{
  "sources": [
    {
      "url": "https://api.imd.fun/artifacts/0d8194e9f6e0017dc4e515cdb04c048efc292935bb1d18e3336091fe81154786",
      "quote": "{\"status\":\"validated_core_external_runtime_required\",\"item\":\"kit\",\"search_performed\":false,\"candidate\":null,\"sim_zb64\":\"eNqdGdty47b1XV+BOg8mZYoSZcm27NVO0plNttNu4lm7eeiOo4FISKJNAQwA2pQ7/feeA4A3WXLSenYlCjg49yt4cnJyF9OMSrKl8olpIlmepTElYkXodjuINzTLGF8z8v3F8mI2jlZXYe8r+71IJVOEF9t8d4NfS0pSRUSuU8FpRrw8oykn"
    },
    {
      "url": "https://api.imd.fun/artifacts/ebbd90c663c396598965b23341aea97a4501b4c971db183a8fe94a880f399979",
      "quote": "{\"item\":\"continue-g\",\"blocked\":null,\"hashes\":{\"HA_code\":\"99e3a2eceae0e5543d27473859e10b005d0ce0aa7067f2e1a24d1e0e4a7390fe\",\"T_code\":\"ebd9de9bf6af6e55d5e308fd15dbb88edec11fd684891d02a30aec185acd19b3\",\"replica_source\":\"1e4c44b4bffbdeed65872ee15d46dbde209bf5aa11014329d13d100507b4040c\",\"RS2_port_source\""
    },
    {
      "url": "https://api.imd.fun/artifacts/afd5183e9ff3235334e041c00b0cd19079599a2c6464db95f8c1586712105557",
      "quote": "{\"status\":\"completed: candidate valid (0 OOG, afterSwap peak 234849 <= 245000); real +1.749 se 0.040 over T and +0.592 se 0.030 over HA on 512 fresh seeds\",\"item\":\"inputs-b\",\"batch\":\"e2-ha-round1\",\"hashes\":{\"HA_code_sha256\":\"99e3a2eceae0e5543d27473859e10b005d0ce0aa7067f2e1a24d1e0e4a7390fe\",\"T_code_s"
    },
    {
      "url": "https://api.imd.fun/artifacts/2801efc8190e86318dd922d4a7179f63684d77a226047fc2f3c78232963eb690",
      "quote": "{\"key\":\"head-a\",\"blocked\":null,\"gates\":{\"A\":{\"passed\":true,\"sequences\":50,\"swap_callbacks\":100000,\"max_fee_diff_bps\":1.2216616607219066e-09,\"threshold_bps\":0.01,\"seeds\":[310000,310049]},\"B\":{\"passed\":true,\"n\":4096,\"seeds\":[31000000,31004095],\"gain\":0.2420270811033297,\"se\":0.01692058854320023,\"requir"
    },
    {
      "url": "https://raw.githubusercontent.com/benedictbrady/amm-challenge/6b6921f8d463314aa7c96234e1be25a25245f704/amm_competition/competition/match.py",
      "quote": "    def _build_configs(self) -> list[amm_sim_rs.SimulationConfig]:\n        \"\"\"Build simulation configs with optional variance.\"\"\"\n        import numpy as np\n\n        configs = []\n        for i in range(self.n_simulations):\n            rng = np.random.default_rng(seed=i)\n\n            retail_mean_size"
    }
  ]
}