Sanjeevni Dhir’s Post

8.7% of Python package hallucinations by AI models match valid JavaScript package names. Think about that. An AI suggests a Python package that happens to share a name with a real JS package. The developer doesn't find it on PyPI, gets confused, and either installs the wrong thing or an attacker registers it on PyPI first. Cross-ecosystem confusion is a real and growing attack vector.

𝗣𝘆𝘁𝗵𝗼𝗻 𝗠𝗲𝘁𝗮𝗰𝗹𝗮𝘀𝘀𝗲𝘀 𝗘𝘅𝗽𝗹𝗮𝗶𝗻𝗲𝗱 Everything in Python is an object. Classes are objects too. They are instances of a metaclass. Type is the default metaclass. It builds your classes. Call type to create a class without the class keyword. Python follows four steps to build a class: - Find the metaclass. - Set up the namespace. - Run the class body. - Create the class object. Use the new method to change the class before it exists. Use the init method to record the class after it exists. Most developers do not need metaclasses. Use the init subclass method instead. It handles registration and interface checks. It is simpler to read. Avoid metaclasses in app code. They are hard to debug. Use them for frameworks. Otherwise, use decorators. The best metaclass is the one you do not write. Source: https://lnkd.in/gMfJU9Nx

While digging deeper into environment setup, I noticed an interesting difference between Python and Node.js: Python installs packages globally by default. Unless you create a virtual environment, all dependencies go into a shared global space. Node.js installs packages locally by default. Every project gets its own node_modules directory. At first, this made me think: → Python focuses more on isolation and optimization → Node.js doesn’t care as much But that’s not completely true. Both ecosystems solve the same problem — dependency management — but in different ways: Python → requires you to explicitly create isolation (virtual environments) Node.js → gives you isolation by default (per-project dependencies) Trade-offs: Python → cleaner environments, but extra setup Node.js → easier start, but larger project size and duplication Key insight: It’s not about which is better — it’s about understanding the design decisions behind each ecosystem. This kind of detail matters when you switch between stacks or design scalable systems. #Python #Nodejs #Backend #SoftwareEngineering #LearningInPublic

Interviewer: An OTP is valid for 60 seconds, but the server never stores it. How does it verify a code it doesn’t even 'know'? #developers #ai #otp #authentication #flutter #js #python

🚀 Comments (Python) Comments are used to add explanatory notes to your code. They are ignored by the Python interpreter. Single-line comments start with a `#` symbol. Multi-line comments are enclosed in triple quotes (`'''` or `"""`). Comments are crucial for improving code readability and maintainability. They help other developers (and yourself) understand the purpose of the code. #Python #PythonDev #DataScience #WebDev #professional #career #development

🔍 *Master Python String Functions – A Must-Have Skill* Strings are everywhere—data cleaning, APIs, apps. Knowing how to handle them makes your code faster and cleaner. ✅ *Case Conversion* upper(), lower(), capitalize(), title() ✅ *Cleaning & Formatting* strip(), replace() ✅ *Search & Analysis* find(), count() ✅ *Validation* startswith(), endswith(), isalnum(), isnumeric() ✅ *Split & Join* split(), "delimiter".join() 💡 *Pro Tip* : Strings are immutable—every operation creates a new string, so use them efficiently. Strong string skills = cleaner code + better performance 🚀

We're just released a Python SDK for Liveblocks. Use Python as your Liveblocks back-end. Built realtime AI agents, update sync engine data, create multiplayer rooms, more. There's 85+ methods in total. https://lblcks.io/TJF1J4n

Async IO in Python is single-threaded. No mutexes, no race conditions, no surprise context switches. Coming from multi-threaded code, this felt like cheating. With threads, anything can interrupt anything. You lock shared state, hope you got it right, and debug it six months later when you didn't. With async, control only transfers at await. That's it. Your data is safe between those points by definition, not by luck. The payoff was immediate. Refactored a GitHub API client to fetch a user profile and repo list at the same time using asyncio.gather(). Two concurrent HTTP calls. No threads, no locks. The mental model shift took longer than the code change. If you've been avoiding async because threads burned you before, it's not the same thing.

Python 3.4 Sets New Standard for High-Performance Concurrent Systems 📌 Python 3.4 cracked the code on high-performance concurrency by introducing asyncio, turning Python into a powerhouse for scalable web systems. With standardized async I/O and unified file paths via pathlib, it erased callback hell and messy string manipulations. Though retired in 2019, its architecture still shapes modern Python frameworks - proving that one release can redefine an entire ecosystem. 🔗 Read more: https://lnkd.in/dbW2_R5P #Python34 #Concurrency #Standardlibrary #Highperformance #Concurrentsystems

Django, FastAPI, or Flask? One image. No confusion. Pick the right tool for you and ship faster. The best framework isn't the fastest or the most popular. It's the one that ships your idea. 🚀 #Python #Django #FastAPI #Flask #WebDevelopment #BackendDevelopment #SoftwareEngineering #PythonDeveloper