What if recording starts on its own? Built a Smart Event Recorder that listens   and reacts It detects sound, starts recording automatically, and stops when silence returns. No manual clicks — just smart automation.  Python | OpenCV | Sound Processing  GitHub: [Add link] #Innovation #Python #Projects #StudentDeveloper

Built a GenAI application that converts YouTube videos into structured articles, downloadable PDFs, and responsive webpages. The system uses a multi-step pipeline: transcript extraction, content cleaning, article generation, and multi-format output creation. Tech stack: Python, Streamlit, LangChain, Groq (LLaMA 3.3), FPDF This project helped me understand how to design end-to-end GenAI workflows beyond simple summarization. 🔗 GitHub: https://lnkd.in/gygZUgGG #Innomaticsresearchlabs #GenerativeAI #Langchain #Python #Datascience

🚀 Built my first RAG (Retrieval-Augmented Generation) Chatbot using Python! Instead of guessing, this chatbot reads, understands, and answers directly from custom data 📄➡️🤖 Powered by FAISS, HuggingFace embeddings, and Groq LLM, it delivers fast and context-aware responses. 💡 From static text → to intelligent conversations This is a small step into the world of AI-powered applications, but a big leap in how machines interact with knowledge. #AI #MachineLearning #LangChain #Python #RAG #GenAI #DataScience

Master Figures, Lines & Arrows in Matplotlib! The matplotlib module can plot geometric figures such as rectangles, circles, and triangles. These figures can then illustrate mathematical, technical, and physical relationships. This blog post demonstrates the creative options of matplotlib through three examples by illustrating the Pythagorean theorem: a gear representation, a pointer diagram, and a current-carrying conductor in a homogeneous magnetic field. #Python #DataViz #Matplotlib #CodeMagic #RheinwerkComputingBlog Dive in now and transform your graphs! https://hubs.la/Q04byPg90

🗓 7 April 2026 LeetCode Problem #128 – Longest Consecutive Sequence Solved the problem of finding the longest consecutive sequence in an unsorted array. Key insight: use a set for O(1) lookups and only start counting sequences from numbers that are the beginning of a sequence. Takeaways: - Using the right data structure reduces time complexity from O(n²) to O(n). - Avoid redundant work while scanning arrays. - Handle edge cases like empty or single-element arrays efficiently. This problem reinforces how a smart approach beats brute force every time! #LeetCode #Algorithms #Python #DataStructures #ProblemSolving #Coding #TechLearning

Day 2 of my Plant Disease AI Detector project 🌱🤖: Today I went deeper into FastAPI and started structuring real logic behind the endpoints. I learned how to: -Work with multiple routes instead of just a single endpoint -Use Python loops like enumerate() to handle data more efficiently -Think about how the backend will process and return results from an AI model It’s starting to feel less like just “running a server” and more like building an actual system. Step by step, turning this into a real AI-powered project. 🚀 #Python #FastAPI #AI #MachineLearning #BackendDevelopment

How can you evaluate an AI model's robustness before real-world failures occur? In this webinar, we’ll demonstrate how to use the open source Natural Robustness Toolkit (NRTK) to create reproducible workflows for testing model performance. You’ll learn how to: ✅ Install and configure NRTK in Python ✅ Apply perturbations to expand existing datasets ✅ Design parameter sweeps to measure performance degradation ✅ Evaluate models under simulated operational conditions 📅 April 15, 2026 | 12–1 PM 👉 Register here: https://ow.ly/Ncnr50YBmK7 #AIResearch #MachineLearning #ModelValidation #NRTK #Python

Excited to share Muse Spark from Meta Superintelligence Labs! ✨ It's a strong natively multimodal model with many surprising properties that emerged. Here, the model is able to use Python tools to make a playable Sudoku game on the web from an image input of the board. https://lnkd.in/grJhdADG

Meta Muse Spark is here ✨ 🥑 9 months in. It still feels surreal 🌪️ First job, and I got to roll up my sleeves across the full stack — from multimodal pretraining data to large-scale RL agentic post-training. Built from scratch, broke things, fixed things, and learned a ton from the incredible talents at TBD and FAIR. Some takeaways: Training a model end-to-end is a lot like raising a child 👶. First, you teach it to see the world — that's compression, learning representations from massive data at scale. Then, you let it experience the world — that's environment, putting it into different agentic scenarios, nudging it carefully, and watching it grow. Somewhere along the way, my own thinking shifted too: intelligence isn't just about compression. It emerges when a model learns to act in the world. More powerful multimodal agentic models are on the way 🤖 🚀