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README.md
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# Cubic β AI and Cybersecurity Research
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Building accessible AI systems that run on consumer hardware. Our projects focus on CPU-first training, open-source research, and practical deployment.
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## Key Metrics
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- **27 models** β 1 research paper β 3 team members
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- **$0.004** min training cost β **100%** open source
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## Research Focus
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- **AI Development** β CPU-first architectures and open-source models
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- **Cybersecurity** β AI-powered security tools and threat detection
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## Projects
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### AXL β CPU-First Code Generation
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- **27 models** from 566K to 318M parameters
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- **Architecture**: Multi-scale byte-level transformer
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- **License**: Apache 2.0
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- Click the project card on the org page to see the full AXL detail page
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## Team
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- **Kennedy**: CEO & Head of AI Research
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- **Jasser**: CTO & Head of Cybersecurity
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- **Taem**: Head of Marketing/Sales/Technical Assist
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## Interactive Tools
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The org card includes
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- **Inference Speed Calculator** β estimated CPU tokens/sec by parameter count and thread count
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- **GGUF File Size Calculator** β file size from parameter count and quantization level (Q4/Q5/Q8/F16/F32)
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- **Context Length Calculator** β KV-cache memory for a given context window, layer count, and hidden dimension
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- **Memory Usage Estimator** β total estimated RAM from weights plus KV-cache overhead
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- **Training Time Estimator** β estimated training time from cost and effective hourly CPU rate
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- **Model Comparison Tool** β side-by-side comparison of two AXL model sizes on params, file size, and inference speed
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## Key Features
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- Dark/Light theme toggle
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- Search across projects
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- Animated counters on metrics
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- Keyboard shortcuts (/ to search, Esc to close)
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- Print-friendly CSS
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- Responsive design
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## Links
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- [GitHub](https://github.com/Cubic-Labs)
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- [HuggingFace](https://huggingface.co/Cubic)
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---
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*CPU-first, AI for everyone.*
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app_file: index.html
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pinned: false
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---
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# Cubic β AI and Cybersecurity Research
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Building accessible AI systems that run on consumer hardware. Our projects focus on CPU-first training, open-source research, and practical deployment.
|
|
|
|
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## Key Metrics
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- **27 models** β 1 research paper β 3 team members
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- **$0.004** min training cost β **100%** open source
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## Research Focus
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- **AI Development** β CPU-first architectures and open-source models
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- **Cybersecurity** β AI-powered security tools and threat detection
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## Projects
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### AXL β CPU-First Code Generation
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- **27 models** from 566K to 318M parameters
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- **Architecture**: Multi-scale byte-level transformer
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- **License**: Apache 2.0
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- Click the project card on the org page to see the full AXL detail page
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## Team
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- **Kennedy**: CEO & Head of AI Research
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- **Jasser**: CTO & Head of Cybersecurity
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- **Taem**: Head of Marketing/Sales/Technical Assist
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## Interactive Tools
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The org card includes two live calculators, grounded in AXL's real published numbers:
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- **Memory Usage Estimator** β total estimated RAM from weights (at your chosen quantization) plus KV-cache overhead
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- **Inference Speed Calculator** β estimated CPU tokens/sec by parameter count and thread count
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## Key Features
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- Dark/Light theme toggle
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- Two live calculators (memory & inference speed)
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- Print-friendly CSS
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- Responsive design
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## Links
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- [GitHub](https://github.com/Cubic-Labs)
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- [HuggingFace](https://huggingface.co/Cubic)
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---
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*CPU-first, AI for everyone.*
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