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type-two 85b81982b0 Tutorial: goal-driven quest cards, optional and persistent
Nine-step tutorial card that watches sim state and advances itself:
wire the PC, first video, cooling, the ALGO HEAT treadmill, releasing
a model version, the VC deal, building a rig, shipping + hosting, and
a graduation card. Steps the save has already accomplished are skipped
automatically;  chip toggles it off/on (persisted), New Game resets
it. No text dumps — each card is one goal and why it matters.

Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
2026-08-01 18:16:30 +10:00
.claude Tier 1 playable slice: bedroom slop empire 2026-08-01 09:55:55 +10:00
public/assets Deploy config: relative base for monsterrobot.games/games/gigaslop/ + cover art 2026-08-01 17:01:11 +10:00
src Tutorial: goal-driven quest cards, optional and persistent 2026-08-01 18:16:30 +10:00
tools M8: warehouse tier, immersion cooling, SaaS subscribers 2026-08-01 16:24:19 +10:00
.gitignore Ignore __pycache__ 2026-08-01 10:05:45 +10:00
CLAUDE.md M8: warehouse tier, immersion cooling, SaaS subscribers 2026-08-01 16:24:19 +10:00
index.html Tier 1 playable slice: bedroom slop empire 2026-08-01 09:55:55 +10:00
package-lock.json Tier 1 playable slice: bedroom slop empire 2026-08-01 09:55:55 +10:00
package.json Tier 1 playable slice: bedroom slop empire 2026-08-01 09:55:55 +10:00
README.md Tier 1 playable slice: bedroom slop empire 2026-08-01 09:55:55 +10:00
tsconfig.json Tier 1 playable slice: bedroom slop empire 2026-08-01 09:55:55 +10:00
vite.config.ts Deploy config: relative base for monsterrobot.games/games/gigaslop/ + cover art 2026-08-01 17:01:11 +10:00

GigaSlop: Compute Tycoon

Factorio meets Software Inc., wrapped in a satirical sim of the AI slop economy. Start with a budget PC in your bedroom generating $0.40/day of AI spam videos; end up draining the county grid to feed slop-generation clusters.

Stack: TypeScript + Vite + PixiJS v8. The simulation (src/sim/) is a deterministic fixed-tick (10 Hz) module with zero renderer dependencies — the Pixi layer (src/render/) and DOM HUD (src/ui/) just read state.

Run

npm install
npm run dev   # http://localhost:5173

Play (Tier 1 slice)

  • You start with a PC, desk, and router. Press W, click the PC, then the router to wire them — slop production starts.
  • Watch the breaker (1400 W bedroom circuit): trip it and your training checkpoint corrupts.
  • Heat is spatial. Hot PCs throttle at 85°C; fans spread heat around, the window AC deletes it.
  • Uplink caps monetizable tokens — saturated cables mean dropped slop.
  • The TRAIN/INFER slider trades cash now (inference → videos → ad revenue) for model tiers later (higher revenue multipliers).

Art pipeline

All art is generated on the local MODELBEAST farm (tools/mb_gen.py): FLUX for the room shell + hardware sprites (bg-removed, autocropped), and the in-game slop thumbnails are genuine SD/FLUX slop — the game about AI slop is made of actual AI slop. Titles baked to src/data/sloptitles.json.

Layout

src/sim/      balance.ts (all tunables) · catalog.ts (hardware defs)
              state.ts · sim.ts (step, place/wire/sell commands)
src/render/   iso.ts (grid↔screen mapping) · scene.ts (Pixi world)
src/ui/       hud.ts + hud.css (DOM dashboards, shop, SlopTube feed)
tools/        mb_gen.py (MODELBEAST batch art generation)