A hierarchical multi-interface reasoning system with Quality Translation (QT) capabilities, multi-language support, 24-hour uptime tracking, and enterprise-grade code management.
What's New in v3.1?
Major feature release introducing Quality Translation (QT) system:
- 🌍 Quality Translation (QT) — Replaces DLP with multi-language support (English, Spanish, Arabic, French)
- ⏰ 24-Hour Uptime Calendar — Real-time hourly availability tracking with metrics
- 🎯 Quality Scoring — Content assessment system (0-100 scale) for data integrity
- 📊 Translation Metrics — Store and analyze translation quality across all languages
- 🔄 Enhanced Protection — Language-aware content validation with quality thresholds
- 🚀 Production Ready — 0 errors, 8/8 tests passing
This application is an AI-enhanced hierarchical reasoning system that processes complex queries through multiple layers of intelligent analysis. It combines deep learning principles with practical software engineering to deliver:
- 📊 Multi-layer Reasoning: Process information through AbstractCore, State, Depth, and Aim interfaces
- 🌍 Quality Translation (QT): Multi-language support with quality scoring for 4 languages
- ⏰ 24-Hour Uptime Calendar: Real-time hourly tracking of system availability and events
- 💾 Persistent Storage: SQL Server integration with Entity Framework Core 9.0.0
- 📊 Quality Metrics: Assess, score, and store translation quality (0-100 scale)
- 📝 Code Intelligence: Store, review, and manage entire codebase with quality metrics
- ⚙️ Custom Commands: Define and execute protocol-based string commands from database
- 🔄 Smart Workflows: Automated review cycles with priority management and status tracking
- 🧠 Adaptive Processing: Configurable depth levels (1-10) for reasoning complexity
- 🔐 Data Integrity: Quality-aware blocking of low-quality updates
- 📚 Interactive Learning: FAQ system, translator, and protocol documentation built-in
- 🎯 v3.1 New: Quality Translation replacing Data Loss Prevention
- 🚀 Latest Packages: EF Core 9.0.0, .NET Test SDK 17.13.0
- Quality Translation System — Multi-language support (English, Spanish, Arabic, French) with quality assessment
- 24-Hour Uptime Calendar — Hourly tracking of system availability with real-time metrics
- Hierarchical Architecture — Multi-interface design with AbstractCore, State, Depth, and Aim layers
- Quality Scoring — Content assessment (0-100 scale) with configurable thresholds
- Interactive Menu — User-friendly CLI with 8 menu options for full functionality
- Multilingual Translator — Translate 60+ phrases into Spanish, Arabic, or French
- Translation Database — Store and manage custom translation rules with priority matching
- Code Repository System — Store, review, and manage source code with quality scoring
- Review Workflows — Complete code review lifecycle with status tracking
- String Commands — Define and execute custom commands from database
- Comprehensive Testing — 8 XUnit tests covering all core functionality
- Full Documentation — Multiple guides for different user roles
- SQL Integration — Entity Framework Core 9.0.0 with SQL Server
- CI/CD Automation — Multi-platform releases via GitHub Actions
- Protocol Translation — Instruction translation following hierarchical protocol
- Advanced Type Safety — Enhanced nullable reference handling and null safety
| Audience | Resource | Purpose |
|---|---|---|
| First-timers | Getting Started | Build, run, and understand the basics |
| Developers | Architecture Guide | System design and components |
| Language Learners | Translator Feature | Multi-language translation guide |
| Database Users | Translation Management | Store and manage translations |
| Code Reviewers | Code Repository | Review workflows and quality tracking |
| Security-minded | Quality Translation Guide | QT system deep dive |
| Testers | Testing Guide | Writing and running tests |
| Contributors | Contributing | Development workflow & standards |
| All | Full Wiki | Complete reference |
- .NET 10.0 SDK or higher
- Git (for cloning)
# Clone the repository
git clone https://github.com/quickattach0-tech/DeepLearningProtocol.git
cd DeepLearningProtocol
# Build
dotnet build
# Run
dotnet run --project DeepLearningProtocol/DeepLearningProtocol.csproj
# Test (8 tests pass ✅)
dotnet testVS Code: Press Ctrl+Shift+B to run, F5 to debug.
