Python Support Added to OpenRewrite for Cross-Language Refactoring

🚀 Python, API & Backend Fundamentals – Quick Revision for Developers Recently revised some core concepts that frequently appear in Python developer assessments and backend interviews. Sharing a crisp summary 👇 🔹 1️⃣ Python List Operations - Remove by value → "remove()" - Remove by index → "pop()" / "del" - Clear all elements → "clear()" 🔹 2️⃣ Bitwise Operations - Flip specific bits → "^" (XOR) - AND → "&" - OR → "|" - NOT → "~" 🔹 3️⃣ Object-Oriented Programming (OOP) - Use Classes & Objects for scalable systems (e.g., Car Rental, Payroll) - Use Encapsulation → private attributes ("__var") + getter/setter - Use Abstract Base Classes ("abc" + @abstractmethod`) to enforce implementation in subclasses 🔹 4️⃣ REST API Design Principles - Use an API Proxy/Gateway for: ✔ Authentication ✔ Logging ✔ Rate Limiting ✔ Traffic monitoring - Implement proper CORS configuration to allow only authorized domains - Prefer Token-based authentication (JWT) over open/public APIs 🔹 5️⃣ Microservices & Resilience - Avoid tightly coupled sequential service calls - Use Message Queues / Asynchronous communication for fault tolerance - Improves scalability and reduces cascading failures 🔹 6️⃣ Database & Data Handling Basics - Protect sensitive fields (e.g., salary) via controlled access - Use proper schema design instead of loosely structured storage - Follow separation of concerns (business logic ≠ direct DB manipulation everywhere) - Prefer ORM layers in backend frameworks for maintainability 💡 Strong fundamentals in Python + API architecture + database design form the backbone of modern backend systems (FastAPI, Django, Microservices, Cloud-native apps). Master the basics. System design becomes easier. #Python #BackendDevelopment #APIDesign #Microservices #DatabaseDesign #SoftwareEngineering #InterviewPreparation

Day 67 of #100daysofcode While building my todo list app (which I talked about in my last post found here: https://lnkd.in/d9wd2NUi), I thought about how I would design my solution before writing any code. As a programmer, you need to understand that a programming language is a TOOL that helps you to solve a problem or bring an idea to life. The programmer still needs to THINK OF and DESIGN a solution, and then translate that solution into code. (Don’t let ChatGPT do ALL your thinking for you 😔) However, writing entire solutions in your head can be really hard, or even impossible, especially when it has multiple steps, or has several conditions that need to be checked, or it contains complex loops, and so on and so forth. So my next few posts will talk about how you can design solutions as a developer. Firstly we’ll talk about Pseudocode. Pseudocode is a high-level, human-readable description of an algorithm or computer program. It is called “pseudo” because it is like “fake” code. Pseudocode is not representative of any specific programming language, and if well written, it can actually be adapted into any programming language. So a JavaScript developer can design a solution with Pseudocode and give it to a Python developer. Here’s an example below: SET password TO "secure123" FUNCTION checkPassword(password) IF password EQUALS password THEN PRINT "Access Granted" ELSE PRINT "Access Denied" END IF END FUNCTION CALL checkPassword("secure123") Now, this code can be easily written in Python, JavaScript, Java, etc. Writing pseudocode can be a great way for a programmer to put their thoughts to paper and test out solutions before writing any real code. It can also be great for 2 developers who write different languages to communicate with each other. In my next post I’ll be talking about Flowcharts, and how that can be a very useful tool as well when designing a solution. Here is a link to an article that talks more about the usefulness of Pseudocode: https://lnkd.in/dqA4-bAw

Day 68 of #100daysofCode In my last post, I talked about the usefulness of pseudocode when designing a solution to a programming problem. In this post, I will discuss flowcharts. A flowchart is like a human-readable diagram of an algorithm. You could say its a visual representation of pseudocode. Different shapes represent different programming constructs. For example, a parallelogram represents an INPUT or OUTPUT, while a diamond represents a condition. Here is the pseudocode example from my last post: SET password TO "secure123" FUNCTION checkPassword(password) IF password EQUALS password THEN PRINT "Access Granted" ELSE PRINT "Access Denied" END IF END FUNCTION CALL checkPassword("secure123") Attached to this post is an image of the pseudocode above represented as a flowchart. Notice how easy it is to read! The best thing about flowcharts is that they can be easily understood by non-programmers! Flowcharts can also be used to solve non-programming problems. Many times, a type of flowchart is used to plan out user-interface flows and how the user will interact with the system step by step. Programming a lot of the time is the manipulation of data to get a particular result. Storing and managing data can get complicated. There’s a diagram to help in that regard as well, and I will be discussing that in my next post.

