#transformers

Local-first social memory search engine with browser capture, hybrid AI retrieval, and optional C++ acceleration.

This course is designed to take you from the basics to advanced concepts, providing hands-on experience in building, deploying, and optimizing AI models using Langchain and Huggingface. Perfect for AI enthusiasts, developers, and professionals

Trilha aberta e gratuita, do zero ao avançado, para construir assistentes educacionais inteligentes: NLP, Machine Learning, Deep Learning, Transformers, LLMs, RAG, agentes e Learning Analytics.

A structured, hands-on learning hub for Large Language Models (LLMs). Journey from Deep Learning foundations to building production-ready RAG systems, agents, and custom LLM applications.

A tiny, custom-built Kannada Language Model (NudiLLM) trained from scratch on Kannada Wikipedia using a custom Transformer architecture and SentencePiece tokenizer.

GenAI Career Roadmap 2026 🚀 | AI Job Paths & Skills Guide

Multi-stage RAG chatbot for mental health support — combining language detection, emotion classification, intent routing, and Qdrant-powered retrieval with Groq LLM streaming responses.

🧠 A persistent memory RAG chatbot that never forgets. Uses Phi-3-mini LLM, ChromaDB vector database, and Ebbinghaus memory decay curves.

Production-grade LLM fine-tuning with LoRA/QLoRA — dataset synthesis, rigorous eval, cost analysis

Identify bird species from images along with audio and explore them with AI-powered insights.

Journey of Agentic AI

An LLM inference engine written from scratch in Python and C++/Metal — tokenizer, forward pass, KV cache, and INT4 quantization implemented by hand. No PyTorch, no llama.cpp.

AI-powered application prototypes with model integration, APIs, databases, and deployable interfaces.

Group of agents working over vector based routing