FastAPI for Python Backend Development Simplified

We just ranked #1 on Google for "Django ORM practice online". If you haven't tried it yet, Django ORM Playground is a free browser-based tool to write and run ORM queries instantly. No setup needed. What you get: Live ORM execution with instant results SQL tab showing exact query Django generates Pre-loaded models: Book, Author, Review, Rating Snippets for filters, Q objects, select_related, prefetch_related, aggregations Try it free: https://lnkd.in/gV4bi6j5 #Django #Python #InterviewPrep #BackendDevelopment #SoftwareEngineering

Day 46/60 of #60DaysOfMiniProjects Built a Smart Study & Mood Tracker using Flask! Excited to share my latest project where I combined productivity tracking with a touch of intelligent suggestions Features: • Track daily study sessions with mood & notes • Smart suggestions based on mood and activity • Productivity score calculation • Daily streak tracking • Search, edit, and manage past sessions • Clean and simple user interface Tech Stack: Python | Flask | JSON | HTML/CSS This project helped me understand how small data insights can improve consistency and focus in daily routines. Would love your feedback and suggestions to improve it further! #Python #Flask #WebDevelopment #StudentProjects #Productivity #CodingJourney #OpenToLearn

In a world where the machine writes most of the code, Python lacks solid type enforcement, Rust is overly strict with complex lifetimes, while Go strikes the right balance by catching critical issues without hindering development velocity. The article argues in favor of Go over Python and Rust for AI-generated code due to Go's efficiency, simplicity, clear error-handling, and easy deployment capabilities. https://lnkd.in/eEgmd4qW --- Like what you see? Subscribe 👉 https://faun.dev/join

Build a production AI agent in 10 lines of Python. Strands Agents SDK: ```python from strands import Agent from strands.models.bedrock import BedrockModel from strands_tools import calculator, web_search agent = Agent( model=BedrockModel("anthropic.claude-sonnet-4-20250514-v1:0"), tools=[calculator, web_search] ) response = agent("What's the GDP per capita of the top 5 economies?") ``` That's it. Tool calling, conversation management, streaming, multi-turn context is all handled. Why Strands over LangChain for AWS: - Built for Bedrock integration (not retrofitted) - Works with any Bedrock model - ToolSimulator for agent testing (just released) - Strands Evals for evaluation pipelines - Open-source, runs locally For production: pair with Bedrock AgentCore for managed deployment, guardrails, and observability. The stack: Strands (build) → ToolSimulator (test) → Strands Evals (evaluate) → AgentCore (deploy) Full lifecycle. Open-source foundation. Managed deployment when ready. Start: https://strandsagents.com #AWS #AIAgents #Python #Strands #Bedrock

Your users are waiting. And leaving. Because your response takes too long. You send everything at once. Big mistake. Django has a hidden weapon: StreamingHttpResponse. Instead of waiting… It sends data in chunks. User sees data instantly ⚡ No loading pain. No frustration. Use it when: → Large files → Real-time output → Slow processing Stop making users wait. Stream it. #Django #Python #WebDevelopment #BackendDevelopment #DjangoTips

"Why is my Rust code slower than Python?" That was the first benchmark result I saw when I started building rmath. My Scalar type: 480ns per addition. Python's float: 65ns. It was 7× slower. In a "fast" language. The problem wasn't Rust. It was the boundary. Every time Python calls Rust, there's a ~200ns tax: → Extract the Python object → Type-check it → Cross the FFI bridge → Do the actual math (1ns) → Wrap the result → Cross back For a single number, that tax is everything. So I changed the question. Instead of "how do I make one operation fast?" I asked "how do I make one million operations fast?" The answer: never leave Rust. One FFI crossing, 100K elements: sqrt → 162× faster dot → 89× faster std_dev → 71× faster LazyPipeline (zero intermediate allocations): .sqrt().add(1).sum() → 92× faster 308 tests. 0 failures. 42× average speedup. The lesson isn't about Rust vs Python. It's about where you draw the boundary. #Rust #Python #Performance #Engineering #PyO3 #SystemsProgramming #OpenSource #softwareEngineering

