- Seed checks for any admin role instead of username='admin'
- Username change open to all registered users (was admin-only)
- New change-email endpoint requiring password confirmation
- Settings page: inline editing for username and email
- Invitations: await refresh after generate so list updates visibly
- Invitations: revoke button to delete unused invites (admin only)
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Use ?? instead of || so empty string (same-origin) is preserved
while undefined still falls back to localhost:3001 for dev.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
VITE_API_URL was /api but all API paths already included /api/,
resulting in /api/api/config etc. Config call failed silently and
defaulted to requireInvite:false. Set VITE_API_URL to empty string
so paths like /api/auth/anonymous go through nginx as-is.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Frontend API_BASE is /api in production, so health check was hitting
/api/health which didn't exist. Added /api/health on the server and
removed the now-unnecessary separate nginx /health proxy rule.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
"Unknown error" was hiding the actual HTTP status (likely 502 from
nginx). Now shows "HTTP 502 Bad Gateway" etc. Network TypeErrors
(connection refused) also get a clear message.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
The frontend builds with VITE_API_URL=/api so all API calls target
the same origin. Without a proxy rule, nginx was serving index.html
for API paths, causing JSON parse errors.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Frontend now checks /health before starting auth flow. Shows a clear
"Cannot Connect to Server" screen with retry button when backend is
unreachable. Stale non-JWT tokens in localStorage are detected and
cleared automatically. All API calls have a 10s timeout via AbortController.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Run Drizzle migrations before seeding admin user so tables exist
on fresh database. Migration files generated from current schema.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
The production Docker stage was copying pnpm symlinks between stages
which broke module resolution. Now does a fresh pnpm install --prod
in the production stage and runs from the server working directory.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Churned subscribers no longer generate revenue the tick they leave,
and the price churn multiplier cap is raised from 10 to 1000 so
astronomical prices empty the subscriber pool in a single tick.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Players could set astronomical prices and still retain subscribers because
price elasticity floored at 10% for any price above $100, satisfaction
ignored pricing entirely, and churn had no price component.
Introduces perceived value per tier (model quality × reputation), replaces
the broken linear formula with sigmoid decay, adds price-aware satisfaction
blending, and applies per-tier price-based churn multipliers.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Propagate per-era duration/bottleneck, serving utilization, cash-flow nadir/peak,
and late-game revenue growth through the worker→CSV→interpret pipeline. Add
simulation health archetype classification, per-era bottleneck frequency,
unused-feature frequency table, failed-run AGI gate analysis, and log-scale
variance for exponential metrics. All new CSV columns parse defensively for
backward compatibility with older summary files.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Eliminates the 22K-object switchRegistry that caused O(n×m) scans 4x per tick.
Network health is now tracked as aggregate counts per tier (totalByTier/healthyByTier)
with RepairBatch timers, cutting late-game tick cost from ~50ms to ~0.3ms.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Fleet template is now rebuilt only when deploymentVersion changes (~68 times per
28,800-tick run instead of every tick). Reuses module-level Maps, arrays, and
utilization objects instead of allocating new ones each tick. Replaces 4x
Object.values().reduce() with single-pass aggregation and sorts fleet in-place.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Model quality for market segments and product lines now derives from deployed
model capabilities (coding, reasoning, agents, etc.) instead of requiring a
separate manual benchmark evaluation step. This eliminates an unbounded
benchmarkResults[] array that was scanned 5x per tick and removes ~480 lines
of dead-weight UI, types, and engine code.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Switch from exec() to spawn() for streaming stderr, add onProgress
callback to runner, and emit per-run progress lines from workers.
CI now shows live percentage, tick count, and era during long runs.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
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>
Remove misleading Reputation -> Era Gates connection (score 0 meant
"already sufficient," not broken). Add diagnosis and eventLabel fields
to each connection. Group output: broken links first with [!!] and
plain-language explanation, then healthy links as compact one-liners.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Adds a full simulation harness (game-simulation package) with greedy/random strategies,
36-metric diagnostics, multi-run orchestration via child processes, and a statistical
interpreter. Includes 2.3x engine performance optimizations (research bonus caching,
per-DC dirty tracking, reduced allocations in tick pipeline, single-pass loops).
