Python Module Loading Internals: A 4-Step Pipeline

🐍 Coming from Python. Last week I confidently wrote this in Rust: for i in range(1..100) { // 💥 sum += i; } Error: cannot find function 'range' in this scope My brain: “But Rust it’s just Python with semicolons?” That tiny mistake became one of the best lessons I’ve had in systems programming. 🔍 Corrected Rust vs Python Rust fn main() { let number: u32 = 92; if number % 2 == 0 { println!("{} is even", number); } else { println!("{} is odd", number); } let size = match number { 1..=20 => "small", 21..=50 => "medium", 51..=100 => "large", _ => "out of range", }; let mut sum = 0; for i in 1..=100 { sum += i; } println!("Sum = {}", sum); } Python number = 92 if number % 2 == 0: print(f"{number} is even") else: print(f"{number} is odd") if 1 <= number <= 20: size = "small" elif 21 <= number <= 50: size = "medium" elif 51 <= number <= 100: size = "large" else: size = "out of range" total = sum(range(1, 101)) print(f"Sum = {total}") Both do the same job. But the how is completely different. 🧠 Under the Hood – What Rust Forces You to Respect 1. No hidden pointers Python’s number = 92 is a full PyObject on the heap (refcount + type pointer + value). Rust’s u32 lives on the stack one CPU register. Modulo is a single instruction. 2. Zero-cost abstractions Rust’s 1..=20 in match compiles to two integer comparisons (disappears after optimization). No method calls, no temporary objects. 3. The loop reality Rust’s 1..=100 iterator lives entirely on the stack. 100 stack increments. Python’s range(1, 101)creates 100 Python integer objects behind the scenes (heap + refcounting + GC pressure). 4. Match is lightning fast Rust turns match into a jump table or optimized branches. Python’s version (even structural pattern matching) still has runtime overhead. ⚡ The Discipline Rust Teaches Python lets you be productive. Rust forces you to be precise. No more `range()` → Use the `..` syntax directly. Forgetting `mut`? Compiler stops you. Semicolon on last expression? You just returned `()`. This explicitness about memory and ownership is exactly why Rust code is so reliable and fast. Pro tips for Python devs: Treat the compiler like a strict but honest mentor. Understand stack vs heap early — it makes ownership click. Run `cargo clippy` religiously. 💬 Final Thought Rust won’t let you forget that every variable occupies real memory, every loop touches real silicon, and every decision has consequences. Python is a fantastic friend. Rust is a disciplined mentor that makes you a better programmer. Your Python experience is an advantage once you unlearn a few habits. Like ❤️ if you’ve ever tried to use Python syntax in Rust Repost 🔁 to help other Pythonistas Comment💬: What Python habit was hardest for you to break in Rust? #RustLang #Python #RustForPythonDevs

I nodded along in code reviews for months before I actually understood what half these Python terms meant. Nobody tells you this when you’re learning. You pick up the syntax, you get things working, and you quietly hope nobody asks you to explain what a decorator actually does or why the GIL exists. So here’s a honest breakdown of the Python terms most people pretend to know. Virtual environments are not optional extras. Every serious project uses them because without one, a single package install can quietly break three other projects you forgot you had. Decorators are functions that wrap other functions. That’s it. Every time you see @login_required in Django or @app.route in Flask, that’s a decorator doing its job in the background. The GIL is one of those things that sounds scary until you understand it. Python only lets one thread run at a time by keep memory safe. For I/O heavy work it barely matters. For CPU heavy computation you reach for multiprocessing instead. Generators are underused by most people who aren’t working with large data. The yield keyword lets you process values one at a time instead of loading everything into memory. Reading a 1GB file without crashing your machine is the classic example. List comprehensions are just a cleaner way to build lists. Faster, more readable, and they signal to anyone reviewing your code that you actually know Python. The interpreter vs compiler distinction explains why Python is slower than C but easier to debug. It runs line by line. Most production systems compensate with optimisations layered on top. Pickle lets you save Python objects to disk and reload them later. It’s used constantly in ML for saving models. The one rule is to never unpickle files from sources you don’t trust. It’s a real security risk that catches people off guard. Pip handles Python packages. Conda handles packages, Python versions and environments together. Use pip for web projects and Conda for data and ML work. Mixing them randomly is how you end up with a broken environment at the worst possible time. The gap between writing code that works and actually understanding what it’s doing is bigger than most people admit. Closing that gap is what separates someone who can code from someone who can engineer. Which of these did you have to quietly google after pretending you already knew it? Credits: this.girl.tech #Python #SoftwareEngineering #Developers #Programming #TechEducation #AI

