Heptabase: Refining the Future of Knowledge Management and Learning

27 min readDec 20, 2023

I am Marvix, committed to reducing the information asymmetry in psychology among the general public.

I remember that winter, I had only a vague understanding of the concept of bi-directional links, and I didn’t even quite grasp what a page or a database was. Notes on political philosophy were like hot potatoes in my hands, catching me off guard. The political philosophy course opened the door of wisdom for me, and the joy of this intellectual collision was an unprecedented experience. Coincidentally, it also made me understand what bi-directional links and connections are.

In class, the teacher’s frequent questions and thoughtful remarks forced me to memorize and recall information repeatedly, even at 9 a.m. when I was still half asleep. On cold, gloomy winter mornings, I had to continuously absorb and review information for 75 minutes, whether sequentially or simultaneously, I could no longer tell.

Then, on a friend’s recommendation, I learned about Heptabase; its card and visualization relationship was second to none at the time, and I promptly paid for it. It was easy to use; I just had to double-click to create a new card on the whiteboard, enter content, and connect them with arrows or freely arrange them on the board.

It took me three hours to transfer all my political philosophy notes from Notion to Heptabase. While copying, analyzing, and deconstructing this content, I also reviewed and organized my original notes. The relationships between the cards and the core knowledge gradually became clear during the organizing process.

When I finished organizing them, I suddenly realized I remembered almost all of the content, their connections, the different interpretations of the unity concept by Hobbes and Locke, and even the relevant original sentences, chapters, or page numbers very clearly. As a result, I often accurately answered the teacher’s questions in class, and when the teacher looked for original texts in the book, I could promptly remind him (since I sat in the first row). During mid-term and final paper writing, I was also able to outline and write introductions much faster, completing them in just half a day, a significant improvement over before.

Although Heptabase had few features initially, its rapid software updates were the reason we stayed. If I remember correctly, during the beta test, I received updates almost every week. Now, it has many more features, and the team not only has extraordinary ambition for AI but also seems to have mature plans for To-Do.

In my use, I gained some personal insights, which I share with you here, hoping to provide a different perspective.

Don’t be afraid to differ in opinion, because every now-accepted idea was once an outlier.
— Bertrand Russell, “The Ten Commandments of Free Thought”

The ease of use of Heptabase is almost leading. By the time the guide interface ended, I understood how to use it. Although it looked complicated, it was well-arranged and not overwhelming; unlike other software, I didn’t have to search for videos on how to use it. More importantly, I didn’t have to worry about design or sharing issues like I would with Notion or Capacities, such as how to display a Database or consider Relations, etc. With Heptabase, I could start typing immediately without considering design issues, focusing directly on the content, which I really liked.

Most people just want a solution that solves their problem out of the box. Most people are not concerned with all the concepts and capabilities introduced in your system. And no matter how good your technology is, if not many people use it, it will ultimately lead to failure. Therefore, these companies often have to spend a lot of time improving usability, simplifying their products, and understanding their users’ needs.

Now, they also have a Zen mode (similar to Flomo), allowing me to focus more on my content without distractions. Therefore, Heptabase might be the only knowledge management software I would recommend to beginners. Because of its features, people from various professions, such as doctors, lawyers, marketers, students, historians, and even animators like Makoto Shinkai, can use it to create their own style or workflow of knowledge management and organization.

This is nothing short of astonishing for an emerging software.

What does each part of Heptabase mean?

Card, Section, Whiteboard

If you’re a frequent user of page-based software like Notion, you might initially, like I did, struggle to differentiate between a Card and a Page and how to use the cards effectively.

Here’s how I understand Cards, Sections, and Whiteboards. Although in terms of information density, Card < Section < Whiteboard, from a hierarchical perspective, they can be seen as equal in level.

Speaking of hierarchy, we need to return to a fundamental question: How do we categorize? Or, how do we decompose concepts?

We could break down concepts in this manner.

In 1829, James Mill proposed that complex ideas could be the result of repeatedly pairing two or more simple sensations. These complex ideas themselves could combine into duplex ideas. He suggested that every complex thought could be broken down into at least two simple ideas, forming through their continuous combination.