Your Tech Stack Didn't Change. It Just Made Some Smart New Friends. ----- Remember when your tech stack was simple? C#, Java, SQL, Python, JavaScript. You knew exactly what tools you needed. Then suddenly everyone's talking about Cursor, Claude, Copilot, Gemini, and Grok. Wait, are these programming languages or AI assistants? (Spoiler: they're your new coding buddies) Here's What Actually Happened: My old stack: - C# and Java for backend - SQL for databases - Python and JavaScript for everything else My new stack: - All of the above, PLUS - Cursor (smart code editor) - Claude (AI assistant for debugging) - GitHub Copilot (autocomplete on steroids) - Gemini and Grok (more AI helpers) The punchline? I'm using both. At the same time. Every single day. "A Day in My Life" (2024 Edition) 9 AM: Write C# code for an API 9:15 AM: Ask Copilot for boilerplate code 9:20 AM: Fix what Copilot got wrong (it's not perfect, folks) 10 AM: Hit a weird bug 10:05 AM: Ask Claude "Why is this breaking?" 11 AM: Write SQL queries 11:30 AM: Ask Gemini to optimize them 3 PM: Still cursing at JavaScript (some things never change) "The Truth Nobody Wants to Hear" AI isn't replacing developers. It's making us faster. - SQL isn't dying. You still need to understand databases. - C# and Java are still running the world's critical systems. - Python is more important than ever (how do you think we build AI?) The difference? When I'm stuck on a problem at 3 PM, I can ask an AI for help instead of searching Stack Overflow for 2 hours. "What Really Changed" We didn't throw away our hammers and buy nail guns. We added nail guns to the toolbox. "Good developers now": - Write code AND prompt AI effectively - Know when to trust AI suggestions and when to ignore them - Understand both classic algorithms AND how to use AI tools - Debug their own code AND the AI's hallucinations. "My Hot Take" The tech stack didn't change. It expanded. You need to know Java AND how to use AI coding tools. You need SQL skills AND the ability to ask Claude good questions. You need debugging skills AND the wisdom to catch when Copilot is confidently wrong. It's not old vs new. It's old + new = better. "Let's Talk" I want to hear from you: - What's your current tech stack? Old school, new school, or both? - Have you caught an AI tool suggesting terrible code? Share your horror stories! - What "old" tech will you never give up? I'll go first: SQL. Always SQL. Drop your thoughts below. Tell me I'm right, tell me I'm wrong, tell me about that time Copilot tried to import a library that doesn't exist. Let's talk about where we're really headed. P.S. For hiring managers: If you're wondering whether to hire "traditional" developers or "AI-savvy" ones, the answer is developers who can do both. We're the new normal. #TechStack #AI #SoftwareDevelopment #Developer #Coding #Programming #Technology #CareerDevelopment #AITools

