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Yostina | Bytephilosopher
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a curious mind exploring tech, AI, and social change || ORTHODOX CHRISTIAN || A2SVian
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This is cool guys🔥 Let's show her some love guys go and star her repo on github.
@byte_philosopher
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Have a productive week
@byte_philosopher
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#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
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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
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#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
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Forwarded from kin
Introducing ExyRead – your all in one AI powered reading companion

After months of building, I’m excited to share that 90% of the core features are now complete!
ExyRead is designed to support students and anyone who wants to make their reading and studying smarter and easier.

Key Features:

AI Chat – ask questions, get instant support

One-click PDF summaries – save time, grasp key points faster

Note-taking – write down ideas while studying

AI note summaries – turn long notes into short takeaways

Instant explanations – highlight text and get AI-powered clarity

Progress tracking – stay motivated as you read

Smart organization – create folders to manage files easily

Study reminders – never miss your next session


Your feedback will play a big role in shaping the future of the app.
💡 Be one of the first to try it here.

#my_project
@kintechno
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ሀሙስ የቀን ቅዱስ😉

@byte_philosopher
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Forwarded from Orthodox Spirituality
“Let us acquire reverence, dignity, and meekness towards all people, as well as precise knowledge of them. so that we may be able to avoid familiarity, which is the mother of all evils.”

+Abba Moses
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#August_27_28_29_LeetCode_Grind

HashMaps (Python dict) Recap

A HashMap stores data in key–value pairs for super-fast access .
Think of it like a dictionary 📖: words = keys, meanings = values.

Key Features:

Fast lookups, inserts, deletes → O(1) average

Keys are unique

Perfect for counting, mapping & caching


hashmap = {"apple": 2, "banana": 5}

print(hashmap["apple"]) # 2
hashmap["banana"] = 10 # update
hashmap["grape"] = 7 # insert
del hashmap["apple"] # delete

🔥 Use Cases:
✔️ Word frequency counters
✔️ Caching results
✔️ Graph adjacency lists

@byte_philosopher
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Happy Sunday y'all 😊

@byte_philosopher
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#August_30_LeetCode_Grid

📌 Graphs in DSA

A Graph = 🟢 Nodes (called vertices) + 🔗 Connections (called edges).
They’re everywhere — social networks, maps, recommendations, even computer networks!

Types of Graphs:

Directed vs Undirected ➡️ / ↔️

Weighted vs Unweighted ⚖️

Cyclic vs Acyclic 🔄 / 🚫

Ways to Store Graphs:

Adjacency List (efficient)

Adjacency Matrix 🔲 (easy but heavy)

Popular Graph Algorithms:

BFS (Breadth-First Search) 🔍 level by level

DFS (Depth-First Search) 🌊 go deep first

Dijkstra 🛣️ shortest path

Kruskal & Prim 🌐 minimum spanning tree

👉 Mastering graphs = mastering real-world problem solving.

@byte_philosopher
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This is insane guys read it
Are you ready for tomorrow it's Monday plus sep 1?

@byte_philosopher
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