Ludovic ODDOS’ Post

The following is a blog that I published which is a practical hands on MCP guide to building a MCP Client and Server using MCPs Python SDK. MCP is a standard that is being adopted in the industry quickly as AI agents take over many workflows and giving them the right context is the biggest hurdle to making them useful. MCP is quickly proving to be a pretty essential tool for that. For anyone who is starting out in AI Engineering or Software Engineering, MCP is a concept that is becoming important to learn and this practical implementation is a solid starting point to learn what happens under the hood. https://lnkd.in/e-QuVRts #ArtificialIntelligence #AIAgents #MCP #Beginnerfriendly #Python

uv vs pip. Why should you use uv rather than pip for managing packages and dependencies in your Python project? - uv generates a uv.lock file that pins the exact versions of every dependency and sub-dependency. This makes environments reproducible across machines and deployments. - When you add a package with uv add, it updates the project definition for you. With pip, you typically have to remember to run pip freeze to update requirements.txt. - uv handles virtual environments as part of the workflow instead of requiring separate setup. - Dependency resolution with uv is dramatically faster than pip. I wrote a short breakdown explaining how this works and why it matters for production Python projects. #python #packagemanagement #uv #pip #backend #pythonprojects

Tkinter Tutorial: Building a Simple Interactive To-Do List with Categories Are you looking to organize your life, boost your productivity, and learn a valuable skill all at once? In today's digital age, to-do lists are essential for managing tasks, both big and small. But, a simple list can quickly become overwhelming. This tutorial will guide you through building a dynamic and organized to-do list application using Tkinter, Python's built-in GUI library....

Talking to people at the Python booth at SCALE expo, two resources were popular. The other is Automate the Boring Stuff: https://lnkd.in/ggQaX2bs People are excited by "free" :) One person was annoyed at having to learn programming, and was using AI to write his code... I told him with Automate you can learn *and understand* the 30% or so that you really need, so you can write and debug your own work.

Day 54: Python Basics: Functions, Modules, and Packages When learning Python, three words appear very often: Functions, Modules, and Packages. They may sound technical, but the idea behind them is actually very simple. Let’s understand 👇 🔹 1️⃣ Function – A Small Task A function is like a small helper that does one specific job. Example: If you want to calculate the sum of two numbers many times, instead of writing the same code again and again, you create a function. def add(a, b): return a + b Now whenever you need addition, just call: add(5, 3) ✔ Functions help us reuse code and keep programs clean. --- 🔹 2️⃣ Module – A File with Functions A module is simply a Python file that contains functions or code. For example, Python already provides a module called "math". import math print(math.sqrt(16)) Here, we are using a function from the math module. ✔ Modules help us organize related functions in one file. --- 🔹 3️⃣ Package – A Collection of Modules A package is a folder that contains multiple modules. Think of it like this: 📦 Package     📄 Module     📄 Module     📄 Module This helps when projects become large and we need better organization. ✔ Packages help manage big projects easily. --- 💡 Simple way to remember: • Function → Does a single task • Module → A file containing functions • Package → A folder containing modules Learning these concepts makes your Python code clean, reusable, and professional. If you're starting Python for DevOps, automation, or scripting, mastering these basics will help you a lot. #Python #DevOps #Programming #Coding #PythonForBeginners #LearnToCode

Unofficial Python API and agentic skill for Google NotebookLM. Full programmatic access to NotebookLM's features—including capabilities the web UI doesn't expose—via Python, CLI, and AI agents like Claude Code, Codex, and OpenClaw. https://lnkd.in/dUPanrdc

Python TIP : filter() vs List Comprehension After working with Python in production systems for years, one thing I’ve noticed is how often we need to filter data efficiently.... especially in backend services and data pipelines. A simple example: filter(lambda amount: amount > 800, transactions) What this does: • Iterates through each item • Applies the condition (amount > 800) • Returns only the matching values Example output: [900, 1300, 2200] My take after using this in real projects: • filter() is concise and works well in functional-style pipelines • It’s useful when chaining transformations (especially with map()) • That said, in many production codebases, I still prefer list comprehensions for readability Equivalent using list comprehension: [amount for amount in transactions if amount > 800] Why this matters: • Readability often beats cleverness in team environments • Consistency across the codebase is more important than personal preference • Choosing the right approach depends on context, not just syntax One quick reminder: filter() returns an iterator, so wrap it with list() if needed. After years of writing and reviewing code, I lean toward clarity first, but it’s always good to know both approaches. #Python #Programming #SoftwareDevelopment #Coding #Developer #PythonTips

Python Roadmap Start learning Python step by step https://lnkd.in/deqpUNgX Recommended courses Python for Everybody https://lnkd.in/dw3T2MpH CS50’s Introduction to Programming with Python https://lnkd.in/dkK-X9Vx Step by step Python learning roadmap 1 Foundations Basic syntax Variables Data types Operators Conditionals Loops Functions Modules and imports Exceptions Type hints Virtual environments 2 Object oriented programming Classes and objects Methods and attributes Inheritance Composition vs inheritance Dunder methods Abstract base classes 3 Data structures and algorithms Lists and arrays Stacks and queues Hash tables Trees Graphs Recursion Sorting algorithms Searching algorithms Time and space complexity 4 Advanced Python List and dictionary comprehensions Generators Iterators Context managers Regular expressions Lambda functions Decorators Async and await Concurrency basics 5 Package and environment management pip PyPI venv conda poetry 6 Databases and SQL SQL basics SELECT and joins Aggregations Indexes SQLite PostgreSQL ORMs 7 Web development HTTP fundamentals REST APIs Django Flask FastAPI Authentication Authorization 8 Automation and scripting File handling Web scraping API automation Task scheduling GUI automation Network automation 9 Testing and quality Unit testing Integration testing End to end testing pytest Mocking Test driven development 10 Developer practices Git basics Debugging Logging Code formatting Linting Documentation Follow this roadmap to move from beginner to advanced Python developer. #Python #Programming #LearnPython #DeveloperRoadmap #ProgrammingValley

Tkinter Tutorial: Build a Simple Interactive GUI for a Basic Calculator Ever wished you could build your own calculator? Not just use one, but build one? In this tutorial, we'll dive into Tkinter, a powerful Python library that lets you create graphical user interfaces (GUIs). We'll go step-by-step to build a simple, yet functional, calculator. This isn't just about learning code; it's about empowering you to create tools that solve real-world problems....

Why every Python backend dev needs Docker in 2026: Reproducibility, team consistency, and easy scaling. Step-by-step: Build images, run containers, optimize with Dockerignore, use env vars, and avoid common pitfalls. Bookmark this one: https://lnkd.in/eRKMAEqQ In the April bootcamp, you'll Dockerize real projects I.E APIs, queues, databases. As part of the full pipeline to job-ready. DM if deployment is your next goal! Enroll here: http://masteringai.dev/ #PythonBackend #DockerTutorial #SoftwareEngineering