# 34 Session Fingerprints — Jan 28-31, 2026

## Summary
- **Substantive sessions**: 28 (worth analyzing)
- **Trivial sessions**: 6 (misfires, instant aborts, no real interaction)
- **Total time with AI**: 4h 46m across substantive sessions only

---

## Trivial Sessions (skip these)

| # | Filename | Events | Duration | Why trivial |
|---|----------|--------|----------|-------------|
| 3 | rollout-2026-01-28T17-07-55 | 20 | 9.6s | Aborted instantly — "how to improve my model?" but interrupted before any response |
| 4 | rollout-2026-01-28T17-08-14 | 9 | 0.1s | Duplicate of #3, no assistant response at all |
| 8 | rollout-2026-01-29T10-18-05 | 9 | 0.2s | "test loss not decreasing" — no assistant response, retried in next session |
| 11 | rollout-2026-01-29T12-06-02 | 9 | 0.4s | Said "hi" and immediately aborted |
| 12 | rollout-2026-01-29T13-21-42 | 25 | 6.8s | Pasted recommendation request, aborted before response — retried in #13 |
| 27 | rollout-2026-01-31T06-23-05 | 40 | 22s | Quick read-only analysis of recommend.py speed — no code changes, 1 turn |

---

## Substantive Sessions (ordered by date)

### [1] rollout-2026-01-28T15-51-17 — "Wandb install fails (Go not found)"
- **Type**: setup | **Outcome**: resolved | **Turns**: 6 | **Duration**: 1m 48s
- **What happened**: pip install wandb tried to build from source and failed because Go wasn't installed. Codex confirmed wandb is optional in the codebase and offered workarounds.
- **First prompt**: > "pip uninstall wandb ... pip install wandb ... error: subprocess-exited-with-error ... Did not find the 'go' binary."
- **Key moment**: > "In this repo, wandb is optional and safely disabled by default: see col/src/utils/wandb_utils.py:1 and col/src/config/config.py:1."

### [2] rollout-2026-01-28T15-57-48 — "Wandb login link points to localhost"
- **Type**: config/debugging | **Outcome**: resolved | **Turns**: 11 | **Duration**: 12m 5s
- **What happened**: Wandb login kept showing a localhost:8080 authorize link. Multi-turn debugging revealed a hardcoded API key in driver.py. Codex patched it out and flagged the security risk.
- **First prompt**: > "wandb login --relogin ... link doesnt open"
- **Key moment**: > "Never hardcode your API key in code. Treat the key you pasted as compromised and rotate it in W&B, then set WANDB_API_KEY outside your repo."

### [5] rollout-2026-01-28T17-08-38 — "How to improve my model (attempt 3)"
- **Type**: ML improvement | **Outcome**: abandoned | **Turns**: 8 | **Duration**: 3m 57s
- **What happened**: Third attempt at "how to improve my model?" Codex read the codebase, identified overfitting, and was mid-patch (early stopping, LR scheduler) when the user interrupted.
- **First prompt**: > "how to improve my model?"
- **Key moment**: > "Next, I'll add early stopping, LR scheduler, weight decay, optional pos_weight, and gradient clipping to training.py, and introduce corresponding knobs in config.py."

### [6] rollout-2026-01-28T20-00-48 — "Deep ML architecture discussion"
- **Type**: ML improvement | **Outcome**: resolved | **Turns**: 10 | **Duration**: 21m 30s
- **What happened**: Comprehensive model improvement session. Codex proposed pos_weight, weight decay, feature crosses, two-tower architecture. Patched model.py with cross features. User critically questioned whether element-wise product is redundant with cosine similarity.
- **First prompt**: > "how can i improve my model?"
- **Key moment**: > "Cosine is a single scalar that captures only angle (direction). It discards magnitude and all per-dimension interactions. Element-wise product keeps a full vector of per-dimension alignments. They're complementary, not redundant."

### [7] rollout-2026-01-28T21-21-52 — "NEG_PER_POS 7→20 dropped test loss"
- **Type**: ML analysis | **Outcome**: resolved | **Turns**: 6 | **Duration**: 1m 47s
- **What happened**: User reported changing NEG_PER_POS from 7 to 20 lowered test_loss from 0.12 to 0.08. Codex explained class imbalance effects on BCE loss and warned about calibration issues.
- **First prompt**: > "i changed NEG_PER_POS from 7 to 20, the test_loss changed from 0.12 to 0.08"
- **Key moment**: > "More negatives per positive increases batch size to B*(1+neg_per_pos), making the BCE loss more dominated by negatives. This often lowers average loss if the model learns to suppress negatives better."

