LLM Wiki v2
LLM Wiki v2 introduces a refined approach to building personal knowledge bases with LLMs. It emphasizes the importance of memory lifecycle, confidence scoring, and knowledge retention to enhance the utility of wikis. The document also outlines the need for a structured knowledge graph to improve information retrieval and organization.
- ▪The original LLM Wiki concept is expanded with lessons learned from practical application in building a persistent memory engine.
- ▪A key insight is that knowledge should have a lifecycle, with newer information automatically superseding older claims.
- ▪The proposed system includes confidence scoring for facts, allowing the LLM to assess the reliability of information based on source support and recency.
Hacker News (AI / LLM) files mainly under ai. We currently carry 3,302 of its stories.
Story provenance
Source · retrieval · rights · ranking — open for full record
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Story provenance
Attribution is not the same as permission. This drawer separates discovery metadata, excerpts, WeSearch-generated summaries, reuse status, and whether the publisher receives the visit. Nothing here claims a legal grant the publisher has not made.
Record
| Original publisher | Gist |
| Canonical URL | https://gist.github.com/kanmadigital/2369c4f5ea410cb8f6a1647b40c0e2a1 |
| Publication time | Wed, 20 May 2026 04:25:26 +0000 |
| Retrieval time | 2026-05-20T04:29:59.651Z |
| Last seen | 2026-05-20T04:29:59.651Z |
| Headline source | Publisher (no WeSearch rewrite) |
| Excerpt source | publisher body |
| Excerpt method | First ~120 words (~800 chars) of extracted publisher body, fair-use limited. |
| Summary | WeSearch · cerebras-chat (WeSearch summarizer) |
| Summary source text | contentText |
| Citation coverage | Summary is a WeSearch-generated derivative; primary citation is the original publisher URL. |
| Cluster | None |
| Cluster logic | Not yet clustered, or no peer story found in the clustering window. |
| Ranking reason | Story pages are not engagement-ranked. Hub feeds use recency, with optional source-diversified chronological ordering (cap consecutive stories per source). No personalized ranking. |
| Publisher visit | Yes — open original |
| Substitutes article? | No — link-out required for full text |
Rights status (four layers)
WeSearch handling by dimension
| Indexing | May the item be indexed (stored, ranked, made findable)? | Allowed |
| Snippet | May a short excerpt of the publisher's text be shown? | Allowed |
| AI summary | May WeSearch generate its own short summary of the article? | Limited |
| Retrieval / RAG | May the content be exposed for third-party retrieval-augmented generation? | Not asserted |
| Model training | May the content be used to train AI models? | Not asserted |
| Commercial reuse | May the content be reused commercially? | Not permitted |
Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
Opening excerpt (first ~120 words) tap to expand
LLM Wiki v2 A pattern for building personal knowledge bases using LLMs. Extended with lessons from building agentmemory 10K Stars ⭐️, a persistent memory engine for AI coding agents. This builds on Andrej Karpathy's original LLM Wiki idea file. Everything in the original still applies. This document adds what we learned running the pattern in production: what breaks at scale, what's missing, and what separates a wiki that stays useful from one that rots. Currently, Working on AKBP: Agent Knowledge Base Protocol based on my findings, a protocol for creating, updating, retrieving, and sharing durable knowledge across AI agents. What the original gets right The core insight is correct: stop re-deriving, start compiling. RAG retrieves and forgets. A wiki accumulates and compounds.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at Gist.
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