Some of the most familiar objects provide examples of these unions of complex and duplex ideas. Brick is one complex idea, mortar another; these ideas, combined with notions of position and quantity, form my idea of a wall. Similarly, my complex ideas of glass, wood, and others, compose my duplex idea of a window; these duplex ideas, united, and formed my idea of a house, which is made of various duplex ideas.

Although this method of decomposition is somewhat outdated, it has indeed inspired our understanding of basic and complex concepts.

As the world has evolved, Mill’s theory seems less convincing and outdated.

We categorize things based on knowledge learned from family or external environments, allowing us to recognize objects and events and infer their properties. First, we have concepts (like ‘cat’), and mental representations used for various cognitive functions.

Then, we integrate concepts into categories, collections of all possible examples of a specific concept. For example, the category ‘cat’ includes leopards, wildcats, and Persians. Categorization is the process of grouping things into what we call categories.

For instance, PARA has four categories: Projects, Areas, Resources, Archives; the process of putting different things into these four categories is categorization.

Categories are useful as they help us understand previously unencountered examples, provide rich general information about a project, and allow us to recognize specific features of a particular project.

We often categorize things using the following methods:

The definitional approach is based on whether an object meets the definition of a category to determine its membership. For example, we have dictionaries and authoritative textbooks in psychology and pathology to ensure that professionals in the same field can communicate effectively. However, this method is not ideal because not all members of everyday categories have the same defining features. For example, there are many subcategories in games (such as card games and electronic games), as well as different types of chairs or tables.

Secondly, family resemblance refers to the similarity between items within a category in many aspects. It addresses the problem of incomplete definitions covering all category members and allows for variation within the category. For example, a chair can be defined as “a place for sitting” and “used for supporting the back.”

Another approach is the prototype approach. A prototype is a typical example of a category that represents the characteristics of its concept members. Essentially, it summarizes past experiences with similar members encountered before. For example, not all birds are like sparrows and magpies. Some birds such as owls and penguins also belong to the bird category.

Rosch viewed internal variation within categories as indicative of differences in prototypicality: high prototypicality means that a category member closely resembles its prototype, while low prototypicality indicates that the member is less similar to standard examples in that classification.

In 1975, Rosch quantified this concept by asking participants to rate how well each of about 50 members represented their respective categorical titles (such as “bird” or “furniture”). Participants used a scale from 1–7; where “1” indicated excellent representativeness for that category and “7” indicated unsuitability or non-membership. He found that most people considered sparrows to be typical birds while tea and sofas were considered typical furniture items.

Furthermore, Rosch and Mervis (1975) conducted a study where participants were asked to list as many shared characteristics of chairs and sofas as they could. Their findings indicated a strong positive correlation between similarity and prototypicality, suggesting that an object’s category similarity increases when its features align with numerous other items within the same category.

The Sentence Verification Technique is another method used to measure how quickly individuals categorize objects. Participants are asked to respond “yes” or “no” to statements based on their truthfulness. Studies have shown that responses are quicker for highly prototypical objects, such as identifying an apple as a fruit, compared to less typical ones like pomegranates. This faster judgment for more representative objects is known as the Typicality effect. Interestingly, this cognitive process reveals that objects with higher prototypicality yield faster reaction times.

The Exemplar Approach offers yet another perspective by proposing that multiple instances represent a concept rather than just one prototype. These instances refer to actual members of the category instead of abstract averages. In this approach, new items must be compared against stored examples for classification purposes. The primary distinction between exemplar-based and prototype-based approaches lies in their optimal application areas: exemplar-based methods tend to work better for smaller categories or specific situations while prototype-based methods prove more effective for larger categories.

Hierarchical Organization

In our exploration of prototypes and exemplar-based methods, we’ve examined category examples and their constituents, such as diverse types of furniture including chairs. This is known as hierarchical organization, which breaks down larger or more general categories into smaller or more specific ones. The hierarchical model comprises three levels: superordinate (global), basic, and subordinate (specific).

