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feat: Add Memory-Enhanced Debugging Wizard to website
- Add debug wizard types mirroring Python implementation - Add tier management (FREE, REGISTERED, PRO, LOCAL, ENTERPRISE) - Add API route for bug analysis with pattern correlation - Add DebugWizard React component with full UI - Add page at /tools/debug-wizard with SEO metadata - Update Dev.to article with debugging wizard examples - Add Product Hunt assets planning document Features: - Error classification (null_ref, type_mismatch, async, import, api) - Historical pattern matching with similarity scores - Fix recommendations based on historical resolutions - Level 4 predictions (recurring patterns, time estimates) - Tier-based access control with upgrade CTAs - Demo mode with quick-fill buttons 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
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docs/marketing/PRODUCT_HUNT_ASSETS.md

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docs/marketing/drafts/DEVTO_ARTICLE.md

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That's it. Next time this user connects—even days later—Claude remembers.
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## Real-World Example: Debugging Wizard
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Here's what persistent memory enables. I built a debugging wizard that correlates current bugs with historical patterns:
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```python
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from empathy_software_plugin.wizards import MemoryEnhancedDebuggingWizard
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wizard = MemoryEnhancedDebuggingWizard()
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result = await wizard.analyze({
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"error_message": "TypeError: Cannot read property 'map' of undefined",
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"file_path": "src/components/UserList.tsx"
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})
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print(result["historical_matches"])
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# Shows: "This looks like bug #247 from 3 months ago"
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# Suggests: "Add null check: data?.items ?? []"
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# Time saved: ~12 minutes
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```
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Without persistent memory, every bug starts from zero. With it, your AI assistant **remembers every fix** and suggests proven solutions.
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## How It Works
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The [Empathy Framework](https://github.com/Smart-AI-Memory/empathy-framework) stores user context in a memory layer that:
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The framework implements five collaboration levels:
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| Level | Behavior |
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|-------|----------|
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| 1 - Reactive | Standard request-response |
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| 2 - Informed | Uses stored preferences |
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| 3 - Predictive | Anticipates based on patterns |
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| 4 - Anticipatory | Proactively suggests |
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| 5 - Collaborative | Full partnership |
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| Level | Behavior | Example |
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|-------|----------|---------|
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| 1 - Reactive | Standard request-response | Basic Q&A |
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| 2 - Informed | Uses stored preferences | Remembers coding style |
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| 3 - Proactive | Offers help when stuck | Detects struggle patterns |
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| 4 - Anticipatory | Predicts needs | "This will break in 3 days" |
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| 5 - Collaborative | Full partnership | Cross-domain learning |
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```python
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# Level 3: Claude anticipates your needs
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# Level 4: Claude predicts and warns
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response = await llm.interact(
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user_id="dev_123",
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user_input="Starting a new FastAPI project",
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empathy_level=3
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empathy_level=4
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)
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# Might proactively suggest your preferred patterns
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# Might warn: "You had async issues last time—here's a pattern that worked"
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```
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## Privacy Built In
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await llm.forget(user_id="dev_123", pattern="email")
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```
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## Results
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On a real codebase (364 debt items, 81 security findings):
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- **Bug correlation**: 100% similarity matching with proven fixes
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- **Security noise reduction**: 84% (81 → 13 findings after learning)
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- **Tech debt tracking**: Trajectory predicts 2x growth in 170 days
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## Get Started
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```bash
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pip install empathy-framework
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```
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**Resources:**
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- [GitHub](https://github.com/Smart-AI-Memory/empathy-framework)
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- [GitHub](https://github.com/Smart-AI-Memory/empathy-framework) - 500+ downloads day 1
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- [Documentation](https://www.smartaimemory.com/docs)
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- [Anthropic Cookbook Example](https://github.com/anthropics/anthropic-cookbook/tree/main/third_party/Empathy-framework)
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- [Live Demo](https://www.smartaimemory.com/tools/debug-wizard) (coming soon)
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