Nikolay M.’s Post

🚀 Why 𝗙𝗮𝘀𝘁𝗔𝗣𝗜 is the Game-Changer in Python API Development If you're building high-performance, modern APIs in Python, you need to know about 𝗙𝗮𝘀𝘁𝗔𝗣𝗜. It's rapidly becoming the go-to framework for its incredible speed and developer experience. ✨ Key Features & Benefits: ⚡ 𝗕𝗹𝗮𝘇𝗶𝗻𝗴 𝗙𝗮𝘀𝘁 𝗣𝗲𝗿𝗳𝗼𝗿𝗺𝗮𝗻𝗰𝗲: It's one of the fastest Python web frameworks, thanks to being built on 𝗦𝘁𝗮𝗿𝗹𝗲𝘁𝘁𝗲 and leveraging Python's asynchronous features (async/await). It handles high traffic efficiently. 🐍 𝗧𝘆𝗽𝗲 𝗦𝗮𝗳𝗲𝘁𝘆 & 𝗙𝗲𝘄𝗲𝗿 𝗕𝘂𝗴𝘀: FastAPI uses standard Python 𝗧𝘆𝗽𝗲 𝗛𝗶𝗻𝘁𝘀(integrated with 𝗣𝘆𝗱𝗮𝗻𝘁𝗶𝗰). This provides: Automatic data 𝘃𝗮𝗹𝗶𝗱𝗮𝘁𝗶𝗼𝗻 and serialization. 𝗚𝗿𝗲𝗮𝘁 𝗲𝗱𝗶𝘁𝗼𝗿 𝘀𝘂𝗽𝗽𝗼𝗿𝘁 (autocompletion and type checks). A reported 𝟰𝟬% 𝗿𝗲𝗱𝘂𝗰𝘁𝗶𝗼𝗻 in human-induced errors. 📖 𝗔𝘂𝘁𝗼𝗺𝗮𝘁𝗶𝗰 𝗗𝗼𝗰𝘂𝗺𝗲𝗻𝘁𝗮𝘁𝗶𝗼𝗻: It automatically generates interactive API documentation (using 𝗢𝗽𝗲𝗻𝗔𝗣𝗜/𝗦𝘄𝗮𝗴𝗴𝗲𝗿 𝗨𝗜 and 𝗥𝗲𝗗𝗼𝗰), saving significant development time and making your API easy for others to consume and test. 🛠️ 𝗣𝗿𝗼𝗱𝘂𝗰𝘁𝗶𝗼𝗻 𝗥𝗲𝗮𝗱𝘆: It includes built-in support for 𝗗𝗲𝗽𝗲𝗻𝗱𝗲𝗻𝗰𝘆 𝗜𝗻𝗷𝗲𝗰𝘁𝗶𝗼𝗻 and 𝗦𝗲𝗰𝘂𝗿𝗶𝘁𝘆 (like OAuth2 and JWT). 🎯 Ideal Use Cases: FastAPI is an excellent choice for: Building high-speed 𝗥𝗘𝗦𝗧𝗳𝘂𝗹 𝗔𝗣𝗜𝘀. Developing scalable 𝗠𝗶𝗰𝗿𝗼𝘀𝗲𝗿𝘃𝗶𝗰𝗲𝘀. Creating 𝗥𝗲𝗮𝗹-𝗧𝗶𝗺𝗲 𝗔𝗽𝗽𝗹𝗶𝗰𝗮𝘁𝗶𝗼𝗻𝘀 (like chat apps or streaming services). #Python #FastAPI #WebDevelopment #API #Microservices #TechStack #Developers

🐞 BugBoy — A Lightweight, Colorful Python Logger BugBoy is a minimal, zero-dependency, developer-friendly logger for Python. Designed for debugging, CLI tools, backend services, and everyday development. Clean API. Colorful output. Helpful utilities. No complexity. --- 📦 Installation pip install bugboy --- --- 🚀 Features ✔️ Color-coded log messages ✔️ Simple helper functions (info(), bug(), warn(), error()…) ✔️ Structured JSON formatting ✔️ Scoped log blocks ✔️ Function-call logging decorator ✔️ Built-in Timer utility ✔️ Zero external dependencies ✔️ Works in any Python project (FastAPI, CLI, scripts) 🔧 Usage Basic Logging from logger import info, error, bug info("Server started") bug("Something unexpected happened") error("Failed to load config") Timer from logger import Timer, info with Timer("Processing"): info("Work in progress...") Decorator Example from logger import log_function_call @log_function_call def add(a, b): return a + b Log Block from logger import log_block, info with log_block("startup"): info("Initializing modules...") --- 🎨 Output Format BugBoy uses ANSI color codes for readable terminal output: INFO → Blue SUCCESS → Green WARN → Yellow ERROR / CRITICAL → Red BUG → Magenta When JSON mode is enabled, output becomes structured and machine-readable

