Microsoft Releases Agent Framework 1.0 for AI Agents and Multi-Agent Workflows

Free Threading — Python's way to "goroutines", sort of. I’ve been experimenting with the new python3.14t builds. By combining Free-threading with AsyncIO, you can now: 1. Spawn worker threads (Parallelism). 2. Run an AsyncIO loop inside each (Concurrency). 3. Use queue.Queue as your "Channels" for thread-safe communication. The Result? True parallelism without the memory overhead of multiple processes. I’ve broken down the benchmarks and the "serialization tax" of subinterpreters in my latest write-up. If you're building high-scale backends, this is required reading. Read more: https://lnkd.in/gp3KBR_G #Python #DistributedSystems #Concurrency #Scalability #Performance

Python 3.4 Sets New Standard for High-Performance Concurrent Systems 📌 Python 3.4 cracked the code on high-performance concurrency by introducing asyncio, turning Python into a powerhouse for scalable web systems. With standardized async I/O and unified file paths via pathlib, it erased callback hell and messy string manipulations. Though retired in 2019, its architecture still shapes modern Python frameworks - proving that one release can redefine an entire ecosystem. 🔗 Read more: https://lnkd.in/dbW2_R5P #Python34 #Concurrency #Standardlibrary #Highperformance #Concurrentsystems

#Python includes a handy built-in function called `exec()` that can execute arbitrary code Never expose your APIs to this or the related `eval()` function since that could allow people to harm your computer!

🔄 Input & Output in Python input() → take user input print() → show output Use case: Take server name → display status Simple but powerful. #PythonBasics

Every Python developer needs to see this. We write clean, elegant Python and feel like we're driving a Ferrari. Meanwhile C++ is literally towing us the entire time. This isn't just a meme it's actually true. Python's most powerful tools NumPy, TensorFlow, PyTorch, OpenCV are all C/C++ under the hood. The abstraction is beautiful. But knowing what's underneath makes you a better engineer. Learn Python. Ship fast. Understand C++. Think deep. Tag a Python dev who needs to humble themselves today! #Python #CPlusPlus #Programming #Developer #TechHumor #SoftwareEngineering #Coding #ComputerScience #Tech

STREAMLIT A new Python library that lets you make web-based user interfaces with pure Python. And since I discovered this, it's been my absolute go-to for spinning up simple Python websites. It's extremely useful for data science and AI-related apps: https://streamlit.io/ #WebApps#WebAppDevelopment#AntonGlenbovith#Dashboards#PythonProgramming

Want to check if two #Python #Pandas datetimes are in the same month/quarter/year? Convert them both to periods, and compare with ==: date1.to_period('M') == date2.to_period('M') # same month? date1.to_period('Q') == date2.to_period('Q') # same quarter?

𝗪𝗵𝘆 𝗱𝗼𝗲𝘀 𝗣𝘆𝘁𝗵𝗼𝗻 𝗰𝗼𝗱𝗲 𝗳𝗲𝗲𝗹𝘀 𝘀𝗹𝗼𝘄 𝗱𝗲𝘀𝗽𝗶𝘁𝗲 𝘂𝘀𝗶𝗻𝗴 𝗺𝘂𝗹𝘁𝗶𝗽𝗹𝗲 𝘁𝗵𝗿𝗲𝗮𝗱𝘀 ? The secret lies in how Python handles execution. I’ve put together a 12-slide deep dive into Python Concurrency, moving from absolute basics to the future of Python 3.13. What’s inside? ✅ Synchronous vs. Async: Why "𝘄𝗮𝗶𝘁𝗶𝗻𝗴" is the biggest bottleneck. ✅ The Event Loop: How 𝗮𝘀𝘆𝗻𝗰𝗶𝗼 manages thousands of tasks on a single thread. ✅ The 𝗚𝗜𝗟 (𝗚𝗹𝗼𝗯𝗮𝗹 𝗜𝗻𝘁𝗲𝗿𝗽𝗿𝗲𝘁𝗲𝗿 𝗟𝗼𝗰𝗸): Why traditional Python threading isn't always "parallel." ✅ The 𝗙𝘂𝘁𝘂𝗿𝗲 (𝗙𝗿𝗲𝗲-𝗧𝗵𝗿𝗲𝗮𝗱𝗶𝗻𝗴): How Python 3.13+ finally enables true multi-core parallelism. 🟪 𝗧𝗵𝗲 "𝗞𝗶𝘁𝗰𝗵𝗲𝗻" 𝗔𝗻𝗮𝗹𝗼𝗴𝘆: Think of a single cook (Thread) multitasking between a gas stove (I/O) and a cutting board. That’s Async. Now imagine a kitchen with multiple cooks and multiple gas stoves. That’s Modern Free-Threading. Whether you're building 𝘄𝗲𝗯 𝘀𝗰𝗿𝗮𝗽𝗲𝗿𝘀 (𝗜/𝗢-𝗯𝗼𝘂𝗻𝗱) or 𝗵𝗲𝗮𝘃𝘆 𝗱𝗮𝘁𝗮 𝗽𝗶𝗽𝗲𝗹𝗶𝗻𝗲𝘀 (𝗖𝗣𝗨-𝗯𝗼𝘂𝗻𝗱), choosing the right model is key to performance. Check out the slides below! #Python #Programming #SoftwareEngineering #Concurrency #AsyncIO #Multithreading #Python313 #TechLearning

Every #Python developer has a well-worn toolkit. Pandas for data, requests for HTTP, logging when something needs to be logged, print() when you’re debugging, and nobody’s looking. These tools work, and they’re battle-tested for good reason. But the Python Package Index now hosts more than 800,000 projects, and Python Software Foundation keeps the ecosystem growing through grants, infrastructure, and community programs that fund the people building the next generation of libraries. Some of the most useful ones never quite break into the mainstream. Most developers use only a fraction of what’s available, so take a gander at what’s new once in a while. (And sign up for #PyConUS, it starts on May 13th! https://us.pycon.org/2026/) Read more about these libraries like #Rich, #Nuff, #Typer:\ https://lnkd.in/ejw3G5Pf

This is how a lot of Python code starts: A simple function. A few flags. A couple of if-statements. And then one more rule gets added… and another… and suddenly everything lives in one place. The code still works, but it becomes harder to extend, test, and reason about. In today’s video, I show how the Policy Pattern helps you move away from big conditional functions toward small, composable rules. Instead of branching logic, you build a pipeline of policies that you can easily reorder, enable, or disable. It’s a simple shift, but it scales much better as your codebase grows. 👉 Watch the full video here: https://lnkd.in/dWuQ_wu3. #python #softwaredesign #designpatterns #cleancode #developers