kamrun Nahar’s Post

I built a small tracer from scratch in Python. It automatically creates spans for function calls (like requests.get) using: 𝗠𝗼𝗻𝗸𝗲𝘆 𝗽𝗮𝘁𝗰𝗵𝗶𝗻𝗴 – intercepting existing library functions and wrapping them with tracing logic, without modifying the original code. 𝗖𝗼𝗻𝘁𝗲𝘅𝘁 𝘃𝗮𝗿𝗶𝗮𝗯𝗹𝗲𝘀 (𝗰𝗼𝗻𝘁𝗲𝘅𝘁𝘃𝗮𝗿𝘀) – these are like thread-local storage but designed for async and concurrent programs. They let each coroutine or thread safely keep its own tracing context, so even with parallel requests, the tracer knows which span belongs to which flow. 𝗪𝗵𝗮𝘁’𝘀 𝗮 𝘀𝗽𝗮𝗻?  A span represents one unit of work — for example, a function execution or an API call.  Spans can be nested (parent-child), forming a tree that shows the entire flow of a request across different components. Each span is logged to a file (trace_log.jsonl), capturing parent-child relationships and timing details. Later, this data can be visualized to see how requests flow through a system — similar to what full-fledged tracing tools like OpenTelemetry do. GitHub: https://lnkd.in/gcR7XCwc

Library Management System using Python & Streamlit I am pleased to share my recent project — a Library Management System developed using Python, Streamlit, and Pandas. This web-based application allows users to efficiently manage a library’s collection through the following key functionalities: 📘 View Books: Display the complete list of books available in the library. ➕ Add Books: Add new book entries with title, author, and availability status. 📖 Issue Books: Update the record when a book is issued. 🔁 Return Books: Mark an issued book as returned. 💾 Persistent Storage: Data is stored and managed using a CSV file for easy access and updates. 🧠 Technologies Used: Python – Core programming logic and data handling Streamlit – Web interface and user interaction Pandas – Data manipulation and storage File Handling (CSV) – Persistent local data storage This project enhanced my understanding of data management, Streamlit-based UI development, and Python file operations. 🔗 Live Application: https://lnkd.in/gDMXu4-E 💻 GitHub Repository: https://lnkd.in/gdjNRSaa #Python #Streamlit #DataScience #SoftwareDevelopment #WebApplication #ProjectShowcase #LibraryManagementSystem #GitHub #LearningByBuilding

💻 Python Command-Line Quiz Game Project (Project 17)! I just wrapped up a fun little project: a fully functional, object-oriented quiz game implemented right in the Python command line! This project was a great way to practice Object-Oriented Programming (OOP) principles in Python by breaking the game down into logical, reusable components. 🌟 Key Features OOP Structure: The project is organized into three main classes, making the code clean, readable, and easy to maintain: Question: Simple class to hold the text and the correct answer for each question. QuizBrain: The core logic class that manages the question list, tracks the user's score, checks answers, and moves to the next question. main.py: Drives the game. It takes raw data, converts it into Question objects, and uses the QuizBrain to run the quiz loop. Dynamic Question Flow: The QuizBrain class handles the entire game flow, prompting the user for answers and automatically keeping score. Case-Insensitive Answer Checking: The check_answer method ensures the quiz is user-friendly by accepting "True," "true," "tRuE," etc., as correct. 🛠️ Tech Stack & Files File NamePurposequestion_model.pyDefines the Question class.quiz_brain.pyContains the QuizBrain logic (score tracking, answer checking, next question).game_data.pyStores the raw list of questions and answers.main.pyInitializes the game, builds the question bank, and runs the main quiz loop.🚀 How It Runs The game loop continues as long as there are questions left, giving the user their score after every question, and finally printing a final score summary. Python # Output Example: Q.1. Ada Lovelace is often considered the first computer programmer. (true/false)?: true you got it right the correct answer is True your score is : 1/1 Q.2. JavaScript derives from a later version of Java (true/false)?: true that's wrong the correct answer is False your score is : 1/2 you've completed the game, your final score is 8/10 github link: https://lnkd.in/eKCuaQTv This was a great exercise in applying fundamental Python concepts and good coding structure! #Python #OOP #Programming #CodingProject #CommandlineGame #QuizGame

