Core Insights - The article discusses the use of embedding vectors for AI search in note-taking applications, emphasizing the importance of maintaining semantic proximity in high-dimensional vector spaces [1][38] - It highlights the application of the t-SNE algorithm for visualizing high-dimensional data in a lower-dimensional space while preserving local similarities [1][38] Group 1: Application Features - The application (cflow) allows users to visualize over 3000 notes as points in a 2D coordinate system based on their embedding vectors [2][4] - Users can interact with the visualization by clicking on points to view note content and see connections between notes through visual links [4][8] - The application supports advanced search functionalities, allowing users to highlight search results and quickly access related notes through tags [4][6] Group 2: User Experience - Users can explore the visualization to find interesting notes and their relationships, leading to the discovery of "orphan notes" that lack further connections [8][9] - The clustering of notes based on shared themes or experiences, such as restaurant reviews, demonstrates the effectiveness of the embedding algorithm [9][13] - The application allows users to input new text and see its position in relation to existing notes, providing a playful exploration of semantic relationships [24][25] Group 3: Insights on Note Clustering - The clustering of notes related to personal achievements shows how unrelated notes can be grouped based on semantic similarity [13][15] - The proximity of notes related to different tags, such as AI and CODING, indicates overlapping themes in the user's knowledge management [17][19] - The article notes the separation of investment-related notes into distinct clusters, suggesting potential mislabeling or thematic divergence [21][22] Group 4: Additional Features and Reflections - The application includes features for creating automated spaces for saving and organizing notes, enhancing user experience [36] - The author reflects on the revolutionary nature of embedding technology in machine learning, highlighting its ability to transform textual data into meaningful vector representations [38]