Andrej Karpathy's LLM Wiki pattern went viral this month. 5,000+ stars, 3,700 forks, dozens of...
I rebuilt Karpathy's LLM Wiki gist: what's missing
Karpathy's original LLM Wiki gist, explained and rebuilt as a working system. 44 open-source commands, plus the five things the gist leaves out.
LLM Wiki v2 — extending Karpathy's LLM Wiki pattern with lessons from building agentmemory
LLM Wiki v2 — extending Karpathy's LLM Wiki pattern with lessons from building agentmemory · GitHub
Karpathy's LLM Wiki: The Complete Guide to His Idea File
Karpathy's follow-up gist went viral. Complete breakdown with implementation examples.
Karpathy's LLM Wiki v2: What to Keep, What to Skip
I rebuilt it, ran it daily for months, and most of the v2 hype is overkill.
Yuchen Jin on Twitter / X
Karpathy’s “LLM Wiki” pattern: stop using LLMs as search engines over your docs. Use them as tireless knowledge engineers who compile, cross-reference, and maintain a living wiki. Humans curate and think.Diagram generated by my Claude agent knowledge worker. https://t.co/5u5i1GeFK8 pic.twitter.com/NIaq3KlAok— Yuchen Jin (@Yuchenj_UW) April 4, 2026
Andrej Karpathy on Twitter / X
LLM Knowledge BasesSomething I'm finding very useful recently: using LLMs to build personal knowledge bases for various topics of research interest. In this way, a large fraction of my recent token throughput is going less into manipulating code, and more into manipulating…— Andrej Karpathy (@karpathy) April 2, 2026
Shann³ on Twitter / X
5 upgrades you can do to Karpathys LLM wikisave this blueprint and send it to your agents https://t.co/clFZiHARGn pic.twitter.com/T7LWgjtu9y— Shann³ (@shannholmberg) May 5, 2026
Patina: Turning Karpathy’s LLM Wiki Pattern into a Rust CLI
Useful, local, and slightly fun. There is a simple but important idea behind Andrej Karpathy’s LLM Wiki pattern: useful synthesis should not disappear into chat history.
klöss on Twitter / X
let me explain what Karpathy just sharedhe’s spending way less time using AI to write code and more time using it to build personal knowledge basesthe full breakdown: → he dumps raw sources (articles, papers, repos, datasets, images) into a folder. then has an LLM organize… https://t.co/Kdq1Q48S5P pic.twitter.com/XajJKR7xgT— klöss (@kloss_xyz) April 4, 2026
Karpathy's LLM Knowledge Base Wiki for Enterprise | Vijoy Pandey posted on the topic | LinkedIn
There's a new kind of computer media in the enterprise: Write once, Read never. The docs are perpetually stale, constantly diverging from reality, and scattered across Confluence, SharePoint, GitHub, Webex (or Slack) threads, Notion, Obsidian - and in my personal life, add Apple Notes, Goodnotes, web clippings, and multiple Google Drives worth of docs and slides that nobody is ever going back to. Karpathy tweeted his LLM knowledge base wiki architecture which went viral last weekend and I decided to give it a run yesterday. Verdict: You *have* to try this out. Prediction: You won’t be able to live without it soon. There were a few mods and decisions I made to the base Karpathy provided. First, the vault / folder structure in Obsidian. I already use Obsidian as a human. Instead of creating separate vaults and dealing with the sync nightmare, I just have folders for Human and Agent, and a Raw folder. (1) The Human/ folder is where I write long form articles and notes independent of the knowledge base wiki. No LLM or agent touches this folder. (2) I do have Arnold Layne, my OpenClaw agent, doing background tasks for me. Raw/ is where both Arnold and I, dump raw snippets. Inclusive of diverse kinds of media. (3) The Agent/ folder is where the LLM (Claude in my case) synthesizes the wiki. No human touches this folder. Second, some customizations to CLAUDE.md for enterprise-like usage - (4) Domain extensions - the agent needs to know that quantum computing and agentic AI have different entity types and different provenance thresholds. (5) Primary source protection, when I drop in my own original work, secondary sources can extend it or raise questions against it, but they cannot overwrite it. It sounds like a small thing but its’s not, especially at enterprise scale where provenance actually matters. Karpathy is upfront that what he’s built is working memory for a single agent, and it’s truly remarkable at that. The jump to Shared Context across teams, reconciling conflicting beliefs at org scale, ontologies that don’t collapse under the weight of a hundred contributors - those are much harder problems and what we are exploring with the Internet of Cognition. PS: The screenshot shows my Obsidian vault after just two runs: one with Karpathy’s original tweet and gist file itself (so meta!) and one with our Internet of Cognition paper. Claude (Sonnet) read it, compiled it into structured summaries, entity pages, concept pages, backlinks, merged all the information cohesively, and keeps it all maintained from there. You just read the Wiki. It’s simply magical.
Karpathy's LLM Wiki as Agent Memory - Agentic AI Foundation (AAIF)
At work, I’m building agents to handle various operational tasks and have found Karpathy’s LLM Wiki design to be an excellent solution for implementing most ty…
Andrej Karpathy on Twitter / X
Personal update: I've joined Anthropic. I think the next few years at the frontier of LLMs will be especially formative. I am very excited to join the team here and get back to R&D. I remain deeply passionate about education and plan to resume my work on it in time.— Andrej Karpathy (@karpathy) May 19, 2026
Karpathy shares 'LLM Knowledge Base' architecture that bypasses RAG with an evolving markdown library maintained by AI
Karpathy proposes something simpler and more loosely, messily elegant than the typical enterprise solution of a vector database and RAG pipeline.
elvis on Twitter / X
Diagram of the LLM Knowledge Base system.Feed this to your favorite agent and get your own LLM knowledge base going. https://t.co/4AQSFOv4PV pic.twitter.com/nPSNi4Ayqv— elvis (@omarsar0) April 3, 2026