Exploring a clean way to serve ML models using FastAPI and Docker. 🧠 Trained a RandomForestClassifier on the Iris dataset ⚙️ Served predictions via FastAPI 🐋 Containerized with Docker for portability 🔥 Key learnings: - How to expose ML models as REST APIs - How to containerize and run in consistent environments - Why FastAPI + Docker is perfect for lightweight ML services Code: https://lnkd.in/dYZiss6Z #python #machinelearning #restapi #dataengineer #mlengineer #genai #fastapi

I’ve created and uploaded a Python repository on GitHub — perfect for beginners who want to practice Python, review core concepts, or simply understand the syntax in an easy and organized way. This repository includes well-structured examples and simple scripts that can help anyone starting their Python journey or refreshing their knowledge. 📂 GitHub Repository: https://lnkd.in/ecHVT7F2 Whether you’re learning, revising, or exploring Python, this repo can be a great starting point! Feel free to fork, explore, and contribute. 💻 #Python #Programming #GitHub #Coding #Developers #PythonForBeginners #LearnToCode

🛡️ Learning to Protect APIs — One Rate Limiter at a Time As part of my journey into system design, I recently built a small but important backend feature: a Rate Limiter using FastAPI. Why does this matter? In real-world systems, it's critical to protect your APIs from being overloaded — whether by accident or misuse. Rate limiting is one of the simplest and most effective ways to do that. 🎯 Goal: Limit each user to 5 API requests per minute using an in-memory dictionary and Python's time module. 💡 How I built it: Used FastAPI middleware to intercept every incoming request. Tracked requests per IP using a Python dictionary. Filtered out old timestamps to calculate request frequency in the last 60 seconds. Returned a 429 Too Many Requests response if the user crossed the limit. 📌 Key Concepts Covered: Rate limiting logic using timestamps Middleware for request handling Stateless, in-memory tracking Testing with Postman & local API simulation 💭 Takeaway: System design isn't only about complex architectures — it's also about simple protections that make systems reliable. This mini project helped me understand how to build smarter, safer APIs. Github Repo: https://lnkd.in/gry_zvjx #SystemDesign #FastAPI #BackendDevelopment #RateLimiting #Python #APIProtection #LearningByDoing #BuildInPublic #FullStackDeveloper #DeveloperJourney

🚀 FastAPI Learning – Day 1 Today I kicked off a new learning project using FastAPI (Python framework for building APIs). Instead of jumping into complex features, I focused on doing the fundamentals properly: Set up the project with a virtual environment Installed FastAPI & Uvicorn Created a simple route (/) and tested it with Swagger UI Cleaned the repository by removing venv/ and __pycache__ Added a proper .gitignore Pushed the code to GitHub with clear commits 🔧 Tech used: Python FastAPI Uvicorn Git & GitHub for version control 📌 Repo link: 👉 https://lnkd.in/dFNvtTFD On the surface, this is a simple setup, but it matters because: If the foundation is messy, the entire project becomes chaos later. Tomorrow, I’ll start working on API models and database integration (SQL). If anyone here has built production APIs with FastAPI, I’d love to hear your advice on best practices.

I’ve been exploring TOON (Token-Oriented Object Notation) 🧩 It’s like a lightweight, token-efficient alternative to JSON — super handy when working with LLMs. To understand it better, I tried a few exercises and shared them here 👇 🔗 https://lnkd.in/dyvymUki #LLM #AI #Python #MachineLearning #GenerativeAI #OpenSource #DataScience #JSON #TOON #PromptEngineering

🚀 Exploring Python Web Scraping with SerpAPI! Hi everyone, I recently started experimenting with Python to explore the power of web scraping and API usage. As a first step, I built a small project to fetch coffee shop data in Lahore using SerpAPI and extract readable details from each location. I used the following Python libraries: aiohttp for asynchronous HTTP requests asyncio to run multiple requests concurrently BeautifulSoup to parse HTML content pandas to store and export the results into Excel logging for clean, structured logging 💡 Note: Please make sure to add your own SerpAPI API key if you want to run this project. The goal was to learn how to combine APIs, asynchronous requests, and data extraction into a simple, practical project. What I learned: How async tasks speed up data collection How to fetch and parse web pages efficiently Exporting structured data professionally to Excel I’ve shared the full project code on GitHub for anyone interested: 🔗 https://lnkd.in/dxfVqerw This was a fun first experiment, and I’m looking forward to expanding it further with advanced data processing! #Python #WebScraping #AsyncProgramming #SerpAPI #DataScience #LearningByDoing