Introducing easy-rag-pdf: Simplified PDF-based RAG with LangChain

I just published my first Python package to PyPI! 🎉 conviertlo - a Terminal User Interface that makes media files conversion and compression accessible through natural language. ⭕️ The problem: FFMPEG is powerful but has a steep learning curve. Remembering codec flags and conversion syntax shouldn't be a prerequisite for image optimization. 🏁 The solution: A clean TUI built with Textual that: 👉️ Accepts requests in plain language ("compress this image to 2MB", “increase video apeed by 2x”) 👉️ Uses GitHub's new Copilot CLI SDK to generate the FFMPEG command 👉️ Shows you the command before execution (transparency + learning opportunity) 👉️ Handles the conversion with progress tracking Why this matters: This is my first open-source package contribution to the Python, so in reality it only matters to me and you if you find the tool useful 😁. 🔨 Used these amazing tools (Textualize.io, GitHub Copilot SDK). 📦 pip install conviertlo 🔗 https://lnkd.in/eN4EQKTE

As part of my Python learning journey at CodeGnan, I practiced and implemented multiple core concepts through hands-on coding in Jupyter Notebook. ✅ Topics Covered: 🔹 String operations & functions (upper(), lower(), title(), capitalize(), swapcase()) 🔹 String slicing, concatenation & repetition 🔹 find(), strip(), split() methods 🔹 String validation methods (isdigit(), isalpha(), isalnum(), islower(), isupper()) 🔹 Username validation logic 🔹 Character count in a string using loops 🔹 break, continue, pass, and for-else 🔹 Functions & return values 🔹 Positional arguments 🔹 Multiplication table using loops 🔹 Simple calculator using functions & menu-driven program 🔹 Even/Odd & Positive/Negative logic 🔹 Working with lists & nested lists 🔹 Calendar module usage 🔹 List manipulation & conditional logic This practice really helped me strengthen my logic building, loop control, and function concepts in Python. 📌 All the practice code is uploaded on GitHub 👇 https://lnkd.in/gftWtChK #Python #CodeGnan #LearningPython #JupyterNotebook #Programming #LogicBuilding #100DaysOfCode #GitHub #StudentDeveloper #PythonBasics

Day 38 of #100DaysOfCode — API POST Requests & Headers Worked through Intermediate+ Habit Tracking Project lessons focused on making authenticated API requests and managing data via HTTP methods. Learning Highlights: → HTTP POST Requests — Sending data to an API endpoint to create new resources. → HTTP Headers for Authentication — Using custom headers to securely pass API tokens. → strftime (Python) — Auto-generating today’s date in the required API format. → HTTP PUT & DELETE Requests — Updating and removing existing API data. Technical Implementation / Projects: → Practiced sending POST requests to an API endpoint. → Implemented authentication using HTTP headers. → Added a habit-tracking “pixel” via a POST request challenge. → Auto-filled request dates using strftime. → Explored updating and deleting data with PUT and DELETE requests. Key Takeaways: Understanding HTTP methods and headers is essential for building and maintaining real-world API-driven applications. Hashtags: #100DaysOfCode #Python #APIs #HTTP #BackendDevelopment #HabitTracker #LearnInPublic

Day 17 Learning | Python Sets & Dictionaries 🚀 Today, I explored Sets in Python, focusing on their core properties, operations, and built-in functions such as: Union, Intersection, Difference, Symmetric Difference I also practiced adding, removing, and updating elements, and understand how sets help manage unique data efficiently. To apply these concepts practically, I built a small Library Management System using Sets and Dictionaries, where I worked on: Managing unique book records Efficient data handling using set operations Structuring and accessing data effectively with dictionaries This hands-on project strengthened my understanding of data structures, logic building, and real-world problem-solving in Python. 🔗 GitHub Repository: https://lnkd.in/gPTy4_Xz Consistent learning and implementation are key steps toward becoming a better developer. Looking forward to learning more and building stronger projects. #Python #PythonLearning #DataStructures #SetsInPython #DictionariesInPython #LibraryManagementSystem #HandsOnLearning #CodingPractice #LogicBuilding #SoftwareDevelopment #ProgrammingJourney #LearningByDoing #CareerGrowth #SkillDevelopment #Consistency #TechCareers #FutureDeveloper

🧠 LeetCode Insight — Minimum Window Substring Lately, I’ve been focusing on strengthening my core problem-solving fundamentals, especially patterns that show up repeatedly in real-world engineering problems. One such problem is Minimum Window Substring, which combines: sliding window frequency tracking and careful state management 💡 Core Logic The goal isn’t just to find a valid window — it’s to find the smallest valid window. To do that efficiently: Track required character counts using a frequency map Maintain a dynamic window over the string Expand the window to satisfy constraints Shrink it only when validity is preserved The balance between correctness and optimality is what makes this problem interesting. ✅ Python Implementation: https://lnkd.in/gN-93eB8 🧩 Why This Matters Problems like this test more than syntax — they test: how you manage state how you reason about constraints how you optimize without breaking correctness These are the same skills required when working on scalable backend systems and data pipelines. 🎯 Takeaway The biggest learning for me here was: Sliding window problems aren’t about moving pointers — they’re about knowing exactly when a condition becomes true and when it breaks. Getting that right is what leads to clean, reliable solutions. 👉 Curious how others reason about shrinking windows — what’s your mental model for this pattern? #Python #ProblemSolving #SlidingWindow #DataStructures #SoftwareEngineering #LeetCode #LearningInPublic

