Full implementation is open-source: https://github.com/iusztinpaul/ai-research-os-workshop Agent Engineering: Building Multi-Agent Systems Course: https://academy.towardsai.net/courses/agent-engineering Turning thousands of notes, videos, documents, and repositories into usable AI context requires more than a bigger context window. It requires memory and context engineering: organizing sources, indexing what matters, and loading only what the model needs. The talk shows how the authors turn an Obsidian vault of 10,000+ notes, documents, videos, and repositories from a passive knowledge archive into live context for AI agents. A system that the authors use daily as their personal AI research OS for writing code and creating content. You'll learn to design a deep research algorithm that runs across both the open web and your personal "Second Brain", then store what it finds as a research memory you and your agents can maintain, visualize, and grow. All based on the authors' 3 major iterations of the system over the past 18 months. You'll walk away knowing how to: - Choose the right tool for the job: Codex/Claude Code vs. NotebookLM vs. RAG vs. a personalized research memory - Design deep-research pipelines across Obsidian, NotebookLM, GitHub, Google Drive, Readwise, and YouTube - Build a token-efficient memory layer from plain files, not a vector or graph DB - Plug tens of thousands of personal notes into an LLM knowledge (or wiki) base that scales - Implement the memory layer between your Second Brain and any agent harness (Codex, Claude Code, or your own) For: Engineers ready to move beyond hoarding notes and turn their "Second Brain" into a context that their AI agents can research, maintain, and grow. Speaker info: Paul Iusztin - X: https://x.com/pauliusztin_ - in: https://www.linkedin.com/in/pauliusztin/ - youtube: https://www.youtube.com/@itsdecodingai Louis-François Bouchard - X: https://x.com/Whats_AI - in: https://www.linkedin.com/in/whats-ai/ - youtube: https://www.youtube.com/@WhatsAI Timestamps: 00:00- Introduction to the "Second Brain" concept 00:49- The core problem: Losing research and finding meaningful notes 01:32- Building an AI Research OS 02:05- Meet the presenters: Louis-François Bouchard and Pauline 03:31- Choosing the right tools for research (Google vs. LLMs) 04:51- Why NotebookLM and vector databases aren't always ideal 07:08- The need for a personalized research assistant 09:16- Moving data to Obsidian for local file management 11:02- Overview of the AI Research OS repository 12:48- The three-layer system: Raw content, Index, and Wiki 13:28- Architecture of the Deep Research algorithm 18:28- Version 3: Adding the Wiki layer on top of knowledge bases 20:13- How the file-based index works (no database required) 21:26- Exploring the Wiki structure: Comparisons, concepts, and entities 22:49- How to query the Wiki efficiently 25:01- Managing snapshots and personal notes using the PARA method 27:04- Demo 1: Researching agent engineering 31:58- Demo 2: Ingesting and comparing GitHub repositories 34:25- Demo 3: Ingesting custom web links 36:50- Future improvements: Connectors, memory compaction, and source provenance 38:45- The Agent Engineering course overview Speakers: - Paul Iusztin: Paul Iusztin is the Founder & CEO of Decoding AI and the author of the bestselling LLM Engineer's Handbook. | Louis-François Bouchard is the Co-founder & CTO of Towards AI and author of Building LLMs for Production. X/Twitter: https://x.com/pauliusztin_ LinkedIn: https://www.linkedin.com/in/pauliusztin/ GitHub: https://github.com/iusztinpaul