FastAPI vs Node.js for REST APIs

You don't need Node.js for real-time applications. This is the assumption I challenged when I required WebSocket support in a Django project. The common advice suggests adding a Node service for the real-time components, which introduces a second language, a second deployment pipeline, and an additional point of failure. Django Channels combined with Redis demonstrated that this approach is unnecessary. Here’s the setup: - Daphne replaces Gunicorn as the ASGI server, managing both HTTP and WebSocket on the same process. - ProtocolTypeRouter effectively splits traffic: HTTP requests are directed to Django, while WebSocket connections are handled by Channels consumers. - Redis acts as a message broker and serves as the channel layer, enabling pub/sub functionality across all connected consumers. - Consumers are async Python classes with methods like receive(), group_send(), and disconnect(). The outcome is that a message sent by one client reaches Redis, propagates to every consumer in the group, and connects with all clients, without leaving the Python ecosystem. No Node. No socket.io. No separate service to maintain. Everything operates within the same Docker container as the rest of the backend, utilizing the same codebase, deployments, and logs. Sometimes, the seemingly boring choice is actually the most intelligent one. #django #python #webdev #backend #software #architecture

I just shipped something I’ve been building for a while BackendKit is officially live on npm! 🚀 It's a CLI scaffolding tool designed to get you from zero to a running, production-ready backend API in under 60 seconds. What’s under the hood? ✅ Massive Variety: 46 templates across NestJS, Express, FastAPI, Django, and Flask. ✅ Database Ready: Instant setup for PostgreSQL, MySQL, and MongoDB (via Prisma, TypeORM, SQLAlchemy, or native drivers). ✅ GraphQL Support: 9 dedicated templates specifically for NestJS. ✅ Universal Workflow: bkitdev dev handles hot reloading for both Node.js and Python projects. ✅ Instant Scaffolding: bkitdev generate <name> to add modules, routes, or models to existing projects. ✅ Robust Safety: Built-in .env validation that warns you about missing keys before they break your app. No config to write. No boilerplate to copy. Just pick your stack and start building. Try it out: npm create bkitdev@latest I’d love some feedback from the community what stack combinations are you reaching for most these days? #opensource #nodejs #python #nestjs #fastapi #backend #developer #typescript #cli #npm

"Does the following code look malicious?" We asked an LLM that. The answer was useless. We tested AI generated code with tools like Claude and Codex across 7 frameworks: Flask, Django, Rails, SpringBoot, ExpressJS, .NET, and Laravel. It got the obvious right: SQL injection, parameterized queries, ORMs. But it dropped context-specific protections: CSRF tokens missing on POST forms, unscoped queries creating IDORs, hardcoded fallback secrets shipping to production. Telling it to “write secure code” or to review its own output didn’t fix it. Longer prompts actually made it worse. ✅ Here’s what improved the results: >> Be explicit, not generic Define what “secure” looks like in your stack. Use examples. >> Use framework-specific references Show exactly how CSRF, secrets, and redirects are implemented; don’t just say that they matter. >> Keep prompts tight More context didn’t help. Focused inputs performed better. >> Treat it as iterative Fix one class of issues, rerun, refine. New gaps will appear. >> Validate downstream SAST and manual review still catch what generation misses. Full breakdown in the carousel. 👇

You'll want to see these practical tips to help you prompt the most secure AI generated code. 👇 Read the full post at https://lnkd.in/gQZK7DHP

I've built production backends with both FastAPI and Express.js. Here's my no-BS comparison for 2026. FastAPI (Python): → Auto-generated API docs (Swagger/ReDoc) — zero extra work → Type validation with Pydantic — catch errors before they hit your DB → Async by default — handles concurrent requests beautifully → Perfect for ML/AI backends (Python ecosystem) → 3x less boilerplate than Flask Express.js (Node.js): → Massive ecosystem — middleware for everything → Same language as frontend (JavaScript/TypeScript) → Websocket support is more mature → Easier to find developers who know it → Battle-tested at massive scale (Netflix, PayPal) My decision framework: Choose FastAPI when: • Your app involves ML models or data processing • You need auto-generated documentation • Type safety is non-negotiable • Your team knows Python Choose Express.js when: • Full-stack JS/TS is your goal • Real-time features are core (chat, live updates) • You need maximum middleware flexibility • Your team is JavaScript-first My current default? FastAPI for AI-heavy backends. Express for everything else. What's powering YOUR backend in 2026? . . . . #FastAPI #ExpressJS #Python #NodeJS #BackendDevelopment #APIDesign #WebDevelopment #TypeScript #Pydantic #SoftwareEngineering #FullStack #REST #WebFramework #TechComparison #Programming #DevCommunity #AsyncProgramming #MLOps #BuildInPublic #TechStack2026

