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Hacker News (AI / LLM) files mainly under ai. We currently carry 5,429 of its stories.
Story provenance
Source · retrieval · rights · ranking — open for full record
inspect →
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 | RAW |
| Canonical URL | https://rawhq.io |
| Publication time | Fri, 18 Sep 2026 02:26:14 +0000 |
| Retrieval time | 2026-09-18T02:28:44.624Z |
| Last seen | 2026-09-18T02:28:44.624Z |
| 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 | qfcdN0Rxlz3b · 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
Your modelsdeserve anicer home. Real metal.Real root.Really yours.100% API — Servers for AIServers for AI, not another AWS.Create servers. Scale on demand. 100× cheaper, 100× faster, 100× better than AWS. CPU from $9/mo. GPU from $304/mo. Create a server Read the docs Runs your whole stackvLLMOllamaPyTorchCUDALlamaQdrantand anything with rootBuilt for models, agents, and inferenceDedicated GPU and CPU servers you create and scale over a single API. No AWS markup. No shared GPUs. No hypervisor tax.LLM inferencevLLM, Ollama, TGI. Dedicated GPUs for Llama, Mistral, DeepSeek. OpenAI-compatible endpoints. 100× cheaper than Bedrock.Training & fine-tunesFull CUDA, 96 GB VRAM, NVMe.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at RAW.