35f01286b8
Fixes governance violations (inst_016/017/018) missed in previous commit: - Replace "production-ready" → "operational"/"validated" (inst_018) - Replace "perfect"/"guaranteed" → "absolute assurance terms" (inst_017) - Add [NEEDS VERIFICATION] to uncited GPU projections (inst_016) Files fixed: - al-integration/IMPLEMENTATION_SUMMARY.md (5 violations) - al-integration/README.md (3 violations + 1 absolute term) - docs/UPDATE_PLAN.md (1 uncited statistic) 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com>
372 lines
7.9 KiB
Markdown
372 lines
7.9 KiB
Markdown
# Documentation & Stress Testing Plan
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**Date**: November 3, 2025
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**Purpose**: Update all references to Agent Lightning + CPU stress testing
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---
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## Part 1: Documentation Updates
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### A. Website Pages to Update
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#### 1. Homepage (`public/index.html`)
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**Current status**: Says "Now integrating with Agent Lightning"
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**Update needed**: "Agent Lightning integration operational (CPU training)"
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**Locations**:
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- Hero subtitle
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- "What's New" section
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- Community section
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**Action**: Update wording from "integrating" to "operational"
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---
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#### 2. Persona Pages
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##### `public/researcher.html`
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**Check**: What does it say about AL?
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**Update**: Reflect operational status + research opportunities
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##### `public/implementer.html`
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**Check**: Implementation guides accurate?
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**Update**: Add real integration examples
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##### `public/leader.html`
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**Check**: Business case still accurate?
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**Update**: Real metrics from stress testing
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---
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#### 3. Integration Page (`public/integrations/agent-lightning.html`)
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**Status**: ✅ Already updated today
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**Content**: Accurate operational status
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---
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### B. Documentation Files
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#### 1. GitHub README (`docs/github/AGENT_LIGHTNING_README.md`)
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**Status**: Pushed to GitHub
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**Check**: Still accurate after today's changes?
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**Update**: May need operational status update
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#### 2. Integration Guides
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- `docs/integrations/agent-lightning.md`
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- `docs/integrations/agent-lightning-guide.md`
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**Update**: Add real implementation examples, stress test results
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#### 3. Demo Documentation
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- `demos/agent-lightning-integration/README.md`
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- Demo 1 & 2 READMEs
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**Update**: Clarify conceptual vs real integration
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---
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### C. Translation Files
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Check if translations need updates for:
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- "integrating" → "operational"
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- New status messaging
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**Files**:
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- `public/locales/en/common.json`
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- `public/locales/de/common.json`
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- `public/locales/fr/common.json`
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---
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## Part 2: CPU Stress Testing
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### A. Test Suite Design
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#### Test 1: Analyzer Performance Benchmark
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**Purpose**: Measure analysis speed, accuracy, consistency
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**Metrics**:
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- Time per analysis (ms)
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- Throughput (analyses/second)
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- Memory usage (MB)
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- CPU utilization (%)
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**Dataset**: 100 synthetic feedback examples (varied types)
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**Expected**:
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- <5 seconds per analysis (acceptable)
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- <1 second per analysis (good)
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- <500ms per analysis (excellent)
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---
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#### Test 2: Reward Function Consistency
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**Purpose**: Verify rewards are stable across runs
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**Test**:
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- Run same feedback through analyzer 10 times
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- Measure reward variance
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- Check category consistency
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**Expected**:
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- Same feedback → same category (100% consistency)
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- Reward variance <0.1 (stable scoring)
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---
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#### Test 3: Concurrent Load Testing
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**Purpose**: Test multiple feedback submissions simultaneously
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**Test**:
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- 10 concurrent analyses
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- 50 concurrent analyses
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- 100 concurrent analyses
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**Metrics**:
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- Response time degradation
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- Error rate
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- Memory pressure
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- CPU saturation point
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**Expected**:
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- 10 concurrent: <10% slowdown
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- 50 concurrent: <50% slowdown
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- 100 concurrent: Identify CPU limit
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---
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#### Test 4: Error Handling
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**Purpose**: Verify graceful degradation
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**Tests**:
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- Invalid feedback (empty comment)
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- Extremely long feedback (10,000 chars)
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- Malformed data
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- LLM timeout/failure
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**Expected**:
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- No crashes
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- Appropriate error messages
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- Reward penalties (-0.5) for failures
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---
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#### Test 5: Category Accuracy (Manual Validation)
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**Purpose**: Validate analyzer categorizations
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**Process**:
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1. Run analyzer on 50 diverse examples
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2. Manually review each categorization
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3. Calculate accuracy rate
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4. Identify problem patterns
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**Expected**:
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- >80% accuracy (acceptable)
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- >90% accuracy (good)
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- >95% accuracy (excellent)
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---
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#### Test 6: MongoDB Query Performance
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**Purpose**: Test feedback data pipeline
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**Tests**:
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- Load 1000 feedback entries
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- Query by type/rating/page
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- Aggregate statistics
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- Concurrent reads
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**Metrics**:
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- Query time (ms)
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- Index effectiveness
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- Connection pooling
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---
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### B. Baseline Metrics to Collect
