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Product Requirements Document (PRD)

Project: NeuralForge - Local-First AI IDE

Last Updated: 2024-10-31
Status: Phase 1-2 Complete (Backend), Phase 3 Starting (Tauri IDE)

🚨 Major Pivot (2024-10-31)

OLD Vision: VS Code fork with embedded SLMs
NEW Vision: Lightweight Tauri-based IDE with local AI + optional cloud providers

Reason: User feedback requested simpler IDE, full AI chat with project manipulation, YOLO mode, learning system, and RAG for framework docs. VS Code fork was too complex and restrictive.

1. Executive Summary

NeuralForge is an open-source local-first AI IDE built with Tauri, offering intelligent code completion, AI chat with project manipulation, adaptive learning, and framework documentation search.

Target Goals (v0.1.0)

  • Binary Size: ~50MB (Tauri efficiency)
  • RAM Usage: ~300MB idle (75% less than Electron)
  • Inference Speed: <50ms (local AI)
  • Cost: $0 for local AI (optional cloud providers)
  • Privacy: 100% local by default, optional cloud

2. Product Vision

Mission Statement

"Empower developers with a lightweight, privacy-first AI IDE that learns their coding style, provides intelligent assistance, and optionally connects to cloud AI providers - all while running primarily on local hardware."

Core Principles

  1. Local-First: Default to local AI, optional cloud providers
  2. Lightweight: Tauri (50MB) vs Electron (200MB) architecture
  3. Privacy-Respecting: Code stays local unless user chooses cloud
  4. Community-Driven: Open-source with transparent development
  5. Adaptive Learning: AI learns user's coding style over time
  6. Developer-Centric: Built by developers, for developers

3. Target Audience

Primary Users

  • Individual Developers: Privacy-conscious developers wanting local AI
  • Small Teams: Startups needing lightweight tools with optional cloud
  • Enterprise Developers: Companies preferring on-premise with optional external AI
  • Students: Learning to code with AI mentoring
  • Open Source Contributors: Developers building in public

User Personas

Persona 1: "Privacy-First Developer"

  • 5+ years experience
  • Works on sensitive/proprietary projects
  • Wants AI help without mandatory cloud dependency
  • Appreciates option to use cloud AI when needed

Persona 2: "Lightweight User"

  • Student or junior developer
  • 4GB-8GB RAM laptop
  • Cannot afford heavy Electron apps
  • Needs fast, responsive IDE

Persona 3: "Hybrid Worker"

  • Uses local AI for most tasks
  • Occasionally needs GPT-4/Claude for complex problems
  • Values choice: local by default, cloud optional
  • Cares about performance and privacy

4. Core Features (v0.1.0 Target)

4.1 Lightweight IDE (Tauri-Based)

Monaco Editor Integration

Features:
├── Syntax highlighting (50+ languages)
├── IntelliSense
├── Multi-file tabs
├── File explorer tree view
├── Settings panel
└── Theme support (dark/light)

Why Tauri?

  • 50MB RAM vs 200MB (Electron)
  • Native performance
  • Smaller binary size (~50MB vs ~200MB)
  • Faster startup (<3s vs 5-8s)

4.2 AI Code Completion (Ghost Text)

Inline Completion System

interface GhostTextEngine {
  - Real-time suggestions as you type
  - Gray text overlay (opacity 0.6)
  - Tab to accept, Esc to reject
  - 500ms debouncing (smart request throttling)
  - YOLO mode (auto-accept completions)
}

Performance Targets

  • Latency: <50ms (local model)
  • Accuracy: Context-aware (file + cursor position)
  • Memory: <2GB model footprint (CodeT5+ 220M)

4.3 AI Chat Panel

Chat Features

UI:
  - Side panel (resizable)
  - Message bubbles (user right, AI left)
  - Code blocks with syntax highlighting
  - Copy button for code snippets
  
Context-Awareness:
  - Knows about open files
  - Understands current selection
  - Analyzes compile errors
  
Providers:
  - Local (default, CodeT5+ 220M)
  - OpenAI (optional, user's API key)
  - Claude (optional, user's API key)
  - Gemini (optional, user's API key)
  - Custom (user's endpoint)