Testfixtures 10 is out! 🎉 If you've ever struggled with writing clear, maintainable test assertions in Python - comparing complex objects, checking API responses, or validating database results - testfixtures can help. 🧵 https://lnkd.in/e6SRzQce 🎯 like() - Partial object comparisons: from testfixtures import compare, like compare(api.get_users(), expected = [like(User, email='alice@example.com', role='admin')]) Don't worry about attributes you don't care about! ✅ contains() - Check only specific items are present: from testfixtures import contains compare(event_log, expected=contains([ Event(type='user.login'), Event(type='purchase.completed'), ])) For logging, though, check out LogCapture: https://lnkd.in/emKa_jyu) 🔄 unordered() - Order-independent exact matching Database queries don't guarantee order? No problem: from testfixtures import unordered compare(query_results, expected=unordered([ User(id=1, name='Alice'), User(id=3, name='Charlie'), ])) 📊 sequence() - Flexible sequence comparisons Full control over ordering and partial matching: from testfixtures import sequence compare(results, expected=sequence(partial=True, ordered=False)( Record(id=3), Record(id=5), ])) ...oh I wish LinkedIn supported posting code snippets properly :-/

🚀 A Python trick that 99.9% developers don’t know — yet it can make your app load faster instantly! Most Python devs focus on optimizing loops or database queries. But there’s a hidden performance gem right inside the language — almost nobody uses it. It’s called module-level lazy loading (introduced in Python 3.7 via PEP 562). Imagine your app importing only what’s actually used — not the entire ocean of dependencies during startup. That’s what this feature allows. Instead of loading all heavy submodules when your package imports, you can set up your module to load them only when accessed. Result? ✅ Faster startup time ✅ Lower memory usage ✅ Cleaner public API Big Python libraries quietly use this to stay “instant” — yet 99% of codebases don’t. I’ve personally seen: 40–80% faster CLI startup Massive latency drops for serverless functions Easier migration paths when refactoring large packages It’s elegant, built-in, and surprisingly underused. --- 💡 Pro tip: If your Python app feels “heavy” at startup, you can probably make it instant with this one technique — no external libraries needed. If you’d like me to share a simple before-and-after example or benchmark, comment “Lazy me!” below 👇 Let’s make Python snappier, one import at a time. ⚡

Introducing the Python Code Harmonizer: Quantifying the Gap Between Code Intent and Execution I'm excited to share a project I've been working on: the Python Code Harmonizer. It's a tool that performs semantic analysis to detect a specific class of logical errors—when a function's name and documentation promise one thing, but its implementation does another. How it works in brief: 1. Parses Python code into an Abstract Syntax Tree (AST). 2. Analyzes the semantic intent (from names/docstrings) vs. the execution logic (from the code body). 3. Quantifies the discrepancy, generating a "Semantic Disharmony Score." 4. Reports which functions have the highest divergence, prioritizing refactoring and review efforts. Why it matters for production code: These semantic mismatches are often the root of subtle bugs, technical debt, and misunderstandings in large or legacy codebases. While traditional linters check for syntax and style, this tool provides a metric for conceptual integrity. Potential use cases: -  Objective Code Reviews: Move from "this function name feels wrong" to "this has a high disharmony score." -  Legacy Code Triage: Quickly identify the most semantically confusing functions when onboarding to a new codebase. -  CI/CD Gates: Flag new functions with high intent-execution gaps before they hit production. This is an open-source project built to be simple, extensible, and integration-friendly. #Python #SoftwareEngineering #CodeQuality #OpenSource #DevOps #StaticAnalysis #TechnicalDebt 🔗 GitHub Repo: https://lnkd.in/drNjpcBm

