Analysis and Advice Paper (Yuri BC, ChatGPT4, 2023-08-02)

Fibery Analysis and Advice Paper (Yuri BC, version 2023-08-02). This paper has been created with the help of ChatGPT 4.

Table of contents

  1. Introduction
  2. Competitive Analysis
  3. Current State Analysis of
  4. Proposed Improvements
  5. Implementation Strategy
  6. Conclusion
  7. References

1. Introduction

In the rapidly evolving field of digital note-taking and knowledge management, a new paradigm has emerged, often referred to as the ‘collective collaborative brain’. This concept represents a shift towards more interconnected, flexible, and collaborative tools that mimic the associative nature of the human brain. is a versatile tool that has made significant strides in this field. It offers a unique blend of features, combining elements of traditional project management software with the flexibility of a custom database and the interconnectivity of a networked note-taking app.

However, the landscape is competitive, with platforms like Roam Research, Obsidian, Logseq, and offering compelling features that resonate with users seeking brain-like functionality.

The objective of this paper is to provide a comprehensive analysis of in the context of these competing platforms, identify its current strengths and limitations, and propose specific improvements. The ultimate goal is to enhance’s capabilities in the realm of ‘collective collaborative brain’, positioning it as the most advanced tool in this field.

2. Competitive Analysis

In the realm of ‘collective collaborative brain’ tools, several key competitors stand out, each with unique features that contribute to their success in this field. The apps chosen for comparison - Roam Research, Obsidian, Logseq, and Anytype - are leading innovators in the note-taking and collaboration field. They each offer unique features that align with the ‘collective collaborative brain’ concept, making them directly relevant for comparison with This analysis will provide valuable insights for potential improvements in

Roam Research: Roam Research is a networked note-taking app that allows users to create bi-directional links between notes, fostering a web of interconnected ideas. It supports block-level linking, which enables users to create links at the granular level of individual blocks of text. This feature, combined with the ability to view the network of connections in a graph view, makes Roam Research a powerful tool for associative thinking and knowledge management. However, it has been noted that Roam’s user interface can be complex and intimidating for new users.

Obsidian: Obsidian is a versatile note-taking app that stores data in plain text markdown files, ensuring long-term accessibility and compatibility. It supports linking between notes and offers a graph view to visualize the connections. Unlike Roam Research, Obsidian does not natively support block-level linking, which limits the granularity of interconnections. However, its local-first data storage approach and robust plugin ecosystem make it a popular choice for users who value data privacy and customization.

Logseq: Logseq is a privacy-focused, open-source platform for knowledge sharing and management. It supports block-level linking and provides a graph view for visualizing connections. Logseq’s key strength lies in its combination of Roam-like features with a data storage model that respects user privacy. It stores data in plain text files and allows users to choose where their data is stored. Anytype is a decentralized app that allows users to create, organize, and collaborate on content. It supports block-level linking and offers a unique system of ‘objects’ that can represent anything from a simple note to a complex project. Anytype’s decentralized architecture ensures that users have full control over their data. However, as a relatively new entrant in the market, it may lack some of the advanced features and polish of more established apps.

Each of these competitors offers valuable lessons for in terms of features that resonate with users in the ‘collective collaborative brain’ field. The next sections will delve into a detailed analysis of’s current state and propose specific improvements to enhance its capabilities in this realm.

1. Logseq

  • Pros:
    • Supports block-level linking and provides a graph view for visualizing connections.
    • Privacy-focused, open-source platform that allows users to choose where their data is stored.
  • Cons:
    • Lacks a robust ecosystem of plugins and integrations compared to some competitors.
    • The user interface might be less polished and intuitive compared to more mature tools.
    • Missing advanced database and view customization features.


  • Pros:
    • Supports block-level linking and offers a unique system of ‘objects’.
    • Decentralized architecture ensures that users have full control over their data.
  • Cons:
    • Being relatively new, it may lack advanced features such as a robust plugin ecosystem, advanced search and filtering capabilities, and mature user interface design.
    • The unique ‘objects’ system, while innovative, may require a learning curve and may not be as intuitive for some users.
    • Missing advanced database and view customization features.

3. Obsidian

  • Pros:
    • Stores data in plain text markdown files, ensuring long-term accessibility and compatibility.
    • Has a robust plugin ecosystem and supports linking between notes.
  • Cons:
    • Does not natively support block-level linking, which limits the granularity of interconnections. This is a key feature for creating a ‘collective brain’.
    • While it has a graph view, it may not be as advanced or interactive as those provided by some competitors.
    • Missing advanced database and view customization features.

