LLM Wiki Implementation
LLM Wiki A personal knowledge base that builds itself. LLM reads your documents, builds a structured wiki, and keeps it current. LLM Wiki is a cross-platform desktop application that turns your documents into an organized, interlinked knowledge base — automatically.
- ▪LLM Wiki A personal knowledge base that builds itself.
- ▪LLM reads your documents, builds a structured wiki, and keeps it current.
- ▪LLM Wiki is a cross-platform desktop application that turns your documents into an organized, interlinked knowledge base — automatically.
Hacker News (AI / LLM) files mainly under ai. We currently carry 3,304 of its stories.
Story provenance
Source · retrieval · rights · ranking — open for full record
inspect →
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 | GitHub |
| Canonical URL | https://github.com/nashsu/llm_wiki |
| Publication time | Mon, 20 Jul 2026 09:21:26 +0000 |
| Retrieval time | 2026-07-20T10:35:37.613Z |
| Last seen | 2026-07-20T10:35:37.613Z |
| 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 | -BYtf88gad62 |
| 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
LLM Wiki A personal knowledge base that builds itself. LLM reads your documents, builds a structured wiki, and keeps it current. What is this? • Features • Tech Stack • Installation • Credits • License English | 中文 | 日本語 | 한국어 Features Two-Step Chain-of-Thought Ingest — LLM analyzes first, then generates wiki pages with source traceability and incremental cache Multimodal Image Ingestion — extract embedded images from PDFs, generate factual captions with a vision LLM, surface them in image-aware search results with lightbox preview and jump-to-source Multi-format Document Parsing — ingest PDF, Office documents, EPUB/MOBI, Org mode, images, media, web clips, and batches of URLs, with built-in, cloud, or local MinerU PDF processing Flexible Model Configuration — configure models per…
Excerpt limited to ~120 words for fair-use compliance. The full article is at GitHub.