So you've heard the saying about the multiple cat-skinning methods? Well, sure, there's diversity in approaches, but let's zero in on the champion of them all.

And by the way, no felines are at risk here; we're talking metaphorically, okay?

Even the Zen of Python nods to this, urging:

"There should be one-- and preferably only one --obvious way to do it."

But hold on, don't take this as gospel that enshrines a single pathway or an obsessive-compulsive need to perfect your every move. That path is a surefire way to stall and stutter in your progress.

So, what's the deal?

The keyword here is "obvious." It's not about the one and only way, but the one that's most obvious to you. It's the one that makes the most sense to you, the one that you can wrap your head around.

It's all about balancing the equation between the problem's demands and the resources you're willing to commit - basically, your time and energy.

Blind optimization? Nah, it's not the go-to. Overzealous tweaking can be a resource hog. Optimize smartly, when the gains truly offset the grind.

And herein lies the magic formula: reusability and scalability, two buzzwords that programmers cling to like a lifeline, particularly in the object-oriented haven.

When you polish a solution, that shiny tool should not just sit there. You should be able to whip it out again in the future, and it ought to carry its weight against more significant challenges.

This is the path. The golden path. This is the essence of best practice.

Don't toss your energy into a black hole working on unscalable, single-use brainchildren. Why swing a sledgehammer to swat a fly?

There's wisdom from the East that puts it elegantly: "견문발검(見蚊拔劍)," or "You don't use a sword to kill a mosquito." This pearl of wisdom keeps us sane and saves us from overkill.

Better grab that swatter, or even slicker, a snippet of code that does the trick neatly, efficiently, sans overkill.

When I'm coding, I have to admit, I often dive right in. I lay down lines of code, following the flow of logic like I'm sketching a quick masterpiece, not pausing to ponder the finer points of optimization at the get-go. "Let's get it working first, fine-tune it later"—that’s my jam.

Now, Python? It's tailor-made for this kind of approach. Write now, ask questions later. The beauty of Python is that it’s a breeze to both write and understand, plus it bends without breaking. You can circle back for that optimization lap when you’re ready.

For those who’ve just dipped their toes into the coding pool, here’s a nugget of wisdom: Post-brainstorming with Python, a lot of coders switch gears to other languages to put on the finishing, speed-enhancing touches. Python is not the Olympian sprinter of programming languages—it’s more about agility and ease. Hence, the enduring appeal of C. C is the rocket ship in the coding universe, not your idea incubator. Sure, it’s lightning-fast, courtesy of its whispering sweeties relationship with the machine, but that also makes it a bit of a beast to tame and nurture.

Looking back on the early days of my coding journey, there's a sense of nostalgia for how we Korean developers grappled with the intricacies of assembly languages. We were like digital artists, coaxing ancient x86 machines to display Korean characters on the monochrome tapestry of MS-Dos, in a time when screens knew only text. We'd get under the digital hood, tinkering our way to a kind of primitive graphics mode to make our language visible on monitors. Who remembers Hercules Graphics? Or CGA, EGA, VGA? Those were our color palettes, expanding from a few humble hues to a kaleidoscope onscreen—glimpses of a new era, really.

But I’m getting sidetracked.

Today, assembly language feels like a relic of a bygone era.

All those trends that felt so crucial back then, like chiseling stone tablets. Norton Commander, anyone? He wrote a fascinating book on assembly language I pored over. Now, it's a fun historical footnote at most.

Nowadays, with hardware speed rivaling that of light itself, we've earned the luxury to ease off the optimization gas pedal. We can indulge in the Pythonic way, where elegance and readability win.

The crux of the matter is optimizing thought processes rather than code lines—the true form of optimization.

I always circle back to the same point: Whether it's object-oriented design, normalization, vectorization, data structures, algorithms—these aren't just technical terms; they're a mindset, sharpened tools for the perpetual problem-solver in all of us. After all, life’s nothing but an endless stream of puzzles waiting for solutions.

Back in the day, Pascal was like the gateway to object-oriented thinking; Turbo Pascal and Borland Pascal were my go-tos before the landscape evolved. C++ dressed up C with object orientation, then Java added its unique flavor, with Python eventually joining the party, each iteration bringing a little something extra to the table.