### [9] rollout-2026-01-29T10-18-14 — "Root-cause test loss instability"
- **Type**: bug-fix | **Outcome**: resolved | **Turns**: 15 | **Duration**: 20m 26s
- **What happened**: Test loss oscillating around 0.254-0.259 across epochs. Root cause: test dataloader was re-sampling negatives each epoch, making eval non-deterministic. 7 patches applied: deterministic test eval, LR scheduler, early stopping, gradient clipping, W&B fixes.
- **First prompt**: > "test loss not decreasing"
- **Key moment**: > "the test dataloader was re-sampling negatives each epoch, so the eval distribution changed and made test loss bounce."

### [10] rollout-2026-01-29T11-47-50 — "Why is test loss not decreasing (deep dive)"
- **Type**: bug-fix | **Outcome**: partial | **Turns**: 5 | **Duration**: 18m 8s
- **What happened**: Deep ML debugging session identifying false negatives in the popularity sampler, BatchNorm issues, and regularization tuning. Session ended with Codex offering 3 fixes but user responded with just ".".
- **First prompt**: > "why is my test loss not decreasing?"
- **Key moment**: > "Primary issue found: false negatives from the sampler — negatives are drawn globally and then concatenated with positives, without excluding the current positive series ID."

### [13] rollout-2026-01-29T13-23-37 — "Build end-to-end recommendation pipeline"
- **Type**: feature-build | **Outcome**: resolved | **Turns**: 10 | **Duration**: 24m 38s
- **What happened**: Built complete recommendation module: created recommend.py, modified training.py for checkpoints, integrated into driver.py, consolidated config. Agent initially scattered env vars; user caught it and requested centralization.
- **First prompt**: > "i want to now generate recommendations for users. the recommendation should be top 50 contents out of all contents in series meta."
- **Key moment**: > "Hooked recommendations into the driver flow so training saves a model and the same driver run produces top-50s using the latest per-user interaction."

### [14] rollout-2026-01-29T20-07-43 — "Code review finds recommend shards unused"
- **Type**: code-review | **Outcome**: resolved | **Turns**: 7 | **Duration**: 4m 56s
- **What happened**: User asked if data_prep.py's recommendation shard code was correct. Codex found two blocking bugs: recommend.py never reads the shards, and a filename mismatch (floats.npy vs float.npy).
- **First prompt**: > "is my save for recommendation code in data_prep.py correct?"
- **Key moment**: > "Blocking: recommend.py never reads the recommend shards you write, so your new output is ignored and recommendation still uses train/test history computed with include_current=False."

### [15] rollout-2026-01-29T20-30-24 — "Fix model.py dimension mismatch"
- **Type**: bug-fix | **Outcome**: partial | **Turns**: 7 | **Duration**: 7m 27s
- **What happened**: "Can you fix error in model.py" — no traceback provided. Codex traced through 6 files to find a linear head input dimension mismatch (4 continuous features should be 5). Fixed model.py but couldn't fix training.py without a traceback.
- **First prompt**: > "can you fix error in model.py"
- **Key moment**: > "Adjusted the head input sizing to make the 5 continuous features and 3 cosine features explicit, which prevents linear head shape mismatches."

### [16] rollout-2026-01-29T20-37-57 — "Fix undefined total_batches in training.py"
- **Type**: bug-fix | **Outcome**: resolved | **Turns**: 6 | **Duration**: 4m 48s
- **What happened**: "Can you fix error in training.py" — again no traceback. Codex found 3 bugs: undefined total_batches NameError, test_shard_dir incorrectly set to None in prod mode, wrong directory in get_pos_series.
- **First prompt**: > "can you fix error in training.py"
- **Key moment**: > "Fixed the undefined total_batches reference and kept the test shard path intact for MODE=prod so training doesn't pass None into the dataloader."

### [17] rollout-2026-01-29T20-44-10 — "Test loader determinism deep-dive"
- **Type**: deep-discussion | **Outcome**: resolved | **Turns**: 9 | **Duration**: 7m 13s
- **What happened**: User asked if test_loader generates same examples across epochs/runs. Codex identified 3 sources of non-determinism (unsorted shards, multi-worker order, per-worker sampler copies) and explained negative sampling mechanics.
- **First prompt**: > "will my test_loader generate same positive and negative examples across epochs and across different runs given data preparation is same?"
- **Key moment**: > "It's not 'negatives are tied to a positive by ID,' it's 'negatives are tied to the position of that positive in the iteration.' If you change batch size, shard splits, or the row order, you can get different negatives for the same positive ID even with single worker."

