[
  {
    "filename": "rollout-2026-01-29T22-46-03-019c0bef-60b4-7d82-a153-a19f0bb4612c.jsonl",
    "total_events": 72,
    "duration_seconds": 90.6,
    "turn_count": 6,
    "model": "gpt-5.2-codex",
    "session_type": "SUBSTANTIVE",
    "user_message_count": 6,
    "first_user_message": "(nik_gpu) ubuntu@ip-192-168-1-56:~/nikhil/cold-ranker/col/src$ python driver.py \ngetting series meta\nseries vocab len: 1961\n100%|...| 4/4 [00:05<00:00,  1.47s/it]\nWrote shard 0 rows [0:1961)\ngetting user meta\n100%|...| 83/83 [01:33<00:00,  1.13s/it]\ngetting interaction data\ngetting other derived columns\n... RuntimeWarning: divide by zero encountered in log2\n... RuntimeWarning: invalid value encountered in log2",
    "last_exchange": {
      "last_user_message": "[Paste of driver.py output with log2 divide-by-zero and invalid-value RuntimeWarnings]",
      "last_assistant_response": "I traced the warnings to np.log2 on age_days and added a guard so negative or missing ages won't produce -inf/NaN. Changes: col/src/data_prep/user_meta.py: clamp age_days to >= 0 and fill missing before log2."
    },
    "key_moment": "I traced the warnings to np.log2 on age_days and added a guard so negative or missing ages won't produce -inf/NaN.",
    "classification": "SUBSTANTIVE",
    "notes": "User pastes runtime warnings from a cold-ranker ML pipeline; Codex CLI fixes log2 of negative/zero age_days in user_meta.py. Single-turn bug fix, resolved quickly."
  },
  {
    "filename": "rollout-2026-01-29T22-48-55-019c0bf2-00e0-74e2-8ab1-1a83ee3d73c1.jsonl",
    "total_events": 228,
    "duration_seconds": 525.4,
    "turn_count": 5,
    "model": "gpt-5.2-codex",
    "session_type": "SUBSTANTIVE",
    "user_message_count": 5,
    "first_user_message": "this is my main file\n\ncol/src/driver.py\n\nlook at all the files and function it triggers , if there are any logical error or any errors in my prohram",
    "last_exchange": {
      "last_user_message": "Crash in training dataloader: age_days and total_interactions are read from ints.npy, but they are written to floats.npy in data prep. This will raise a KeyError on self.shard_int_idx[\"age_days\"] / [\"total_interactions\"] (or silently read the wrong column if indices were ever present). Use batch_floats_data with self.shard_float_slices instead. dataset.py (line 114), dataset.py (line 116)\n\nCehcek now",
      "last_assistant_response": "Checked col/src/model/dataset.py - it already reads age_days and total_interactions from batch_floats_data with self.shard_float_slices. So the crash you described is already fixed in this file."
    },
    "key_moment": "Hardcoded secrets in source: API keys and DB credentials are committed and set at import-time, which is a severe security risk and overrides real env config in any environment.",
    "classification": "SUBSTANTIVE",
    "notes": "Comprehensive code audit of a cold-ranker ML pipeline. Codex found 5 concrete issues including hardcoded API keys, crash-on-empty DataFrames, dataloader type mismatch, and datetime parsing risks. User then asked it to verify a specific fix that was already applied."
