Python Type Safety with isinstance()

Recently I published an open-source utility I built back in 2024: skeletonpy. TL;DR: If you do AI-assisted Python coding and want a simple way to include your project's code summary without bloating the context, with pretty much zero setup or installation, give it a try. Simply run this in your project's source root: uvx skeletonpy src And include the generated `summary.py.txt` file in your favorite LLM's context. The Backstory I built this because I had no luck with RAG. Granted, back then it was just naive RAG, and there were very few good reranking models. Maybe RAG is simply not a good fit for code, or maybe it was just me. Either way, I always ended up with a context half-full of irrelevant garbage. Furthermore filling up the prompt often leads to standard context rot or the "lost in the middle" phenomenon, where models just start ignoring or confusing data. Today top-tier models got better but some performance degradation is still there. I use skeletonpy occasionally when coding Python projects even today, especially when working with coding assistants like Cline and I want to have full control and insight into the generated code (no vibes :-) ). It gives the AI a focused, accurate map of the repo with class-level resolution to quickly find what it needs. How it works Skeletonpy is deliberately "simple and stupid": it does exactly one thing. It parses your source code offline using AST (no LLMs, no vector DBs, no complex local indexers) to generate a highly compressed skeleton of your repository. It squashes a few pages into a few lines while still providing references back to the original code so the LLM agent can "dig deeper" in the right location. At the same time, the output summary perfectly resembles Python. I've tested it against various LLMs over the last two years and they all had no problems navigating and understanding this structural pseudo-code. https://lnkd.in/dW7QYBkF

Python Coding Tip String Formatting in Python: f-strings vs .format() One important skill for writing clean and readable Python code is string formatting, the ability to dynamically insert variables or values into a string. Python has evolved over the years, and today there are two common approaches developers use: formatted string literals (f-strings) and the .format() method. Using f-strings Formatted string literals, commonly known as f-strings, were introduced in Python 3.6 (PEP 498). They allow you to embed variables or expressions directly inside a string by prefixing the string with f. The expressions inside curly braces {} are evaluated at runtime and inserted into the string. F-strings are widely preferred in modern Python because they are: • More readable • More concise • Generally faster • Easier to maintain in complex expressions For most everyday formatting tasks, f-strings are the recommended approach. Using .format() The .format() method was introduced earlier in Python 2.6 and Python 3.0 (PEP 3101) as an improvement over the older % string formatting style. With .format(), you define placeholders inside a string and then pass values that will be inserted into those placeholders. Although f-strings are now the most common approach, .format() is still extremely useful in scenarios such as: • Building dynamic string templates • Formatting strings programmatically • Working with long reusable text templates I still use .format() frequently in my AI projects while constructing prompt templates. In many cases, the prompt is a long string defined separately with placeholders, and .format() is used to inject values dynamically. The same approach is also useful when generating structured emails, reports, or long formatted messages. Understanding when to use f-strings for quick inline formatting and when to use .format() for reusable templates helps you write cleaner, more flexible Python code. Subscribe to my YouTube Channel where I teach AI Engineering and Coding in general. https://lnkd.in/ePQf7EPh Which of the two methods do you use more often and why? lets discus in the comment session

Post 2: The Rise of AI-Powered Python Development Headline: Coding in 2026 Feels Different: How AI is Reshaping Python Editors Python development has changed. The editors and IDEs we use today are no longer passive tools that simply display text on a screen. They have become active participants in the coding process . AI is now built into the workflow at every level. In PyCharm, AI tools suggest clearer logic and better structure. They point out unused variables and recommend simpler ways to write long functions. For large projects that expand over time, this guidance keeps code clean and maintainable . Visual Studio Code integrates AI assistants that suggest the next lines based on common patterns. When you work with lists or file input, the editor displays loops or conditions that fit similar tasks. It speeds up daily work while keeping the code easy to follow . Wing IDE takes a different approach with its AI Coder and AI Chat tools. AI Coder writes, redesigns, or extends code directly in the current editor. AI Chat lets you ask questions about code or iterate on designs without modifying anything. Wing now supports OpenAI, Claude, Grok, Gemini, and any provider using the OpenAI completions API . Even newer entrants like Windsurf are designed with AI at the core rather than as an added feature. It understands the entire project, not just one file. Change a function name and it updates related files automatically . The message is clear. AI is not replacing developers. It is handling repetitive tasks, catching errors early, and suggesting cleaner logic while we write. But strong Python fundamentals remain essential. The tools assist, but they do not think for you . How has AI changed your coding workflow this year? #Python AI MachineLearning Coding DeveloperTools VS Code #PyCharm WingIDE TechTrends

