🎥 Python just removed the GIL after 33 years. It will change everything for multi-threaded code Python 3.14 ships with a free-threaded version that unlocks true multi-core performance. No more single-threaded bottlenecks. No more heavy multi-processing overhead. (🎥 Full breakdown with code examples in my new video, link in comments👇🏽) 𝗧𝗵𝗲 𝗯𝗮𝗰𝗸𝘀𝘁𝗼𝗿𝘆 Python was created in 1991. Multi-core processors didn't exist until 2001 (IBM's Power4). When they added threading support in 1992, they used the Global Interpreter Lock (GIL) to keep things simple and thread-safe. The GIL is a single lock that only allows one thread to execute at a time. Launch 10 threads or 1 thread? Same performance on a 16-core machine. 𝗪𝗵𝘆 𝘄𝗲 𝗸𝗲𝗽𝘁 𝗶𝘁 👉🏽 Low overhead for single-threaded programs 👉🏽 Simple memory management (reference counting) 👉🏽 Compatibility with non-thread-safe C libraries 👉🏽 Easy to implement (one lock vs fine-grained locking) 𝗧𝗵𝗲 𝗽𝗿𝗼𝗯𝗹𝗲𝗺 Your multi-threaded code runs sequentially. Thread 1 executes, releases the GIL, Thread 2 executes, releases the GIL. Zero parallelism. You had to use multi-processing with massive memory overhead and separate interpreters. 𝗣𝘆𝘁𝗵𝗼𝗻 𝟯.𝟭𝟰 𝗰𝗵𝗮𝗻𝗴𝗲𝘀 𝘁𝗵𝗲 𝗴𝗮𝗺𝗲 Two versions ship together: - Standard Python (with GIL) - Free-threaded Python (no GIL) Same code. Actual parallelism. Multi-core performance with threading. 𝗛𝗼𝘄 𝘁𝗼 𝘁𝗿𝘆 𝗶𝘁 Install: - Install with UV in seconds: - uv python install 3.14t Run your code: - No GIL: PYTHON_GIL=0 python script[.]py - With GIL: PYTHON_GIL=1 python script[.]py That's it. Same threading code, multi-core performance unlocked. 𝗣𝗲𝗿𝗳𝗲𝗰𝘁 𝗳𝗼𝗿 - Data processing pipelines - Scientific computing - Image/video processing - Any CPU-bound workload After 30+ years, Python finally leverages all your cores with threading. No more choosing between simple threading and heavy multi-processing. 𝘈𝘳𝘦 𝘺𝘰𝘶 𝘶𝘴𝘪𝘯𝘨 𝘊𝘗𝘜-𝘪𝘯𝘵𝘦𝘯𝘴𝘪𝘷𝘦 𝘵𝘢𝘴𝘬𝘴 𝘪𝘯 𝘗𝘺𝘵𝘩𝘰𝘯? 𝘛𝘩𝘪𝘴 𝘪𝘴 𝘺𝘰𝘶𝘳 𝘱𝘦𝘳𝘧𝘰𝘳𝘮𝘢𝘯𝘤𝘦 𝘶𝘯𝘭𝘰𝘤𝘬. 🎥 Full breakdown with code examples in my new video (link in comments). 👇🏽 #python #ai #performance #opensource

🔥 Python 3.14 introduces true multithreading — the GIL is finally optional! After decades of limitation, Python 3.14 now ships with a free-threaded (no-GIL) build, officially enabling parallel execution of Python threads across multiple CPU cores. 🧠 What This Means The Global Interpreter Lock (GIL) prevented multiple threads from executing Python bytecode at the same time — effectively serializing all CPU-bound code. With the new build: • The GIL is removed • Reference counting is atomic & thread-safe • Multiple threads can run concurrently and in parallel ⚙️ Quick Example import threading def cpu_heavy(): sum(range(50_000_000)) threads = [threading.Thread(target=cpu_heavy) for _ in range(8)] for t in threads: t.start() for t in threads: t.join() 🧩 In Python ≤3.13 → Only one thread executes at a time 🚀 In Python 3.14 (no-GIL build) → All 8 threads run in true parallelism 📈 Key Takeaways • ✅ True Multithreading: Threads run on different cores • ⚙️ Optional Build: GIL build still exists; use the --disable-gil version for free-threaded mode • 🧩 Extension Update Needed: Libraries like NumPy and Pandas need thread-safe adjustments • ⚡ Performance: Slight single-thread overhead, massive gains for multi-core workloads 🔮 What’s Next This is officially Phase II of Python’s no-GIL adoption. The community will refine it further before it becomes the default build. Once that happens — Python moves into the same parallel performance league as Java and C++. 🐍 Python 3.14 isn’t just another release — it’s the start of Python’s true multithreading era. #Python #NoGIL #Python314 #Multithreading #Concurrency #AI #MachineLearning #ParallelComputing #Developers #Tech