Code Actions

// AI can directly manipulate project files
interface CodeActions {
  createFile(path: string, content: string): void;
  modifyFile(path: string, changes: FileDiff): void;
  deleteFile(path: string): void;
  
  // With preview and confirmation
  previewChanges(actions: FileAction[]): void;
  applyChanges(actions: FileAction[]): void;
  undoChanges(actionId: string): void;
}

4.4 Learning System

Style Analyzer

class StyleAnalyzer {
  // Detects user preferences from codebase
  - Naming conventions (camelCase vs snake_case)
  - Formatting style (tabs vs spaces, brace position)
  - Comment density and style
  - Import organization
  - Test patterns
}

Adaptive Suggestions

class StyleAdapter {
  // Applies learned style to AI output
  - Match user's naming conventions
  - Follow user's formatting
  - Suggest libraries user already uses
  - Maintain project consistency
}

Learning Dashboard

// Show user what AI learned
interface LearningDashboard {
  namingStyle: "camelCase" | "snake_case" | "mixed";
  librariesUsed: string[];  // ["Spring Boot", "JUnit 5", "Lombok"]
  testFramework: string;     // "JUnit 5"
  codeStyle: StyleProfile;   // indent, braces, etc.
}

4.5 RAG System (Framework Documentation)

Qdrant Vector Database

Features:
├── Embedded mode (no external services)
├── Semantic search (CodeBERT embeddings)
├── Framework auto-detection (pom.xml, package.json)
├── Auto-crawler (Spring Boot, React, etc. docs)
└── Code snippet extraction

Usage Examples

// User asks in chat:
"How to use @Transactional in Spring Boot?"

// RAG System:
1. Detects Spring Boot from pom.xml
2. Queries Qdrant for "Spring @Transactional"
3. Returns top 5 relevant doc snippets
4. AI synthesizes answer with examples

4.6 Vibe Coding (Context-Aware Multi-Step Tasks)

Context Detection

// AI understands:
- Compile errors (from editor diagnostics)
- Cursor position (what user is working on)
- Selected code (refactoring target)
- Project structure (files, dependencies)

Multi-Step Execution

// User: "Refactor this class to use dependency injection"
// AI:
1. Shows plan (5 steps)
2. User approves
3. AI executes with progress tracker:
   ✅ Create interface for service
   ✅ Update constructor with @Autowired
   🔄 Modify test cases... (in progress)
   ⏸️ Update configuration
   ⏸️ Verify build passes

Codebase Fingerprinting

Project Profile:
├── Language distribution
├── Framework patterns
├── Naming conventions
├── Comment density
├── Testing patterns
├── Error handling style
└── Architecture patterns

→ Creates unique 512-dimension embedding
→ Finds similar projects for transfer learning

4.6 Swarm Intelligence Mode

Parallel Model Execution

async def swarm_inference(prompt):
    results = await asyncio.gather(
        model1.generate(prompt, temp=0.7),
        model2.generate(prompt, temp=0.8),
        model3.generate(prompt, temp=0.9)
    )
    return weighted_voting(results)

Collaborative Filtering

  • Models vote on best completion
  • Confidence scores determine weight
  • Disagreement triggers user choice

4.7 AI Mentor Mode

Interactive Learning Assistant

interface MentorFeatures {
  codeReview: {
    complexity: "O(n²) detected, optimize?",
    security: "SQL injection risk here",
    performance: "Consider caching this",
    style: "This violates team conventions"
  },
  
  teaching: {
    explanations: "Here's why this works...",
    alternatives: "3 other ways to do this",
    bestPractices: "Industry standard approach",
    edgeCases: "Don't forget null check"
  }
}

4.8 Time Travel Debugging

Pattern-Based Bug Prediction

  • Identifies similar past bugs
  • Shows historical fixes
  • Predicts potential issues
  • Links to relevant commits

4.9 Adapter Marketplace

Community Sharing Protocol

# Share your fine-tuned adapter
neural-forge push adapter my-style --public

# Download specialized adapters
neural-forge pull adapter facebook/react-hooks
neural-forge pull adapter google/angular-best

