J.A.R.V.I.S.
Initializing neural map
J.A.R.V.I.S.
Neural Graph
Domains--
Memories--
Entities--
Nodes--
Synapses--
Clusters--
Initializing | L0 |
Monitoring Log
Bio:
Domain
Entry
Pattern
Tool
Memory
Entity
Bridge
Graph Glossary
What each element represents
Nodes
Domain
A project or knowledge area you work in (e.g. "frontend", "devops"). Domains are the large hub nodes — everything else orbits around them. Size reflects how many sessions you've had in that domain.
Entry Point
The typical way you start working in a domain — recurring first-message patterns clustered from your session history. For example, "fix bug in auth" or "add API endpoint" are entry points if you frequently begin sessions that way.
Pattern
A recurring behavioral pattern detected across your sessions — keyword sequences that appear repeatedly. These reveal your habitual workflows, e.g. "read → grep → edit → test" or specific technical terms you consistently use together.
Tool
A Claude Code tool you frequently use in this domain (Read, Edit, Grep, Bash, etc.). Size reflects usage ratio — larger means you use it more often. Shows your preferred workflow style per domain.
Feature
A behavioral feature learned by sparse dictionary analysis of your session patterns. These are abstract dimensions like "exploration depth" or "refactoring tendency" — computed from 27 behavioral signals, not named by you.
Memory
A piece of information stored by JARVIS — decisions, facts, errors, or context you asked it to remember. Memories have heat (how recently/frequently accessed) and importance. They cool over time and compress from full text to summaries.
Entity
A named thing extracted from memories — functions, files, dependencies, technologies, errors, or decisions. Entities form the nodes of the knowledge graph, connected by typed relationships like "imports", "calls", or "resolved_by".
Edges
Bridge
A cross-domain connection — shared patterns, vocabulary, or structural links between two different domains. Bridges show how your knowledge transfers across projects.
Co-occurrence
Two entities that frequently appear together in the same memories. The more often they co-occur, the stronger (brighter) the connection.
Causal (caused_by / resolved_by)
A cause-effect relationship between entities — e.g. an error caused by a dependency, or a bug resolved by a fix. Red edges indicate causal chains.
Imports / Calls
Code-level relationships — one module importing another, or one function calling another. Extracted from memory content automatically.
Biological Mechanisms
Emotional Tagging
Memories with emotional charge get colored by their dominant emotion: red = urgency, dark red = frustration, green = satisfaction, amber = discovery, purple = confusion. Emotional memories have pulsing rings — faster pulse = higher arousal. Based on amygdala-hippocampal coupling (Wang & Bhatt 2024).
Neuromodulation
Four chemical channels modulate how strongly memories are encoded: Dopamine (reward from fixing errors/passing tests), Norepinephrine (arousal from error density), Acetylcholine (novelty enhancement), Serotonin (exploration vs exploitation balance). These affect node size and glow intensity.
Synaptic Plasticity
LTP: entity edges strengthen when both entities co-appear in memories (Hebbian learning). LTD: inactive edges weaken over time. STDP: when entity A consistently appears before B, the A→B direction strengthens — learning causal structure from temporal ordering.
Microglial Pruning
During consolidation, weak edges (low weight + stale + cold endpoints) get eliminated, and orphaned entities get archived. Like brain microglia that prune unused synapses to keep the network efficient.
Synaptic Tagging
When a strong (important) memory is stored, older weak memories that share entities with it get retroactively promoted — their importance and heat are boosted. Like protein synthesis tags in biology (Frey & Morris 1997).
Visual Encoding
Node size — reflects importance: session count for domains, frequency for patterns, heat+importance×emotional boost for memories, usage ratio for tools.
Node glow — intensity reflects heat (recency). Hot nodes glow brighter, cold nodes are dim.
Edge brightness — reflects relationship weight/strength. Stronger connections are brighter.
Flow particles — animated dots traveling along edges, representing active information flow between connected nodes.
Cluster shells — translucent spheres grouping nodes by domain. Visible when zoomed out (L1/L2 zoom levels).
Controls
Click node — select it, see details + connections, camera flies to it
Click background — deselect
Scroll — zoom in/out (zoom level changes: Neural → Constellation → Galaxy)
Drag — orbit camera
M — toggle monitoring log
R — reset camera
Esc — close panels