Built a Python + PyQt5 text editor with Google Drive sync

⚙️ Master REST APIs with Python in 2025 APIs are the backbone of the modern web — from login systems to AI integrations. If you know how to build and use REST APIs, you can connect anything to everything 🌐 Here’s what you can do with Python + REST APIs: 1️⃣ Build APIs — with Flask or FastAPI 2️⃣ Consume APIs — using the Requests library 3️⃣ Secure APIs — with JWT & OAuth2 4️⃣ Document APIs — using Swagger / OpenAPI 5️⃣ Deploy APIs — on Render, AWS, or Heroku 💡 Pro Tip: Start small — build a simple “To-Do List” API and connect it to a frontend later! APIs make your apps talk to the world 🌎 💬 Have you ever built or used a REST API? #Python #API #RESTAPI #WebDevelopment #FastAPI #Flask #BackendDevelopment #SoftwareEngineering #LearningJourney #Programming

The library you learn first isn't the one you scale with. httpx picks up where Requests stops-async, HTTP/2, the works. Requests works great for getting started, but httpx is where serious Python projects are heading. The difference becomes obvious once you hit real-world scaling challenges. Here's what makes httpx worth the switch: • Dual mode support: Same API for both sync and async requests. No more juggling different libraries when you need concurrent HTTP calls. • HTTP/2 protocol: Built-in support means faster, more efficient connections without extra setup. • Better connection pooling: Advanced timeout controls and resource management that actually matter under load. • Drop-in compatibility: The API feels familiar if you know Requests. Migration is straightforward. Requests handles simple scripts just fine. But when you're dealing with hundreds of concurrent requests or integrating multiple third-party APIs efficiently, httpx's async support becomes the difference between a system that works and one that performs. The performance gains in high-concurrency scenarios are substantial. Plus, you're future-proofing your HTTP client layer instead of painting yourself into a corner. For new projects that need to scale, httpx is the modern choice. For quick scripts, Requests still gets the job done. #Python #BackendDevelopment #AsyncProgramming

I built SkillBridge to move beyond simple LLM prompts, demonstrating a production-ready Retrieval-Augmented Generation (RAG) architecture. This full-stack application generates hyper-personalized learning roadmaps by vectorizing real job data and performing semantic search using PostgreSQL + pgvector. The Google Gemini API consumes the retrieved context (skill gaps) to output actionable, step-by-step guidance. The application is built for performance: Python FastAPI handles asynchronous backend operations, while Next.js 14 (TypeScript) ensures a robust and scalable frontend. Live demo link: https://lnkd.in/eJtSi77U #RAG #VectorSearch #GeminiAPI #FastAPI #NextJS #FullStack #Python #SoftwareEngineering

🚀 Day 3 of Codematic Technology Services Hackathon with Google Cloud Today, I explored FastAPI — one of the fastest-growing Python frameworks — to handle data processing and analysis from PDF files that will power my project’s backend (integrated later with NestJS). I learned how to: ✅ Build and structure API routes ✅ Use Pydantic models for validation ✅ Apply async/await for high-performance data tasks ✅ Leverage built-in Swagger UI for documentation FastAPI’s simplicity and power make it perfect for the data-processing layer of my microservice architecture. Read my full blog here 👉 https://lnkd.in/diAUdAnF #BuildwithGoogle #BuildwithCodematic #FastAPI #Hackathon #GoogleCloud #BackendDevelopment #Python #NestJS #VertexAI #BuildwithFortunePrecious

🚀 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

🚀 Python 3.14 is Here – And It’s FAST! 🔥 The latest benchmarks are in: #Python314 delivers its fastest CPython experience yet, with up to 27% speedups over 3.13 in key workloads! 🏎️ Key takeaways for devs & tech leads: 🚀 Single-threaded code runs up to 27% faster than 3.13 (benchmarks: fib(40), bubble sort). 🔥 The new Free-Threading (FT) interpreter in 3.14 makes multi-threaded CPU-bound code 3x faster than standard builds—finally, multi-core Python really shines! 🧑💻 Experimental JIT compiler is available, but don’t expect major speedups just yet—it’s still maturing. 💡 PyPy continues to be the speed champion in pure Python, but for day-to-day dev, CPython 3.14 is now the performance default. 🛡️ Python 3.14 also brings better async, memory management, and security for business/AI/automation apps. Big thanks to the #Python community for raising the bar for performance and developer experience! If your stack is still on 3.10 or earlier, this is your sign to plan that upgrade. More details: Miguel Grinberg’s deep-dive benchmarking post & the official #Python314 release notes are must-reads for engineers watching Python’s future speed trajectory. Are you upgrading to Python 3.14? Drop your benchmark results or migration tips below! ⬇️ #Python #Programming #DevCommunity #Release #OpenSource #Performance #AI

⚡ 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

💡 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 and API Design , Simplicity with Structure In backend systems, Python plays a central role in building scalable and maintainable APIs. Frameworks like FastAPI, Flask, and Django REST Framework simplify request handling, routing, and serialization. Modern API design in Python focuses on asynchronous execution, dependency management, and layered architecture, ensuring clean and efficient service communication. #SystemDesign #Python #API #FastAPI #Flask #DjangoREST #BackendDevelopment #SoftwareArchitecture #AsynchronousProgramming #DistributedSystems