FastAPI for Python Backend Development

FastAPI has become the standard for building high-performance Python backends. It successfully combines the developer experience of a lightweight framework with the speed required for modern, asynchronous applications. Here is why it is a go-to for production-ready systems: • Performance: Built on Starlette and Pydantic, it is one of the fastest Python frameworks available. • Efficiency: Features like automatic OpenAPI (Swagger) docs and built-in data validation reduce boilerplate significantly. • Async Support: Native support for asynchronous programming makes it ideal for real-time apps and ML model deployment. • Reliability: Leveraging Python type hints ensures fewer bugs and better editor support during development. Whether you are architecting microservices or a simple REST API, FastAPI provides the scalability and speed that modern software demands. Are you still using Flask for your initial prototypes, or have you made the full switch to FastAPI? . . . #Python #FastAPI #Backend #WebDev #Microservices #Coding

🚀 Designing scalable APIs is more than just writing endpoints — it's about building systems that can handle real-world load efficiently. Checkout my new blog on designing production-ready REST APIs in Python 👇 https://lnkd.in/gHH5gJhx A good reminder to always think about scalability, reliability, and performance from day one. #Python #APIDesign #BackendDevelopment #SystemDesign #SoftwareEngineering

agentpad is live. A multi-language runtime for AI agents that actually runs your code — bash, Python, JavaScript, SQL - against a real project directory. Not a sandbox pretending to be one. Most agent runtimes fake the environment. agentpad doesn't. You get full control over what the agent can touch, what it can write, and exactly what happened when it's done. > Overlay mode to stage changes before they stick. > Read-only mode when writes aren't allowed. > Glob allowlists and timeouts so nothing runs wild. > Structured output every time - stdout, stderr, exitCode, files written. > A full session run log so nothing is a black box. > OpenAI tool helpers so you're not rewriting the function-calling loop from scratch. Just run npm install agentpad Or pip install agentpad Website/Docs: https://slaps.dev/agentpad #openSource #aiAgents #developerTools #typescript #python

When I was building agents-js and Slapify, the browser side was only half the story. The other half was always: run this in the repo - a script, a test, a SQL check, a one-off patch - and get back something structured I could log, replay, or hand to the next step. I didn't want another bespoke subprocess wrapper per project. agentpad is that layer. Bash, Python, Node, SQL against a real working tree, with timeouts, glob allowlists, and a clear result object every time. Same idea in TypeScript and Python, because agents don't care which language your stack speaks. The piece I reach for most is overlay mode - full temp copy of the directory, agent edits there, then apply or discard. It made "let the model touch files" feel survivable in production, not reckless. If you're wiring agents, CI, or internal copilots on top of real codebases - try it and tell me what breaks. Github: https://lnkd.in/gi4Ccqb2 Website: https://lnkd.in/grJXTicc #openSource #aiAgents #developerTools #typescript #python

Oh how dandy! I have personally used dandy to use AI to pull useful insights out of raw data. If you have experience AI returning inconsistent data between prompts, or simply need to turn raw data into something useful, give this a try!

Stack Overflow is declining. I have been following and contributing to it consistently for the past 12 years whenever I had some free time or needed solutions to complex problems. The rate of new questions in the most popular tags has dropped significantly by around 80% in recent years. I can’t believe that tags like JavaScript, Python, Java, and C# now have fewer than 100 questions a day; it used to be hundreds per hour. Do you think Stack Overflow will face a similar fate to MSDN, CodeProject, or other technical communities? #Stackoverflow #LLM #AI

Python 3.15 Delivers Explicit Lazy Imports After Three-Year Development Effort 📌 Python 3.15 brings explicit lazy imports-a major performance boost after three years of development. Real-world benchmarks show startup times slashed by up to 80% and memory use reduced by 90%, thanks to deferring imports until they’re actually needed. This change empowers devs to optimize CLI tools without breaking existing codebases. 🔗 Read more: https://lnkd.in/dqEp3BkA #Python315 #Lazyimports #Pep810 #Explicitimports

🚀 Trending in Tech Today! Exploring the latest innovations in AI, PHP frameworks, and Python development! 💡 The technology landscape continues to evolve with groundbreaking developments across: 🤖 Artificial Intelligence & Machine Learning 🔧 PHP Frameworks & Backend Development 🐍 Python Innovations & Data Science 🤔 What's your take on the latest developments in tech? #AI #PHP #Python #TechTrends #SoftwareDevelopment #Innovation

I recently built an open-source Python project called Limify. It’s a Redis-backed rate limiting engine designed for modern backend systems. While working on APIs and microservices, I kept rewriting the same rate-limiting logic again and again — usually tightly coupled to a framework. So I decided to build a reusable, framework-agnostic solution. Limify features: • Redis-backed token bucket algorithm   • Atomic Lua execution   • Framework adapters for FastAPI / Starlette   • Plan-based rate limiting (SaaS-friendly)   • Clean architecture design  The goal is to treat rate limiting as infrastructure rather than framework code. I wrote a short article explaining the motivation and architecture: 👉 https://lnkd.in/ecr-ms79 The project is open source if you'd like to explore it: GitHub:   https://lnkd.in/ei3Ndvzg Docs:   https://lnkd.in/e-vHUhgg Feedback and contributions are very welcome.

This article explores the significance of real-time data in modern software and provides insights on implementing WebSockets using Python and FastAPI. I found it interesting how crucial real-time communication has become in various applications from finance to collaborative tools. What are your thoughts on the importance of real-time data in your industry?