How My Python Brain Almost Broke My Rust Code🐍🦀 "I typed return, added a semicolon, and Rust looked at me like I’d just tried to put ketchup on a five-star steak." Coming from Python, we’re treated to a world where functions are like polite requests: "Please do this, and return this value when you're done." It’s explicit. It’s comfortable. It’s what we know. But then I met Rust. I tried to write a simple area function and my Python instincts screamed: “Declare the variables! Use the keyword! End the line!” The result? A syntax error that felt like a personal intervention. ## Under the Hood: The "Semicolon" Secret 🤐 In Python, almost everything is a statement. A statement is a command—it does something. In Rust, the magic lies in Expressions. With a semicolon (;): You’ve created a Statement. It performs an action, returns "unit" (), and effectively "kills" the value's journey. Without a semicolon: You’ve created an Expression. The value is "live," and because it’s the last thing in the block, Rust "yields" it upward to the function caller. You may ask? Why does Rust hate my return 🤷? It doesn't! You can use return, but idiomatic Rust treats the last expression as the "final result" of the block. Think of a Python function as a vending machine (you have to press a button to get the result out). Think of a Rust function as a waterfall (the value naturally flows out the bottom unless you put a dam—a semicolon—in its way). I wanna spoil your evening dear python Devs who are transitioning 🤣: 1. Stop declaring "Return variables": You don't need result = x * y. Just let x * y be the last line. 2. The Semicolon is a Wall: If you want a value to leave a function, don't block it with a ;. 3. Types are Friends: While Python guesses what you’re returning, Rust demands you sign a contract by declaring the return type before you even type the type the logic 🫣 eg.(-> i32) . It feels strict and very manual, but it’s actually Rust’s way of promising your code won't crash at 3 AM 😂. Conclusion: Transitioning from Python to Rust isn't just about learning new syntax; it’s about moving from a "Command" mindset to a "Flow" mindset. Python tells the computer what to do; Rust describes how data should transform brick by brick. Ditch the semicolon, ditch the indentation,embrace the expression, and let your code flow! 🦀✨ #RustLang #Python #CodingHumor #SoftwareEngineering #LearnInPublic

You could spin up 100 threads in Python. Only one would run Python code at a time. For 30 years. As of 3.14, that's finally changing. And I think it matters way more for the AI era than anyone is giving it credit for. I maintain langchain-litellm (https://lnkd.in/eAYYe3vq), the adapter between LangChain and LiteLLM AI Gateway's 100+ provider routing. A lot of people use it to build agentic pipelines where the same code might call Claude, GPT-4o, and Gemini depending on the task. When I started thinking about free-threading in that context, it clicked why this matters right now specifically. Agentic workloads are concurrent at the system level. You're routing a request to one model while embedding a document and parsing a previous response — ideally all at the same time. The network I/O was always fine, async handles that. But the compute sitting around those calls was bottlenecked by the GIL, a lock deep inside CPython that serialized thread execution no matter how many cores you had. The GIL is now optional. You opt into python3.14t, and threads actually run in parallel. What this doesn't change: you still don't manage memory manually, the garbage collector is unchanged. What it does change: race conditions are now your problem, same as in Go or Java. The single-threaded overhead is around 5-10%, so it's not free. And a lot of packages haven't updated yet — they'll silently re-enable the GIL on import until they do. Track ecosystem support at https://lnkd.in/ejHh3knW. GIL-disabled-by-default is probably 2028-2029 and doesn't even have a PEP yet. But if you're building Python AI infrastructure, run your test suite against python3.14t now. Not to ship it — just to know what breaks. PEP 703 (peps.python.org/pep-0703) is surprisingly readable, and the official HOWTO (https://lnkd.in/eiiYFrQA) is the clearest practical guide on this. If you've tried 3.14t on real workloads — what broke first? #Python #LLM #AIEngineering #OpenSource #LangChain