💡 𝗔 𝗦𝗺𝗮𝗹𝗹 𝗦𝘁𝗼𝗿𝘆 𝗔𝗯𝗼𝘂𝘁 𝗢𝗢𝗣 𝗶𝗻 𝗣𝘆𝘁𝗵𝗼𝗻, 𝗣𝗛𝗣, 𝗮𝗻𝗱 𝗝𝗮𝘃𝗮𝗦𝗰𝗿𝗶𝗽𝘁 — 𝗮𝗻𝗱 𝗪𝗵𝗮𝘁 𝗜𝘁 𝗠𝗲𝗮𝗻𝘀 𝗳𝗼𝗿 𝗠𝗼𝗱𝗲𝗿𝗻 𝗪𝗲𝗯 𝗗𝗲𝘃𝗲𝗹𝗼𝗽𝗺𝗲𝗻𝘁 Recently while revising Python OOP, I started thinking about how different programming languages approach Object-Oriented Programming and how it affects real-world systems. Let’s imagine three developers building a large HRMS platform. 🐍 𝗔 𝗣𝘆𝘁𝗵𝗼𝗻 𝗱𝗲𝘃𝗲𝗹𝗼𝗽𝗲𝗿 builds backend services. Python supports strong OOP concepts like 𝗲𝗻𝗰𝗮𝗽𝘀𝘂𝗹𝗮𝘁𝗶𝗼𝗻, 𝗶𝗻𝗵𝗲𝗿𝗶𝘁𝗮𝗻𝗰𝗲, 𝗮𝗻𝗱 𝗽𝗼𝗹𝘆𝗺𝗼𝗿𝗽𝗵𝗶𝘀𝗺.. This makes the system easier to structure and scale. Sensitive data can be protected properly using private attributes, which improves security in large applications. 🐘 𝗔 𝗣𝗛𝗣 𝗱𝗲𝘃𝗲𝗹𝗼𝗽𝗲𝗿 builds the admin system using frameworks like 𝗟𝗮𝗿𝗮𝘃𝗲𝗹. PHP follows classical OOP principles similar to languages like 𝗝𝗮𝘃𝗮. With features like 𝗶𝗻𝘁𝗲𝗿𝗳𝗮𝗰𝗲𝘀, 𝘁𝗿𝗮𝗶𝘁𝘀, 𝗮𝗻𝗱 𝗱𝗲𝗽𝗲𝗻𝗱𝗲𝗻𝗰𝘆 𝗶𝗻𝗷𝗲𝗰𝘁𝗶𝗼𝗻, enterprise applications become easier to maintain. This is one reason why many large platforms still rely heavily on PHP-based systems. ⚡ 𝗔 𝗝𝗮𝘃𝗮𝗦𝗰𝗿𝗶𝗽𝘁 𝗱𝗲𝘃𝗲𝗹𝗼𝗽𝗲𝗿 builds the user interface and real-time features. JavaScript uses 𝗽𝗿𝗼𝘁𝗼𝘁𝘆𝗽𝗲-𝗯𝗮𝘀𝗲𝗱 𝗢𝗢𝗣, which makes it extremely flexible. It powers modern stacks like 𝗥𝗲𝗮𝗰𝘁 𝗮𝗻𝗱 𝗡𝗼𝗱𝗲.𝗷𝘀,, enabling high concurrency and real-time applications such as dashboards, chat systems, and notifications. But there is an interesting takeaway. Each language has its strengths. 🐍 𝗣𝘆𝘁𝗵𝗼𝗻 → clean architecture and powerful backend services 🐘 𝗣𝗛𝗣 → structured enterprise applications and admin systems ⚡ 𝗝𝗮𝘃𝗮𝗦𝗰𝗿𝗶𝗽𝘁 → dynamic interfaces and scalable real-time systems Modern web development is not about choosing one language over another. It is about 𝗰𝗼𝗺𝗯𝗶𝗻𝗶𝗻𝗴 𝘁𝗵𝗲 𝘀𝘁𝗿𝗲𝗻𝗴𝘁𝗵𝘀 𝗼𝗳 𝗺𝘂𝗹𝘁𝗶𝗽𝗹𝗲 𝘁𝗲𝗰𝗵𝗻𝗼𝗹𝗼𝗴𝗶𝗲𝘀 to build scalable systems. That’s why many modern architectures look like this: 𝗙𝗿𝗼𝗻𝘁𝗲𝗻𝗱 → 𝗥𝗲𝗮𝗰𝘁 𝗕𝗮𝗰𝗸𝗲𝗻𝗱 𝗔𝗣𝗜𝘀 → 𝗡𝗼𝗱𝗲.𝗷𝘀 / 𝗣𝘆𝘁𝗵𝗼𝗻 𝗘𝗻𝘁𝗲𝗿𝗽𝗿𝗶𝘀𝗲 𝗺𝗼𝗱𝘂𝗹𝗲𝘀 → 𝗣𝗛𝗣 𝗼𝗿 𝗝𝗮𝘃𝗮 𝗗𝗮𝘁𝗮𝗯𝗮𝘀𝗲 → 𝗣𝗼𝘀𝘁𝗴𝗿𝗲𝗦𝗤𝗟 / 𝗠𝗼𝗻𝗴𝗼𝗗𝗕 Understanding how different languages approach programming paradigms like OOP helps developers design better systems. 💬 𝗖𝘂𝗿𝗶𝗼𝘂𝘀 𝘁𝗼 𝗵𝗲𝗮𝗿 𝗳𝗿𝗼𝗺 𝗼𝘁𝗵𝗲𝗿 𝗱𝗲𝘃𝗲𝗹𝗼𝗽𝗲𝗿𝘀 𝗵𝗲𝗿𝗲: Which language do you prefer for building scalable systems and why? #WebDevelopment #Python #PHP #JavaScript #OOP #SoftwareArchitecture #FullStackDevelopment

