📓 NoteWave 👋
NoteWave is an AI-powered "Second Brain" workspace that utilizes a hardened RAG pipeline, high-speed Llama 3.3 orchestration, and a state-managed neural audio engine to transform static research into interactive multi-modal studios. click here 🫵
I’m honestly tired of people underestimating what this project does. This is NoteWave, a full-stack AI engineering showcase that actually works end-to-end, not just another flashy wrapper. It combines a hardened RAG pipeline with semantic chunking, metadata filtering in Pinecone that remains pinpoint accurate at scale, and real-time chat streaming synchronized across a complex 3-column layout. Beyond simple chat, it features a specialized Podcast Studio that generates multi-speaker, scripted "Deep Dive" dialogues from raw PDFs—a task requiring intense state management and prompt chaining. Built with Next.js 16 and ElevenLabs, this isn’t a collection of buzzwords; it’s a high-integrity system where every component is engineered with architectural intention. If you want to see what happens when you prioritize engineering over hype, click here.
The Concept
NoteWave serves as a specialized research hub designed to transform static, overwhelming PDF documents into dynamic knowledge assets. Inspired by Google's NotebookLM, it solves the "Static Content Problem" by converting inert documents into a living knowledge base. Whether through a RAG-powered research assistant with verifiable citations, interactive 3D-flip flashcards for active recall, or an AI-hosted audio podcast, NoteWave provides users with multiple cognitive entry points to master complex material. It is designed for the power user who demands academic rigor and a fluid, distraction-free environment.
Technical Implementation
The High-Integrity RAG Pipeline
The backend is powered by Llama 3.3 70B (via Groq) for ultra-low latency reasoning and Pinecone Serverless for vector storage. Utilizing Hugging Face (all-MiniLM-L6-v2) for embeddings, the system implements a semantic ingestion pipeline where text is contextually split and stored with strict filename metadata. This ensures the AI assistant only retrieves context from active "Sources," effectively eliminating cross-document hallucinations and maintaining a professional, honest system resistant to adversarial jailbreaks.
"NoteWave transforms passive reading into active synthesis by bridging the gap between vector retrieval and multi-modal output."
The frontend is a masterclass in modern Next.js 16 (Turbopack) architecture, utilizing Tailwind CSS and Shadcn/UI for a high-integrity interface. It features a custom-engineered, resizable 3-column system that allows for "Wide Mode" deep work or a "Studio Hub" directory view. The entire experience is navigated through a keyboard-first "/" command palette, allowing users to swap between the Chat Assistant, Podcast Studio, and Flashcard Studio without ever breaking their flow.
Critical Engineering Challenge: Synchronous Multi-Modal State Management The hardest part was solving the "text stuttering" in streaming responses while managing the neural audio engine. I implemented a custom accumulator pattern and "TextDecoder" handling to capture streaming tokens and process them into structured scripts for the multi-speaker podcast generator via the ElevenLabs API. Solving these layout shifts and synchronization issues while maintaining a global state for the 3D flashcard UI required deep work with React’s concurrent rendering and low-level stream manipulation to ensure the UX felt like a single, cohesive unit.
[ PDF INGESTION ] --(HuggingFace Embeds)--> [ PINECONE VECTOR DB ]
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| (Metadata Filtering)
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[ NEXT.JS 16 CORE ] <---(Groq / Llama 3.3)--- [ LLM ORCHESTRATOR ]
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|---(Command Palette /)----------------> [ STUDIO ECOSYSTEM ]
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|---(State-Managed Audio)--------------> [ ELEVENLABS API ]
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[ 3-COLUMN LAYOUT ] <------------------------- [ NEURAL PODCAST OUT ]