StrangerGraphs is a fan theory prediction engine that applies graph database analytics to the chaotic world of Stranger Things fan theories on Reddit.
The company scraped 150,000 posts and ran community detection algorithms to identify which Stranger Things fan groups have the best track records for predictions. Theories were mapped as a graph (234k nodes and 1.5M relationships) that track characters, plot points and speculation and then used natural language processing to surface patterns across seasons. These predictions are then mapped out in a visualization for extra analysis. Top theories include ■■■ ■■■■■ ■■■■, ■■■ ■■■■■■■■ ■■ and ■■■■ ■■■■■■■■ ■■■ ■■ ■■■■. (Editor note: these theories have been redacted to avoid any angry emails about spoilers.)
Transform Claude's Hidden Memory Into Interactive Knowledge Graphs
Transform Claude's Hidden Memory Into Interactive Knowledge Graphs
Universal tool to visualize any Claude user's memory.json in beautiful interactive graphs. Transform your Claude Memory MCP data into stunning interactive visualizations to see how your AI assistant's knowledge connects and evolves over time.
Enterprise teams using Claude lack visibility into how their AI assistant accumulates and organizes institutional knowledge. Claude Memory Viz provides zero-configuration visualization that automatically finds memory files and displays 72 entities with 93 relationships in real-time force-directed layouts. Teams can filter by entity type, search across all data, and explore detailed connections through rich tooltips.
The technical implementation supports Claude's standard NDJSON memory format, automatically detecting and color-coding entity types from personality profiles to technical tools. Node size reflects connection count, while adjustable physics parameters enable optimal spacing for large knowledge graphs. Built with Cytoscape.js for performance optimization.
Built with the philosophy "Solve it once and for all," the tool works for any Claude user with zero configuration. The visualizer automatically searches common memory file locations, provides demo data fallback, and offers clear guidance when files aren't found. Integration requires just git clone and one command execution.
This matters because AI memory has been invisible to users, creating trust and accountability gaps in enterprise AI deployment. When teams can visualize how their AI assistant organizes knowledge, they gain insights into decision-making patterns and can optimize their AI collaboration strategies.
👩💻https://lnkd.in/e__RQh_q | 10 comments on LinkedIn
Transform Claude's Hidden Memory Into Interactive Knowledge Graphs
Navigating the EU AI Act: Introducing the AI Act Requirements Graph
Navigating the EU AI Act: Introducing the AI Act Requirements Graph As the EU AI Act moves closer to becoming law, understanding its complex web of… | 11 comments on LinkedIn
Navigating the EU AI Act: Introducing the AI Act Requirements Graph