LLM Wiki app Chunker – transform documents into navigable knowledge trees
The Chunker app transforms documents into navigable knowledge trees by processing them into self-sufficient chunks and multi-level summaries. This approach allows users to explore documents progressively, starting from high-level overviews and drilling down into details without loading the entire text. By utilizing intelligent chunking and bottom-up aggregation, Chunker preserves the document's natural structure and enhances navigation.
- ▪Chunker processes documents into a hierarchy of self-sufficient chunks and multi-level summaries.
- ▪It uses an LLM to find semantically complete split points for chunking, ensuring each chunk is a complete thought.
- ▪The app builds a navigable tree where every node is self-contained, allowing for efficient exploration of document content.
Hacker News (AI / LLM) files mainly under ai. We currently carry 3,301 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 | GitHub |
| Canonical URL | https://github.com/sermakarevich/chunker |
| Publication time | Tue, 19 May 2026 08:22:22 +0000 |
| Retrieval time | 2026-05-19T08:29:57.426Z |
| Last seen | 2026-05-19T08:29:57.426Z |
| 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 | vVSxvpGBPYcc |
| 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
Chunker Transform documents into navigable knowledge trees. Chunker processes a document into a hierarchy of self-sufficient chunks and multi-level summaries, producing a set of linked markdown files that an AI model (or a human) can explore through progressive disclosure -- starting from a high-level overview and drilling into details on demand, without ever loading the entire document. The Problem When an AI model needs to work with a long document, the standard approaches are wasteful: Full context loading feeds the entire document into a prompt. This burns tokens, dilutes attention, and hits context window limits. Naive chunking (split every N tokens) produces fragments that start and end mid-thought.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at GitHub.