Fixes a critical balance bug where training pipelines stalled on insufficient VRAM would
permanently block training slots — the engine never re-checked stalled pipelines, and the
greedy strategy didn't pre-check VRAM requirements. This caused 20-25% of seeds to get
stuck in Scale-up era. All three fixes (engine un-stalling, strategy VRAM pre-check,
stalled pipeline cancellation) bring pass rate from 75% to 100% across 20 random seeds.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Add compute history time-series (capacity vs demand chart), revenue vs expenses
dual-line chart, enhanced system status (training allocation, network uptime,
model freshness), active operations panel, market position bars, and competitor
snapshot. Stat cards expand from 3 to 6 as player progresses through eras.
Graceful v9→v10 save migration preserves existing games.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
The old overload policy had dead controls (maxQueueDepth, rateLimitPerCustomer never read)
and trivial flat penalties. This replaces it with a full serving pipeline where deployed
models form a fleet, requests route through priority/degradation logic, and policy choices
create meaningful strategic tradeoffs.
New serving pipeline: fleet building from deployed models (size/quant/MoE multipliers),
demand categorization by 5 priority tiers, enterprise capacity reservation, priority-ordered
serving with overflow behaviors (queue/reject/degrade), auto-degradation to faster models
under load, and Batch API to fill idle capacity at discounted rates.
4 new research nodes gate features progressively: Intelligent Request Routing, Priority
Queue System, Request Batching, and Auto-Scaling. New dedicated Serving page with pipeline
metrics, model fleet utilization, and research-gated policy controls.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Training pipeline now requires SFT specializations and alignment method configured at start — no more
mid-training configuration step. Model families support multiple size tiers (Nano/Small/Medium/Large/Flagship)
trained independently, mimicking real AI company model families. Point releases iterate on deployed models
with 40% training time and 8% capability gain. Distillation and fine-tuning variants removed — players
train smaller size tiers or configure SFT during initial training instead. Only quantization remains as
a variant type.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
- Lift modelsTab state into Zustand store so actions can navigate tabs
- Add toast notifications + auto-tab-switch to all 10 model actions
(train, configure SFT/alignment, distill, fine-tune, quantize, eval, deploy, open-source)
- Add actionable toast buttons with navigation (e.g., "Go to Families" on training complete)
- Fix post-training config: remove 50% deadline, show until pretraining completes,
always-visible warning prompt outside card expand, engine reminder at 75%
- PostTrainingConfig now hides already-configured sections independently
- Add tab badges: pulsing dot for active jobs, count for undeployed models, warning for no deployment
- Replace empty states with actionable buttons guiding next steps
- Stage bars show "(skip)" in warning color for unconfigured SFT/Alignment stages
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
onModelDeployed was defined but never invoked — lastModelReleaseTick
stayed at 0 so the freshness guard always returned 0. Now deployModel
updates obsolescence state, setting freshness to 1.0 with proper decay.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
- Create researchBonuses utility to aggregate tech tree effects into all game systems
(infrastructure energy costs, compute efficiency, training speed, model capability, reputation)
- Rework model capability from sqrt(compute) to 4-pillar formula (params + compute + data + research)
- Make context window affect benchmarks and inference speed
- Add MoE tradeoffs: 1.5x VRAM, 0.8x training speed
- Enforce research point costs as a gate for unlocking research
- Add real consequences to data contamination events (reputation hit, legal costs)
- Scale talent costs from $0.03 to $5/tick per headcount
- Scale compliance costs 100x to be meaningful
- Rework competitor acquisition: cheaper but grants headcount, RP, and reputation
- Remove dead code: sfxVolume, autoSaveInterval, notificationsEnabled,
FAST_FORWARD_BATCH_SIZE, CHINCHILLA_OPTIMAL_RATIO
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
josh