⚡ 5 Ways to Make Your Python Code Run Faster Python is one of the most flexible and readable languages out there but it’s often called slow. In reality, Python just needs the right execution strategy for the right type of workload. Here are some techniques that can dramatically improve performance 👇 🧠 1️⃣ Synchronous (Default) Code runs line by line. Simple and reliable, but slow for tasks like file I/O or API calls. 💡 Best for: small scripts and quick automation. ⚙️ 2️⃣ Multithreading Runs multiple threads in the same process. Great for I/O-bound tasks like web scraping, API requests, or reading files. 💡 Try: ThreadPoolExecutor for simple and powerful parallelism. 🔥 3️⃣ Multiprocessing Spawns separate processes each with its own Python interpreter. Perfect for CPU-bound work like data processing, math, or image tasks. 💡 Try: multiprocessing.Pool to run CPU-heavy functions in parallel. 🌐 4️⃣ Asyncio Executes asynchronous code with async / await. Excellent for handling thousands of concurrent network operations efficiently. 💡 Try: aiohttp or asyncio.gather() for high-performance network I/O. 📊 5️⃣ tqdm A tiny but powerful library for progress bars. Adds instant visibility into long-running loops or downloads. 🚀 Key Takeaway Python isn’t “slow” it’s just synchronous by default. Once you understand when to use threads, processes, or async, you can unlock serious performance gains with just a few lines of code. 💬 What’s your go-to method for speeding up Python scripts? Let’s share tips and tools that make Python even more powerful! #Python #Performance #Asyncio #Multithreading #Multiprocessing #tqdm #SoftwareEngineering #LearningInPublic

Python’s runtime is slow. Interpreter + single-threaded design impose a hard ceiling. Bytecode is executed by the interpreter, not the CPU. Performance “fixes” like Cython help by sprinkling C into Python, but they never replace the core limitations. Python is fast to ship. Developer time beats runtime speed. A project that takes hours or days to wrestle through Rust’s borrow checker or C++’s complexity can be delivered in Python in minutes. For most workloads, delivery velocity matters more than peak performance. Execution speed is often irrelevant. In ML, the heavy lifting happens in C/CUDA; Python just orchestrates. For automation, data tasks, bots, and internal tooling, Python is “fast enough.” End users rarely notice runtime loss. Static typing enables codebase sprawl. TypeScript showed what happens when you make a scripting language scalable: people use it to build systems it wasn’t designed for. Optional Python typing risks the same trap. Practical boundary: • Use Python when velocity and flexibility matter • Use Rust/C++ when latency, throughput, and predictability matter Optimizing Python for raw speed is usually wasted effort. The win is rapid iteration, not theoretical maximum throughput. What do you think, does Python belong in production webservers?

