Get Ready for Quantum Computers

(If You Think AI Is a Big Deal)

If large language models (think ChatGPT) and AI feel like a shock to the system, it’s worth quietly bracing for what comes next.

Quantum computers aren’t faster laptops or smarter phones. They’re a different way of computing entirely—built on rules that don’t match our everyday intuition. And for a certain class of problems, that difference isn’t a small upgrade. It’s a new kind of leverage.

This isn’t a hype piece. It’s a plain-English explanation of what quantum computers are, what they’re good at, and—just as important—what they’re not.

Classical Computers: One Path at a Time

A normal computer works with bits. Each bit is either a 0 or a 1. Everything—photos, passwords, video, spreadsheets—is ultimately reduced to long chains of those choices.

When a classical computer searches for something (a password, a missing record, a match in a database), it checks possibilities one at a time. It can do that incredibly fast—but it’s still fundamentally sequential.

If the password is four digits, the computer tries combinations until it hits the right one:

0000 → 0001 → 0002 → … → 5893

That’s manageable at 10,000 possibilities.

But scale the space up—millions, billions, trillions, astronomically large—and “one path at a time” starts to feel like trying to find a specific grain of sand by examining the beach with tweezers.

Quantum Computers: Shaping the Whole Search

Quantum computers don’t replace classical machines. Think of them more like a different tool you bring out for specific jobs.

Instead of bits, they use qubits.

A qubit can behave like a 0, a 1, or—most importantly—a controllable blend of both at the same time. That’s not a metaphor. It’s a real physical state you can measure and manipulate.

Here’s the key idea most people miss:

Quantum computers don’t “try everything at once” the way hype articles say it.
They represent many possibilities at once, then use carefully designed steps so the wrong possibilities interfere away and the right ones become more likely.

Not magic. Not guessing.

More like: setting up a system where physics helps you tilt probability toward the answer.

The catch is huge, though: you only get that advantage when the problem can be expressed in the right mathematical form.

Example 1: The Owl’s Eyes (A Rule You Can Actually Picture)

Say we want to answer a simple question:

What color are this owl’s eyes?

Possible answers:

• Orange

• Brown

• Black

• Blue

A classical computer could try to “decide” by checking reference photos, running image analysis, comparing patterns—whatever. A quantum computer doesn’t work like that. It needs a rule it can evaluate.

So the programmer builds the problem like a funnel.

Imagine we know a few basic facts (not perfect facts—just usable constraints):

• In this species, brown eyes are common (say ~80% of the population).

• Blue eyes are extremely rare.

• We have a quick test result that suggests the owl carries the pigment trait associated with brown or orange, not blue.

• Under the lighting in the photo, the eye reflection pattern matches orange or brown, not black.

The programmer encodes those constraints as checks:

• “Boost answers that match the likely genetics.”

• “Reduce answers that conflict with the pigment test.”

• “Reduce answers that don’t match the lighting/reflection signature.”

Now the quantum system holds all four answers as possibilities at once, but after the rules are applied:

• Brown gets reinforced (it satisfies multiple rules and is statistically common).

• Orange might also stay in the running (it fits the lighting pattern).

• Blue gets suppressed (rare + conflicts with test).

• Black gets suppressed (doesn’t match reflection signature).

Then you measure the system.

The answer you see isn’t “invented.” It’s the one the rules pushed to the top—often brown, unless the evidence strongly favors orange.

That’s the key: quantum computing isn’t a mind. It’s a way of applying constraints so the “most consistent” answer survives.

Example 2: The Missing Card

You draw one card from a full deck and hide it. The rest of the deck is laid out.

A classical computer might check each card:

“Two of Clubs? Present.”
“Three of Clubs? Present.”
…
“Jack of Diamonds? Missing.”

A quantum approach aims for something more like:

Represent all cards as possibilities → apply a rule that flags the missing one → amplify that outcome.

As the algorithm runs, the “present” cards don’t get reinforced. The “missing” one does.

Measure the system, and it collapses to:

Jack of Diamonds.

Again: not guessing. Not inference.
Just structured math that makes the correct state stand out.

The Programmer’s Role (This Is the Part People Skip)

Quantum computers are not general-purpose thinkers. They’re precision tools.

To get the quantum advantage, the programmer has to:

• Define what “correct” means in a way the machine can evaluate

• Build an algorithm that uses quantum effects to amplify correct outcomes

• Accept that it only works when the problem fits the model

If there’s no clear rule—no structure, no verifiable check—quantum computing doesn’t help.

