Flask Book Collection Tracker App with Python and MySQL

✂️ Just shipped my first full-stack web project a URL Shortener called Snip! Snip lets you paste any link and get a clean, short URL in under a second with one-click copy, a full history page, and click tracking built in. Here's what went into building it: → Backend: Python + Flask for routing and API logic → ORM: SQLAlchemy to interact with the database cleanly → Database: SQLite to store all original & shortened URLs → Frontend: HTML, CSS, JavaScript + Bootstrap Icons → URL Validation: urlparse to verify scheme and domain before saving → Deduplication: same URL always returns the same short code 📌 Key features I'm proud of: Instant URL shortening with 6-char alphanumeric codes One-click copy to clipboard History page with search, delete & click counters Live stats — total links and total clicks Clean dark UI with smooth animations Building this taught me so much about how the web actually works — HTTP redirects, database relationships, REST API design, and tying a backend to a frontend. This is just the beginning. Next up: user auth, custom aliases, and QR code generation. 👀 The full code is on my GitHub 👇 🔗 https://lnkd.in/gRqiFjMW #Python #Flask #WebDevelopment #FullStack #OpenSource #100DaysOfCode #BuildInPublic #SQLAlchemy #StudentDeveloper

🚀 Day 6/7 — Building a Decision-Based Game Engine with Django As part of my 7-day Django challenge, I wanted to build something beyond typical CRUD apps — something that focuses on system design and backend logic. So I built a Decision Maze / Story Engine 👇 🎮 What it does: • Each scenario is a node • Each choice leads to another node • Different decisions → different paths → different endings 🧠 Key Features: ✔ Graph-based architecture (node → choice → node) ✔ Player progress tracking (stateful system) ✔ RPG elements (health, inventory, damage) ✔ Admin-powered story creation ✔ Typing animation & dynamic backgrounds ✔ Sound effects & smooth transitions ✔ 🗺️ Interactive graph map visualization 💡 What I learned: • Designing self-referencing models in Django • Managing user state & progression • Building backend systems like a game engine • Visualizing relational data as a graph This project really helped me shift from building “apps” to building systems. GITHUB => https://lnkd.in/gSgnUVs9 👉 Day 7 coming next — going even more advanced 💪 Would love feedback or suggestions! 🙌 #Django #Python #BackendDevelopment #WebDevelopment #Projects #LearningInPublic #100DaysOfCode #FullStack #ComputerScience

My not so "hidden” engine behind my latest dashboard. A few weeks back, I “finished” version 1 of a full-stack financial dashboard that tracks real-time stocks and weather data. While the UI is nice and built using React, the real story is what’s happening “behind the scenes” in the backend. Essentially, I didn't want the frontend to do its own "shopping". If you call API fetching “shopping”. Most apps have the user's browser call five different APIs at once. Instead, I built a BFF (Backend-for-Frontend) using FastAPI. The BFF acts like a Personal Assistant. Instead of the frontend running around to the "Weather Store" and the "Stock Market Store" itself, it just asks the assistant. The assistant (FastAPI) fetches everything, hides my private API keys, cleans up the data, and hands the frontend one perfectly organized package. That's great and all, but then I started to ask myself “what about the analytics?” So I needed to come up with a solution for real time analytics that is fast. For the heavy math—calculating stock indicators like RSI or Moving Averages across massive datasets—Python can sometimes be the bottleneck. So, I went with one of the best. I wrote a custom Compute Engine in C++ for the raw math and used pybind11 to bridge it directly into my Python code. “Why bother with all this?” First, and most importantly, speed. Delegating C++ to handle the math frees up time as it’s a fraction of the time compared to python. - Speed: The C++ engine handles the math in a fraction of the time. - UI/UX: The frontend React app stays snappy because it isn't bogged down by data processing. Building this was a great reminder that "Full Stack" isn't just about making things look pretty—it's about making sure the engine under the hood is built for the job. #WebDev #SoftwareArchitecture #Python #Cpp #FullStack

Ever notice how small food businesses still rely on printed menus that constantly go outdated? I’ve been building a QR-based digital menu system to solve that problem, allowing owners to update their menu in real-time without reprinting anything. Built using JavaScript for the frontend experience and a simple backend system to manage menu data, updates reflect instantly once changes are made. One key insight: simplicity beats complexity. Most businesses don’t need “advanced systems” they need something fast, easy, and practical that fits their daily workflow. Still refining how to make it even more intuitive for non-technical users. How do you usually balance simplicity and flexibility when building tools for real-world users? #Python #JavaScript #WebDevelopment #SoftwareEngineering #BuildInPublic

