🍽️ Menu Management Web Application (Flask) I built a simple web application that helps restaurant staff manage their menu digitally. With this application, staff can: • Add new dishes with images • Update existing items • Delete dishes when needed • Mark items as available or unavailable This helps reduce manual effort by avoiding repeated updates to physical menus and making changes instantly in one place. Tech Stack: Python (Flask), SQLAlchemy, SQLite, HTML, CSS What I learned: • Building CRUD applications using Flask • Connecting backend with frontend (templates) • Working with databases using SQLAlchemy • Handling file uploads (images) • Managing routes and form data 🔗 GitHub: https://lnkd.in/gKWFQCBn Looking forward to building more practical applications. #Flask #Python #WebDevelopment #Projects #SoftwareDevelopment

🚀 𝗙𝗹𝗮𝘀𝗸 𝘃𝘀 𝗙𝗮𝘀𝘁𝗔𝗣𝗜 If you have worked with Python for backend development, you have probably come across Flask and FastAPI. Both are powerful, but they serve slightly different purposes depending on your use case. 🔹 Flask is a lightweight and flexible micro-framework. It’s been around for years and has a huge community. You get full control over how you structure your application. However, that flexibility comes at a cost — you often need to write more boilerplate code and manage things like validation and async handling manually. 🔹 FastAPI, on the other hand, is relatively newer but built for modern APIs. It leverages async programming and type hints, making it incredibly fast and developer-friendly. ⚡ Why is FastAPI faster? FastAPI is built on Starlette (for async support) and Pydantic (for data validation). It uses asynchronous request handling, which allows it to process multiple requests efficiently without blocking the server. 🐢 Why is Flask slower? Flask is primarily synchronous. While you can use async with Flask, it’s not its core strength. For high-concurrency applications, this can become a bottleneck. 🧠 When to use Flask? 1. Small to medium projects 2. Simple APIs or web apps 3. When you need flexibility and full control ⚡ When to use FastAPI? 1. High-performance APIs 2. Microservices architecture 3. Real-time or async-heavy applications 4. When you want automatic validation and documentation 𝗦𝘂𝗺𝗺𝗮𝗿𝘆 - Flask is like a blank canvas — simple and flexible. FastAPI is like a smart toolkit — optimized and ready for scale. Both are great — the choice depends on your project needs, not just speed. #Python #FastAPI #Flask #BackendDevelopment #WebDevelopment #APIDesign #SoftwareEngineering #Programming #Developers #TechCommunity #CodingLife #LearnToCode #AsyncProgramming

🚨 I was wrong about polyglot runtimes. PythonMonkey changed my mind. Last year, I hit a nightmare in data pipeline using both Python and Node. Every tiny bridge between them added milliseconds that turned into hours at scale. I tried subprocesses, sockets, and shared memory-ugly patches. Then I found PythonMonkey. It doesn’t treat JS as an external tool. It runs the Mozilla SpiderMonkey JS engine inside Python itself. Same process. Same memory space. No serialization. No IPC. No more JSON dumps between Python and JS. Strings, lists, and dicts move natively. You can pm.require("crypto-js") right inside Python and decrypt data with JS tooling instantly. JS Promises map to Python await objects. Async just works. It even ships console, setTimeout, XMLHttpRequest-full JS globals-directly into your Python VM. This isn’t just about convenience. It’s the first serious path toward unified multi-language runtimes. AI agents, data pipelines, webapps-all sharing one memory system. Some say it's overkill. It’s the start of something huge. When Python can run NPM modules without context switching, you’re not just saving time-you’re blurring the boundary between languages. The landscape is evolving faster than people realize. Follow me so you don’t miss the next runtime revolution. #Python #JavaScript #Developers #AIagents #GitHub #OpenSource #DataEngineering #SoftwareArchitecture

🚀 FastAPI – Hello World Example (Getting Started) FastAPI is a modern Python web framework used to build fast and high-performance APIs. The first step in creating a FastAPI application is to create an application object from the FastAPI class. This object acts as the main interface between the server and the client. 🔹 Step 1: Create FastAPI Application from fastapi import FastAPI app = FastAPI() Here, app is the main application object used by the Uvicorn server to listen for incoming client requests. 🔹 Step 2: Create a Path Operation A path operation links a URL path with a specific HTTP method and function. Example: @app.get("/") async def index(): return {"message": "Hello World"} When a user visits the root URL (/), this function executes and returns a JSON response. FastAPI can also return data types like dictionaries, lists, strings, integers, or Pydantic models. 🔹 Step 3: Run the Server Save the code as main.py and start the FastAPI server using the Uvicorn command: uvicorn main:app --reload The --reload option automatically reloads the server whenever the code changes. 🔹 Step 4: View the Output Open a browser and visit: http://localhost:8000 You will see the JSON response: {"message": "Hello World"} This confirms that your FastAPI application is running successfully. 💡 FastAPI is widely used for building REST APIs, microservices, and machine learning model APIs because of its speed, automatic documentation, and async support. #Python #FastAPI #WebAPI #BackendDevelopment #PythonProgramming #RESTAPI #SoftwareDevelopment #AshokIT

