Are we using LLMs for Personal Knowledge Management all wrong? 🤔
The article discusses a new approach to using large language models (LLMs) for personal knowledge management, termed the 'LLM Wiki.' This method allows for the compounding of knowledge by having the LLM maintain a structured and interlinked markdown wiki instead of merely retrieving information. The author emphasizes the benefits of this approach, including improved organization and reduced manual bookkeeping.
- ▪Andrej Karpathy introduced the concept of the 'LLM Wiki' as a shift in document interaction.
- ▪The LLM Wiki approach allows for compounding knowledge by updating existing information rather than rediscovering it.
- ▪Users curate sources and ask questions while the LLM handles summarization and organization.
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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 | DEV.to (Top) |
| Canonical URL | https://dev.to/vhagar/are-we-using-llms-for-personal-knowledge-management-all-wrong-2fjm |
| Publication time | Wed, 20 May 2026 23:54:47 +0000 |
| Retrieval time | 2026-05-21T00:05:03.135Z |
| Last seen | 2026-05-21T00:05:03.135Z |
| 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 | A9bGLYpVZb3N |
| Cluster logic | Grouped by semantic title/content similarity across sources within a rolling window. Same-publisher template collisions are excluded from coverage comparison. |
| 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
try { if(localStorage) { let currentUser = localStorage.getItem('current_user'); if (currentUser) { currentUser = JSON.parse(currentUser); if (currentUser.id === 3759520) { document.getElementById('article-show-container').classList.add('current-user-is-article-author'); } } } } catch (e) { console.error(e); } G Gokulnath Posted on May 20 Are we using LLMs for Personal Knowledge Management all wrong? 🤔 Andrej Karpathy recently shared a fascinating concept called the "LLM Wiki"—a brilliant shift from how most of us currently interact with our documents. Right now, the standard approach is RAG (Retrieval-Augmented Generation). You upload files, ask a question, and the LLM retrieves chunks to generate an answer.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at DEV.to (Top).
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