Tom Reid's PythoC Tutorial: Compiling Native Apps in C

PythoC is an interesting library. It allows you to create, compile and run C executable code all within your familiar Python eco-system. If you want to learn more, check out my latest article on the Towards Data Science blogging platform for more details on how to install and use it, together with some some coding examples. https://lnkd.in/eWAku8Wg

This is a short post about why plain Python logging is not enough when you move to real‑world distributed systems. It shows how we went from simple text logs to structured JSON and basic context passing so that debugging and observability actually work. #Python #Logging #DevOps #Observability #SoftwareEngineering #Backend #PythonDev #Kubernetes #ELK #Datadog https://lnkd.in/eRXbuSP9

🐍 CPython Internals: Your Guide to the Python 3 Interpreter 📖 ➡ Read and navigate the CPython 3.9 interpreter source code.  ➡ Master CPython’s memory management capabilities. ➡ Debug C and Python code like a true professional. And much more... https://lnkd.in/ejS9mzT

Imagine being able to write C code using Python? That just became a reality with the PythoC library. PythoC is a Domain-Specific Language (DSL) compiler that allows developers to write C-like programs and create .exe's using standard Python syntax. You can call the compiled library directly from Python like this, # test1.py from pythoc import compile, i32 @compile def add(x: i32, y: i32) -> i32: return x + y # Compile to native code @compile def main() -> i32: return add(10, 20) result = main() print(result) Then run it like this, python test1.py # prints 30 Or you can create a stand-alone .exe to run. My Towards Data Science article takes you through the details. Read it for free below. https://lnkd.in/eWAku8Wg

Writing code in C++ for a multi-target tracking system has some advantages over python such as speed. For me code development is much slower, and of course there is the unit test code. Since I already have a python real time multi target simulator with gui, data logger, performance metrics, data logging, post processing and analysis, I decided to create python bindings for the C++ code. It worked quite well. Numpy is mapped to the armadillo data structures. The python simulator allows me to see the overall high performance of the various tracking algorithms. https://lnkd.in/gRM_X3xA

🐍📰 How to Convert Bytes to Strings in Python Convert bytes to strings using the correct encoding and validate with error handling. #python

🧵 **Understanding Multithreading in Python — Simplified** While working with Python, I recently explored **Multithreading** — and it completely changed how I think about performance 🚀 💡 **What is Multithreading?** Multithreading allows a program to run multiple tasks (threads) *concurrently* within the same process. 👉 Instead of waiting for one task to finish, Python can handle multiple operations at the same time (especially useful for I/O tasks). 🔹 **Where is it useful?** * API calls 🌐 * File handling 📂 * Web scraping 🕸️ * Background tasks ⚠️ **Important Note:** Due to the **GIL (Global Interpreter Lock)** in Python, multithreading doesn’t always speed up CPU-bound tasks—but it works great for I/O-bound operations. 📌 **Key Learning:** Choosing the right approach (Multithreading vs Multiprocessing) is what makes your code efficient. 🚀 Small optimization → Big performance impact Have you used multithreading in your projects? Share your experience 👇 #Python #Multithreading #Programming #DataEngineering #Coding #TechLearning #CareerGrowth

🐍📰 How to Convert Bytes to Strings in Python Turn Python bytes to strings, pick the right encoding, and validate results with clear error handling strategies #python

Experimenting with PEB traversal on python.exe and I came across .pyd files in the module list. Turns out .pyd hides a surprisingly quiet execution space to execute from within a trusted binary. here's what I found https://lnkd.in/ekukmRci #windowsinternals #tradecraft Playing with Pyd