I just shipped django-query-doctor — an open-source package that diagnoses slow Django ORM queries and prescribes exact code fixes with file:line references. pip install django-query-doctor Every Django developer knows the ritual. Page loads slow. Open the toolbar. Stare at SQL. Ctrl+F through views. 40 minutes later you find the missing select_related. Ship it. Next sprint, same thing. I got tired of being the detective. So I built the tool that does the detective work for you. What makes it different from debug-toolbar, silk, or nplusone: Those tools tell you WHAT queries ran. django-query-doctor tells you WHERE in your code the problem is, WHY it's slow, and gives you the exact fix — copy, paste, done. → N+1 in blog/views.py:42? Here's the select_related() call to add. → 15 duplicate queries? Here's the cache pattern to eliminate them. → SELECT * on 47 columns? Here's the .only() you need. → DRF serializer triggering hidden queries? Caught and explained. But detection was just the start: → python manage.py fix_queries — auto-patches your code (with dry-run preview) → python manage.py diagnose_project — scans every URL, scores every app → pytest plugin that fails your build if someone introduces an N+1 → CI mode that only flags queries in files YOUR PR touched → Celery, async/ASGI, OpenTelemetry, JSON + HTML reports → One line in settings.py. Zero config. Never crashes your app. 434 tests. 87% coverage. Python 3.10–3.13. Django 4.2–6.0. Built with Claude as my AI development partner — 42 source files, 44 test files, idea to PyPI in weeks instead of months. The architecture and decisions are mine, but I'm not going to pretend this scope was possible solo without AI. GitHub: https://lnkd.in/dPE59U5p PyPI: https://lnkd.in/dwXcJbg7 Try it on your slowest endpoint. Star it, break it, open issues. #Django #Python #OpenSource #DeveloperTools #BackendEngineering #BuildInPublic #AIAssistedDevelopment

Today, I’ve been working on Django Views to bridge the gap between user input and the database. 💻 Key Learning: Why use commit=False? I learned that using item = form.save(commit=False) is crucial when you need to modify an object before saving it to the database. In this case, I used it to manually attach the currently logged-in user to the new item. Other implementations today: ✅ Access Control: Secured views with the @login_required decorator. ✅ Dynamic Queries: Used get_object_or_404 for better error handling and filtered related items for a better UX. ✅ Request Handling: Managed the flow between GET (viewing the form) and POST (submitting data). Backend development is like solving a puzzle—every piece of logic matters! 🧩 #Django #Python #BackendDeveloper #CleanCode #SoftwareEngineering #CodingJourney #WebDevelopment

A view works fine in development. Slows down in staging. Crawls in production. What is the infamous N+1 query problem? TL;DR - N+1 is a query that looks like one DB call but fires hundreds. How does it happen? 1. One query fetches a list of objects. 2. Then inside the loop, a related object is accessed. 3. Django doesn't have it. So it fetches it. Once per row. So, 10 orders → 11 queries. 100 orders → 101 queries. No errors. No warnings. The reason is lazy evaluation of queries as discussed in last post. How to identify N+1 query problem? Django Debug Toolbar shows exact query counts per request, use it to check the query count. How to fix this issue? - Fetch related data upfront in single query. - select_related for ForeignKey and OneToOne relationships. Uses a SQL JOIN. One query in total. - prefetch_related for ManyToMany and reverse ForeignKey. Uses a second query, joins in Python. The loop stays identical. The query count drops from N+1 to 2 at most. N+1 is not a Django problem. It's a misunderstanding of when evaluation happens and how it works! I’m deep-diving into Django internals and performance. Do follow along and tell your experiences in comments. #Python #Django #DjangoInternals #SoftwareEngineering #BackendDevelopment

