Leonardo Montenegro’s Post

For the Python developers who's follwing me, that a good post to know which framework use depending on your project!! 🐍

🚀 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

Stop writing Dockerfiles manually. Every Node.js or Python backend eventually needs a container. And almost every time, we: – Google best practices – Copy from old projects – Fix broken builds – Debug port mismatches – Rewrite multi-stage Dockerfiles It’s repetitive. It’s slow. And it’s easy to get wrong. After doing this one too many times, I decided to automate it. So I built DockerMind. A CLI tool that analyzes your project and generates a production-ready Dockerfile automatically. Just run: dockermind init That’s it. DockerMind detects: • Language (Node.js or Python) • Framework (Express, FastAPI, Django, etc.) • Runtime version • Package manager (npm, yarn, pnpm, pip) • Build step • Entry point • Port And generates: • Optimized multi-stage Dockerfile • Non-root container configuration • Clean .dockerignore • Optional docker-compose.yml No AI APIs. No cloud calls. Fully offline. Deterministic. Three commands from source code to running container: dockermind init dockermind build dockermind run Built with Python, Typer, and Rich. Open-source. Lightweight. Installs via pip. If you build Node.js or Python backends and are tired of rewriting Dockerfiles for every project, try it and break it. I’d genuinely appreciate real feedback. PDF documentation is attached below with full technical details. #Docker #DevOps #BackendDevelopment #Python #NodeJS #OpenSource #DeveloperTools #CLI #SoftwareEngineering #BuildInPublic

🕸️ I chose Sync API over Async for web scraping—and I don’t regret it. While building a Playwright scraper, I deliberately avoided async. Here’s why 👇 🔹 Why Sync API worked better for me • Linear, predictable execution (open → extract → store) • Easier debugging without async state issues • Faster iteration cycles during development • Fewer race conditions to worry about 💡 In my case: scraping hundreds of pages sequentially worked reliably—without needing concurrency. 🔹 Where Async does make sense • Large-scale scraping (thousands of requests) • Parallel pipelines and distributed systems • Async-first ecosystems ⚖️ Key insight: • Most scraping problems are I/O-bound—but not always concurrency-bound. • Adding async too early can increase complexity faster than it improves performance. 👀 Question: Have you actually seen real performance gains with async scraping—or just added complexity? #WebScraping #Playwright #Python #Automation #AsyncProgramming #SoftwareEngineering #DataEngineering

I recently built 𝗖𝗵𝗲𝗺𝗗𝗮𝘁𝗮 𝗩𝗶𝘀𝘂𝗮𝗹𝗶𝘇𝗲𝗿 as part of the 𝗦𝗰𝗿𝗲𝗲𝗻𝗶𝗻𝗴 𝗧𝗮𝘀𝗸 𝗳𝗼𝗿 𝗮 𝗪𝗶𝗻𝘁𝗲𝗿 𝗜𝗻𝘁𝗲𝗿𝗻𝘀𝗵𝗶𝗽 𝗣𝗿𝗼𝗴𝗿𝗮𝗺. The goal was to design a cross-platform system for monitoring, analyzing, and visualizing chemical process data, combining modern web technologies with scientific computing tools. Below is a brief overview of the project. • 𝗣𝗿𝗼𝗷𝗲𝗰𝘁 𝗢𝘃𝗲𝗿𝘃𝗶𝗲𝘄 A full-stack platform designed for chemical equipment monitoring and data analysis, enabling engineers to visualize trends, analyze operational data, and generate structured reports. • 𝗪𝗲𝗯 𝗗𝗮𝘀𝗵𝗯𝗼𝗮𝗿𝗱 Built using React + TailwindCSS to create a responsive and interactive dashboard for exploring datasets and monitoring parameters. • 𝗗𝗲𝘀𝗸𝘁𝗼𝗽 𝗔𝗽𝗽𝗹𝗶𝗰𝗮𝘁𝗶𝗼𝗻 Developed with PyQt5 + Matplotlib to support offline-capable scientific plotting and advanced data visualization. • 𝗕𝗮𝗰𝗸𝗲𝗻𝗱 & 𝗔𝗣𝗜 Implemented using Django REST Framework, providing secure API endpoints for data processing, authentication, and communication between system components. • 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 Used Pandas and NumPy for statistical analysis, data manipulation, and trend analysis of engineering datasets. • 𝗔𝘂𝘁𝗼𝗺𝗮𝘁𝗲𝗱 𝗥𝗲𝗽𝗼𝗿𝘁𝘀 Implemented PDF report generation for documenting analytical insights. • 𝗗𝗲𝗽𝗹𝗼𝘆𝗺𝗲𝗻𝘁 Containerized using Docker for reproducible environments and scalable deployment. • 𝗞𝗲𝘆 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴𝘀 – Full-Stack System Architecture – REST API Development – Scientific Data Visualization – Cross-Platform Application Development – Docker-Based Deployment • 𝗟𝗶𝘃𝗲 𝗟𝗶𝗻𝗸𝘀 Web Dashboard: https://lnkd.in/gr4ha9mm Backend API: https://lnkd.in/gtznD2xq Demo Video: https://lnkd.in/gfXxzy-N GitHub Repository: https://lnkd.in/gNpEy3rp I would appreciate feedback from developers and engineers working in data visualization, full-stack development, and scientific computing systems. #DataVisualization #ChemicalEngineering #FullStack #Python #React #Django #PyQt5 #DataScience #Docker

