How to Master REST APIs with Python in 2025

I’m excited to share a modern, feature-rich text editor I built with Python + PyQt5 — think Notepad, but with syntax highlighting, themes, and Google Drive sync. Key features: file operations (New/Open/Save), advanced Find & Replace, customizable toolbar, Light/Dark themes, multi-language syntax highlighting, auto-save, and seamless Google Drive integration for cloud sync. Repo & install instructions are in the README Github repo: https://lnkd.in/gtX-_ie4 #opensource #python #pyqt #desktopapps #productivity #devtools #googledrive #softwaredeveloper

🚀 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

Consuming RESTful APIs with Python, then visualizing the received JSON data with JavaScript, is one of the most valuable skill sets I rely on every day. On a nightly basis I build working prototypes for various APIs, in order to find hidden force multipliers; pockets of exponential value. For example, tonight I worked with two marketplace APIs. Such as in the application screenshot attached to this post. I am also combining my love for open source intelligence (OSINT) into this mix. Which involves using locally deployed Mistral AI models to summarize large quantities of recent topical news stories and relevant YouTube video transcripts. All sourced from APIs as well. Right now is an especially fortuitous time to be able to work with data derived from RESTful APIs. Because there are a lot of valuable, niche data sources to choose from. And it seems surprisingly few are acting on the value these tools represent when brought together. That is the opportunity I see at the moment for software engineers.

💡 How we support 5 incompatible Python libraries in one platform ? The Problem: I wanted to support Elasticsearch versions 5 through 9 in a single SaaS platform, ElasticDoctor. Each version needs a different Python client library, and they're mutually incompatible. Install them together? Dependency hell. Build separate apps? Code duplication nightmare. The Solution: What if we isolated dependencies at the infrastructure level, not the code level? Our Solution: 🐳 5 Docker containers (one per ES version) 📦 Each with isolated, version-specific dependencies 🔄 All sharing the same diagnostic logic (22+ health checks) 🎯 Smart gateway that routes requests to the right container Architecture in 3 layers: 1️⃣ API Gateway detects ES version 2️⃣ Routes to correct service (ES5→:8005, ES6→:8006, etc.) 3️⃣ All services import shared diagnostic engine Result: ✅ Support for ES 5.x through 9.x ✅ 90%+ code reuse across versions ✅ Zero dependency conflicts ✅ Easy to scale and add new versions Sometimes the best solution isn't changing your code, it's changing where your code runs. 🔗 Want to see it in action? Try ElasticDoctor for free: https://elasticdoctor.com (See architecture diagram below 👇) #Docker #Microservices #SoftwareEngineering #Elasticsearch #TechArchitecture #DevOps #Python #Containerization #SoftwareArchitecture #CloudComputing #SaaS #FastAPI #NextJS #SystemDesign #TechInnovation #DeveloperTools #Backend #FullStack #WebDevelopment #TechLeadership

Why FastAPI is Becoming the Go-To Framework for Modern Python APIs If you’ve been working with Python for a while, you’ve probably noticed an important shift: APIs are now at the core of most applications. FastAPI is becoming a preferred choice for many developers and teams, and here is why it stands out: 1. High Performance FastAPI is built on ASGI and supports asynchronous programming, which allows it to deliver performance comparable to Node.js and Go. In modern applications, speed is not a luxury anymore; it is a necessity. 2. Built-in Interactive Documentation As soon as you start your FastAPI application, you get clean, interactive documentation generated automatically through Swagger and ReDoc. No additional configuration, no manual setup. 3. Strong Data Validation FastAPI uses Pydantic for data validation and type management. This reduces bugs, improves clarity, and ensures more reliable APIs without extra work. 4. Aligned with Modern Python Standards FastAPI embraces type hints, async functionality, and clean, readable code. It feels natural for developers who appreciate clarity and structure. 5. Scales Effectively Whether you are building a small personal project or a large microservices architecture, FastAPI scales smoothly. It is trusted in production environments by companies such as Microsoft, Uber, and Netflix. If you are a Python developer looking to build APIs with less boilerplate, better structure, and faster development time, FastAPI is worth exploring. #FastAPI #Python #APIs #PythonDevelopers

🚀 Unlock the power of distributed DynamoDB rate limiting! Harness Amazon S3 & Python Boto3 to smartly adjust request rates in your multi-client apps. Say goodbye to throttling issues with the dynamic #DynamoDBMonitor class. Efficient and scalable, this AWS-native solution is perfect for managing consumption across nodes. Dive into adaptive control without extra dependencies! 🌐 #AWS #DynamoDB #Python #CloudInnovation #TechLeaders

🚀 Unlock the power of distributed DynamoDB rate limiting! Harness Amazon S3 & Python Boto3 to smartly adjust request rates in your multi-client apps. Say goodbye to throttling issues with the dynamic #DynamoDBMonitor class. Efficient and scalable, this AWS-native solution is perfect for managing consumption across nodes. Dive into adaptive control without extra dependencies! 🌐 #AWS #DynamoDB #Python #CloudInnovation #TechLeaders

⚡ Shipped: Full-Stack Milestone Tracker with Smart Analytics Just deployed a production-ready learning management dashboard with some interesting technical features: 🔧 Technical Highlights: • RESTful API with FastAPI (async/await patterns) • MongoDB aggregation pipeline for real-time metrics • JWT-based authentication system • Auto-backup scheduling with APScheduler • Docker containerization for consistent deployments • CI/CD via GitHub Actions → Render • Keep-alive workflow to maintain uptime on free tier 📊 Features: • Dynamic question-to-milestone mapping algorithm • 6-stage progress tracking (Ready → Complete) • Chart.js visualizations + Excel/PDF exports • Responsive UI with dark mode • Admin panel for CRUD operations 🌐 Live: https://lnkd.in/dxvsWFaA 📚 Stack: Python 3.11 | FastAPI | Motor (async MongoDB) | Vanilla JS | Chart.js Built initially for personal use at IITian Academy Milestone Tracker. Planning to generalize it for wider adoption! Open to feedback from the dev community! What would you add? 💬 #FastAPI #Python #MongoDB #FullStack #WebDevelopment #DevOps #GitHubActions #Docker #APIs #BackendDevelopment #iitian