The basic level is distinctive because it bridges both higher and lower levels. For instance, if my mother wants to purchase a bed (basic level) from IKEA, we bypass all other sections to head straight for the bedding department (superordinate level), where we select our preferred style (subordinate level).

Rosch et al.’s 1976 study found that people generally use basic terms like fish, guitar or pants; however experts tend to employ more specific terminology while non-experts typically stick with basic names. Thus the usage of specific vocabulary hinges on an individual’s familiarity with certain objects.

This type of hierarchical organization pervades our daily lives in methodologies like OKR and tools such as mind maps.

Understanding this hierarchical structure allows us to delve deeper into semantic networks — systems that organize concepts similarly to how they’re arranged in our thought processes. Collins and Quillian suggested in 1969 that each node within these networks represents a category or concept interconnected by associations between concepts and attributes in our thinking process. In their model, categories can be organized according to various hierarchical structures — ranging from macroscopic to local levels.

Each concept’s attributes are represented by their corresponding nodes. By traversing the lines that connect these concepts within the network, we can discover additional attributes. For instance, tracing from “canary” to “bird” reveals that canaries have feathers and wings and possess flight capabilities. We must also consider the principle of cognitive economy, which suggests that shared attributes are stored only in higher-level nodes.

In the diagram, assigning the attribute “able to fly” to each bird node (e.g., canaries, sparrows, vultures) is inefficient as it consumes excessive storage space. Exceptions exist in subordinate nodes; for example, an “ostrich” is labeled as “unable to fly”. Studies show a correlation between a person’s information retrieval time for concepts and the distance traveled through the network — longer distances result in longer response times.

Another theory is spreading activation: activity extending along any link connected to an activated node. Activation refers to a node’s arousal level; primed concepts receiving this activation become more easily accessible from memory.

Understanding these relationships clarifies everything.

My method involves creating a new card on a whiteboard and expanding it into card format while incorporating all subtitles and content highlights from chapters etc., resulting in one comprehensive card. The next step involves extracting each toggle headline separately into basic cards; further review and division yield many specific cards which I then assemble into one basic section with added titles using three methods (definitional, exemplar, prototype). These sections may be encompassed within higher-order sections where original basic cards become specific or even more subordinate ones. Whiteboards follow similar principles. Embedded whiteboards could be viewed as basic-level whiteboards featuring detailed sections and cards…

The hierarchical structure of Heptabase offers diversity freely adaptable according to different scenarios making it ideal for note-taking beginners like me who want quick knowledge recording without being restricted by traditional text structures.

As your number of whiteboard cards increases, you may face a dilemma: whether to create new whiteboards. Initially, I avoided embedding whiteboards within whiteboards and used Sections for classification. I attempted to mimic these knowledge scenes in my mind — an infinitely expanding space similar to Heptabase’s whiteboard. However, as the number of cards grew, navigating the whiteboard became increasingly laggy. The official recommendation is around 150 cards per board; however, from my experience, lagging only occurs with over 200 cards on one board prompting me to consider embedded whiteboards.

For my ethics course requiring weekly reading of ethical codes each with its own lecture notes, I transferred each rule into Heptabase as a card. To avoid bloating and lags, I created embedded boards for them.

In cases where chapter content (typically about 30 pages) consists of approximately 20 cards, a particularly important and extensive chapter requires reading about 60 textbook pages. I also created an embedded board for this chapter.

The logical relationship between the Section and embedded boards can be expressed as follows:

When the number of cards in a Section exceeds 25, we can convert that Section into an embedded board.
When you need to create a Project, you can embed whiteboards.

Color

After we have finished processing the textual content and overall structure, we use colors to highlight important content and objects.

I differentiate objects based on the characteristics of their content:

Red: Most important
Orange: Important
Yellow: Moderately important
Green: Special things (such as timelines or knowledge that differs from one’s own thoughts)
Blue: Relevant questions or chart information
Purple: Highlighted words or phrases (occasionally using yellow)
Gray: Less important auxiliary information

The process of color annotation is from top to bottom, from outer to inner, and from broad to narrow. When we annotate objects with colors, we actually complete the rehearsal in short-term memory and review it, thereby unconsciously deepening our impression of these objects.

