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├── 📁 core_identity
│ ├── bio.txt
│ └── config.json
├── 📁 research_frontiers
│ ├── vision.py
│ ├── llm.py
│ └── agents.py
├── 📁 deployments
│ ├── yolo_pt
│ └── gemini-cli
└── 📁 telemetry
└── stats.log# NJX-Internal-v1
architecture:
input:
- source: "Curiosity"
- dtype: "Raw_Data"
encoder:
- layer: "CS_Fundamentals"
- activation: "Deep_Learning"
attention:
- heads: ["Vision", "LLM"]
- mechanism: "System_Design"
decoder:
- task: "Engineering"
- output: "Innovation" |
I treat AI research not just as academic exploration, but as system architecture. My goal is to understand the emergent properties of large models and engineer the infrastructure that makes them accessible.
class ResearchInterests(nn.Module):
def __init__(self):
super().__init__()
self.vision = "Vision Transformers (ViT), Object Detection (YOLO)"
self.llm = "Architecture Design, PEFT, KV Cache Optimization"
self.agents = "Multi-Agent Orchestration, Tool Use & Planning"
self.infra = "High-performance Inference, Quantization"
def forward(self, x):
return self.agents(self.llm(self.vision(x)))
# Initialize connection handshake (try it!)
curl -s https://njx-njx.github.io/api/v1/contact.json | python3 -m json.tool |
