🎧💡 EchoSummarize: A YouTube video summarizer using the Phi-3.5-mini LLM, providing fast and accurate summaries of video transcripts.

AronaOS is an offline AI-powered personal assistant that helps you manage tasks, set reminders, and recall context-aware conversations. Built with Python, Flask, and local LLMs like Phi-3 Mini, it's designed for both privacy and productivity.

Hybrid AI orchestration stack combining local LLMs (Ollama), vector search (Qdrant), and Azure AI Foundry for scalable RAG, Agentic AI, and Vision. Built with .NET 8 and Python.

Serving and deploying LLMs with Bento Ecosystem.

An AI analyst with a hybrid LLM architecture 🤖. Uses a fine-tuned Phi-3 Mini (3.8B) for local RAG answer generation & Gemini 1.5 Pro for query analysis of SEC filings (AAPL, MSFT, GOOG, AMZN, META).

AronaOS is an offline AI-powered personal assistant that helps you manage tasks, set reminders, and recall context-aware conversations. Built with Python, Flask, and local LLMs like Phi-3 Mini, it's designed for both privacy and productivity.

Fully automated AI development stack with Ollama, n8n, React, and Node.js. One-command deployment with cross-platform support (Linux/macOS/Windows).

Small POC for a STT Python module using faster-whisper to convert wav files into parsed text.

To build a Retrieval-Augmented Generation (RAG) system from scratch, you must first create an ingestion pipeline to load and process your data into a vector store (by chunking documents, generating embeddings, and storing them). Then, you build a retrieval pipeline to embed a user's query, search the vector store for relevant chunks, and pass these

Our approaches for amazon ml challenge 2025