Flattening Arrow Code with Clean Architecture in Python

🚀✨ Sharing something I’ve been building — Code Crawler 🕷️ Ever joined a large Python codebase and spent days 😵💫 figuring out what calls what? Yeah… that frustration led me to build this 👇 💡 Code Crawler helps you understand any Python codebase visually and instantly. Just point it to a GitHub repo (or even a local folder 📂), and it parses everything using AST to generate a function & class-level lineage graph 🧬 🔍 What makes it powerful: ⚙️ Resilient crawl pipeline (Temporal) — survives restarts & scales smoothly 🔗 Cross-repo lineage — track relationships across repos + inheritance chains 🌿 Branch comparison — see what changed & what it impacts downstream ▶️ Run functions from UI — no test setup needed 🧪 Smart mocking — auto-detect dependencies & stub them easily 📊 Batch testing — run multiple test cases together with instant results 📂 Local support — drag & drop projects, no GitHub required 🏢 Multi-tenant architecture — full isolation + invites + admin panel 💭 Why I built this: Understanding a new codebase shouldn’t feel like solving a puzzle 🧩 Instead of jumping across hundreds of files: 👉 Visualize relationships 📈 👉 Trace flows instantly 🔄 👉 Run functions with real inputs ⚡ All in one place. 🚧 Still building, but already super useful for exploring codebases I didn’t write Would love feedback from folks who’ve struggled with large Python projects 🙌🔥 🔗 https://lnkd.in/gtD-5juu #Python 🐍 #SoftwareEngineering 💻 #DevTools 🛠️ #OpenSource 🌍 #FastAPI ⚡ #React ⚛️

I just learned something that no LeetCode problem ever taught me. How do you sort 200 GB of data when your RAM is only 5 GB? 🤯 I came across this in a real interview question today — and honestly, I had no clue. The answer? External Merge Sort. Here's how it works in simple terms 👇 📦 Phase 1 — Break it down: • Read 5 GB of data into RAM • Sort it using QuickSort • Write it back to disk as a sorted "chunk" • Repeat 40 times → now you have 40 sorted files 🔀 Phase 2 — Merge using a Min-Heap: • Open all 40 files at once • Push the first element of each file into a Min-Heap (size = just 40!) • Pop the minimum → write to output → push next element from that file • Repeat until all 200 GB are merged The genius part? The heap never holds more than 40 elements at a time. Not 200 GB. Just 40. All those Heap and Merge Sort problems on LeetCode? This is exactly what they're preparing you for — just at a massive scale. This is why Big Tech companies ask System Design questions. Real-world data doesn't fit in an array. 🌍 📸 Attached the full Python implementation above — Phase 1 (Run Creation) + Phase 2 (K-Way Merge) with comments explaining every step. Drop a 🙋 if you had no idea this concept existed before today! And tell me — what's the most surprising DSA concept YOU'VE come across recently? 👇 #DSA #LeetCode #SystemDesign #SoftwareEngineering #Python #CodingInterview #ExternalSorting

The Secret to Clean Code: Snake Case 🐍 Have you heard about snake variable 🐍? If you are diving into Python or working within a team of developers, you’ve likely seen variable names like 'user_login_count' or 'total_price_usd' This specific style—where words are written in lowercase and joined by underscores is known as snake_case. Why does it even matter? In the world of coding, readability is just as important as functionality. Here is why snake case is a standard for many: Readability—It mimics natural spacing, making it easy for the human eye to distinguish separate words at a glance. Context— Instead of vague abbreviations like ua, using "user_age" tells anyone reading your code exactly what data is being stored. Consistency— Following naming conventions ensures that a large codebase remains professional and maintainable, whether you're working solo or with a global team. Writing clean code is a form of professional etiquette. It respects the time of your future self and your colleagues. What’s your preferred naming convention? Let’s discuss below! 👇 #Python #CleanCode #ProgrammingTips #DataScience #WebDevelopment #TechCommunity

My first version of the Task Manager CLI was embarrassing. Everything in one file. No error handling. Variables named x, temp, data2. It worked — until it didn't. And when it broke, I had no idea where to start debugging. That moment of staring at my own code and not understanding it was one of the most important moments in my development as an engineer. I refactored the entire project. Broke it into modules. Added structured exception handling. Rewrote the validation logic. Debugging effort dropped by 40%. More importantly, I could actually read my own code. The lesson wasn't technical. It was about ego. Bad code is often the result of being in a hurry to "finish" — instead of being willing to do it right. I now build slower and ship cleaner. And I'm better for it. Have you ever looked back at old code and learned something important about yourself? #Refactoring #CleanCode #Python #SoftwareEngineering #LessonsLearned

My CLAUDE.md was 281 lines. Claude loaded all of it every session - whether I was writing a post or debugging a Python script. Post rules. Format guides. Note conventions. Voice guidelines. All in context, all the time. Even when none of it was relevant. Longer context = lower adherence. The docs say it plainly. I ignored it until I ran this prompt: --- *Review my CLAUDE.md against https://lnkd.in/g6UNtsC7 and suggest specific improvements - what to add, what to move to .claude/rules/ files, and what to cut.* --- Claude read the official docs, compared them against my file, and came back with a diagnosis. The biggest one was `.claude/rules/` - a directory most people don't know exists. Here's what it told me to do: Rules in `.claude/rules/` load just like CLAUDE.md - but you can scope them to specific file paths. ``` paths: - "posts/**" ``` That one frontmatter block means post rules only enter context when Claude is working inside that folder. Format guides, note conventions - same thing. Zero tokens wasted when they don't apply. After the restructure: - CLAUDE.md: 281 lines → 69 lines - Always-loaded context: ~180 lines total - 3 rule files load on demand, only when relevant The path-scoped rules are in the official docs at https://lnkd.in/g6UNtsC7. Almost nobody uses them. Run `/memory` in Claude Code to see exactly what's loaded in your current session. If it's over 200 lines, that's where the drift is coming from. What rule do you keep repeating to Claude because it keeps forgetting? #ClaudeAI #AITools #BuildInPublic #ProductManagement #DeveloperTools

