Are we using LLMs for Personal Knowledge Management all wrong? 🤔

G Gokulnath — Wed, 20 May 2026 23:54:47 +0000

Andrej Karpathy recently shared a fascinating concept called the "LLM Wiki"—a brilliant shift from how most of us currently interact with our documents.

Right now, the standard approach is RAG (Retrieval-Augmented Generation). You upload files, ask a question, and the LLM retrieves chunks to generate an answer. It works, but there's a catch: the LLM is rediscovering knowledge from scratch every single time. Nothing compounds.

Enter the LLM Wiki approach: 🧠
Instead of raw retrieval at query time, the LLM acts as a dedicated maintainer of a persistent, structured, and interlinked markdown wiki.

Here is how it changes the game:
1️⃣ Compounding Knowledge: When you add a new source, the LLM doesn't just index it. It reads it, extracts key info, updates existing entity pages, and flags contradictions. The knowledge is compiled once and kept current.
2️⃣ Division of Labor: You don't write the wiki. You are in charge of curating sources, exploring, and asking the right questions. The LLM does the grunt work—summarizing, cross-referencing, filing, and bookkeeping.
3️⃣ The Architecture: 🔹 Raw Sources: Your immutable files (PDFs, articles).
🔹 The Wiki: The LLM-generated markdown files (the synthesis).
🔹 The Schema: The instructions that tell the LLM exactly how to structure and maintain your specific domain.

Karpathy notes: "Obsidian is the IDE; the LLM is the programmer; the wiki is the codebase." 💻

Imagine using this for deep-dive research, tracking your personal goals over years, building companion wikis for complex books, or maintaining an internal team knowledge base that actually stays up to date!

The biggest bottleneck of maintaining a knowledge base has never been the thinking—it’s the bookkeeping. And LLMs don't get bored of bookkeeping.

Have you tried using AI to actively maintain a personal wiki yet, or are you still relying on standard RAG? Let’s discuss below! 👇

AI #MachineLearning #Productivity #LLM #KnowledgeManagement #AndrejKarpathy #TechTrends #Obsidian

Unlocking True AI Collaboration: Understanding Short-Term and Long-Term Memory in Agents

G Gokulnath — Mon, 11 May 2026 00:09:02 +0000

How can AI agents go from being simple conversational tools to becoming genuine collaborators? The answer lies in effective memory systems.

Memory is crucial for AI agents. It lets them remember previous interactions, learn from feedback, and adapt to user preferences. As agents tackle increasingly complex tasks with numerous user interactions, this capability becomes essential for both efficiency and user satisfaction.

But not all memory is the same. There are two primary types, differentiated by their recall scope:

🧵 1. Short-Term Memory (Thread-Scoped)
This tracks the ongoing conversation by maintaining message history within a single session. In LangGraph, this is managed as part of your agent’s state. This state is persisted to a database using a checkpointer, allowing the thread to be resumed at any time. Short-term memory updates with each interaction.

📚 2. Long-Term Memory
This stores user-specific or application-level data across sessions and is shared across conversational threads. It can be recalled at any time and in any thread. Memories are scoped to custom namespaces, not just within a single thread ID. LangGraph uses stores to let you save and recall long-term memories.

Understanding this distinction is key to designing seamless, personalized, and robust AI systems.

How are you implementing memory capabilities in your AI agent projects? Share your insights and challenges in the comments! 👇

DEV Community: G Gokulnath

Are we using LLMs for Personal Knowledge Management all wrong? 🤔

AI #MachineLearning #Productivity #LLM #KnowledgeManagement #AndrejKarpathy #TechTrends #Obsidian

Unlocking True AI Collaboration: Understanding Short-Term and Long-Term Memory in Agents