Flask vs FastAPI — Which Should You Choose in 2025? Both are powerful Python frameworks, but they serve different mindsets: Flask 1. The OG — simple, flexible, minimal. 2. Perfect for small APIs, prototypes, or legacy systems. 3.Huge ecosystem and community support. 4. lacks native async support and can feel slow for high-concurrency workloads. FastAPI 1. Built for the async era. 2. Type hints + automatic validation = fewer bugs and instant docs (Swagger UI!). 3.Blazing fast thanks to Starlette and Uvicorn under the hood. 4. Ideal for modern ML, data, or real-time backends. In short: If you want simplicity → go with Flask. If you want speed, scalability, and modern tooling → go with FastAPI. Example benchmark insight: FastAPI can handle 2–3x more concurrent requests than Flask when properly tuned. Flask = stable classic. FastAPI = future-ready engine. #Python #FastAPI #Flask #WebDevelopment #BackendEngineering #APIDesign #DeveloperTips

🚀 From 5-second lag to 50ms bliss. We just cut our API response time by 99%. Scenario: Talk about a real-world problem. A critical service (e.g., a data-processing pipeline or a high-traffic API endpoint) was a bottleneck. Maybe it was written in another language like Python or Node.js and couldn't handle the concurrent load. Solution: Explain why Go was the answer. The original Python/Node.js service struggled with 1000s of concurrent requests. We identified the bottleneck and rewrote this microservice in Go. By leveraging goroutines and channels, we were ableto process requests in parallel without the massive memory overhead. Takeaway & CTA (Call to Action): "The result? A 99% drop in P95 latency and a 70% reduction in server costs. A massive win for performance and our users." CTA: "What's the biggest performance jump you've seen after switching to Go? Share your story!" #Golang #Go #Performance #Backend #Microservices #Optimization

⚡ Day 277: Think Fast, Code Faster with asyncio Imagine sending 10 emails at once or downloading data from multiple APIs simultaneously — without your code waiting on one thing to finish. That’s what asyncio is all about: asynchronous magic. It helps Python multitask — efficiently. 👉 Here’s a glimpse: import asyncio async def greet(name): print(f"Hello, {name}") await asyncio.sleep(1) print(f"Goodbye, {name}") asyncio.run(greet("Alice")) 💡 Pro tip: Perfect for web scraping, real-time chat apps, or anything that waits for I/O. If you’ve ever heard “non-blocking code,” this is it! 🚀 🔹 Challenge: Build an async function that fetches three websites concurrently and prints the time taken. #Python #Asyncio #Performance #CodeSmarter

The more I build with FastAPI, the more I realize why companies really like it ✨ Auto-generated Swagger Docs ⚡ Insane Speed thanks to async support 🧩 Pydantic models make validation clean and predictable. 🌟 When you understand HTTP exceptions, error handling becomes intuitive. 💫 Folder structure matters, and moving main.py to /app taught me how imports & module paths actually work. Debugging has taught me more than any theory could : 🎯 Wrong JSON casing → 422 error 🎯 Missing comma → JSON decode error 🎯 Shadowing status function → unexpected AttributeError 🎯 In-memory lists resetting after reload → why databases matter Missing package imports → server loading forever All of these issues became "aha!" moments. #learningjourney #python #FastAPI #VisualisingTech

Why FastAPI is a Game-Changer for Modern Backends When I first tried FastAPI, I expected “just another Python framework.” But what I found changed how I build and think about APIs completely. Here’s why, 1. Speed — for both devs and servers Built on Starlette and Pydantic, FastAPI is blazingly fast, not just in response time, but also in development time. Type hints and automatic validation mean fewer bugs, less boilerplate. 2. Documentation that writes itself Every endpoint you define comes with interactive Swagger and ReDoc docs. No more separate Postman collections, your API explains itself. 3. Async support out of the box Concurrency is native. You can handle multiple requests at once without complex thread management. Perfect for high-performance apps, microservices, or real-time APIs. 4. Type safety = fewer surprises Your IDE becomes your debugging buddy, auto-suggestions, validation, and error catching before runtime. 5. Great fit for modern stacks From ML inference APIs to microservices and even serverless functions, FastAPI scales with you, not against you. #FastAPI #Python #BackendDevelopment #WebDev #APIs #TechLeadership

MetaResolve is excited to announce HarveyVaultApp, a small open-source desktop utility built in Python that allows users to bulk-upload documents directly into their Harvey AI vault via API. Read more about why we decided to build it and how you can benefit in our press release: https://lnkd.in/gKb5czXE

I wrote a short blog post introducing MetaResolve’s new open-source Harvey.ai bulk uploader. Easy, straightforward, and simple solution for those that have been plagued by the platform’s limitations. Go give it a read, you might find the app useful!

This week, I revisited how API performance can be improved with just a few small tweaks in backend design and it reminded me how sometimes simple changes make the biggest difference. One thing that stood out was how much database queries impact response time. 🌟 select_related(): it reduces extra queries when fetching related objects. And: 🌟prefetch_related(): it helps when working with many-to-many or reverse relationships. The difference may not look big in small datasets but in real systems, it can reduce dozens of queries per API call. It’s a good reminder that "Writing code is one thing" 👉 Understanding how it executes is where performance comes from If you’ve come across a small optimization recently that made a real difference, I’d love to hear about it. #Python #Django #BackendDevelopment #Performance #LearningJourney #CleanCode

I was so frustrated watching my Mac's disk space vanish into node_modules folders everywhere. So I decided to build something about it. I created a Python CLI called cleanup-nodemodule that recursively finds and removes build artifacts like node_modules, .next, and dist folders. The best part? It's completely safe by default with dry-run mode. This is also my first PyPI package. Thanks to AI, I figured out the entire publishing workflow so other devs can actually install and use it with pip. Honestly, publishing to PyPI felt intimidating, but breaking it down step by step made it doable. How to use it pip install cleanup-nodemodule # Safe dry-run first (shows what would be deleted) cleanup-nodemodule -p /path/to/project # Actually delete (when you're ready) cleanup-nodemodule -p /path/to/project --no-dry-run It's simple, safe, and saves a ton of disk space. I tested it on macOS and it works smoothly. If you try it and find bugs, please let me know or contribute. What other folders should I add? Thinking .turbo, .cache, .vercel, coverage. For more instruction click on comment link #python #javascript #nodejs #react #cli #opensource #devtools #productivity #firstproject #learning #ai #developer #coding

I tried to break this launch on purpose. It passed. Reflex just launched an AI app builder that keeps everything in Python. Front end in Python. Backend in Python. No JavaScript handoff. Same flexibility and speed you expect from a JS app, but you stay where data work actually lives. Good stuff below – What I built in one sitting - Opened Reflex and picked the Databricks integration - Queried a Delta table - Cleaned data with pandas - Added a small LLM insight panel - Charted results with Plotly - Dropped in authentication using a familiar PyPI package Why it felt different - I could use any package on PyPI without waiting for a plugin to exist - State, UI, and logic sat next to each other in Python - Performance felt like my custom React builds - Where I see this landing - Internal decision assistants for pipeline owners - Quality gates with explainable summaries - Fast proof to pilot when a product team asks for a demo by Friday If your team lives in notebooks and wants to ship real apps without a second stack, start here: https://lnkd.in/dfaj78NY #reflex #ravenaondata