What if there are too many objects of the same color on the whiteboard? Then it may indicate two things:

These objects are very important to us, so we need to review them promptly.

A certain color may have been overused. You need to reexamine and evaluate the content on this whiteboard. Reassess the importance of cards or sections. In this case, you need to carefully and bravely let go.

Relation

By understanding the concepts of classification, objects, and colors, we can begin to reorganize and rearrange our items.

However, relying solely on hierarchical structures is insufficient as it has its shortcomings.

Meyer and Schvaneveldt’s 1971 study demonstrated that recalling a word from memory could activate adjacent areas in the network. However, this model falls short in explaining the typicality effect — why we confirm “an ostrich is a bird” quicker than “a parrot is a bird.”

The efficiency of the cognitive economy comes into question because some individuals might be more prone to associate the feature of “having wings” with a canary rather than “singing.”

Consequently, researchers have introduced another theory known as the Connectionist Approach or Parallel Distributed Processing (PDP). This method is employed to develop computer models representing cognitive processes. The circles within these models symbolize units designed to emulate neurons in our brain.

In our exploration of neural networks, we delve into the intricacies of how concepts and their attributes are represented through different activity patterns within the network. The lines in this model illustrate the connections between objects, facilitating information exchange between units, akin to axons in the brain. Among these units, some can be stimulated by environmental cues or signals from other units. Those activated by environmental stimuli are known as input units.

In this simplified network model, the input units send signals to hidden units, which in turn transmit them to output units. The concept of ‘connection weight’ plays a pivotal role here. It determines the amplification or reduction of activity from one unit to the next, mirroring the synaptic signal transmission between neurons. Higher connection weights imply a greater likelihood of intensely stimulating subsequent units, whereas lower weights indicate reduced stimulation. Interestingly, negative weights can decrease excitability or inhibit activation.

The activation of network units depends on two key factors: (1) the signals originating from input units, and (2) the propagation of connection weights throughout the network. This dual-factor approach sheds light on the dynamic and complex nature of neural network operations.

The diagram demonstrates that the activation of both “canary” and “can” units triggers a network-wide response. This in turn activates attribute units associated with “canary can”, such as growth, movement, flying, and singing.

Arrows can be used to illustrate these connections. They not only visualize the relationships between objects but also facilitate quicker and more dynamic associations within our brain. Another effective tool is a mind map which displays individual details while simultaneously linking interdependent elements for enhanced context on specific items. Furthermore, an invisible double chain link allows us to swiftly associate potentially related objects during data input.

Arrow

AtInitially, the misuse of Arrow led to a cluttered whiteboard page that was difficult for me to decipher. It wasn’t until recently that I began using Arrow to denote relationships between objects.

We can illustrate the intensity of object relationships and the corresponding attention needed through line thickness and dashes. A thicker line signifies a stronger relationship requiring more focus; conversely, a thinner line suggests a less significant connection needing minimal attention — mere awareness suffices. Dashed lines represent weak or optional object relationships, implying they can be acknowledged sporadically with an open-minded approach.

I adhere to three principles when using Arrow:

1. Objects must maintain a certain distance from each other.
2. Each object should retain its independence.
3. Recognized relationships between objects should be marked.

Arrow is only utilized if these three conditions are satisfied.

When extracting content from books or textbooks, I select diverse elements because they possess individuality (like specific terms, chapter titles, subheadings within chapters, etc.), yet their interrelation in the text remains tight-knit.

If objects already share a close relationship, such as being under the same subheading or contextually related, I refrain from connecting them with arrows. Instead, I arrange each object closely in a grid-like format known as ‘bento’.

When we review and arrange objects, we must also pay attention to what is happening in our brains. When I drag the cards out, my brain has already told me what their relationship is, so I directly annotate the text on the arrow and connect it with the objects. In the above figure, you can see that I used a lot of bold lines because they are events that occurred at that time on the timeline.

