# Session Analysis: rollout-2026-01-31T06-14-13-019c12b0-0e85-7bb1-9460-e06dd4eabcf9.jsonl

**Date**: 2026-01-31 | **Duration**: 512.2s | **Turns**: 7 | **Model**: gpt-5.2-codex
**Type**: performance-optimization | **Outcome**: abandoned | **Specificity**: 2/5 | **Struggle**: 7/10

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## Journey

The developer opened `col/src/model/recommend.py` in the cold-ranker project (a PyTorch-based CTR recommendation system) and asked Codex to analyze whether the file could be made faster. The agent spent over 8 minutes reading through the codebase: `recommend.py` (in two passes, lines 1-220 and 220-520), `model.py` (the neural network definition), `utils/utils.py` (data loading utilities), searched for callers of `recommend.driver`, and inspected `driver.py`. The agent identified multiple optimization opportunities and began writing a patch -- adding a `_slice_to_device` helper function and a `prepare_series_meta_tensors` function via `apply_patch`. However, the session was then interrupted: the sandbox mode changed from `read-only` to `workspace-write`, new system context was injected, and the session ended without the agent ever delivering a final text response to the user. The developer appears to have abandoned this session and started fresh (sessions 4 and 5 followed within minutes with the same question).

## Prompting Assessment (2/5)

**What worked:**
The developer provided IDE context including the active file, which gave the agent a clear starting point.

> "can recommend.py be made faster? how?"

**What was vague or could improve:**
The prompt is extremely open-ended. It provides no information about what "faster" means -- inference latency? throughput? training speed? There is no mention of the current bottleneck, dataset size, GPU vs CPU, or acceptable tradeoff. This forced the agent into an exhaustive 8-minute codebase exploration.

> "can recommend.py be made faster? how?"

## Outcome: abandoned

**Evidence:**
The session's last user message was a new system context injection (sandbox mode change) with no follow-up human message. No assistant text response was ever delivered despite the agent spending 512 seconds reading code and beginning to write patches. The agent's internal reasoning was active:

> "Designing device-aware series meta preparation -- I'm refining the helper to slice series metadata tensors efficiently on target device with proper dtype and non-blocking options."

But the user never received any output. The session ended abruptly at line 75 of the raw JSONL with only system context messages, no closing assistant message.

## Struggle Points

The primary struggle was the agent fighting the sandbox. The very first `ls` command failed with a landlock panic, requiring escalation. Every subsequent command needed `require_escalated` permissions. This added overhead to each of the 8 tool calls.

> "thread 'main' (12054) panicked at linux-sandbox/src/linux_run_main.rs:30:9: error running landlock: Sandbox(LandlockRestrict)"

The agent also spent significant time in deep code exploration (reading `model.py`, `utils.py`, `driver.py`, searching for callers) before starting to write any response. By the time it began generating the `apply_patch`, over 3 minutes had passed. The session was set to `xhigh` reasoning effort, which contributed to the slow, thorough analysis that ultimately went undelivered.

> "Analyzing performance bottlenecks in recommend.py -- I've identified costly repeated CPU-to-GPU transfers and per-user loops in scoring and feature loading"

## Aha Insight

This session exposed hardcoded secrets in `driver.py` -- the agent read and cached an OpenAI API key, MySQL credentials, and a WandB API key that were set as environment variables in plain text. The agent's tool output shows these credentials in full. This is a significant security concern: AI code assistants will read and process whatever files they encounter, and if sessions are logged or shared, credentials become exposed.

**Evidence:**
> "os.environ[\"OPENAI_API_KEY\"] = \"sk-proj-qRoswT4wSHS3R4i7ZcIbP3FW5XT0xI_WFPd_ZkNr0OXrihudRu2ZzXthe8u_9BvNahk8fsEPhOT3BlbkFJ..."

## Coach Tip

When asking an AI to optimize code, front-load the constraints: specify whether you are running on GPU or CPU, mention the approximate data scale (number of users, number of series), and state which part feels slow. The developer asked this exact same question three times across three sessions. In session 4, Codex answered in 22 seconds with `medium` effort. The difference was not the prompt -- it was the effort level. Consider using `medium` effort for exploratory questions and saving `xhigh` for implementation tasks, rather than the other way around.
