ThinkGraph
1. Motivation/Idea
Does your ChatGPT have a little infographic saying “your memory is full”? I’m sure it does for most of you who use LLMs even for basic searches. You’re passing your brain’s cognitive load to the LLM, letting it do the thinking for you. The memory may be full — but do you actually remember even 10% of it? Can you really recall?
Check this out: a 2024 MIT study, Your Brain on ChatGPT: Accumulation of Cognitive Debt when Using an AI Assistant for Essay Writing Tasks, found that individuals using an LLM for writing showed weaker neural connectivity and greater difficulty recalling what they had written compared to control groups.
The core issue is that current LLM interfaces are not designed to support long-term knowledge consolidation. They lack persistent visual structures that would allow users to see relationships, revisit previous lines of inquiry, and build a coherent mental model over time.
Yet information seeking through Large Language Models (LLMs) like ChatGPT and Gemini has become central to us — offering unprecedented speed and access to knowledge. But this efficiency creates a paradox: while users can get answers faster, they may retain less. The linear, ephemeral nature of chat interfaces encourages a pattern of “cognitive offloading” — outsourcing the process of thinking and making connections to the machine.
Proposed Solution: A "Second Brain" for AI-Powered Exploration
Note: This is definitely not the answer. I simply see it as the first step in the right direction — a starting point sure, but not yet the entire solution.
ThinkGraph is a simplified note-taking application designed to merge structured information management with dynamic visualization. Unlike traditional note-taking tools that rely solely on text and linear organization, ThinkGraph enables users to view and interact with their ideas as interconnected entities within a virtual universe. Each project is represented as a star, and within these stars, notes function as nodes connected through meaningful relationships. The application emphasizes a user-friendly, immersive experience, where zooming, panning, and connecting notes create an intuitive way to structure and revisit ideas.
My primary goal of ThinkGraph right now is to provide a Minimum Viable Product (MVP) that demonstrates the potential of this approach while laying the groundwork for future iterations, including advanced graph-based visualization, AI-assisted knowledge structuring, and cognitive augmentation.
Expected Impact:
This research aligns directly with the principles of Concept-Driven Visual Analytics. By providing a tool for users to visually construct and test hypotheses, it empowers them to move beyond passive information consumption toward active knowledge creation. The project's ultimate goal is to provide empirically validated design guidelines for the next generation of human-centered AI systems, ensuring that these powerful technologies serve to augment, not replace, human cognition.
ThinkGraph Lite - Minimal Viable Product - Scope Doc
ThinkGraph Lite is a note-taking application designed to merge structured information management with dynamic visualization. Unlike traditional note-taking tools that rely solely on text and linear organization, ThinkGraph enables users to view and interact with their ideas as interconnected entities within a virtual universe.
Scope of the Project
The scope of ThinkGraph Lite is deliberately focused on simplicity and usability to ensure that a functional prototype can be built quickly and expanded over time.
Core Features:
Project-as-Stars Metaphor: Users can create projects represented visually as stars in a universe-like canvas.
Note Creation: Within each project, users can create and edit notes.
Connections: Users can draw edges between notes to represent relationships, ideas, or dependencies.
Zoom and Navigation: A dynamic zooming interface allows switching between universe view (projects as stars) and project view (notes as nodes).
Basic Persistence: Notes, connections, and projects are saved locally (e.g., in a JSON file or lightweight database).
Constraints:
Limited to single-user interactions in the MVP.
Basic visualization features only (no advanced analytics or AI integration yet).
Focused on usability over scalability.
3. Technology Stack
To develop ThinkGraph Lite efficiently, a lightweight but powerful technology stack will be used:
Frontend:
React.js (for interactive UI)
D3.js or Cytoscape.js (for graph visualization)
Tailwind CSS (for clean and responsive styling)
Backend:
Node.js with Express or Flask (Python) for lightweight server-side operations (if required).
File-based Storage (JSON) or SQLite for early-stage persistence.
Additional Tools:
Canvas/WebGL (Three.js) for universe/star visualization.
