Tinashe Crispen Garidzira (Crispen Gari) is a software developer and AI Researcher focused on mobile health (mHealth), mobile agriculture (mAgric), and intelligent systems. With a strong background in deep learning, machine learning, and full‑stack development, he builds practical AI‑powered tools that improve real‑world decision‑making. He is the creator of dataloom and helperfns, and continues to contribute to open‑source AI tools and research. His 2026 focus areas include:
- 🧬 AI for mHealth (disease detection, patient monitoring, telemedicine models)
- 🌱 AI for mAgric (crop health analysis, yield prediction, pest classification)
- 📱 Mobile & cross‑platform development (React Native, Flutter, web dashboards)
- 🧠 Deep Learning (DL) & ML research
"In 2026, I aim to push the boundaries of AI in healthcare and agriculture — building intelligent tools that improve lives, communities, and future technologies."
| TOOLS AND TECHNOLOGIES | BADGE |
|---|---|
| CLOUD PLATFORMS |       |
| OPERATING SYSTEM |     |
| PROGRAMMING LANGUAGES |      |
| FRAMEWORKS |       |
| IDE & ENVIRONMENTS |      |
| VERSION CONTROL |   |
| SERVERS |    |
| DATABASES |      |
| DESIGN |   |
| ML/DL/AI |        |
| DEVOPS |  |
| TOOLS |     |
# AI for mHealth — Example: CNN Inference Script
import torch
from model import CNNModel
model = CNNModel().eval()
img = torch.randn(1, 3, 224, 224)
pred = model(img)
print("Prediction:", pred.argmax().item())// React Native 2026 — Health Monitoring Snippet
const App = () => {
return (
<View>
<Text>mHealth AI Dashboard — 2026</Text>
</View>
);
};
export default App;// C++ + LibTorch 2026 Example
#include <torch/torch.h>
int main(){
auto tensor = torch::rand({3,3});
std::cout << tensor << std::endl;
}2026: Building AI for Health & Agriculture.