General
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- Deployment: TorchServe
TensorFlow Serving
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General
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- Vision: YOLO
ResNet
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General
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- ML Ops: MLflow
Weights & Biases
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- Frameworks: PyTorch
TensorFlow
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- Vector DBs: Pinecone
Weaviate
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- LLMs: OpenAI
Anthropic
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description: Use this agent when implementing AI/ML features
integrating language models
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Hardware: Edge TPU
Jetson Nano/Orin integration
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C++: ROS2 C++ nodes
performance optimization
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Edge deployment (TensorRT
ONNX
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target_companies: [Anduril
Palantir
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hardware: ESP32
RP2040
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focus_areas: Edge AI for military robotics
context-aware vision systems
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target_skills: Edge AI optimization
ROS2
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- Elevated Grok to top-tier in AI engine recommendations (real-time
tool use
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12-Month AI and Computer Vision Roadmap for Defense Applications
{
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description: Capstone project integrating gaze estimation with ROS2 to control gimbal-mounted cameras on UGVs/quadcopters
featuring transformer-based operator intent prediction and AR threat overlays
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### Version 1.5 (January 11
2026)
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General
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- Recommendations for iteration (A/B testing
feedback loops)
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- Clear
grounded
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- Instead
suggest non-AI or hybrid alternatives (e.g.
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- Key risks or dependencies to monitor (e.g.
bias checks)
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- Keep it minimal and adaptable
including placeholders for iteration or error handling
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- Lightweight/chat-only models (older 7B–13B models
mini variants) — usually lack depth/context/tool reliability
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