That’s why quantum computers are promising for things like:

• Searching enormous spaces (in certain structured ways)

• Factoring large numbers (relevant to some encryption systems)

• Optimization (finding good solutions among ridiculous possibilities)

• Simulation of quantum systems (chemistry/materials)

And why they’re not good at:

• Web browsing

• Writing documents

• Running your email

• Replacing everyday software

A quantum computer won’t be your new laptop.

It’s more like a specialized engine you call when the terrain is brutal.

Why Passwords Are in Trouble (But Not Overnight)

A four-digit password has 10,000 possibilities.

A classical computer tries them one at a time:

0000 → 0001 → 0002 → … → 5893

A quantum computer can sometimes reduce the number of steps needed to find a correct answer by using probability amplification—so you don’t have to grind through the full list in the same way.

Important nuance: this isn’t an instant cheat code that makes passwords meaningless tomorrow. The speedups depend on the exact problem and the type of security involved.

But scale the idea up to modern cryptography, and the direction is clear:

Some of today’s widely used security assumptions won’t hold forever—especially the ones that rely on certain math problems staying “too hard” to solve.

That doesn’t mean panic.
It means migration: new standards, new encryption, long transitions.

(And yes, people are already working on that.)

The Quiet Truth About Quantum Computing

Quantum computers won’t replace classical computers.
They won’t replace AI.
They won’t solve everything.

What they will do is crack open certain problem classes that were previously impractical—and force us to rethink things we treat as permanent: security, simulation, optimization, and what “feasible” even means.

If AI feels like a leap in intelligence,
quantum computing is a leap in possibility space.

Different tool. Different rules.
Same world—about to feel a little stranger.

The Top 5 Best Cereals (and Prizes) of the 1980s

Saturday morning meant cartoons before anyone else woke up. He-Man, GoBots, Dungeons & Dragons, Muppet Babies. The TV volume low. Pajamas still on. The house quiet in that specific way it only ever was once a week.

Cereal was part of that ceremony — the other part the prize.

You ate fast, dug carefully, and hoped no one else in the house had already opened the box. The cereal was temporary. The reward was real.

Here’s my completely subjective ranking.

1. Freakies

Freakies wins because it understood kids didn’t want friendly — they wanted strange.

The cereal itself wasn’t great, but the stickers were everything. Little alien-monster characters living together in a tree like it made perfect sense. They weren’t polished mascots; they felt otherworldly, slightly unhinged, and committed to being weird.

You stuck them on trapper keepers, doors, notebooks, desks — anywhere you could leave evidence you’d been there. Freakies didn’t care if you liked them. They existed anyway.

That mattered.

2. Honeycomb

Honeycomb actually tasted great — loud, crunchy, unapologetically sweet. But the real prize was the bicycle license plate.

They were plastic, not metal, but to a kid they felt official. Heavy enough. Glossy. Important. You clipped one on and suddenly the sidewalk felt like open territory.

You weren’t just riding your bike — you were registered.

3. Cocoa Pebbles

Cocoa Pebbles were perfect. Chocolate cereal that turned the milk into a second, better course.

And at least once, they included coin holders — little plastic organizers that made you feel responsible even if you didn’t actually have money. You could hear the coins rattle. That was wealth.

If I can’t fully remember the prize but still remember the cereal this clearly, that tells you everything.

4. Apple Jacks (Wacky WallWalkers Era)

Apple Jacks were solid on their own, but they earn this spot because of Wacky WallWalkers.

The WallWalker wasn’t a toy — it was an experiment. You threw it at the wall and watched it slowly crawl downward like gravity was optional and physics was negotiable.

No instructions. No goals. Just chaos.

Perfect for a morning already filled with cartoons about swords, robots, and impossible odds.

5. Cookie Crisp

Cookie Crisp felt like a loophole in the system. Tiny cookies in a bowl with milk. Someone had approved this. You benefited. I recall getting baseball cards once.

However, the thrill wasn’t the prize — it was the knowledge that breakfast rules had briefly collapsed.

That counts.

Special Mentions

• Cap’n Crunch — Delicious. Violent. Unapologetic.

• Anything With the Prize Loose in the Box — A test of patience and restraint.

The 1980s understood something modern cereal forgot: kids didn’t want apps, codes, or “interactive experiences.” They wanted something they could hold. Lose. Trade. Stick to a wall.

You finished your cereal, wiped the milk from your face, and flipped back to the TV just in time to catch the theme song.

And for a few hours, that was everything.

