Arthur Danjou
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n8n Automations

Academic ProjectIn progress

An academic project exploring the automation of GenAI workflows using n8n and Ollama for self-hosted AI applications, including personalized research agents and productivity hubs.

March 15, 2026 2 min read
n8nGeminiSelf-HostedAutomationRAGProductivity

Overview

This project focuses on designing and implementing autonomous workflows that leverage Large Language Models (LLMs) to streamline productivity and academic research. By orchestrating Generative AI through a self-hosted infrastructure on my ArtLab, I built a private ecosystem that acts as both a personal assistant and a specialized research agent.

Key Workflows

1. Centralized Productivity Hub

I developed a synchronization engine that bridges Notion, Google Calendar, and Todoist.

  • Contextual Sync: Academic events, such as course schedules and exam dates, are pulled from Notion and reflected in my calendar and task manager.
  • Daily Briefing: Every morning, the system triggers a workflow that compiles my schedule, pending tasks, and a local weather report into a single, centralized email summary. This ensures a frictionless start to the day with all critical information in one place.

2. Intelligent Research Engine (RSS & RAG)

To stay at the forefront of AI research, I built an automated pipeline for academic and technical monitoring.

  • Multi-Source Fetching: The system monitors RSS feeds from arXiv, Hugging Face, Hacker News, selfho.st, and major industry blogs (OpenAI, Google Research, Meta).
  • Semantic Filtering: Using LLMs, articles are filtered and ranked based on my specific research profile, with a focus on robust distributed learning.
  • Knowledge Base: Relevant papers and posts are automatically stored in a structured Notion database.
  • Interactive Research Agent: I integrated a chat interface within n8n that allows me to query this collected data. I can request summaries, ask specific technical questions about a paper, or extract the most relevant insights for my current thesis work.

Technical Architecture

The environment is built to handle complex multi-step chains, moving beyond simple API calls to create context-aware agents.

Integrated Ecosystem

  • Intelligence Layer: Integration with Gemini (API) and Ollama (local) for summarization and semantic sorting.
  • Data Sources: RSS feeds and Notion databases.
  • Notifications & UI: Gmail for briefings and Discord for real-time system alerts.

Key Objectives

  1. Privacy-Centric AI: Ensuring that sensitive academic data and personal schedules remain within a self-hosted or controlled environment.
  2. Academic Efficiency: Reducing the "noise" of information overload by using AI to surface only the most relevant research papers.
  3. Low-Code Orchestration: Utilizing n8n to manage complex logic and API interactions without the overhead of maintaining a massive custom codebase.

This project is currently under active development as I refine the RAG (Retrieval-Augmented Generation) logic and optimize the filtering prompts for my research.