Start learning Python by writing code. Not by watching tutorials. Not by saving playlists. Actually writing code. Because Python looks simple on the surface… but the real value comes when you start using it. Most people stop at basics like: print statements   loops   if-else  And then say “I know Python.” But real understanding starts when you go deeper. When you learn things like: • how data structures actually behave   • how functions organize logic   • how OOP helps structure real systems   • how APIs, files, and databases connect to code   • how automation and scripting solve real problems  That’s when Python starts becoming useful. This PDF is helpful because it doesn’t just show syntax. It walks through Python step-by-step — from fundamentals to real-world concepts like APIs, file handling, multithreading, and more. :contentReference[oaicite:0]{index=0} So instead of jumping between random tutorials, you can build understanding in one structured flow. A simple way to use it: 1. Pick one concept   2. Write code for it   3. Modify it and break it   4. Try to apply it in a small use case  That’s how skills actually stick. Because Python is not about knowing everything. It’s about being able to use it when needed. And that only happens through practice. Not passive learning. Save this sheet so you can revisit it while practicing. Comment "Python" and I’ll send the full PDF. Follow Sahil Hans for daily tech job openings and practical interview prep resources that make switching easier.

I know many of you, and even more if you're starting within one of many several branches of IT will surely appreciate this material as Python has been one of the main skills in IT. #StartingInIT #Python #ITForBeginners

Writing Python tests can be incredibly tedious when you have to manually hardcode complex expected values. 𝗶𝗻𝗹𝗶𝗻𝗲-𝘀𝗻𝗮𝗽𝘀𝗵𝗼𝘁 changes how we handle test assertions. Instead of typing out massive dictionaries or strings, you let the tool write the code for you. You simply replace your expected test output with an empty snapshot function call. When you run your test suite, it automatically records the correct values directly into your source file. This workflow drastically reduces boilerplate and keeps your focus entirely on application logic. Here is what makes this library a game changer for Python developers. ✅ It integrates seamlessly with pytest to automate value recording. ✅ It supports normal assertions without requiring complex new syntax. ✅ It strictly preserves Black formatting so your codebase stays completely uniform. ✅ It can instantly outsource large string payloads to external files. ✅ It easily handles nested structures and creates sub-snapshots on demand. 🔗 Link to repo: github(.)com/15r10nk/inline-snapshot --- ♻️ Found this useful? Share it with another builder. ➕ For daily practical AI and Python posts, follow Banias Baabe.

We asked 278 Python developers about how they use AI for coding. The pattern was consistent: AI helps with small, isolated tasks, but breaks down on real projects. Context gets lost. Code gets pasted back and forth. Files the AI doesn't know about break when you apply its suggestions. 65% said they're stuck at this point. The problem isn't the AI. It's the workflow. Agentic coding tools like Claude Code work differently. Instead of chatting in a browser, the AI runs in your terminal. It reads your files, edits them directly, runs your tests, sees the errors, and fixes them. It works across your whole codebase, not one snippet at a time. We're running a 2-day live course (March 21-22) where you'll build a complete Python CLI application from scratch using this workflow. Not toy examples, but a real project with Click, Textual, uv, git, and tests. You'll leave with a working project and a portable set of skills you can apply to your own code. Details and enrollment: https://lnkd.in/gvS-KzVn

🚀 Understanding Polymorphism in Python – OOPS Practice As part of strengthening my Python Object-Oriented Programming (OOPS) concepts, I practiced Polymorphism and its different implementations in Python. Polymorphism means “one interface, multiple behaviors.” It allows the same function, method, or operator to behave differently depending on the object. For example: 5 + 3 = 8 "Hello " + "World" = "Hello World" The same + operator works for numbers and strings but produces different results. This is a simple example of polymorphism. To understand this concept better, I implemented several examples using operator overloading and comparisons in Python. 📚 Concepts Practiced ✔ Operator Overloading (__add__, __sub__) ✔ Comparison Operators (__eq__, __lt__, __gt__) ✔ Object-to-object operations ✔ Implementing custom behavior for operators ✔ Writing clean class-based logic 🧑🎓 Student Class Examples • Adding marks of multiple students using + operator • Comparing student marks using ==, <, and > 🛒 Cart / Product Examples • Subtracting product prices using - operator • Comparing item prices inside a cart system 📖 Polymorphism Types Covered 🔹 Method Overloading Using the same method name with different arguments. 🔹 Method Overriding Child class redefining a method already defined in the parent class. 🔹 Constructor Overloading Initializing objects in multiple ways using default parameters. 🔹 Constructor Overriding Child class redefining the parent constructor. 🔹 Operator Overloading Allowing operators like +, -, ==, < to work with objects. 💡 Why Polymorphism is Important ✔ Improves code readability ✔ Encourages reusable code ✔ Makes applications flexible and scalable ✔ Reduces complexity in large systems Strong understanding of OOPS concepts is essential for: Backend Development System Design Writing scalable applications Learning step by step. Improving every day 💪 📄 Code and explanation attached in the PDF. 🙏 Thanks to 10000 Coders venubabu vajja Kotesh bommu and Akshaya Koyedi for continuously motivating me to learn and grow. #Python #OOPS #Polymorphism #OperatorOverloading #BackendDeveloper #CodingJourney #Learning #SoftwareDevelopment

