[
  {
    "filename": "rollout-2026-01-31T06-23-05-019c12b8-2a3d-7d32-924b-b4bd714b3fd1.jsonl",
    "total_events": 40,
    "duration_seconds": 22.3,
    "turn_count": 4,
    "model": "gpt-5.2-codex",
    "session_type": "TRIVIAL",
    "user_message_count": 1,
    "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": "Listed prioritized performance improvements for recommend.py: move series meta tensors to device once (biggest win), use torch.inference_mode(), batch users, replace batch topk merge, optional AMP/torch.compile. Offered 3 next steps."
    },
    "key_moment": "You can make this materially faster, mainly by reducing per-batch tensor creation and Python loops. Biggest hotspot is score_user() doing torch.from_numpy(...).to(device) and expand() every batch, for every user.",
    "notes": "Single-turn read-only analysis session. 22 seconds, no code changes made. Codex read the file and gave advice only. Trivial due to duration <30s and only 1 real user message."
  },
  {
    "filename": "rollout-2026-01-31T06-25-34-019c12ba-6f73-7bd3-ae3f-f00f3582c344.jsonl",
    "total_events": 62,
    "duration_seconds": 193.0,
    "turn_count": 5,
    "model": "gpt-5.2-codex",
    "session_type": "SUBSTANTIVE",
    "user_message_count": 2,
    "first_user_message": "can recommend.py be made faster? how?",
    "last_exchange": {
      "last_user_message": "Precompute series tensors once, Replace batch topk merge:, Use torch.inference_mode() in score_user - do these",
      "last_assistant_response": "Implemented the three requested optimizations in recommend.py: precompute series tensors once on device, switch to torch.inference_mode(), and change the top-k merge to only merge batch top-k with the running top-k."
    },
    "key_moment": "Implemented the three requested optimizations in col/src/model/recommend.py: precompute series tensors once on device, switch to torch.inference_mode() in score_user, and change the top-k merge to only merge batch top-k with the running top-k.",
    "notes": "Follow-up to session 27. User asked same question, got analysis, then explicitly requested 3 specific optimizations. Codex implemented all three. First real code changes to recommend.py."
  },
  {
    "filename": "rollout-2026-01-31T06-30-50-019c12bf-4336-7a50-ade9-38988c069e48.jsonl",
    "total_events": 170,
    "duration_seconds": 1068.7,
    "turn_count": 12,
    "model": "gpt-5.2-codex",
    "session_type": "SUBSTANTIVE",
    "user_message_count": 9,
    "first_user_message": "can recommend.py be made faster? how?",
    "last_exchange": {
      "last_user_message": "still Generating recommendations: 0%| | 896/1362277 [00:22<9:26:06, 40.08it/s]",
      "last_assistant_response": "You're compute-bound now (GPU util ~99%), so it/s won't improve just by changing batching. To move the needle, you need to make each forward cheaper. Biggest next win: mixed precision + TF32."
    },
    "key_moment": "That's expected with the current logic: I auto-scale series_batch_size down when you increase user_batch_size, so the total work per forward stays about the same. You're still doing the same number of (user, series) pairs per step, so it/s (users/sec) doesn't improve much.",
    "notes": "Long iterative performance tuning session. User kept pasting nvidia-smi output and throughput numbers. Codex implemented user batching, then discovered auto-scaling was canceling gains. Removed auto-scaling. Still stuck at ~40 it/s with 1.36M users. Real-time debugging on a Tesla T4 GPU."
  },
  {
    "filename": "rollout-2026-01-31T06-49-43-019c12d0-8ef7-77b1-81a6-2bb34a4f9d5e.jsonl",
    "total_events": 85,
    "duration_seconds": 246.8,
    "turn_count": 6,
    "model": "gpt-5.2-codex",
    "session_type": "SUBSTANTIVE",
    "user_message_count": 3,
    "first_user_message": "how to make my recommend.py faster?",
    "last_exchange": {
      "last_user_message": "yes",
      "last_assistant_response": "Which do you want next? 1) Inspect the running GPU process (PID 17305) for command line + which script it's running. 2) Tune user_batch_size / series_batch_size (and maybe add mixed precision) to improve throughput. Reply with 1 or 2."
    },
    "key_moment": "GPU util is pegged at 100% on a Tesla T4, with ~7.2 GiB / 15.4 GiB in use by a single python process (PID 17305). Power is near cap (66W / 70W).",
    "notes": "Another iteration of the same performance question. Codex reshaped tensors to avoid large flat batches. Ran nvidia-smi showing GPU at 100% util with 7.2GiB/15.4GiB used. Session ended with user confirming 'yes' but not picking a next step."
  },
  {
    "filename": "rollout-2026-01-31T06-55-05-019c12d5-790d-7e93-be0a-c17f7b491299.jsonl",
    "total_events": 261,
    "duration_seconds": 2866.0,
    "turn_count": 14,
    "model": "gpt-5.2-codex",
    "session_type": "SUBSTANTIVE",
    "user_message_count": 11,
    "first_user_message": "currently recommend.py takes 8:47:41 time to run, i want to run in 3 hrs, what changes should id have to do?",
    "last_exchange": {
      "last_user_message": "i have changed files so can you re read user_meta.py now , i do compute platform_age",
      "last_assistant_response": "Re-read done. You do compute platform_age now in get_user_created_at(). Given that, your filter up_df[up_df['platform_age'] < 31] will work as intended, because platform_age is raw days from today (not log2, not z-score), and you excluded it from mapper_and_normalizer."
