Bytephilosopher

Bereket Engida for the 2025 JavaScriptLandia Awards Bereket (@imbereket) is being nominated for Outstanding Contribution from a New Arrival for his impactful work on BetterAuth, which is making authentication simpler and helping many developers. The community has already started nominating him. Let’s add our support. 📅 Nominations close Sept 19 🏆 Winners announced at JSConf 2025 (Oct 14–16, Maryland) Nominate Bereket 👉 here. Original: x post Follow: DevNotes

🌿 August in BytePhilosopher 🌿 This month turned out better than I expected 🙌 ✨ Stayed consistent for 20+ days on GitHub & LeetCode ✨ Finished the fundamentals of DSA (still a tough journey 😭) ✨ Started reading spiritual books for the first time ✨ Spent quality time with my family back home in Bahirdar ✨ Internship gave me the chance to meet many inspiring people August wasn’t just a month — it was a season of personal & spiritual growth. What I realized 👉 habit + consistency = real progress. Even though I failed in many goals, I’m grateful for every step forward. 💫 #Gratitude #Consistency #Growth @byte_philosopher

#August_31_september_01_LeetCode_Grind 🚀 Dynamic Programming (DP) – The Brain of DSA 🧠 Dynamic Programming is one of the most powerful techniques in problem-solving. It’s all about breaking a complex problem into smaller overlapping subproblems, solving each subproblem once, and storing the results for reuse. ✨ Why DP matters? Avoids repeated work ➝ makes algorithms efficient. Solves problems that plain recursion or brute force cannot handle in time. Forms the backbone of advanced algorithms in AI, bioinformatics, and optimization. 📌 Key Ideas in DP: 1. Overlapping Subproblems → Solve once, reuse results. 2. Optimal Substructure → The solution to a problem depends on solutions to smaller subproblems. 3. Memoization (Top-Down) → Store results of recursive calls. 4. Tabulation (Bottom-Up) → Build a table iteratively to reach the final answer. ⚡ Classic DP Problems: Fibonacci Numbers 🔢 Longest Common Subsequence Unique Paths in a Grid 🤖 Knapsack Problem 🎒 👉 Mastering DP = unlocking the ability to solve the toughest algorithmic problems efficiently. 💡 Remember: Think recursive, optimize with memory. #DSA #DynamicProgramming #Coding @byte_philosopher

Happy Monday, have a productive week❤️ @byte_philosopher

Are you ready for tomorrow it's Monday plus sep 1? @byte_philosopher

Psalms 14:1 "The fool says in his heart, ‘There is no God.’" 2017 Reflections 2. I almost went insane... https://telegra.ph/I-Almost-Went-Insane-08-31

This is insane guys read it

#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

Happy Sunday y'all 😊 @byte_philosopher

#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

“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

ሀሙስ የቀን ቅዱስ😉 @byte_philosopher

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

#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

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_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

Have a productive week❤ @byte_philosopher

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

Finally i deployed lesson of the day bot on python anywhere. 🔎 What can Lesson Of The Day Bot do: 📘 Show today’s lesson instantly. 🕰 Browse through past lessons easily. 🔖 Bookmark and revisit lessons you like. 📅 Search by date. 👉 @lesson_of_day_bot here is the Github repo https://github.com/Lidiya-Bokona/Lesson_of_the_day_bot.git #project

መልካም ዕለተ ሰንበት 💛 Have a blessed sunday @byte_philosopher