Exploratory Programming with Jupyter and IPython

I used to think Object-Oriented Programming (OOP) was overcomplicating things. I was wrong. 🛑 For a long time, I stuck to simple scripts. But as my projects grew, I realized that writing functional code is one thing—writing scalable code is another. Today, I sat down to master the Class structure in Python, and it finally clicked. By building this Animal class, I realized why OOP is a game-changer: ✅ Reusability: I can create 100 different objects without rewriting the logic. ✅ Organization: Data (attributes) and actions (methods) live together in one neat package. ✅ Readability: Anyone can look at dog.speak() and know exactly what is happening. It’s a simple script, but it’s a foundational step toward building more complex software. Small wins lead to big builds! 🚀 Question for the devs: What was the hardest part of OOP for you to wrap your head around when you first started? For me, it was definitely understanding self! 👇 Zakir Hussain #Python #SoftwareEngineering #CodingJourney #BuildInPublic #Programming #OOP #LearningToCode #PythonProgramming

“𝗛𝗮𝗿𝗱𝗲𝘀𝘁 𝗽𝗿𝗼𝗯𝗹𝗲𝗺 𝗳𝗼𝗿 𝗽𝗿𝗼𝗴𝗿𝗮𝗺𝗺𝗲𝗿𝘀? 𝗡𝗮𝗺𝗶𝗻𝗴 𝘁𝗵𝗶𝗻𝗴𝘀.” We can build scalable systems… but naming a variable? That takes forever. We’ve all written things like: temp, data, x, finalData_v2 Even programming languages aren’t great at naming: • Python → not about snakes • JavaScript → not really Java • Go → sounds like a command • Rust → not about corrosion • Swift → not just about speed Naming has always been hard. But in real projects, bad names = confusion, bugs, and slow development. Good naming is simple: • Clear • Meaningful • Easy to understand 𝗥𝗲𝗰𝗲𝗻𝘁𝗹𝘆 𝗿𝗲𝗮𝗱 𝗮 𝗱𝗼𝗰 𝘁𝗵𝗮𝘁 𝗰𝗵𝗮𝗻𝗴𝗲𝗱 𝗵𝗼𝘄 𝗜 𝘁𝗵𝗶𝗻𝗸 𝗮𝗯𝗼𝘂𝘁 𝗻𝗮𝗺𝗶𝗻𝗴. Link: https://lnkd.in/gMgBWdqz #learning

Day 17 / 90 — Software Engineering Challenge Today I focused on revising Object-Oriented Programming (OOP) in Python to write more structured and maintainable code. Concepts Revised • Classes and Objects • Encapsulation and Abstraction • Inheritance and Polymorphism • Constructors and method definitions • Instance vs class variables • Writing modular and reusable code • Organizing logic using classes instead of functions • Improving code readability and maintainability Practical Thinking: Understanding how OOP helps in backend development: • Models → represent data (e.g., User, Task) • Services → handle business logic • Better separation of concerns Well-structured code is easier to debug, extend, and maintain. #90DaysOfCode #Python #OOP #SoftwareEngineering #LearningInPublic

Write code that doesn't break in production...! When building end-to-end pipelines, reading data from GitHub or external URLs is common. But relying on a "happy path" is a mistake. For robust development, always implement: Logging: To track the flow and capture specific error details. Exception Handling: To prevent the entire app from crashing and get clear "Unable to load" alerts. It’s a simple habit, but it’s what separates a beginner from a Pro Developer. #Python #MLOps #CleanCode #SoftwareEngineering #DataScience #CodingTips

From "It Works on My Machine" to Shipping Code That Works Everywhere! Most data projects don't fail because of bad code. They fail because of broken environments. Library version mismatches. Pipelines that break overnight. Onboarding that takes days instead of minutes. Docker solves all of that by packaging your code, Python version, and dependencies into a single container that runs identically anywhere. I wrote a full breakdown of the business and technical impact, and what it looks like in a real data project: 👉 https://lnkd.in/gh8-r3Bj #Docker #BusinessIntelligence #BI #DataScience #Python

🎯 Tech Learning Journey - Day 03: Web APIs & Requests - Talk to the Internet! Web APIs are how your Python code talks to websites and online services. You send a request, just like asking for information, and the API sends back data that you can use in your application. import requests # Get data from a public API response = requests.get\('https://lnkd.in/gUpgfHqa) data = response.json\(\) print\(f"User: \{data\['login'\]\}"\) print\(f"Repos: \{data\['public\_repos'\]\}"\) Where I use this: Fetching weather updates, getting user data from databases, and integrating third-party services into my apps. #Python #Coding #Programming #WebAPI