Every engineer talks about learning. Few show what they actually did with it. It has been more than two years since I stopped just “learning” code and started “building with it”. That one shift changed the entire trajectory of how I grow as an engineer. Earlier, I spent a lot of time going deep into the fundamentals- memory management, system design, algorithms, web basics. Genuinely valuable. But the real leap happened when I moved from solo experiments to building full applications with structured frameworks. That's when I came across Django - a high-level Python web framework that follows the Model-Template-View (MTV) architectural pattern. And things started clicking differently. Here are three projects that marked that transition for me: 🔗 Video Streaming Platform: https://lnkd.in/dTeWxaQ8 This project helped me understand how to manage media flows, route requests, and structure backend logic with Django’s architecture — bridging data models to real interfaces. 🔗 Django Auction Platform: https://lnkd.in/d9Txrf6q Here, I built a multi-feature platform with user authentication, auction logic, and dynamic interactions, pushing me to think holistically about data flow and system state. 🔗 Django Wikipedia Platform: https://lnkd.in/dMYJtkKB This one taught me how to structure content, organise search and navigation, and turn backend schema into intuitive user experiences. What these projects taught me goes well beyond "how to use Django": ✔ Frameworks aren't shortcuts — they're architectural tools that make your systems maintainable ✔ Separating data, logic, and presentation isn't just theory — it's what lets you actually scale ✔ Real applications are about flows — auth, routing, content, UI — all working together predictably Good engineering isn't just writing code that runs. It's building systems that hold up when real users show up. All three repos are open source because I believe building in public is one of the fastest ways to grow, and I'd rather invite people into the process than build behind closed doors. If you're on a similar path, what was the project that made you feel like a builder, not just a learner? I'd love to hear it. #BuildInPublic #OpenSource #Python #Django #TorontoTech

🚀 Why I’m Exploring Reflex – A Pure Python Web Framework As someone who works deeply in Python-based systems, I’m always looking for ways to move faster from idea → prototype → production. Recently, I’ve been exploring Reflex, a modern Python web framework that lets you build full-stack web apps using only Python — no separate frontend JavaScript required. 💡 What makes Reflex interesting? ✅ Build UI components using Python ✅ Automatic frontend + backend integration ✅ Real-time updates with reactive state ✅ Clean developer experience For Python developers who want to build dashboards, internal tools, AI apps, or data-driven web platforms — Reflex removes a lot of the traditional frontend complexity. Instead of switching between: Python (backend) JavaScript (frontend) API wiring You stay inside Python and ship faster. 🔥 My Take For AI-powered apps, admin panels, and rapid internal tooling, Reflex feels lightweight, productive, and aligned with Python-first teams. Still early in exploration, but definitely promising. #Python #WebDevelopment #Reflex #FullStack #AIApps #DeveloperExperience

