klöss on Twitter / X

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

Andrej Karpathy Stopped Using AI to Write Code. He’s Using It to Build a Second Brain Instead

His new workflow turns raw research into a self-maintaining wiki.No vector databases, no RAG pipelines, just markdown files and an LLM that…

Digital Science on Twitter / X

An AI-native workspace that already knows your project?The new Papers AI from Digital Science keeps your drafts, data & references together, so the AI assistant has full context of your work - instead of starting fresh each time. 👉 Available now: https://t.co/DNcG173GDk… pic.twitter.com/FQWxdfKfS4— Digital Science (@digitalsci) August 4, 2026

Andrej Karpathy on Twitter / X

Wow, this tweet went very viral!I wanted share a possibly slightly improved version of the tweet in an "idea file". The idea of the idea file is that in this era of LLM agents, there is less of a point/need of sharing the specific code/app, you just share the idea, then the… https://t.co/4UAmYYFzCw— Andrej Karpathy (@karpathy) April 4, 2026

elvis on Twitter / X

arXiv Papers → LLM ArtifactsThis is how I keep up with AI research now.It's like having access to the most personalized arXiv feed.Automations run everyday to curate papers based a set of rules and insights.Curated papers are indexed and power the artifacts.Agent… pic.twitter.com/5UCxF8ZsT0— elvis (@omarsar0) May 6, 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.

Daily Dose of Data Science on Twitter / X

Claude Code fully dissected!Researchers from UCL reverse-engineered the leaked Claude source. What they found changes how you should think about agent design.Only 1.6% of the codebase is AI decision logic.The other 98.4% is operational infrastructure. Permission gates, tool… https://t.co/5HYH7qV3wQ pic.twitter.com/5SBHQy5wFH— Daily Dose of Data Science (@DailyDoseOfDS_) June 13, 2026

JUMPERZ on Twitter / X

karpathy is showing one of the simplest AI architectures that actually works..dump research into a folder, let the model organise it into a wiki, ask questions, then file the answers back in.the real insight is the loop...every query makes the wiki better. it compounds.. now… https://t.co/vRhImgTQuN pic.twitter.com/Uq2D3ONvGT— JUMPERZ (@jumperz) April 2, 2026

What Karpathy's LLM Wiki Is Missing (And How to Fix It)

Andrej Karpathy's LLM Wiki pattern went viral this month. 5,000+ stars, 3,700 forks, dozens of...

The website that created an AI clone of its editor in chief

Every CEO Dan Shipper on doubling headcount while automating everything, building an agent out of 30,000 copyedits, and the “dirty secret” of writing with AI

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

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