#August_18_LeetCode_Grind

📌 Topic: Binary Search 🔍

Today’s grind is all about sharpening our Binary Search skills.
Binary Search is one of the most fundamental algorithms in coding interviews – efficient, elegant, and powerful for solving search-based problems in O(log n) time.

✨ Why it matters?

Used in searching sorted arrays & matrices 📊

Core for solving advanced problems like search in rotated arrays, peak finding, etc.

Reduces brute force and saves runtime ⚡


🔥 Your challenge for today:

Revisit the classic Binary Search template

Solve 2–3 LeetCode problems that apply it in creative ways


💡 Pro tip: Always think about the midpoint and whether to move left or right. Mastering this logic unlocks many problems beyond simple searching.
@byte_philosopher

So here is all about today

ደህና እደሩ 🫶

Me: waking up በዓል ለማክበር

also the rain: መጣና ባመቱ እረ እንደምን ሰነበቱ
ሆያ ሆዬ....እርገጠው:)

Happy ቡሄ ቤተሰብ🎉
@byte_philosopher

Look what I have got on the street of bole መድሃኒያለም. Idk who are they tho but love it👀

@byte_philosopher

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#OpenSource #CLI
@Exodus_Tola

#August_20_LeetCode_Grind

today I was just exploring what trees are and doing leetcode problems. This topic is a bit vast and with doing side projects it feels overwhelming . But here we are struggling to face the challange. Today I also noticed that I have only 2 or 3 weeks to start my 2nd year. Somehow it feels guilty when it comes to accomplishing your goal. And at the same time you need to rest but your mind can't . So here we are " we struggle to satisfy our mind and we kill the feeling of rest to feed the satsfaction for our soul"

መስራት ያስከብራል ሀገርን ያኮራል

@byte_philosopher

እንኳን ለእናታችን ለእመቤታችን ቅድስት ድንግል ማርያም በዓለ ዕርገት እና ትንሳኤ አደረሳችሁ አደረሰን 💛

📸ሐመረ ኖህ እንጦጦ ኪዳነምህረት

@byte_philosopher

#August_21_22_23LeetCode_Grid

I finished my study on trees here is the short summery

🌳 Trees in Data Structures 🌳

Think of a Tree like a family tree or folder system:

🌱 Root → the starting point

🌿 Branches (children) → connect to other nodes

🍂 Leaves → end nodes with no children

✨ Popular Trees:

🌲 Binary Tree → max 2 children per node

🔎 Binary Search Tree (BST) → left < root < right

🏗 Heaps & Balanced Trees → used in priority queues, databases

⚡ Why Learn Trees?
✔ Organize data efficiently
✔ Fast search & sorting
✔ Used in file systems, AI, compilers, HTML DOM

📌 Next time you open folders on your PC… you’re walking through a tree 🌳😉

@byte_philosopher

ደህና እደሩ🫶

@byte_philosopher

This is cool guys🔥 Let's show her some love guys go and star her repo on github.
@byte_philosopher

Have a productive week❤
@byte_philosopher

#August_25_LeetCode_Grid

🚀 Backtracking in DSA – Quick Recap

Backtracking is like exploring all paths in a maze. You try options, go forward, and undo your choices if they don’t work.

How it works:

Choose: Pick a possible option.

Explore: Move forward recursively with that choice.

Backtrack: Undo the choice to try other possibilities.

✅ Key idea: Explore all possibilities systematically, but prune paths that fail early.
✅ Use cases:

Subsets & permutations

Combination sum problems

Sudoku & N-Queens

Maze & pathfinding problems

💡 Tips:

Always have a base case to stop recursion.

Make sure to undo changes before returning to explore other options.

Example in LeetCode: Combination Sum, Subsets, N-Queens

@byte_philosopher

This days what I understand from solving leetcode is, you always have to think in the reverse way. (ገልብጦ ማሰብ😁 ) is the better way.

For ex: if your first thought for the solution was addition then use substraction boom it works😁

@byte_philosopher

#August_26_LeetCode_Grid

👑 Heap: The King of Efficiency 👑

Ever wondered how to always grab the biggest or smallest number FAST? ⚡
That’s where Heaps come in!

🔥 What’s a Heap?

A tree-like structure 📚

Min-Heap → smallest on top (root).

Max-Heap → largest on top (with a trick in Python 😉).

⚡ Why are they cool?

📍 Always gives you the top element in O(1) time.

➕ Insert / ❌ Remove in O(log n).

Used in priority queues, scheduling, Dijkstra’s algorithm, and finding kth largest/smallest element!

🐍 Python Example:

import heapq

nums = [5, 2, 8, 3, 1]

heapq.heapify(nums)   # min-heap
print(nums[0])        # 👉 1 (smallest)

# max-heap trick
nums = [-x for x in nums]
heapq.heapify(nums)
print(-nums[0])       # 👉 8 (largest)

💡 Remember:
Heaps don’t fully sort data, they just keep the top element ready at all times 🚀.

⚔️ Next time you need efficiency → Just call the Heap King 👑
@byte_philosopher