A hierarchical multi-interface reasoning system with Data Loss Prevention (DLP) capabilities.
- Hierarchical Architecture — Multi-interface design with AbstractCore, State, Depth, and Aim layers
- Data Loss Prevention — Detects meme/binary content and backs up states automatically
- Interactive Menu — User-friendly CLI with 10-question FAQ system
- Comprehensive Testing — 8 XUnit tests covering all core functionality
- Full Documentation — Multiple guides for different user roles
- CI/CD Automation — Multi-platform releases via GitHub Actions
| Audience | Resource | Purpose |
|---|---|---|
| First-timers | Getting Started | Build, run, and understand the basics |
| Developers | Architecture Guide | System design and components |
| Security-minded | DLP Guide | Data protection 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.
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: DataLossPrevention (DLP) layer detects suspicious content and backs up states.
DeepLearningProtocol/
├── DeepLearningProtocol/ Core protocol implementation
│ ├── Program.cs Main logic + DLP + 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)
- DLP-protected state management
Automatically detects and blocks:
- Image-like content (
.png,.jpg,base64) - Meme-related keywords
- Suspicious binary payloads
- State backups to
./.dlp_backups/
Browse 10 pre-written answers about:
- How to use the protocol
- Architecture details
- DLP functionality
- Customization options
# 1. Update Program.cs
# 2. Add tests to DeepLearningProtocol.Tests/DeepLearningProtocolTests.cs
# 3. Run tests
dotnet testPress F5 in VS Code for interactive debugging.
GitHub Actions runs on every push:
- ✅ Multi-platform builds (Linux, Windows, macOS)
- ✅ Unit tests (8 tests)
- ✅ Code coverage collection
- ✅ Release artifact creation
See .github/workflows/dotnet.yml for details.
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 — 8 tests, all 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.
- Full Wiki — Complete reference
- Issues — Bug reports & feature requests
- Actions — CI/CD pipeline status
- Releases — Pre-built binaries
Last Updated: December 18, 2025 | Maintained by: @quickattach0-tech