If you already know Python basics and you’re still writing slow, repetitive, and inefficient code… Then you’re not stuck You’ve just not learned Advanced Python Operations yet I just released a session on “Advanced Python Operations for Machine Learning Beginners” and this is where things start to separate beginners from serious builders. Because this is the stage where Python stops being “just code” And becomes a tool for speed, efficiency, and real-world problem solving ⸻ 1. You stop writing long code and start writing smart code At this level, it is not about doing more It is about doing things better You begin to use: List comprehensions Lambda functions Ternary operations Instead of writing 10 lines, you write 2 clean lines that do the same thing That is how professionals think ⸻ 2. You understand how Python actually works under the hood Most beginners use Python Few understand it Here you start learning: Iterators and generators Memory efficiency How loops really execute This is critical when working with large datasets especially in health research where data can be massive ⸻ 3. You master vectorization and stop using slow loops This is a big shift Instead of looping through data manually, you use: NumPy operations Pandas vectorization This alone can make your code 10x faster And in data science, speed is not luxury It is necessity ⸻ 4. You write reusable and scalable code Now you are thinking like a system builder You start using: Functions properly Modular code structure Reusable pipelines Because in research and machine learning, you will run the same process multiple times Efficiency matters ⸻ 5. You handle data like a pro At this stage, your data manipulation becomes sharper You are now comfortable with: Advanced filtering Groupby operations Merging and joining datasets Handling complex missing data This is the real backbone of machine learning work ⸻ 6. You begin to think in workflows, not just scripts Beginners write scripts Advanced users design workflows You now understand: Data pipeline thinking Step by step transformation Reproducibility in research And this is exactly what separates academic work from impactful research ⸻ 7. You move closer to real machine learning Advanced Python operations are the bridge Between: “I can code” And “I can build models that solve problems” Without this level, machine learning becomes guesswork With it, everything becomes structured and intentional Link https://lnkd.in/eK3CDZFT

🚀 Python Operators – My First Step into Programming Logic I’ve started building my Python portfolio step by step, and this is one of my foundational projects where I explored Python Operators with clear examples and documentation. 🔗 Project Link: https://lnkd.in/dkP-ax4U --- 📊 What This Project Covers This script demonstrates the use of Python operators, which are essential for writing any program: ✔ Arithmetic Operators ("+", "-", "*", "/", "%", "**") ✔ Comparison Operators ("==", "!=", ">", "<", ">=", "<=") ✔ Logical Operators ("and", "or", "not") ✔ Assignment Operators ("=", "+=", "-=", etc.) ✔ Operator precedence and combined operations --- 💡 Why This Project is Important Understanding operators is the foundation of programming. They are used in: - Calculations - Decision-making - Conditions and loops - Real-world problem solving --- 📈 Conclusion This project helped me understand how Python performs calculations and logical decisions. By adding a proper README: ✔ The code becomes easy to understand ✔ Concepts become clear for beginners ✔ The project becomes portfolio-ready Now anyone can open this file and quickly learn: 👉 How operators work 👉 How to combine them 👉 How to use them in real programs --- 🎯 What I Learned - Core programming logic using operators - Writing clean and readable Python code - Documenting projects professionally - Building a strong foundation for Data Science --- 🔥 Next Step I will continue building more projects step by step, covering: 👉 Data Types 👉 Loops 👉 Functions 👉 Data Analysis --- If you are starting Python: 👉 Master operators first — everything builds on them! 💬 Feedback is always welcome!