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My Vision - The Roadmap

The Roadmap-1 Photo by Matt Duncan / Unsplash

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This article was published on May 1, 2023 by Alan Chan, co-founder of Heptabase, one year after releasing the public beta version of Heptabase.

Foreword

It has been a year and a half since I last published a My Vision article. The reason it took me so long to write this article today is because I believe that only after interacting extensively with real-world users can I see more clearly the vaguer parts of my vision. Therefore, over the past year and a half, the Heptabase team has been building products, talking to users, and validation our hypothesis through continuous iteration.

In the conclusion of the previous article, I described Heptabase’s vision as follows:

In short, from the perspective of “The lifecycle of human knowledge work,” we are building an ecosystem of tools to help knowledge workers integrate their knowledge lifecycle of exploring → collecting → thinking → creating → sharing. Our guiding principle is to optimize information interoperability, context retrieval, and collective knowledge creation, with the ultimate aim of evolving a contextualized knowledge internet.

This way of describing the vision has the advantage of providing a framework for the knowledge lifecycle to guide our product development, as well as three major principles to be aware of in execution. However, for a general audience, there are still many questions after reading this description.

First, although I presented some ideas and directions for information interoperability, context retrieval, and collective knowledge creation in the previous article, I did not delve deep into our roadmap at the execution level.

Second, the description of this vision is relatively abstract and academic, and after reading it, you may still be a bit confused: why do we need a new knowledge internet? What benefits can preserving the context of ideas bring us? What primitive human need does Heptabase’s knowledge internet want to solve?

In this article, I will delve deeper into these questions that were not answered clearly in the previous article, clarify Heptabase’s core goals, and let Heptabase users better understand our roadmap.

Purpose

Before discussing the roadmap, I want to rephrase Heptabase’s vision in a more straightforward way: We want to create a world where anyone can effectively establish a deep understanding of anything.

In the era of information explosion dominated by Google, social media, and ChatGPT, acquiring knowledge has become extremely easy. However, this knowledge is often just the tip of the iceberg in the vast knowledge structure and thinking context of humanity, and most people still have no idea what the actual shape of these icebergs is, nor have their ability to deeply understand complex things significantly improved.

At Heptabase, we believe that the biggest challenge modern people face in learning, researching, and problem-solving is not the lack of knowledge, but the lack of context to connect countless pieces of knowledge and the tools to construct and preserve these contexts. If we can preserve the context of knowledge and let all humanity share these contexts, when others want to learn and research the same knowledge, they can use these contexts to establish a more comprehensive and in-depth understanding.

Based on this vision, I have set four progressive stages for the company’s development. The significance of these four stages is to build an “open hyperdocument system” that can carry our contextualized knowledge internet and build the infrastructure needed for this system layer by layer at each stage. I will discuss the goals and challenges of these four stages in detail and describe this system more completely.

Stage 1 — Contextualize Your Brain

In stage 1, our goal is to create a thinking tool that helps everyone learn and research complex topics. The core task of this tool is to enable users to build thinking frameworks on top of a large amount of information, extract important ideas and knowledge, connect the “collect → think → create” stages of the knowledge lifecycle, and preserve the user’s thinking context for these topics.

From the perspective of the final knowledge internet to be built, the significance of this stage is to create two foundational infrastructures: the contextual layer and the descriptive layer.

Contextual Layer​

In Heptabase, the basic unit that carries ideas and knowledge is the card, and the contextual layer is the layer used to preserve the thinking context for these cards, corresponding to Heptabase’s whiteboard function. People who have not used Heptabase may think that the purpose of the whiteboard is visualization just by looking at its appearance, but in fact, visualization is only a means. Its real purpose is to trace each card back to its thinking context in different whiteboards.

For this reason, in the early stages of developing the whiteboard, we did not spend too much time on creating common whiteboard product features such as handwriting, shapes, lines, styles, etc., but focused on developing features related to “preserving thinking context,” such as the reuse of cards in multiple whiteboards, bidirectional linking between cards and the whiteboard, hierarchical structure between whiteboards, grouping and indexing of knowledge cards in the whiteboard, and interaction between card editors and whiteboards.