🚀 I just shipped something I wish existed years ago. Introducing app-generator-cli — a developer-friendly CLI to scaffold production-ready Python projects in seconds. Tired of copy-pasting boilerplate every time I started a new project, I built a tool that gets you from zero to a clean, working codebase with a single command: app-generator-cli create fastapi my_api --docker --postgres --redis What it scaffolds for you: ✅ FastAPI backend with async DB session, Pydantic settings & health checks ✅ FastAPI + Jinja2 full-stack app with templated frontend ✅ LangChain / LangGraph AI apps with a ReAct agent, RAG chain & tool registry ✅ Optional Docker, PostgreSQL, Redis — all wired up out of the box ✅ uv-powered dependency bootstrapping (blazing fast) ✅ Tests, .env setup, and clean project structure included No more spending the first hour of a project configuring folders. Just build. 🔗 GitHub: https://lnkd.in/dM4yrsEp 📦 PyPI: pip install app-generator-cli If you work with FastAPI, LangChain, or LangGraph — give it a try and let me know what you think! Stars and feedback are always welcome ⭐ #Python #FastAPI #LangChain #LangGraph #OpenSource #DeveloperTools #CLI #uv #BuildInPublic

Lately I have been working on building crazy looking websites using streamlit, and i'll let you guys know the crazy secret on how to build amazing ,animated looking web apps Streamlit is a Python framework used to build interactive web apps for data, ML, and dashboards without needing frontend skills. You write pure Python Streamlit automatically creates the UI in the browser, Every time your code runs, the UI updates instantly In simple terms: Python code - Streamlit - Web app UI What is import streamlit.components.v1 as components (The crazy secret) This line imports the Components API of Streamlit. import streamlit.components.v1 as components What it does: Gives you access to functions that allow custom HTML, CSS, and JavaScript Lets you go beyond Streamlit’s built-in UI Why you need it Normally Streamlit: Does not allow JavaScript Controls UI rendering With components: You can run HTML + CSS + JS Embed custom frontend code Use external libraries #data #streamlit #cloud #dataengineer #cloudengineer

🚀 Setting up a FastAPI backend does not have to be complicated. I recently documented a clean, beginner-friendly setup guide for a FastAPI project and I want to break it down so you can follow along, whether you are just starting out or looking to standardize your workflow. Here is what a well-organized FastAPI project typically looks like: 📁 your-project/ ├── environmentfoldername/  (virtual env, excluded from Git) ├── main.py         (your FastAPI app) ├── requirements.txt     (your dependencies) └── README.md        (your setup guide) Simple. Predictable. Easy to hand off to a teammate. 💡 The key insight: a clean project structure is not about being fancy. It is about saving your future self (and your team) from confusion at 11pm before a deadline. If you are building Python backends, FastAPI is one of the fastest ways to get a production-ready API running. And the setup takes less than 10 minutes once you know the steps. Follow along this week as I break down the full setup, step by step. 👇 #FastAPI #Python #BackendDevelopment #WebDevelopment #SoftwareEngineering