# Merge multiple adapters
neural-forge merge adapter1 adapter2 --output custom

Popular Adapters

  • spring-boot-microservices
  • react-hooks-typescript
  • clean-architecture-java
  • competitive-programming-cpp
  • data-science-python

4.10 Federated Learning

Privacy-Preserving Improvements

class FederatedLearning:
    def share_gradients(self, local_gradients):
        # Differential privacy
        noise = generate_gaussian_noise(epsilon=1.0)
        private_gradients = local_gradients + noise
        
        # Secure aggregation
        encrypted = homomorphic_encrypt(private_gradients)
        return send_to_federation(encrypted)

5. Technical Architecture

5.1 System Architecture

┌─────────────────────────────────────────────────┐
│                VS Code Fork (Electron)          │
├─────────────────────────────────────────────────┤
│            Extension API Compatibility           │
├─────────────┬───────────────────────────────────┤
│   Editor    │          AI Service               │
│   Core      │    ┌─────────────────────┐       │
│             │    │   Java Backend      │       │
│   Monaco    │◄───┤   Spring Boot       │       │
│   Editor    │    │   Embedded JVM      │       │
│             │    └──────┬──────────────┘       │
│             │           │                       │
│   File      │    ┌──────▼──────────────┐       │
│   System    │    │   Model Engine      │       │
│             │    │   DJL/ONNX Runtime  │       │
│   Terminal  │    └──────┬──────────────┘       │
│             │           │                       │
│   Git       │    ┌──────▼──────────────┐       │
│   Integration│   │   Vector Store      │       │
│             │    │   Qdrant Embedded   │       │
└─────────────┴────┴─────────────────────┴───────┘

5.2 Model Serving Architecture

Model Server:
├── Model Registry
│   ├── Active Models (hot)
│   ├── Available Models (cold)
│   └── Downloading Queue
│
├── Inference Engine
│   ├── ONNX Runtime (CPU)
│   ├── CUDA Provider (GPU optional)
│   └── Quantization (INT4/INT8)
│
├── Adapter Manager
│   ├── LoRA Loader
│   ├── Merge Engine
│   └── Weight Calculator
│
└── Cache Layer
    ├── KV Cache (attention)
    ├── Result Cache (completions)
    └── Embedding Cache (RAG)

5.3 Learning Pipeline

class IncrementalLearningPipeline:
    def __init__(self):
        self.buffer_size = 1000  # interactions
        self.replay_buffer = ExperienceReplay()
        self.ewc_fisher = {}  # Elastic weights
        
    async def process_interaction(self, code, action, feedback):
        # 1. Store in buffer
        self.replay_buffer.add(code, action, feedback)
        
        # 2. Check if training needed
        if self.should_train():
            await self.mini_batch_training()
            
        # 3. Update Fisher information
        if self.is_important_knowledge():
            self.update_ewc_weights()

5.4 RAG Architecture

RAG Pipeline:
├── Code Indexer
│   ├── AST Parser (Java/TS/Python)
│   ├── Semantic Chunker
│   └── Dependency Analyzer
│
├── Embedding Engine
│   ├── CodeBERT embeddings
│   └── Custom fine-tuned embedder
│
├── Vector Database (Qdrant)
│   ├── Project vectors
│   ├── Documentation vectors
│   └── Stack Overflow cache
│
└── Retrieval Strategy
    ├── Semantic search
    ├── Keyword matching
    └── Graph traversal

6. Model Strategy

6.1 Base Models Selection

Model Size License Purpose Context
CodeT5+ 770M 1.5GB Apache 2.0 Primary completion 2K
StableCode 3B 6GB (Q4) Apache 2.0 Long context tasks 16K
SantaCoder 1.1B 2.2GB Apache 2.0 Multi-language 2K
StarCoder 15B 9GB (Q4) Apache 2.0 Complex generation 8K
TinyStories-Code 33M 66MB MIT Comments/docs 512

6.2 Quantization Strategy

Quantization Levels:
├── FP16: Development/Fine-tuning
├── INT8: Default deployment
├── INT4: Memory-constrained
└── INT2: Experimental ultra-light