### [18] rollout-2026-01-29T20-53-38 — "Fix recommend.py end-to-end"
- **Type**: bug-fix | **Outcome**: resolved | **Turns**: 6 | **Duration**: 5m 50s
- **What happened**: "fix my recommend.py" — 3-word prompt. Codex read 8+ source files, found model's forward() expected total_interactions but recommend.py never passed it. Fixed driver params, output file creation, and feature ordering.
- **First prompt**: > "fix my recommend.py"
- **Key moment**: > "Fixed recommend.py so recommendations run end-to-end: the driver now accepts sane defaults/overrides, writes to a real output file, and the scorer passes the full feature set (including total_interactions) into the model in the correct order."

### [19] rollout-2026-01-29T22-46-03 — "Log2 divide-by-zero on negative age_days"
- **Type**: bug-fix | **Outcome**: resolved | **Turns**: 6 | **Duration**: 1m 31s
- **What happened**: User pasted full pipeline output with log2 RuntimeWarnings. Codex traced to negative age_days values (interactions before signup) and added a clip+fillna guard.
- **First prompt**: > "RuntimeWarning: divide by zero encountered in log2 ... RuntimeWarning: invalid value encountered in 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."

### [20] rollout-2026-01-29T22-48-55 — "Full codebase audit from driver.py"
- **Type**: code-review | **Outcome**: resolved | **Turns**: 5 | **Duration**: 8m 45s
- **What happened**: User asked Codex to audit all files triggered by driver.py for errors. Read 15+ files, found 5 bugs including hardcoded API keys/secrets, crash-on-empty DataFrames, dataloader type mismatch.
- **First prompt**: > "this is my main file ... look at all the files and function it triggers, if there are any logical error or any errors in my program"
- **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."

### [21] rollout-2026-01-29T23-07-18 — "IndexError from numpy 3D shape bug"
- **Type**: bug-fix | **Outcome**: resolved | **Turns**: 4 | **Duration**: 2m 7s
- **What happened**: DataLoader crash — IndexError: index 839 out of bounds for axis 0 with size 256. Root cause: [:, None] expanded age_days to 3D, making np.tile duplicate along wrong axis.
- **First prompt**: > "IndexError: index 839 is out of bounds for axis 0 with size 256"
- **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)."

### [22] rollout-2026-01-29T23-17-23 — "Does MODE=prod use all data?"
- **Type**: codebase-inquiry | **Outcome**: resolved | **Turns**: 4 | **Duration**: 49s
- **What happened**: Quick Q&A. User asked if MODE='prod' uses all data. Codex traced through dataset.py, training.py, data_prep.py to confirm prod merges train+test and skips evaluation.
- **First prompt**: > "if i use MODE='prod', will i be using all the data for training?"
- **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."

### [23] rollout-2026-01-30T13-43-01 — "Disk exhaustion from .npy files"
- **Type**: advisory | **Outcome**: resolved | **Turns**: 10 | **Duration**: 9m 9s
- **What happened**: User running out of disk writing .npy shard files. Pure Q&A session (no tool calls) comparing .npy vs .npz vs zarr. Ended with Codex providing np.savez_compressed read/write syntax.
- **First prompt**: > "exhausting permanent memory during write of data prep files - train, test, recommend. what can we do? just tell dont edit code"
- **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."

### [24] rollout-2026-01-30T16-43-54 — "KeyError after .npy→.npz migration (aborted)"
- **Type**: bug-fix | **Outcome**: abandoned | **Turns**: 5 | **Duration**: 2m 57s
- **What happened**: User hit KeyError slicing an NpzFile like an ndarray after switching from .npy to .npz. Aborted mid-investigation — continued in next session.
- **First prompt**: > "KeyError: '(slice(None, None, None), 0) is not a file in the archive'"
- **Key moment**: > "KeyError: '(slice(None, None, None), 0) is not a file in the archive'" — user aborted before Codex could respond.

