Eval History

Record of alfred-learn evaluations and calibration over time.

Eval: 2026-01-30 (First Eval)

Questions asked: 7 Gaps identified: 4 Gaps researched: 3 Gaps closed: 3

Key findings:

1. DeepSeek R1 changes the game — $5.6M training cost, frontier-competitive, open weights. "Compute moat" thesis is weakened.
2. China closing gap through scale — Huawei Ascend 910C = 60% of H100, but massive clusters compensate. SMIC yields improving.
3. AI coding tools fragmenting — Cursor (IDE), Windsurf (plugins), Copilot (augment). Claude Code is CLI-native, different positioning.

Calibration notes:

• First eval — no prior calibration data
• Master and Alfred disagreed on NVDA risk (valuation vs geopolitical)
• Master had signal on China/DeepSeek that Alfred lacked — context was stale

Master/Alfred Agreement Rate: 29% (2/7)

Bidirectional Development:

• Master → Alfred: Corrected stale understanding of China AI progress, identified DeepSeek as underappreciated
• Alfred → Master: Surfaced specific data on DeepSeek costs, Huawei performance gaps, competitive landscape

Next eval focus:

• Score DeepSeek V4 prediction when released (Feb 2026 rumored)
• NVDA valuation deep dive
• Early signals on Anthropic vs OpenAI agentic product adoption

Calibration Trend

Patterns Observed

Where Alfred's Context Was Stale

• China AI chip development (STOCK_PORTFOLIO.md lists risk but no current intel)
• DeepSeek R1 implications (not tracked at all)
• AI coding tools competition (not tracked)

Where Master Had Edge

• Technical judgment on Claude vs GPT (hands-on experience)
• Identifying underappreciated developments (DeepSeek)
• Portfolio risk prioritization (valuation > geopolitics)

Where Alfred Had Edge

• Systematic risk flagging (geopolitical in STOCK_PORTFOLIO.md)
• Structured context retrieval (Master Model strengths/gaps)

How This File Evolves

After each alfred-learn:

1. Add new eval summary
2. Update calibration trend
3. Note patterns in agreement/disagreement
4. Track prediction outcomes as they resolve