Python Threads Appear to Run in Parallel but Don't

Day 2 of my Python Full Stack journey. ✅ Today I covered the very first building block of Python: → Variables → Data Types (int, float, str, bool) → f-strings to print dynamic output Looks simple. But this is the foundation everything else is built on. Here's what I actually typed today: name = "Punith" # str age = 24. # int score = 9.5 # float is_dev = True # bool print("Hi, I'm {Punith}, age {24}") and many. One thing that clicked today: Python figures out the data type automatically. No need to declare it like in Java or C. This is called dynamic typing — and it makes Python so much cleaner to write. 45 minutes. Committed to GitHub. Showing up again tomorrow. If you're learning to code right now — what was the first concept that actually made sense to you? #PythonFullStack #Day2 #BuildingInPublic #100DaysOfCode #Bangalore

🚨 Most Python developers are using concurrency WRONG. Yes, I said it. If you're still confused between Multithreading and Multiprocessing, you're probably leaving performance on the table. Let’s fix that 👇 🧵 Multithreading → Same process, shared memory → Fast & lightweight → Perfect for I/O tasks (API calls, file handling, DB queries) BUT… Thanks to Python’s GIL, threads DON'T run in true parallel for CPU tasks. 👉 Translation: Your CPU-heavy code is still slow. ⚙️ Multiprocessing → Separate processes, separate memory → Uses multiple CPU cores → TRUE parallel execution 👉 Best for: Heavy computations Data processing ML workloads 💥 The Reality: If you're using: Threads for CPU tasks ❌ (Wrong choice) Processes for simple I/O ❌ (Overkill) You're wasting resources. 🧠 Simple Rule: 👉 I/O-bound → Use Multithreading 👉 CPU-bound → Use Multiprocessing 🔥 Pro Tip: Top developers don’t just write code… They choose the RIGHT execution model. 💬 What are you using more in your projects — Threads or Processes? #Python #Multithreading #Multiprocessing #BackendDevelopment #SystemDesign #CodingTips

I recently discovered assert_never in Python's typing module and it's one of those small things worth knowing about. Say you add a new status to your workflow, like "𝙘𝙖𝙣𝙘𝙚𝙡𝙡𝙚𝙙", and forget to handle it somewhere in the code. Nothing warns you. It runs fine until that status hits in production. The common fix is a raise 𝚅̲𝚊̲𝚕̲𝚞̲𝚎̲𝙴̲𝚛̲𝚛̲𝚘̲𝚛̲("𝚄̲𝚗̲𝚎̲𝚡̲𝚙̲𝚎̲𝚌̲𝚝̲𝚎̲𝚍̲ ̲𝚟̲𝚊̲𝚕̲𝚞̲𝚎̲") at the end. It works at runtime, but the type checker stays silent until something actually breaks. 𝗮𝘀𝘀𝗲𝗿𝘁_𝗻𝗲𝘃𝗲𝗿 does the same thing at runtime, and mypy and pyright will flag the branch statically, right in your IDE, before you ever run the code. Available in 𝘁𝘆𝗽𝗶𝗻𝗴 from Python 3.11+, or via 𝘁𝘆𝗽𝗶𝗻𝗴_𝗲𝘅𝘁𝗲𝗻𝘀𝗶𝗼𝗻𝘀 for older versions.

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

Small Change, Big Performance Gain I recently tested a simple optimization in Python using ThreadPoolExecutor for an API-heavy workflow. First Case : 2 workers Total tasks: 52 ( external API calls) Time taken:  👉 436.489 seconds Second Case : 10 workers Total tasks: 52 ( external API calls) Time taken:  👉 90.856 seconds 💥 Simple takeaway If your workload involves: 1. API calls 2. database queries 3. external service calls then improving concurrency can* have a big impact.

If you used Rcpp back in the R days, this will feel familiar. I vibe-coded something over the weekend that does the same thing for Python: write C++ inline, call it like a normal function, get results back as numpy arrays. No build system, no separate files, no compilation step. ``` fn = cppFunction(''' std::vector<double> simulate(int n, double mu) { ... } ''') fn(10000, 2.5) ``` Projects like pybind11 and Cython are better for production C++ projects, but this is for when you just need one fast function right next to your Python code. The code compiles on the first call and is cached forever after. Armadillo works out of the box if you have it installed. https://lnkd.in/gt6YQENq

🧠 How Python Works Internally (Big Picture Explained) Python is one of the most popular programming languages today, used in web development, data science, automation, and artificial intelligence. But most developers only scratch the surface. If you truly want to master Python, you need to understand what happens behind the scenes when your code runs. The Big Picture When you run a Python program, it goes through this pipeline: Source Code → Bytecode → Python Virtual Machine → Output Unlike languages such as C or C++, Python does not directly execute your code or compile it into machine code. When you run: x = 5 print(x) Internally: 1. Code is tokenized 2. Converted to AST 3. Compiled to bytecode 4. Executed by PVM 5. Memory managed automatically Have you ever explored Python internals before? Which concept surprised you the most? Let’s discuss in the comments 👇

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

Built a mini C compiler from scratch in Python. No libraries. No shortcuts. Just pure compiler theory turned into working code. The pipeline: Source code → Lexer → Parser → Semantic Analyser → Code Generator → x86-64 Assembly What it covers: → Tokenizes raw C source into a typed token stream → Recursive-descent parser builds a full AST → Symbol table with type checking and use-before-declare detection → Emits real x86-64 AT&T assembly, linkable with gcc → Supports functions, arrays, pointers, if/else, while, for loops Also includes a sliding window test case (max sum subarray of size k) compiled and run entirely through the custom compiler all 6 test cases pass. It's a great exercise if you want to actually understand what happens between writing C and running a binary. Full source + README on GitHub. https://lnkd.in/gj5A3-xb #C #Compilers #Python #SystemsProgramming #LowLevelProgramming #OpenSource

🚀 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