### [25] rollout-2026-01-30T16-47-16 — "Fix .npz loading across all files"
- **Type**: bug-fix | **Outcome**: resolved | **Turns**: 7 | **Duration**: 8m 26s
- **What happened**: Continuation of #24. Codex explained NpzFile is a zip archive not an array, added load_npz_array helper to utils.py, patched loaders in training.py, dataset.py, and recommend.py.
- **First prompt**: > "KeyError: '(slice(None, None, None), 0) is not a file in the archive' ... what is the reason of this error"
- **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."

### [26] rollout-2026-01-31T06-14-13 — "Make recommend.py faster (abandoned)"
- **Type**: performance | **Outcome**: abandoned | **Turns**: 7 | **Duration**: 8m 32s
- **What happened**: User asked how to speed up recommend.py. Codex read through the full pipeline but session ended before delivering a response. User retried in sessions 27-28.
- **First prompt**: > "can recommend.py be made faster? how?"
- **Key moment**: > "can recommend.py be made faster? how?" — 8 minutes of investigation with no delivered answer.

### [28] rollout-2026-01-31T06-25-34 — "Implement 3 recommend.py optimizations"
- **Type**: performance | **Outcome**: resolved | **Turns**: 5 | **Duration**: 3m 13s
- **What happened**: User got analysis from #27 and explicitly requested 3 optimizations: precompute series tensors, use inference_mode(), fix top-k merge. Codex implemented all three.
- **First prompt**: > "can recommend.py be made faster? how?"
- **Key moment**: > "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."

### [29] rollout-2026-01-31T06-30-50 — "GPU pegged at 100% — auto-scaling cancels gains"
- **Type**: performance | **Outcome**: partial | **Turns**: 12 | **Duration**: 17m 49s
- **What happened**: Iterative performance tuning with live nvidia-smi output. Added user batching, then discovered auto-scaling was canceling throughput gains. Tesla T4 at 99% GPU util, ~40 it/s on 1.36M users.
- **First prompt**: > "can recommend.py be made faster? how?"
- **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 doesn't improve much."

### [30] rollout-2026-01-31T06-49-43 — "GPU analysis — compute-bound at 100%"
- **Type**: performance | **Outcome**: partial | **Turns**: 6 | **Duration**: 4m 7s
- **What happened**: Another performance iteration. Codex ran nvidia-smi showing GPU at 100% util, 7.2GiB/15.4GiB used. Offered mixed precision + TF32 as next step.
- **First prompt**: > "how to make my recommend.py faster?"
- **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)."

### [31] rollout-2026-01-31T06-55-05 — "Reduce 8h47m to 3h — filter by recency"
- **Type**: performance/feature | **Outcome**: resolved | **Turns**: 14 | **Duration**: 47m 46s
- **What happened**: Longest session. Pivoted from pure GPU optimization to reducing problem size — filter to users created in last 30 days. Deep discussion about log2 + z-score normalization of age_days. User made live code changes and asked Codex to re-read files.
- **First prompt**: > "currently recommend.py takes 8:47:41 time to run, i want to run in 3 hrs, what changes should i have to do?"
- **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."

### [32] rollout-2026-01-31T07-51-36 — "Switch output from JSONL to AVRO"
- **Type**: feature-build | **Outcome**: resolved | **Turns**: 4 | **Duration**: 5m 1s
- **What happened**: User pasted ~150 lines from another repo's inference.py and asked Codex to replicate the AVRO output format with S3 upload and gzip. Single-turn implementation.
- **First prompt**: > "similar to this i want to generate the avro file in the same form in the end by recommend.py not the jsonl. please do the needful"
- **Key moment**: > "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."

### [33] rollout-2026-01-31T07-59-10 — "Filter out already-read series"
- **Type**: feature-build | **Outcome**: resolved | **Turns**: 4 | **Duration**: 11m 9s
- **What happened**: User wanted to exclude already-read series from recommendations. Codex modified data_prep.py to persist per-user read history as read_series.npz and updated recommend.py to filter and output top 30.
- **First prompt**: > "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."
- **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."

### [34] rollout-2026-01-31T08-17-13 — "Refactor recommend.py — strict config, proper functions"
- **Type**: refactoring | **Outcome**: resolved | **Turns**: 4 | **Duration**: 20m 28s
- **What happened**: User asked to break recommend.py into proper functions, remove silent fallbacks (throw errors instead), and centralize config at top of file. Codex did a full restructure with _MISSING sentinel pattern and strict validation.
- **First prompt**: > "can you break recommend.py into proper functions? also the code assumes backup values if something is not available, remove them and actually throw error if the value is not present."
- **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."