🚀 Day 19/60 – Iterators (Understand How Python Loops Work Internally ⚡) Yesterday you learned Decorators. Today, let’s go deeper into how Python actually loops 👇 🧠 What is an Iterator? An iterator is an object that lets you loop through data one item at a time. 👉 Implements __iter__() and __next__() 👉 Used behind every for loop 🔄 How for loop works internally numbers = [1, 2, 3] iterator = iter(numbers) print(next(iterator)) # 1 print(next(iterator)) # 2 print(next(iterator)) # 3 👉 StopIteration is raised at the end ⚡ Custom Iterator class CountUp: def __init__(self, max): self.max = max self.current = 1 def __iter__(self): return self def __next__(self): if self.current > self.max: raise StopIteration val = self.current self.current += 1 return val for num in CountUp(5): print(num) 🔍 Iterator vs Iterable 👉 Iterable → Object you can loop over (list, tuple, string) 👉 Iterator → Object that actually produces values 🔥 Why Iterators Matter? ✅ Memory efficient ✅ Lazy evaluation ✅ Core of generators ❌ Common Mistake Confusing iterable with iterator ❌ 👉 Not every iterable is an iterator 🔥 Pro Tip 👉 Use iter() to convert iterable → iterator 👉 Use next() to manually fetch values 🔥 Challenge for today 👉 Create a custom iterator 👉 That returns numbers from 1 to 3 👉 Use next() manually Comment “DONE” when finished ✅ Follow Adeel Sajjad to stay consistent for 60 days 🚀 #Python #PythonProgramming #LearnPython #Coding #Programming #Developer

I’ve spent the last few weeks moving beyond just analyzing data to actually building the systems that create it. It’s been a massive learning curve, but I’ve finally started to wrap my head around:   𝗠𝗼𝗱𝗲𝗹𝘀 & 𝗔𝗱𝗺𝗶𝗻: How the database actually structured.   𝗩𝗶𝗲𝘄𝘀 & 𝗧𝗲𝗺𝗽𝗹𝗮𝘁𝗲𝘀: Passing variables between the backend and the UI.   𝗣𝗲𝗿𝗺𝗶𝘀𝘀𝗶𝗼𝗻𝘀: Managing users and groups.   𝗕𝗼𝗼𝘁𝘀𝘁𝗿𝗮𝗽: Keeping things clean and responsive. Huge shoutout to my mentor Rathan Kumar for the guidance. Check out the live site - https://lnkd.in/g-jkXR5x On to the next build! #Django #Python #DataEngineering #Learning #FullStack

My first trained model sat in a Jupyter notebook for two weeks. I had no idea how to let anyone else use it. That is the gap between knowing ML and doing ML engineering. Knowing how to serve a model is a different skill from knowing how to train one. Here is how to go from a saved model file to a live REST API in under 30 lines of Python. The key insight that took me too long to learn: never load the model inside the endpoint function. Load it once on startup. Every call after that is instant. FastAPI also generates an interactive docs page automatically at /docs. Zero extra work. Point anyone at the URL and they can test your API from the browser. Four things to add before real traffic: input validation beyond types, request logging, structured error handling, and a /health endpoint for your load balancer. Swipe through for the complete code. What was your first production ML deployment? Flask, FastAPI, something else? #Python #FastAPI #MLOps #MachineLearning

Julia vs Python isn’t a technical debate. It’s a business one. Python wins on ecosystem and hiring. Julia wins on performance and elegance. In enterprise: - ecosystem usually wins - maintainability beats purity Context decides. Takeaway: Pick what reduces total system friction. I've seen Julia annihilate Python from a performance perspective, yet I've never released a Julia production app. Have you seen Julia outperform Python in production?