6.3 Model Routing Logic

function selectModel(context) {
    const complexity = analyzeComplexity(context);
    const fileSize = context.file.length;
    const language = context.language;
    
    if (fileSize > 1000 && complexity.requiresFullContext) {
        return 'stablecode-3b';  // 16K context
    }
    
    if (complexity.score > 0.8) {
        return 'starcoder-15b';  // Most capable
    }
    
    if (language in ['Java', 'Python', 'JavaScript']) {
        return 'codet5-770m';  // Best for common languages
    }
    
    return 'santacoder-1.1b';  // Good default
}

7. Fine-Tuning Pipeline

7.1 Automated Training Pipeline

Training Triggers:
├── Scheduled: Daily at low-activity hours
├── Threshold: Every 1000 interactions
├── Manual: User-initiated training
└── Git-hook: On major commits

Training Data Sources:
├── Accepted completions (positive)
├── Rejected completions (negative)
├── Manual corrections (highest weight)
├── Code review comments
├── Test results (pass/fail)
└── Build logs (success/error)

7.2 Adapter Architecture

class AdapterStack:
    """
    Composable LoRA adapters for different aspects
    """
    def __init__(self):
        self.adapters = {
            'base': None,  # Frozen
            'language': LoRAAdapter(r=4, alpha=8),    # 10MB
            'framework': LoRAAdapter(r=8, alpha=16),   # 40MB
            'project': LoRAAdapter(r=8, alpha=16),     # 40MB
            'personal': LoRAAdapter(r=4, alpha=8),     # 10MB
            'team': LoRAAdapter(r=4, alpha=8),         # 10MB
        }
        
    def combine_adapters(self, weights=None):
        """Weighted combination of multiple adapters"""
        if weights is None:
            weights = self.auto_calculate_weights()
        
        combined = sum([
            adapter * weight 
            for adapter, weight in zip(self.adapters.values(), weights)
        ])
        return combined

7.3 Continuous Learning Strategy

class ContinualLearningStrategy:
    def __init__(self):
        self.strategies = {
            'ewc': ElasticWeightConsolidation(),
            'replay': ExperienceReplay(buffer_size=10000),
            'distill': KnowledgeDistillation(),
            'maml': ModelAgnosticMetaLearning(),
        }
    
    def train_incremental(self, new_data):
        # Prevent catastrophic forgetting
        old_knowledge = self.strategies['replay'].sample(100)
        
        # Mix old and new data
        training_data = self.mix_data(
            new_data, 
            old_knowledge, 
            ratio=0.8  # 80% new, 20% old
        )
        
        # Apply EWC regularization
        loss = self.compute_loss(training_data)
        loss += self.strategies['ewc'].penalty()
        
        return loss

8. Unique Differentiators

8.1 Design Goals vs Competitors

Our focus is on:

  • 100% Local: All processing on user's machine
  • Privacy First: No data collection or telemetry
  • Resource Efficient: Optimized for lower-end hardware
  • Open Source: Fully transparent development
  • Community Driven: Adapters and extensions from users

8.2 Target Feature Set

Features we're working toward:

  1. Continuous Learning: Model improves from user feedback
  2. Project-Specific Adapters: Unique model per project
  3. Multi-Model Support: Different models for different tasks
  4. Git Integration: Learn from commit history
  5. Offline First: No internet required
  6. Adapter Marketplace: Community-shared models
  7. Resource Efficiency: Runs on modest hardware
  8. Complete Privacy: Zero data leaves your machine

9. User Experience

9.1 First Run Experience

Installation (5 minutes):
1. Download (150MB base + lazy load models)
2. Launch → Automatic setup wizard
3. Select languages you use
4. Choose model size (small/medium/large)
5. Optional: Import Git history for learning
6. Ready to code!