4. Roam Research

  • Pros:
    • Supports block-level linking and provides a graph view for visualizing connections.
    • Powerful tool for associative thinking and knowledge management.
  • Cons:
    • User interface can be complex and intimidating for new users, which could hinder adoption and usage.
    • While it supports block-level linking and has a graph view, it lacks some of the advanced database and view customization features that other tools offer.

3. Current State Analysis of, a distinctive contender in the realm of collaborative work management tools, is built on the philosophy that every organization is unique and should have the freedom to shape its own operational processes. This principle, articulated by the founder in various articles, is embodied in the adaptability of, which can be tailored to accommodate any workflow.

The architecture of is centered around entities and their relations. Entities, which can represent anything from tasks and projects to teams and user-defined types, form the building blocks of the system. Relations, on the other hand, establish the connections between these entities. This flexible framework empowers users to mirror their unique processes and knowledge structures within, thereby facilitating easy access and utilization of information. This design choice is a direct reflection of the founder’s vision to create a tool that grows and evolves with the organization.

Key Features:

  • Entities and Relations: Fibery’s core architecture is based on entities and relations. Entities can represent anything from a simple note to a complex project, and relations allow entities to be linked in meaningful ways.
  • Customizability: Fibery allows users to define their own types of entities and relations, and to customize the fields of each entity type. This makes it highly adaptable to a wide range of use cases.
  • Collaboration: Fibery supports real-time collaboration, with features like shared views, comments, and notifications. It also integrates with other tools like Slack and Google Calendar.
  • Browser Extension: Fibery offers a browser extension for Chrome that allows users to add ideas, insights, competitors, and articles from a web page to their Fibery workspace in two clicks.
  • Map View: Fibery has a map view feature that allows users to visualize customer locations, events, offices, or other spatial data.
  • Forms: Fibery supports the creation of forms for data entry, which can be shared externally.
  • Experimental Grid View: Fibery is working on a new grid view feature, which is expected to replace the current table view.

Challenges and Limitations:

Despite its strengths, also has some challenges and limitations that could be addressed to make it a more powerful tool for ‘collective collaborative brain’:

  • Lack of Block-Level Linking: Unlike some of its competitors, Fibery does not currently support block-level linking. This limits the granularity of interconnections that users can create.
  • Lack of Graph View: Fibery does not currently offer a graph view feature to visualize the connections between entities. This could make it harder for users to understand and navigate their network of information.
  • User Experience: Some users have reported that Fibery’s user interface can be complex and intimidating, especially for new users. The list of relations can be overwhelming, and it can be difficult to combine relations into meaningful content.
  • Difficulty in Creating New Documents: Users have reported difficulty in creating new documents that consist of a selection of other entities. This could be addressed by improving the process of creating and embedding entities.

The next section will propose specific improvements to address these challenges and limitations, and to enhance’s capabilities in the realm of ‘collective collaborative brain’.

4. Proposed Improvements

To enhance’s capabilities in the realm of ‘collective collaborative brain’, the following improvements are proposed:

Unified Block System:

  • Implementation: Consider every piece of content in Fibery, whether it’s a block of text in a rich text field or an entire entity, as a “block”. Each block would have its own unique identifier and could contain other blocks. Entities would be treated as special types of blocks that have additional properties and capabilities.
  • Nested Blocks: Allow blocks to be nested within other blocks. A block of text within a rich text field could contain links to other blocks, and an entity could contain other entities or blocks of text.
  • Transclusion: Enable the ability to embed blocks within other blocks. This would allow the content of a block to be displayed in multiple places, and any changes to the original block would be reflected in all places where it’s embedded.

Graph View:

  • Implementation: In the graph view, each block (whether it’s a rich text field block or an entity) could be represented as a node. Links (both relations and block-level links) could be represented as edges connecting nodes.
  • Interactivity: Make the graph view interactive, allowing users to click on nodes to navigate to the corresponding block, and to drag nodes around to rearrange the graph.

Improved User Experience:

  • Contextual Navigation: Implement a more contextual navigation system, where clicking on a relation takes you to the related block, but also provides a way to navigate back to the original block or to other related blocks.
  • Improved Search and Filtering: Enhance the search and filtering capabilities to make it easier to find and focus on the blocks that are most relevant to the task at hand. This could include the ability to search within the content of blocks, not just their titles, and to filter relations based on various criteria.
  • Simplified Document Creation: Improve the process of creating new documents that consist of a selection of other entities. This could involve making it easier to create and embed entities, and providing more flexible options for arranging and formatting entities within a document.
  • Drag-and-Drop Interface: Implement a drag-and-drop interface for arranging blocks within a document. This would make it easier for users to visually organize their content and could be especially useful for large documents with many blocks.
  • Suggested Blocks: Introduce a section of ‘suggested blocks’ that are automatically listed based on criteria like semantic search on keywords and similar relations. This could help users discover relevant content that they might want to include in their document.
  • Block Palette or Library: Consider implementing a ‘block palette’ or ‘block library’ feature. This would be a place where users could save blocks that they use frequently, and then easily insert them into new documents with a single click or drag-and-drop action.

