36 lines
1.9 KiB
Markdown
36 lines
1.9 KiB
Markdown
# Example: AI Model Evaluation Blog Post Review
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## Context
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Blog post titled "6款AI模型iOS开发能力深度评测" based on @solidus's evaluation data.
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## First Review (6.5/10) — Key Issues Found
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### Critical Factual Errors
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1. **Opus scoring misleading**: 95/100 based on only 8 core practical questions, while other models scored on 84 questions. Placed in same table without caveat.
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2. **"Two evaluation systems" described as three**: Title said "两套" but listed three.
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3. **GLM highest main score but ranked 3rd**: No explanation of why (XII pressure test only 79 vs Sonnet 87).
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### Fairness Issues
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4. **Double standard on API fabrication**: MiMo's fabricated `sending` syntax got bold + "最危险的失败模式", while Sonnet's fabricated iOS API got only "翻车" (casual). Fix: equal treatment.
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5. **Selective month-end drift comparison**: Only showed Opus (best) vs Kimi (worst), ignoring DeepSeek/GLM also solved it correctly.
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### Depth Issues
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6. **5 "deep analysis" questions were just rephrased** from the source report's summary section.
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7. **Scenario recommendations copied verbatim** from source report.
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### Missing Content
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8. Kimi's `fatalError` in production code (critical engineering flaw)
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9. GLM's CSV export syntax error (won't compile)
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10. Sonnet's TWO failures in graphics test (API fabrication + ACES formula)
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## Second Review (8.2/10) — Remaining Low-Priority Issues
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1. SE proposal number reference (SE-0371 vs SE-0427)
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2. Opus 95-score description could be more precise
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3. Missing "legacy Swift 5 project" recommendation scenario
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## Lessons Learned
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- Always add caveats when comparing scores with different sample sizes
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- Equal treatment: if you harshly criticize one model for X, do the same for all models that did X
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- Original analysis frameworks (failure mode taxonomy, cost/perf analysis) add genuine depth
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- Subagent review with NO context forces independent verification against source data
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