"Python is slow." Every developer has heard this. And technically, it's true. Pure Python loops are 50-100x slower than C/C++. That part is real. But here's what nobody tells you — Python doesn't do the heavy lifting. It tells C and Rust what to do. 𝗪𝗵𝗮𝘁 𝗮𝗰𝘁𝘂𝗮𝗹𝗹𝘆 𝗿𝘂𝗻𝘀 𝘂𝗻𝗱𝗲𝗿𝗻𝗲𝗮𝘁𝗵: → numpy.dot() → Intel MKL / OpenBLAS (C/Fortran) → torch.matmul() → cuBLAS (CUDA C++) → Pydantic v2 → pydantic-core (Rust) → Uvicorn HTTP → httptools (C) → orjson.dumps() → Rust JSON serializer → pandas.read_csv() → C parser Python is the steering wheel. The engine is C/Rust. 𝗧𝗵𝗲 𝗻𝘂𝗺𝗯𝗲𝗿𝘀 𝘁𝗵𝗮𝘁 𝗺𝗮𝘁𝘁𝗲𝗿: Matrix multiplication (1000x1000): • Pure Python → 450 seconds • C++ → 0.8 seconds • Python + NumPy → 0.03 seconds Read that again. Python + NumPy is 26x faster than raw C++ because it calls hand-tuned BLAS libraries with CPU SIMD optimization. ResNet-50 training on ImageNet: • PyTorch (Python) → 28 min/epoch • LibTorch (pure C++) → 27 min/epoch Same speed. Because Python is just orchestrating — the math runs in compiled CUDA kernels. 𝗔𝗣𝗜 𝗽𝗲𝗿𝗳𝗼𝗿𝗺𝗮𝗻𝗰𝗲 (𝗿𝗲𝗾𝘂𝗲𝘀𝘁𝘀/𝘀𝗲𝗰): → Gin (Go) → 45,000 → Spring Boot (Java) → 18,000 → Express.js (Node) → 15,000 → FastAPI (Python) → 12,000-15,000 → Django (Python) → 1,200 → Rails (Ruby) → 900 → Laravel (PHP) → 800 FastAPI sits right next to Express and Spring Boot. 15x faster than Laravel. 𝗪𝗵𝘆 𝗻𝗼 𝗹𝗮𝗻𝗴𝘂𝗮𝗴𝗲 𝗰𝗮𝗻 𝘁𝗼𝘂𝗰𝗵 𝗣𝘆𝘁𝗵𝗼𝗻 𝗶𝗻 𝗠𝗟: → 500,000+ pre-trained models on HuggingFace → PyTorch, TensorFlow, JAX — all Python-first → Native GPU acceleration (CUDA) → Go has zero mature ML frameworks → PHP has zero ML frameworks → Java has DL4J... and that's it Even if Go is 3x faster at raw computation — 3x faster at nothing is still nothing. You'd spend 6 months building what Python gives you in one pip install. 𝗧𝗵𝗲 𝗯𝗼𝘁𝘁𝗼𝗺 𝗹𝗶𝗻𝗲: Python is slow. Python's ecosystem is not. And in 2026, the ecosystem is what ships products — nobody writes matrix math by hand anymore. #Python #FastAPI #MachineLearning #SoftwareEngineering #WebDevelopment #AI

🚀 Python Series – Day 15: Exception Handling (Handle Errors Like a Pro!) Yesterday, we learned how to work with files in Python 📂 Today, let’s learn how to handle errors smartly without crashing your program ⚠️ 🧠 What is Exception Handling? Exception handling is a way to manage runtime errors so your program continues running smoothly. 👉 Without it → program crashes ❌ 👉 With it → program handles error gracefully ✅ 💻 Understanding try and except try: # risky code (may cause error) except: # runs if error occurs 🔍 How it Works: ✔️ Python first executes code inside try ✔️ If NO error → except is skipped ✔️ If error occurs → Python jumps to except ⚡ Example 1 (Basic) try: num = int(input("Enter number: ")) print(10 / num) except: print("Something went wrong!") 👉 If user enters 0 or text, error is handled. 🔥 Why Avoid Only except? Using only except is not a good practice ❌ 👉 It hides the real error. ✅ Best Practice: Handle Specific Errors try: num = int(input("Enter number: ")) print(10 / num) except ZeroDivisionError: print("Cannot divide by zero!") except ValueError: print("Please enter a valid number!") ⚡ Multiple Exceptions in One Line except (ZeroDivisionError, ValueError): print("Error occurred!") 🧩 else Block (Less Known 🔥) try: num = int(input("Enter number: ")) except ValueError: print("Invalid input") else: print("No error, result:", num) 👉 else runs only if no error occurs 🔒 finally Block (Very Important) try: print("Trying...") except: print("Error") finally: print("This always runs ✅") 👉 Used for cleanup (closing files, database, etc.) 🎯 Why This is Important? ✔️ Prevents crashes ✔️ Makes programs professional ✔️ Used in real-world apps, APIs, ML projects ⚠️ Pro Tips: 👉 Always use specific exceptions 👉 Use finally for cleanup 👉 Don’t hide errors blindly 📌 Tomorrow: Modules & Packages (Organize Your Code Like a Pro) Follow me to master Python step-by-step 🚀 #Python #Coding #Programming #DataScience #LearnPython #100DaysOfCode #Tech #MustaqeemSiddiqui

