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June 12, 2026 · Islamabad Rawalpindi

Obsidian: Build Your 2nd Brain

Learn how to build your "LLM Knowledge Base" using Obsidian and Claude. This talk demonstrates extracting information from notes, organizing wikis, and drafting articles, simplifying your learning process.

Claude Code Sonnet Obsidian LLM RAG
Overview

It’s the “LLM Knowledge Base,” a lighter version of “RAG” in AI terminology.

How it works:
Taking in raw notes > extracting meaningful information (concept, learning, people etc) out of it > jotting in your wikis. Why? As a human, you cannot keep an eye on tons of notes repeating

Live Demo:

  • Step 1: /ingest “Topic, path to notes etc.” add a source to wikis, based on SCHEMA
  • Step 2: /ask “what projects do I have?” and show it reading the Index, synthesizing from multiple pages, and returning a structured answer
  • Step 3: /lints > Actuall weeekly clean up as well > I’ll do this in demo and will keep the raw notes of last week to showcase
  • Step 4: /draft > The most important part; i’m consuming knowledge, my LLM is maintaining it too but I need to share that too. So draft outs an article to share from any topic, notes etc.
  • The Log (0 - Everything/Log.md) as a full audit trail of every operation Claude has run
Links
Tech stack
  • Claude Code
    Anthropic's agentic coding tool: Unleash Claude's raw power directly in your terminal or IDE to turn complex, hours-long workflows into a single command.
    Claude Code is Anthropic’s powerful agentic coding assistant, designed for high-velocity development. It operates natively within your terminal, IDE (VS Code, JetBrains), or via a web interface, allowing you to delegate complex tasks like feature building, bug fixing, and codebase navigation. The agent plans, edits files, executes commands, and creates commits, maintaining awareness of your entire project structure. Internally, Anthropic engineers using Claude Code reported a 67% increase in productivity, demonstrating its capacity to deliver significant gains for Pro and Max plan users.
  • Sonnet
    Sonnet is Anthropic's powerful, mid-tier AI model, balancing frontier intelligence with high-speed, cost-efficient performance for production-scale deployments.
    Sonnet (currently Claude Sonnet 4.5) is Anthropic’s versatile model, optimized for complex agentic workflows and coding tasks. It delivers state-of-the-art performance, achieving 77.2% on the SWE-bench Verified coding benchmark (cite: 2.2, 2.4). The model is engineered for high-volume, real-time applications like customer support automation and financial analysis, supporting a 200K token context window (cite: 2.8). Pricing is set for efficiency: $3 per million input tokens (cite: 2.8). This makes Sonnet the recommended choice for developers needing top-tier reasoning and coding capability at a practical, scalable cost.
  • Obsidian
    Obsidian is a powerful, local-first knowledge base: it uses plain Markdown files for notes and links them bi-directionally to build a personal, interconnected knowledge graph.
    Obsidian is a cross-platform note-taking application designed for building a 'second brain.' It operates on a local folder of plain Markdown files (a 'vault'), ensuring you own your data and maintain long-term portability. The core strength lies in bi-directional linking, which connects concepts using the `[[link]]` syntax, allowing you to visualize relationships via an interactive Graph View. With thousands of community plugins and themes, users can customize the platform for diverse workflows, from Zettelkasten to project management, making it a flexible and future-proof tool for serious knowledge workers.
  • LLM
    Large Language Models (LLMs) are deep learning models, built on the Transformer architecture, that process and generate human-quality text and code at scale.
    LLMs are a class of foundation models: massive, pre-trained neural networks (often with billions to trillions of parameters) that leverage the self-attention mechanism of the Transformer architecture (introduced in 2017) to predict the next token in a sequence. Trained on vast datasets (e.g., Common Crawl's 50 billion+ web pages), these models—like GPT-4, Gemini, and Claude—acquire predictive power over syntax and semantics. They function as general-purpose sequence models, enabling critical applications such as complex content generation, language translation, and automated code completion (e.g., GitHub Copilot). Their core value: generalizing across diverse tasks with minimal task-specific fine-tuning.
  • RAG
    RAG (Retrieval-Augmented Generation) is the GenAI framework that grounds LLMs (like GPT-4) on external, verified data, drastically reducing model hallucinations and providing verifiable sources.
    RAG is a critical GenAI architecture: it solves the LLM 'hallucination' problem by inserting a retrieval step before generation. A user query is vectorized, then used to query an external knowledge base (e.g., a Pinecone vector database) for relevant document chunks (typically 512-token segments). These retrieved facts augment the original prompt, providing the LLM (e.g., Gemini or Llama 3) the specific, current, or proprietary context required. This process ensures the final response is accurate and grounded in domain-specific data, avoiding the high cost and latency of full model retraining.
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