Most beginner backend projects die in refactoring. Here's the structure I use to prevent that. When I built my Task Manager CLI, I learned this the hard way — a monolithic file that worked until it very much didn't. After refactoring, here's the structure I now start with: Before writing a single line: → Define your data model first → Identify all operations (CRUD) you'll need → Map inputs, outputs, and error states While building: → One module per concern (routes, models, utils, exceptions) → Validate inputs at the boundary — not deep inside logic → Handle errors explicitly — no silent failures Before shipping: → Test the unhappy paths, not just the happy ones → Read your own code like a stranger would This approach reduced my debugging effort by 40% on a real project. It works at any scale — from a CLI tool to a FastAPI service. What's the first thing you do when starting a new backend project? #BackendDevelopment #Python #FastAPI #SoftwareEngineering #CodingTips

🐍 Exploring NumPy Operations in Python Today I practiced different operations on NumPy arrays, which are essential for Data Science and Machine Learning 🚀 🔹 Created arrays using np.arange() 🔹 Performed element-wise operations: ✔ Addition (arr + arr) ✔ Subtraction (arr - arr) ✔ Multiplication (arr * arr) ✔ Division (arr / arr) 🔹 Applied scalar operations: ✔ arr + 4, arr * 3 🔹 Explored mathematical functions: ✔ Square root (np.sqrt) ✔ Exponential (np.exp) ✔ Trigonometric functions (np.sin) 🔹 Found useful statistics: ✔ Maximum (np.max) ✔ Minimum (np.min) ⚠️ Important Observation: Dividing by zero gives inf or nan values Example: 1/0 → inf, 0/0 → nan 💡 Key Takeaway: NumPy allows fast and efficient element-wise operations, but handling edge cases like division by zero is very important in real-world data. 📌 Full code available here: 👉 https://lnkd.in/dCMhYQey #Python #NumPy #DataScience #MachineLearning #100DaysOfCode #LearningJourney

Ever wondered how your computer actually manages its memory? 🧠💻 I’m excited to share my latest project: a Memory Management Visualizer that brings complex Operating System concepts to life. Moving from textbook theory to a functional implementation was a huge learning curve, but seeing the logic in action makes it all worth it. Key Features I built: ✅ Paging: Real-time visualization of FIFO and LRU algorithms using a Flask-powered backend. ✅ Segmentation: Implementation of First Fit and Best Fit allocation strategies with a dynamic memory map. ✅ Virtual Memory: A full simulation featuring Page Tables, TLB cache, and page fault handling. Tech Stack: - Backend: Python (Flask) 🐍 Frontend: HTML, CSS, JavaScript, Chart.js 📊 Tools: Conda, REST APIs Check out the repo here: [https://lnkd.in/gdAnxj7E] Team: Shaurya Raj, Sreenath YV Greatful to Arjun Saini for guidance and support throughout the project. #OperatingSystems #ComputerScience #FullStack #Python #Flask #WebDevelopment #CodingProject

💻 Why Follow My GitHub Journey? Because here, code is not just code. It’s solutions, learning, and creativity. ✅ Highlights: Projects in Python, SQL, React, and AI/ML Real-world datasets for hands-on learning Clear documentation & reusable code 👀 Take a look at my repositories: https://lnkd.in/dqgHkRQm⁠� 💬 CTA: Star ⭐ your favorite repo, fork it, or drop me a message. I love collaborating with like-minded data enthusiasts!

🚀 Day 16/100: Stepping into Object-Oriented Programming (OOP)! 🏗️🤖 💡 Did you know? Procedural programming is like following a recipe, but OOP is like managing a kitchen with specialized chefs (Objects) who each know their own job. This shift is what allows modern software to scale to millions of lines of code! I’ve hit Day 16 of #100DaysOfCode, and it’s a game-changer. I transitioned from procedural logic to Object-Oriented Programming (OOP) using the Turtle Graphics library and PrettyTable. Key technical takeaways: ✅ Classes vs. Objects: Learning that a Class is the blueprint (the DNA) and the Object is the actual instance (the Organism). ✅ Attributes & Methods: Accessing data (attributes) and triggering actions (methods) within an object. ✅ External Packages: Using PyPi to install and implement libraries like PrettyTable for structured data visualization. ✅ Abstraction: Learning how to use complex code written by others without needing to see the internal "wiring." Mastering OOP is like unlocking a new Grimoire—it changes how you view and build every project from here on out! 🛡️ Check out my first OOP steps here: 🔗 https://lnkd.in/gDWQXAHW The evolution continues. Day 17, I'm coming for you! 🚀 #Python #100DaysOfCode #OOP #ObjectOrientedProgramming #CleanCode #SoftwareArchitecture #DevCommunity