9.2 Daily Workflow

// Morning startup
- Models load in background (3 seconds)
- Yesterday's learnings applied
- New team commits analyzed

// While coding
- Ghost text appears (30ms)
- Tab to accept, Esc to reject
- Cmd+K for AI commands
- Right-click for "AI Explain"

// End of day
- Auto-training from today's work
- Adapter saved to profile
- Optional: Share improvements with team

9.3 UI/UX Components

New UI Elements:
├── AI Status Bar
│   ├── Active model indicator
│   ├── Confidence percentage
│   ├── Learning progress
│   └── Resource usage
│
├── AI Side Panel
│   ├── Chat interface
│   ├── Explanation view
│   ├── Suggestion history
│   └── Training dashboard
│
├── Inline Overlays
│   ├── Complexity warnings
│   ├── Performance hints
│   ├── Security alerts
│   └── Alternative suggestions
│
└── Settings Page
    ├── Model management
    ├── Training configuration
    ├── Privacy settings
    └── Adapter marketplace

9.4 Keyboard Shortcuts

Shortcut Action
Tab Accept completion
Cmd+K AI command palette
Cmd+I Inline chat
Cmd+Shift+E Explain selection
Cmd+Shift+R Refactor with AI
Cmd+Shift+T Generate tests
Cmd+Shift+D Generate docs
Alt+Enter Show alternatives

10. Community Features

10.1 Adapter Marketplace

Marketplace Structure:
├── Official Adapters
│   ├── Verified by core team
│   ├── Tested on large codebases
│   └── Guaranteed compatibility
│
├── Community Adapters
│   ├── User ratings/reviews
│   ├── Download counts
│   ├── Source code visible
│   └── Fork and modify
│
└── Enterprise Adapters
    ├── Private sharing
    ├── Team-only access
    └── Encrypted storage

10.2 Federated Learning Network

class FederationNode:
    def join_network(self, network_id):
        """Join learning network while preserving privacy"""
        self.network = network_id
        self.share_gradients = True
        self.receive_updates = True
        
    def contribute(self):
        """Share learning without sharing code"""
        gradients = self.compute_gradients()
        encrypted = self.apply_differential_privacy(gradients)
        self.network.submit(encrypted)

10.3 Community Channels

Current Status: Early development phase

For now, please use:

  • GitHub Issues: Bug reports and feature requests
  • GitHub Discussions: General questions and ideas
  • Pull Requests: Code contributions welcome

Note: Dedicated community channels will be established as the project matures.

11. Performance Requirements

11.1 System Requirements

Minimum

OS: Windows 10/macOS 10.14/Ubuntu 20.04
CPU: 2 cores @ 2.4GHz
RAM: 4GB
Storage: 10GB
GPU: None required

Recommended

OS: Latest stable
CPU: 4 cores @ 3.0GHz
RAM: 8GB
Storage: 20GB
GPU: Optional (6GB VRAM)

11.2 Performance Targets

Metric Target Maximum
Startup time <5s 10s
First completion <100ms 200ms
Subsequent completions <50ms 100ms
Model switching <1s 2s
Training time (1K samples) <5min 10min
RAM usage (idle) <1GB 2GB
RAM usage (active) <3GB 4GB

11.3 Optimization Strategies

Optimizations:
├── Speculative Decoding
│   └── 3x speedup with draft model
├── KV Caching
│   └── 50% reduction in compute
├── Flash Attention
│   └── 2x memory efficiency
├── Quantization
│   └── 75% model size reduction
├── Lazy Loading
│   └── Load models on demand
└── Incremental Parsing
    └── Parse only changed code

12. Security & Privacy

12.1 Data Privacy

Privacy Guarantees:
✓ No telemetry collection
✓ No cloud connectivity required
✓ All models run locally
✓ Code never leaves machine
✓ Git data stays local
✓ Opt-in federation only

12.2 Security Measures

class SecurityLayer:
    def __init__(self):
        self.features = {
            'code_sanitization': self.sanitize_input,
            'injection_prevention': self.prevent_injection,
            'model_isolation': self.sandbox_execution,
            'adapter_verification': self.verify_adapter_signature,
        }
    
    def scan_adapter(self, adapter_file):
        """Verify adapter safety before loading"""
        checksum = self.compute_hash(adapter_file)
        signature = self.verify_signature(adapter_file)
        malware_scan = self.scan_for_malware(adapter_file)
        return all([checksum, signature, malware_scan])

12.3 Compliance

  • GDPR: Full compliance (no data collection)
  • SOC 2: Audit trail capabilities
  • HIPAA: Medical code safe (no data exposure)
  • Enterprise: On-premise deployment ready

