FastAPI Testing with Pytest for Beginners

FastAPI with Pytest: A Comprehensive Guide for API Testing In the world of web development, building APIs has become a cornerstone of modern applications. FastAPI, with its speed and ease of use, has quickly become a favorite for developers looking to create efficient and robust APIs. But building an API is only half the battle. Ensuring its reliability, functionality, and security is where testing comes in. This is where Pytest, a powerful and versatile testing framework for Python, shines....

FastAPI with pytest-asyncio: Testing Asynchronous APIs In the rapidly evolving world of web development, building high-performance APIs is crucial. FastAPI, with its asynchronous capabilities and modern Python syntax, has emerged as a popular choice for developers. However, writing robust and reliable APIs requires rigorous testing. This is where pytest-asyncio comes in, providing a seamless way to test asynchronous FastAPI applications. Why Test Asynchronous APIs? Asynchronous programming allows your API to handle multiple requests concurrently, leading to improved performance and responsiveness....

Added to AGENTS.MD in a frontend + Django Python API project. Simple Codex instructions like creating a feature or writing PR comments were consuming ~30% of context memory per call. These notes brought that down to roughly 10–17%. here it goes : To reduce context size and memory usage, follow these rules when analyzing the repository: ### Step 1 — Diff First 1. Inspect the **git diff** before performing any repository exploration. 2. List the files modified in the PR. ### Step 2 — Targeted Analysis 3. Only open files that appear in the diff. 4. Do NOT explore the repository before analyzing the diff. 5. Do NOT read the entire repository. ### Step 3 — Limited Context Expansion 6. If additional context is required, use **repository search first**. 7. Only open additional files if strictly necessary to understand the change. 8. Any file opened that is not part of the diff must be **explicitly justified**. ### Step 4 — Exploration Limits 9. Maximum automatic file reads: **5 files**. 10. Prefer reading **small sections of files** instead of entire files. ### Step 5 — PR Review Output 11. After minimal exploration, generate the PR review comment. 12. The review must be written in a **human-like format using Markdown**. 13. Respect task scope. If the task is backend-only, do not inspect frontend code and vice versa.

There is a website you use regularly that has no Application Programming Interface (API). You want to automate your interactions with it — searching for people, managing your profile, sending messages. The traditional approach would be to spend days reverse-engineering the site’s Hypertext Markup Language (HTML) structure, writing a scraper, building a command-line interface (CLI), setting up tests, configuring Continuous Integration (CI), and deploying. What if you could do all of that in under ninety minutes, producing a well-tested, well-structured Python package with over two hundred tests and nearly five thousand lines of code? This essay describes exactly that process. It is not theoretical. Every claim here is verifiable from session logs, git history, and GitHub pull request records. The project is a Python CLI tool managed with uv (a fast Python package manager from Astral) for a website that has no public API. The tool uses authenticated Hypertext Transfer Protocol (HTTP) requests, scrapes HTML responses with BeautifulSoup, validates data with Pydantic models, and presents results through a Click command-line interface. It was built from a template project to a working tool with auth, search, and member-view commands in eighty-four minutes of wall-clock time. https://lnkd.in/giC9RzsP

IDE vs. Code Editor – Understanding the Difference Python in 2026: IDE or Code Editor? The Choice Matters More Than You Think. Every Python developer starts with the same question: What do I write my code in? The answer used to be simple. Today, the landscape is more nuanced, and the choice between an IDE and a code editor defines your workflow. Let us clarify the difference. An Integrated Development Environment, or IDE, is a comprehensive toolkit. It bundles a text editor with a debugger, build automation tools, version control integration, and often a profiler. Everything comes in one package, ready to work together. PyCharm is the classic example. It understands your code deeply, points out errors as you type, and manages complex projects out of the box . A code editor is the lightweight alternative. Tools like Visual Studio Code or Sublime Text start simple. They are fast, flexible, and customizable. But they require plugins and extensions to gain IDE-like features. You build your own environment piece by piece . So which path is right for you? Choose a full IDE like PyCharm if you want everything configured from day one. It is ideal for large, organized projects where structure matters and you prefer a batteries-included approach . Choose a code editor like VS Code if you value speed and customization. You can add exactly what you need and nothing more. It scales from simple scripts to microservices, especially with its Python extension and Jupyter support . The right tool depends on your project and your preferences. There is no single correct answer. But understanding the trade-offs between a complete IDE and a modular editor will save you hours of frustration. What is your daily driver for Python development and why? #Python IDEs CodeEditors VS Code #PyCharm DeveloperTools Programming Productivity

A dashboard is the soul of a business reflected in a system. A good dashboard should invite users to take action. Without that, the system becomes nothing more than CRUD operations with a beautiful graphical interface. A dashboard is a reflection of the organization’s KPIs (Key Performance Indicators). It transforms raw data into insights that guide decisions and drive results. Here are some dashboard screens from a recent project I worked on. Python + Django + DRF React + Js + TailwindCSS

FastAPI with Tortoise ORM: Building Asynchronous APIs with Databases In the world of web development, building fast, efficient, and scalable APIs is crucial. Asynchronous programming has emerged as a key technology to achieve this, allowing developers to handle multiple tasks concurrently without blocking the main thread. FastAPI, a modern, fast (high-performance), web framework for building APIs with Python 3.7+ based on standard Python type hints, is perfectly suited for asynchronous development....

Kivy Tutorial: Build a Multi-Step Wizard App in Python In the world of software development, user experience is paramount. One common pattern that significantly enhances user experience for complex processes is the multi-step wizard. Whether you're guiding users through a lengthy setup, a complex configuration, or a detailed data entry process, breaking it down into manageable steps makes the journey less daunting and more intuitive. In this tutorial, we'll dive deep into building a multi-step wizard application using Kivy, a powerful Python framework for creating cross-platform GUIs....

Day 54 of #90DaysOfCode Today I built a Python automation that interacts with Instagram to discover and follow users from a target account’s follower list. The script logs into Instagram, opens the followers dialog of a selected profile, scrolls through the dynamically loaded list, and automatically follows selected users. How the automation works • Logs into Instagram using Selenium WebDriver • Navigates to a target account profile • Opens the followers modal dialog • Scrolls the followers container to load more users • Identifies Follow buttons within the modal • Automatically follows selected profiles Key engineering concepts used • Selenium WebDriver for browser automation • Explicit waits using WebDriverWait and ExpectedConditions • Handling dynamically loaded content inside modal dialogs • JavaScript execution for scrolling dynamic containers • Exception handling for intercepted clicks This project helped deepen my understanding of how automation scripts interact with dynamic front-end applications and modern web interfaces. GitHub Repository https://lnkd.in/gKbk3f2W #Python #Selenium #Automation #WebAutomation #SoftwareEngineering #90DaysOfCode

JavaScript got React. Python got Django. Ruby got Rails. Go got its standard library. Every language ecosystem that scales eventually adopts frameworks, libraries, and reusable components. The most successful ones have registries and discovery. Infrastructure is no different. A Terraform module is a validated piece of production experience in a reusable component. The next team that needs a VPC doesn't rediscover the edge cases. Modules are the components. A proven framework is what ties them together.