Show HN: Llama-dash – local LLM operators dashboard and proxy
Llama-dash is a local AI gateway that provides a unified interface for managing AI model states and request histories. It supports OpenAI and Anthropic clients while offering features like request logging, model management, and customizable routing policies. The dashboard also includes GPU monitoring and metrics for performance tracking.
- ▪Llama-dash turns a self-hosted local inference box into an observable AI gateway.
- ▪It supports OpenAI-compatible and Anthropic-compatible clients with a single public entrypoint.
- ▪The dashboard features live stats, model management, request logging, and GPU monitoring.
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Story provenance
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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/ndom91/llama-dash |
| Publication time | Tue, 19 May 2026 17:15:07 +0000 |
| Retrieval time | 2026-05-19T17:19:57.909Z |
| Last seen | 2026-05-19T17:19:57.909Z |
| 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 | hz-QxDW7ZLr8 |
| 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
llama-dash llama-dash turns a self-hosted local inference box into an observable, policy-controlled AI gateway: one UI for model state, request history, API keys, routing rules, proxy metrics, and client setup. The implemented inference backend is currently llama-swap over llama.cpp. It is the single public entrypoint for OpenAI-compatible and Anthropic-compatible clients. llama-dash owns proxy policy, logging, auth, routing, and backend normalization, your selected inference backend owns local model processes and inference when traffic is routed to local models.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at GitHub.