Commit Graph

8 Commits

Author SHA1 Message Date
josh c1cc70eeb9 Rename AI Tycoon to Token Empire across entire codebase
Balance Check / balance-simulation (pull_request) Successful in 38s
Balance Check / multi-run-balance (pull_request) Successful in 13m44s
Full rebrand: UI display text, package scope (@ai-tycoon/* -> @token-empire/*),
localStorage keys, Docker/CI image paths, database names, and documentation.

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
2026-04-27 21:04:07 -04:00
josh 416b6bfe8d Add research money costs, longer research times, era-scaled talent costs, and persona strategy
Balance Check / balance-simulation (push) Successful in 11m19s
Balance Check / multi-run-balance (push) Has been cancelled
CI / build-and-push (push) Successful in 40s
Research now costs money (drained per-tick) with ~2.5-3.5x longer durations by category.
Early-game talent budget costs reduced via era multiplier (startup 0.2x → bigtech 1.0x).
New seed-driven PersonaStrategy with 8 axes of variation for meaningful multi-run testing.
CI multi-run switched from greedy to persona strategy.

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
2026-04-26 16:14:27 -04:00
josh 09a5cb69a7 Overhaul market system with shared TAM competition, multi-tier pricing, enterprise pipeline, and developer ecosystem
CI / build-and-push (push) Successful in 42s
Replaces the simplified single-subscriber market with a full competitive simulation:
shared TAM with softmax market shares across 4 segments, multi-tier consumer
subscriptions (Free/Plus/Pro/Team) and API tiers (Free/PAYG/Scale/Enterprise),
enterprise sales pipeline (Lead→Qualification→POC→Negotiation→Active→Renewal)
with SLA tracking, developer ecosystem flywheel, technology obsolescence pressure,
seasonal demand cycles, and two new product lines (Code Assistant, AI Agents Platform).

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
2026-04-25 08:30:24 -04:00
josh 4c1c0e9ff2 Overhaul model system with multi-stage training, variants, benchmarks, and eval
CI / build-and-push (push) Successful in 32s
Replace the single-stage training + flat capability score with a realistic AI
development pipeline: pre-training with Chinchilla scaling laws, SFT with
specializations, alignment with safety/capability tradeoffs (RLHF/DPO/Constitutional),
model families with distillation/fine-tuning/quantization variants, named benchmark
suite with compute-costing eval jobs, and segment-specific market quality.

Phases 1-6 of the model rework plan: new types, engine rewrite, save migration,
training events/risk system, concurrent training, variant creation, benchmark
evaluation with leaderboard, and market integration.

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
2026-04-25 07:36:34 -04:00
josh c799f2e359 Redesign infrastructure to hypercluster scale with 4-level hierarchy
CI / build-and-push (push) Successful in 43s
Replace flat DataCenter/Rack model with Cluster > Campus > Data Center > Racks
hierarchy. Individual rack entities eliminated in favor of statistical batch
simulation using deployment cohorts. Adds tiered network topology (ToR/agg/core)
with proportional outage model, DC retrofitting, bulk operations, and drill-down
UI navigation with breadcrumbs. First cluster and campus are free to preserve
early game flow. Rebalances starting economy ($600K), funding rounds, and
cohort scaling for hypercluster-scale gameplay.

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
2026-04-24 23:15:41 -04:00
josh 0005e580a7 Overhaul infrastructure: replace GPU model with rack-centric system
CI / build-and-push (push) Successful in 33s
Replace flat GPU buying with a realistic data center + rack pipeline:
- 4 DC tiers (small/medium/large/mega) with construction time, dual
  capacity constraints (rack slots + power budget kW), and era/research
  gating
- 10 predefined rack SKUs from consumer GPUs through custom ASICs, each
  with unique FLOPS, power draw, cost, and pipeline timings
- 6-stage procurement pipeline (order → mfg → receive → install → test
  → production) with Kanban UI, talent-influenced speed bonuses
- Test failures (5-25% base rate) reduced by cooling, ops talent, and QA
  research; auto-repair with cost and re-test cycle
- Production failures at low per-tick rate, racks sent to repair pipeline
- Cooling and redundancy upgrades per DC (reduce failure rates)
- 4 new tech tree nodes (DC Engineering II/III/IV, Quality Assurance)
- Save version bump (1→2) with migration that resets old saves
- Updated economy system to account for rack repair costs
- Redesigned Infrastructure page with pipeline Kanban, capacity bars,
  rack ordering, and DC upgrade panels

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
2026-04-24 19:41:55 -04:00
josh 0ff8a32b95 Add Week 4 social features, regulation, and safety tradeoffs
Leaderboard page with category tabs and score submission, shareable
company stats card with clipboard copy, dynamic regulation system
(compliance costs scale with capability and era, regulatory standing
tracks safety research), 6 geopolitical events (export controls, energy
crisis, natural disaster, AI safety summit, immigration policy, data
sovereignty), safety-capability tradeoff (safety score affects benchmark,
low safety triggers incidents with reputation damage), and enhanced
event consequence handling for regulation and talent types.

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
2026-04-24 18:02:30 -04:00
josh fdc8e544ae Initial scaffold: AI Tycoon monorepo with core game loop
Turborepo monorepo with three packages:
- packages/shared: TypeScript types for all 14 game systems + balance constants + formatting utils
- packages/game-engine: Pure TS simulation engine with tick processor, economy, infrastructure, compute, research, market, and reputation systems
- apps/web: React + Vite + Tailwind + Zustand frontend with sidebar dashboard layout, new game screen, dashboard with charts, infrastructure management, and model training pages

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
2026-04-24 16:53:46 -04:00