Madhana Gopal Thirunavukkarasu’s Post

Python is like that high school ex you run into: you remember the good times, but five minutes in, you're reminded exactly why you moved on. 🐍 After being fully immersed in the TypeScript ecosystem, jumping back into a Python project felt... weird. Going from the safety of strict typing and seamless ESM imports back into the land of manual labor was a wake-up call. Here is the "Welcome Back" package I didn't ask for: The Venv Ritual: Why am I still manually creating and activating virtual environments in 2026? It feels like hand-cranking a car engine just to go to the grocery store. 🛠️ The "Invisible" Packages: That moment when uv list shows the package is there, the interpreter is set correctly, but Python still insists it doesn't exist. Ghosting at its finest. 👻 Manual Everything: Coming from a world where the tooling feels like it has your back, Python's setup feels like it's actively trying to trip you up over a stray .env or a path mismatch. Don't get me wrong, Python is a powerhouse for AI and Data Science, but the developer experience gap is becoming hard to ignore. When you're used to the speed of tools like Bun or the reliability of TS types, those "silly" import errors feel a lot less like a minor bug and a lot more like a tax on your productivity. Has anyone else felt this "syntax shock" lately, or have I just been spoiled by the TS ecosystem? #SoftwareEngineering #Python #TypeScript #WebDevelopment #FullStack #CodingLife

Great summary! I’ve always seen Java and Python as complementary rather than rivals. Java is my go-to for performance-critical heuristics where every millisecond counts, thanks to its rigid structure. But for rapid modeling and solver integration, Python is unbeatable. My approach is to treat Python with a "Java mindset," using type hinting and strict docstrings to ensure that rapid prototyping doesn't turn into technical debt later on.

#GenAI #RagforJAVA Few months ago I tried to build a RAG system in Java. I found 47 Python tutorials and exactly zero complete Java examples. So I figured it out the hard way : and wrote down everything I learned. Here's what nobody tells you about RAG in Java: The concept is simple. Two pipelines: #Ingest your docs (chunk → embed → store) #Answer questions (embed query → retrieve → prompt → LLM) The implementation is where it gets interesting. 𝗖𝗵𝘂𝗻𝗸𝗶𝗻𝗴 matters more than the model. I've seen teams obsess over GPT-4 vs Claude while their chunks are 2000 tokens with no overlap. Fix chunking first. 𝗬𝗼𝘂 𝗱𝗼𝗻'𝘁 𝗻𝗲𝗲𝗱 𝗮 𝗳𝗮𝗻𝗰𝘆 𝘃𝗲𝗰𝘁𝗼𝗿 𝗗𝗕 𝘁𝗼 𝘀𝘁𝗮𝗿𝘁. If you already use PostgreSQL, install pgvector. One extension. You're done. 𝗦𝗽𝗿𝗶𝗻𝗴 𝗔𝗜 𝗵𝗮𝘀 𝗺𝗮𝘁𝘂𝗿𝗲𝗱 𝗮 𝗹𝗼𝘁. VectorStore, EmbeddingModel, ChatClient — it's actually pleasant to work with now. 𝗘𝗺𝗯𝗲𝗱𝗱𝗶𝗻𝗴𝘀 𝗮𝗿𝗲 𝗰𝗵𝗲𝗮𝗽. 𝗚𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝗼𝗻 𝗶𝘀 𝗻𝗼𝘁. Stop worrying about embedding costs. Watch your LLM token usage instead. 𝗦𝗶𝗺𝗶𝗹𝗮𝗿𝗶𝘁𝘆 𝘁𝗵𝗿𝗲𝘀𝗵𝗼𝗹𝗱𝘀 𝗮𝗿𝗲 𝗺𝗮𝗻𝗱𝗮𝘁𝗼𝗿𝘆. Without one, your LLM gets handed irrelevant junk and hallucinates confidently. Always set withSimilarityThreshold(). I wrote a full guide: architecture diagrams, working Spring Boot code (IngestionService, RagService, REST controller), pgvector setup, chunking strategies, advanced patterns like HyDE and reranking, and a production checklist. All Java. No Python. #Java #SpringBoot #RAG #GenerativeAI #BackendEngineering #LLM

Most teams do not reach for #PyFlink because Python feels nicer. They reach for it after paying the production cost of splitting one ML system across two ecosystems: Python for training, Java for prediction, and months lost to subtle feature mismatches, latency, and debugging. That is the real adoption driver. New on ML-Affairs: "If the real source of friction in your system is that your training, feature logic, and model-adjacent code live naturally in Python, then "just use Java #Flink" is not a neutral suggestion. It is an architectural trade, and often an expensive one."

