Idempotency in APIs: Preventing Duplicate Orders with Idempotency-Key

🐍 Python Developer Nuggets — Day 17 Retries in APIs Why do APIs fail even when everything looks correct? Because failures happen… and your system doesn’t retry smartly. The problem: Request fails once and stops User sees an error Even though it might have worked on retry The solution: Retry the request Add delay between retries Limit number of retries request = call_api() # retried on failure What changes: First attempt → may fail Retry → succeeds Better user experience Fewer visible failures Don’t retry everything Retry only when: Retry when: Network issues Timeout Temporary failures Don’t retry when: Wrong input Validation errors Golden rule: Retry only when failure is temporary Retry turns small failures into success #Python #Django #BackendEngineering #SystemDesign #CleanCode #Performance #DeveloperTips

Docker tip that every Python developer should know: 🐳 Always use multi-stage builds for production Python images. Stage 1 (builder): Install all deps, compile extensions Stage 2 (runtime): Copy only what you need Result: My AI agent image went from 1.2GB to 180MB. Smaller images mean faster deploys and smaller attack surface. Bonus: combine with `uv` instead of pip for 10x faster dependency resolution. What Docker optimization has saved you the most time? #Docker #Python #DevOps #CloudNative #BackendDev

Learn Python web development with our comprehensive guide, covering setting up a development environment, choosing a framework, and building a simple web application with Python https://lnkd.in/gTDFTgpV #PythonWebDevelopment Read the full article https://lnkd.in/gTDFTgpV

If you're building LLM apps in Python - RAG pipelines, agents, graph workflows and you're tired of debugging abstractions you didn't write: https://lnkd.in/d2fGSPkX Async-native. Streaming-first. 2 dependencies. 30 LLM providers behind one interface. 1,500+ tests. Apache 2.0. pip install synapsekit

🚀 Just Built My First API Integration Project in Python! Today I worked on integrating a live Quotes API using Python and the requests library. GITHUB Repo Link:- https://lnkd.in/gdx-b94v The project fetches random motivational quotes from an online API and displays them in real-time. 💡 Key learnings from this project: How to work with APIs and endpoints Handling JSON responses in Python Using response.raise_for_status() for error handling Writing clean exception handling with try-except Saving API data into a file with timestamps 📌 Built Features: Fetch random quotes from API Display quote & author in terminal Save quotes in a text file for future use This small project helped me understand how real-world applications communicate with external services. Next step: Building a GUI-based Quote Generator App 🚀 #Python #APIs #BeginnerProjects #CodingJourney #100DaysOfCode #Developers #LearningByDoing

 I learned about JWT Authentication in Django 🔐 Session Token vs JWT Token Session Token – Server stores the session data and checks it for every request. JWT Token – The token contains the data and is verified by the server, making it more secure and scalable for APIs. The server gives an access token for authentication and a refresh token to get a new access token when it expires. #Python #Django #WebDevelopment #BackendDeveloper #Learning

Your Python app is one bad API response away from a production crash Pydantic fixes that in 5 lines. #Pydantic

Learn Python web development with our comprehensive guide, covering the basics, best practices, and latest trends in the field of python web development https://lnkd.in/gVbkdUhz #PythonWebDevelopment Read the full article https://lnkd.in/gVbkdUhz

🐍 Most Python developers use try/except… but ignore else and finally. That’s a missed opportunity. If you’re only using try/except, your code might be less clear and harder to maintain than it needs to be. The underrated parts: else: runs only if no exception occurs finally: runs no matter what (even after return or errors) Why this matters: ✅ Enforces clear separation between execution logic and failure handling ✅ Reduces the risk of masking hidden bugs due to overly broad try blocks ✅ Guarantees deterministic cleanup of critical resources example: try: file = open('data.txt', 'r') content = file.read() except FileNotFoundError: print("File not found!") else: print("File read successfully!") finally: file.close() # Always runs! #Python #Programming #CodingTips #SoftwareDevelopment #LearnToCode #PythonDeveloper #ExceptionHandling