FastAPI: High-Performance Python Backend Framework

Outlines a Python monorepo setup that pairs uv workspaces with Dagger and BuildKit caching. Builds container stages programmatically. Keeps things cache-friendly and predictable. Parses pyproject.toml and extracts the workspace graph. Copies required local packages into intermediate stages. Installs them in editable mode so caches survive and rebuilds stay fast. uv and Dagger turn CI from ad-hoc scripts into workspace-driven, cache-first monorepo builds. The result: per-package containerization that scales and caches sensibly. https://lnkd.in/e5GYVy2d --- Love it? Get our newsletter 👉 https://faun.dev/join

I used to think my Python UI code was "fine"—until I tried to write a unit test for it. 🤯 If you've ever felt trapped between Choosing #Streamlit or #Gradio, or if you've struggled with code that’s impossible to test without launching a browser, this article is for you. I've been working on #AgnosticPyUI to prove that we can apply the same clean architecture patterns we use in our backends to our frontend Python code. Decoupling your UI library from your logic isn't just about flexibility; it's about peace of mind. Read more about it in my latest Medium post: https://lnkd.in/eZpjQxqA I'd love to hear your thoughts on this! Which one do you prefer using? #PythonDevelopers #DataScience #Testing #SoftwareEngineering #OpenSource

We’ve all been there… “Code works perfectly on local” Production: 💥 I wanted to fix that gap — not with theory, but with something practical and repeatable. So I built a production-ready Python service setup: • Docker for consistency • systemd for reliability • Nginx + Blue-Green for zero-downtime deployments The idea was simple: 👉 Your service should never go down during deployment This blog is what I wish I had when I started building real systems. If you’re moving from scripts to production systems, this will help: https://lnkd.in/gZThnvK8 Would love your thoughts 🙌 #DevOps #Python #SystemDesign #Backend #Tech

NiceGooey Lets Python CLI Tools Generate Web UIs in One Line 📌 Turn your Python CLI tools into sleek web interfaces in one line with NiceGooey - no HTML, CSS, or GUI coding needed. Built on Vue and Tailwind, it auto-generates responsive forms that mirror your command-line logic, bridging the gap between power tools and non-technical users. A fresh, AI-free successor to legacy Gooey, it’s already reshaping how devs share and use CLI utilities. 🔗 Read more: https://lnkd.in/d5EzMzTV #Nicegooey #Pythoncli #Webui #Onelinecode #Opensource

💡 𝗧𝗶𝗽 𝗼𝗳 𝘁𝗵𝗲 𝗗𝗮𝘆 — 𝗗𝗷𝗮𝗻𝗴𝗼 𝗗𝗶𝗱 𝘆𝗼𝘂 𝗸𝗻𝗼𝘄? Django’s "annotate()" lets you add 𝗰𝗮𝗹𝗰𝘂𝗹𝗮𝘁𝗲𝗱 𝗳𝗶𝗲𝗹𝗱𝘀 𝗱𝗶𝗿𝗲𝗰𝘁𝗹𝘆 𝗶𝗻 𝘆𝗼𝘂𝗿 𝗾𝘂𝗲𝗿𝘆𝘀𝗲𝘁𝘀. Instead of processing data in Python after fetching it, you can compute values at the 𝗱𝗮𝘁𝗮𝗯𝗮𝘀𝗲 𝗹𝗲𝘃𝗲𝗹. 🔧 𝗖𝗼𝗺𝗺𝗼𝗻 𝘂𝘀𝗲 𝗰𝗮𝘀𝗲𝘀: - Counting related objects ("Count") - Calculating averages ("Avg") - Adding computed fields to API responses This reduces data processing in your app and leverages the power of your database. Smarter queries = faster apps. #Django #Python #BackendDevelopment #WebDevelopment #DatabaseOptimization #PerformanceOptimization #SoftwareEngineering #CodingTips #FullstackDeveloper

🚀 Turn any Python CLI script into a modern GUI – with zero extra dependencies. I just open‑sourced PyScript-to-GUI, a tool that instantly wraps your command‑line scripts into a clean, functional graphical interface. ⚡ No more boring terminals. Your users get a real window with dark mode, real‑time output, and interactive input dialogs – without writing a single line of GUI code. ✨ Key features: ✅ Zero external dependencies – uses only tkinter (built into Python) ✅ Smart input() handling – automatically converts prompts into pop‑up dialogs ✅ Live logging – all print() output appears in a scrollable terminal‑style area ✅ Multi‑threaded – the GUI never freezes, even during heavy tasks ✅ Hacker aesthetic – dark grey + lime green theme, ready to impress 🔧 Perfect for: Sharing your scripts with non‑technical colleagues Building quick internal tools with a professional look Teaching Python without scaring beginners with the terminal 🔗 GitHub repo: https://lnkd.in/dDpXCYSk 👨💻 Built by NULL200OK – because every script deserves a beautiful face. #Python #GUI #Tkinter #OpenSource #DeveloperTools #CLItoGUI #PyScriptToGUI #Coding

The flasgo website is now live. You want a fast async typed Python web framework that has security built in from the start(follows owasp 2025) requires Python 3.14 and has a small attack surface as possible with django like security primatives but is easy as using flask? Then you have come to the right place. #python #webdevelopment #web

While digging deeper into environment setup, I noticed an interesting difference between Python and Node.js: Python installs packages globally by default. Unless you create a virtual environment, all dependencies go into a shared global space. Node.js installs packages locally by default. Every project gets its own node_modules directory. At first, this made me think: → Python focuses more on isolation and optimization → Node.js doesn’t care as much But that’s not completely true. Both ecosystems solve the same problem — dependency management — but in different ways: Python → requires you to explicitly create isolation (virtual environments) Node.js → gives you isolation by default (per-project dependencies) Trade-offs: Python → cleaner environments, but extra setup Node.js → easier start, but larger project size and duplication Key insight: It’s not about which is better — it’s about understanding the design decisions behind each ecosystem. This kind of detail matters when you switch between stacks or design scalable systems. #Python #Nodejs #Backend #SoftwareEngineering #LearningInPublic

🐍📈 Flask by Example — You're going to start building a Flask app that calculates word-frequency pairs based on the text from a given URL. This is a full-stack tutorial covering a number of web development techniques #python #learnpython

Built my first Python API using FastAPI! Coming from a MERN background, I decided to explore Python backend development—and it’s been an eye-opening experience. What I built: A simple REST API with GET & POST endpoints Request validation using Pydantic models Auto-generated API docs (Swagger UI) Key Learnings: How FastAPI handles routing (similar to Express but cleaner) Request body validation without extra libraries Importance of virtual environments (and debugging them the hard way) Running production-ready APIs using Uvicorn One thing that really stood out: FastAPI feels like TypeScript + Express, but with built-in validation and performance advantages. Example: Created a POST /user endpoint that validates incoming data using a schema and returns structured responses. GitHub Repo: https://lnkd.in/gF4FFR2u Would love feedback from the community #Python #FastAPI #BackendDevelopment #LearningInPublic #100DaysOfCode