𝐉𝐚𝐯𝐚 𝐯𝐬 𝐏𝐲𝐭𝐡𝐨𝐧: 𝐖𝐡𝐢𝐜𝐡 𝐨𝐧𝐞 𝐢𝐬 𝐛𝐞𝐭𝐭𝐞𝐫 ? One of the most debated questions in programming. Both languages aim to do the same thing: turn human-written code into a working program. But the journey from writing code to running it is where they differ. 🔷 𝐉𝐚𝐯𝐚’𝐬 𝐰𝐨𝐫𝐤𝐟𝐥𝐨𝐰 Java follows a structured, multi-step process. → You write code in a .java file → The javac compiler converts it into bytecode (.class) → The Java Virtual Machine (JVM) executes that bytecode → The same program runs across different operating systems This is why Java is known for 👉 “Write Once, Run Anywhere.” 🔷 𝐏𝐲𝐭𝐡𝐨𝐧’𝐬 𝐰𝐨𝐫𝐤𝐟𝐥𝐨𝐰 Python takes a more direct and flexible approach. → Code is written in a .py file → Python compiles it into bytecode → The Python Virtual Machine (PVM) executes it → Dynamic typing and libraries are handled during runtime This is why Python is often described as: 👉 “Write Fast, Run Instantly.” 𝐈𝐧 𝐬𝐢𝐦𝐩𝐥𝐞 𝐭𝐞𝐫𝐦𝐬 🧑💻 𝐉𝐚𝐯𝐚 → compiled + interpreted → strongly typed → optimized for performance and large systems 🧑💻 𝐏𝐲𝐭𝐡𝐨𝐧 → interpreted execution model → flexible and beginner-friendly → faster for development and experimentation Neither language is “better”. They are designed for different goals and different types of problems. Java powers many enterprise systems and large scale applications. Python dominates in data science, AI, automation, and rapid development. The real question is not which language is better. It’s which language is better for the problem you’re solving. ⁉️ If you had to pick one for your daily work, which would it be: Java or Python? ♻️ Repost if this helped you learn something new about data analysis tools 🔔 Follow Abhisek Sahu for more insights on AI, data, and tech tools ♻️ I share cloud , data analysis/data engineering tips, real world project breakdowns, and interview insights through my free newsletter. 🤝 Subscribe for free here → https://lnkd.in/ebGPbru9 #Programming #Java #Python #SoftwareDevelopment #Coding #Developers

Python APIs don't have to be messy. FastAPI + Pydantic changed how I think about backend development. Here's what makes this stack production-ready from day one: ✅ Type-safe request & response models via Pydantic ✅ Automatic input validation — no manual checks needed ✅ Auto-generated OpenAPI / Swagger docs, always in sync ✅ Blazing-fast serialization with Pydantic v2 (Rust core) ✅ Async support out of the box for high-concurrency workloads ✅ Clean dependency injection system for services, DB sessions, auth The real superpower? Your schema IS your documentation IS your validation IS your serializer. One source of truth. This reduces the gap between what your API contract promises and what it actually delivers — which is exactly what you want in production. Whether you're building REST APIs, GenAI tool backends, or internal services, FastAPI + Pydantic gives you the developer experience of modern TypeScript frameworks — but in Python. Have you used FastAPI in production? What's your experience been? #FastAPI #Pydantic #Python #APIDesign #BackendEngineering #GenAI

Strands AI Functions for system prompting feels a bit like Spring Boot for Java. Instead of wiring everything through prompts, you define functions with clear inputs and outputs, error handling, response basic validation and let the model operate within that structure. It doesn’t make things deterministic as you can’t reliably validate semantics without heavy logic, but it definitely makes them more manageable from an engineering point of view. Thanks to Chamika Bandara for pointing me to this, worth exploring. https://lnkd.in/g53ARJ4G

Building High-Performance APIs with Python + FastAPI Over the past few days, I’ve been exploring FastAPI, and it’s impressive how quickly you can build modern, high-performance APIs with it. Why FastAPI is becoming a popular choice among developers: High Performance – One of the fastest Python frameworks, comparable to NodeJS and Go. Automatic API Docs – Built-in interactive documentation with Swagger UI. Type Hints Powered – Uses Python type hints for validation and better developer experience. Async Support – Perfect for scalable, high-concurrency applications. Easy Integration – Works seamlessly with databases, ML models, and microservices. For developers working with AI, ML, or microservices, FastAPI is a powerful tool to expose models and services through clean REST APIs. I’m currently experimenting with Python + FastAPI for building scalable backend services and AI-powered applications. #Python #FastAPI #BackendDevelopment #APIs #SoftwareDevelopment #Programming #WebDevelopment #AI

I’ve been a Java fan for years—and after JavaOne, I’m more excited than ever about where it’s heading 🚀There’s a common narrative that AI belongs to Python. But what I saw at JavaOne tells a different story: Java is becoming a serious contender for real-world AI systems. Java isn’t trying to replace Python in AI—it’s carving out its own space where reliability and scale matter most.Curious to see how far this goes—but one thing is clear: Java + AI is no longer a question mark.

Many engineering teams are looking at language migration as part of modernizing their platforms. This could mean upgrading Python, moving to a modern Java framework, or shifting JavaScript stacks. These efforts are important, but they can be complex and teams often worry about breaking existing functionality or slowing down releases. AI is starting to change how teams approach this work. It can analyze large codebases, identify patterns, generate an initial version of converted code, and expand test coverage to validate behavior after the migration. This helps teams move faster while keeping releases stable. If language migration is on your roadmap, I would be happy to connect you with one of our experts for a short 30 minute discussion. #AI #SoftwareEngineering #QualityEngineering #TechModernization