brianpetro/obsidian-smart-connections

Find related notes and excerpts while writing. Your link building copilot displays relevant content in graph + list view. A local embedding model powers semantic search. Zero setup. No API key.

Documentation — Terminal Graph

Download Terminal Graph beta, learn the shortcuts, nodes, and dataflow model.

Desire Paths for Wikipedia

An extension that remembers the path of a cursor over the linked pages of Wikipedia, “wearing” them into the page.

knowledgespace.md

knowledgespace.md · GitHub

Higher-Order Knowledge Representations for Agentic Scientific Reasoning

Scientific inquiry requires systems-level reasoning that integrates heterogeneous experimental data, cross-domain knowledge, and mechanistic evidence into coherent explanations. While Large Language Models (LLMs) offer inferential capabilities, they often depend on retrieval-augmented contexts that lack structural depth. Traditional Knowledge Graphs (KGs) attempt to bridge this gap, yet their pairwise constraints fail to capture the irreducible higher-order interactions that govern emergent physical behavior. To address this, we introduce a methodology for constructing hypergraph-based knowledge representations that faithfully encode multi-entity relationships. Applied to a corpus of ≈\approx 1,100 manuscripts on biocomposite scaffolds, our framework constructs a global hypergraph of 161,172 nodes and 320,201 hyperedges, revealing a scale-free topology (power law exponent ≈\approx 1.23) organized around highly connected conceptual hubs. This representation prevents the combinatorial explosion typical of pairwise expansions and explicitly preserves the co-occurrence context of scientific formulations. We further demonstrate that equipping agentic systems with hypergraph traversal tools, specifically using node-intersection constraints, enables them to bridge semantically distant concepts. By exploiting these higher-order pathways, the system successfully generates grounded mechanistic hypotheses for novel composite materials, such as linking cerium oxide to PCL scaffolds via chitosan intermediates. This work establishes a “teacherless” agentic reasoning system where hypergraph topology acts as a verifiable guardrail, accelerating scientific discovery by uncovering relationships obscured by traditional graph methods.

Discourse Graphs

A tool and ecosystem for collaborative knowledge synthesis

Yuchen Jin on Twitter / X

Karpathy’s “LLM Wiki” pattern: stop using LLMs as search engines over your docs. Use them as tireless knowledge engineers who compile, cross-reference, and maintain a living wiki. Humans curate and think.Diagram generated by my Claude agent knowledge worker. https://t.co/5u5i1GeFK8 pic.twitter.com/NIaq3KlAok— Yuchen Jin (@Yuchenj_UW) April 4, 2026

Termboard - Knowledge Graphs Made Simple

Finally, knowledge graphs anyone can build.

Agent Guide — Stigmergic

Stigmergic is an AT Protocol appview for publishing and browsing knowledge-graph islands. An island is a compact, inspectable cluster of source URLs connected by typed edges and accompanied by a short synthesis.

Getting started - Docs

Discourse Graphs are a tool and ecosystem for collaborative knowledge synthesis, enabling researchers to map ideas and arguments in a modular, composable graph format.

letta-ai/social-cli

A unified CLI to connect artificial intelligence to the social web.

I Built a Knowledge Base That Writes Itself. Here Is What Andrej Karpathy Got Right.

Andrej Karpathy posted about using LLMs to build personal knowledge bases. I took his workflow, wired it into my Obsidian vault with Claude Code, and within an hour had 21 cross-linked wiki articles compiled from YouTube transcripts. Here is how it works and why it matters.