Find agent skills by outcome

C++: ROS2 C++ nodes

performance optimization

Hardware: Edge TPU

Jetson Nano/Orin integration

Edge deployment (TensorRT

ONNX

target_skills: Edge AI optimization

ROS2

target_companies: [Anduril

Palantir

focus_areas: Edge AI for military robotics

context-aware vision systems

Education

current_skills: OpenCV (learning)

PyTorch (familiar)

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

{

Data

- Suggested follow-up actions (prototype scope

data prep

- Recommendations for iteration (A/B testing

feedback loops)

- Clear

grounded

Enhancement: Refactor Reva CLI with prompts

The current CLI is a bit cumbersome to use with users having to type commands

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

bias checks)

- Keep it minimal and adaptable

including placeholders for iteration or error handling

- Instead

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

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

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

Creative

- Generate a simple

practical starter prompt

Education

Always explain your ranking in the specific context of the user's process

inputs

Data

- Llama 4 / Llama 405B variants (Meta) — Best open-source frontier performance

excellent for self-hosting

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

fast