Toaster Chef¶
A simulation platform and incubator for two real-world initiatives: Kitchen Garage (a nonprofit community kitchen) and siMPowerUp (a parameterized simulation of the MPowerUP peer mutual aid network).
Status: Active — Layer 0 complete, Layer 1 next Repo: (not yet created) Org: Big Nerd Idea, LLC
What It Is¶
Toaster Chef started as a 2D multiplayer community-kitchen game. It has been reframed as a simulation platform and incubator.
The game layer — electric trikes, toasters, a Studio Ghibli-style neighborhood — is a visualization surface. The primary deliverable is the headless analytical simulation: scenario-driven, agent-calibrated, exporting SimulationOutcome JSON that informs real design decisions for MPowerUP and a potential real Kitchen Garage.
Why this matters for MPowerUP: The Red Team Analysis established that MPowerUP's core theories (token economy, mutual aid incentives, SSI benefit interactions) cannot be validated with documentation alone. Simulation is the earliest possible test of whether those theories survive contact with realistic conditions — before real users are in the system.
Incubator Targets¶
| Initiative | What it models | Why simulation first |
|---|---|---|
| Kitchen Garage | Nonprofit community kitchen: staffing ratios, food production, trike delivery, economic sustainability | Validates operating budget assumptions before committing real money |
| siMPowerUp | MPowerUP Circle/HelpRequest lifecycle, MPWR token economy (Layer 2+), bad-actor facilitator dynamics | Narrows [HYPOTHESIS] uncertainty on MPowerUP theories before pilot launch |
Architecture¶
Visualization Layer Phaser 3 (secondary — interactive display)
Simulation Engine Colyseus — Kitchen Garage + siMPowerUp, 100ms tick
Analytics Layer Per-tick outcome accumulation, SimulationOutcome export
Scenario Layer server/scenarios/*.json — ScenarioConfig per named scenario
Agent Ecosystem Research agents calibrate parameters with real-world data
Full architecture: docs/architecture.md (repo TBD)
Tech Stack¶
- Client: Phaser 3 (browser, TypeScript)
- Server: Colyseus 0.17.x (Node.js 22, TypeScript) — server-authoritative
- Shared types:
shared/package —ScenarioConfig,SimulationOutcome, simulation state types - Map generation: rot-js, simplex-noise, WFC (server-side only)
Named Scenarios¶
Three baseline scenarios are defined in server/scenarios/. All have mpwr.enabled: false until Layer 2.
| Scenario | Purpose | Key parameter |
|---|---|---|
baseline.json |
Tier-2, moderate need — performance floor | badActorFacilitatorFraction: 0.0 |
high-need.json |
Tier-3 urban, demand > helpers | npcCount: 100, responseRate: 0.55 |
bad-actor.json |
15% predatory facilitators | badActorFacilitatorFraction: 0.15 |
Simulation Validation Status¶
All simulation outputs are tagged [SIMULATION OUTPUT] — a validation marker sitting between [HYPOTHESIS] and [PILOT VALIDATED]. They narrow uncertainty but do not replace field validation. Claims derived from simulation runs cannot be promoted to [PILOT VALIDATED] without real-user data.
Phase Roadmap¶
| Layer | Deliverable | Status |
|---|---|---|
| 0 | Formal model: types, scenario JSON, doc updates | ✅ Complete |
| 1 | Simulation tick: Circle/HelpRequest lifecycle, analytics, SimClock | Next |
| 2 | Scenario system: JSON loader, REST endpoints, headless rooms | Upcoming |
| 3 | Agent data collection — calibrate parameters with real-world research | Ongoing |
| 4 | Headless batch simulation + SimulationOutcome export |
Upcoming |
| 5 | Analysis visualization | Later |
Sub-Agent Ecosystem¶
Eight research agents are defined in toasterchef/CLAUDE.md:
ToasterChef-specific: Food Systems Research, Nonprofit Operations, Community Need Mapping, Simulation Validation, Scenario Design
siMPowerUp-specific: Mutual Aid Network Research, Token Economy Scenario, Bad Actor & Risk Scenario
Cross-cutting (inherited from big-nerd-idea): Red Team, Regulatory Intelligence, Grant Intelligence
Related Pages¶
- MPowerUP — the real-world peer mutual aid app siMPowerUp models
- MPowerUP Token Economy — MPWR spec; Layer 2 scenario parameters calibrated from this
- Red Team Analysis — the adversarial findings that motivated the simulation-first approach