Open Telemetry in Python: Foundations and First App

🚀 Day 50 of My Python Journey Today I solved Complement of Base 10 Integer on LeetCode. 🔍 Problem Overview: The task is to find the bitwise complement of a given base-10 integer. The complement is obtained by flipping all bits in its binary representation — changing every 0 to 1 and every 1 to 0. 🧠 Approach: 1️⃣ Convert the integer into its binary representation. 2️⃣ Traverse the binary string and flip each bit (1 → 0, 0 → 1). 3️⃣ Convert the resulting binary string back to a decimal integer. ⚡ Key Learnings: • Practiced binary representation and bit manipulation • Improved understanding of number systems (binary ↔ decimal) • Strengthened string manipulation and logical thinking in Python 📊 Complexity: • Time Complexity: O(n) • Space Complexity: O(n) Under the Guidance of : Rudra Sravan kumar and Manoj Kumar Reddy Parlapalli #Day50 #Python #LeetCode #DataStructures #Algorithms #CodingJourney #ProblemSolving #100DaysOfCode 🚀

Over the past few days, I built another Python library: 𝗔𝗴𝗲𝗻𝘁𝗞𝘂𝗯𝗲-𝗠𝗶𝗻𝗶. A tiny agent orchestration engine.  ~239 lines of Python, zero dependencies It started with a simple question: What is the minimum needed to orchestrate multiple agents? Not frameworks. Not abstractions. Just the core. So I stripped it down to: Task DAG → agents + dependencies Scheduler → runs independent tasks in parallel Event system → observe every step Shared memory → pass outputs downstream If you look at most multi-agent systems, they follow the same pattern: nodes (agents), edges (dependencies), executed by a scheduler Everything else is layering. It’s for a different purpose: → to understand orchestration by reading it end-to-end Try: `𝙥𝙞𝙥 𝙞𝙣𝙨𝙩𝙖𝙡𝙡 𝙖𝙜𝙚𝙣𝙩𝙠𝙪𝙗𝙚-𝙢𝙞𝙣𝙞` Because once you see it in ~200 lines, a lot of the “magic” disappears and that’s a good thing. Curious, how are you orchestrating your multi-agent workflows today?

🐍 Day 12 of My 30-Day Python Learning Challenge Today I worked on a real-world concept: File Handling in Python. 📌 Problem: Read a file and count how many words it contains. 📌 Code: file = open("sample.txt", "r") content = file.read() words = content.split() print(len(words)) file.close() 📌 Output: Total number of words in the file 💡 Why this matters? File handling is used in: • Data processing • Log analysis • Backend development 📊 Quick Question What will happen if the file does NOT exist? A) Error B) Empty output C) None D) 0 Answer tomorrow 👇 #Python #FileHandling #CodingJourney #LearningInPublic #SoftwareDeveloper

If you're not automating your daily workflows in 2026, you're missing out on massive time savings. 🛑⏳ Python has always been the king of automation, but the libraries leading the charge this year are taking things to a whole new level. From simple task automation to complex orchestration, there’s a modern tool for exactly what you need. 🛠️✨ Curious about which Python libraries you should be adding to your tech stack right now? I found a great guide that ranks and reviews the best options available today. 🔗 The link is in the first comment! Let me know what tasks you are automating this year. #WorkflowAutomation #PythonCoding #TechTrends #ProductivityHacks #SoftwareEngineering #Python

Started my #30DaysOfPython journey today. 🚀 Here are some of the basics I picked up: 1. Python is a high-level, interpreted, open-source, object-oriented language created by Guido van Rossum. 2. Unlike many languages, Python uses indentation instead of curly brackets to define blocks of code. 3. I also revisited core data types like: (i) Numbers (ii) Strings (iii) Booleans (iv) Lists (v) Dictionaries (vi) Tuples (vii) Sets 4. One simple but useful thing: type() helps check the data type of any value. Day 1 done. One step closer to becoming more confident with Python. 🐍 #Python #30DaysOfPython #SoftwareDevelopment #LearningInPublic #CareerTransition

Pandas vs. Polars: A Complete Comparison of Syntax, Speed, and Memory Need help choosing the right #Python dataframe library? This article compares #Pandas and #Polars to help you decide. If you've been working with data in Python, you've almost certainly used pandas. It's been the go-to library for data manipulation for over a decade. But recently, Polars has been gaining serious traction. Polars promises to be faster, more memory-efficient, and more intuitive than pandas. But is it worth learning? And how different is it really? In this article, we'll compare pandas and Polars side-by-side. You'll see performance benchmarks, and learn the syntax differences. By the end, you'll be able to make an informed decision for your next data project. Read: https://lnkd.in/gh_GtBsA

Python Tip — Tuples: Small Feature, Big Signal Most developers see tuples as “lists you can’t modify.” That’s surface-level thinking. Tuples are about immutability and intent. When you use a tuple, you’re telling other developers: “This data should not change.” They’re: - Faster than lists - Hashable (usable as dictionary keys) - Safer for fixed data - Perfect for returning multiple values Use lists for collections that evolve. Use tuples for data that represents a fixed structure. In Python, the right data structure isn’t just technical, it communicates design. FOLOW FOR MORE PYTHON TIPS & INSIGHTS #Python #DataStructures #CleanCode #SoftwareEngineering #ProgrammingTips

Day 22/100 – #100DaysOfCode 🚀 Solved LeetCode #560 – Subarray Sum Equals K (Python). Today I learned how prefix sum and hashmap can be used together to efficiently count subarrays with a given sum. Approach: 1) Initialize count = 0 and curr_sum = 0. 2) Use a hashmap to store prefix sum frequencies (start with {0:1}). 3) Traverse the array and keep adding elements to curr_sum. 4) Check if (curr_sum - k) exists in the hashmap. 5) If it exists, add its frequency to count. 6) Update the hashmap with the current prefix sum. Time Complexity: O(n) Space Complexity: O(n) Understanding prefix sum + hashmap pattern is a game changer 💪 #LeetCode #Python #DSA #HashMap #PrefixSum #ProblemSolving #100DaysOfCode