Mindmaps are a common tool for visualizing object relationships, but traditional versions can be difficult to use and may not aid in learning effectively.

In my experience, XMind prioritizes aesthetics over usability; it looks impressive but isn’t user-friendly. Mindnode demands constant adjustments and design input from the user, detracting focus from content creation. The input process is also inconvenient. Mubu stands out among these tools as it allows users to concentrate on content creation, yet formatting issues still arise during input or review stages.

Heptabase distinguishes itself by extending mindmaps into cards that can be transformed into detailed subtopics. These cards offer flexibility — they can be collapsed, expanded, moved around freely and linked with arrows; a significant improvement over previous formats. Additionally, Heptabase enables the creation of ordered card sets.

However, users might confuse Heptabase’s features due to their similar functionality of linking objects together. The key difference lies in object independence: In Arrow mode each object is autonomous and movable while in Mindmap mode objects seem predetermined around a specific theme making them inseparable — akin to pulling one hair moving the whole body.

Determining when to use Mindmap can be challenging. In my view, it is most useful in two scenarios: 1) for simple concept classification, such as organizing a few sentences or words, and for illustrating straightforward hierarchies like rigid company structures; 2) when the independence of these objects isn’t strong and there’s a specific order to follow, like a timeline or sequence of steps.

One reason I don’t frequently use Mindmap is its lack of customization options compared to other software. It also lacks flexibility which doesn’t align with my note-taking approach. The examples provided are drawn from my notes over the past six months.

Hyperlink

In addition to the apparent links, there’s also an unseen connection: bidirectional links, or hyperlink.

Previously, while using software like Notion, I read numerous articles about bidirectional links but struggled to grasp their concept. However, my understanding became clear when I started using Heptabase.

My first encounter with bidirectional links was during the organization of a glossary for my teaching materials. I loaded nearly 400 terms onto one card which initially crashed my computer (this no longer happens). At that point, I envisioned each term as a separate and actionable entity.

As time passed and more notes were added to knowledge cards utilizing this layout frequently, it became evident that some content seemed familiar or similar to previous knowledge. Employing “@” to search for keywords quickly linked these objects; even allowing me to create new cards directly. This seamless experience reminded me of using flash capsules — swiftly capturing fleeting thoughts without the frustration of missing them.

A significant feature is Heptabase’s support in dragging items into cards — an invaluable tool once used becomes indispensable due to its enhancement on user experience.

Nowadays, living without bidirectional links seems impossible. Whenever a keyword or main content sparks associations in my mind from material studied before, I immediately use it to check if related concepts are available.

Tag and Searching

I appreciate the simplicity of Heptabase’s tagging system. Despite advancements in tag technology, manual tagging can’t keep up with our brain’s natural ability to categorize and label content. Users who employ tags often face challenges managing them or end up overusing them.

Tags can also shape our thinking by encouraging us to label everything we encounter online or in real life. This habit can limit our thought processes and restrict the versatility of cards themselves. For instance, Flomo has transformed tags into hierarchical content using parent-child titles instead of card relationships, which eliminates potential connections between cards and creates a disconnect between knowledge fragments. Supernotes and Tana have similar issues.

Heptabase offers a solution by replacing traditional parent tags with more user-friendly Groups, facilitating flexible implementation of the PARA (Projects — Areas — Resources — Archives) system within its platform. My Psychology group is an example: it includes a Newsletter project; various areas like developmental psychology, experiments, therapy techniques; resourceful cards; as well as archived terms and drugs.

During retrospectives, I prefer reviewing and organizing contents on whiteboards or card boxes while labeling specific or PARA-related cards to avoid misuse. If this method doesn’t suit you, Heptabase provides an alternative: use it like Notion software treating it as a database without any hindrance.

Another standout feature is Heptabase’s search function — far superior to Notion’s cumbersome one that lacks precision despite careful categorization efforts. A robust search function should be prioritized in all note-taking software — a feat achieved by Heptabase through global searches within specific whiteboards using shortcuts covering extensive search scopes accurately previewing card content along with searching for tags sections & whiteboards enabling precise content connection.

