Big-o-notation Memes

Posts tagged with Big-o-notation

Binary Search My Life
Algorithms Programming
5 days ago 230.5K views 1 shares
Binary Search My Life
Binary search requires O(log n) time complexity, but only if your array is sorted first. Otherwise you're just randomly guessing in the middle of chaos. Kind of like trying to find the exact moment your life went off the rails by checking your mid-twenties, then your teens, then... wait, it's all unsorted? Always has been. The brutal honesty here is that you can't efficiently debug your life decisions when they're scattered across time in no particular order. You need that sweet O(log n) efficiency, but instead you're stuck with O(n) linear search through every regret. Sort yourself out first, then we'll talk algorithms.

A Bit Of Advice
Algorithms Programming
18 days ago 270.3K views 1 shares
A Bit Of Advice
So you learned binary search in your algorithms class and now you think you can apply it to real life? Cool, cool. Just remember that in the real world, guessing someone's age by saying "50" and then "25" is basically telling them they look 50 first. Congratulations, you just optimized your way into sleeping on the couch with O(log n) efficiency. Pro tip: some problems are better solved with linear search, even if it's slower. Like maybe start at 21 and work your way up slowly? Your relationship will thank you for the extra time complexity.

Can't Find Happiness In Log N
Algorithms Programming
18 days ago 184.5K views 1 shares
Can't Find Happiness In Log N
Ah yes, the classic existential crisis wrapped in algorithm complexity. You want to binary search your way to happiness with that sweet O(log n) efficiency, but turns out life isn't a sorted array—it's more like a linked list with random pointers and memory leaks everywhere. The brutal truth hits harder than a stack overflow: you can't apply your fancy data structures to find meaning when your entire existence is basically unsorted chaos. No amount of optimization is gonna help when the input data is just... a mess. Should've read the prerequisites before enrolling in Life 101.

Can't Find Happiness In Log N
Algorithms Math Programming
19 days ago 250.9K views 0 shares
Can't Find Happiness In Log N
When you try to optimize your life with computer science algorithms but reality hits different. Binary search requires your life to be sorted first—you know, organized, stable, having your stuff together. Spoiler alert: most of us are living in O(n²) chaos. The brutal honesty here is *chef's kiss*. You can't just slap efficient algorithms onto a messy existence and expect miracles. It's like trying to use a hash map when your keys are all undefined. The monkey's deadpan delivery of "your life isn't sorted" is the kind of existential debugging message nobody wants to see but everyone needs to hear. Pro tip: Before implementing any O(log n) life improvements, make sure to run a quick isSorted() check on your existence. Otherwise you're just gonna get undefined behavior and segfaults in your happiness.

Cloth Cache
Algorithms Hardware Programming Backend
2 months ago 386.2K views 0 shares
Cloth Cache
When you've been optimizing cache hit ratios all day and suddenly your entire life becomes a systems architecture problem. The justification is technically sound though: L1 cache for frequently accessed items (today's outfit), sized large enough to prevent cache misses (digging through the closet), with O(1) random access time. The chair is essentially acting as a hot data store while the closet is cold storage. The real genius here is recognizing that minimizing latency when getting dressed is mission-critical. Why traverse the entire closet tree structure when you can maintain a small, fast-access buffer of your most frequently used items? It's the same reason CPUs keep L1 cache at 32-64KB instead of just using RAM for everything. The only thing missing is implementing a proper LRU eviction policy—but let's be honest, that pile probably uses the "never evict, just keep growing" strategy until Mom forces a cache flush.

Bad News For AI
AI Algorithms Math Programming
2 months ago 291.2K views 0 shares
Bad News For AI
Google's AI Overview just confidently explained that matrix multiplication "is not a problem in P" (polynomial time), which is... hilariously wrong. Matrix multiplication is literally IN the P complexity class because it can be solved in polynomial time. The AI confused "not being in P" with "not being solvable in optimal polynomial time for all cases" or something equally nonsensical. This is like saying "driving to work is not a problem you can solve by driving" – technically uses the right words, but the logic is completely backwards. The AI hallucinated its way through computational complexity theory and served it up with the confidence of a junior dev who just discovered Big O notation yesterday. And this, folks, is why you don't trust AI to teach you computer science fundamentals. It'll gaslight you into thinking basic polynomial-time operations are unsolvable mysteries while sounding incredibly authoritative about it.