Python — Asyncio: Write Faster I/O Without Threads Want concurrency without the headache of threads? If you’ve ever tried using threads in Python to speed up your program, you probably ran into synchronization issues, race conditions, or the infamous Global Interpreter Lock (GIL). Fortunately, Python offers a cleaner and more efficient way to achieve concurrency—asyncio. Asyncio introduces an event loop, which enables cooperative multitasking. Instead of running multiple threads that compete for CPU time, asyncio allows your program to pause one task when it’s waiting for I/O (like a network request or file read) and resume another in the meantime. This happens seamlessly using the await keyword. When a coroutine (an async function) hits an await, it yields control back to the event loop. This means your program isn’t blocked while waiting for data to arrive—it’s busy doing something else useful. That’s why asyncio is perfect for I/O-bound applications such as web servers, API clients, chat apps, or database connectors. You can scale to thousands of concurrent tasks without creating thousands of threads, making it far more memory-efficient. Combine it with libraries like aiohttp for async web requests or asyncpg for PostgreSQL database operations, and you’ll see dramatic performance improvements. Here’s the catch: asyncio isn’t magic for everything. It won’t speed up CPU-bound workloads like image processing or complex calculations, since the GIL still applies. For that, use the multiprocessing module or offload heavy work to a separate process or thread pool. To keep your async code elegant and bug-free: Use async with and async for consistently. Avoid mixing sync and async code arbitrarily. Wrap blocking calls (like traditional file I/O or CPU work) with run_in_executor() to prevent freezing the event loop. Asyncio gives you concurrency with clarity—no tangled threads, no shared-state chaos, just smooth, cooperative execution. Pro Tip: Don’t fight the event loop—embrace it. CTA: Follow and subscribe to my newsletter for more practical Python performance tips and async coding patterns. #Python #Programming #Asyncio #Threading #Concurrency #Pythonperformance

Ever made your Python code “concurrent” and still felt it’s running like a snail? Yeah… same. 😅 That’s because Python gives you multiple ways to do concurrency — but each one solves a different problem. Earlier I was using native threads but soon realized there is now a better way for coordation. Let’s make this simple 👇 🧵 1️⃣ ThreadPoolExecutor — When your code is mostly waiting Think of API calls, database queries, or file reads. Threads let your program handle multiple “waiting” tasks at once instead of blocking everything. 👉 It’s like calling 10 people and putting them all on hold together instead of one by one. ⚙️ 2️⃣ ProcessPoolExecutor — When your code is burning CPU This one actually uses multiple cores. Great for math-heavy or data processing tasks where the CPU is sweating. It’s like having 4 chefs in 4 kitchens instead of 1 chef doing everything alone. ⚡ 3️⃣ asyncio.create_task — When you want async I/O magic This is pure event-loop goodness — no threads, no processes. It just switches super fast between tasks that are waiting on I/O. 👉 It’s like cooking multiple dishes — while one simmers, you chop the veggies for the next. 🔄 4️⃣ asyncio.to_thread — When async meets blocking code Sometimes you’re in an async app but still need to run an old, blocking function. This lets you sneak it into a background thread without freezing everything. 👉 It’s like having someone else hold your place in line while you grab a coffee. ☕ 💡 Quick guide: 🧵 ThreadPoolExecutor → I/O bound (lots of waiting) ⚙️ ProcessPoolExecutor → CPU bound (heavy lifting) ⚡ asyncio.create_task → Async I/O (event loop magic) 🔄 asyncio.to_thread → Mix sync with async safely Threads handle waiting. Processes handle computation. Async handles coordination. Once you get this mental model — concurrency in Python finally clicks. #concurrency #python #asynchronous #learning

🚀 Beyond the GIL: Python 3.14’s Hidden Performance & DevX Upgrades The core challenge for Python has always been true multi-core utilization. While the optional "free-threaded" build addresses this for CPU-bound work, these other features provide direct, immediate performance and quality-of-life wins today: Native Subinterpreters (PEP 734): This is the true multi-core parallelism engine. It allows multiple, isolated Python interpreters to run in parallel within a single process. The Shortcut: Use the new concurrent.interpreters module for parallel processing with less memory overhead than traditional multiprocessing. Think isolated, concurrent ML model serving. Template Strings (T-Strings): A new string literal type that defers evaluation, making them ideal for safer string processing. Actionable Tip: Use T-Strings for securely generating SQL queries or configuration files, drastically reducing the risk of injection attacks compared to raw f-strings in certain contexts. Incremental Garbage Collector: Say goodbye to those annoying, split-second application freezes! The new GC works in small, quick steps, resulting in much smoother latency for web servers and GUIs. This isn't just a new Python version; it's a re-architecture for the multi-core, high-concurrency world. Which one of these 3.14 features will have the biggest impact on your current project's system design? Share your thoughts! 👇 #SystemDesign #AI #Tech #CloudComputing #DeveloperTools #LearningJourney

