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A production-grade neural AI OS interface built with Next.js 15, running local large models entirely via Ollama without external cloud APIs, enabling maximum privacy protection and high-performance cognitive enhancement.
Section 01
J.A.R.V.I.S-X is a production-grade neural AI OS interface built with Next.js 15. It runs local large models entirely via Ollama without relying on external cloud APIs, achieving maximum privacy protection and high-performance cognitive enhancement. It addresses the data security risks of cloud-based AI assistants and provides users with a powerful yet private intelligent assistant solution.
Section 02
With the development of AI technology, most AI assistants rely on cloud APIs, which pose privacy and security risks. J.A.R.V.I.S-X emerged as a solution—leveraging tools like Ollama to enable local operation of open-source models. Its core philosophy is to provide advanced AI capabilities while ensuring complete privacy and local control of user data. The frontend uses Next.js 15 + React 19 + Tailwind CSS with a glassmorphism-style interface, communicating with local Ollama via WebSocket, and all inference tasks are completed locally.
Section 03
Contains multiple modules:
Section 04
Frontend framework: Next.js15 + App Router; UI components: React19, Tailwind CSS, ShadCN UI, etc.; AI orchestration: Genkit1.x; Local inference: Ollama (supports llama3, llava, gemma2); Voice processing: Faster-Whisper, Piper TTS, openWakeWord; Memory & RAG: ChromaDB, PostgreSQL16 + pgvector; Charts: Recharts; Animations: Framer Motion; Backend: FastAPI (Python3.11 asynchronous), WebSockets; Automation: Playwright, PyAutoGUI; Infrastructure: Docker Compose, Nginx, Redis7, PostgreSQL16.
Section 05
Applicable to:
Section 06
Deployment steps:
ollama pull llama3, ollama pull llava;Section 07
J.A.R.V.I.S-X represents the development direction of AI assistants: maintaining powerful functions while returning data control to users. The localized architecture solves privacy issues and provides low latency and high customization. As local large model technology advances, its practicality and performance will continue to improve. Its open-source nature allows community contributions for improvements, making it an ideal choice for privacy-sensitive users.
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