    },
    "key_moment": "Yes -- in your pipeline age_days is log2-normalized. So the value stored in recommend shards is log2(days_since_created_at_last_interaction + 1), not raw days. If you want 'within last 30 days' using this field, the cutoff is log2(30 + 1) ~= 4.954.",
    "notes": "Longest session in batch (~48 min). Started with 'reduce 8h47m to 3h' goal, pivoted to filtering users by recency (platform_age < 31 days). Deep discussion about normalized vs raw values, log2 + z-score transforms. User made live code changes and asked Codex to re-read files. Collaborative data pipeline debugging."
  },
  {
    "filename": "rollout-2026-01-31T07-51-36-019c1309-3467-7613-a009-e6e76be0c721.jsonl",
    "total_events": 229,
    "duration_seconds": 301.3,
    "turn_count": 4,
    "model": "gpt-5.2-codex",
    "session_type": "SUBSTANTIVE",
    "user_message_count": 1,
    "first_user_message": "similar to this i want to generate the avro file in the same from in the end by recommend.py not the jsonl. please do the needful",
    "last_exchange": {
      "last_user_message": "[pasted full inference.py code from another repo] similar to this i want to generate the avro file in the same from in the end by recommend.py not the jsonl. please do the needful",
      "last_assistant_response": "Updated recommend.py to emit AVRO in the same structure as your other repo (and optional .gz), instead of JSONL by default. It now builds widget/algorithm/contentList records, maps series_id -> firstPratilipiId, and writes AVRO in batches."
    },
    "key_moment": "Updated col/src/model/recommend.py to emit AVRO in the same structure as your other repo (and optional .gz), instead of JSONL by default. It now builds widget/algorithm/contentList records, maps series_id -> firstPratilipiId, and writes AVRO in batches.",
    "notes": "User pasted ~150 lines of code from another repo's inference.py and asked Codex to replicate the AVRO output format. Codex read extensively across the codebase (229 events, 25+ tool calls) and produced a complete AVRO writer with S3 upload, gzip, and schema support. Single-turn but highly substantive."
  },
  {
    "filename": "rollout-2026-01-31T07-59-10-019c1310-23c0-7082-8e3d-b7f03d13cd72.jsonl",
    "total_events": 118,
    "duration_seconds": 668.7,
    "turn_count": 4,
    "model": "gpt-5.2-codex",
    "session_type": "SUBSTANTIVE",
    "user_message_count": 1,
    "first_user_message": "i actually want to remove series that have already been read by the user, please do the needful. Probably store data during data preperation like series read by the user. and in recommend.py load them to filter out. after filter out recommend top 30 only.",
    "last_exchange": {
      "last_user_message": "i actually want to remove series that have already been read by the user, please do the needful. Probably store data during data preperation like series read by the user. and in recommend.py load them to filter out. after filter out recommend top 30 only.",
      "last_assistant_response": "Updated data prep to persist per-user read series and updated recommendation to load that file, filter out already-read series, and emit only the top 30 after filtering."
    },
    "key_moment": "Updated data prep to persist per-user read series (read_series.npz with user_ids, offsets, and series_ids) and updated recommendation to load that file, filter out already-read series, and emit only the top 30 after filtering.",
    "notes": "Feature request: filter out already-read series from recommendations. Codex modified both data_prep.py and recommend.py to persist and load a read_series.npz file. Changed output from top-k to top-30 after filtering. Single-turn implementation across two files."
  },
  {
    "filename": "rollout-2026-01-31T08-17-13-019c1320-aa65-7333-9cf7-3600126c1761.jsonl",
    "total_events": 248,
    "duration_seconds": 1227.6,
    "turn_count": 4,
    "model": "gpt-5.2-codex",
    "session_type": "SUBSTANTIVE",
    "user_message_count": 1,
    "first_user_message": "can you break recommend.py into proper functions? also the code assumes backup values if something is not avaialable, remove them and actually throw error if the value is not present. Also all the config values that is required can you keep them at top of the file.",
    "last_exchange": {
      "last_user_message": "can you break recommend.py into proper functions? also the code assumes backup values if something is not avaialable, remove them and actually throw error if the value is not present. Also all the config values that is required can you keep them at top of the file.",
      "last_assistant_response": "Refactored recommend.py into smaller, single-purpose helpers and tightened config validation so missing values now raise errors instead of silently falling back. Split driver logic into helpers for validation, path resolution, batch prep, mapping, and output writing. Centralized required config names at the top and added a _MISSING sentinel to force explicit config."
    },
    "key_moment": "Important behavior change: driver() now requires explicit values for data_dir, interaction_dirs, series_meta_dir, output_path, model_path, device, top_k, series_batch_size, user_batch_size, output_format, output_top_n, and read_series_path. Calling recommend.driver() with no args will now raise.",
    "notes": "Major refactoring session. User asked for 3 things: break into proper functions, remove fallback defaults (throw errors instead), centralize config. Codex did a full restructure of recommend.py with _MISSING sentinel pattern, single-purpose helpers, and strict validation. ~20 min of work, 248 events."
  }
]
