Embedding CPython in Go for Simplified Deployments

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

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

⚡ Meet Ty: The New Generation Python Type Checker by Astral If you're still using traditional type checkers and feeling the slowdown 👉 it might be time to look at ty Built by the team behind ruff and uv, ty is a blazingly fast Python type checker and language server written in Rust  💡 Why Ty is getting attention ✅ Extremely fast compared to traditional tools ✅ Works as both a type checker and a language server ✅ Rich and actionable diagnostics ✅ Handles partially typed codebases well ✅ Near-instant feedback with incremental analysis 🔍 What makes it really interesting Ty is not just about speed It also introduces advanced typing capabilities like • Intersection types • Smarter type narrowing • Better reachability analysis 🔥 The bigger picture Astral is building a full Python tooling ecosystem ruff for linting uv for packaging ty for type checking 📦 If you care about performance and modern Python tooling, this is definitely one to watch 👉 GitHub repo: https://lnkd.in/eNB37cVa #Python #DataEngineering #TypeChecking #DeveloperTools #Programming #Astral

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

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

Stop Wasting Memory! Conquer Python’s Deadliest Trap: The Reference Cycle. Think your Python code is perfectly memory-efficient just because you use del? Think again. You could be leaving massive memory leaks on the table, and it’s time to see exactly why. I’ve put together this definitive four-panel visualization to expose the inner workings of CPython’s memory management and, more importantly, why it sometimes fails without help. Reference cycles are silent performance killers. When objects get stuck pointing to each other, they become isolated ‘zombies’ that refuse to die, driving up your application’s footprint and potentially triggering crashes. This guide isn’t just theoretical—it's essential knowledge for writing production-ready, scalable Python: See the Illusion: We show how innocently two lists get created. Witness the Trap: Watch as simple append operations create an unbreakable bond—the cycle is born, and ref counts skyrocket. Feel the 'Deadlock': The scariest part. You delete the variables, but the objects live on. They are unreachable, invisible, yet still consuming precious RAM. Meet Your Savior: We introduce the Cyclic Garbage Collector (GC). It’s the only tool powerful enough to break this deadlock and reclaim that trapped memory. Understanding this mechanism isn't optional; it's the difference between a leaky script and robust software. Study this diagram, understand the stakes, and start writing cleaner, smarter Python. What’s your biggest memory optimization challenge? Share it in the comments! 👇 #Python #CPython #MemoryManagement #Programming #TechExplainer #CodingBestPractices #SoftwareEngineering #DataStructures

One thing that significantly improved my Python code quality: Static analysis is not optional at scale. For a long time, I relied on code reviews to catch issues. Eventually, I realized something: 👉 Humans are bad at consistently spotting patterns. 👉 Tools are not. That’s where static analysis changed everything. Without running the code, these tools analyze your source and detect: bugs code smells complexity issues type inconsistencies All before production The combination that worked best for me: Ruff → fast linting and code quality Replaces multiple tools (flake8, isort, etc.) and runs extremely fast Mypy → type checking Uses type hints to catch bugs before runtime, bringing discipline to Python’s dynamic nature Radon → complexity analysis Measures cyclomatic complexity and highlights functions that are hard to maintain. #Python #StaticAnalysis #BackendEngineering #Django #CleanCode #SoftwareEngineering #DevOps

I recently built a small Python backend service to make one thing easier to inspect: good backend engineering transfers across stacks. Python is not my primary language, so the goal was not to “learn Python in public” or chase source parity with an existing system. The goal was to reimplement the core shape of a real orchestration service in a bounded way: - thin API, - orchestration-owned flow, - persisted job and step state, - provider boundary, - tests, - CI/CD, and a small infrastructure baseline. What mattered most to me was not the language switch itself, but preserving the system design and operational model. That’s usually the part that scales beyond any one stack. https://lnkd.in/eF-j-dn3

We have written a python Qt program to load multiple las files representing different run on the same well, depth shift each run to the Base run and then merge these files into a merged las file – all available on GitHub We have just released our beta version of our Merge  petrophysical software written in python using Qt. For me, this has really been a time saver. Give it a try. What it can do today: ·      Load multiple LAS files ·      Display multiple runs on a typical Andy McDonald depth plot (Red box on depth plot representing the extent a GR-type tool available in each run). ·      Choose your base run and then each run to be shifted to the Base run. ·      Determine numerous automatic depth shifts for each run or you can do this manually too ·      Save depth shifted data for that run and then go to the next run ·      After all runs have been depth shifted to the Base run, then the program merges all files and save a single merged las file to be used later on. We would still like to add the capabilities to trim each run, estimate missing curves and QC all data. This is the link on GitHub: https://lnkd.in/gHVEVdsC and to launch this program use this command from the app’s root directory; python -m apps.merge_gui.main #Petrophysics #Python #OpenSource #ReservoirCharacterization #DataScience #ArtificialIntelligence

Most Python code looks simple until you realize how much is happening under the surface. Take this for example: _C = (1, 2, 3) a, b, c = _C print(a) This is iterable unpacking, more precisely Python’s way of doing positional destructuring assignment. What actually happens: _C is evaluated as an iterable Python matches elements positionally Each value is bound in a single atomic assignment step So internally: a = _C[0] b = _C[1] c = _C[2] This pattern is not just syntactic sugar, it is widely used in production code: Function return unpacking (return x, y) Iteration over structured data API responses and tuple-based records Why it matters: Removes manual indexing (less error prone) Improves intent readability Makes transformations explicit and compact One important constraint: If the structure does not match, Python fails fast with a ValueError, which is often a feature, not a bug. Clean syntax, strict alignment, predictable behavior. That is the philosophy behind Python’s design. Which Python feature felt too simple until you saw it in real systems? #Python #SoftwareEngineering #CleanCode #Programming #PythonTips #Coding #Developer #SystemDesign