4. Future Scope
While the MVP provides the foundation, the full vision of ThinkGraph extends far beyond note-taking.
Advanced Graph Features:
Complex graph layouts and clustering.
Automated relationship suggestions using AI.
Weighted edges to show strength of relationships.
Cognitive Augmentation:
AI-powered summarization and reflection.
Semantic search across notes and projects.
Smart grouping of notes into concepts and themes.
Collaboration:
Multi-user shared universes.
Real-time collaborative editing and visualization.
Integrations:
API connections with tools like Notion, Apple Notes or Google Keep.
Export options for academic and research workflows.
5. Impact
The impact of ThinkGraph Lite and its eventual evolution into a full-fledged ThinkGraph system lies in its intersection of education, human-computer interaction, visualization, learning, and cognitive augmentation.
5.1 Education
ThinkGraph provides students with a radically new way of structuring knowledge. Instead of linear notes, learners can represent concepts spatially, making it easier to see relationships across disciplines. For example, a student studying history could connect political, cultural, and economic events into a dynamic map rather than memorizing isolated facts. This shift from rote memorization to conceptual visualization promotes deeper understanding and long-term retention.
5.2 Human-Computer Interaction (HCI)
At its core, ThinkGraph is an HCI project. It redefines how users interact with digital knowledge—not through static lists or search boxes, but through spatial exploration, zooming metaphors, and node-based interactions. This kind of interface aligns closely with natural human cognition, where spatial memory and visual association play critical roles in recall. The system bridges human intuition with computational organization, setting a precedent for more immersive and user-centric digital tools.
5.3 Visualization, Learning, and Understanding
Visualization is not just an aesthetic choice—it is a cognitive tool. By turning abstract information into visual structures, ThinkGraph helps users recognize patterns, clusters, and gaps in their thinking. This makes the act of learning both interactive and reflective. Learners can “see” their thought process, reorganize ideas on the fly, and gain insights from the structure of their knowledge itself. The shift from text-only to visual-structural learning can improve comprehension across disciplines ranging from STEM to the humanities.
5.4 Cognitive Augmentation
The ultimate ambition of ThinkGraph is cognitive augmentation—the idea that technology can extend human thought. By externalizing memory, relationships, and reasoning structures into a visual system, users effectively build a “second brain.” With AI integrations, ThinkGraph could evolve into a collaborative thinking partner, helping generate new connections, challenge assumptions, and accelerate creativity. This augmentation is not about replacing thought but about enhancing the brain’s ability to navigate complexity.
5.5 Broader Implications
Beyond individual users, ThinkGraph has the potential to influence research, team collaboration, and even institutional knowledge management. Its visual-first paradigm could become a model for how organizations map problems, design solutions, and manage collective intelligence.
ThinkGraph
1. Motivation/Idea
Does your ChatGPT have a little infographic saying “your memory is full”? I’m sure it does for most of you who use LLMs even for basic searches. You’re passing your brain’s cognitive load to the LLM, letting it do the thinking for you. The memory may be full — but do you actually remember even 10% of it? Can you really recall?
Check this out: a 2024 MIT study, Your Brain on ChatGPT: Accumulation of Cognitive Debt when Using an AI Assistant for Essay Writing Tasks, found that individuals using an LLM for writing showed weaker neural connectivity and greater difficulty recalling what they had written compared to control groups.
The core issue is that current LLM interfaces are not designed to support long-term knowledge consolidation. They lack persistent visual structures that would allow users to see relationships, revisit previous lines of inquiry, and build a coherent mental model over time.
Yet information seeking through Large Language Models (LLMs) like ChatGPT and Gemini has become central to us — offering unprecedented speed and access to knowledge. But this efficiency creates a paradox: while users can get answers faster, they may retain less. The linear, ephemeral nature of chat interfaces encourages a pattern of “cognitive offloading” — outsourcing the process of thinking and making connections to the machine.
Proposed Solution: A "Second Brain" for AI-Powered Exploration
Note: This is definitely not the answer. I simply see it as the first step in the right direction — a starting point sure, but not yet the entire solution.