🚀 DTL vs Jinja2 — A Backend Engineering Perspective When working with Python web frameworks, templating engines play a critical role in rendering dynamic content. Two common choices are: • Django Template Language (DTL) • Jinja2 Both solve the same problem — generating dynamic HTML — but their design philosophy and engineering trade-offs are quite different. 🧠 Design Philosophy DTL (Django Template Language) Built with the principle that templates should focus only on presentation, not programming. Jinja2 Designed to be more Pythonic and expressive, giving developers greater flexibility inside templates. ⚡ Performance In most benchmarks, Jinja2 is faster. Why? • Templates compile into Python bytecode • Optimized rendering pipeline • Efficient expression evaluation This often makes Jinja2 1.5×–3× faster in rendering-heavy workloads. 🧩 Flexibility & Pythonic Design DTL intentionally restricts logic in templates to maintain separation of concerns. Example limitation: {{ users[0].name }} <!-- Not allowed in DTL --> Jinja2 allows more expressive syntax, closer to Python: {{ "Admin" if user.is_admin else "User" }} It also supports powerful features like: • Macros (template functions) • Advanced filters • Template imports • Inline expressions 🏗 Ecosystem Usage DTL • Primarily used within Django • Common in server-rendered Django applications Jinja2 • Flask • FastAPI • Ansible • Static site generators • Infrastructure automation This broader usage makes Jinja2 a popular choice beyond web frameworks. ⚖️ Engineering Trade-off DTL prioritizes: ✔ Safety ✔ Simplicity ✔ Strict separation of logic and presentation Jinja2 prioritizes: ✔ Speed ✔ Flexibility ✔ Pythonic syntax 💡 Engineering takeaway DTL works great for Django-driven applications, while Jinja2 excels in modern Python ecosystems where flexibility and performance matter. #Python #BackendEngineering #Django #Jinja2 #SoftwareArchitecture #WebDevelopment

Today’s dev headline: Python just joined the semantic refactoring party. Moderne announced Python support for OpenRewrite — extending its Lossless Semantic Tree (LST) model across Java, JavaScript, TypeScript, and now Python. For GitHub-heavy teams managing multi-repo, multi-language stacks, this is significant. Instead of: ▪️ Manually hunting deprecated APIs ▪️ Writing custom migration scripts ▪️ Fixing dependency drift repo by repo You can model code semantically and apply repeatable “recipes” across projects. Think: 🔄 Python version upgrades 📦 Dependency add/remove/replace 🔐 Cross-language vulnerability remediation 🧹 Consistent formatting and cleanup Modern systems rarely evolve in isolation. A Java service exposes an API. A Python integration consumes it. A shared library touches frontend and backend. Coordinated change is becoming a core competency. If you manage active GitHub orgs, this trend is clear: Refactoring is moving from manual effort to orchestrated campaign. Are your repos ready for multi-language modernization at scale?

𝘖𝘯𝘦 𝘵𝘩𝘪𝘯𝘨 𝘐 𝘭𝘪𝘬𝘦 𝘢𝘣𝘰𝘶𝘵 𝘈𝘐 𝘪𝘴 𝘵𝘩𝘦 𝘴𝘵𝘳𝘶𝘤𝘵𝘶𝘳𝘢𝘭 𝘸𝘢𝘺 𝘪𝘵 𝘱𝘳𝘪𝘯𝘵𝘴 𝘤𝘰𝘥𝘦. 📚 𝐁𝐨𝐨𝐤 𝐑𝐞𝐯𝐢𝐞𝐰: Clean Architecture with Python I finished reading this resource written by Sam Keen, and even from the preface and early chapters, it’s clear that this book is designed for developers who want to move beyond simply writing working code to designing maintainable, scalable systems. It focuses on applying Clean Architecture principles to Python applications, demonstrating how architectural thinking can transform software structure. 𝐏𝐲𝐭𝐡𝐨𝐧’𝐬 𝐟𝐥𝐞𝐱𝐢𝐛𝐢𝐥𝐢𝐭𝐲 𝐢𝐬 𝐚 𝐬𝐭𝐫𝐞𝐧𝐠𝐭𝐡, but without discipline it can easily lead to complex and difficult-to-maintain systems. This guide tackles that challenge by combining theory with practical Python implementations. 𝐓𝐨𝐩𝐢𝐜𝐬 𝐜𝐨𝐯𝐞𝐫𝐞𝐝 𝐢𝐧𝐜𝐥𝐮𝐝𝐞: 1️⃣ Applying SOLID principles in Python 2️⃣ Using type hints to strengthen architectural boundaries 3️⃣ Building domain models and isolating business logic 4️⃣ Structuring applications into clear layers (Domain, Application, Interface Adapters, Frameworks & Drivers) 5️⃣ Implementing controllers, presenters, and adapters 6️⃣ Designing testable architectures 7️⃣ Integrating external frameworks (e.g., web interfaces) without coupling them to core logic 8️⃣ Adding observability, logging, and monitoring while maintaining clean boundaries 9️⃣ Strategies for refactoring legacy Python systems The book emphasises separation of concerns and dependency inversion, which are critical for building systems that evolve. The inclusion of testing strategies and observability patterns shows that architecture is about operationally reliable systems. This is especially valuable for: 🙋♀️ Python backend developers working on large or evolving systems 👨💻 Software engineers interested in domain-driven design and architectural patterns 🧑💼 Technical leads and architects responsible for maintainable system design 🤷♂️ Developers dealing with legacy Python codebases that need refactoring Clean Architecture with Python appears to provide a structured roadmap for building robust, modular, and adaptable Python applications. It reinforces an important lesson: 𝘨𝘰𝘰𝘥 𝘢𝘳𝘤𝘩𝘪𝘵𝘦𝘤𝘵𝘶𝘳𝘦 𝘪𝘴 𝘢𝘣𝘰𝘶𝘵 𝘤𝘳𝘦𝘢𝘵𝘪𝘯𝘨 𝘴𝘺𝘴𝘵𝘦𝘮𝘴 𝘸𝘩𝘦𝘳𝘦 𝘣𝘶𝘴𝘪𝘯𝘦𝘴𝘴 𝘭𝘰𝘨𝘪𝘤 𝘳𝘦𝘮𝘢𝘪𝘯𝘴 𝘪𝘯𝘥𝘦𝘱𝘦𝘯𝘥𝘦𝘯𝘵, 𝘵𝘦𝘴𝘵𝘢𝘣𝘭𝘦, 𝘢𝘯𝘥 𝘳𝘦𝘴𝘪𝘭𝘪𝘦𝘯𝘵 𝘵𝘰 𝘤𝘩𝘢𝘯𝘨𝘦. 💳 Order it here! Link in comments... 🙏 Thanks Sonali from Packt for the continuous trust!

