Introducing app-generator-cli for FastAPI projects

Shipped v1.0 of Weftlyflow today. A self-hosted, Python-native workflow automation platform. Visual node-graph editor, 127 built-in nodes, AI agents as a first-class primitive, Fernet-encrypted credentials, three-layer sandbox for user code. One command to run it: docker compose up -d Apache 2.0. All original code. mypy --strict, 172 test files, FastAPI + Vue 3 + Vue Flow under the hood. Built it because every existing tool forced one of three compromises — pay-per-task forever, Python-as-afterthought, or AI bolted on. This is the version I wanted to exist. Repo in the comments. If you've been duct-taping cron jobs and wishing for something better, take it for a spin. Repo: https://lnkd.in/gzYwz9es Stack: Python 3.12 · FastAPI · SQLAlchemy 2 · Celery · Redis · Postgres · Vue 3 · Vue Flow #Python #OpenSource #WorkflowAutomation #SelfHosted

🚀 FastAPI: Build Powerful APIs with Less Code If you're building APIs in Python and not using FastAPI yet… you're missing out. Here’s why FastAPI is gaining massive popularity 👇 ⚡ Blazing Fast Performance Built on ASGI and Starlette, FastAPI delivers performance close to Node.js and Go. 🧠 Automatic Data Validation Thanks to Pydantic, you get type validation using Python type hints — clean and powerful. 📄 Auto-Generated Docs Swagger UI & ReDoc are generated automatically. No extra effort needed. 🔐 Easy Authentication & Security Supports OAuth2, JWT, and other modern security standards out of the box. 🔧 Developer-Friendly Less code, more productivity. You write less boilerplate and focus on logic. 💡 Example: from fastapi import FastAPI app = FastAPI() @app.get("/") def home(): return {"message": "Hello FastAPI 🚀"} 🔥 Whether you're building microservices, AI APIs, or backend systems — FastAPI is a game changer. Start learning today and level up your backend skills 💪 #FastAPI #Python #BackendDevelopment #WebDevelopment #APIs #Programming #SoftwareEngineering #100DaysOfCode #DeveloperLife #Coding

Recently, I worked on a background removing tool — and it turned out to be way more than just integrating an API. The popular options like remove.bg charge per image once you pass the free tier. Not sustainable at scale. So I found rembg on GitHub — open-source, runs the U2-Net model locally, completely free. Wrapping it into a FastAPI service was the first challenge. Then came hosting. Tried Render.com's free tier — the model downloaded fine, then the server crashed. 512MB RAM isn't enough for an AI model. Moved to a VPS, got FastAPI running behind Nginx, and connected it to Laravel with a single HTTP call. That was it. Longer than expected. But the result is unlimited, self-hosted background removal with zero ongoing cost. Full Article: https://lnkd.in/gckebJG6 Try Tool: https://lnkd.in/grmnuNH8 #WebDevelopment #Python #Laravel #SelfHosted #BuildInPublic

🚀 Day 55 – Exploring FastAPI (Modern Backend Magic!) Today I dived into FastAPI, one of the fastest and most efficient web frameworks for building APIs with Python. ⚡ 💡 What is FastAPI? FastAPI is a modern web framework that helps you build APIs quickly using Python, with automatic validation, documentation, and high performance. 🔥 Why FastAPI stands out: ✔️ Super fast (built on ASGI & Starlette) ✔️ Automatic API docs with Swagger UI 📄 ✔️ Type hints = better code + fewer bugs ✔️ Easy to learn and implement ✔️ Async support for high performance 🛠️ What I learned today: 🔹 Creating a basic API 🔹 Handling GET & POST requests 🔹 Path & Query parameters 🔹 Request validation using Pydantic 🔹 Auto-generated interactive docs 💻 Simple Example: from fastapi import FastAPI app = FastAPI() @app.get("/") def read_root(): return {"message": "Hello World 🚀"} 📌 Key Takeaway: FastAPI makes backend development simple, fast, and production-ready with minimal code. Consistency is the real power 💪 #Day55 #FastAPI #Python #BackendDevelopment #APIs #100DaysOfCode #LearningJourney 🚀

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

✂️ 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

🚀 Excited to share my latest project — Smart To-Do & Productivity Tracker! A full-stack web app built with Python Flask + SQLite that helps you organize tasks and stay productive. ✅ User authentication (signup/login) ✅ Task priority levels (High / Medium / Low) ✅ Time scheduling for each task ✅ AI-powered productivity suggestions ✅ Dark mode support ✅ Live productivity summary 🔗 Live Demo: https://lnkd.in/g5iJZBtA Would love your feedback! 🙌 #Python #Flask #WebDevelopment #FullStack #SQLite

Decoupling logic in Django is always an interesting architectural challenge. Recently, I’ve been relying more on Django Signals to keep my models clean and enforce a strict separation of concerns. For those who haven't dug into how they work under the hood: Django signals essentially implement the Observer design pattern. There is a central dispatcher, when a specific action occurs in the application (the sender), the dispatcher routes that event to any function "listening" for it (the receiver), allowing them to execute their own logic independently. In the snippet below, I’m using the post_save signal. Whenever a new Student instance is successfully created, this receiver catches the signal and automatically generates a CreditWallet for them. Why use a signal here instead of just overriding the save() method on the Student model? It comes down to encapsulation. Overriding save() works fine for simple apps, but as a project grows, it can lead to massive, bloated models. By using signals, the Student model remains strictly responsible for student data, while the financial/wallet logic is encapsulated in its own domain. It makes the codebase much easier to maintain, scale, and test. I’m curious to hear from other developers on here: What is the most complex, creative, or technically challenging way you have utilized Django signals in a project? I'd love to learn from your experiences! #Django #Python #SoftwareEngineering #WebDevelopment #Architecture #Coding

Mastered the HTTP Request-Response Cycle & Methods! 🐍 Moving deeper into my Python Backend journey, I’ve realized that a great API isn't just about the code—it’s about how it communicates. Today, I took a deep dive into HTTP (HyperText Transfer Protocol), the backbone of every interaction on the internet. Here is what I explored today: 🔄 The Request-Response Cycle: Learned how a Client (Browser) and Server (Backend) talk to each other. Understanding that every request I send from the frontend needs a structured response from my FastAPI server. 🛠️ The "Big Four" HTTP Methods: GET: Fetching data safely (The "Read" operation). POST: Sending new data to the server (The "Create" operation). PUT: Replacing or updating existing data (The "Update" operation). DELETE: Removing data from the system (The "Delete" operation). 🚦 Status Codes & Headers: Started identifying the "secret language" of servers—from the successful 200 OK to the dreaded 404 Not Found and 500 Internal Server Error. This knowledge is the bridge between my local Python scripts and the global web. I'm now ready to start building RESTful APIs that can handle real-world traffic! #Python #WebDevelopment #HTTP #BackendDeveloper #CodingJourney #FastAPI #SoftwareEngineering #RESTAPI #TechCommunity #ContinuousLearning

🚀 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