FastAPI: Beyond "Async Python" FastAPI's rise in API development isn't just about async versus non-async. It signifies a significant shift in how Python interacts with the web. The Interface Evolution: WSGI vs ASGI When your browser sends a request, it doesn't directly communicate with your Python code. It speaks HTTP, while your app communicates through Python functions. This necessitates a mediator - WSGI or ASGI, defining the communication between the web server (like Gunicorn or Uvicorn) and your Python app. WSGI - Traditional Approach WSGI (Web Server Gateway Interface) is the longstanding standard (since 2003) utilized by frameworks like Flask and Django. It functions by: - Receiving an HTTP request from the web server - Converting it into a Python-callable format (app(environ, start_response)) - Waiting for your app's response before sending it back as an HTTP response While WSGI is simple and reliable, it operates synchronously, handling one request per worker sequentially, akin to a single phone line scenario. ASGI - The Contemporary Solution ASGI (Asynchronous Server Gateway Interface) modernizes this concept by offering a multi-lane expressway approach. It: - Supports various protocols like HTTP, WebSockets, and background tasks - Enables non-blocking async execution (async def app(scope, receive, send)) - Excels in real-time applications, AI model deployment, and high-concurrency APIs FastAPI leverages ASGI, utilizing servers such as Uvicorn or Hypercorn, tailored for async Python operations. The Significance In today's landscape, systems are not solely serving web pages; they handle diverse tasks like API calls, AI model operations, data streams, and live updates. This necessitates an interface that efficiently communicates across multiple channels, precisely what ASGI provides. FastAPI simplifies this process, offering a user-friendly interface atop this advanced architecture. #FastAPI #Python #ASGI #WSGI #BackendDevelopment #WebArchitecture #SoftwareEngineering #APIs

Pass-by-reference vs pass-by-value — explained with a 🍌banana. And why you might be wrong about how Python works. There are two core ways to pass data to functions: 0/ By value - a copy is created and passed to the function 1/ By reference - a reference to the same data is passed to the function Languages like C/C++ make you explicitly choose - a pointer* or a value. But what about other languages? 👽 JavaScript & Java is pass-by-value.... BUT: object type is a reference, so you pass a copy of a reference to the function, so it looks like pass by reference. 🐍 What about Python? By reference, right?... Python has "names" that refer to "values". Everything is referenced by a name, and many names can reference the same value. You pass references to functions. Where it get's confusing: For immutable types (numbers, strings, tuples, ...), when assigning a new value, you're rebinding the name, rather than changing the value. So you're creating a new name, and it looks like pass by value: def gotcha(y): y = 3 x = 1 gotcha(x) print(x) 🔁 Repost for other devs to see this... 👋 Follow, Miko Pawlikowski 🎙️, for dev insights that don’t 🍌 slip past the details.

Over the past week, I wanted to challenge myself to build something end-to-end in Python — a full-stack project that’s simple, fast, and practical. The result is Expense Trackr, a clean and modular Expense Tracking Application powered by FastAPI (backend) and Streamlit (frontend). What I built: - Backend: FastAPI for building high-performance RESTfuls - Frontend: Streamlit for an intuitive & interactive interface - Database: MySQL to store and manage expense data - Testing: Pytest for ensuring reliability and maintanability Highlights: - Real-time expense tracking with CRUD operations - Clear data flow between UI →  API → Database - Modular project structure - Simple to deploy and extend - Learn new Python modules (Pandas, Pydantic) Tech Stack: Python, FastAPI, Streamlit, MySQL, Pytest, Pandas, Pydantic, Uvicorn GitHub Repo: https://lnkd.in/gZPRjRYq #Python #FastAPI #Streamlit #FullStackDeveloper #Pandas The architecture shows a smooth data flow from UI → API → Database

I wanted to share this. It's a look at the Agent Development Kit (ADK) Samples repository. This is a great collection of ready-to-use AI agents. It's designed to help developers speed up their work. The repository has samples for both Python and Java. You can find everything from simple conversational bots to more complex multi-agent workflows. If you're working with AI agents, this looks like a very useful resource for getting started and seeing how they are built. You can find the full repository here: https://lnkd.in/gDRux4qe

Python package management is a pain. It becomes obvious when one moves to Go, where it’s a much better experience. One way to solve the pain, especially in larger teams, is to start from the very first thing: package manager. Homebrew is a good start. In the past I’ve successfully used pkgsrc. Then there are others like Conda, nix, & Macports. I’m sure there are many others I don’t even know about. After you choose a package manager, you build your own base Python. You start building dependencies from requirements.txt or pyproject.toml or whatever as native packages. It’s not an easy solution at all. But it gives you the most control to serve your target developers. You can then choose escape hatches to lower your maintenance burden. For example, don’t build your own base Python but choose the one built by your package manager maintainers. Once you have a reliable build pipeline that can continuously build newer releases & a team to shepherd them, it becomes an internal service. This idea is a variation of the “use Docker containers to package & ship your application”. Only in this case you start from a level lower than containers (which can also use your package manager of choice). Is Python worth all this effort? A resounding yes! Once you start thinking this way, you can even move upstream & contribute to official packages, helping not just your team but others, too. Maybe I’m the lone warrior who loves to build packages. I’ve done that in various roles throughout my career. Maybe I enjoy it too much to propose such a heavy investment as a “simple” solution to others. But if you have already invested your team in the Python ecosystem, it’s worth a look at doing it better than what’s available to you.