curl https://rotastellar-cae.subhadip-mitra.workers.dev/v1/presets \
  -H "Origin: https://rotastellar.com"

import requests

response = requests.get(
    "https://rotastellar-cae.subhadip-mitra.workers.dev/v1/presets",
    headers={"Origin": "https://rotastellar.com"}
)

for preset in response.json()["presets"]:
    print(f"{preset['id']}: {preset['steps']} steps, {preset['data_flow']['overall_reduction']} reduction")

{
  "presets": [
    {
      "id": "onboard-ml-inference",
      "name": "On-Board ML Inference",
      "description": "Run ML inference on-board to achieve 190:1 data reduction before downlink...",
      "category": "ml-inference",
      "steps": 4,
      "onboard_steps": 4,
      "ground_steps": 0,
      "total_compute_s": 170,
      "data_flow": {
        "initial_capture_mb": 2000,
        "final_output_mb": 10.5,
        "overall_reduction": "190:1"
      },
      "needs_downlink": true,
      "needs_uplink": false,
      "security": {
        "encryption": "aes256",
        "data_classification": "restricted",
        "allowed_ground_stations": null,
        "require_authenticated_uplink": true,
        "key_rotation_orbits": 24
      },
      "policy": {
        "objective": "min_latency",
        "deadline_orbits": 3,
        "max_data_loss_fraction": 0.001,
        "min_delivery_confidence": 0.99
      }
    }
  ],
  "count": 5
}