josh
4c1c0e9ff2
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>
26 lines
781 B
TypeScript
26 lines
781 B
TypeScript
import type { GameState, Era } from '@ai-tycoon/shared';
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import { ERA_THRESHOLDS } from '@ai-tycoon/shared';
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export function checkEraTransition(state: GameState): Era | null {
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const current = state.meta.currentEra;
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const eraOrder: Era[] = ['startup', 'scaleup', 'bigtech', 'agi'];
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const currentIdx = eraOrder.indexOf(current);
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const nextEra = eraOrder[currentIdx + 1];
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if (!nextEra) return null;
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const thresholds = ERA_THRESHOLDS[nextEra as keyof typeof ERA_THRESHOLDS];
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if (!thresholds) return null;
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const bestModel = state.models.bestDeployedModelScore;
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if (
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state.economy.totalRevenue >= thresholds.revenue &&
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bestModel >= thresholds.capability &&
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state.reputation.score >= thresholds.reputation
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) {
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return nextEra;
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}
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return null;
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}
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