𝐏𝐡𝐚𝐬𝐞 𝐈 — 𝐓𝐡𝐞 𝐅𝐨𝐮𝐧𝐝𝐚𝐭𝐢𝐨𝐧 𝐁𝐮𝐢𝐥𝐝𝐢𝐧𝐠 𝐢𝐧 𝐩𝐮𝐛𝐥𝐢𝐜: 𝐅𝐫𝐨𝐦 𝐳𝐞𝐫𝐨 𝐭𝐨 𝐚 𝐜𝐥𝐨𝐮𝐝-𝐧𝐚𝐭𝐢𝐯𝐞 𝐝𝐢𝐬𝐭𝐫𝐢𝐛𝐮𝐭𝐞𝐝 𝐬𝐲𝐬𝐭𝐞𝐦 I recently completed a 5-phase hackathon that challenged me to evolve a simple Python script into a production-grade, AI-powered platform deployed on Google Cloud. 𝗛𝗲𝗿𝗲'𝘀 𝗣𝗵𝗮𝘀𝗲 𝗜 — where it all started. 𝐓𝐡𝐞 𝐂𝐡𝐚𝐥𝐥𝐞𝐧𝐠𝐞: Build a fully functional task management CLI app in Python using Spec-Driven Development (SDD). No "vibe coding" allowed — every line of code had to trace back to a written specification. 𝐖𝐡𝐚𝐭 𝐈 𝐁𝐮𝐢𝐥𝐭: • A Python console app with full CRUD operations: Add, List, Update, Delete, and Mark Complete • In-memory task storage with a clean menu-driven interface • Input validation and error handling at every step 𝐓𝐡𝐞 𝐏𝐫𝐨𝐜𝐞𝐬𝐬 (𝐒𝐃𝐃): Before writing a single line of code, I followed a strict workflow: 1. Write the specification (what to build) 2. Clarify ambiguities (ask targeted questions) 3. Plan the architecture (how to build it) 4. Break into atomic tasks (testable work units) 5. Implement with checkpoints This might sound like overkill for a CLI app — but it laid the foundation for everything that followed. 𝐓𝐞𝐜𝐡 𝐒𝐭𝐚𝐜𝐤: • Python 3.13+ • UV package manager • Reusable core module (`core/models.py`, `core/manager.py`) 𝐊𝐞𝐲 𝐓𝐚𝐤𝐞𝐚𝐰𝐚𝐲: The core business logic was designed to be reusable from Day 1. The same `TaskManager` class that powered the CLI would later be imported by the web API, the AI agent, and the cloud deployment. Architecture decisions made early compound over time. 𝐆𝐢𝐭𝐡𝐮𝐛 𝐋𝐢𝐧𝐤: https://lnkd.in/dRiRz3dU #Python #SoftwareEngineering #BuildInPublic #SpecDrivenDevelopment #CLI #Hackathon Hassam Rauf Hasnain Ali Afnan Rauf

