---
title: "Ocho vs. Karpathy's second brain"
description: "The viral LLM-wiki pattern proved the idea. Ocho is the product it calls for."
canonical: https://ocho.bot/docs/market-comparison/ocho-vs-karpathys-second-brain
last_updated: 2026-07-12
---

# Ocho vs. Karpathy's second brain

> The viral LLM-wiki pattern proved the idea. Ocho is the product it calls for.

In April 2026, Andrej Karpathy posted how he maintains a personal research wiki: raw sources go in a folder, an LLM agent writes and cross-references the wiki pages, an index file keeps it navigable. The [gist](https://gist.github.com/karpathy/442a6bf555914893e9891c11519de94f) drew millions of views — deservedly. His core insight is exactly right: *"The tedious part of maintaining a knowledge base is not the reading or the thinking — it's the bookkeeping."* Let the AI do the bookkeeping, and knowledge compounds instead of evaporating after every chat.

Here's the thing: it's a pattern, not a product. Karpathy describes his own tooling as "a hacky collection of scripts" — and adds, *"I think there is room here for an incredible new product."*

## What the LLM wiki does well

- Zero infrastructure: markdown in a git repo, readable anywhere, forever.
- The LLM maintains cross-references and consistency across pages.
- Answers get filed back as pages, so knowledge compounds.

## Where the pattern stops

- **You are the ingestion pipeline.** Every source is hand-captured — clipped,

pasted, or dropped in a folder. No connectors to where company knowledge   actually lives. Nothing syncs; if a source changes, the wiki doesn't know.

- **It's single-player by design.** Markdown folders have no permissions, no

concurrent-writer safety, no audit trail. A team can't share one.

- **It hits a ceiling fast.** The whole approach relies on fitting the index

in the model's context window — practitioners report strain around   100–200 articles   ([one write-up](https://tomnguyenit.medium.com/i-built-karpathys-llm-wiki-for-my-day-job-here-s-what-actually-works-0d4ec6d1e433)).   A company's corpus is thousands of documents on day one.

- **No verifiable citations, no governance, no support.** Fine for personal

research; disqualifying for anything auditable.

## What Ocho does differently

Ocho is the productized version of the same conviction. The bookkeeping — ingestion strategy, indexing, entity extraction, cross-referencing, freshness — is done by agents, at corpus scale, continuously:

- **Start from what exists.** Connect SharePoint, Notion, Google Drive,

GitHub, or any API; Ocho ingests and keeps syncing. No clipping.

- **The wiki writes itself — as a graph.** Instead of LLM-authored pages, Ocho

extracts an actual [knowledge   graph](/docs/market-comparison/what-is-a-knowledge-graph) that updates when   documents change.

- **Built for teams.** Roles, permissions, usage budgets, and one shared brain

the whole company queries — with page-level citations on every answer.

- **No scale ceiling.** [Agentic

retrieval](/docs/market-comparison/what-is-agentic-rag-search) searches the   corpus; nothing needs to fit in a context window.

## Which should you use?

If you're one technical person organizing personal research, run the gist — it's elegant, free, and yours. If you're a team that wants the same compound returns on knowledge you already have, without becoming your own infrastructure, that's the product Karpathy said someone should build. We built it.

All docs: https://ocho.bot/docs

---

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