FastAPI Backend Development Simplified

Struggling with API abuse or unexpected traffic spikes? This guide breaks down rate limiting in a simple, practical way to building scalable systems. Learn key algorithms and real-world use cases, then implement it in FastAPI using middleware or dependency-based limits for efficient request control. #python #fastapi #API #BackendDevelopment #softwareengineer #SystemScalability

I used to think my Python UI code was "fine"—until I tried to write a unit test for it. 🤯 If you've ever felt trapped between Choosing #Streamlit or #Gradio, or if you've struggled with code that’s impossible to test without launching a browser, this article is for you. I've been working on #AgnosticPyUI to prove that we can apply the same clean architecture patterns we use in our backends to our frontend Python code. Decoupling your UI library from your logic isn't just about flexibility; it's about peace of mind. Read more about it in my latest Medium post: https://lnkd.in/eZpjQxqA I'd love to hear your thoughts on this! Which one do you prefer using? #PythonDevelopers #DataScience #Testing #SoftwareEngineering #OpenSource

Just shipped LLMPrice 🚀 A lightweight Python + TypeScript library for LLM pricing lookup. - 2500+ models - Offline-first - Search / compare / CLI - Auto-synced pricing data Built for developers who want pricing data only, without pulling in a heavy LLM stack. Links in first comment. #Python #TypeScript #LLM #OpenSource #AIEngineering #DeveloperTools

14/30 Performance Post 7: Async Processing Nobody likes a loading spinner. If your API is waiting for an email to send or a PDF to generate before responding to the user, you’re creating a bottleneck. Asynchronous Processing is the secret to building "snappy" apps that handle heavy workloads without breaking a sweat. Swipe through to see how to move your slow tasks to the background and keep your main thread fast! #BackendDevelopment #SystemDesign #SoftwareEngineering #Async #RabbitMQ #NodeJS #Python #LearnInPublic

While digging deeper into environment setup, I noticed an interesting difference between Python and Node.js: Python installs packages globally by default. Unless you create a virtual environment, all dependencies go into a shared global space. Node.js installs packages locally by default. Every project gets its own node_modules directory. At first, this made me think: → Python focuses more on isolation and optimization → Node.js doesn’t care as much But that’s not completely true. Both ecosystems solve the same problem — dependency management — but in different ways: Python → requires you to explicitly create isolation (virtual environments) Node.js → gives you isolation by default (per-project dependencies) Trade-offs: Python → cleaner environments, but extra setup Node.js → easier start, but larger project size and duplication Key insight: It’s not about which is better — it’s about understanding the design decisions behind each ecosystem. This kind of detail matters when you switch between stacks or design scalable systems. #Python #Nodejs #Backend #SoftwareEngineering #LearningInPublic

By far one of the books that most changed how I see backend applications in Python. Architecture Patterns with Python (Cosmic Python) helped me move from code that just works to thinking about building systems that are actually robust and scalable. I’ve been applying a lot of the ideas from this book in my day to day work, and it made a real difference in how I design and structure backend services. If you’re a Python developer and want to level up, I highly recommend it. Free here: https://lnkd.in/d3CWyWiX

Python Web Apps Cut Memory Use by 31% via Architectural Optimization 📌 Python web apps slashed memory use by 31% through smart architectural tweaks-cutting 3.2GB of RAM from a 16GB server by swapping sync frameworks for async ones, isolating imports, and ditching heavy ORMs. The results? Faster throughput, denser deployments, and less hardware strain-all without code refactoring. 🔗 Read more: https://lnkd.in/dEEMwHdu #Python #Memoryoptimization #Webapps #Architectural #Cluster

Python 3.4 Sets New Standard for High-Performance Concurrent Systems 📌 Python 3.4 cracked the code on high-performance concurrency by introducing asyncio, turning Python into a powerhouse for scalable web systems. With standardized async I/O and unified file paths via pathlib, it erased callback hell and messy string manipulations. Though retired in 2019, its architecture still shapes modern Python frameworks - proving that one release can redefine an entire ecosystem. 🔗 Read more: https://lnkd.in/dbW2_R5P #Python34 #Concurrency #Standardlibrary #Highperformance #Concurrentsystems

Bringing Python-powered AI directly into Node.js apps ⚡ Built PyCall-Node — a package that enables seamless interaction between Node.js and Python, with automatic data conversion and efficient execution (no more heavy subprocess overhead 🚀). Perfect for integrating ML models into real-time web applications. 🔗 NPM: https://lnkd.in/g4Z5Gprn 🌐 Demo: https://lnkd.in/gytqStUs Would love your feedback! #AI #MachineLearning #NodeJS #Python #OpenSource #FullStack #BuildInPublic

Deploying a FastAPI or modern Python web app soon? Don't leave your production performance up to chance. ⚙️🚀 The "Gunicorn vs. Uvicorn" debate comes up all the time, but the truth is, you often need both to build a fast, reliable, and scalable backend. I put together this quick breakdown of how they differ and how to combine them for the ultimate production setup. Swipe through to optimize your next deployment! 👉 What server setup are you currently using for your Python apps? Let's discuss in the comments! 👇 #Python #FastAPI #BackendDevelopment #WebDevelopment #SoftwareEngineering #DevOps #Uvicorn #Gunicorn