Async ≠ Non-Blocking (A common mistake I see in FastAPI apps and with Django async views) As backend engineers, we often assume that using async def automatically makes our APIs scalable and non-blocking. But here’s the catch Using blocking code inside async functions can freeze your entire server. The Problem @app.get("/blocking-bad") async def blocking_bad_behavior(): time.sleep(10) # Blocks the event loop! Even though this is an async function, time.sleep() blocks the event loop, meaning: * No other requests are processed * Your API appears “down” for those 10 seconds * No Concurrency The Correct Approach @app.get("/blocking-fixed") async def blocking_fixed(): await asyncio.to_thread(time.sleep, 10) # Offloaded to thread Now: * Event loop stays free * Other requests are handled instantly * True concurrency Even a normal def works safely in FastAPI: @app.get("/normal-def") def normal_def_endpoint(): time.sleep(8) # Runs in thread pool automatically FastAPI smartly runs it in a thread pool — so your event loop is still safe. Key Takeaways async def does NOT guarantee non-blocking behavior Avoid blocking calls like time.sleep(), heavy CPU tasks, or sync I/O Use: * await asyncio.sleep() for async waits * asyncio.to_thread() for blocking work * or stick to def when appropriate Final Thought Async is powerful — but only when used correctly. Misusing it can be worse than not using it at all. If you’re building high-performance APIs with FastAPI, this is something you cannot ignore. #FastAPI #Python #AsyncIO #BackendDevelopment #SystemDesign #Concurrency #SoftwareEngineering

Every language ecosystem that scales eventually adopts frameworks and reusable components. Every single one. JavaScript got React and Next.js. Python got Django and Flask. Ruby got Rails. Go got standard library patterns and well-known packages. Rust got crates.io. Python developers reach for Django because it encodes years of web development knowledge. React exists because managing UI state at scale benefits from shared patterns. The value is in the accumulated experience, not just the syntax. Infrastructure is no different. Declarative languages benefit from logical reductions just as much as imperative ones. A Terraform module is a logical reduction. It takes a complex set of resources, encodes opinions about how they should work together, and presents a clean interface. This isn't about limiting flexibility. It's about encoding knowledge. When a VPC module handles IPv6 dual-stack, flow logs, transit gateway attachments, and AZ capacity constraints, that's hundreds of hours of production experience distilled into a reusable component. The next team that needs a VPC doesn't need to rediscover those edge cases. The pattern is universal: ecosystems that scale adopt packages, frameworks, and shared components. Infrastructure has reached the scale where this isn't optional anymore. Platform teams who adopt this pattern build on foundations. The ones who've made this shift tell us the biggest change is how much faster new engineers become productive.

FastAPI vs Django: Which Wins in 2026? The best framework isn’t the one with the most features—it’s the one that solves your specific bottleneck. If you’re choosing for your next project, here’s a breakdown: Why the industry is shifting to FastAPI - Performance: Built on Starlette and Pydantic, it’s one of the fastest Python - - frameworks. Ideal for high-concurrency or I/O-bound tasks. - Developer velocity: Automatic OpenAPI (Swagger) docs and Python type hints reduce time spent on documentation. - Modern stack: Designed for microservices, AI/ML deployments, and modern frontends like React or Next.js. Why Django isn’t going anywhere - Batteries included: Comes with admin panel, authentication, and ORM out of the box. - Security: Built-in protection against common web vulnerabilities. - Stability: Strong structure for large-scale monoliths and enterprise applications. The verdict Use FastAPI if you want a high-performance engine for modern APIs or microservices. Use Django if you need a fully equipped framework for complex, data-heavy applications. Which side are you on? Are we moving toward a “FastAPI-first” world, or does Django’s ecosystem still reign supreme? #Python #WebDevelopment #FastAPI #Django #SoftwareEngineering #Backend #CodingTips

🚀 Master RESTful APIs: From Zero to Production API design is one of the most critical skills for any backend developer, yet it’s easy to get the "rules" tangled up. Whether you are working with Python, Flask, or any other framework, these core principles remain the same. I’ve put together a comprehensive breakdown of the RESTful API Masterclass cheat sheet. Here is what you need to know to build professional-grade interfaces: 🔹 The Anatomy of REST: It’s not just a library; it’s an architectural style based on Representational State Transfer. 🔹 Verbs matter: Use GET for reading, POST for creating, PUT for full replacements, and PATCH for partial updates. 🔹 Statelessness is key: Every request must contain all the information the server needs—no "guessing" based on previous interactions. 🔹 Clean URLs: Use plural nouns (/books) instead of verbs (/getBooks). Keep nesting shallow to stay readable. 🔹 Security: Transition from simple API Keys for server-to-server talk to JWT (JSON Web Tokens) for modern, scalable auth. Pro-Tip from the guide: Always keep your v1 running while launching v2. Never make breaking changes without versioning!. Check out the full cheat sheet attached below for a deep dive into status codes and Flask implementation. ⬇️ #Python #Flask #WebDevelopment #API #Backend #CodingTips #RESTAPI

Last week at work, while building e2e tests for a feature in a NestJS project, I ended up discovering Testcontainers, and it was one of those tools that immediately felt practical. The idea is simple: instead of mocking every dependency, you spin up real services in disposable Docker containers during the tests. In my case, I used it to create MongoDB and MySQL-compatible instances, which made the tests feel much closer to how the application actually behaves in a real environment. What I found especially interesting was performance. With the raw images, startup was quite slow, taking around 30 seconds, but after switching to MariaDB instead of the regular MySQL image and using Mongo with ephemeralForTest, startup time dropped to around 10 seconds, which made the feedback loop much better while developing and rerunning the suite. For unit testing, I still see in-memory versions or mocking as the best fit, because the goal there is usually speed, isolation, and focus. But for integration and e2e testing, Testcontainers feels like a great alternative, because it gives much more confidence that the application really works when talking to real infrastructure. I also like that this approach fits well in ecosystems like Node.js and Python, while still supporting other platforms too. After using it last week, it definitely became one of those tools I want to explore more deeply. #Testcontainers #NestJS #IntegrationTesting #E2ETesting #NodeJS #Python #BackendDevelopment #SoftwareEngineering #Testing