Master SQL JOINs like a pro INNER, LEFT, RIGHT, FULL - all in one place! Stop memorizing, start understanding Save this cheat sheet now You'll thank yourself later Follow us PlaceMateAI afor more information about programming and much more. #sql #datascience #programming #codinglife #learntocode

𝗜𝗻𝘁𝗿𝗼𝗱𝘂𝗰𝗶𝗻𝗴 𝗞𝗮𝗶𝗿𝗼! About a year and a half ago, my friends and I started building a programming language. We'd been writing systems-level code across C++, Rust, and lower-level tooling for long enough to have strong opinions about what was missing. C++ gives you performance and the richest library ecosystem in systems programming, but the language fights you at every turn undefined behavior, header hell, decades of accumulated complexity. Rust solves the safety problem but introduces real cognitive overhead and a syntax that trades readability for compiler appeasement. We wanted something that didn't force that tradeoff. So we built Kairo a statically typed systems programming language with bidirectional C++ interoperability, readability-first syntax, and a memory safety model that gives you both manual and automatic memory tracking without fighting a borrow checker. 𝗪𝗵𝗲𝗿𝗲 𝘄𝗲 𝗮𝗿𝗲 𝗻𝗼𝘄 The project has come a long way. We shipped a Stage 0 bootstrap compiler, then used it to build a Stage 1 self-hosted compiler; Kairo is being written in Kairo itself! The language, toolchain, and ecosystem are actively under development and roughly halfway to a production release. 𝗪𝗵𝗮𝘁 𝗺𝗮𝗸𝗲𝘀 𝗞𝗮𝗶𝗿𝗼 𝗱𝗶𝗳𝗳𝗲𝗿𝗲𝗻𝘁 - Bidirectional C++ FFI call C++ from Kairo and Kairo from C++, no bindings layer required. - Readability-first design. Code should be readable by default, not after three months of pattern memorization. - BCIR/AMT memory safety model manual and automatic memory tracking with safety guarantees, without Rust's borrow checker annoyance. - Planned interop expansion to Python and Rust. - Full access to the existing C++ library ecosystem from day one. 𝗖𝗼𝗻𝘁𝗿𝗶𝗯𝘂𝘁𝗼𝗿𝘀 :Arnav Goyal , Dhruvan Kartik and Me 𝗟𝗶𝗻𝗸𝘀 - Website: https://www.kairolang.org - Github: https://lnkd.in/gZEdW2Gi We're a three-person team and the project has 360+ GitHub stars. If this interests you, a star, an issue report, or a contribution all help. #CompilerDev #Compiler #tech #buildinpublic #SystemsEngineering