Vibe Coding: Building Things You’re Not “Qualified” to Build

These days, the air feels electric. AI is everywhere, the workplace is shifting under our feet, and a lot of people seem stuck between two moods: panic or hype. I’ve been trying a third option.

I build things.

Not because I suddenly became a “real developer,” but because these new tools let me move faster than my skillset used to allow. That’s the whole spirit of vibe coding—and honestly, it’s the most fun I’ve had with technology in years.

What is vibe coding?

Vibe coding is when you describe what you want a piece of software to do in plain language—almost like you’re describing a feeling, a behavior, a vibe—and a large language model drafts the code. You’re not handing over the wheel. You’re steering. You guide, test, refine, and keep asking for adjustments until the thing becomes real.

It shifts your role from “typing perfect code” to “directing the outcome.” Less blank-page dread. More momentum.

And momentum is everything.

The real power: creativity without permission

The biggest surprise with vibe coding isn’t speed. It’s confidence.

When the technical gatekeeping drops, you start thinking differently. You stop asking, “Am I allowed to do this?” and start asking, “What if this existed?” Then you try it. Then you tweak it. Then you learn something accidentally.

That “accidentally” part matters. Because you don’t learn vibe coding by sitting in a classroom. You learn it by trying to build something slightly beyond you… and then pulling it back into reality one small revision at a time.

Building First, Figuring It Out Later

If you’ve ever wanted to automate something with Python, build a little web tool, or create a tiny app—but felt like you didn’t have the background—vibe coding is basically a shortcut around the intimidation.

You can start with:

• “I want a script that takes a list and removes duplicates.”

• “I want a page that saves my notes locally.”

• “I want a button that changes the theme and remembers the setting.”

The model gives you a first draft. It probably won’t be perfect. That’s fine. You respond like you’re working with a slightly overconfident assistant:

• “That broke when I clicked it twice.”

• “Make the text bigger.”

• “Don’t reset the score.”

• “Make it work on mobile.”

• “Okay, now make it less ugly.”

And over time, you start picking up the logic of it all. Not from textbooks—just from repetition and curiosity. It’s almost like coding by osmosis.

Prototyping at the speed of thought

Before vibe coding, a lot of ideas died in the planning stage. You’d think of something cool and then remember the mountain of steps between “idea” and “working.”

Now you can prototype in minutes.

This is especially true if you’re using low-code and no-code tools, or mixing them with small bits of generated code. You can get a rough version running quickly, then improve it in layers instead of trying to build perfection from scratch.

It’s like sketching instead of sculpting marble.

Automation: small lever, big lift

One of the most practical uses for vibe coding is automation—especially for people who aren’t “technical” in a traditional sense.

If you live inside email, spreadsheets, forms, Teams/Slack, or recurring tasks that make you quietly resent your job… automation is where vibe coding starts paying rent.

You can describe your workflow in plain language and get help building it:

• route info from one place to another

• format it cleanly

• reduce repetitive steps

• catch errors

• generate summaries

• turn chaos into a process

And even if you never become a hardcore programmer, you become something just as valuable: someone who can design systems that save time.

Gaming: the sneaky creativity lab

This one surprised me the most: vibe coding makes game-making oddly accessible.

Even small games teach you a lot—logic, rules, feedback loops, balance, pacing. You’re forced to think like a designer and a tester. And those skills spill into everything else: building tools, automating workflows, even solving boring problems at work.

It’s also just… fun.

Sometimes you need fun again. Sometimes that’s the whole point.

Where do you actually vibe code?

You can vibe code anywhere you can talk to an AI and test what it gives you. The simplest way is to use an LLM (like ChatGPT, Claude, or Gemini), describe what you want, and then paste the code into a place that can run it. For quick experiments, that might be an online editor like Replit, CodePen (for web stuff), or a basic local setup on your computer. If you’re building something bigger, you can use an editor like VS Code and have the AI help you write and revise the code as you go. And if you’re more into workflow automation than “coding,” the same vibe applies inside tools like Power Automate or Make—describe the workflow, build a first version, then iterate until it feels right.

Why I think vibe coding matters right now

I don’t think vibe coding is about pretending you’re a senior engineer. It’s not cosplay.

It’s about getting your ideas out of your head and into the world.

It’s about learning by doing. Creating before you feel ready. And discovering that the boundary of “what you can build” is more flexible than you thought—especially when you have an AI collaborator that never gets tired, never gets annoyed, and will happily rewrite the same function ten times until it behaves.

The world is changing fast. That part is true.

But this is also the first time in a long time that regular people can build tools, workflows, and weird little experiments without needing a formal invitation from the tech gate.