  },
  {
    "filename": "rollout-2026-01-29T23-07-18-019c0c02-d7b7-7c30-acc5-4f66b8839635.jsonl",
    "total_events": 84,
    "duration_seconds": 126.9,
    "turn_count": 4,
    "model": "gpt-5.2-codex",
    "session_type": "SUBSTANTIVE",
    "user_message_count": 4,
    "first_user_message": "(nik_gpu) ubuntu@ip-192-168-1-56:~/nikhil/cold-ranker/col/src$ python driver.py\n...\nIndexError: Caught IndexError in DataLoader worker process 0.\nOriginal Traceback:\n  File \".../dataset.py\", line 157, in __iter__\n    age_days = age_days[idx]\nIndexError: index 839 is out of bounds for axis 0 with size 256",
    "last_exchange": {
      "last_user_message": "[Full traceback of IndexError: index 839 is out of bounds for axis 0 with size 256 in dataset.py line 157]",
      "last_assistant_response": "The crash came from age_days (and similarly total_interactions) being expanded to 3D by [:, None], so np.tile(..., (dup, 1)) duplicated along the wrong axis. That left axis-0 at the original batch size (256), but the shuffle index was sized for the duplicated label count (e.g., 840). I removed the extra axis so these arrays stay 2D and tile correctly."
    },
    "key_moment": "The crash came from age_days being expanded to 3D by [:, None], so np.tile(..., (dup, 1)) duplicated along the wrong axis. That left axis-0 at the original batch size (256), but the shuffle index was sized for the duplicated label count (e.g., 840).",
    "classification": "SUBSTANTIVE",
    "notes": "DataLoader IndexError during training - a subtle numpy shape mismatch caused by extra axis from [:, None] before np.tile. Codex diagnosed the root cause precisely and fixed dataset.py."
  },
  {
    "filename": "rollout-2026-01-29T23-17-23-019c0c0c-0fec-7e31-b81d-a2b730ad8416.jsonl",
    "total_events": 63,
    "duration_seconds": 48.8,
    "turn_count": 4,
    "model": "gpt-5.2-codex",
    "session_type": "SUBSTANTIVE",
    "user_message_count": 4,
    "first_user_message": "if i use MODE='prod', will i be using all the data for training?",
    "last_exchange": {
      "last_user_message": "if i use MODE='prod', will i be using all the data for training?",
      "last_assistant_response": "Yes - MODE='prod' trains on both the train and test shards, so it uses all data that data_prep wrote to those two folders, and it skips test evaluation. All data in ../../data/{LANGUAGE_KEY}/train + test is used for training, and you won't get a held-out test loss in prod."
    },
    "key_moment": "MODE='prod' trains on both the train and test shards, so it uses all data that data_prep wrote to those two folders, and it skips test evaluation.",
    "classification": "SUBSTANTIVE",
    "notes": "Quick question-answer about MODE='prod' vs 'test' behavior. Codex traced through dataset.py, training.py and data_prep.py to explain that prod mode combines train+test splits and skips evaluation. Short but meaningful code comprehension session."
  },
  {
    "filename": "rollout-2026-01-30T13-43-01-019c0f24-95e2-70b1-b46e-99c38c5a062b.jsonl",
    "total_events": 65,
    "duration_seconds": 549.1,
    "turn_count": 10,
    "model": "gpt-5.2-codex",
    "session_type": "SUBSTANTIVE",
    "user_message_count": 10,
    "first_user_message": "exhausting permanent memory during write of data prep files - train, test, recommend\nwhat can we do? just tell dont edit code",
    "last_exchange": {
      "last_user_message": "how to read and write npz",
      "last_assistant_response": "Write: np.savez_compressed(\"floats.npz\", floats=floats). Read: data = np.load(\"floats.npz\"); floats = data[\"floats\"]. If you want uncompressed .npz, use np.savez(...) instead of np.savez_compressed(...)."
    },
    "key_moment": ".npy is uncompressed, so disk usage is basically: bytes = itemsize * prod(shape) (plus tiny header). So if you're exhausting permanent memory while writing .npy, it's just running out of disk/quota.",
    "classification": "SUBSTANTIVE",
    "notes": "Conversational Q&A session about disk space exhaustion when writing .npy shard files. No tool calls at all - pure advisory. User asks progressively about npy vs npz vs zarr formats, compression tradeoffs, speed differences, and read/write syntax. 7 real user turns over 9 minutes."