🚀 𝐃𝐚𝐲 𝟏: 𝐃𝐢𝐯𝐢𝐧𝐠 𝐢𝐧𝐭𝐨 𝐭𝐡𝐞 𝐒𝐩𝐞𝐞𝐝 𝐨𝐟 𝐅𝐚𝐬𝐭𝐀𝐏𝐈! ⚡ After building robust applications with Django and DRF, I’ve decided to level up my backend game. Today marks Day 1 of my journey into FastAPI, and I’m already blown away by its efficiency! Here’s a breakdown of what I explored today: 🔍 𝙒𝙝𝙖𝙩 𝙞𝙨 𝙁𝙖𝙨𝙩𝘼𝙋𝙄? It’s a modern, high-performance web framework for building APIs with Python, based on standard Python-type hints. It’s designed to be easy to use while being one of the fastest Python frameworks available. 🌟 𝙒𝙝𝙮 𝙩𝙝𝙚 𝙝𝙮𝙥𝙚? (𝘼𝙙𝙫𝙖𝙣𝙩𝙖𝙜𝙚𝙨) 𝗦𝗽𝗲𝗲𝗱: It’s on par with Node.js and Go, thanks to Starlette and Pydantic. 𝐀𝐮𝐭𝐨-𝐃𝐨𝐜𝐬: It generates Swagger UI and ReDoc automatically—no more manual API documentation! 𝐓𝐲𝐩𝐞 𝐒𝐚𝐟𝐞𝐭𝐲: Fewer bugs thanks to rigorous data validation. Asynchronous: Native support for async and await. 🏢 𝙒𝙝𝙤’𝙨 𝙪𝙨𝙞𝙣𝙜 𝙞𝙩? FastAPI isn't just a trend; industry giants like Microsoft, Uber, Netflix, and Tesla are leveraging it to power their services. Plus, the community is massive and growing every day! 📈 🛠️ 𝙏𝙚𝙘𝙝𝙣𝙞𝙘𝙖𝙡 𝙁𝙤𝙪𝙣𝙙𝙖𝙩𝙞𝙤𝙣𝙨 𝘾𝙤𝙫𝙚𝙧𝙚𝙙: 𝗧𝗵𝗲 𝗨𝘃𝗶𝗰𝗼𝗿𝗻 𝗦𝗲𝗿𝘃𝗲𝗿: Learned how this lightning-fast ASGI server keeps everything running. 𝐇𝐓𝐓𝐏 𝐌𝐞𝐭𝐡𝐨𝐝𝐬:Refreshed my knowledge on GET, POST, PUT, and DELETE in the context of FastAPI routes. 𝐓𝐡𝐞 𝐃𝐨𝐜𝐮𝐦𝐞𝐧𝐭𝐚𝐭𝐢𝐨𝐧: Spent time exploring the official FastAPI docs and their GitHub repo—the DX (Developer Experience) is top-tier! I’m excited to see how my Django background helps me master this "fast" new world. Stay tuned for more updates! #Python #FastAPI #WebDevelopment #BackendDeveloper #CodingJourney #LearningEveryday #Django #SoftwareEngineering #TechCommunity

Really excited to share my latest full-stack project: Campus Connect! I set out to build a lightweight social networking web app with Python and Flask, but I wanted to challenge myself by pushing the boundaries of what happens directly in the browser. While the app features the standard core mechanics: secure authentication, dynamic feeds, and asynchronous interactions, the technical highlight is the automated photo tagging system. Instead of relying on heavy server-side processing for computer vision, I architected a client-side pipeline using face-api.js. Here is how it works under the hood: Local Compute: When a user uploads an image, the browser handles the facial detection locally. Data Extraction: It extracts the spatial coordinates and prompts the user to tag their friends. Optimized Storage: Only the lightweight coordinate data is persisted to the SQLite backend. Dynamic Rendering: When the feed loads, Vanilla JS uses that data to create interactive visual overlays on the images. This architecture offloads the compute-heavy ML tasks from the server to the client, resulting in a highly scalable and seamless user experience. Tech Stack: Python, Flask, SQLite, Vanilla JS, HTML/CSS, and face-api.js. You can check out the source code and documentation here: https://lnkd.in/gtATWDnM I would love to hear your thoughts on integrating ML directly into the frontend architecture! #SoftwareEngineering #WebDevelopment #ComputerVision #Python #Flask #JavaScript #MachineLearning

Ever wondered what actually happens in the database when you create a folder? I built a tool to make it visible. 𝗙𝗼𝗹𝗱𝗲𝗿 𝗘𝘅𝗽𝗹𝗼𝗿𝗲𝗿 a mock file system UI with a live backend visualizer. Right-click → New Folder. Looks simple on screen. But under the hood: → FastAPI receives a POST /nodes request → SQLite inserts a row with an auto-incremented id → parent_id links it to its parent folder → depth tracks how nested it is → ON DELETE CASCADE handles cleanup automatically That's it. A folder is just a row in a table. 𝗪𝗵𝗮𝘁 𝗜 𝗯𝘂𝗶𝗹𝘁: • React + Vite frontend with a right-click context menu tree • FastAPI backend with 4 clean REST endpoints • SQLite with a self-referencing foreign key schema • A "Visualize Backend" button that renders the live node graph every folder/file as a box, every relationship as an edge 𝗧𝗵𝗲 𝗸𝗲𝘆 𝗹𝗲𝗮𝗿𝗻𝗶𝗻𝗴: File systems feel hierarchical. Databases are flat. The trick is parent_id one column that turns a flat table into an infinite tree. This pattern powers: Google Drive, Notion, Figma layers, file explorers pretty much every nested UI you've ever used. Building the visualizer made the concept click in a way no diagram ever did. If you're learning backend fundamentals build something you can see move. 🛠 Stack: React · TypeScript · FastAPI · SQLite · Tailwind #buildinpublic #webdev #python #react #sqlite #backend #learnbycoding