Laptop AI – local AI memory for your files
Laptop AI is a local-first AI memory engine designed for personal computers. It allows users to query files on their computer using local language models, providing contextually relevant answers. The system emphasizes security by indexing only user-selected folders and avoiding whole-home indexing.
- ▪Laptop AI indexes selected folders into a custom disk-backed vector database.
- ▪The system retrieves answers based on local LLMs while ensuring user privacy.
- ▪It includes features like a secret scanner and a denylist to prevent accidental indexing of sensitive information.
Hacker News (AI / LLM) files mainly under ai. We currently carry 2,530 of its stories.
Story provenance
Source · retrieval · rights · ranking — open for full record
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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/takshd15/Laptop-AI |
| Publication time | Tue, 26 May 2026 14:47:03 +0000 |
| Retrieval time | 2026-05-26T14:57:49.919Z |
| Last seen | 2026-05-26T14:57:49.919Z |
| 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 | MEtIHgYqQ_gN |
| 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
Laptop AI Private AI memory infrastructure for personal computers. Laptop AI is a local-first AI memory engine that lets users ask questions about files on their computer using local LLMs. It indexes selected folders into a custom disk-backed vector database and retrieves relevant context for private, source-cited answers. Demo Assets For a short project post: Main visual: terminal GIF Backup visual: architecture diagram Backup visual: benchmarks and security proof Setup Requirements: Go 1.22+ Ollama running locally at http://localhost:11434 Ollama models: nomic-embed-text and llama3 ollama serve ollama pull nomic-embed-text ollama pull llama3 go build ./cmd/laptop-ai Run the verified demo: ./laptop-ai init ./laptop-ai index ./examples/notes ./laptop-ai ask "what controls movement in my…
Excerpt limited to ~120 words for fair-use compliance. The full article is at GitHub.