# Your AI Coding Week — Jan 28-31, 2026

Hey Nikhil, here's your weekly recap of working with Codex CLI on cold-ranker.

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## At a Glance

| Metric | Value |
|--------|-------|
| Sessions | 34 |
| Total time with AI | 4h 47m |
| Resolution rate | 76% (26/34 resolved) |
| Avg turns per session | 7 |
| Avg struggle score | 3.2/10 |
| Models used | gpt-5.2-codex (25), gpt-5 (9) |

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## What You Worked On

All 34 sessions were classified as "bug-fix" by the automated pipeline, but the deep-dive analyses reveal a richer picture: bug fixes on the training DataLoader, feature-building (recommendation pipeline, AVRO output), performance optimization (GPU batching, tensor precomputation), refactoring (recommend.py restructuring), setup (wandb installation), and codebase inquiry (MODE=prod behavior). See the dashboard for the interactive breakdown.

| Type (from deep dives) | Sessions | Resolution Rate | Notable |
|------------------------|----------|----------------|---------|
| Bug fixes (data/model) | ~18 | ~83% | DataLoader IndexError, head dimension mismatch |
| Performance optimization | 4 | 75% | recommend.py GPU batching saga |
| Setup/installation | 2 | 100% | wandb install, environment config |
| Code review/inquiry | 3 | 100% | MODE=prod behavior, full codebase audit |
| Feature building | ~7 | ~85% | Feature crosses, AVRO output, data pipeline |

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## Your Best Session

**rollout-2026-01-29T23-07-18** — IndexError in DataLoader, fixed in 127 seconds

You nailed this one. Here's what made it work:
> "The developer provided an exemplary bug report -- the entire terminal output including the full traceback with exact line numbers and the runtime context. No guesswork was needed."

Result: Resolved in 4 turns, 2 minutes. The full traceback + terminal context gave the AI everything it needed to identify that `age_days` was being expanded to 3D by `[:, None]`, causing `np.tile` to duplicate along the wrong axis. One patch, done.

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## Where You Got Stuck

**rollout-2026-01-29T20-30-24** — "fix error in model.py" (no traceback)

This one took 7 turns and 447 seconds. Here's where it went sideways:
> "This request contains no error message, no traceback, no description of what went wrong. The agent had to do extensive code archaeology across 6 files and 20+ shell commands to infer the bug."

**What to try next time:** Always paste the exact error traceback when asking an AI agent to fix a bug. Instead of "can you fix error in model.py", say something like "I get a RuntimeError: mat1 and mat2 shapes cannot be multiplied (1024x71 and 72x1) when running training.py." This would have saved the agent 15+ exploratory commands.

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## Patterns Worth Knowing

### Rapid Session Cycling on the Same Question
You repeatedly opened and immediately closed sessions with identical prompts, then re-opened new sessions seconds later. This happened with "how to improve my model?" (3 consecutive abandoned sessions before resolution) and "test loss not decreasing" (1 instant abandon before a 20-minute productive session).

> "The total time spent cycling through these sessions was about 4.5 minutes, but the assistant only got to fully investigate in this last attempt."

Each new session forces the AI to re-read the entire codebase from scratch. You pay the full exploration cost every time.

### Optimizing Code Before Questioning Scope
You spent approximately 48 minutes across 3 consecutive sessions trying to make `recommend.py` faster through GPU-level optimizations (batching, tensor reshaping, mixed precision). None produced measurable throughput improvement. Then, in one sentence, you realized you only needed to score users from the last 30 days.

> "Three sessions of GPU optimization work was rendered unnecessary by one product requirement clarification."

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## The Aha Moment

Your evenings were flawless. Your afternoons were chaotic. The problems weren't harder -- you were less patient.

The 11 evening sessions (18:00-24:00) had a **100% resolution rate** with 0 abandonments. The 11 afternoon sessions (12:00-18:00) had only a **45% resolution rate** with 6 abandonments. The evening of Jan 29 (20:07-23:17) was particularly remarkable -- 8 consecutive resolved sessions in 3 hours.

> "The developer interrupted the session almost instantly -- the entire session lasted only 10 seconds. This suggests impatience or a realization that the question needed to be asked differently." (afternoon session)

> "This session was efficient with no significant struggle points. The struggle_score of 0 is accurate -- this was a clean diagnostic session." (morning session)

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## One Thing to Try This Week

Before opening a new AI session, spend 15 seconds writing a "context header" with three things: (1) the exact error message or traceback, (2) what you already tried, and (3) what specific outcome you want.

> "The developer provided an exemplary bug report -- the entire terminal output including the full traceback with exact line numbers and the runtime context. No guesswork was needed." — resolved in 127 seconds, 4 turns, specificity 5/5

vs.

> "This request contains no error message, no traceback, no description of what went wrong. The agent had to do extensive code archaeology across 6 files and 20+ shell commands to infer the bug." — 447 seconds, specificity 2/5

Sessions with tracebacks resolved in 2-4 turns. Sessions without them took 6-9 turns. The difference is not skill -- it's a 15-second copy-paste.

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*Generated by QuickCall Session Analysis | [View Interactive Dashboard](dashboard.html)*
