Today I elevated Nevin Chatbot AI Math Learning Assistant from prototype phase to a deployed Gemini-powered AI tutoring app.I concentrated on integrating real AI, building multi-turn learning capabilities, implementing step-by-step math problem-solving, error trapping, security and accessibility as well as SEO elements, while building a professional-looking user experience for students and teachers. Lesson Learned: Building AI is not just about getting the model to answer but testing, expanding the architecture, troubleshooting what doesn’t work and continuously improving the overall user experience. Live AI Demo: https://lnkd.in/eSFaxrmd GitHub: https://lnkd.in/d8EUsUPM Thank you @decordingdatascience and @Mohammad Ahmad for keeping the challenge going and continuing to encourage us to learn by building. 🚀 #DDSNextGen #DDSAmbassador #DecodingDataScience #BuildingAI #BuildInPublic #GeminiAI #ArtificialIntelligence #AIAgents #GenerativeAI #StudentBuilder #LearningByBuilding

Excited to share the first deployed prototype of Nevin Chatbot – an AI Math Learning Assistant. 🥳😎 The mission with this tool is provide students the ability to understand the math being done, not just the answer! We do this with step-by-step learning, explanations of concepts and math, and most importantly by focusing on the student experience. Prototype UI built ✔️ Github repo set up ✔️ Deployed website via Github pages ✔️ ⏰ Next Up - Build AI brain and hook up the LLM model 🧠 Live Demo Website Link: https://lnkd.in/d5N4BSth Live Project Repository Link: https://lnkd.in/d8EUsUPM Thanks to Decoding Data Science, Mohammad Arshad and the rest of the DDS community for pushing us to learn by creating! 🙌🏽 hashtag#DDSNextGen hashtag#DDSAmbassador hashtag#DecodingDataScience hashtag#ArtificialIntelligence hashtag#StudentBuilder hashtag#LearningByBuilding

Meet Nevin – Your AI Powered Educational Math Sidekick! 👋🎓🤖 I have officially started the Day 1 Building Agentic AI Application Challenge by Decoding Data Science. Hello everyone, I’ve created Nevin Chatbot, an AI-powered mathematics chatbot that helps students solve Maths problems by learning step by step. Key Philosophy behind Nevin Chatbot: Learning Maths is more than just solving for an answer. 👨🎓 Rather than outputting an answer, Nevin Chatbot aims to: 1️⃣ Understand the Maths problem posed 2️⃣ Explain the maths concept(s) related to the problem. 3️⃣ Walk students through problems step-by-step. 4️⃣ Promote problem solving skills. 5️⃣ Allow students to build a better conceptual understanding. My project aims to unlock the potential of Artificial Intelligence in the learning journey. Rather than simply searching for a quick answer, I believe students can benefit from having AI as a learning tool that walks them through problems. I look forward to improving Nevin into a comprehensive interactive learning tool to help students better understand how to tackle Mathematics. 🤖 Another project I built while learning, building, experimenting and implementing AI into real-world solutions.🚀 #NevinChatbot #ArtificialIntelligence #GenerativeAI #AIChatbot #StudentLearning #ProblemSolving #MachineLearning #AIProject #BuildInPublic #Innovation #FutureOfEducation

During the development phase of my Physics AI Chatbot for the DDS AI Application Challenge I had a moment when I realised I had built something cool. My chatbot was able to take a physics problem and output a step-by-step solution instead of providing a final numerical answer. It really clicked for me that I had stopped just learning about AI and started building with AI. That’s why MC07: Hugging Face – Building an Open-Source Project led by Mohammad Arshad resonated so much with me. Mohammad guided us through the process of taking an AI prototype and transforming it into something that can be used, tested, reproduced, and improved by the community. 🧠 Three things I learned: 1. It takes more than a model: Data, evaluation, deployment, licensing, and documentation are important considerations. 2. There’s a builder workflow you can follow: explore → test → evaluate → deploy → improve. 3. Documentation is your product: clear model cards with specific limitations and intended uses will make your open-source AI more responsible, useful. I left motivated to look at my own Physics AI Chatbot with new eyes. How can I transform what I have built into something transparent and shareable? How can I open-source my AI project? Thank you to Mohammad Arshad and the Decoding Data Science (DDS) community. You’ve helped me learn something new once again. 🤗 #HuggingFace #OpenSourceAI #ArtificialIntelligence #MachineLearning #DDSCommunity #DDSAmbassador #AIProjects

Wrapping up Workshop 1 in the @Decoding Data Science "AI Accelerator Bootcamp", we kicked off our project blueprint for the 'DDS HR Policy Chatbot' Agent. Before building the RAG pipeline, we mapped out the core foundations: The User & Problem: Solving employee friction in navigating dense HR handbooks and policy PDFs. The Stack: OpenAI embeddings & LLMs, LlamaIndex, Pinecone, and a Gradio interface. The Guardrail: Zero hallucinations. The bot must strictly answer from policy documents and clearly state when information isn't available. Always great to revisit foundational product thinking and refine the architecture before opening the code editor. Ready for the build phase! 💻 hashtag#Decoding Data Science hashtag#DDS Business Circle Mohammad Arshad hashtag#AIBuilders hashtag#RAG hashtag#DAY1 hashtag#GenerativeAI hashtag#LlamaIndex hashtag#HRChatbot hashtag#LLM hashtag#Python hashtag#Gradio hashtag#AIEngineering hashtag#OpenAI hashtag#ProductThinking hashtag#ContinuousLearning hashtag#BuildInPublic hashtag#Upskilling