KaaS – Knowledge as a Service: an out-of-the-box LLM wiki compiler
KaaS (Knowledge as a Service) is an open‑source tool that converts scattered notes, documents, and transcripts into a searchable personal wiki using a four‑phase LLM pipeline. It compiles content into human‑readable Markdown rather than a vector store, allowing users to edit, version‑control, and query the knowledge base via web UI, Docker, or CLI. The system integrates a Go backend, a Python AI engine, and supports MCP access for coding agents, working with any OpenAI‑compatible API.
- ▪KaaS was created to prevent knowledge loss when employees change roles or leave an organization.
- ▪The pipeline processes raw content through extraction, classification, writing, and indexing to produce structured Markdown articles.
- ▪The platform offers both Docker and command‑line installations and can connect to any OpenAI‑compatible endpoint.
- ▪It includes features such as concurrent workers, streaming chat with citations, incremental compilation, and git‑friendly output.
Hacker News (AI / LLM) files mainly under ai. We currently carry 2,798 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/bybit-exchange/kaas |
| Publication time | Wed, 29 Jul 2026 16:45:38 +0000 |
| Retrieval time | 2026-07-29T16:56:08.026Z |
| Last seen | 2026-07-29T16:56:08.026Z |
| 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 | v8QF7UwlOFZ2 · 1 stories |
| 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
KaaS — Knowledge as a Service English · 中文 Turn scattered notes, documents, and transcripts into a searchable, queryable personal Wiki — powered by LLM-driven knowledge compilation. Why We Built This KaaS started as an internal tool. Our knowledge lived scattered across documents, meetings, and email — and every time someone changed roles or left, the context they'd built up walked out with them. New people spent weeks piecing it back together. A distillation pipeline fixed that. It compiles each person's scattered material into a wiki tied to their role rather than their identity — so when someone moves on, the raw data goes but the distilled judgment stays for whoever fills the seat next. The payoff is the same either way: the organization stops re-answering the same questions.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at GitHub.