Big-o-notation Memes

Posts tagged with Big-o-notation

Cloth Cache
Algorithms Hardware Programming Backend
2 hours ago 39.6K 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
3 days ago 172.6K 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.

When You Start Using Data Structures Other Than Arrays
Algorithms Programming Debugging
14 days ago 135.5K 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
15 days ago 171.0K 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
26 days ago 132.8K 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
1 month ago 160.5K 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
2 months ago 232.7K 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
3 months ago 233.2K 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.

The Potato Dilemma
Algorithms Hardware Programming Debugging
4 months ago 219.9K views 0 shares
The Potato Dilemma
The eternal struggle of budget hardware! Imagine writing code that's so inefficient it causes both exponential memory usage AND runtime simultaneously. That's not just a memory leak—that's a memory Niagara Falls. The poor developer's ancient laptop (affectionately dubbed "glorified potato") is about to melt into a hash brown while they watch helplessly as their O(n²) algorithm devours every available resource. The real question: will the machine BSOD or just spontaneously transform into french fries first?

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Quantum Search Algo Where Are You
Algorithms Programming Debugging
4 months ago 231.7K views 0 shares
Quantum Search Algo Where Are You
Ah, the eternal struggle of enterprise software! While computer science students slave away learning elegant O(log n) binary search trees and O(√n) quantum algorithms, some poor dev in 1997 just threw in a linear O(n) search and called it a day. Now we're all sitting here like Bigfoot—evolved beings contemplating why we tolerate scrolling through 10,000 records when a proper index would fix everything. The real miracle isn't the search algorithm—it's the supernatural patience of users who've been conditioned to believe that computers just take that long to find things. Stockholm syndrome, but for terrible UX.

Constant Time Solution
Python Gamedev Algorithms Programming Debugging
5 months ago 280.1K views 1 shares
Constant Time Solution
When your friend asks you to "just code a simple chess game," and you realize you need to handle every possible board state individually. That's 2.6 million lines of if-else statements because who needs algorithms when you can hardcode each move? The beautiful part is that technically it's an O(1) solution! Chess engines hate this one weird trick - just write out every possible game state and skip all that fancy minimax algorithm nonsense. Bonus: your git commits will make it look like you're the most productive developer in history. "Added support for knight moves - 400,000 lines changed."

Time To Grind Sorting Algo
Algorithms Math Programming
6 months ago 176.1K views 0 shares
Time To Grind Sorting Algo
Watching an algorithm tutorial at 4:55 AM while chugging water and flexing is apparently the secret sauce to passing technical interviews. Nothing says "I'm committed to understanding QuickSort" like bicep curls at dawn. The duality of programming: one minute you're watching a mild-mannered instructor explain Big O notation, the next you're transformed into a hydrated code warrior ready to battle merge sort with your bare hands. This is what they mean by "grinding leetcode" – literal physical preparation for the mental marathon ahead. Somewhere between desperation and dedication lies the path to algorithm enlightenment.