F.R.E.E.D.O.M.

In a single word, Heptabase epitomizes FREEDOM. It offers an unparalleled sense of liberty, far from the constraints of traditional frameworks. Its features extend beyond simple text highlighting or color-coding cards; it allows us to explore logical relationships between objects based on our unique card placements. As we interact with these “toys” and assign them attributes, much like children at play, we inadvertently reinforce our knowledge and deepen our understanding.

One key advantage is its ability to captivate users completely in their content, making them lose track of time. This underscores its primary function — enhancing learning rather than merely boosting productivity systems. Upon launching the software, users can swiftly enter a state of flow and concentrate on studying or researching topics that interest them most without realizing they’re using a software program.

Our high performance is 100% focused, while flow state is 110%.

Focus

Focus is defined as the capacity to concentrate on specific stimuli or locations in our surroundings. It’s a common understanding that an average person can’t remember everything, leading to the forgetting of irrelevant details — a phenomenon known as selective attention. However, with the information age, Fear Of Missing Out (FOMO) has surfaced and people have started worrying about missing out on seemingly important current events. To counteract this fear of forgetting, numerous collection tools have been developed. These tools don’t necessarily aid us in learning but rather alleviate our immediate fears and anxieties. We simply add items into these collections without truly storing them in our memory because there exists a filtering mechanism within our brain that discards “useless” information enabling us to focus better on the present moment. The following two models will provide further insight into this filtering mechanism.

Broadbent’s Filter Model

In this model, it feels like memory briefly retains all incoming information before transferring them to the filter. The filter identifies relevant messages based on their physical features and only selected messages are passed on to the next stage — the detector. The detector then processes this information to determine higher-level properties of the message. Subsequently, short-term memory receives this output from the detector and retains it for 10 to 15 seconds before possibly transferring it to long-term memory.

There are two examples:

Cocktail party phenomenon: Have you ever been in a noisy party, engaged in a conversation, and suddenly heard someone mention your name from the other side of the room, causing you to immediately pay attention?

In the experiment “Dear Aunt Jane” (Gray & Wedderburn, 1960), participants were instructed to shadow the information they heard in their left ear. However, they reported hearing a message: “Dear Aunt Jane,” which started in the left ear, jumped to the right ear, and then returned to the left ear.

Treisman’s attenuation model

Such experimental results inspired Trisman, and he came up with a new model:

In this mode, the information system can distinguish between attended and unattended messages early in the processing stage, with message selection occurring later.

This mode is divided into two components: an attenuator and a lexical unit. The attenuator replaces Broadbent’s filter, analyzing incoming information based on psychological traits (such as high or low pitch, fast or slow speed), language (syllable or word combinations), and semantics (how words form meaningful phrases).

Attended messages pass through the attenuator at full strength while unattended ones are significantly weakened. Lexical units store words according to activation thresholds. Common or important words have lower thresholds whereas uncommon words have higher ones. This explains why some unusual-looking vocabulary may be easier to remember than more common terms.

While there are many updated explanatory models available today, we won’t delve into them here. It’s safe to assume that Heptabase complements this attenuation model under certain circumstances.

Through annotation, highlighting, and organizing object structures and relationships, we filter information by importance — distinguishing what requires attention from what doesn’t; allowing all messages to pass through filters as much as possible. As such, investing more time and attention in Heptabase allows us not only to retain most of the content but also recall it effectively — unlike other note-taking tools which merely store knowledge without facilitating its retrieval.

Loads of Attention Capacity

Let’s revisit the concept of attention capacity.

Processing capacity refers to the volume of information a person can handle at any given moment. The term “perceptual capacity” denotes the intricacy of specific tasks. Tasks with high loads are more demanding and require greater processing capacity, akin to a computer’s RAM usage. This implies that there are no spare resources for handling unattended or irrelevant stimuli, thus reducing the likelihood of divided attention. Conversely, simpler tasks have a lower load and need less processing capacity, potentially leaving room for managing unattended or irrelevant stimuli.

