SynapseMind v1.0.0 is a prototype knowledge graph explorer billed as an AI collaboration tool.
The interface has three panels: a left sidebar for focus area filtering, a central canvas
showing an interactive force-directed node graph, and a right-side Node Inspector panel
that displays metadata for whichever node is currently selected. The aesthetic is dark and
technical, using cyan and magenta as primary accent colors against a near-black background.
Observed Behavior
The left sidebar lists three Focus Areas as filter pills: Quantum Computing, Culinary Arts, and Urban Planning. All appear selected or at minimum highlighted in teal.
The central canvas renders a small force-directed graph with three visible nodes labeled ND-8821 (cyan), ND-1109 (magenta), and ND-4402 (magenta). Two directed edges connect them: a "prerequisite" edge from ND-8821 to ND-1109, and an "analogous" edge from ND-8821 to ND-4402. A hint reads "Drag nodes to explore. Click to inspect."
The Node Inspector on the right is populated with data for ND-8821, the currently selected node: title "The Geometry of Flavor," tags Molecular Gastronomy / Chemistry / Design, a short description, Verified: Yes, Sentiment: 0.72, and a "View" source link.
The header shows "AI_Collaborator" with a teal status dot in the top-right corner, implying some kind of AI agent or session context.
Things That Did Not Work as Expected
Only three nodes are present in a knowledge graph app. This is far too sparse to demonstrate the value of a graph-based exploration interface. The concept only makes sense with dozens of interconnected nodes at minimum.
The three Focus Areas (Quantum Computing, Culinary Arts, Urban Planning) are thematically unrelated and do not obviously connect to the one inspected node ("The Geometry of Flavor," which straddles Culinary Arts and Chemistry). It is unclear whether clicking a focus area actually filters the graph or does nothing.
ND-1109 and ND-4402 have no visible labels or names on the canvas — only their IDs. The Node Inspector only populates for the pre-selected node, so there is no way to know what those two nodes represent without clicking them, and it is unclear whether clicking is actually wired up in this static prototype.
The "View" source link in the Node Inspector is almost certainly a placeholder pointing nowhere, which undercuts the "Verified: Yes" credibility indicator right next to it.
The "AI_Collaborator" label in the header implies an active AI agent, but no AI functionality is visible or accessible anywhere in the UI — no chat, no suggestion, no query interface.
Sentiment (0.72) is shown as a raw float with no explanation of scale, direction, or meaning. A user would not know if higher is better, what it refers to, or how it was computed.
The node color distinction between cyan and magenta has no legend or explanation — it is not clear whether color encodes type, status, focus area, or something else entirely.
Improvement Prompt
You are building SynapseMind, an AI-powered knowledge graph explorer. Improve the current prototype with the following changes:
1. Expand the graph to at least 15-20 nodes with realistic interconnections across the three focus areas (Quantum Computing, Culinary Arts, Urban Planning). Use at least four relationship types: prerequisite, analogous, contradicts, and supports. Edge labels should always be visible on hover.
2. Make Focus Area pills in the sidebar act as real filters. Clicking one should highlight only nodes belonging to that domain and fade out unrelated nodes. Clicking again deselects the filter. Allow multi-select.
3. Add a node color legend. Color should encode the focus area (e.g. cyan for Quantum Computing, magenta for Culinary Arts, amber for Urban Planning). Display the legend below the Focus Areas panel.
4. Fix the Node Inspector for all nodes, not just the pre-selected one. Clicking any node on the canvas should populate the inspector with that node's title, tags, description, verified status, sentiment, and a working source link. Clicking blank canvas space should clear the inspector.
5. Replace the raw sentiment float with a simple visual indicator: a horizontal bar from 0 to 1 with a label like "Positive" or "Mixed" based on the value.
6. Wire up the AI_Collaborator. Add a small text input at the bottom of the Node Inspector that lets the user ask a question about the selected node (e.g. "How does this relate to Urban Planning?"). Use a language model API call to generate a short contextual answer based on the node's metadata and its graph neighbors.