Quick guide to pip 💡 Here's the deal: Pip is Python's official package manager, a simple command-line tool to install, update and remove libraries for your projects. It connects to PyPI's huge store of over 500,000 free packages and handles dependencies automatically. Like an app store for Python code, it saves time with one command. 🌟 Example: Building a web scraper for job sites like Naukri? Run pip install requests to grab the library, download from PyPI and start coding with import requests right away. 🏭 Popular in: • Install libraries fast: pip install pandas for handling Excel/CSV data in reports or analysis. • Set up team projects: Use requirements.txt and pip install -r requirements.txt to match versions across machines. • Keep code updated: pip install --upgrade numpy for latest math tools in ML models. #pip #Technology #Tech #Innovation #TechLearning 📖 Learn more: https://lnkd.in/dfqVQYc4

Understanding Ducky: Desktop Network Utility In Python Written by $DiligentTECH💀⚔️ What if you could summon a digital tools to do some advance job for you, —a feathered friend in the code—to map the shadows and bridge the gaps? Today, we are going to share useful tips on Ducky, the Python-powered desktop sentinel that turns network troubleshooting from a headache into a stroll through the park. The Digital Active Tool Building a network utility isn't just about stacking lines of code; it's about crafting an extension of your own senses. Ducky lives on your desktop, acting as a translator between the raw, chaotic whispers of hardware and your human brain. By utilizing Python’s robust libraries, Ducky scans for active residents on your Wi-Fi, checks the pulse of your connection, and identifies open gateways. $SlimRich147: "Wait, so Ducky is basically a digital bloodhound for my IP addresses?" https://lnkd.in/dnTYtq9c

Just discovered "Pydantic" today and it's a game-changer! If you're building APIs in Python or dealing with data management, this library makes life so much easier. What I learned: -> Automatic data validation - no more manual checks -> Works perfectly with FastAPI -> Clean settings and config management You get validation, error handling, and type safety automatically. If you work with APIs(FastAPI) or external data, definitely check it out. Perfect Source: Chai Aur Code-Pydantic Crash Course(YouTube) #Python #Pydantic #API

🚀 Documenting while learning — then building with it Over time, I’ve uploaded complete Python backend documentation to my FastAPI-experiments repo — from basics to advanced, written with industry-level guidance and real backend use cases. Honestly, these docs helped me a lot. Without following random YouTube tutorials, I was able to: understand backend concepts deeply connect topics logically (FastAPI, SQLAlchemy, async, DI, transactions, concurrency) learn at my own pace, by reading, implementing, and documenting Now, I’m applying everything I’ve learned into a real project — 👉 a Flash Sale Engine designed for high concurrency and future scalability. This repo became my reference notebook, and now it’s turning into the foundation for my production-level project. Sharing this for learners like me — sometimes documenting your learning is the fastest way to grow 🚀 🔗 Repo: https://lnkd.in/gDJYSEzT #Python #FastAPI #BackendDevelopment #LearningInPublic #SystemDesign #SQLAlchemy #DeveloperJourney #BuildInPublic

𝗥𝗮𝗻𝗱𝗼𝗺 𝘁𝗵𝗼𝘂𝗴𝗵𝘁𝘀 𝗳𝗿𝗼𝗺 𝗮 𝗰𝗵𝗶𝗹𝗹 𝗦𝘂𝗻𝗱𝗮𝘆 𝘄𝗼𝗿𝗸 𝘀𝗲𝘀𝘀𝗶𝗼𝗻 𝗼𝗻 𝗕𝗹𝘂𝗲𝗚𝗿𝗲𝗲𝗻: • The omz kubectl plugin is absolutely 🐐 goated. I've been using omz plugins for a few years and will never go back https://lnkd.in/de9qDekd • The 𝘬𝘥𝘦𝘭𝘱 command made me think of Del Piero for the first time since 2006. I don't even like football • 𝘊𝘵𝘳𝘭 + 𝘟 then 𝘊𝘵𝘳𝘭 + 𝘌 in a bash terminal opens your editor with the command preloaded. Extremely useful for writing long ass inline YAML configs for kubernetes when you wanna test stuff quickly • Python is painful and objectively horrible. 𝘱𝘺𝘵𝘩𝘰𝘯 vs 𝘱𝘺𝘵𝘩𝘰𝘯𝟥, 𝘱𝘪𝘱 vs 𝘱𝘪𝘱𝟥… I thought this v2.x vs v3.x war was already over. Anywho, BlueGreen runbooks now have rudimentary support for composable runtimes. I'll write more on that in the foreseeable future, but here's a sneak peek of how I use it to build & release Bluegreen. Coming Soon™