ThinkGraph is a simplified note-taking application designed to merge structured information management with dynamic visualization. Unlike traditional note-taking tools that rely solely on text and linear organization, ThinkGraph enables users to view and interact with their ideas as interconnected entities within a virtual universe. Each project is represented as a star, and within these stars, notes function as nodes connected through meaningful relationships. The application emphasizes a user-friendly, immersive experience, where zooming, panning, and connecting notes create an intuitive way to structure and revisit ideas.
My primary goal of ThinkGraph right now is to provide a Minimum Viable Product (MVP) that demonstrates the potential of this approach while laying the groundwork for future iterations, including advanced graph-based visualization, AI-assisted knowledge structuring, and cognitive augmentation.
Expected Impact:
This research aligns directly with the principles of Concept-Driven Visual Analytics. By providing a tool for users to visually construct and test hypotheses, it empowers them to move beyond passive information consumption toward active knowledge creation. The project's ultimate goal is to provide empirically validated design guidelines for the next generation of human-centered AI systems, ensuring that these powerful technologies serve to augment, not replace, human cognition.
ThinkGraph Lite - Minimal Viable Product - Scope Doc
ThinkGraph Lite is a note-taking application designed to merge structured information management with dynamic visualization. Unlike traditional note-taking tools that rely solely on text and linear organization, ThinkGraph enables users to view and interact with their ideas as interconnected entities within a virtual universe.
Scope of the Project
The scope of ThinkGraph Lite is deliberately focused on simplicity and usability to ensure that a functional prototype can be built quickly and expanded over time.
Core Features:
Project-as-Stars Metaphor: Users can create projects represented visually as stars in a universe-like canvas.
Note Creation: Within each project, users can create and edit notes.
Connections: Users can draw edges between notes to represent relationships, ideas, or dependencies.
Zoom and Navigation: A dynamic zooming interface allows switching between universe view (projects as stars) and project view (notes as nodes).
Basic Persistence: Notes, connections, and projects are saved locally (e.g., in a JSON file or lightweight database).
Constraints:
Limited to single-user interactions in the MVP.
Basic visualization features only (no advanced analytics or AI integration yet).
Focused on usability over scalability.
3. Technology Stack
To develop ThinkGraph Lite efficiently, a lightweight but powerful technology stack will be used:
Frontend:
React.js (for interactive UI)
D3.js or Cytoscape.js (for graph visualization)
Tailwind CSS (for clean and responsive styling)
Backend:
Node.js with Express or Flask (Python) for lightweight server-side operations (if required).
File-based Storage (JSON) or SQLite for early-stage persistence.
Additional Tools:
Canvas/WebGL (Three.js) for universe/star visualization.
4. Future Scope
While the MVP provides the foundation, the full vision of ThinkGraph extends far beyond note-taking.
Advanced Graph Features:
Complex graph layouts and clustering.
Automated relationship suggestions using AI.
Weighted edges to show strength of relationships.
Cognitive Augmentation:
AI-powered summarization and reflection.
Semantic search across notes and projects.
Smart grouping of notes into concepts and themes.
Collaboration:
Multi-user shared universes.
Real-time collaborative editing and visualization.
Integrations:
API connections with tools like Notion, Apple Notes or Google Keep.
Export options for academic and research workflows.
5. Impact
The impact of ThinkGraph Lite and its eventual evolution into a full-fledged ThinkGraph system lies in its intersection of education, human-computer interaction, visualization, learning, and cognitive augmentation.
5.1 Education
ThinkGraph provides students with a radically new way of structuring knowledge. Instead of linear notes, learners can represent concepts spatially, making it easier to see relationships across disciplines. For example, a student studying history could connect political, cultural, and economic events into a dynamic map rather than memorizing isolated facts. This shift from rote memorization to conceptual visualization promotes deeper understanding and long-term retention.
5.2 Human-Computer Interaction (HCI)
At its core, ThinkGraph is an HCI project. It redefines how users interact with digital knowledge—not through static lists or search boxes, but through spatial exploration, zooming metaphors, and node-based interactions. This kind of interface aligns closely with natural human cognition, where spatial memory and visual association play critical roles in recall. The system bridges human intuition with computational organization, setting a precedent for more immersive and user-centric digital tools.
