Visualize Your Knowledge Base with Recall AI! | Graph View 2.0 Review & Tutorial

TL;DR

Recall automatically organizes stored resources using tags and filters, reducing the need for manual tagging.

Briefing Cornell Notes

Briefing

Recall is positioned as a personal AI knowledge base for people drowning in saved links, articles, and notes—turning that pile into something searchable, connected, and actively studyable. Instead of relying on manual organization, it automatically surfaces stored resources on a Home screen using tags and filters, while still letting users add new entries from URLs, Wikipedia, Google Knowledge Graph, or Wikidata, or by writing notes directly. A key workflow centers on linking ideas: highlight text in a note, create a connection to an existing concept (like “artificial intelligence”), and instantly see related cards and resources grouped under that node.

The platform then extends beyond storage into interaction. An AI chat feature lets users query their entire knowledge base, with @mentions and tag-based search narrowing results to specific topics or contexts. For learning, a Review area functions like a space repetition system, generating study questions tied to the cards a user wants to practice and tracking performance as questions are answered. For content capture, a Chrome extension generates concise AI summaries for saved media and supports timestamped note-taking inside the summary—plus the ability to chat with the source content, read a full transcript, and generate AI quizzes that feed back into the Review workflow.

Where Recall differentiates most sharply is Graph View 2.0, built to make relationships visible and customizable. Nodes represent concepts, resources, and tags; the more connections a node has, the larger it appears, helping users spot “hot” themes at a glance. Directed links (arrows) reflect how one resource points to another through stored connections—so an article about sleep may connect to sleep directly, while also linking onward to sources like CNN when that article originated there. Graph View 2.0 adds practical controls: filtering by tag, source, name, or search (including the ability to remove items with a minus sign), showing unconnected or leaf nodes, and grouping nodes by matching queries so entire clusters light up (for example, everything related to technology or AI).

The interface also offers deeper graph mechanics: timeline views show when cards were added; layout settings adjust node spacing, link lengths, link force, and pull toward the center; and visual options control label visibility, arrow display, and link thickness. A “pathfinder” tool lets users click two nodes and reveal the connecting route—surfacing intermediate concepts and the specific cards that bridge them. Users can color-code by tag, lock the graph to keep a manual arrangement, and save a customized view as a preset so different perspectives (journal-only, a single topic, or a tag-focused layout) can be recalled later.

Finally, Recall treats people and recommendations as first-class connections. A user can create an entity like “Sally,” record that she recommended a particular article, tag her as a contact, and later trace her influence across the graph—seeing Sally connected to multiple nodes. A mobile app rounds out the workflow by enabling sharing a podcast directly into Recall for later reading and study. The overall message is clear: Recall aims to turn passive saving into an evolving, connected system for discovery and spaced learning, with Graph View 2.0 as the centerpiece for finding new paths through accumulated knowledge.

Cornell Notes

Recall is a tool for building an AI-powered knowledge base from saved web content, Wikipedia/knowledge graph sources, and handwritten notes. It emphasizes linking: highlighting text and connecting it to concepts instantly organizes related cards under shared nodes and tags. Users can chat with their knowledge base using @mentions and tag filters, then study via a Review area that generates space-repetition style questions. The Chrome extension streamlines capture by producing AI summaries, timestamped note-taking, transcript access, and AI quizzes that feed into Review. Graph View 2.0 is the standout feature, visualizing nodes and directed connections, offering filtering/grouping, pathfinding between concepts, and saving customized graph presets.

How does Recall turn scattered saved items into an organized knowledge base without heavy manual tagging?

On the Home screen, Recall shows stored resources with tags and filters in the sidebar. The transcript emphasizes that filters and tags are added automatically, so users don’t have to manually tag every entry. New content can be added via a URL (including YouTube videos, websites, and articles), from sources like Wikipedia, Google Knowledge Graph, or Wikidata, or by writing entries directly. When users highlight text in a note and create a connection to a concept (e.g., linking to “artificial intelligence”), Recall links the new card into the existing node so it appears in the relevant context.

What learning loop does Recall support beyond search—especially for studying efficiently?

Recall combines chat-based retrieval with a dedicated Review area. In Review, users can start space-repetition style practice where questions are drawn from the cards they want to study. The transcript also describes generating AI quizzes for a specific resource (from the card’s quiz feature), answering those questions, and then connecting that learning back into the Review section. This creates a cycle: capture → summarize/organize → quiz → spaced review.

How does the Chrome extension change the workflow for saving and studying video or web content?

Using the Recall icon in the Chrome extension, users get an AI-generated concise summary immediately. They can add their own notes inside the extension, including notes within the AI summary text. For video, the summary supports clickable timestamps that jump to relevant sections as the video plays. The extension also enables chatting with the source content, reading the full transcript in a Reader view, and generating AI quizzes tied to the resource.

What makes Graph View 2.0 different from a simple list of notes?

Graph View 2.0 visualizes relationships as a network: circles are nodes, and node size increases with the number of connections. Directed arrows show how one resource points to another based on stored references—so a card can connect to multiple downstream topics and sources. The view includes filtering (tag, source, name, search, and removing items with a minus sign), timeline progression of when cards were added, toggles for unconnected and leaf nodes, and grouping by query so clusters (like “technology” or “AI”) highlight together.

How does pathfinding help users discover connections they might miss?

Pathfinder lets users select a starting node (e.g., “adenosine”) and a target node (e.g., “nervous system”) to reveal the connecting route. The transcript describes intermediate connecting points—such as an article about caffeine-free effects, then concepts like coffee culture and sleep—before reaching the target. This turns the graph into a guided discovery tool rather than just a static visualization.

How can people and recommendations become part of the knowledge graph?

Graph View 2.0 supports editing inside nodes. If a colleague recommended an article, the user can create a person entity (example: “Sally”), then record that Sally recommended the article in Connections. The person can also be tagged (e.g., “contacts”) and given notes (like interests). Later, searching the graph for Sally shows which nodes she connects to—making recommendations traceable and reusable across the knowledge base.

Review Questions

Describe the end-to-end workflow from saving a YouTube video to studying it in Review using Recall’s tools.
In Graph View 2.0, how do node size and arrow direction help interpret relationships between resources?
What graph controls (filtering, grouping, layout, presets, or pathfinding) would you use to answer a question like “How does concept A relate to concept B through intermediate topics?”

Key Points

1
Recall automatically organizes stored resources using tags and filters, reducing the need for manual tagging.
2
Users can add knowledge from URLs, Wikipedia, Google Knowledge Graph, Wikidata, or by writing entries directly.
3
Highlight-and-connect linking turns notes into a connected network where new cards appear under relevant concept nodes.
4
AI chat supports retrieval across the knowledge base using @mentions and tag-based narrowing.
5
The Review area provides space-repetition style practice using generated questions tied to selected cards.
6
The Chrome extension streamlines capture with AI summaries, timestamped note-taking, transcript access, and AI quiz generation.
7
Graph View 2.0 visualizes nodes and directed connections, supports filtering/grouping, pathfinding between concepts, and saving customized presets.

Highlights

Graph View 2.0 makes relationships visible: node size reflects connection density, and arrows show directed links between resources and sources.

Pathfinder reveals the route between two concepts, listing intermediate cards and topics that connect them.

The Chrome extension supports timestamped note-taking inside AI-generated summaries, then feeds quizzes into the Review space-repetition workflow.

People like “Sally” can be added as entities so recommendations become traceable connections across the graph.

Topics

AI Knowledge Base
Graph View 2.0
Space Repetition
Chrome Extension
Knowledge Graph