C++ powers Python's speed and performance

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

🚀 Python 3.13+ is a game-changer: Free-threading (no-GIL mode) and experimental JIT boost multithreaded code by 2-5x! Speed gains are real for CPU-heavy tasks. Tested a simple parallel sum script—3x faster than 3.12. Python 3.15 stabilizes JIT fully. Here’s the snippet: # Run with: python3.13 -X free-threading import threading def compute(n): return sum(i*i for i in range(n)) threads = [threading.Thread(target=compute, args=(10**7,)) for _ in range(4)] for t in threads: t.start() for t in threads: t.join() print("Done!") Who’s upgraded? Share your benchmarks below! 👇 #Python #Python313 #Programming

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

Day 3/365: Comparing Two Strings Character by Character 🧵🧠 Today I worked on a simple but fundamental logic problem: checking if two strings are the same, without directly using a built-in equality check. First, I compare the lengths of both strings. If lengths differ, they can’t be the same. If lengths match, I loop through each index and compare characters one by one. If any character is different, I break and print that the strings are not the same. If the loop finishes without finding a mismatch, the else block of the for loop runs and prints that the strings are the same. The interesting part is the for-else in Python. The else only runs when the loop completes normally (no break). This makes it a clean way to express: “if I didn’t find any mismatch in the entire loop, then the strings are equal.” Day 3 done ✅ 362 more to go. #100DaysOfCode #365DaysOfCode #Python #LogicBuilding #StringComparison #ForElse #CodingJourney #LearnInPublic #AspiringDeveloper

Clean code isn't clever. It's clear. 5 Python patterns every developer should know: 1️⃣ Flatten nested list: flat = [x for sub in nested for x in sub] 2️⃣ Merge dicts (Python 3.9+): merged = dict_a | dict_b 3️⃣ Most frequent item: max(set(lst), key=lst.count) 4️⃣ Swap variables: a, b = b, a 5️⃣ Read + strip file lines: lines = [l.strip() for l in open("file.txt")] --------------- These aren't tricks. They're idiomatic Python. When your code communicates intent: ✅ Reviews go faster ✅ Bugs surface sooner ✅ Onboarding is smoother Write for the developer reading it at 2am before a deployment. That developer is usually you. #Python #CleanCode #Programming #CodingTips #SoftwareEngineering

People sometimes ask me why I still like to work with Python notebooks given that LLMs can also generate web-apps for us. The difference to me comes down to the fact that I really like it when my software is usable like legos. https://lnkd.in/eVDdgBEw

The generator expression that relies on lazy evaluation (iterator) is the most underrated approach for processing large sequence of non strictly numerical data. #GeneratorExpression #Python

Here’s a tiny Python change that pays off fast. Python tip: use `@dataclass(slots=True)` for high-volume models. It removes per-instance `__dict__`, which usually means lower memory usage and slightly faster attribute access. Great for DTOs, parser outputs, event payloads, and cache objects where shape is fixed. Mini rule: if the object schema is stable, add `slots=True` by default. #Python #Performance #CodeQuality #SoftwareEngineering

C++26 Reflection & Python Bindings Writing bindings manually is tedious: * Extra code you need to read, maintain(,and write). * Extra dependencies in the project. * Extra bugs. I’ve been exploring C++26 reflection and built a small prototype: automatic Python bindings without writing bindings. Here’s how it works 👇 Post: https://lnkd.in/gwJYhnnF Code: https://lnkd.in/gbQqPVNr #cpp #reflection #c++26

I’ve published my first technical article: a walkthrough of the SOLID principles—with Python examples. It started as “I’ve heard these letters everywhere—what do they actually mean in code?” Turning that into something concrete helped me more than skimming another diagram. In the post I break things down into bite-sized pieces, including: • Single Responsibility: One job per module—easier to reason about and change. • Open/Closed: Extend behavior without rewriting existing code. • Liskov Substitution: Subtypes that don’t break expectations. • Interface Segregation: Small, focused contracts instead of fat interfaces. • Dependency Inversion: Depend on abstractions, not concrete details. Beyond the theory, each section includes short Python snippets so the ideas map to something you can run and tweak—not just memorize. The full post is here: https://lnkd.in/gFXSE4d9 #SoftwareEngineering #SOLID #Python #CleanCode #OOP #DesignPatterns