Copying vs Reference 🐍 I thought this would create a copy: b = a It didn’t. a = [1, 2, 3] b = a b.append(4) print(a) 👉 [1, 2, 3, 4] Both variables point to the same list. No copy was made. In Python, variables are just labels, not containers. When you use =, you aren’t duplicating data, you’re just giving the same memory address a second name. To actually copy: b = a.copy() Looks the same. Behaves completely differently. 💡 And also: .copy() only goes one layer deep and that's why we need deep copy. ➡️ Assignment ≠ Copy Day 17/30 #Python #30DaysOfCode #SoftwareEngineering

Most implementations of the State pattern in Python look very “clean”. Lots of small classes. A base interface. One class per state. But if you’ve ever worked with one in a real project, you know the downside: transitions are scattered, behaviour is hard to see in one place, and adding new states often means touching multiple files. In today’s video, I rebuild the State pattern in a very different way. Instead of relying on inheritance, I make the state machine explicit as data and use decorators to define transitions. The result is a small, reusable engine where the entire flow becomes visible at a glance. If you’re interested in writing Python that’s easier to reason about and extend, this is a pattern worth understanding. 👉 Watch here: https://lnkd.in/e9Y3xGNF. #python #softwaredesign #designpatterns #statemachine #cleancode

Most implementations of the State pattern in Python look very “clean”. Lots of small classes. A base interface. One class per state. But if you’ve ever worked with one in a real project, you know the downside: transitions are scattered, behaviour is hard to see in one place, and adding new states often means touching multiple files. In today’s video, I rebuild the State pattern in a very different way. Instead of relying on inheritance, I make the state machine explicit as data and use decorators to define transitions. The result is a small, reusable engine where the entire flow becomes visible at a glance. If you’re interested in writing Python that’s easier to reason about and extend, this is a pattern worth understanding. 👉 Watch here: https://lnkd.in/eg22yEHR. #python #softwaredesign #designpatterns #statemachine #cleancode

I got tired of scrolling through messy file names… so I fixed it with a small Python script. While reading One Piece manga PDFs, the file names were all over the place: chapter-1112, one-piece-chapter-1222, onepiece-1123, OP-Chapter-1123… Finding the correct order every time was annoying. So I wrote a simple script that: Extracts the chapter number from any format Renames files into a consistent structure Automatically arranges them in readable order Nothing fancy just solving a small personal problem and saving time. This reminded me: You don’t always need big projects. Even small scripts that remove friction from your daily life are worth building. Clean input → Clean output → Peace of mind 😌 #Python #LearningByDoing #Automation #OnePiece #Coding

Managing Python environments shouldn’t be a nightmare. But for many — it still is. Here’s how to keep things clean, fast, and production-ready in 2026: 🛠️ Use uv or poetry to manage environments & dependencies. Faster, safer, and simpler than old-school pip+venv. ⚡ Speed matters – use polars, numpy, and numba to vectorize heavy loops. Even small tweaks can give 10× performance wins. 🧼 Lint + Format + Type-check = non-negotiable Ruff for linting Black for formatting Pyright or Pyrefly for fast type-checks 💡 Bonus tip: Use Typer to build CLIs in minutes. So clean, it feels like magic. 💬 What’s one Python setup rule you wish you knew earlier? #Python #CodeQuality #Productivity #DevTools #DataEngineering

Something that’s starting to click for me is how simple ideas in code mirror how the internet actually works. When you type something into a website, it’s not just “magic” happening in the background it’s a request being sent, processed, and responded to. I explored this by writing small Python programs that: take user input make decisions using conditions return different outputs based on logic At a basic level, even something like a login check or an age category program is a reflection of how backend systems work. I also learned to be more careful with small details—like how input is always treated as text unless converted, and how conditions need to be logically complete. It’s interesting how much clarity you get when you slow down and really understand the basics instead of rushing ahead. #Python #BackendDevelopment #LearningInPublic #TechJourney

In 𝗙𝗮𝘀𝘁𝗔𝗣𝗜, 𝗿𝗲𝘁𝘂𝗿𝗻 𝘁𝘆𝗽𝗲 and 𝗿𝗲𝘀𝗽𝗼𝗻𝘀𝗲_𝗺𝗼𝗱𝗲𝗹 describe the success body: a single Pydantic model, or a list of that model—whatever the handler returns on a normal 2xx. When a lookup fails or a rule is broken, you raise 𝗛𝗧𝗧𝗣𝗘𝘅𝗰𝗲𝗽𝘁𝗶𝗼𝗻 so the client gets a proper 𝟰𝘅𝘅 𝘀𝘁𝗮𝘁𝘂𝘀 and a 𝗱𝗲𝘁𝗮𝗶𝗹 𝗽𝗮𝘆𝗹𝗼𝗮𝗱. That is separate from the return type, because raise is not return. 𝗿𝗲𝘀𝗽𝗼𝗻𝘀𝗲_𝗺𝗼𝗱𝗲𝗹 defines how the 𝘀𝘂𝗰𝗰𝗲𝘀𝘀 𝗿𝗲𝘀𝗽𝗼𝗻𝘀𝗲 is built and documented; HTTPException is how you answer with an error without faking a 200. 👉 Detailed breakdown here: https://lnkd.in/gcySJqZX 👉 Code Here: https://lnkd.in/gyf4663s #FastAPI #Python #API #BackendDevelopment #SoftwareEngineering #Pydantic #OpenAPI

Finally moved Python & AI project from local to live! 🎈 I’ve been experimenting with Python lately and finished building a simple Personal Assistant for my morning routines and productivity. It was fun to step outside .NET and try out some new tools: ⚡ Groq : Really impressed with how fast the responses are. 🐍 Python: Getting more comfortable with the syntax every day. 🖥️ Streamlit: Made it super easy to put a clean UI on top. It’s work in progress, but you can check out V1 here: https://lnkd.in/gwyuaGZt #Python #Streamlit #PersonalProject #BuildingInPublic #WomenInCode

Today I worked on an interesting string problem — counting how many times a substring appears in a string without using built-in methods like count(). At first, it seemed straightforward… until I realized an important twist 👇 👉 Built-in count() does not handle overlapping substrings So I implemented a manual sliding window approach: 🔹 Traverse the string from left to right 🔹 Extract substrings using slicing 🔹 Compare each slice with the target substring 🔹 Increment count when a match is found 💡 Example: String → ABCDCDC Substring → CDC There are 2 occurrences, not 1 — because overlapping is allowed. This small problem helped me understand: How string slicing works internally Why built-in functions aren’t always sufficient The importance of handling edge cases like overlapping 🧠 Key takeaway: Sometimes writing logic manually gives deeper insight than relying on shortcuts. Learning step by step and enjoying the process 🔥 #Python #CodingJourney #100DaysOfCode #ProblemSolving #DataStructures #Learning

I built a small Python tool for LM Studio that compresses prompts/context into a shorter “caveman” style. Why? Local LLMs are great, but token limits and small context windows can get in the way. This project: shortens markdown/context files preserves code, URLs, and headings works with LM Studio local server saves the original file as backup It was a fun way to learn prompt compression and build something practical for local AI workflows. Repo: https://lnkd.in/gtJf_s6c #Python #LMStudio #LocalLLM #AI #OpenSource