The Roadmap-2

Descriptive Layer​

The second foundational infrastructure to be built in stage 1 is the descriptive layer of the card, which is responsible for adding types and attributes to the card, corresponding to Heptabase’s tag and property functions.

In Heptabase, you can add different tags to cards and specify different properties that can be reused by different tags. For example, I use the tags #research-note-taking and #research-communication to manage the relevant cards for my research in note-taking and communication software. Both tags are related to research and share properties such as document type, insight, and importance.

The Roadmap-3

For individual users, such functions can help them better manage homogeneous cards in a database format, and even create different views and filters like common project management systems to view these cards from different perspectives, such as tables and Kanban.

The Roadmap-4

Of course, just as the purpose of the whiteboard is not solely for visualization, the purpose of tags and properties is not solely for card management. Their important long-term purpose lies in stage 4, where third-party developers can build different software for different scenarios based on Heptabase’s card system, thereby expanding the reusability and contexts of these knowledge cards.

Stage 2 — Contextualize Outer Sources

When Heptabase has already built a “good enough” thinking tool in stage 1, we will enter stage 2 to help users not only preserve their own thinking context but also bring external information into this context for thinking, connecting the “explore → collect” stages of the knowledge lifecycle.

From the perspective of the knowledge internet, the significance of this stage is to build two foundational infrastructures responsible for integrating external information into the Heptabase system: the annotation layer and the integration layer.

Annotation Layer​

In today’s internet, there is a lot of knowledge saved in different formats such as PDF, video, audio, image, and webpages. If we want to build a knowledge internet that can trace the context of all knowledge, we must bring these different formats of knowledge into our knowledge internet, so that users not only can extract important ideas from them but also trace the sources of these important ideas.

In Heptabase, our goal is to provide corresponding card types for all mainstream formats that carry knowledge, such as PDF cards, video cards, etc., so that they can not only be placed on the whiteboard, added with tags and properties, but users can also do highlight and annotation on them.

For example, Heptabase already supports PDF cards. Users can use text-selection or area-selection to pull out one Highlight card after another from the content of the PDF card and integrate these Highlight cards into the existing thinking context on the whiteboard. Users can not only write annotations on these Highlight cards but also locate them back to their original position in the PDF card with just one click.

The Roadmap-5

In the future, in addition to PDF, we will design and develop highlight and annotation functions for other data formats such as video, audio, images, and webpages, and all highlight and annotation will eventually use our annotation layer as a universal interface.

Integration Layer​

In addition to files and static webpages, there is a lot of knowledge in this world saved in different products with special data structures (e.g., Facebook’s Posts, Twitter’s Tweets, Notion’s Pages, Readwise’s Highlights). If we want to bring this type of third-party information into the Heptabase system, we must build interfaces that can synchronize with this third-party information, and create card aliases for this third-party information in Heptabase. This is the core task of the Integration Layer.

For example, if a user connects Readwise with Heptabase, all their Readwise Highlights will be instantly converted into Heptabase’s Highlight cards. If we develop Google Sheet integration in the future, we may support turning each row into a card, and specific columns will be written into the properties of the card.

Whether it is the annotation layer for annotating on static files or the integration layer for creating aliases for third-party data, their common goal is to bring external information into the user’s thinking context in Heptabase, allowing us to build a new contextualized knowledge internet on top of all existing human knowledge.

Stage 3 — Contextualize Collective Knowledge

In the first and second stages, Heptabase aimed to create the best “personal thinking tool.” However, starting from the third stage, we will build a communication tool on top of this thinking tool, allowing a group of users to collectively research complex topics, create collective knowledge, and bridge the “share → explore” gap in the knowledge lifecycle.

From the perspective of a knowledge internet, the task of this stage is to create a communication layer for knowledge.

Communication Layer​

Before designing anything, we always need to think clearly about what problem this design is meant to solve. When people hear “communication software,” they may immediately think of messaging, commenting, collaboration, and co-editing. However, from the designer’s perspective, the “purpose and model of communication” is the most important thing that needs to be emphasized, rather than implementing these functions.