🚀 𝑹𝒆𝒄𝒆𝒏𝒕𝒍𝒚 𝑮𝒐𝒊𝒏𝒈 𝑫𝒆𝒆𝒑 𝒊𝒏𝒕𝒐 𝑳𝒐𝒂𝒅 𝑻𝒆𝒔𝒕𝒊𝒏𝒈 𝒘𝒊𝒕𝒉 𝑷𝒚𝒕𝒉𝒐𝒏 & 𝑳𝒐𝒄𝒖𝒔𝒕 Over the past few days, I’ve been diving deep into 𝐋𝐨𝐚𝐝 & 𝐏𝐞𝐫𝐟𝐨𝐫𝐦𝐚𝐧𝐜𝐞 𝐓𝐞𝐬𝐭𝐢𝐧𝐠 𝐮𝐬𝐢𝐧𝐠 𝐏𝐲𝐭𝐡𝐨𝐧-𝐛𝐚𝐬𝐞𝐝 𝐋𝐨𝐜𝐮𝐬𝐭 🦗 So far, I’ve explored and understood: • Fundamentals of Performance Testing • Load vs Stress vs Spike vs Endurance Testing • Scalability & Volume Testing • Throughput vs Response Time • Concurrent Users vs Requests • Think Time & Pacing • Identifying Bottlenecks - Application bottlenecks - Database bottlenecks - Network bottlenecks • Locust Architecture (Master–Worker Model) • How Virtual Users Simulate Real-World Traffic 𝐎𝐧𝐞 𝐭𝐡𝐢𝐧𝐠 𝐈 𝐫𝐞𝐚𝐥𝐢𝐳𝐞𝐝 👇 𝑷𝒆𝒓𝒇𝒐𝒓𝒎𝒂𝒏𝒄𝒆 𝒕𝒆𝒔𝒕𝒊𝒏𝒈 𝒊𝒔 𝒏𝒐𝒕 𝒋𝒖𝒔𝒕 𝒂𝒃𝒐𝒖𝒕 𝒔𝒆𝒏𝒅𝒊𝒏𝒈 𝒕𝒓𝒂𝒇𝒇𝒊𝒄. 𝑰𝒕’𝒔 𝒂𝒃𝒐𝒖𝒕 𝒖𝒏𝒅𝒆𝒓𝒔𝒕𝒂𝒏𝒅𝒊𝒏𝒈 𝒉𝒐𝒘 𝒔𝒚𝒔𝒕𝒆𝒎𝒔 𝒃𝒆𝒉𝒂𝒗𝒆 𝒖𝒏𝒅𝒆𝒓 𝒑𝒓𝒆𝒔𝒔𝒖𝒓𝒆 𝒂𝒏𝒅 𝒊𝒅𝒆𝒏𝒕𝒊𝒇𝒚𝒊𝒏𝒈 𝒕𝒉𝒆 𝒘𝒆𝒂𝒌𝒆𝒔𝒕 𝒍𝒊𝒏𝒌 𝒃𝒆𝒇𝒐𝒓𝒆 𝒑𝒓𝒐𝒅𝒖𝒄𝒕𝒊𝒐𝒏 𝒖𝒔𝒆𝒓𝒔 𝒅𝒐. 📌 𝐍𝐞𝐱𝐭 𝐒𝐭𝐞𝐩 𝐢𝐧 𝐌𝐲 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠 𝐏𝐚𝐭𝐡: Instead of directly writing Locust scripts, I’m going deeper into: 🔹 How API Testing actually works 🔹 Understanding HTTP methods (GET, POST, PUT, DELETE) 🔹 Sending requests using Python (requests library) 🔹 Handling headers, payloads, authentication 🔹 Understanding status codes & response validation 𝐁𝐮𝐢𝐥𝐝𝐢𝐧𝐠 𝐬𝐭𝐞𝐩 𝐛𝐲 𝐬𝐭𝐞𝐩 𝐭𝐨𝐰𝐚𝐫𝐝 𝐦𝐚𝐬𝐭𝐞𝐫𝐢𝐧𝐠 𝐥𝐨𝐚𝐝 𝐭𝐞𝐬𝐭𝐢𝐧𝐠 𝐚𝐧𝐝 𝐩𝐞𝐫𝐟𝐨𝐫𝐦𝐚𝐧𝐜𝐞 𝐞𝐧𝐠𝐢𝐧𝐞𝐞𝐫𝐢𝐧𝐠. I’ve also documented my learning journey in detail on Medium. Here is a post of mine https://lnkd.in/g8QUxXzR #PerformanceTesting #LoadTesting #APITesting #Locust #Python

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