╔════════════════════════════════════════════════════════╗
║ Deep Learning Protocol - Interactive Menu ║
╚════════════════════════════════════════════════════════╝
1. Run Interactive Protocol
2. View FAQ
3. Translate Text
4. View System Data Map
5. Translate & Store Text
6. Manage Translation Rules
7. Code Repository & Review
8. Exit
Choose an option (1-8):
Enter your question: What is the meaning of artificial intelligence?
Enter your aim (goal): Understand AI concepts
Enter depth level (1-10): 5
Processing through AbstractCore...
Running IAimInterface with goal: Understand AI concepts
Recursive depth processing (Level 5 of 10)...
✓ Query processed successfully
State backed up to ./.dlp_backups/state_20260125_134512_789.txt
Enter text to translate (or 'back'): Hello World
Spanish: Hola Mundo
Arabic: مرحبا العالم
French: Bonjour le Monde
Press Enter to continue...
Storing "Hello World" to database...
Stored Translation ID: 142
Spanish: Hola Mundo (Quality: 95)
Arabic: مرحبا العالم (Quality: 92)
French: Bonjour le Monde (Quality: 94)
View in database? (y/n): y
╔════════════════════════════════════════════════════════╗
║ Code Repository & Review System ║
╚════════════════════════════════════════════════════════╝
1. Store Project Source Files
2. View Code Files Index
3. Review Code File
4. Add Code Review Record
5. View Review Workflow
6. Update Review Status
7. Get Files by Status
8. Back to Main Menu
Choose an option (1-8): 1
Scanning /workspaces/DeepLearningProtocol/DeepLearningProtocol...
✓ Stored 17 source files to code repository.
Enter file ID: 5
Enter review type: Quality
Quality score (0-100): 87
Feedback: Well-structured with comprehensive error handling
Issues found: Consider adding more inline documentation
Recommended changes: Add XML documentation comments
✓ Code review added with quality score: 87
✓ Auto-calculated priority: 2 (score 87 = low priority)
✓ Quality Assessment: Content quality score: 85/100
✓ Translation to Spanish: "protocolo de aprendizaje profundo"
✓ 24-Hour Uptime Status: 18 active hours, 75% availability
✓ Metric Stored: Translation quality tracked and recorded
Current state recovered. Try again with safe content.
✓ How do I run the program?
Three ways:
1. VS Code: Press Ctrl+Shift+B (default task)
2. CLI: dotnet run --project DeepLearningProtocol/DeepLearningProtocol.csproj
3. Interactive: Run it and follow the menu prompts
Press Enter to continue...
════════════════════════════════════════════════════════
SYSTEM DATA MAP
════════════════════════════════════════════════════════
SYSTEM STATES:
• Processing: Active query/task in execution
• Waiting: Awaiting user input or confirmation
• Idle: Ready for new input
• QualityCheck: Assessing content quality metrics
• Error: Encountered recoverable error
INTERFACE OPERATIONS:
• Aim.SetGoal(): Define strategic objective
• Depth.Process(level): Recursive analysis at level 1-10
• State.Save(): Persist current state to database
• QT.AssessQuality(): Check content quality (0-100)
════════════════════════════════════════════════════════
The protocol implements four core components:
| Component | Purpose | Responsibility |
|---|---|---|
| AbstractCore | Deepest reasoning layer | Fundamental processing logic |
| IAimInterface | Goal-directed processing | Strategic objectives & targets |
| IDepthInterface | Recursive hierarchical processing | N-level depth control |
| IStateInterface | State management | Current state tracking & updates |
Plus: QualityTranslation (QT) layer assesses content quality with multi-language support and uptime tracking.
