# Review by Andrej Karpathy (Round 2)

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## Review: Andrej Karpathy — AI Engineering & Prompt Systems

**127 tests pass.** All 5 functional requirements implemented. 8 commits, +1,252 / -29 lines across 6 files.

### Key Assessment

This is a well-architected feature that follows the cardinal rule of LLM application engineering: **treat model output as structured-but-noisy data, apply deterministic post-processing, degrade gracefully on malformed input.**

The implementation makes zero new LLM calls. It extracts signal from existing artifacts (`task_results`, `FINDINGS:`/`SYNTHESIS:` sections) using simple string operations — the right level of engineering for semi-structured output that *mostly* follows a template but isn't guaranteed. The three-tier fallback in `_extract_review_findings_summary()` (FINDINGS → SYNTHESIS → first non-empty line) handles real-world LLM output variance gracefully.

Sanitization is at the correct abstraction layer: applied to individual content fragments before interpolation into mrkdwn templates, so our own formatting is preserved while untrusted content is escaped. After the fix iteration, truncation is intrinsic to formatters (not caller-dependent), which eliminates a maintenance hazard.

### Findings (Non-Blocking)

- **Double truncation in `_run_pipeline()`** — formatters now truncate internally, making the outer `_truncate_slack_message()` wrappers redundant (idempotent, harmless, but dead code)
- **Magic numbers** (72, 80, 6, 3000) could be named constants — cosmetic only, PRD explicitly defers configurability

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VERDICT: **approve**

Review artifact saved to `cOS_reviews/reviews/andrej_karpathy/20260331_194500_round1_when_you_output_followups_in_the_thread_on_slack_a938599349.md`.
