Why httpx is the better choice for Python projects that scale

💡 Flask 101: request.args vs request.form — The Real Difference One line of code can save hours of debugging. 👇 ✅ request.args → Data from the URL (GET) username = request.args.get("username") # /login?username=Shubh ✅ request.form → Data from a submitted form (POST) username = request.form.get("username") 🔹 args = comes from the address bar 🔹 form = comes from user input Once you get this, Flask starts to feel effortless. Your frontend and backend finally speak the same language. #Flask #Python #WebDevelopment #CodingTips #BackendDevelopment #LearningJourney

🚀 Python 3.14 has officially arrived this month, and there are three key features that stand out: 🔹 Deferred annotations (PEP 649): Type hints are now evaluated lazily, simplifying forward references and reducing startup costs. 🔹 Official free-threaded support and improved concurrency: The “no-GIL” build is now officially supported, allowing for greater parallelism in CPU-intensive workloads. 🔹 Template string literals (“t-strings”, PEP 750): This new templating syntax enables interception or validation of interpolation at runtime. Additionally, there are several bonus improvements, including smarter error messages, standard library support for multiple sub interpreters, safer debugging hooks, and internal interpreter enhancements (tail-call style interpreter) that promise approximately 10 -15% faster execution in many benchmarks. 💸 FinOps angle: Even a modest 10% runtime gain can translate directly into lower GB-seconds (and $) on Lambda, especially on Graviton. A/B test 3.13 vs 3.14 with the same memory, then right-size using Lambda Power Tuning and trim package size to reduce cold starts. Small duration drops at scale ⇒ double-digit cost savings with no app changes. #python #serverless #finops

The more I build with FastAPI, the more I realize why companies really like it ✨ Auto-generated Swagger Docs ⚡ Insane Speed thanks to async support 🧩 Pydantic models make validation clean and predictable. 🌟 When you understand HTTP exceptions, error handling becomes intuitive. 💫 Folder structure matters, and moving main.py to /app taught me how imports & module paths actually work. Debugging has taught me more than any theory could : 🎯 Wrong JSON casing → 422 error 🎯 Missing comma → JSON decode error 🎯 Shadowing status function → unexpected AttributeError 🎯 In-memory lists resetting after reload → why databases matter Missing package imports → server loading forever All of these issues became "aha!" moments. #learningjourney #python #FastAPI #VisualisingTech

Exploring Local Model Support in GPT-5 Pro Gemini Deepthink Setting Up the Environment for GPT-5 Pro / Gemini Deepthink Prerequisites Before diving into the setup, ensure you have the following installed: Python \(version 3.8 or newer\) pip \(Python package installer\) Git \(for cloning repositories\) Virtual environment \(recommended for Python dependency management\) Create and Activate a Virtual Environment: python -m venv gpt5-env source gpt5-env/bin/activate # On Windows use `gpt5-env\Scripts\activate` Clone the Repository: git clone https://lnkd.in/gQJT92TZ cd gpt5-pro Install Required Packages: pip install -r requirements.txt To run GPT-5 Pro / Gemini Deepthink locally, you need to configure the model settings appropriately. This involves setting up the model's architecture and specifying the path to the pretrained model weights. Download Pretrained Model Weights: ./models/. Configure Model Settings: config.json file in your project directory to include the path to your model w https://lnkd.in/gqq7F2zg

We built distil-localdoc. py - a tool that generates Python docstrings entirely on your laptop - your code shouldn't leave your machine to get documented. In practice, we trained a tiny 0.6B Qwen3 model using knowledge distillation. It runs locally via Ollama and generates Google-style docstrings with LLM quality. Why this matters: - No more IP exposure or compliance headaches - No API costs or rate limits   - Actually works offline We tested it on 250 functions across different domains. It handles async code, error handling, complex parameters: the stuff developers actually write. It's open source. GitHub and model links in the comments.

This week, I revisited how API performance can be improved with just a few small tweaks in backend design and it reminded me how sometimes simple changes make the biggest difference. One thing that stood out was how much database queries impact response time. 🌟 select_related(): it reduces extra queries when fetching related objects. And: 🌟prefetch_related(): it helps when working with many-to-many or reverse relationships. The difference may not look big in small datasets but in real systems, it can reduce dozens of queries per API call. It’s a good reminder that "Writing code is one thing" 👉 Understanding how it executes is where performance comes from If you’ve come across a small optimization recently that made a real difference, I’d love to hear about it. #Python #Django #BackendDevelopment #Performance #LearningJourney #CleanCode

REST is great — until your microservices start talking too much. 🧠 That’s when you bring in gRPC — fast, binary, and built for machines that talk to machines. In this post: 🔹 Why REST slows down internal communication 🔹 How gRPC solves latency with Protocol Buffers 🔹 Python gRPC server + client example 🔹 REST + gRPC hybrid architecture 💡 gRPC is like switching from handwritten letters to instant phone calls — same message, way faster. 💬 Have you ever used gRPC in production or still living the REST life? #SystemDesign #Python #gRPC #FastAPI #BackendDevelopment #Microservices #APIDesign

🚀 FastAPI isn’t just fast by name — it’s fast by design. One of the main reasons FastAPI outperforms many Python frameworks is its asynchronous request handling and Pydantic-based validation. Here’s what happens under the hood 👇 When you define your routes with async def, FastAPI uses Starlette’s event loop to handle multiple concurrent requests without blocking I/O — meaning your API can process thousands of requests per second without adding new threads. Meanwhile, Pydantic handles input validation and type enforcement at lightning speed using pure C under the hood — far faster than the typical Django serializer approach. 🔧 Quick Tip: If you’re serving I/O-bound workloads (like database or API calls), always prefer: @router.get("/users") async def get_users(): return await fetch_users_from_db() and pair it with an async database library like encode/databases or asyncpg. This small shift can easily cut your average response time by 20–30%. 🧠 Takeaway: FastAPI shines not just because it’s modern — but because it’s built on asynchronous design principles that scale naturally. #FastAPI #BackendDevelopment #Python #APIs #Microservices #Scalability #BackendEngineering

JSON vs TOML — Which One Should You Choose? Both are popular configuration and data serialization formats, but they serve slightly different purposes JSON (JavaScript Object Notation): 1. Best for APIs and data exchange between systems. 2. Simple, lightweight, and supported almost everywhere. 3. Lacks comments (which can make configs harder to explain). TOML (Tom’s Obvious Minimal Language) 1. Built for configuration files — clean, human-readable, and organized. 2. Supports comments, arrays, and tables natively. 3. Increasingly used in tools like Python’s pyproject.toml and Rust’s Cargo. Rule of Thumb: Use JSON for data interchange. Use TOML for configuration management. #Python #Developers #JSON #TOML #Programming #DataEngineering #SoftwareDevelopment #OpenSource