Enhanced Views Architecture:

  • Treat Views as Blocks: Extend the block-based architecture to include views. This would allow views to be embedded within other blocks, linked to other blocks, and included in the graph view.
  • Dynamic View Creation: Allow views to be dynamically created based on certain criteria. This would make views more flexible and responsive to the user’s needs.
  • View Templates: Offer a selection of view templates that users could use as a starting point for creating their own views. Users could also have the ability to save their own custom views as templates for future use.
  • Improved View Customization: Offer more options for customizing the appearance and functionality of views. This could include more options for sorting and filtering, more control over the layout and formatting of views, and the ability to add custom fields or calculations to views.
  • View Interactions: In addition to viewing data, views could also support interactions. For example, a user could click on a block in a view to navigate to that block, or drag and drop blocks within a view to rearrange them or change their properties.

Enhanced Database Functionality:

  • Filtering on Entity Types: Allow users to create filters that include conditions based on the entity type. For example, a user could create a filter that only includes entities of a certain type, or that excludes entities of a certain type.
  • Sorting on Entity Types: Allow users to sort views based on the entity type. This could involve adding a special ‘Entity Type’ field that can be used in sorting operations.
  • Entity Type Fields: Consider adding special fields to each entity that represent properties of the entity type, such as the entity type’s name or description. These fields could then be used in filtering and sorting operations.
  • Advanced Query Builder: For more complex queries that involve multiple entity types, consider adding an advanced query builder that allows users to construct queries using a visual interface.

Each of these improvements would address one or more of the current challenges and limitations of, and together they would significantly enhance its capabilities in the realm of ‘collective collaborative brain’. The next section will discuss a suggested approach for implementing these improvements.

5. Implementation Strategy

The implementation of the proposed improvements will be a significant undertaking, requiring careful planning, prioritization, and execution. Here is a suggested strategy:

5.1 Phased Approach:

Given the scope of the proposed improvements, a phased approach would be beneficial. This would allow for the gradual introduction of new features, minimizing disruption for users and allowing for ongoing testing and refinement.

  • Phase 1: Implement the unified block system, including both rich text field blocks and entities as blocks. This will form the foundation for many of the other improvements.
  • Phase 2: Enhance the user experience, focusing on intuitive navigation, drag-and-drop functionality, and a ‘suggested blocks’ feature.
  • Phase 3: Introduce the graph view, providing users with a powerful new way to visualize and navigate their data.
  • Phase 4: Improve the views and database functionality, including the introduction of dynamic views and enhanced filtering and sorting capabilities for databases.

5.2 Prioritization of Improvements:

Prioritize improvements based on their potential impact and feasibility. User feedback can be invaluable in this process, helping to identify the most needed and desired improvements.

5.3 User Feedback and Iteration:

Throughout the implementation process, actively seek out and incorporate user feedback. This will ensure that the improvements are meeting user needs and expectations, and will allow for ongoing refinement and improvement.

5.4 Technical Considerations:

Consider the technical implications of each improvement, including the potential impact on system performance and stability. Ensure that the necessary infrastructure and resources are in place to support the new features.

By following this strategy, can successfully implement the proposed improvements, enhancing its ‘collective collaborative brain’ capabilities and positioning itself as a leader in the field of note-taking and collaboration tools.

6. Conclusion

Alignment with Founder’s Vision This paper aligns with the founder’s vision for as a tool that grows and evolves with the organization. The proposed enhancements aim to further increase its flexibility and interconnectivity, augmenting its capacity to serve as a collective collaborative brain.

Proposed Improvements The introduction of block-level linking, enhancement of the graph view, and integration of views and databases into the entity system address current challenges. These enhancements increase’s capacity to model complex knowledge structures and processes, making it more user-friendly and intuitive.

Positioning as a Leader By implementing these improvements, could position itself at the forefront of the next generation of collaborative work management tools. This would truly embody the founder’s vision of a tool that adapts to and grows with its users, emphasizing the immense potential that has to revolutionize the way organizations manage their work and knowledge, making it a truly brain-like, interconnected system.