13. Distribution Strategy

13.1 Release Channels

Stable (Monthly):
├── Full testing suite passed
├── Community beta tested
└── Production ready

Beta (Weekly):
├── Feature complete
├── Known issues documented
└── Feedback collection

Nightly (Daily):
├── Latest features
├── Experimental
└── Automated builds

13.2 Platform Packages

Platform Package Size Auto-Update
Windows .exe installer 200MB Yes
macOS .dmg 200MB Yes
Linux .AppImage 200MB Yes
Linux .deb/.rpm 200MB Via package manager
Universal .tar.gz 200MB No

13.3 Model Distribution

# Progressive download
- Core editor: 200MB (immediate)
- Essential model: 500MB (background)
- Additional models: On-demand

# CDN Strategy
- GitHub Releases (primary)
- Hugging Face Hub (models)
- Torrent (community mirrors)

14. Success Metrics

14.1 Target Adoption Metrics

Metric Target Goal
GitHub Stars Growing community interest
Active Contributors Sustainable development
Adapters Created Vibrant ecosystem
User Feedback Positive reception

14.2 Technical Quality Targets

Code Quality Goals:
├── Acceptance Rate: >60%
├── False Positive: <20%
├── Latency P95: <200ms
└── Stability: Minimal crashes

User Experience Goals:
├── Easy Installation
├── Smooth Performance
├── Clear Documentation
└── Responsive Support

15. Development Roadmap

Note: This is an early-stage project. The roadmap represents our vision and planned features.

Current Status

✓ Project initialization
✓ Architecture design
✓ Documentation foundation

Planned Phases

Phase 1: Foundation (Target: Q1 2026)

- [ ] VS Code fork setup
- [ ] Basic completion engine
- [ ] Model integration (CodeT5+)
- [ ] Local model serving
- [ ] Essential UI components

Phase 2: Core Features (Target: Q2 2026)

- [ ] Multi-model support
- [ ] Smart model routing
- [ ] Git integration basics
- [ ] Basic adapter system
- [ ] Performance optimization

Phase 3: Advanced Features (Target: Q3-Q4 2026)

- [ ] Incremental learning
- [ ] Adapter marketplace
- [ ] Advanced completions
- [ ] Team collaboration features
- [ ] Documentation and polish

Timeline Disclaimer: These are aspirational targets subject to community involvement and development progress.

16. Technical Stack

16.1 Complete Technology List

Component Technology License Purpose
Frontend
Editor Base VS Code OSS MIT Core editor
UI Framework Electron MIT Desktop app
Editor Component Monaco MIT Code editing
Backend
Runtime Java 21 + GraalVM GPL+CE Main backend
Framework Spring Boot Apache 2.0 Service layer
ML Framework DJL Apache 2.0 Model serving
Inference ONNX Runtime MIT Model execution
AI/ML
Base Models Various Apache/MIT See section 6.1
Fine-tuning Custom LoRA Apache 2.0 Adaptation
Vector DB Qdrant Apache 2.0 Embeddings
Embeddings CodeBERT MIT Code understanding
Data
Database H2 MPL 2.0/EPL Metadata
Cache Caffeine Apache 2.0 Performance
Serialization Apache Arrow Apache 2.0 Data format
Infrastructure
Build Gradle Apache 2.0 Build system
CI/CD GitHub Actions N/A Automation
Package electron-builder MIT Distribution
Utilities
Math Apache Commons Apache 2.0 Calculations
Async Project Reactor Apache 2.0 Reactive
Logging SLF4J + Logback MIT/EPL Logging

16.2 Development Tools

Required:
├── Node.js 18+
├── Java 21+
├── Python 3.10+ (model conversion)
├── Git
└── 16GB RAM (for building desktop app)

Optional:
├── CUDA Toolkit (GPU acceleration)
└── VS Code (dogfooding)

NOT Used:
├── Docker (not needed - desktop app)
├── Kubernetes (not needed - desktop app)
└── Web servers (not needed - desktop app)

17. Risk Analysis

17.1 Technical Risks

Risk Impact Likelihood Mitigation
Model performance issues High Medium Multiple model fallbacks
Memory constraints High Low Aggressive quantization
Compatibility breaks Medium Medium Extensive testing
Security vulnerabilities High Low Security audits

