Show HN: Lumabri – What if LLMs worked like Napster?
Lumabri is a platform that allows users to run large mixture-of-experts models from a swarm of peers, with the colibri engine. The engine was built for CPU and SSD first, and a GPU only makes it faster, never different. The platform allows users to join a swarm, donate disk space or compute power, and switch between different models.
- ▪Lumabri allows users to run huge mixture-of-experts models from a swarm of peers with the colibri engine.
- ▪The engine is built for CPU and SSD first, and a GPU only makes it faster, never different.
- ▪Users can join a swarm, donate disk space or compute power, and switch between different models.
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Story provenance
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Record
| Original publisher | GitHub |
| Canonical URL | https://github.com/JustVugg/lumabri |
| Publication time | Sun, 09 Aug 2026 22:24:24 +0000 |
| Retrieval time | 2026-08-09T22:55:47.439Z |
| Last seen | 2026-08-09T22:55:47.439Z |
| 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 | j-CsZ9zhwJpz · 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
Run huge mixture-of-experts models from a swarm of peers, with the colibri engine. Pure C, no dependencies. One machine shares a model. Any other machine chats with it: nothing is downloaded up front, the bytes an inference actually touches arrive from the peer on first use and stay in a local mirror. The second question is served from local disk at full speed. The engine binary is unmodified. The founding principle: any machine may join, GPU or not. The engine was built for CPU and SSD first; a GPU only makes it faster, never different, and the output is byte-identical either way. A swarm with no GPU at all is a working swarm. Networks that pool GPUs recruit from the few; lumabri recruits from everyone.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at GitHub.