~/talk

Optimising Games for a Complex World

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Describes a pipeline that turns per-frame telemetry into dashboards and stats: client telemetry (aggregated every 15–30 seconds), stored in Influx and visualized with Grafana + Influx telemetry, Python + pandas reprocessing millions of profiler rows into pandas box‑plots and KPIs (example: 90% time ≥55 FPS, 95th percentile 32.8 ms). Shows a real problem — emulation was 10–15% faster than real hardware — and a working mitigation: per-main and worker performance scalar (plus emergency mode) that throttles spawn rates, AI budgets and shadow/animation costs to hit targets.

AI-written summary · about

  1. 0:00Game optimization challenges
  2. 2:12Measuring frame rate
  3. 7:15Performance telemetry
  4. 16:25Prioritizing optimizations
  5. 23:08Data science for performance
  6. 39:05Scaling techniques
  7. 45:02Key takeaways
  8. 47:25Q&A
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