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#### Performance Metrics:
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- Analysis time (mean, p50, p95, p99)
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- Throughput (analyses/second)
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- Memory usage (idle, peak)
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- CPU utilization (mean, peak)
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#### Quality Metrics:
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- Category accuracy (%)
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- Severity accuracy (%)
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- Reward consistency (variance)
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- False positive rate (%)
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#### System Metrics:
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- MongoDB query time (ms)
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- Network latency (ms)
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- Error rate (%)
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- Uptime (%)
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---
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### C. Stress Test Implementation
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**File**: `al-integration/testing/stress_test.py`
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**Features**:
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- Automated test suite
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- Metrics collection
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- Report generation
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- Baseline documentation
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**Output**:
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- `STRESS_TEST_REPORT.md`
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- Metrics JSON for tracking
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- Performance graphs (optional)
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---
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### D. Comparison: CPU vs GPU (Future)
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**CPU Baseline** (Today):
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- Analysis time: X ms
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- Throughput: Y analyses/sec
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- Memory: Z MB
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**GPU Target** (MS-S1 Max):
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- Analysis time: X/10 ms (10x faster) [NEEDS VERIFICATION]
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- Throughput: Y*10 analyses/sec [NEEDS VERIFICATION]
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- Memory: Z MB + GPU VRAM
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**This validates "5% performance cost" claims with REAL DATA**
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---
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## Part 3: Update Deployment Strategy
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### Phase 1: Audit (30 minutes)
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1. Check all pages for AL mentions
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2. Document current wording
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3. Identify what needs changing
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### Phase 2: Updates (1-2 hours)
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1. Update homepage (hero, what's new)
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2. Update persona pages (researcher, leader, implementer)
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3. Update documentation files
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4. Update translations if needed
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### Phase 3: Stress Testing (2-3 hours)
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1. Build stress test suite
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2. Run all tests
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3. Collect baseline metrics
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4. Document results
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### Phase 4: Documentation (1 hour)
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1. Create STRESS_TEST_REPORT.md
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2. Update integration docs with real metrics
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3. Update website with performance data
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### Phase 5: Deployment (30 minutes)
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1. Deploy all website updates
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2. Commit stress test code
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3. Push documentation updates
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---
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## Part 4: Expected Outcomes
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### Documentation Updates:
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✅ All pages reflect "operational" status
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✅ No false claims remain
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✅ Real implementation examples
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✅ Accurate technical details
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### Stress Testing:
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✅ CPU baseline metrics documented
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✅ Performance bottlenecks identified
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✅ Error handling validated
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✅ Category accuracy measured
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✅ Real data for claims validation
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### Benefits:
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✅ Confidence in CPU deployment
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✅ Baseline for GPU comparison
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✅ Data-driven optimization
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✅ Honest performance claims
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✅ Research integrity maintained
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---
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## Priority Order
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**High Priority** (Do first):
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1. Stress test suite (proves it works)
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2. Collect baseline metrics (proves performance)
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3. Homepage update (most visible)
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4. Integration docs update (technical accuracy)
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**Medium Priority**:
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5. Persona pages update
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6. Translation files
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7. GitHub README review
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**Low Priority** (Can wait):
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8. Demo documentation polish
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9. Planning documents archive
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---
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## Success Criteria
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### Documentation:
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- [ ] All pages say "operational" not "in development"
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- [ ] Real metrics cited (from stress tests)
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- [ ] No false claims
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- [ ] Translations updated
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### Stress Testing:
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- [ ] All 6 test categories passed
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- [ ] Baseline metrics documented
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- [ ] Performance report published
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- [ ] Bottlenecks identified
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### Deployment:
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- [ ] Website live with updates
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- [ ] Docs committed to git
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- [ ] Stress test code in repo
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- [ ] Metrics tracked over time
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---
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## Timeline
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**Session 1 (Today)**:
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- Build stress test suite
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- Run initial tests
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- Document baseline metrics
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**Session 2 (Tomorrow)**:
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- Update all pages
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- Deploy to production
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- Commit documentation
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**Total**: 4-6 hours work
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---
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## Notes
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**Why Stress Testing Matters**:
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- Validates "REAL implementation" claims
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- Provides data for "5% cost" comparison
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- Identifies CPU limitations before GPU
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- Baseline for optimization
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- Research integrity (cite real numbers)
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**Why Documentation Updates Matter**:
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- Removes last false claims
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- Shows progress to community
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- Demonstrates research integrity
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- Attracts collaborators with honest status
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---
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**Status**: Ready to execute
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**Owner**: Claude Code
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**Review**: User approval before deployment
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