Show HN: Fast CPU summarize, eli5, fact-check or translate any text
The article introduces fftext, a tool designed for summarizing, explaining, fact-checking, or translating text without relying on cloud services. It operates solely on CPU, allowing users to process files, URLs, or raw text efficiently. The tool supports various tasks with specific prompts, ensuring user privacy by keeping data local after the initial download.
- ▪fftext can summarize, explain, fact-check, or translate any text, URL, or file without using cloud services.
- ▪It operates on CPU only, utilizing a quantized model that streams results directly to the terminal.
- ▪Users can perform tasks by providing files, URLs, or raw strings, with the model remaining offline after the first run.
Hacker News (Newest) files mainly under programming. We currently carry 5,306 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 | GitHub |
| Canonical URL | https://github.com/kouhxp/fftext |
| Publication time | Wed, 27 May 2026 21:29:00 +0000 |
| Retrieval time | 2026-05-27T21:38:05.389Z |
| Last seen | 2026-05-27T21:38:05.389Z |
| 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 | 6tEa5aaxKskr |
| 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
fftext Summarize, explain, fact-check, or translate any text, URL, or file. No GPU. No cloud. One command. fftext s https://en.wikipedia.org/wiki/Llama.cpp Three bullet points, streamed to your terminal, generated on your CPU. No API key. No round-trip to anyone's server. Why fftext ⚡ Fast on CPU. Powered by a quantized 0.8B Qwen3.5 (Q4_K_M GGUF, ~500 MB) running through llama.cpp. Streams tokens as they're generated so you see the answer build, not a spinner. No CUDA. No Metal-only tricks. Plain old cores. 🌐 Files, URLs, or raw strings. Point it at a .txt, paste an article URL, or just type the text inline. URLs get fetched, run through readability-lxml for main-content extraction, and stripped to clean prose before the model sees them. 📴 Offline after first run.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at GitHub.