llama.cpp
Own your models and conversation data. curl -LsSf https://llama.app/install.sh | sh Prefer Brew or Winget? Follow instructions Pair it with a local coding agent. Run llama serve, install the pi-llama plugin and launch Pi.
- ▪Own your models and conversation data. curl -LsSf https://llama.app/install.sh | sh Prefer Brew or Winget?
- ▪Follow instructions Pair it with a local coding agent.
- ▪Run llama serve, install the pi-llama plugin and launch Pi.
Hacker News (Front Page) files mainly under programming. We currently carry 1,440 of its stories. Top-voted stories on Hacker News.
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 | Llama |
| Canonical URL | https://llama.app |
| Publication time | Wed, 12 Aug 2026 04:51:59 +0000 |
| Retrieval time | 2026-08-12T05:45:45.253Z |
| Last seen | 2026-08-12T05:45:45.253Z |
| 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 | None |
| Cluster logic | Not yet clustered, or no peer story found in the clustering window. |
| 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.cpp AI that lives on your computer. Open-source, private & always local. Run frontier AI entirely on your machine. No API keys, no telemetry, no limits. Own your models and conversation data. curl -LsSf https://llama.app/install.sh | sh Prefer Brew or Winget? Package managers · Rather build from source? Follow instructions Pair it with a local coding agent. Run llama serve, install the pi-llama plugin and launch Pi. It will automatically discover your local model. No config, no API keys. Files stay on your machine, requests never leave it. # 1. Serve a model llama serve # 2. Install the pi-llama plugin pi install git:github.com/huggingface/pi-llama # 3. Run Pi, everything is set pi Optimized for any hardware. From your laptop to a cluster, llama.cpp runs on whatever you have.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at Llama.
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