Overview
Some concepts explored related to Knowledge Engineering, Nostr, Large Language Models, Complexity, and more.
NostrComplexityLarge Language ModelKnowledge Engineering
Overview source: Curated paper metadata.
Methods and contributions
Read the source for the full argument, qualifications, and evidence.
Findings and contributions
- The notes propose linking papers not only by citation edges but by syntactic bridges (keyword cards) and semantic bridges (embeddings).
- The notes argue auto-generated flashcards pose less of an information-overload risk than auto-generated papers, since unused cards are simply ignored and useful ones composed.
- The notes suggest review papers could follow the most popular paths through the card graph and novelty search the least traversed ones.
Methods
- Index cards as hashed Nostr notes (JSON to SHA256 ID) — The notes sketch each index card as a JSON object (username, text, etc.) hashed into a 32-byte SHA256 identifier, with edges defined between card IDs.
- LLM semantic embeddings attached to index cards — Proposes using language-model embeddings, translations and summaries so cards act as semantic bridges between texts.
- Path analysis over composed index-card graphs — Proposes analyzing paths through card graphs for trivial features (overall length) and subtler ones (e.g. which proportion of links are novel reports).
Summary sources: Paper metadata and evidence · Extracted source text.
PDF downloads
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- Index_Card_7_5_2023.pdf PDF · 0.04 MiB · extracted-text source
Citation
Citation metadata follows the unified bibliography.
Claros, Andrew; Friedman, Daniel. 2023. Modern Nostr Index Card-based Knowledge Engineering. Zenodo. DOI: 10.5281/zenodo.8118155. URL: https://doi.org/10.5281/zenodo.8118155.
Catalog details and resources
Related in Active Inference
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