Master One Area in Python Ecosystem for Success

🚀 Built a small AI tool to chat with PDFs I made a project where you can upload a PDF and ask questions. It gives answers only from the document. If the answer is not in the PDF, it shows: “Not found in document”. 🔗GitHub : https://lnkd.in/gbs2ugHp 🛠 Used: Python, LangChain, FAISS, Streamlit Infotact Solutions #AI #Python #RAG

#story BREAKING: China has open-sourced a massive Python framework for building AI agents. It’s called AgentScope, a python framework built around Agent-Oriented Programming that lets you build AI agents visually with MCP tools, memory, rag, and reasoning capabilities. 100% Open Source. https://lnkd.in/gUw9d_ea

Day 28 / #120DaysOfCode – LeetCode Challenge 🚀 Consistency is building momentum 💯 Today’s focus was on String Processing and Frequency Counting, using Python’s built-in tools for efficient solutions. ✅ Problem Solved: • Most Common Word 💻 Language: Python 📚 Key Learnings: • Used regex (re.findall) to extract words cleanly • Applied Counter to count word frequency efficiently • Learned how to handle case-insensitive strings • Filtered out unwanted words using conditions Clean code + built-in functions = powerful solutions 🚀 Every day improving step by step 💪 🔗 LeetCode Profile: https://lnkd.in/gbeMKcv5 #LeetCode #Python #DSA #Strings #Regex #ProblemSolving #CodingJourney #Consistency #120DaysOfCode

Python Series — Day 3 🧠 Let’s level it up a bit 👇 What will be the output of this code? def modify_list(lst): lst.append(4) a = [1, 2, 3] modify_list(a) print(a) Options: A. [1, 2, 3] B. [1, 2, 3, 4] C. Error D. None Think carefully 👀 (Hint: It’s not about functions… it’s about how Python handles data) Drop your answer 👇 Answer tomorrow 🚀 #Python #CodingChallenge #LearningInPublic #DataEngineering #Tech

Hi, friends! I just built my first AI-powered stock prediction app from scratch — as a complete Python beginner. It uses Machine Learning to predict stock price direction for any ticker in the world. Built with Python, scikit-learn, yfinance and Streamlit. Check it out https://lnkd.in/eXAZW4U8 #Python #MachineLearning #DataScience #100DaysOfCode #PythonProgramming

Worked on a small project using LangChain to build a dynamic prompt system. Instead of hardcoded prompts, I used templates that adapt based on inputs like topic, audience, and tone. It helped me understand how real-world prompt engineering works. Still learning 👍 #GenAI #LangChain #Python

When I first started using Pandas, I wrote code the same way I wrote normal Python. Lots of loops. Lots of step-by-step logic. And it worked… at first. But then datasets got bigger. And things slowed down quickly. That’s when I learned something important: 👉 Pandas works best when you think in vectorized operations. Instead of: looping through rows You start thinking in columns. Example mindset shift: Instead of processing each row individually, you transform entire columns at once. This small change made my code: ✔ faster ✔ simpler ✔ easier to read Still learning, but it's one of those small mental shifts that really changes how you work with data. #DataEngineering #Python #Pandas

What if you could improve LLM responses without training bigger models? That’s the idea behind Inferscale 0.1.1. A lightweight Python package that applies inference-time scaling techniques to produce higher-quality outputs—perfect for developers working within tight compute budgets. It’s simple, effective, and ready to use. Explore the repo and README: https://lnkd.in/giq8KJ5g Let me know what you think! #ArtificialIntelligence #LLM #Python #OpenSourceProject #AIInnovation #DeepLearning

Python is the base of growth in today’s tech world. From data analysis to AI, automation to web development—everything starts with Python. If you want to grow faster, start with the foundation. Start with Python. #Python #CareerGrowth #TechSkills #AI #DataScience #day71

𝗣𝘆𝘁𝗵𝗼𝗻 𝗗𝗲𝘃𝗲𝗹𝗼𝗽𝗲𝗿𝘀, 𝗥𝗲𝗷𝗼𝗶𝗰𝗲! A full Python reimplementation of the Claude Code agent architecture is now open source! 🎉 Say goodbye to juggling npm/TypeScript/Rust just to dive into AI agent development. 𝗪𝗵𝘆 𝗱𝗼𝗲𝘀 𝘁𝗵𝗶𝘀 𝗺𝗮𝘁𝘁𝗲𝗿? Python's simplicity and readability make it easier for developers to understand, modify, and run sophisticated AI models locally. This is a significant step forward for the Python community and AI innovation. 𝗪𝗵𝗮𝘁'𝘀 𝘆𝗼𝘂𝗿 𝘁𝗮𝗸𝗲 𝗼𝗻 𝗣𝘆𝘁𝗵𝗼𝗻 𝗮𝘀 𝘁𝗵𝗲 𝗴𝗼-𝘁𝗼 𝗹𝗮𝗻𝗴𝘂𝗮𝗴𝗲 𝗳𝗼𝗿 𝗔𝗜 𝗱𝗲𝘃𝗲𝗹𝗼𝗽𝗺𝗲𝗻𝘁? #AI #MachineLearning #Python #OpenSource #TechInnovation