Studies show that complex tasks lead to longer reaction times compared to simpler ones. In tasks with fewer processing demands, extra processing capacity is available for dealing with stimuli not directly related to the task at hand — an example being the Stroop Effect.

Articulating vocabulary may disrupt our ability to discern the color of its ink, as it’s difficult for us to disregard word meanings due to our ingrained habit of reading. This task demands attention.

Moreover, overt attention research employs tools like eye trackers to monitor eye movements. Two crucial elements in this process are saccades — swift shifts from one point to another — and fixations — short pauses at points of interest. Our eyes linger longer on visually striking and captivating areas. The prominence of these regions depends on features such as color and motion, which are highly influential factors. These high-prominence areas draw our initial gaze, explaining why we use different colors when marking cards on a whiteboard; it immediately attracts our attention and prioritizes viewing these cards.

In exploring this concept further, I identified additional characteristics: F stands for Focus — “the more focused we are, the better we perform.” R represents ReThink or reevaluating ideas. When using Heptabase, even if only slightly new insights emerge from the content; without fresh ideas, revisiting them deepens my understanding and strengthens my memory recall.

On average, our minds wander about 20% to 40% of the time. Mind wandering is crucial for creative insights. The creative process requires gathering information first, focusing on problems genuinely attentively then relaxing… Science and mathematics fields are filled with those who come up with incredible solutions while daydreaming in the shower or riding a bus or walking their dogs… It’s because during mind wandering periods we can connect distant elements in a novel and valuable way. That’s the definition of creative behavior.

However, this knowledge was initially Entangled like a tangled mess. So I need to Engage in it by starting to sort out this knowledge and interact with it not letting it tangle but rather entwining it reasonably for my use. In doing so, I have completed the Disenchantment of any knowledge; O represents Organization, where I organize this knowledge in a rational and orderly manner. With the continuous accumulation of knowledge, spending more time and attention, I can also become an “expert” in a certain field, represented by M as Maestro.

How to use Heptabase Effectively — U.R.E.A.D.C.（You Read Context）

Uninterrupted typing 不间断地输入

We first create a whiteboard. Every time we input on this whiteboard, we create a card first and continuously input. We only need to focus on the content, not the design or layout.

The more disrupted is particularly for young people, the harder it is for them to grasp, to build the cumulative mental models that amount to master in any subject.

Review

After we finish inputting, we need to review the content from beginning to end and make modifications, additions, and deletions:

Bold text
Highlighted phrases
Which content needs to be turned into collapsible lists that can become cards
Which content is related to other cards and needs to be supplemented with double links or needs to be created as double links
…

Extract

Extract the content you have in mind and transform it into a new card.

Arrange

Arrange these new cards.

Determine

Decide object relationships
Create Sections and Embedded Whiteboards
Use color coding to differentiate the importance of objects

connect Context

Use the Arrow to highlight object relationships
Connect and annotate the relationships between objects

Through these six steps, we’ve conducted a detailed analysis of macro objects such as articles, videos, or books. This process not only deepens our understanding and discovery of the subject matter but also provides us with a comprehensive global perspective. Consequently, whether recalling or applying specific knowledge from it, our search and management skills enable swift application. Therefore, we can learn this content more accurately and attentively. UREDC could also be interpreted as ‘You Read Context’, signifying your comprehension of the context.

By implementing these six steps, we regain our focus.

To execute and actualize your ideas, it’s crucial to maintain a focused state of mind.

Heptabase’s visual features allow us to easily track the cumulative results of a project or knowledge point. It becomes evident that notes are not static but rather progressive and continuously evolving. This is demonstrated by altering object connections and supplementing previous cards with additional information like double links and tags.

Thus, over time, these notes serve as tangible evidence of our unending progress.

Outlook

When I began writing this piece, I compared the previous AI Assistant to current platforms like Notion Q&A and Google NoteBookLM. These tools retrieve and recommend cards relevant to your queries while maintaining privacy. However, my experience with them was mediocre at best, and I rarely used them. Despite this, I am hopeful that future AI Assistants will offer citation provision and direct navigation to specific cards or sections for improved search functionality.

