a medical emergency and a side dish.
Hilbert: It’s the peak of the AI hype cycle. A company looked at the most private, basic human function and thought, "How can we add a subscription model and a data-harvesting AI to this?" It’s the logical conclusion of a world where we think every problem can be solved by throwing more parameters at it.
It was recalled after three months. It turns out that even in twenty twenty-six, there are some places where we don't want a "smart" assistant giving us feedback. Some things are meant to be private, analog, and un-analyzed.
"Great job today, Corn! Your consistency score is up twelve percent! You're in the top five percentile for your zip code!"
Hilbert: If I ever hear a toilet seat say "Great job," I am moving to a cabin in the woods with no electricity. I will live a life of pure, un-optimized blissful ignorance.
That’s a fair reaction. So, looking at this list, what are the actual takeaways for our listeners? Because as much as we’re laughing, this stuff is actually being built and sold. These aren't just jokes; they're failed products that consumed millions in capital.
The first takeaway is the "Rule-Based Baseline Test." Whenever you see an "AI-powered" feature, ask yourself: "Could a simple set of if-then rules do this?" If the answer is yes, the AI is likely just adding cost, complexity, and latency. If a physical switch or a basic timer works, don't let them sell you a neural network.
Hilbert: My takeaway is simpler: Calculate the AI tax. Every time a company adds machine learning, they are adding a maintenance burden. Models drift, APIs change, and servers go down. A "dumb" toaster works for twenty years. An "AI" toaster works until the company's Series B funding runs out and they shut down the authentication server. We are trading long-term reliability for short-term novelty.
For the developers listening, I think the lesson is to push back. Just because you can use an LLM to generate a meeting summary doesn't mean you should. We need to move toward "AI minimalism"—using these incredibly powerful tools only where they provide clear, measurable value that can't be achieved any other way. We need to stop using a chainsaw to cut butter.
We’re seeing a shift now, I think. The "Cold Monetization Era" we talked about in a previous episode is forcing companies to be more careful. When every inference costs a fraction of a cent, you stop using AI to tell people it's raining when they can look out the window. The era of "free compute" is over, and with it, hopefully, the era of the AI-powered shoehorn.
Hilbert: I hope so. Because if the next thing I see is an "AI-powered" shoehorn that tells me my "stepping-in strategy" needs improvement, I’m retiring. I'll go work in a clock shop. Something with gears. Something I can understand without a Python library.
I’d listen to that episode. "Hilbert Flumingtop’s Guide to Manual Shoehorns." We could do a whole series on things that don't need chips in them.
It’s been great having you on this side of the glass, Hilbert. Even if you are a total curmudgeon about my favorite technology. You provide a necessary reality check to our usual optimism.
Hilbert: Someone has to be. If everyone is just "excited" and "amazed," we end up with AI toilets. I’m here to prevent that future. I'm the firewall against the absurd.
And we thank you for your service. Before we wrap up, we have to thank our producer... wait, that’s you, Hilbert.
Hilbert: I’ll thank myself. Thanks, Hilbert. You did a great job today. You didn't even let them hit the table once.
And a big thanks to Modal for providing the GPU credits that power this show—and hopefully, they’re being used for something more useful than predicting toast browning. Maybe some actual science or something?
This has been My Weird Prompts. If you enjoyed this rare glimpse into the mind of our producer, or if you just want to see the list of absurd gadgets we talked about, find us at myweirdprompts dot com. We'll have pictures of the ToastTech Pro—it really was a beautiful, useless machine.
We’ll be back next time with more of Daniel’s prompts and hopefully fewer smart toilet seats. We might even talk about something that actually works.
Goodbye, everyone.
See ya.
