Building MCP Server with Python in 2026

📢 How to Build an MCP Server from Scratch Using Python in 2026 This guide provides a detailed walkthrough for developers on building a Model Context Protocol server with Python to enhance AI capabilities. 📖 Read more on Lead AI Dev #AI #AIDev #MCPserver #Python #ainews https://is.gd/22l87B

Sometimes it is useful to go one layer lower than Python. A while ago Nikita showed a simplified print implemented through a direct syscall path to explain what actually happens under the hood: arguments, string representation, buffer length, registers, and then the call into the OS. This is not about rewriting Python in assembly. It is about something more practical: when you understand where the abstraction ends, you make better decisions higher up the stack too. In complex Python systems, many expensive problems start exactly there: - too many layers nobody questions anymore - hidden cost behind “convenient” abstractions - weak understanding of I/O boundaries - performance discussions without a clear model of what the system is actually doing You do not need assembly to build a good backend. But when someone can reason from Python code down to the runtime and OS boundary, they usually see architecture risks earlier than others do. That is one of the reasons architecture reviews are useful: they are not just about code style or framework choices. They are about understanding where the system becomes expensive to change, hard to trust, or harder to scale than it should be.

Today I ran into a UI issue where a blocking I/O operation was taking too long, and the whole Python program would just freeze and become unresponsive. That pushed me to revisit how concurrency in Python works, and what options exist to avoid this kind of situation. Python does not really have one concurrency model. It has several, and each one behaves differently depending on whether you are dealing with I/O, CPU work, or external processes. Here are five practical approaches, with what they are, what they do well, and where they fall short. Read full post: https://lnkd.in/e7-6X3Xw

People try to learn Python by memorizing syntax. But real progress comes from understanding the structure behind it. From basic commands to control flow, from data types to functions everything in Python is designed to help you write clean, logical, and reusable code. This cheat sheet isn’t just for revision. It’s a reminder of how all core concepts connect to build real-world applications. Master these fundamentals once, and you won’t just write code you’ll build systems. #Python #Programming #LearnToCode #DataScience #AI #Developers #CodingLife #SoftwareEngineering

🚀 List vs Tuple in Python — A Fundamental Yet Overlooked Concept Many developers underestimate the importance of choosing the right data structure. In Python: 🔹 Lists are mutable, allowing dynamic changes such as adding or removing elements 🔹 Tuples are immutable, ensuring data integrity and better performance 💡 Why it matters: Tuples are generally faster and more memory-efficient, while lists offer flexibility for dynamic operations Choosing the right structure can improve performance, readability, and scalability of your code. 👉 Read more info: https://lnkd.in/dBs3ikTU #Python #Programming #SoftwareDevelopment #Coding #Developers #DataStructures #CleanCode #TechCareers

If your Python scripts are making 50 API calls, synchronous code spends most of its time waiting around doing absolutely nothing. Suresh Vina has written an intro to async Python using a simple analogy: boiling a kettle and making toast at the same time. Sync code does one, then the other. Async runs both concurrently. Same two tasks: 5 seconds sync, 3 seconds async. It’s not a big deal when making breakfast. But scale that concept across dozens of API calls and the difference adds up. The Infrahub Python SDK supports both sync and async natively. Switching between them is simple. Suresh walks through the core concepts, shows side-by-side code examples, and builds up to running the same operation across multiple sites concurrently. If async Python has been sitting on your "I should learn that someday" list, now’s your chance to *get up to speed*. (See what we did there? 😉) Link in comments 👇

How async/await Works in Python (Simple Explanation) Async programming in Python allows multiple tasks to run without blocking each other. Instead of waiting for one task to finish, Python can switch to another task. Key Concepts: - async → defines a function that runs asynchronously - await → pauses execution until the task is complete How it works: 1. Task starts (e.g., API call) 2. Instead of waiting, Python moves to another task 3. When result is ready → execution continues Example Use Cases: - API requests - Database queries - File handling - Web scraping Why it’s important: - Faster performance for I/O tasks - Better resource utilization - Handles multiple operations efficiently Final Insight: Async is not about doing things faster… It’s about not wasting time while waiting. Follow Saif Modan #Python #Async #Backend #Programming #Tech #LearningInPublic

Most Python code looks simple until you realize how much is happening under the surface. Take this for example: _C = (1, 2, 3) a, b, c = _C print(a) This is iterable unpacking, more precisely Python’s way of doing positional destructuring assignment. What actually happens: _C is evaluated as an iterable Python matches elements positionally Each value is bound in a single atomic assignment step So internally: a = _C[0] b = _C[1] c = _C[2] This pattern is not just syntactic sugar, it is widely used in production code: Function return unpacking (return x, y) Iteration over structured data API responses and tuple-based records Why it matters: Removes manual indexing (less error prone) Improves intent readability Makes transformations explicit and compact One important constraint: If the structure does not match, Python fails fast with a ValueError, which is often a feature, not a bug. Clean syntax, strict alignment, predictable behavior. That is the philosophy behind Python’s design. Which Python feature felt too simple until you saw it in real systems? #Python #SoftwareEngineering #CleanCode #Programming #PythonTips #Coding #Developer #SystemDesign

💡 What I Learned About Python Basics Today When I started Python, I thought it was just a simple language. But I realized — simplicity is its biggest strength. 🔹 Easy syntax 🔹 Powerful libraries 🔹 Used in AI, Web, Automation Example: Instead of writing complex code, Python lets you solve problems in fewer lines. 👉 That’s why beginners AND professionals love it. 📌 My takeaway: Focus on logic, not just syntax. #Python #Coding #SoftwareDevelopment #Beginners #LearnToCode

Just published a deep dive into Advanced OOP Patterns in Python—covering practical design strategies that help build scalable, maintainable, and production-ready systems. If you're aiming to level up your architecture skills beyond basics, this one’s worth your time. #Python #SoftwareEngineering #OOP #CleanCode #DesignPatterns #BackendDevelopment #TechLeadership #CodingExcellence