7. References

Given the nature of this analysis and advice paper, the references are primarily based on the information and data gathered from the official websites, documentation, and user forums of the various tools discussed in chatgpt 4. Here are the primary sources:

  1. Anytype. (2023). Official Website. Retrieved from
  2. Anytype. (2023). Official Documentation. Retrieved from
  3. Anytype Community Forum. (2023). Retrieved from
  4. Anytype. (n.d.). Anytype: A new type of computer. Retrieved from
  5. (2023). Official Website. Retrieved from
  6. (2023). Official Documentation. Retrieved from
  7. Community Forum. (2023). Retrieved from
  8. (n.d.). Fibery | Work Management Platform. Retrieved from
  9. (n.d.). Fibery Blog. Retrieved from
  10. (n.d.). Fibery Philosophy. Retrieved from
  11. (n.d.). How Fibery uses Fibery for Product Development. Retrieved from
  12. (n.d.). 2021 Year in Review. Retrieved from
  13. (n.d.). Fibery Getting Started. Retrieved from
  14. Logseq. (2023). Official Website. Retrieved from
  15. Logseq. (2023). Official Documentation. Retrieved from
  16. Logseq Community Forum. (2023). Retrieved from
  17. Logseq. (n.d.). Logseq: A privacy-first, open-source platform for knowledge sharing and management. Retrieved from
  18. Obsidian. (2023). Official Website. Retrieved from
  19. Obsidian. (2023). Official Documentation. Retrieved from
  20. Obsidian Community Forum. (2023). Retrieved from
  21. Obsidian. (n.d.). Obsidian: A knowledge base that works on local files. Retrieved from
  22. Roam Research. (2023). Official Website. Retrieved from
  23. Roam Research. (2023). Official Documentation. Retrieved from
  24. Roam Research Community Forum. (2023). Retrieved from
  25. Roam Research. (n.d.). Roam Research – A note taking tool for networked thought. Retrieved from

See also the same shared version by Yuri BC: . | Fibery


Have you checked the blog post which discusses Fibery’s positioning?:

It is not quite aligned with this:

The use cases which currently have the highest priority are not exactly those for ‘note-taking and knowledge management’.


Thanks for your enthusiasm and your thoughts.
Here’s my take on this.

  • Competitors. I love the mentioned products, but our strategy indicates we are in a different league. Interestingly, none were included in any previous Fibery vs. X articles. I do not believe comparing ourselves to them would help us position ourselves. However, we are not afraid to learn from them.

  • Block-level linking. While a block-based approach can be helpful, the world is not structured ideally or precisely. For example, one reference could start in the middle of one block and end two blocks later in the middle of a sentence. I believe in real-time semantic linking that could guide users through blocks or highlights. I do not think creating strict block-level links is the right approach.

  • Graph view. As a boomer, I have watched people use Roam Research. They proudly show me their graphs, but when I ask them what value they get from them, they cannot convince me that it is more than a gimmick. The graphs are visually appealing but complex for most users to turn into real value and real actions.
    I have seen YouTube videos, but in most cases, they talk about how to improve the graph model, which is terrific. I have not seen a use case that convinces me that I need this Graph view.
    When I am stuck with something like this, I always try to find a computer game that focuses on solving an interesting problem in an intuitive and fun way. I have not found a good case yet.
    Please let me know how you use the Graph view, and I am happy to learn.

  • User experience. No question, I fully agree. No user experience that cannot be improved. Including Fibery.

  • Implementing blocks, nested blocks, and transclusion. These things keep us awake at night, thinking about how it should work well to organize a knowledge management hub.

  • Implementation startegy. I think you mentioned some fantastic features. I have to tell you most of them are in our backlog, which is amazingly huge. Our strategy today is to build a vertical use case by building features that tightly connect to it. The features you mentioned will come, but probably in a different order, interrupted by other priorities.

I apologize for putting half as much energy into my reply as you did into your analysis. I still wanted to share my thoughts with you, and I am happy to continue the conversation.

Thanks again.


Thank you, I appreciate your review of the paper.

I will motivate the focus on collective brain and graph and ai approaches in another section I will write soon, because I see this as the overall direction in technology and modern society, which impacts all strategies and tools in a way that most organizations may underestimate at this point.

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Here here! And I’m not a boomer, so we’ve got most generations covered now :fist_right::fist_left:

The current system Fibery has to show table relations is all I’ve ever really needed.

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The main benefit of graph view 's seeing the connection of unrelevant things and then create more evergreen, which means permanent notes for new things. This’s what we do in Obsidian
But now with the leverage of AI, I think Fibery can access as this level as InfraNodus

Very interesting. Thanks for sharing!

I think several steps need to be taken before this becomes a problem. However, I don’t think this feature is among the best next steps we should develop.

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