DeepLearningProtocol/
├── DeepLearningProtocol/ Core protocol implementation
│ ├── Program.cs Main logic + QT + Menu system
│ └── DeepLearningProtocol.csproj
├── DeepLearningProtocol.Tests/ Unit tests (8 tests, all passing)
├── docs/ Comprehensive documentation
├── .github/workflows/dotnet.yml CI/CD pipeline with multi-platform builds
├── .vscode/ VS Code tasks & debug config
└── README.md This file
- Custom input questions
- Goal-directed processing
- Configurable depth levels (1-10)
- QT-protected state management
Multi-language support with quality assessment:
- 4 Languages: English, Spanish, Arabic, French
- Quality Scoring: 0-100 scale with configurable thresholds
- 24-Hour Uptime Calendar: Hourly tracking of system availability
- Metrics Storage: Track and analyze translation quality
- Threshold Enforcement: Block updates below quality threshold
Browse pre-written answers about:
- How to use the protocol
- Architecture details
- Quality Translation functionality
- Customization options
The Deep Learning Protocol employs a multi-layered AI reasoning architecture:
Input Query
↓
[Layer 1: AbstractCore] → Fundamental processing & analysis
↓
[Layer 2: IAimInterface] → Goal-directed reasoning with objectives
↓
[Layer 3: IDepthInterface] → Recursive analysis (1-10 depth levels)
↓
[Layer 4: IStateInterface] → State tracking & memory management
↓
[QT Layer: Quality Translation] → Multi-language support & uptime tracking
↓
Output Response
- Multi-depth Reasoning: Process queries at 10 different depth levels
- Level 1: Surface-level quick analysis
- Level 5: Balanced depth analysis
- Level 10: Deep philosophical reasoning
- Goal-Directed Intelligence: Define strategic aims for processing
- State Persistence: All states backed up automatically to database
- Quality Assessment: QT system validates content quality (0-100)
- Multi-Language Support: Translate to Spanish, Arabic, or French
- Adaptive Learning: Translation rules and patterns stored in SQL
| Feature | Implementation | Benefit |
|---|---|---|
| Translator AI | 60+ phrase database with ML scoring | Multi-language with quality feedback |
| Code Analysis | Quality scoring (0-100) with priority | Automated code review workflows |
| Quality Assessment | QT scanning for content quality | Data integrity + multi-language support |
| Command Learning | Store and execute custom patterns | Extensible protocol behavior |
| State Management | Hierarchical state tracking | Recoverable from any point |
- Translation: Input text → AI translates → Quality scored (0-100) → Stored in database
- Code Review: Store code → AI scores quality → Priority calculated → Review tracked
- Protocol Execution: Input query → AI processes at chosen depth → Output with backup
- Command Execution: Define pattern → AI matches rules → Execute with logging
# 1. Update Program.cs
# 2. Add tests to DeepLearningProtocol.Tests/DeepLearningProtocolTests.cs
# 3. Run tests
dotnet testPress F5 in VS Code for interactive debugging.
Feature Development (Local)
↓
[1. Create Feature Branch]
git checkout -b feature/feature-name
↓
[2. Code Changes]
- Implement feature in Program.cs
- Add unit tests to DeepLearningProtocol.Tests/
- Update docs if needed
↓
[3. Local Testing]
dotnet build # Verify no errors
dotnet test # Run all tests (must pass)
dotnet run # Manual testing
↓
[4. Commit & Push]
git add .
git commit -m "feat: description"
git push origin feature/feature-name
↓
[5. Pull Request]
GitHub PR → Code review → Merge to main
↓
[6. CI/CD Automation Triggered]
Debug build (PR)
Release build + tests (main)
Code coverage (main)
Artifact upload (main)
| Branch | Purpose | Protection |
|---|---|---|
| main | Production-ready code | ✅ PR required, CI must pass |
| develop | Feature integration | |
| feature/* | Individual features | ❌ No protection |
-
Create local feature branch
git checkout -b feature/your-feature
-
Implement changes
- Update
/DeepLearningProtocol/Program.csfor core logic - Add tests to
/DeepLearningProtocol.Tests/DeepLearningProtocolTests.cs - Update docs in
/docs/
- Update
-
Test locally
dotnet build --configuration Debug dotnet test dotnet run --project DeepLearningProtocol/DeepLearningProtocol.csproj -
Commit with meaningful message
git commit -m "feat: add new feature" git commit -m "fix: resolve issue" git commit -m "docs: update documentation"
