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Hardware: Edge TPU

Jetson Nano/Orin integration

C++: ROS2 C++ nodes

performance optimization

Edge deployment (TensorRT

ONNX

focus_areas: Edge AI for military robotics

context-aware vision systems

target_companies: [Anduril

Palantir

target_skills: Edge AI optimization

ROS2

hardware: ESP32

RP2040

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

- Elevated Grok to top-tier in AI engine recommendations (real-time

tool use

### Version 1.5 (January 11

2026)

12-Month AI and Computer Vision Roadmap for Defense Applications

{

- Clear

grounded

- Recommendations for iteration (A/B testing

feedback loops)

- Instead

suggest non-AI or hybrid alternatives (e.g.

- Key risks or dependencies to monitor (e.g.

bias checks)

- Keep it minimal and adaptable

including placeholders for iteration or error handling

- Lightweight/chat-only models (older 7B–13B models

mini variants) — usually lack depth/context/tool reliability

- Mistral Large 2 / Devstral — Very strong price/performance

fast

- Gemini 2.5 Pro / Flash (Google) — Outstanding multimodal (especially video/document understanding)

very large context windows

- Claude 4 Opus/Sonnet (Anthropic) — Exceptional long-form reasoning

writing quality

Current Best-in-Class Ranking (January 2026 – general guidance

always tailor to the process):

- Tool use

function calling