Antonio Cuni (20 years PyPy core) dropped SPy: a compiled Python variant designed for performance. 𝗧𝗵𝗲 𝗰𝗼𝗿𝗲 𝗶𝗱𝗲𝗮 Python's dynamism makes it fundamentally hard to optimize. Everything is mutable, dispatch is complex, and pointer chasing destroys cache locality. JITs help but introduce unpredictable performance cliffs. SPy takes a different approach: remove the dynamism that kills performance, but add new features that keep Python's expressiveness intact. 𝗪𝗵𝗮𝘁 𝗺𝗮𝗸𝗲𝘀 𝗦𝗣𝘆 𝗱𝗶𝗳𝗳𝗲𝗿𝗲𝗻𝘁 👉🏽 Import time vs runtime: The world freezes after imports. Modules and classes become immutable at runtime. 👉🏽 Redshifting: Blue expressions evaluate at compile time, red at runtime. It's partial evaluation on steroids. 👉🏽 @blue functions: Write metaprogramming code that runs during compilation. Like C++ templates, but debuggable with Python's interpreter. 👉🏽 Static dispatch: Operator lookup happens at compile time based on static types. The runtime overhead just vanishes. 𝗧𝗵𝗲 𝗿𝗲𝘀𝘂𝗹𝘁? A raytracing example runs 200x faster than CPython. The compiler generates code comparable to C/Rust, with predictable performance. 𝗧𝗿𝗮𝗱𝗲-𝗼𝗳𝗳𝘀 SPy isn't 100% Python compatible (by design). It formalizes constraints that Python devs already follow in practice: stable types, immutable classes, minimal monkey-patching. But you get metaprogramming power back through , which feel surprisingly natural. The project is early stage but the ideas are solid. Worth watching if you care about Python performance without JIT unpredictability. #python #ai #opensource #llm

Introducing rpycbench, an open source python RPC profiler/benchmark. We do a lot of remote AI workload monitoring for CXL.mem at Micron Technology. Our internal application suite taught me a lot about RPyC, a very slick python extension that treats remote objects and functions as if they are local. I thought it would be cool to put together an open source tool to run experiments on how RPyC performs compared to HTTP/REST. You can benchmark your own network topology with bandwidth, latency, server forking/threading strategies, run locally or remotely with zero deployment, visualize recursive network round trips, get telemetry and stats on stack frames and netref object resolution from the command line with no code. THEN, you can turn right around and use the python API to profile + optimize your existing app code using all the same tools. https://bit.ly/3JTGFR3

Python 3.14 adds asyncio introspection. Finally, you can see what your async code is doing Debugging stuck asyncio programs can be painful. 𝘠𝘰𝘶 𝘬𝘯𝘰𝘸 𝘴𝘰𝘮𝘦𝘵𝘩𝘪𝘯𝘨'𝘴 𝘸𝘢𝘪𝘵𝘪𝘯𝘨, 𝘣𝘶𝘵 𝘸𝘩𝘢𝘵? Python 3.14 ships with built-in tools to inspect running async tasks. 𝗧𝗵𝗲 𝗻𝗲𝘄 𝗰𝗼𝗺𝗺𝗮𝗻𝗱𝘀: - python -m asyncio ps PID - python -m asyncio pstree PID The ps command shows a flat table of all tasks: names, coroutine stacks, and what they're awaiting. The pstree command renders a visual async call tree. You see the hierarchy of tasks and how coroutines chain together. 𝗪𝗵𝘆 𝘁𝗵𝗶𝘀 𝗺𝗮𝘁𝘁𝗲𝗿𝘀: Async debugging used to mean adding print statements everywhere or using third-party tools. Now you can inspect a running process and see exactly where it's stuck. Perfect for long-running async services or finding deadlocks in task groups. 𝗪𝗵𝗲𝗻 𝘁𝗼 𝘂𝘀𝗲 𝗶𝘁: 👉🏽 Production debugging (service stuck, but why?) 👉🏽 Development (understanding complex task flows) 👉🏽 Performance analysis (finding bottlenecks in async code) If a task group isn't finishing, pstree shows you which tasks are blocking and their relationships instantly. 𝗧𝗵𝗲 𝗰𝗮𝘁𝗰𝗵: You need access to the process. This works for local development and servers you control. Not for serverless or containerized apps without process access. But for traditional deployments? This is gold. Asyncio just got a lot easier to debug. If you're building async Python apps, this tool will save you hours of head-scratching. What's your biggest asyncio debugging pain point? #python #asyncio #ai #agi