Pattern Learning Explained

Pattern Learning Explained

How pattern learning works in SuperLocalMemory - Automatic detection of your coding preferences and style, all processed locally for privacy.

---

What is Pattern Learning?

Pattern learning is SuperLocalMemory's ability to automatically detect your coding preferences and style by analyzing the memories you save. It learns what frameworks you prefer, how you write code, what testing approaches you use, and more — without any manual configuration.

Example:

After saving memories, SuperLocalMemory learns:

Your Coding Identity:
- Framework preference: React (high confidence)
- Style: Performance over readability
- Testing: Jest + React Testing Library
- API style: REST over GraphQL
- Language: Python for backends

Why this matters: Your AI assistant can automatically match your preferences without you re-explaining them every session.

For technical details on the learning algorithms, see our published research: https://zenodo.org/records/18709670

---

How It Works

Pattern learning analyzes your saved memories across multiple dimensions:

• Technology preferences — What frameworks, languages, and tools you use most
• Architecture choices — Your preferred patterns (e.g., REST vs GraphQL, microservices vs monolith)
• Security approaches — Your go-to authentication and authorization patterns
• Coding style — Performance vs readability, testing depth, code organization
• Domain terminology — Project-specific terms and industry vocabulary

Each detected preference gets a confidence score based on the evidence in your memories. Low confidence with small datasets is correct behavior — the system is conservative by design.

---

Learning Process

Automatic Learning

Triggered on every remember operation:

slm remember "We use FastAPI for REST APIs" --tags python,backend

No manual action required. Patterns update as you add memories.

Manual Update

# Force pattern update
python3 ~/.claude-memory/pattern_learner.py update

When to use:

• After bulk imports
• After database restore
• When patterns seem stale

Get Identity Context

# Get identity context
python3 ~/.claude-memory/pattern_learner.py context 0.5

Output:

Your Coding Identity:

Framework Preferences:
- React (strong confidence)
- FastAPI (strong confidence)

Language Preferences:
- Python for backends
- TypeScript for frontend

Architecture Patterns:
- REST over GraphQL

Security Approaches:
- JWT tokens

Coding Style:
- Performance over readability

---

Identity Context Generation

Identity context is a formatted text summary of your learned patterns that gets injected into AI assistant prompts automatically.

Using with AI Assistants

Automatic injection (Cursor/Claude Desktop):

• MCP server automatically includes identity context
• No manual action needed

Manual injection:

# Get context
context=$(python3 ~/.claude-memory/pattern_learner.py context 0.5)

# Use with any AI assistant
echo "$context\n\nNow help me build a new API endpoint."

---

Adaptive Learning

Patterns Evolve Over Time

Scenario 1: New preference emerges

Month 1: React (dominant)
Month 3: React and Vue emerging equally
Month 4: Pattern learning adapts: "Shifting from React to Vue"

Scenario 2: Temporary spike

Week 1-4: Python dominant
Week 5: JavaScript spike (project context)
Week 6: Python resumes dominance — recognized as temporary

Recency and Decay

Recent patterns are weighted more heavily. Old patterns gradually fade when not reinforced. This ensures your current preferences dominate, not stale ones from months ago.

---

Privacy & Security

100% Local Processing

No data leaves your machine:

• All pattern learning happens locally
• No external API calls
• No telemetry
• No cloud sync

Location: ~/.claude-memory/memory.db

Access control: Standard filesystem permissions

---

Use Cases

1. Onboarding New AI Sessions

Without pattern learning:

You: "Help me build an API"
AI: "Sure! Which framework? Which language? REST or GraphQL?"
You: *explains preferences again*

With pattern learning:

You: "Help me build an API"
AI: [Reads identity context: FastAPI, Python, REST, JWT]
AI: "I'll create a FastAPI REST endpoint with JWT auth"

2. Project-Specific Patterns

Use profiles for different projects:

# Work project
slm switch-profile work
slm remember "Work project uses React + FastAPI"

# Personal project
slm switch-profile personal
slm remember "Personal project uses Vue + Flask"

# Each profile learns separate patterns

3. Debugging Assistance

Pattern learning knows your typical patterns:

AI: "You typically use JWT auth, but this endpoint uses sessions.
     Was this intentional or should I fix it?"

---

Advanced Features

Custom Confidence Threshold

# High confidence only (80%+)
python3 ~/.claude-memory/pattern_learner.py context 0.8

# More patterns included (30%+)
python3 ~/.claude-memory/pattern_learner.py context 0.3

List All Patterns

# View all learned patterns
python3 ~/.claude-memory/pattern_learner.py list

Reset Patterns

# Clear all learned patterns
python3 ~/.claude-memory/pattern_learner.py reset

# Confirmation required
Are you sure? This will delete all learned patterns. [y/N]: y

✓ Patterns reset successfully

---

Troubleshooting

"No patterns learned"

Cause: Not enough memories with relevant content

Solution:

# Check memory count
slm status

# Need at least 20-30 memories for meaningful patterns
slm remember "I prefer React for frontend"
slm remember "I use Python for backend APIs"
slm remember "I prefer performance over readability"

"Patterns seem wrong"

Cause: Conflicting or outdated memories

Solution:

# Review learned patterns
python3 ~/.claude-memory/pattern_learner.py list

# Force pattern update
python3 ~/.claude-memory/pattern_learner.py update

"Confidence too low"

Cause: Not enough samples or conflicting signals

Solution:

# Add more memories about your preferences
slm remember "I always use React for frontend" --tags preference
slm remember "React is my go-to framework" --tags preference

# Or lower confidence threshold
python3 ~/.claude-memory/pattern_learner.py context 0.3

---

Best Practices

1. Be Explicit About Preferences

Good:

slm remember "I prefer React over Vue for this project" --tags preference

Poor:

slm remember "Used React" --tags todo

2. Tag Preferences

slm remember "Team standard: Use TypeScript" --tags team-standard,preference

3. Update Patterns After Major Changes

# Switched from React to Vue
slm remember "Migrated from React to Vue" --tags migration
slm remember "Now using Vue for all frontend" --tags preference

# Force pattern update
python3 ~/.claude-memory/pattern_learner.py update

4. Use Profiles for Different Contexts

# Work profile learns work patterns
slm switch-profile work

# Personal profile learns personal patterns
slm switch-profile personal

---

Related Pages

• Quick Start Tutorial - First-time setup
• Multi-Profile Workflows - Profile management
• Why Local Matters - Privacy benefits
• CLI Cheatsheet - Command reference

---

Created by Varun Pratap Bhardwaj Solution Architect • SuperLocalMemory

GitHub • Issues • Wiki