Péter István Fülöp’s Post

Here’s a tiny Python change that pays off fast. Python tip: use `@dataclass(slots=True)` for high-volume models. It removes per-instance `__dict__`, which usually means lower memory usage and slightly faster attribute access. Great for DTOs, parser outputs, event payloads, and cache objects where shape is fixed. Mini rule: if the object schema is stable, add `slots=True` by default. #Python #Performance #CodeQuality #SoftwareEngineering

Python Challenge – Can you solve this? Today was all about deep-diving into Lists vs. Sets and I came across a common mistake that we can sometimes overlook. Let’s test your Python understanding👇 numbers = [1, 2, 3] numbers.append([4, 5]) print(len(numbers)) A) 3 B) 4 C) 5 D) Error It’s a classic interview question that tests if you truly understand how Python handles memory and lists. Day 15/30 #30DaysOfCode #DataStructures #Day15 #PythonQuiz

Python Dunder Methods: Making code more "Pythonic" 🐍 I’ve been playing around with operator overloading today! Instead of writing a bulky function like add_packages(pkg1, pkg2), Python allows us to use the __add__ magic method. By defining __add__, I can simply use the + operator to combine two package objects. Cleaner syntax? Check. More readable? Absolutely. I also added __str__ to ensure that when I print the result, I get a clear, formatted summary of the dimensions and weight rather than a messy memory address. #Python #CodingTips #SoftwareDevelopment #ObjectOrientedProgramming #CleanCode

Python 3.15 is getting a new builtin: sentinel. Not a loud feature, but a very practical one. It addresses a common API design problem: how to distinguish between “argument was not provided” and “argument was explicitly set to None”. That distinction matters when None is already a valid domain value. Until now, many libraries and codebases had to define their own sentinel objects for this. Now Python will have a standard way: _SENTINEL = sentinel("_SENTINEL") Small change, but useful for cleaner APIs and fewer ad-hoc patterns in production code.

If you use Cursor or Claude Code to write Python you've probably noticed LLMs have some odd habits. Claude especially loves hasattr/getattr calls, unexplained function-scope imports, bare excepts, passing True/False as positional args with no context, and defaulting to dataclasses when your project uses Pydantic. I use agents pretty heavily to build MA-Gym (my open source multi-agent RL library, v-1.0.0 pending soon) and a lot of our internal tooling at DeepFlow. I kept catching the same stuff in review, so I wrote a linter in Rust to do it automatically. slopcop. 16 rules, tree-sitter based, zero false positives on strings/comments, sub 100ms, drops into CI/CLI in one line. pip install slopcop https://lnkd.in/eiUK2j4p Tell me what stupid Python idioms your agents keep writing and I'll add rules for them.

📘 Web Scraping with Python ✍️ Ryan Mitchell A great book to learn how to extract data from real-world websites using Python. It covers tools like BeautifulSoup, Scrapy, and Selenium, and explains how modern websites work, including APIs and JavaScript-based content. Perfect for anyone interested in automation, data collection, or building scraping tools. 🔗 Read here: https://lnkd.in/gKeQG8We⁠ #Python #WebScraping #Automation

Stateful UDFs just changed how Python scales. With @daft.cls, you can turn any Python class into a distributed operator that initialises once per worker and reuses state across every row. That means models, API clients, and database connections no longer get rebuilt on every call. The mental model stays simple: write normal Python classes, add a decorator, and Daft handles execution, scheduling, and parallelism. Find out more: https://lnkd.in/e79SePbN #PythonScaling #DaftCls #DistributedComputing #PythonClasses

🚀 #100DaysOfPython – Day 4: map(), filter(), reduce() These are powerful functional tools in Python 👇 👉 map() – transform nums = [1, 2, 3] squares = list(map(lambda x: x*x, nums)) 👉 filter() – select evens = list(filter(lambda x: x % 2 == 0, nums)) 👉 reduce() – accumulate from functools import reduce total = reduce(lambda a, b: a + b, nums) 💡 But in real-world Python? List comprehensions are often preferred for readability. 🔍 My takeaway: Understand these concepts—but choose readability over cleverness. #Python #FunctionalProgramming #100DaysOfCode

Async Web Scraping in Python: httpx + asyncio for 10x Faster Data Collection Created by Vhub Systems Async Web Scraping in Python: httpx + asyncio for 10x Faster Data Collection Synchronous scraping makes requests one at a time. While you wait for one response, you're doing nothing. Async scraping makes 10-50 requests simultaneously — same time, 10-50x the output. Here's how to actually implem... link https://lnkd.in/e6C3Ccfm pubDate Fri, 03 Apr 2026 03:09:55 +0000

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