17.2 Business Risks

Risk Impact Likelihood Mitigation
Low adoption High Medium Strong marketing
Competition from big tech High High Focus on privacy/local
Maintaining momentum Medium Medium Community building
Sustainability Medium Low Sponsorships/donations

18. Competitive Analysis

18.1 Market Landscape

The AI coding assistant market includes established players like GitHub Copilot, Cursor, and Tabnine. NeuralForge aims to differentiate through:

  • 100% Local Processing: Complete privacy and offline functionality
  • Open Source: Full transparency and community involvement
  • Resource Efficiency: Optimized for modest hardware
  • No Subscriptions: Permanently free
  • Adaptable: Learn from individual coding patterns

18.2 Our Unique Approach

  1. Privacy-First Architecture: No code ever leaves your machine
  2. Community-Driven: Shared adapters and improvements
  3. Transparent Development: Open source from day one
  4. Local Learning: Models adapt to your style
  5. Resource Conscious: Designed for real-world hardware constraints

19. Marketing Strategy

19.1 Positioning

Tagline: "Your AI pair programmer that learns from you, not from you"

19.2 Launch Strategy

Pre-Launch:
├── Beta program (100 developers)
├── Blog posts series
├── YouTube demos
└── Reddit/HN presence

Launch:
├── Product Hunt
├── Hacker News
├── Dev.to articles
├── Twitter/X campaign
└── Conference talks

Post-Launch:
├── Community building
├── Workshop series
├── Partner integrations
└── Enterprise outreach

19.3 Growth Strategy

  1. Open Source First: Full transparency
  2. Developer Evangelism: Conference talks
  3. Content Marketing: Tutorials and guides
  4. Community Challenges: Coding competitions
  5. Enterprise Pilots: Free trials for companies

20. Legal Considerations

20.1 Licensing

Project License: Apache 2.0
Model Licenses: Apache 2.0 / MIT only
Dataset Licenses: Verified open source
Dependencies: Compatible licenses only

20.2 Compliance Requirements

  • No proprietary code in training
  • GDPR compliance built-in
  • Export control compliance
  • Accessibility standards (WCAG)

20.3 Intellectual Property

  • Trademark registration for "NeuralForge"
  • Contributor License Agreement (CLA)
  • Patent review for novel techniques
  • Copyright assignment clarity

21. Appendices

Appendix A: Model Performance Targets

Model Target Context Target Latency Memory Budget
CodeT5+ 770M 2K tokens ~50ms ~1.5GB
SantaCoder 1.1B 2K tokens ~60ms ~2.2GB
StableCode 3B 16K tokens ~100ms ~3.5GB

Note: These are target specifications. Actual performance will vary based on hardware and optimization.

Appendix B: API Documentation

interface AIService {
  // Completion API
  complete(context: CodeContext): Promise<Completion>
  
  // Chat API
  chat(message: string): Promise<Response>
  
  // Training API
  train(dataset: Dataset): Promise<TrainingResult>
  
  // Adapter API
  loadAdapter(path: string): Promise<void>
  mergeAdapters(adapters: Adapter[]): Promise<Adapter>
  
  // Settings API
  configure(settings: Settings): Promise<void>
}

Appendix C: Training Data Format

{
  "version": "1.0",
  "samples": [
    {
      "context": "public class User {",
      "completion": "private String name;",
      "accepted": true,
      "timestamp": 1234567890,
      "metadata": {
        "language": "java",
        "project": "my-app",
        "file": "User.java"
      }
    }
  ]
}

Appendix D: Adapter Format

adapter:
  name: "spring-boot-expert"
  version: "1.0.0"
  base_model: "codet5-770m"
  parameters:
    r: 8
    alpha: 16
    dropout: 0.1
  training:
    samples: 50000
    epochs: 3
    learning_rate: 0.0001
  metadata:
    author: "community"
    license: "Apache-2.0"
    description: "Spring Boot patterns"

Document History

Version Date Author Changes
1.0.0 2024-10-30 Yamaç Bezirgan Initial PRD

End of PRD - This document represents the vision and planned features for NeuralForge, an early-stage open-source project.