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Connectome — A curiosity navigation system

Connectome treats curiosity and confusion as first-class signals. Instead of static Q&A, users annotate what felt unclear or interesting, rank intensity, and watch those signals shape the next response. Structured extraction pulls feedback into a persistent topic graph that accumulates across sessions—a navigable map of your own learning trajectory.

Built as an API-first, human-in-the-loop LLM orchestration layer. Core patterns: feedback-driven personalization, session persistence, EWA-based relevance decay, and force-directed visualization. Designed to explore how memory, topology, and feedback can support open-ended learning without badges, streaks, or dark patterns.

Stack: FastAPI, Claude API, SQLite, D3.

You wander. It maps. Side effects may include genuine curiosity.


Core Patterns

These patterns generalize beyond learning:

Pattern Implementation Generalizes to...
Structured extraction Parse <topology> JSON from natural language responses Document intelligence, entity extraction
Feedback injection Annotations modify next prompt context User corrections, review queues, preference learning
Session persistence Topic graph accumulates in SQLite across conversations Customer history, case continuity, audit trails
Relevance decay EWA-weighted opacity based on visit recency Lead scoring, stale flag detection, attention prioritization
Human-in-the-loop Signals steer without blocking Escalation triggers, QC sampling, compliance review

What You Get

  • Dialogic conversation — an AI that thinks alongside you, not at you
  • Annotation feedback — highlight text to mark "confused" or "curious," shape the next response
  • Topology visualization — force-directed graph of your learning trajectory
  • Honest engagement — no "Great question!", no dark patterns, no gamification

The Topology Map

Your exploration builds a persistent graph:

Visual Meaning
Circle radius Time/depth spent on that topic
Opacity Recency (EWA of visit intervals)
Pink fill Net confusion from annotations
Green fill Net curiosity from annotations
Dashed outline Adjacent topic (suggested, not yet explored)

This is the honest reward: not fake coins, but the actual shape of your intellectual growth.

About

Human-in-the-loop LLM orchestration with structured signal extraction and session persistence. Annotate confusion and curiosity—feedback shapes responses, topology accumulates over time. API-first design, no gamification. FastAPI + Claude + SQLite + D3.

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