#Understanding Python Destructors: A Practical Mini-Project # Python’s __init__ method gets all the spotlight, but what happens when an object’s job is done? That’s where the Destructor (__del__) comes in. I recently explored how to use destructors to manage external resources efficiently. Here is a mini-project demonstrating a Smart File Session Manager. 🛠️ The Project: Automated Resource Cleanup In real-world apps, forgetting to close a file or a database connection can lead to memory leaks. This script ensures that the resource is safely closed the moment the object is destroyed. 📝 Step-by-Step Implementation: Step 1: Define the Class and Constructor We initialize the session and automatically create a log file. Python class SessionManager: def __init__(self, name): self.name = name self.file = open(f"{self.name}.txt", "w") print(f"✅ Session '{self.name}' started and file created.") Step 2: Add Functionality A simple method to write logs to our file. Python def write_log(self, message): self.file.write(message + "\n") print(f"✍️ Logged: {message}") Step 3: Implement the Destructor (__del__) This is the magic part. It triggers automatically when the object is deleted or the program ends. Python def __del__(self): self.file.write("Session Closed.\n") self.file.close() print(f"🗑️ Destructor Called: Resources for '{self.name}' cleaned up.") Step 4: Execute and Observe Python # Creating the object session = SessionManager("User_Activity_Log") session.write_log("User logged in at 10:00 AM") # Manually deleting the object to trigger the destructor del session 💡 Key Takeaways: Automatic Cleanup: The __del__ method ensures that even if you forget to close a file, Python handles it during garbage collection. Resource Management: Great for handling database connections, network sockets, or temporary files. Caution: While powerful, destructors should be used carefully due to how Python's Garbage Collector handles circular references. Building these small utility projects helps in understanding the "under the hood" mechanics of Python. How do you handle resource cleanup in your projects? Do you prefer Destructors or Context Managers (with statements)? Let’s discuss in the comments! 👇 #Python #Programming #SoftwareDevelopment #CleanCode #CodingTips #PythonBeginner #BackendDevelopment

I just finished a deep dive into Python's internal integer handling and it completely changed my perspective on basic variables. In languages like C or Java, an integer is a fixed-width box of 32 or 64 bits. If you try to shove a number larger than 2^63-1 into a 64-bit box, it overflows and breaks. Python avoids this entirely by treating integers as dynamic objects called PyLongObjects. Instead of a single binary value, a Python integer is an array of digits stored in base 2^30. Under the hood, every integer follows a specific C-structure with three main parts. First is the PyObject_HEAD, which handles standard metadata like reference counts and type info. Next is the ob_size field, which is the secret sauce of Python math. This field stores the number of items in the digit array and simultaneously tracks the sign. If the number is negative, ob_size is negative; if the number is zero, ob_size is zero. The third part is the ob_digit array, which actually holds the chunks of your number. You might wonder why Python uses base 2^30 instead of something simpler like base 10. It comes down to pure hardware efficiency and CPU registers. On a 64-bit system, multiplying two 30-bit digits results in a 60-bit value. This 60-bit result fits perfectly inside a single 64-bit CPU register. This allows Python to handle massive multiplication without losing data or needing complex overflow logic for every tiny step. On older 32-bit systems, Python automatically switches to base 2^15 for the exact same reason. Think of a massive Python number as a polynomial where the variable x is 2^30. Python just adds more terms to the polynomial as the number grows, limited only by your available RAM. But this flexibility comes with a significant performance and memory tax. Even a simple number 1 takes up 28 bytes of memory in Python. That is 16 bytes for the header, 8 bytes for the size field, and 4 bytes for the actual digit. This is why data-heavy libraries like NumPy exist—they bypass this overhead by using C-style fixed-width integers. Python essentially trades raw hardware speed for a feeling of mathematical infinity. It is a beautiful example of software abstraction hiding complex engineering to make the developer's life easier. If you have ever written x = 10**1000 and it just worked, this is the architecture that made it happen. Full breakdown of the BigInt paper and internal logic linked in the comments.

