/prototypes

Experiments in Adaptive Design

Theory held in tension, until it clicks.


What is This Space?

Prototypes are not finished.
They are attempts — expressions of living ideas under construction.

This page collects experimental implementations of:

  • DAM-inspired systems (adaptive, layered, recursive)
  • DAF-informed flows (alignment-sensitive, rhythm-aware, intuitive)
  • Hybrid bridges between self and structure

To prototype is to model before you’re sure — and learn by watching it breathe.


Featured Prototypes


1. Time-Density Simulator

A visual tool for exploring how internal time (τᵢ) shifts under stress, energy, or entropy.

  • Input: entropy level, goal deviation, external pressure
  • Output: time contraction/expansion curves, system delays, sync/desync patterns
  • Future: add “subjective coherence” sliders to integrate DAF

Built in: Python + matplotlib → [View Notebook]
Use for: modeling temporal drift in learning systems, pacing rituals, or decision fatigue


2. Error Reflection Loop (DAFxDAM)

A journaling prototype that uses modeling logic to guide adaptive self-reflection.

  • Input: subjective friction points
  • Logic: maps to DAM-like “error vectors” → generates prompts for adaptation or rest
  • Output: alignment report + suggested micro-shift
  • Weekly rhythm: “What changed you? Where did you resist?”

Built in: JavaScript + minimal UI → [Try It Live]
Use for: reflective teams, coaching systems, self-guided flow tuning


3. Meta-Goal Drift Engine

A system that evolves its own priorities over time, based on internal feedback loops.

  • Starts with fixed goal vector H(t)
  • Measures environmental mismatch and coherence over time
  • Mutates goals subtly through adaptive rules (meta-evolution layer)
  • Optional: introduce “observer intention” to steer direction

Built in: networkx + Python logic + entropy tracking
Use for: modeling institutional drift, creative process navigation, personal strategy evolution


Prototype Design Values

Every Autorite prototype follows three principles:

  1. Clarity of Feedback
    → The system should show when it’s adapting — not hide it
  2. Perceptual Coherence
    → The logic should “feel right,” not just compute correctly
  3. Meta-awareness
    → The user and the model both track how they’re changing

Prototypes are conversation partners, not tools.


Future Experiments

  • Alignment mirror: real-time coherence feedback
  • DAF-based writing companion
  • Entropy-aware learning scheduler
  • Systemic ritual planner
  • Observer-in-the-loop simulation game

Related Labs

[Design by intuition → /interface/tactile-thinking]
[Observe system-feeling → /interface/systems-in-skin]
[Test feedback principles → /organism/simulations]
[Prototype your own → /atelier/submit