Used my weekends to upskill in AI (RAG, embeddings, LLMs) and built a chatbot. It lets you ask questions and get instant, context-based answers instead of manually searching through documents. Tech: Python | Streamlit | LangChain | ChromaDB | HuggingFace | Llama 3.3 via Groq 🎥 Demo: https://lnkd.in/gA_Cnv2y 💻 GitHub: https://lnkd.in/g7kcx53b #AI #RAG #LLM #Python #GenerativeAI

🚀 Day 25 of My Generative & Agentic AI Journey! Today’s focus was on advanced concepts of Generators in Python — going deeper into how they work internally. Here’s what I learned: ⏭️ next() Method: • Used to manually get the next value from a generator • Helps control iteration step by step ♾️ Infinite Generators: • Generators can run indefinitely and produce values endlessly • Useful for streams or continuous data generation 📩 Sending Values to Generators: • We can send values into a generator using special methods • This allows dynamic interaction with the generator while it’s running 🔗 yield from: • Used to delegate part of a generator’s operations to another generator • Makes code cleaner when working with multiple generators ⛔ Closing Generators: • Generators can be stopped manually using close() • Helps in releasing resources and stopping execution when needed 💡 Key takeaway: Generators are not just for iteration — they can be controlled, extended, and optimized for handling complex data flows. Diving deeper into advanced Python concepts 🚀 #Day25 #Python #GenerativeAI #AgenticAI #LearningJourney #BuildInPublic

🚀 Just built a RAG AI Assistant! This tool lets users upload PDFs or text files and get context-aware answers instantly using Python, FastAPI, Sentence Transformers, Groq API, and LLaMA 3.1. Key Highlights: Semantic search for accurate and fast responses Handles multiple document formats Scalable and efficient backend 💻 Check it out: [https://lnkd.in/g9BmUMRD] 📝 Feedback and thoughts are welcome! #AI #MachineLearning #Python #FastAPI #RAG #OpenSource

Built an AI agent for our product during a hackathon and won first place. 🤖🏆 Just published a complete guide on how to develop a simple AI agent using OpenAI APIs and Python—complete with working code and architecture patterns. Read it here: https://lnkd.in/gqhSf_Yd #AI #OpenAI #Python #Agents

Turning unstructured video into structured logic. 🧠✨ I've been diving deep into RAG (Retrieval-Augmented Generation) to solve a personal headache: efficient note-taking. RAG Study Buddy doesn’t just summarize; it understands. As shown in the trailer, it can take a lecture on Linear Search and instantly generate: Pseudocode & Python Implementation Complexity Analysis Visual Dry Runs It’s not just a chatbot; it’s a dedicated study partner that speaks 5+ languages. 🌍#AI #Technology #Innovation #GenerativeAI #RAG #EdTech #Python #BuildInPublic #DataScience #MachineLearning Mayank Dubey

Day 11 of My AI Journey 🚀 Today I started working with data structures in Python. Covered: 👉 Lists and how to store multiple values 👉 Iterating over data using loops 👉 Basic operations like adding, removing, and accessing elements What I worked on: 👉 Built small programs using lists to manage and process data 👉 Practiced combining lists with loops and conditions Key takeaway: 👉 Real-world programs don’t deal with single values — they work with collections of data This step is helping me move closer to handling real datasets and preparing for AI concepts. #Python #AI #LearningInPublic #BuildInPublic

I’ll be speaking at PyCon US 2026 about AI-assisted contributions and maintainer load. It’s a topic that is becoming increasingly visible across Open Source projects. Looking forward to the discussion in Long Beach. #Python #OpenSource #PyCon

Built an AI Object Detector using Python! Feed any image → AI finds all objects,  draws boxes and shows confidence scores. Tested on a messy room image and detected: - Bed, couch, books, potted plant - All with 65-82% confidence Tech stack: - Python - HuggingFace Transformers - Facebook DETR model - Pillow + Matplotlib GitHub: https://lnkd.in/dj4PVi2D #Python #AI #ComputerVision #DeepLearning #Portfolio

🚀 Day 15 of My Generative & Agentic AI Journey! Today’s focus was on understanding Variable Scope in Python — how the same variable name can behave differently depending on where it is defined. Here’s what I learned: 🌍 Global vs Local Scope: • Variables defined outside a function are global • Variables inside a function are local 👉 Even if the variable name is the same (like student_name), the one inside the function is completely different from the one outside. 🔁 Nested Function Scope: • Functions can be defined inside other functions • Inner functions can have their own variables, even with the same name 👉 Example use case: A student_name defined in the outer function can be different from the one inside the inner function, and both don’t affect each other. 💡 Key takeaway: Scope controls where a variable can be accessed — understanding this avoids confusion and helps write bug-free code. Going deeper into how Python handles variables behind the scenes 🚀 #Day15 #Python #GenerativeAI #AgenticAI #LearningJourney #BuildInPublic

Day 14 of My AI Journey 🚀 Today I focused on working with real data using file handling in Python. Covered: 👉 Reading and writing files 👉 Processing data from text/CSV files 👉 Combining file data with lists and dictionaries What I worked on: 👉 Built small scripts to read data, process it, and generate outputs 👉 Practiced handling real input instead of hardcoded values Key takeaway: 👉 Working with real data introduces new challenges and requires more structured thinking This step is helping me transition from practice problems to real-world data processing, which is essential for AI systems. #Python #AI #LearningInPublic #BuildInPublic