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HASTY.dev @hasty.dev · 30/04/2025

O(n log n) - Quasi-Linear Time: The workhorse! 💪 Faster than O(n^2), common in efficient sorting like Merge Sort or Heap Sort 📊. Balances speed & practicality nicely #BigO #Sorting #Algorithms

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HASTY.dev @hasty.dev · 30/04/2025

O((log n)^k) - Polylogarithmic Time: Super efficient! ✨ Grows slower than *any* polynomial. Time is based on (log n)^k. Great for massive datasets & advanced structures 💾 #BigO #Efficiency #ComputerScience

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HASTY.dev @hasty.dev · 30/04/2025

O(2^n) - Exponential Time: Grows explosively! 💥 Like finding all subsets of a set or a virus doubling hourly 🦠. Fine for small inputs, but hits a wall fast. Watch out! #BigO #Performance

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HASTY.dev @hasty.dev · 30/04/2025

O(n!) - Factorial Time: Beware the beast! 😨 Grows incredibly fast, like checking every possible route for a salesperson 🚚. Becomes impractical *very* quickly. Avoid if you can! ❌ #BigO #Complexity

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HASTY.dev @hasty.dev · 30/04/2025

Sublinear Time (e.g., O(log n)): Faster than linear! 🚀 These algorithms get relatively quicker as input grows. Think binary search 🔍 or finding a word in a dictionary index. Doesn't need to check every item! #BigO #Algorithms

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HASTY.dev @hasty.dev · 30/04/2025

Big-O: the secret language of how algorithms scale! 💻 We should all know the basic O(1), O(n), O(n^2) ... etc., but let's explore the wild side! 🤩 Get ready for some less common, fascinating (sometimes scary!) complexities. Dive in! 👇 #BigO #Learning

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HASTY.dev @hasty.dev · 26/01/2025

Key Takeaway Space complexity matters! Choose the right algorithm for efficient, scalable code ✍️ #CodingTips #SoftwareDev #BigO

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HASTY.dev @hasty.dev · 26/01/2025

Polynomial Space: O(n^k) O(n^k) space: Memory grows as a power of input size. Storing all pairs of items, for example 😬 #PolySpace #Memory

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HASTY.dev @hasty.dev · 26/01/2025

Log-Linear Space: O(n log n) O(n log n) space: Memory grows faster than linear, slower than quadratic. Like books + a card catalog 🗄️ #LogLinear #Nlogn

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HASTY.dev @hasty.dev · 26/01/2025

Logarithmic Space: O(log n) O(log n) space: Super efficient! Memory grows slowly. Think dictionary search - use the index, not every page! 📖 #LogSpace #Ologn #Efficient

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HASTY.dev @hasty.dev · 26/01/2025

Linear Space: O(n) O(n) space: Memory grows directly with input size. Like needing more bookshelf space for more books 📚 #LinearSpace #On #Memory

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HASTY.dev @hasty.dev · 26/01/2025

Constant Space: O(1) O(1) space: Same memory, no matter the input size! Like a single scratchpad for any calculation 📝 #ConstantSpace #O1 #Memory

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HASTY.dev @hasty.dev · 26/01/2025

Intro to Big-O & Space How much memory does your code use? 🤔 Big-O notation helps us understand space complexity! Let's explore! 🧠 #BigO #SpaceComplexity #Coding

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HASTY.dev @hasty.dev · 25/01/2025

Wacky vs. Organized Chaos: Bogo Sort 🤪 vs. Heap Sort ⛰️ Can blind luck beat a mountain of order? This sorting showdown is pure MAYHEM! Don't miss the results! 🤯 hasty.dev/blog/sorti... #algorithms #programming #sorting #bogo #heap