Moreover, if Heptabase could leverage AI to generate question sections from whiteboard content or facilitate brainstorming in multi-person collaborations based on a card or other scenarios, it would truly excel. There are reference cases available for these applications.

Many users appreciate Heptabase primarily for its card features. If it were to adopt a mobile page creation process similar to Flomo’s or Supernotes’ card input method — shifting from creating pages to creating cards — I believe it would be even more appealing. Another exciting prospect is the ability to copy and share whiteboards within one’s own Heptabase platform akin to Notion.

I also hope that Heptabase will invest more in design aesthetics in the future. While its pragmatic design currently suffices, there is room for improvement aesthetically speaking; Scrintal initially attracted me due to its superior design elements such as animations and color schemes.

However, what brought me back to Heptabase was Alan’s team’s sensible approach towards user suggestions; reading Alan’s blog further solidified my faith in their clear vision for the platform.

While democratic function implementation is commendable, my concern lies with how sustainable this approach will be as both the user base and team expand — it requires careful consideration and restraint. This brings me admiration towards reMarkable: they consider user suggestions aligned with their core principles — a trait I hope Heptabase can emulate too. Nonetheless, community voting does foster a sense of “participation” among users which is valuable.

最后

Can Heptabase replace pen and paper?

No software, including Heptabase, can truly replace the traditional pen and paper. The speed of typing often fails to keep pace with our rapid thoughts and untidy handwriting. When tackling large projects, I don’t simply sit at my computer and begin drafting an outline aimlessly; instead, I initially jot down rough ideas in my Stalogy notebook and sketch a rudimentary network diagram — it doesn’t matter if it’s messy as long as it’s comprehensible. Once this basic framework is established, I employ the UREADC six-step method in Heptabase to record and refine these ideas before consolidating everything. Consequently, whenever I step out now, my Stalogy notebook and Zebra Pitan pen are always within reach.

Pricing

The subscription cost for Heptabase is $11.99 per month or $107.88 annually, which equates to $8.99 per month.

After reading this article, if you believe that Heptabase could be your ideal knowledge system, feel free to try it out for 7 days using my method. If you’re not satisfied with the experience, you can request a full refund. You may even discover your own unique learning approach in the process.

Thank you

Thank you for taking the time to read this article. I hope it has provided you with a fresh perspective on Heptabase, a software that seamlessly blends usability, flexibility and superior user experience. Regardless of your profession or expertise, Heptabase is designed to assist you.

Its unique F.R.E.E.D.O.M (Focus, ReThink, Entangle, Engage, Disenchant, Organize, Maestro) characteristics and U.R.E.A.D.C six-step method are crafted to stimulate creativity when using tools. They facilitate comprehensive organization and profound understanding of knowledge while enhancing our focus on content — ultimately enriching our mastery of the subject matter.

While I’m not particularly fond of the term “second brain,” I believe Heptabase deserves this accolade. It’s more than just my note-taking system; it’s an integral part of my lifelong learning journey.

If you’re looking to regain your ability to concentrate effectively again, why not give Heptabase a shot? You might find it beneficial!

Epilogue

Just as I was about to finish writing this article, my friend shared an interview with Alan from Ness Labs. I took a quick look and found both common ground and admirable aspects.

In the interview, Alan also shared two guiding principles that coincidentally addressed my final section on envisioning software functionality.

He said:

The most important principle in our company is that we want to ensure that everything we’re building is aligned with our ultimate goal of helping people acquire and establish a deep understanding of the things they care about. We’re very conscious of not getting distracted by other purposes. The second principle is that we want the product to be as friendly and intuitive as possible. Our target users should be able to get into the flow of thinking as soon as they open the product.

What truly draws me to Heptabase is not the platform itself, but Alan’s insightful thinking. I resonate with and trust his ideas, which in turn makes me believe in the potential of Heptabase software. As a student, my substantial investment in it speaks volumes.

Heptabase also offers you an opportunity to craft your own intellectual universe.