Kuber Mehta makes a sharp case against the growing trend of “humanising” LLM outputs via prompt instructions (ASD-STE Simplified Technical English, “talk to me like I have ADHD”, short-sentence-only agent.md directives): these are lossy compression applied too early in the pipeline, and they hide exactly the information you most need to see. When a subagent runs six tests, a smoothed summary of “most tests passed, one issue worth looking into” is strictly worse than the raw 5/6 PASS FAIL: test_cache_invalidation CAUSE: stale key survives restart REPRO: cache_test.py:184. Human prose is excellent at glossing over uncertainty, hallucinations, and edge cases — the smoothing is the bug, not the feature. Mehta draws the right architectural parallel: databases don’t store data in dashboard format, compilers don’t make IR pleasant to read, and APIs don’t exchange friendly summaries. The correct pattern is to keep the highest-fidelity representation through the pipeline and transform only at the consumption boundary. LLM tooling does this backwards today, and the viral “make the model talk to me like…” repos are valuable not as end-state solutions but as bug reports pointing at the right fix.