Ihor Shevchenko’s Post

In real, THREADS don’t actually run in parallel in Python… Here’s what really happens: >They just act like they are running in parallel. >Thread A starts working like it owns the system. Thread B is ready to jump in, but Python says, “Wait for your turn.” So, B waits. Then A pauses, B runs, then C gets a chance. It’s super-fast switching, so it looks like everyone is working together. >It’s basically one laptop shared by multiple users. Everyone looks busy, but only one is typing at a time. >This happens because of the Global Interpreter Lock in Python, which allows only one thread to execute at a time. >However, for I/O-bound tasks like API calls or file handling, threads still improve performance because while one waits, another runs. #Important_Point And here’s the key: in languages like Java, C++, and Go, threads can truly run in parallel across multiple CPU cores. #python #threads #multithreading #java #c++ #coding

𝐃𝐚𝐲 𝟔/𝟑𝟎: 𝐇𝐨𝐰 𝐝𝐨 𝐏𝐲𝐭𝐡𝐨𝐧 𝐝𝐞𝐯𝐬 𝐡𝐚𝐧𝐝𝐥𝐞 𝐭𝐡𝐢𝐬? 🐍 Coming from C++, I’m used to the compiler being my safety net. If I try to add a string to an int there, the code won’t even run. ➡️ But yesterday I realized Python is... a different world! def add(a, b): return a + b add(10, "5")💣 Boom! Runtime Error In a tiny script, this is a 2-second fix.. But in a massive codebase with thousands of functions? This feels like a 𝐡𝐢𝐝𝐝𝐞𝐧 𝐫𝐢𝐬𝐤 𝐰𝐚𝐢𝐭𝐢𝐧𝐠 𝐭𝐨 𝐡𝐚𝐩𝐩𝐞𝐧. So, the real question for the experts here: How do you guys stop these "sneaky" errors before they hit production? Is it: A) Just remember everything B) Test everything thoroughly (Unit tests for every single edge case) C) Use Python type hints (a: int, b: int) (👀but do they actually stop the crash?) D) Something else(MyPy? Pylint?) I’ve been digging into this 𝐜𝐥𝐚𝐬𝐡 𝐛𝐞𝐭𝐰𝐞𝐞𝐧 𝐟𝐥𝐞𝐱𝐢𝐛𝐢𝐥𝐢𝐭𝐲 𝐚𝐧𝐝 𝐬𝐚𝐟𝐞𝐭𝐲 𝐭𝐨𝐝𝐚𝐲. I’m hunting for a way to make Python feel just as safe as C++. I'll share what I find tomorrow! Drop your choice below 👇 What’s the standard industry workflow for catching these? #Python #Cpp #LearningInPublic #30DaysOfCode #SoftwareEngineering #Programming

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

**New video: Debug Python the Right Way** Got a Python traceback but no idea why? The VS Code debugger shows the actual data — no print() rewriting, no guesswork. Breakpoints, variables panel, debug console, and watch expressions — all on a real script hitting an IOS-XR device via the free Cisco DevNet Always-On Sandbox. Part 3 of the Month of Developer Productivity series. 🔗 https://cs.co/6047BBKdlW #DeveloperProductivity #CiscoDevNet #NetworkAutomation #PythonDebugging #VSCode

Another video I wanted to share. Debugging #Python took me a while to grasp it, specially since it was difficult for me to use pdb, so prints were always easier. But once I saw how to do it in VSCode I always use it when investigating why something is not working. Worth to get this skill.

I’ve been strengthening my understanding of the fundamentals of software development, starting with core concepts in Python and web development. So far, I’ve covered: • Variables and how they store data • Data types (strings, integers, floats, booleans) • The difference between numbers and text in Python • Basic output using print statements • What a website is and how it’s structured • The role of the backend in handling logic like user authentication and data processing One concept that stood out to me was how small details matter in programming—for example: "10" + "5" gives "105", not 15, because they are treated as text, not numbers. I’m focusing on building a strong foundation and understanding how things work under the hood as I continue learning. #Python #BackendDevelopment #LearningInPublic #SoftwareEngineering #TechJourney

After years of reading and writing code, I find that the dumbest code is the best code. It doesn't matter if it's C#, C++, or Python. Make your code simple. Don't use complex abstractions or difficult syntactic sugar, and you'll have a codebase that anyone can jump into and quickly add features without introducing bugs (or bugs that are less likely to happen). This matters more than anything else.

In this week’s lecture, Dr. Hartmut Kaiser focuses on the seamless integration of C++ and Python. The lecture highlights essential foundational concepts by exploring how to combine the user-friendliness and extensive library ecosystem of Python with the raw performance of C++, setting the stage for highly efficient scientific computing. A core discussion demonstrates how to utilize the Pybind11 library as a lightweight solution for creating Python bindings, specifically addressing the practical challenges of mapping C++ functions, classes, and diverse data types. Finally, critical strategies for integrating NumPy arrays are explored, offering a comprehensive approach to maximizing performance in advanced mathematical and data-driven applications. Watch the full lecture here: https://lnkd.in/dNkKbBWc #ParallelComputing #HPC #Cplusplus #HPX #ScientificComputing #StellarGroup

Opinion: People who deploy Python Apps really doesn't understand how invasive installing Python can be with all the venv bs you have to do

A client wanted to use LLMs for C# code generation, but LLMs tend to struggle with C# compared to Python or C — producing more syntax errors and language-specific logic mistakes. They asked if we could help close the gap. We combined long-context LLMs with a targeted retrieval system to give the model better grounding in C# conventions. The approach delivered a 5.7% accuracy improvement over zero-shot prompting. For more details, see our full post: https://lnkd.in/e3wrX2pn