🔸 🔸 🔸 Classic example to address late-binding and closures in python This 4 line of code talks about multiple important concepts ↘️ Code: funcs = [] for i in range(3):   funcs.append(lambda : i)     print([f() for f in funcs]) 🤔 What do you think ❓ Will it give any result, will it error out of random ❓ 🟰 Well, this prints [2,2,2] over console The idea is to demystify why this code does what it does under the hood. Concepts ↘️ ➡️ Is lambda : i a valid syntax ? If yes, what does it denote It denotes a shorthand notation to define a function that does not accept any input parameters and returns a single value as output. What will lambda : i resolve to ❓ ↘️ def f(): return i ❓ What is the iterative statement doing 🟰 It simply creates a container in memory, denoted by i. Initializes i to 0 and keeps incrementing the value that the container referenced by i contains ➡️ i : 0 -> 1 -> 2 ❓What will the list funcs hold ➡️ It's important to note here, funcs is a list that appends lambda into it. ➡️ Since, lambda is nothing more than a function in python, the list holds function ✅ ❓ But, how can a list hold function and what does it mean ➡️ Python treats function as objects, and in programming objects are nothing more than a memory address. 🟰 So, the funcs list holds memory addresses that individually resolve to functions denoted by lambda ✅ ➡️ Another important pointer to note here , the list holds the memory reference to these functions, not the actual value returned by the function. ✅ ❓ Demistify [f() for f in funcs] 🟰 This is a list comprehension in python. ➡️ We are iterating over all thats contained in the list, referencing each element via local variable f Since, f refers to an in-memory function, f() will simply invoke the function call. ✅ ➡️ When i = 0: Invoke f(), the first function pointed to by the first element of the list funcs ❓ The interesting part Function call always creates a new individual scope in python. You can say it as a standalone local environment specific to that function call. So, for the first element in funcs f() -> invokes a function that returns i ❓ Where is i now ➡️ Well, i comes from the loop scope created earlier and is used in the enclosed scope lambda : i denoted by function. ❓ So , are we talking about a closure concept in python here ? ➡️ Yes, this is a closure as lambda is closing over the variable i which is defined in the outer scope, the outer scope is the loop scope. ❓ Another interesting thing to note here is : ➡️ i is defined over a single outer scope, so it is shared amongst all the functions contained in the list funcs. 🤔 Think of it as : We are evaluating each function in the list funcs, where each function simply returns i. 🟰 The value of i = 2, post loop iteration finished in range(3), and hence each of these function calls will return the value 2 i.e. value of the variable closed over by lambda. #python #closure #lambda #softwareengineering #dataengineering

Most Python code works. That’s not the problem. The problem is - most of it doesn’t scale past the person who wrote it. You’ve probably seen code like this: • full of comments explaining what’s happening • try/finally blocks everywhere • repeated logic for caching, logging, auth • functions doing 5 things at once It works. Until it doesn’t. The shift that changed how I think about Python: 👉 Stop writing logic 👉 Start using language-level patterns Once you start seeing it this way: • with replaces cleanup logic • decorators replace repeated behavior • generators replace unnecessary data structures • dunder methods make your objects feel native The result? Code that explains itself without comments, removes entire classes of bugs, and actually scales across teams. I wrote a deep dive on this - not surface-level tips, but how these patterns actually work, when to use them, and how they reshape your code. 👉 Read the full article: https://lnkd.in/g_9GZDRk Curious — what’s one Python concept that only clicked after real-world experience? For me, it was realizing generators aren’t about syntax - they’re about thinking in streams instead of collections. #Python #CleanCode #SoftwareEngineering #ScalableSystems #DesignPatterns #AdvancedPython #BackendDevelopment