5.3 Visualization, Learning, and Understanding
Visualization is not just an aesthetic choice—it is a cognitive tool. By turning abstract information into visual structures, ThinkGraph helps users recognize patterns, clusters, and gaps in their thinking. This makes the act of learning both interactive and reflective. Learners can “see” their thought process, reorganize ideas on the fly, and gain insights from the structure of their knowledge itself. The shift from text-only to visual-structural learning can improve comprehension across disciplines ranging from STEM to the humanities.
5.4 Cognitive Augmentation
The ultimate ambition of ThinkGraph is cognitive augmentation—the idea that technology can extend human thought. By externalizing memory, relationships, and reasoning structures into a visual system, users effectively build a “second brain.” With AI integrations, ThinkGraph could evolve into a collaborative thinking partner, helping generate new connections, challenge assumptions, and accelerate creativity. This augmentation is not about replacing thought but about enhancing the brain’s ability to navigate complexity.
5.5 Broader Implications
Beyond individual users, ThinkGraph has the potential to influence research, team collaboration, and even institutional knowledge management. Its visual-first paradigm could become a model for how organizations map problems, design solutions, and manage collective intelligence.
ThinkGraph
1. Motivation/Idea
Does your ChatGPT have a little infographic saying “your memory is full”? I’m sure it does for most of you who use LLMs even for basic searches. You’re passing your brain’s cognitive load to the LLM, letting it do the thinking for you. The memory may be full — but do you actually remember even 10% of it? Can you really recall?
Check this out: a 2024 MIT study, Your Brain on ChatGPT: Accumulation of Cognitive Debt when Using an AI Assistant for Essay Writing Tasks, found that individuals using an LLM for writing showed weaker neural connectivity and greater difficulty recalling what they had written compared to control groups.
The core issue is that current LLM interfaces are not designed to support long-term knowledge consolidation. They lack persistent visual structures that would allow users to see relationships, revisit previous lines of inquiry, and build a coherent mental model over time.
Yet information seeking through Large Language Models (LLMs) like ChatGPT and Gemini has become central to us — offering unprecedented speed and access to knowledge. But this efficiency creates a paradox: while users can get answers faster, they may retain less. The linear, ephemeral nature of chat interfaces encourages a pattern of “cognitive offloading” — outsourcing the process of thinking and making connections to the machine.
Proposed Solution: A "Second Brain" for AI-Powered Exploration
Note: This is definitely not the answer. I simply see it as the first step in the right direction — a starting point sure, but not yet the entire solution.
ThinkGraph is a simplified note-taking application designed to merge structured information management with dynamic visualization. Unlike traditional note-taking tools that rely solely on text and linear organization, ThinkGraph enables users to view and interact with their ideas as interconnected entities within a virtual universe. Each project is represented as a star, and within these stars, notes function as nodes connected through meaningful relationships. The application emphasizes a user-friendly, immersive experience, where zooming, panning, and connecting notes create an intuitive way to structure and revisit ideas.
My primary goal of ThinkGraph right now is to provide a Minimum Viable Product (MVP) that demonstrates the potential of this approach while laying the groundwork for future iterations, including advanced graph-based visualization, AI-assisted knowledge structuring, and cognitive augmentation.
Expected Impact:
This research aligns directly with the principles of Concept-Driven Visual Analytics. By providing a tool for users to visually construct and test hypotheses, it empowers them to move beyond passive information consumption toward active knowledge creation. The project's ultimate goal is to provide empirically validated design guidelines for the next generation of human-centered AI systems, ensuring that these powerful technologies serve to augment, not replace, human cognition.
ThinkGraph Lite - Minimal Viable Product - Scope Doc
ThinkGraph Lite is a note-taking application designed to merge structured information management with dynamic visualization. Unlike traditional note-taking tools that rely solely on text and linear organization, ThinkGraph enables users to view and interact with their ideas as interconnected entities within a virtual universe.
