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This article introduces how to use WorkBuddy/Codex + LLM wiki + Obsidian to build a self-growing personal knowledge base, detailing the Raw/Wiki/Schema three-layer architecture and practical steps, and recommending suitable models and tools.
This paper presents a reusable template (llm-wiki-memory-template) that implements the llm-wiki pattern to preserve session memory and failure paths in collaborative knowledge work involving multiple humans, AI agents, and domains. It argues for the substrate's generalizability across three axes and includes case studies demonstrating improved evidence tracking.
The author describes building Oogway, a proactive monitoring agent on OpenClaw that investigates anomalies, proposes fixes, and persistently logs each incident into a wiki, creating a compound knowledge base that avoids re-deriving solutions.
Introducing OpenKnowledge, an open-source, local and private markdown IDE for humans and AI agents, compatible with Claude, Codex, and other agents.
The article provides a detailed analysis of the llm-wiki method proposed by Karpathy: using LLM to compile personal raw materials into interlinked wiki pages, where users only need to read and ask questions without manual editing. The author compares the limitations of RAG and provides a 30-minute setup tutorial, emphasizing the key role of schema files in system discipline.
The author advocates for LLM Wikis as a valuable application of AI, showcasing their PaperWiki project that uses agents to automatically curate and maintain a knowledge base of research papers, improving signal-to-noise ratio and enabling cutting-edge research.
The tweet describes using LLM wikis for agentic coding, calling it extremely powerful, and gives an example of developing a coding harness by ingesting multiple repositories.
LLM Wiki WebUI is a web interface for nashsu's LLM Wiki desktop app, allowing you to access and manage LLM Wiki instances from your browser on the local network. It features project switching, search, knowledge graph visualization, and can be deployed with a single Docker command.
OpenKnowledge is an open source, local-first markdown editor and LLM wiki that integrates with Claude, Codex, and Cursor, offering real-time collaborative AI editing and team sharing via GitHub.
llm_wiki v0.5 is a major update that transitions the personal knowledge base from a RAG-based system to an agent-driven architecture, introducing modular tool calls, intelligent retrieval decisions, and a revamped UI. The project, now at 12.6k stars, incorporates features like a knowledge graph, deep research, and MCP server integration.
A developer built a version of Karpathy's LLM Wiki adapted for code repositories, allowing users to store and retrieve insights from local code with automatic change detection.
Google announced the Open Knowledge Format, an open standard based on Karpathy's LLM wiki concept, designed to provide context for AI agents using simple markdown files.
Brad Mills shares his personal AI agent fitness coaching system using the LLM Wiki tool, which compiles knowledge bases for AI agents. The system coordinates multiple agents to manage workouts and adapt to user feedback.
This tweet introduces the obsidian-wiki project, which implements Andrej Karpathy's LLM Wiki pattern on Obsidian, allowing AI Agents to autonomously ingest multi-source knowledge and organize it into a wikilink-interconnected markdown knowledge base. It supports Delta incremental updates and source attribution (provenance), and has built-in features like query, audit, graph export, etc. Currently at 2k stars.
Google Cloud introduces the Open Knowledge Format (OKF), an open specification that standardizes the LLM-wiki pattern for representing structured knowledge in markdown with YAML frontmatter, aiming to improve data sharing and interoperability for AI agents.
Google has released the Open Knowledge Format, a specification based on Karpathy's llm-wiki, using markdown files for portable knowledge representation, decoupling human-written knowledge from agent consumption.
Google has drafted Andrej Karpathy's LLM Wiki methodology as the Open Knowledge Format standard OKF v0.1. The project author nash_su plans to make his own llm_wiki project compatible with this standard.
This paper formalizes Streaming Knowledge Compilation for LLM wikis, introducing a materiality signal to proactively pin important documents from a streaming corpus under a token budget. It proves an O(√(T log K)) regret bound and validates the approach in finance and Wikipedia domains, showing that regret analysis is a reliable evaluation metric.
A developer shares a month-long experience building an LLM-powered wiki based on Andrej Karpathy's idea, discovering that while setup is easy, ongoing maintenance—like handling stale sources, cost, and integration—poses the real challenge.
This article compares the applicable scenarios and selection suggestions of three knowledge base retrieval schemes: Regular RAG, Knowledge Graph RAG, and LLM Wiki, emphasizing choosing the right scheme based on data morphology and avoiding blind use of complex tools.