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When You Start Using Data Structures Other Than Arrays
Algorithms Programming Debugging
2 months ago 230.8K views 0 shares
When You Start Using Data Structures Other Than Arrays
That moment when you've been forcing everything into arrays for years and suddenly discover linked lists, trees, and hash maps. The sheer existential horror of realizing how much unnecessary O(n) searching you've been doing. Your entire coding career flashes before your eyes as you contemplate all those nested for-loops that could have been O(1) lookups.

We Will Process Only Last 1000 Files They Said
php Algorithms Programming Databases Backend
2 months ago 282.7K views 0 shares
We Will Process Only Last 1000 Files They Said
When your manager says "just process the last 1000 files" but you're dealing with a PHP script that's about to iterate through 2 million files while comparing against a database of 1 million records. The script is literally pulling 1000 records with limit(1000) but then checking EACH of your 2 million files against those 1000 records with in_array() . That's a cool O(n²) operation that's going to take approximately checks notes forever to complete. Your server's CPU is already writing its resignation letter.

Technically Horrifyingly Correct
Javascript Webdev Algorithms Programming
2 months ago 190.5K views 0 shares
Technically Horrifyingly Correct
The code creates a sorting algorithm that's technically O(n) but for all the wrong reasons. Instead of actually sorting the array, it's using setTimeout() with the array value as the delay time in milliseconds. The smallest numbers appear first in the console simply because their timeouts complete faster! It's like telling your friends you've invented a revolutionary sorting algorithm, but you're actually just making each number raise its hand after waiting for X milliseconds where X equals its own value. Pure chaotic genius. The browser's event loop is doing the sorting for free! Computational complexity professors are currently rolling in their graves (even the ones who aren't dead yet).

The L1 Cache Chair: Optimized Clothing Access
Algorithms Programming
3 months ago 217.6K views 0 shares
The L1 Cache Chair: Optimized Clothing Access
THE AUDACITY of parents calling it a "messy pile" when it's CLEARLY an optimized system! Sweetie, this isn't laziness—it's COMPUTER SCIENCE IN ACTION ! My bedroom chair isn't cluttered, it's a sophisticated L1 cache architecture where my most-worn t-shirts achieve BLAZING O(1) access times! The bigger the pile, the fewer cache misses! Do you want me digging through drawers like some kind of BARBARIAN with O(log n) closet lookups?! I am LITERALLY OPTIMIZING MY LIFE while you're over there worried about "tidiness" like it's 1995! The optimization committee has spoken—this pile STAYS!

The Dictator's Guide To Arrays
Algorithms Math Programming
4 months ago 280.1K views 0 shares
The Dictator's Guide To Arrays
Ah, the infamous "StalinSort" – where elements don't get rearranged, they get purged . This "O(n) algorithm" is technically correct in the most horrifying way possible. Sure, you'll end up with a sorted list... mostly because you've executed all the elements that dared to be out of order. It's the same energy as fixing bugs by deleting the code that contains them. Congratulations, you've optimized your way to a solution that would make computer science professors wake up in cold sweats. Efficiency through elimination – the algorithm works because the witnesses don't.

Linear Time: When Your Data Structure Diet Fails
Algorithms Math Programming
5 months ago 284.0K views 0 shares
Linear Time: When Your Data Structure Diet Fails
The classic "yo momma" joke gets a computer science upgrade! Binary trees are efficient data structures with O(log n) operations, while linked lists have O(n) linear time complexity. So flattening a tree to a list is basically making something efficient into something... not so efficient. It's the algorithmic equivalent of taking the expressway and somehow ending up on a dirt road. Every CS grad who spent weeks optimizing their search algorithms just died a little inside.