So if you’ve been feeling that anxious “Where do I fit in?” energy lately—try building something small. Something playful. Something useful. Something slightly beyond you.

You don’t need to be “qualified.”

You just need a vibe… and the willingness to iterate until it works.

The 3 Coolest Make-Believe Alien vs Cryptid Battles

The 3 Coolest Make-Believe Alien vs Cryptid Battles

[Written by Casey T. Duncan]

I don’t know what it says about me that my brain does this, but it does: I’ll be doing something normal—counting my He-Man figures—and suddenly I’m like… who wins in a fight between a Grey alien and Bigfoot? Not “who’s real” (calm down, internet). This is purely imaginary pay-per-view energy.

So here are three matchups I’d pay good money to see.

1) Roswell Grey vs Bigfoot

Location: Pacific Northwest tree line, fog on maximum setting.
Vibe: “I can freeze your mind” vs “I can end your bloodline.”

The Grey steps out like it owns the scene—silent, smooth, with that calm little hand lift like it’s about to tap a touchscreen in the air. Then it does the classic move: freeze the room—mind-state shutdown. Birds stop mid-tweet. The wind holds its breath. Even the fog seems to pause and wait for instructions.

Bigfoot… does not get the instructions.

How it goes:

• The Grey tries to lock Bigfoot in a psychic box: be still, be calm, be studied.

• Bigfoot counters with the most disrespectful response possible: Andre-the-Giant strength with Ric Flair energy.

• We’re talking a full “grab-by-the-torso, spin once, and throw you like a lawn chair” situation.

• The Grey makes a noise that can only be described as “ultra mega super regret.”

Winner: Bigfoot, decisively.
Post-fight headline: “Elusive Ape Man Goes Viral on Instagram”

2) Reptilian vs Werewolf

Location: A foggy Texas pasture, 3:12 AM.
Vibe: Aggressive strategist vs lunar chaos.

Let’s be honest: this one is close.

Reptilians aren’t just “schemey.” They’re strong, fast, and aggressive—like a heavyweight fighter who also took an honors course in intimidation. They don’t posture; they advance. This is the kind of opponent that doesn’t miss twice.

Werewolf shows up looking like a bad decision given fur and teeth. But here’s the key: werewolf power isn’t constant. It’s a dial.

How it goes (and why it’s close):

• The Reptilian comes in hot—clean strikes, ugly slashes, brutal speed, and that cold “I’ve done this before” confidence.

• The werewolf takes damage early. Like, real damage. If you bet on the Reptilian in the first 30 seconds, you’re feeling smart.

• The pasture fog swirls. The fence posts creak. The tension is thick enough to bottle and sell at Buc-ee’s.

Then the moon shifts.

Full moon power hits and the werewolf stops fighting like an animal and starts fighting like a natural disaster.

• The Reptilian lands another heavy blow… and the werewolf barely notices.

• The werewolf’s strength spikes like somebody just plugged it into a wall outlet.

• What was “close” becomes “oh no.”

Winner: Werewolf—by full moon cheat code.
Important note: any other night? This might go the other way.
Post-fight headline: “Texas Pasture Quiet Again After Brief Incident Involving Fog, Teeth, And Regret.”

3) Insectoid vs Fresno Nightcrawler (The Ghost Pants Cryptid)

Location: Fresno, California backyard at 2:17 AM, security cam quality set to “2007.”
Vibe: Ancient hive intelligence vs a pair of haunted trousers on a peaceful stroll.

If you don’t remember the Fresno Nightcrawler: it looks like walking white pants—no torso, no arms, just pure “laundry spirit” energy. It doesn’t menace. It glides. It’s the most polite cryptid of all time.

Insectoid shows up like the universe’s scariest scientist—tall, angular, quiet, with the vibe of someone who judges your entire species in one look.

How it goes:

• The Insectoid tries to scan it.

• The pants do not scan. The pants simply continue being pants.

• The Insectoid pauses, recalculates, and experiences something new: confusion.

• The Nightcrawler drifts away like it’s late for absolutely nothing.

Winner: Fresno Nightcrawler, by being unbothered and unexplainable.
Post-fight headline: “Creepy Insect Dude Defeated By Casual Stroll.”

Final Ranking (Based on Coolness, Not Science)

1. Fresno Nightcrawler (unbothered champion)

2. Bigfoot (forest tank, suplex adjacent)

3. Werewolf (full moon = unfair advantage)

If you disagree, that’s fine. This is Side Column. We can argue politely like adults and then immediately change the subject to action figures.