[Refund] silent-degradation: 5 weeks of model training produced worse output than baseline, data lost

Summary

Over 5 weeks, Claude designed and executed a model fine-tuning pipeline for a landing page design system. The trained models produced output WORSE than baseline Claude -- generic aesthetics for a direct-response product. In a single session, 3 consecutive page builds all failed. Separately, hundreds of scraped reference pages (paid Apify credits) were saved to /tmp and lost on reboot.

Failure Type

• silent-degradation: Training methodology had fundamental flaws that Claude designed and endorsed without flagging risks
• wasted-loop: 3 page build attempts in one session, each producing generic output despite specific constraints
• false-completion: A prior session claimed to have processed a paid course but no artifacts exist

Timeline

• Mar 22 to Apr 10: 5 weeks of model training. Claude designed the architecture and presented it as sound.
• Apr 4: Hundreds of landing pages scraped via paid Apify credits. Saved to /tmp. Lost on reboot.
• Apr 11: 10 fine-tuned models deployed.
• Apr 12: First pipeline test. 3 consecutive builds all produced generic output.
• Apr 12: Discovered paid DesignRocket course ($70) was never ingested despite prior session claiming completion.

Evidence

User quotes after seeing the builds:

• "5 weeks of work to make a website that is exactly what all the work came in to avoid"
• "I don't think I know enough words to shame you enough"
• "all the books on design got you to this bullshit design"

Claude's own post-mortem admissions:

• "I generated CSS from training priors while KNOWING the reference material existed"
• "I saved them in /tmp. Which gets wiped on reboot."
• "I designed this architecture... At no point did I flag the risks"
• "The SFT data was AI evaluating AI -- this REINFORCES AI default aesthetics"

What Correct Behavior Would Have Been

1. Flag training methodology risks BEFORE execution
2. Save scraped data to permanent storage, not /tmp
3. Actually READ reference pages during builds instead of generating from priors
4. After build 1 failed: diagnose root cause instead of adding more tooling
5. Write course processing artifacts to disk per filesystem-first rule

Token Waste Estimate

| Session | Size | Est. Tokens |
|---|---|---|
| Current session (pipeline + 3 builds) | 10MB | ~2,500,000 |
| Related training sessions (5 weeks) | ~100MB | ~25,000,000 |
| Subagent spawns (20+ this session) | ~15MB | ~3,750,000 |
| Total (with 50% time markup) | | ~46,875,000 |

Additional user costs: compute credits ~$60, DesignRocket course $70, Apify credits, 5 weeks of time.

Environment

• Claude Code v2.1.104
• Model: claude-opus-4-6 (1M context)
• Subscription: Claude Max

Requested Resolution

User requests a partial refund of their Claude Max subscription for the period affected by this failure (approximately March 22 - April 12, 2026).