🚀 From Backend APIs to Smart Automation with Core Python A few days ago, I shared my Streamlit Password Generator project. Today, I’m excited to share another project I built using Core Python + Streamlit — a Smart File Organizer. As a backend engineer with 4+ years in PHP/Laravel and Django, I’ve spent most of my career building APIs, payment systems, and transaction-heavy platforms. But recently, I decided to go deeper into core Python fundamentals — not just frameworks. So I built something simple… but powerful. 🧠 The Problem: We all have messy folders filled with random files: Images (.png, .jpg, .jpeg) Documents (.pdf, .docx, .txt) Videos Audio files And “mystery files” 😅 🛠 The Solution: A Smart File Organizer that: • Accepts a folder path • Scans all files • Detects file extensions • Automatically groups them into folders like: Images Documents Videos Audio Others Built with: Core Python (os, shutil, file handling) Streamlit (for a clean interactive UI) 💡 What I Loved About This Project Seeing how powerful Python’s standard library is Writing logic without depending on heavy frameworks Turning backend logic into a usable interface with Streamlit Building something practical that solves a real everyday problem What’s interesting is this: As a backend developer used to Django and Laravel, this experience reminded me that strong fundamentals > frameworks. Frameworks are tools. Core language knowledge is power. I’m really enjoying this phase of building small but practical tools with Python. More projects coming soon 🚀 #Python #Streamlit #BackendDeveloper #SoftwareEngineering #BuildInPublic #LearningJourney #WomenInTech

A myth in the startup world: “Build the prototype in Python. Rewrite it in Java later.” But here’s the uncomfortable truth. You can prototype just as fast in modern Java. More so in the age of AI. Frameworks like Spring Boot, Micronaut, and Quarkus make it possible to spin up a production-ready backend in hours—not weeks. And you get something very valuable from day one: • compile-time safety • strong architectural boundaries • reliable dependency management • safe large-scale refactoring • excellent performance under concurrency In other words: you avoid prototype debt. Because rewriting systems later is extremely expensive. What I see in many companies instead is a different pattern: They launch with a Python backend. It works well initially. But as the system grows, problems start appearing: • fragile refactoring in large codebases • runtime type errors surfacing in production • performance bottlenecks under high load • dependency conflicts in complex environments At that point, parts of the system get rewritten into JVM services anyway. A more robust approach is often: Hybrid architecture from day one Java services for: • core backend systems • high-throughput APIs • complex business logic Python for: • AI / ML pipelines • data processing • experimentation This gives teams the best of both worlds: Fast experimentation and long-term system stability. Over the past few years I’ve been helping companies evolve Python-heavy systems by: • migrating critical services into Java / Spring Boot • introducing stronger architectural boundaries • stabilizing systems while keeping Python where it makes sense The goal isn’t “Java vs Python”. It’s building systems that still work cleanly five years later. Curious what others have experienced. Have you seen successful systems start in Python and stay there long-term? Or do most systems eventually migrate toward more strongly typed architectures?