  },
  {
    "filename": "rollout-2026-01-30T16-43-54-019c0fca-2e0c-79c2-97c6-b28f29740c4b.jsonl",
    "total_events": 68,
    "duration_seconds": 177.3,
    "turn_count": 5,
    "model": "gpt-5.2-codex",
    "session_type": "SUBSTANTIVE",
    "user_message_count": 5,
    "first_user_message": "(nik_gpu) ubuntu@ip-192-168-1-56:~/nikhil/cold-ranker/col/src$ python driver.py\n...\nKeyError: '(slice(None, None, None), 0) is not a file in the archive'\n[traceback from training.py:198 get_pos_series -> ints_data[:,0] on an NpzFile]",
    "last_exchange": {
      "last_user_message": "<turn_aborted> The user interrupted the previous turn on purpose.",
      "last_assistant_response": "[No assistant response - session was aborted while Codex was investigating]"
    },
    "key_moment": "KeyError: '(slice(None, None, None), 0) is not a file in the archive' - user aborted before Codex could respond.",
    "classification": "SUBSTANTIVE",
    "notes": "User hit the npz key error (slicing NpzFile like ndarray) after switching from .npy to .npz format. Aborted mid-investigation - likely continued immediately in session 25 which has the same error. Outcome: abandoned."
  },
  {
    "filename": "rollout-2026-01-30T16-47-16-019c0fcd-4338-7a10-a9d8-490dc0305087.jsonl",
    "total_events": 171,
    "duration_seconds": 506.3,
    "turn_count": 7,
    "model": "gpt-5.2-codex",
    "session_type": "SUBSTANTIVE",
    "user_message_count": 7,
    "first_user_message": "(nik_gpu) ubuntu@ip-192-168-1-56:~/nikhil/cold-ranker/col/src$ python driver.py\n...\nKeyError: '(slice(None, None, None), 0) is not a file in the archive'\n[traceback from training.py:198 get_pos_series -> ints_data[:,0] on an NpzFile]\n\nwhat is the reason of this error",
    "last_exchange": {
      "last_user_message": "yes do it anywhere necesssary. what baout floats.npz",
      "last_assistant_response": "Added a small loader that unwraps single-array .npz files (the arr_0 key) and replaced direct np.load calls so both ints.npz and floats.npz behave like normal ndarrays. Updated loaders in training.py, dataset.py, and recommend.py to use it."
    },
    "key_moment": "np.load('.../ints.npz') returns an NpzFile (a zip archive), not a NumPy array. When you do ints_data[:, 0], NumPy treats the tuple as a key into the archive and raises KeyError because that key doesn't exist.",
    "classification": "SUBSTANTIVE",
    "notes": "Continuation of session 24. Codex explained the .npz vs .npy loading difference (NpzFile archive vs ndarray), added a load_npz_array helper to utils.py, and patched all loaders in training.py, dataset.py, and recommend.py. Clean resolution of a format migration issue."
  },
  {
    "filename": "rollout-2026-01-31T06-14-13-019c12b0-0e85-7bb1-9460-e06dd4eabcf9.jsonl",
    "total_events": 74,
    "duration_seconds": 512.2,
    "turn_count": 7,
    "model": "gpt-5.2-codex",
    "session_type": "SUBSTANTIVE",
    "user_message_count": 7,
    "first_user_message": "can recommend.py be made faster? how?",
    "last_exchange": {
      "last_user_message": "can recommend.py be made faster? how?",
      "last_assistant_response": "[No assistant response captured - session ended with only tool calls, possibly aborted or timed out before response was delivered]"
    },
    "key_moment": "can recommend.py be made faster? how?",
    "classification": "SUBSTANTIVE",
    "notes": "Performance optimization inquiry for recommend.py. Codex read through recommend.py, model.py, utils.py, and driver.py to analyze the recommendation pipeline. Session appears abandoned (no assistant messages captured). 8+ minutes of investigation without a final answer delivered."
  }
]
