Conan vs CRIN — Watch AI Process Your Words, Visually: Features, Pricing & Which Is Better (2026)
A side-by-side comparison of Conan and CRIN — Watch AI Process Your Words, Visually — features, pricing, and ideal use cases — to help you decide which AI tool fits your workflow.
Conan
Conan
Conan is a native macOS app that wraps Claude Code in a live HUD, surfacing every prompt, tool call, skill, and token in real time.
Key features
- Live Timeline: Every command, edit, and tool call streams onto a living timeline as it happens.
- Context Window Meter: Watch the context window fill across system, tools, memory, skills, and messages while tokens burn in real time.
- Session Pulse: A live throughput pulse that spikes when Claude works and calms when it waits.
- Skills & MCP Visibility: See every skill and MCP server in play, surfaced and observable as they fire.
- Native macOS App: A native HUD for Apple silicon Macs running macOS 13+, with no subscription required.
- Claude Radio: Built-in curated audio stations to score your coding sessions.
Best for
- Monitoring Claude Code Sessions: Watch every prompt, tool call, and skill execution in real time without scrolling logs.
- Token Budget Management: Track context window usage and token burn to avoid context rot and surprise costs.
- Debugging Agent Behavior: Observe which skills and MCP servers fire to understand and debug agentic workflows.
- Staying In Flow: Keep a glanceable HUD of session activity while focusing on the work itself.
CRIN — Watch AI Process Your Words, Visually
CRIN (crin.ai)
Interactive visual lessons that show how transformers, attention, embeddings, and tokens work through live animated data flows.
Key features
- Interactive Animated Lessons: Step-through, playable lessons that visualize model internals (tokens, embeddings, attention) as animated node graphs to reveal computation flow.
- Transformer and Attention Visualization: Live depiction of transformer layers and attention weights so users can observe how tokens influence each other in real time.
- Embedding and Token Tracing: Visual tracing of tokenization and embedding vectors across model stages to illustrate representation changes and semantic encoding.
- No-Prior-Knowledge Onboarding: Lesson content crafted to teach core concepts without requiring prior ML expertise, enabling beginners to grasp foundational ideas quickly.
- Developer-Focused Explanations: Explanatory overlays and breakdowns designed to help developers reason about model behavior, architecture choices, and failure modes.
- Animated Data Flows: Node-graph animations that show how data moves and transforms across layers, aiding intuition about otherwise opaque numeric operations.
- Interactive visualizations of transformer internals (tokens, embeddings, attention)
- Live animated data flows showing step-by-step model processing