Scope of the Project
The scope of ThinkGraph Lite is deliberately focused on simplicity and usability to ensure that a functional prototype can be built quickly and expanded over time.
Core Features:
Project-as-Stars Metaphor: Users can create projects represented visually as stars in a universe-like canvas.
Note Creation: Within each project, users can create and edit notes.
Connections: Users can draw edges between notes to represent relationships, ideas, or dependencies.
Zoom and Navigation: A dynamic zooming interface allows switching between universe view (projects as stars) and project view (notes as nodes).
Basic Persistence: Notes, connections, and projects are saved locally (e.g., in a JSON file or lightweight database).
Constraints:
Limited to single-user interactions in the MVP.
Basic visualization features only (no advanced analytics or AI integration yet).
Focused on usability over scalability.
3. Technology Stack
To develop ThinkGraph Lite efficiently, a lightweight but powerful technology stack will be used:
Frontend:
React.js (for interactive UI)
D3.js or Cytoscape.js (for graph visualization)
Tailwind CSS (for clean and responsive styling)
Backend:
Node.js with Express or Flask (Python) for lightweight server-side operations (if required).
File-based Storage (JSON) or SQLite for early-stage persistence.
Additional Tools:
Canvas/WebGL (Three.js) for universe/star visualization.
4. Future Scope
While the MVP provides the foundation, the full vision of ThinkGraph extends far beyond note-taking.
Advanced Graph Features:
Complex graph layouts and clustering.
Automated relationship suggestions using AI.
Weighted edges to show strength of relationships.
Cognitive Augmentation:
AI-powered summarization and reflection.
Semantic search across notes and projects.
Smart grouping of notes into concepts and themes.
Collaboration:
Multi-user shared universes.
Real-time collaborative editing and visualization.
Integrations:
API connections with tools like Notion, Apple Notes or Google Keep.
Export options for academic and research workflows.
5. Impact
The impact of ThinkGraph Lite and its eventual evolution into a full-fledged ThinkGraph system lies in its intersection of education, human-computer interaction, visualization, learning, and cognitive augmentation.
5.1 Education
ThinkGraph provides students with a radically new way of structuring knowledge. Instead of linear notes, learners can represent concepts spatially, making it easier to see relationships across disciplines. For example, a student studying history could connect political, cultural, and economic events into a dynamic map rather than memorizing isolated facts. This shift from rote memorization to conceptual visualization promotes deeper understanding and long-term retention.
5.2 Human-Computer Interaction (HCI)
At its core, ThinkGraph is an HCI project. It redefines how users interact with digital knowledge—not through static lists or search boxes, but through spatial exploration, zooming metaphors, and node-based interactions. This kind of interface aligns closely with natural human cognition, where spatial memory and visual association play critical roles in recall. The system bridges human intuition with computational organization, setting a precedent for more immersive and user-centric digital tools.
5.3 Visualization, Learning, and Understanding
Visualization is not just an aesthetic choice—it is a cognitive tool. By turning abstract information into visual structures, ThinkGraph helps users recognize patterns, clusters, and gaps in their thinking. This makes the act of learning both interactive and reflective. Learners can “see” their thought process, reorganize ideas on the fly, and gain insights from the structure of their knowledge itself. The shift from text-only to visual-structural learning can improve comprehension across disciplines ranging from STEM to the humanities.
5.4 Cognitive Augmentation
The ultimate ambition of ThinkGraph is cognitive augmentation—the idea that technology can extend human thought. By externalizing memory, relationships, and reasoning structures into a visual system, users effectively build a “second brain.” With AI integrations, ThinkGraph could evolve into a collaborative thinking partner, helping generate new connections, challenge assumptions, and accelerate creativity. This augmentation is not about replacing thought but about enhancing the brain’s ability to navigate complexity.
5.5 Broader Implications
Beyond individual users, ThinkGraph has the potential to influence research, team collaboration, and even institutional knowledge management. Its visual-first paradigm could become a model for how organizations map problems, design solutions, and manage collective intelligence.