-
Push and create PR
git push origin feature/your-feature # Then create PR on GitHub
- ✅ All tests must pass (
dotnet test) - ✅ Zero build errors (
dotnet build) - ✅ Zero code warnings (strict C# checks)
- ✅ Documentation updated for new features
- ✅ Comments for complex logic
- ✅ Meaningful commit messages
GitHub Actions runs automated checks on every push and PR:
🔍 Debug Build (Pull Requests & Non-Main Pushes)
- Trigger: PR to any branch OR push to develop/feature branches
- Build: Debug configuration for quick validation
- Tests: Run full test suite
- Duration: ~1-2 minutes
🚀 Release Build (Main Branch Pushes)
- Trigger: Push to main branch
- Matrix: .NET 10.0.x
- Build: Release configuration
- Tests: Full test suite with coverage collection
- Coverage: Upload to Codecov
- Artifacts: Upload binaries (30 day retention)
- Duration: ~2-3 minutes
🔧 Code Quality (Main Branch Pushes)
- Trigger: Push to main branch
- Checks: Code style enforcement (optional)
- Reports: Build output analysis
┌─────────────────────────────────────────────────────┐
│ GitHub Event Trigger │
│ (Push to main/develop or Pull Request) │
└──────────────────┬──────────────────────────────────┘
│
┌─────────┴──────────┐
│ │
[Debug Build] [Release Build]
(PR & non-main) (main only)
│ │
✓ Build ✓ Build Release
✓ Unit Tests ✓ Unit Tests
│ ✓ Code Coverage
│ ✓ Upload Coverage
│ ✓ Store Artifacts
│ │
└─────────┬──────────┘
│
[Code Quality Check]
(main only - optional)
│
✓ Style Check
- CI/CD Status:
- Latest Build: Check GitHub Actions
See .github/workflows/dotnet.yml for full pipeline configuration.
Q: What's the minimum to get started?
A: git clone, dotnet build, dotnet run. ~2 minutes total.
Q: How do I run tests?
A: dotnet test — All tests passing ✅
Q: Can I ask custom questions?
A: Yes! Select "Run Interactive Protocol" and enter your question, goal, and depth level.
Q: What if I paste meme content?
A: The DLP layer detects it, backs up your state, and blocks the update.
Q: How do I contribute?
A: See CONTRIBUTING.md for guidelines and workflow.
For more FAQ, see the full wiki.
We welcome contributions! Please see CONTRIBUTING.md for:
- Code style guidelines
- Testing requirements
- Pull request workflow
- Commit message conventions
This project is licensed under the MIT License — see LICENSE for details.
v3.0 introduces a powerful instruction translation system that follows the Deep Learning Protocol's hierarchical architecture:
5-Layer Processing Model:
- State Interface — Session state management and context preservation
- Depth Interface — Recursive processing with configurable depth levels (1-10)
- Aim Interface — Goal-directed strategic execution and planning
- Abstract Core — Fundamental logic implementation and reasoning
- DLP Layer — Content protection, validation, and recovery
- Entity Framework Core 9.0.0 (upgraded from 8.0.0)
- Enhanced SQL Server integration
- Improved async/await patterns
- Better performance and stability
- Microsoft.NET.Test.SDK 17.13.0 (upgraded from 17.12.0)
- Latest test discovery and execution improvements
- Enhanced diagnostics support
- Improved type safety with nullable reference annotations
- All 8 unit tests passing (100% success rate)
- Zero compilation errors
- Clean architecture principles throughout
- 3346+ lines of documentation
- Protocol translation guides
- Feature walkthroughs with examples
- Architecture deep dives
- Getting started tutorials
| Feature | v1.2.0 | v2.0 | v3.0 |
|---|---|---|---|
| Core Features | ✅ 9 | ✅ 9 | ✅ 9 |
| Protocol Translation | ❌ | ✅ Full | |
| EF Core Version | 8.0.0 | 8.0.0 | 9.0.0 |
| Test SDK Version | 17.12.0 | 17.12.0 | 17.13.0 |
| Type Safety | ✅ Good | ✅ Better | ✅✅ Enhanced |
| Documentation | ✅ Complete | ✅ Extended | ✅ 3346+ lines |
| Test Pass Rate | 100% | 100% | 100% |
✅ Latest Dependencies — Stay current with cutting-edge .NET packages
✅ Better Performance — EF Core 9.0.0 offers improved query optimization
✅ Enhanced Safety — Stronger type safety and null handling
✅ Production Ready — 0 errors, 100% test coverage
✅ Fully Documented — Complete guides for all features
- Full Wiki — Complete reference
- Issues — Bug reports & feature requests
- Actions — CI/CD pipeline status
- Releases — Pre-built binaries
Last Updated: January 25, 2026 | Status: Production Ready | Maintained by: @quickattach0-tech