𝙁𝙧𝙤𝙢 𝘿𝙖𝙩𝙖𝙨𝙚𝙩 𝙩𝙤 𝘿𝙚𝙥𝙡𝙤𝙮𝙚𝙙 𝘼𝙋𝙄: 𝙈𝙤𝙫𝙞𝙚 𝙍𝙚𝙘𝙤𝙢𝙢𝙚𝙣𝙙𝙖𝙩𝙞𝙤𝙣 𝙎𝙮𝙨𝙩𝙚𝙢🎬🚀 I’m really enjoying the journey of bridging the gap between ML and Full-Stack Development . From data preprocessing → model building → API → containerization → deployment → web integration, this project helped me understand the complete workflow of deploying a machine learning system T𝐞c𝐡 𝐒t𝐚c𝐤: Python (Scikit-learn) | FastAPI | Docker | .NET Core | Vue.js 𝑻𝒉𝒆 𝑾𝒐𝒓𝒌𝒇𝒍𝒐𝒘 :- 𝐃𝐚𝐭𝐚𝐬𝐞𝐭: Used the TMDB dataset to train a movie recommendation model. 𝐃𝐚𝐭𝐚 𝐏𝐫𝐨𝐜𝐞𝐬𝐬𝐢𝐧𝐠: Performed preprocessing, selected relevant fields, and generated tags for each movie. 𝐓𝐞𝐱𝐭 𝐕𝐞𝐜𝐭𝐨𝐫𝐢𝐳𝐚𝐭𝐢𝐨𝐧: Applied Bag of Words to convert text into vectors. 𝐓𝐞𝐱𝐭 𝐂𝐥𝐞𝐚𝐧𝐢𝐧𝐠: Implemented stop-word removal and stemming to improve the quality of features. 𝐒𝐢𝐦𝐢𝐥𝐚𝐫𝐢𝐭𝐲 𝐂𝐚𝐥𝐜𝐮𝐥𝐚𝐭𝐢𝐨𝐧: Used cosine similarity to identify the closest movies and generate recommendations. 𝐀𝐏𝐈 𝐃𝐞𝐯𝐞𝐥𝐨𝐩𝐦𝐞𝐧𝐭: Exposed the model through a FastAPI endpoint. 𝐂𝐨𝐧𝐭𝐚𝐢𝐧𝐞𝐫𝐢𝐳𝐚𝐭𝐢𝐨𝐧: Containerized the application using Docker. 𝐃𝐞𝐩𝐥𝐨𝐲𝐦𝐞𝐧𝐭: Pushed the Docker image to Docker Hub and served the model. 𝐅𝐫𝐨𝐧𝐭𝐞𝐧𝐝 & 𝐖𝐞𝐛 𝐈𝐧𝐭𝐞𝐠𝐫𝐚𝐭𝐢𝐨𝐧: Built the web interface using ASP.NET Core MVC with Vue.js. 𝐖𝐚𝐧𝐭 𝐭𝐨 𝐩𝐫𝐚𝐜𝐭𝐢𝐜𝐞 𝐰𝐢𝐭𝐡 𝐦𝐲 𝐦𝐨𝐝𝐞𝐥? If you are learning Frontend, Backend, or API Integration and want a working ML model to test your skills , don't worry . You can pull my trained model directly from Docker Hub and start building your own interface around it: 👉 𝒅𝒐𝒄𝒌𝒆𝒓 𝒑𝒖𝒍𝒍 𝒉𝒂𝒎𝒛𝒂1086/𝒎𝒐𝒗𝒊𝒆-𝒓𝒆𝒄𝒐𝒎𝒎𝒆𝒏𝒅𝒂𝒕𝒊𝒐𝒏-𝒎𝒐𝒅𝒆𝒍 #MachineLearning #DataScience #DotNet #Docker #FastAPI #VueJS #WebDev

Your API isn’t slow — your pagination might be. 📉 It worked perfectly… until your data grew. Then every page got slower than the last. Your code didn’t change. But your dataset did. The culprit? Offset pagination. The deeper the page, the more rows your database has to scan and skip. Page 1 → fast Page 1000 → painful Same query shape. Very different cost. The fix isn’t always caching. Sometimes it’s changing the pattern. Switch to cursor-based pagination. No skipping. Just seeking. In Django REST Framework: Use *CursorPagination* instead of *PageNumberPagination*. Performance stays consistent — even at scale. Because most performance issues aren’t complex. They’re patterns that don’t scale. And most developers don’t notice… until production. #BackendDevelopment #Django #Python #WebDevelopment #SoftwareEngineering #APIPerformance #DatabaseOptimization #SystemDesign #ScalableSystems #DjangoRESTFramework

🚀 Understanding the N+1 Query Problem in Django (From Real Experience) While working on a backend feature recently, I noticed performance issues when fetching related data in one of my APIs. After digging deeper, I realized it was due to the N+1 query problem. Here’s what I understood: 🔹 Django executes one query for the main data and additional queries for each related object 🔹 This leads to multiple database hits and slower API responses 🔹 It may not be noticeable with small data but becomes critical as data grows 💡 What helped me: By using "select_related" and "prefetch_related", I was able to reduce multiple queries into a minimal number of queries and improve response time. This made me realize how important query optimization is while building scalable backend systems. Have you faced similar performance issues in your projects? #Django #Python #BackendDevelopment #PerformanceOptimization #LearningInPublic