It’s true, adopting object-oriented designs can slow things down a bit, but what we get in return—maintainability, reusability, scalability, clarity—is worth the trade. Some folks might stick to their guns and port their creations over to C or languages closer to the machine level for that extra efficiency. And that's fine. We each have our reasons, our own elusive efficiencies to chase. My two cents? It's all about picking the right tool for the job at hand, and sometimes, nothing less than a drill down to the core will do.

Whenever a new conundrum pops up, it's like a little lightbulb moment—we dig through our mental attic for a pattern that's clicked in the past. We're basically inheriting smart ideas from days gone by and remixing them to solve today's puzzles, tossing out the fluff that slows us down. And boom, we're in the groove with the fab four of object-orientation: abstraction, inheritance, polymorphism, and encapsulation.

Ever been swamped with numbers too big or too tiny? We tidy them up to play nice on our mental abacus, kind of like turning an avalanche of frequencies into a more handleable bunch of decibels.

Imagine a database bursting at the seams with tables, each crammed with more repeat fields than a hoarder's trove of treasures? Time to declutter! Tidy it all up by normalizing: assign unique keys and sweep away those duplicates. Voila! You've got yourself a streamlined, spick-and-span data haven.

What about simple arithmetic?

Addition is like adding more fun to the party. Subtraction is like taking a little fun away. For making big things manageable, division is your go-to—it's like cutting a jumbo sandwich into bite-sized pieces so it's easier to eat. Multiplication does the opposite; it's like turning a small sandwich into a jumbo one.

When you've got something like 255 colors and you want to make them easier to work with, you use division to make them simpler—this is called normalization. When you want to return to the original number of colors, you use multiplication to bring them back—this is denormalization. It's really that straightforward.

Visualize the tower of plates at a buffet—stack 'em up and what do you get? A real-life stack data structure, with the last plate down ready to be first up. It's not just plates, this 'Last In, First Out' thing is a classic move some folks use for keeping their stockrooms tidy.

And that orderly queue for coffee? It's a living, breathing queue data structure—'First In, First Out,' no budging!

People mingling at a party are kind of like a human graph data structure—everyone's a point of interest, with chit-chat forming the lines between.

Makes sense, right?

Why keep these neat tricks in one sandbox, though? They're not just for coding or crunching numbers. Let's sprinkle them over everything we do, all over life!

Pondering machine learning and deep learning? Same story.

In Blender, when you're shaping 3D objects, you'll notice every circle is actually a polyshape with straight sides. The more vertices you add, the more it starts to look rounded. But here's where it gets mathematical—when you bring two vertices very close to each other, you’re simulating the essence of calculus in the 3D world. This is just like finding the slope of a tangent to a curve at a point.

This is where you touch upon the concept of limits without even realizing it. By moving these points closer and closer together, you're approaching what calculus terms as the 'limit'. The closer they get, the more you can see the slope of the line that just 'kisses' the curve at that point—the derivative, also known as the gradient.

This idea of 'almost touching' is fundamental to differential calculus, which is the core of gradient descent used in AI. Gradient descent takes this concept and uses it to find the steepest downward path from a mountain's peak. Each step is calculated by finding the gradient at a specific point, and this tells you the direction to take that step. It's all about making tiny, precise adjustments to get to the lowest point, which, in the world of AI and machine learning, means finding the point with the least error or the best solution.

These strategies are like your all-access pass to thinking big and getting hands-on.

So, why sweat the small stuff? There's no need. Instead, take a step back and grab that wide-angle lens to view the situation.

Kicking off with object-orientation is more than a solid start.

Yet, that's just scratching the surface. Each piece of knowledge is ready to be expanded and traded like collectible cards. Regularly press pause to consider how these insights piece together in the big picture of your life.

Come across a golden piece of wisdom? Take it, shape it with abstraction, pass it on through inheritance, spice it up with polymorphism, secure it with encapsulation, then throw the abstract switch again. Transform it, dress it up in your own style, and then, why not broadcast it for others to tune in?

That's not just acing it; that’s crafting a legacy of insight.

And that's the zen of smart effort.

That's what I do.

Remixing wisdom—abstracting, inheriting, morphing, encapsulating, and then sharing it anew.