𝐒𝐩𝐞𝐧𝐭 𝟑 𝐰𝐞𝐞𝐤𝐬 𝐝𝐞𝐜𝐢𝐝𝐢𝐧𝐠 𝐛𝐞𝐭𝐰𝐞𝐞𝐧 𝐏𝐲𝐭𝐡𝐨𝐧 𝐚𝐧𝐝 𝐉𝐚𝐯𝐚𝐒𝐜𝐫𝐢𝐩𝐭. Three. Weeks. I made spreadsheets. Read 47 blog posts. Watched YouTube videos at 2x speed. Compared syntax highlighting on both my monitors. Asked developers who've never heard of me their "honest takes." You know what I actually needed? 𝐓𝐨 𝐣𝐮𝐬𝐭 𝐩𝐢𝐜𝐤 𝐨𝐧𝐞 𝐚𝐧𝐝 𝐬𝐭𝐚𝐫𝐭 𝐛𝐮𝐢𝐥𝐝𝐢𝐧𝐠. Honestly, I think we (developers) spend way too much time in 𝐚𝐧𝐚𝐥𝐲𝐬𝐢𝐬 𝐩𝐚𝐫𝐚𝐥𝐲𝐬𝐢𝐬 mode when choosing a language. Reddit threads say Python is dying. HackerNews says JavaScript is bloated. Someone's always saying "just use Rust." And suddenly you're spiraling, wondering if your choice from 6 months ago was a massive mistake. Here's what I've learned: 𝐓𝐡𝐞 "𝐛𝐞𝐬𝐭" 𝐩𝐫𝐨𝐠𝐫𝐚𝐦𝐦𝐢𝐧𝐠 𝐥𝐚𝐧𝐠𝐮𝐚𝐠𝐞 𝐢𝐬 𝐭𝐡𝐞 𝐨𝐧𝐞 𝐭𝐡𝐚𝐭 𝐬𝐨𝐥𝐯𝐞𝐬 𝐲𝐨𝐮𝐫 𝐩𝐫𝐨𝐛𝐥𝐞𝐦 *𝐭𝐨𝐝𝐚𝐲*, 𝐧𝐨𝐭 𝐭𝐡𝐞 𝐨𝐧𝐞 𝐭𝐡𝐚𝐭 𝐦𝐢𝐠𝐡𝐭 𝐛𝐞 𝐭𝐫𝐞𝐧𝐝𝐲 𝐧𝐞𝐱𝐭 𝐲𝐞𝐚𝐫. Yes, context matters—but it doesn't matter *𝐭𝐡𝐚𝐭*  much. Every major language has: - 𝐆𝐫𝐞𝐚𝐭 𝐥𝐢𝐛𝐫𝐚𝐫𝐢𝐞𝐬 - 𝐆𝐨𝐨𝐝 𝐝𝐨𝐜𝐮𝐦𝐞𝐧𝐭𝐚𝐭𝐢𝐨𝐧 - 𝐀 𝐬𝐮𝐩𝐩𝐨𝐫𝐭𝐢𝐯𝐞 𝐜𝐨𝐦𝐦𝐮𝐧𝐢𝐭𝐲 - 𝐒𝐭𝐚𝐜𝐤 𝐎𝐯𝐞𝐫𝐟𝐥𝐨𝐰 𝐚𝐧𝐬𝐰𝐞𝐫𝐬 𝐟𝐨𝐫 𝐞𝐯𝐞𝐫𝐲𝐭𝐡𝐢𝐧𝐠 The difference in productivity? Usually comes down to *𝐡𝐨𝐰 𝐦𝐮𝐜𝐡 𝐲𝐨𝐮'𝐯𝐞 𝐮𝐬𝐞𝐝 𝐢𝐭 𝐛𝐞𝐟𝐨𝐫𝐞*, not which one you picked. So if you're stuck between options: flip a coin if you have to. Pick one. 𝐁𝐮𝐢𝐥𝐝 𝐬𝐨𝐦𝐞𝐭𝐡𝐢𝐧𝐠. 𝐓𝐡𝐞 𝐫𝐞𝐠𝐫𝐞𝐭-𝐟𝐫𝐞𝐞 𝐥𝐚𝐧𝐠𝐮𝐚𝐠𝐞 𝐢𝐬 𝐚𝐥𝐰𝐚𝐲𝐬 𝐭𝐡𝐞 𝐨𝐧𝐞 𝐰𝐡𝐞𝐫𝐞 𝐲𝐨𝐮 𝐚𝐜𝐭𝐮𝐚𝐥𝐥𝐲 𝐬𝐡𝐢𝐩𝐩𝐞𝐝 𝐬𝐨𝐦𝐞𝐭𝐡𝐢𝐧𝐠. What's the most time you've spent deciding on a language? (Asking for a friend who is me.) 😉 #DeveloperLife #Programming #DeveloperProblems #CodeLife #LearnToCode #CareerGrowth #CodingCommunity

I’ve just wrapped up a major milestone in my backend journey — implementing asynchronous processing in my Task Manager project, and the results are What I built: Sync vs Async API comparison endpoints Concurrent request handling using async routes External API integration with parallel calls Clean UI dashboard to visualize performance differences Results: Sync execution: 2160 ms Async execution: 1586 ms ~574 ms faster with async! This clearly shows how asynchronous programming can significantly improve performance when dealing with multiple I/O operations. Key Takeaways: Async = better scalability & responsiveness Perfect for external API calls & high-load systems Clean architecture makes debugging & scaling easier Tech Stack: FastAPI | Python | Async/Await | HTTPX | SQLite | Custom UI This phase really helped me understand how modern backend systems handle concurrency efficiently. #BackendDevelopment #Python #FastAPI #AsyncProgramming #WebDevelopment #SoftwareEngineering #LearningInPublic #100DaysOfCode