🔗 Structured Links

We deeply understand that isolated knowledge points cannot maximize their value, and only by organically connecting related information can deeper insights be stimulated. Dessix comprehends this deeply, placing structured links at the core of the system design. It not only inherits the advantages of traditional double-linked notes but also extends them to a new dimension, making each link an active neuron in the knowledge network, conveying semantics and context in the process of human-computer collaboration.

Core Capabilities

• Generalized @mention mechanism: Not limited to links between notes, extended to all Block types within the system
• Strongly associated semantic context: Linked content automatically participates in the context construction of AI collaboration
• Bidirectional link tracking: Automatically records and displays all reference relationships
• Semantic alignment consistency: Ensures that the understanding of link relationships between users and AI is completely consistent

Link as Context: Beyond Traditional Double-Linked Notes

🔄 Generalized Link Model: Everything Block Can Be Linked

The double-link function of traditional note tools is usually limited to mutual references between notes, while Dessix adopts a more open and flexible link model:

• In Dessix, any entity defined as a Block can be linked via @mention, including but not limited to:
  • Notes and Articles
  • Threads
  • Actions and Scenes
  • Folders and Collections
  • External content such as Twitter posts

This generalized link model breaks the barriers between different content types in traditional knowledge management systems, allowing you to freely associate any relevant information in a unified semantic network.

🧠 Strong Association Semantics: Link as a Map of Thought

In Dessix, @mention is not just a simple reference mark, but a deliberate cognitive behavior:

• When you link two Blocks via @mention, you are essentially declaring: "There is an important semantic association between these two pieces of information"
• The system records this strong association and automatically considers it in the context when collaborating with AI
• The linked content will participate in the thinking and creation process as a high-priority semantic context

This design ensures that the cognitive associations you express through deliberate linking can be accurately captured by the system and respected and applied in AI collaboration.

🔍 Bidirectional Tracking: A Comprehensive Reference Network

Dessix not only records the links you create but also automatically maintains a complete bidirectional reference relationship:

• Each Block can clearly see "who it references" and "who references it"
• The system automatically constructs and updates these reference relationships without manual maintenance
• Through the reference network, you can easily track the source and impact of information

This bidirectional tracking mechanism ensures that knowledge forms an organic network in the system, rather than isolated information islands.

⚡ Human-Machine Alignment: Consistent Link Understanding Front and Back

The most unique value of Dessix is to ensure that you and AI have a completely consistent understanding of link relationships:

• When you establish a link via @mention, the system simultaneously informs AI of the existence and importance of this relationship
• When AI processes related content, it will automatically consider these strongly associated contexts
• The relationships you emphasize are equally valued by AI, achieving deep alignment in link understanding between humans and machines

This consistent link understanding front and back ensures that AI can truly "see" the knowledge structure you build, providing a collaborative experience that aligns more closely with your way of thinking.

Next Steps

As the user's knowledge network continues to expand, Dessix will continue to optimize the structured link function and explore more possibilities:

• Introduce a link weight mechanism based on usage frequency and contextual relevance
• Support richer link types to express more refined semantic relationships
• Develop visual knowledge graphs to intuitively display the network of information associations
• Explore automatic link suggestion functions combined with large language models