In social media (e.g. Facebook, Twitter), the common communication model is expression-driven. People seem to be discussing a topic, but in reality, they are more often expressing themselves: What are my opinions and positions on a certain issue? Which group of people do I want to attract attention from?

In work software (e.g. Slack, Notion), the common communication model is conclusion-driven. People often discuss back and forth to determine: What is our decision? What do we need to complete at what time?

The design of each communication software is aimed at helping users achieve their goals more effectively. Therefore, social media has more functions related to expression and sharing, while work software has more functions related to task integration.

At Heptabase, we want to create a world where anyone can effectively establish a deep understanding of anything. We believe that true collective wisdom relies not on forcing everyone to reach a consensus immediately, but on allowing each individual to expand their own cognition through others, and to see how their ideas are developed in the context of other people’s thinking.

Therefore, the communication layer we create will be comprehension-driven. Our design goal is to enable multiple people (including AI) to effectively construct their deep understanding of a topic through discussion, learning, and research. The understanding established by this group of people on this topic can be further expanded by other explorers in different contexts. When you want to learn a topic today, you no longer have to find isolated knowledge as in the past, but can explore the knowledge framework established by a group of people in the process of discussion.

Stage 4 — Contextualize Application Ecosystem

In the fourth stage, which is the final stage of the roadmap, our goal is to enable people to build different software on top of Heptabase, using these software to study different systems (physics, chemistry, biology, finance, mechanics, architecture, business, etc.), and create powerful knowledge representation for studying these systems. People can communicate, share, and modify the representations they create on our platform. They can establish a deep understanding of different fields using these representations while preserving the application context of the same knowledge in different representations across different software.

From the perspective of a knowledge internet, the task of this stage is to create an Application Layer for knowledge.

Application Layer​

Regarding the specific implementation of Heptabase’s Application Layer, it would take another entire article to explain it clearly, so I will only briefly mention some core concepts here.

Unlike traditional software development, Heptabase will create an Application Layer that allows users to use cards on the whiteboard to build and assemble the interface of the software. Cards will no longer just be for taking notes, but users will also be able to write programs on cards and communicate with other cards (e.g. read property values of surrounding cards), or integrate with third-party databases, and ultimately present the data they want to display in the representation they choose on the card. Each card will be both an interface that users can directly see and manipulate, and a piece of code that users can directly adjust the logic of. A whiteboard and all the cards on it will add up to a software that users build themselves.

This kind of software development environment will not only allow users to directly manipulate the software they build on the production end, but also allow them to share the cards they make with other users, so that other users can use these cards as basic modules to assemble software with other purposes. Everyone can be both a user and a developer of software at the same time. Creating software will no longer be a commercial activity, but an activity that people use to study and understand different systems.

Once Heptabase creates an Application Layer that allows users to build customized software on the whiteboard, the use case of our knowledge internet will be able to expand to a much wider range of scenarios. For each knowledge card, users will be able to use its properties to trace where and how it is used and presented in other software built by different users, and thus establish a deeper understanding of this knowledge. This level of information interoperability and context tracing is unprecedented on the current internet.

Conclusion

In summary, Heptabase want to create a contextualized knowledge internet that allows everyone in the world to effectively establish a deep and comprehensive understanding of anything they want to learn or research.

This contextualized knowledge internet needs to be supported by a new open hyperdocument system, which will include many layers of infrastructure: the contextual layer for preserving thinking context, the descriptive layer for managing categories and adding properties, the annotation layer for annotating static files, the integration layer for creating aliases for third-party data, the communication layer for enabling a group of people to construct a deep understanding of complex topics, and the application layer for allowing users to build card-based software, and so on.

From an engineering perspective, we know clearly that such a complex system cannot be built in a short period of time. From a business perspective, we also know clearly that no matter how good our system is, if it does not solve real-world problems, no one will use it. Therefore, at Heptabase, we adopt a market-driven R&D logic, using continuous product iteration and extensive conversation with users to understand the market, and then creating a roadmap for building this system based on our understanding of the market and users.

Whether you have used Heptabase or not, we hope this article can help you better understand Heptabase’s vision and the positioning of our product in this vision. We will continue to work hard to make Heptabase evolve and realize our vision of creating a contextualized knowledge internet.