Mintesnot Mengesha
Computer Engineer | AI/ML & Full-stack Developer
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Mintesnot is ready for their next project!
High-Agency ASO Audit Agent (Mastra & TypeScript)
I architected and built an autonomous AI Agent designed to perform deep App Store Optimization (ASO) audits. Moving beyond simple LLM wrappers, this system utilizes the Mastra framework to handle complex, stateful workflows that automate data-gathering and competitive analysis.
Technical Excellence:
Stateful Workflows: Implemented graph-based logic to manage multi-step audit processes autonomously.
Data Integrity: Used Zod for strict schema validation, ensuring 100% type-safe, structured JSON outputs with zero LLM hallucinations.
Scoring Engine: Developed a 10-dimension data-grounded framework to score apps based on real-time market data and keyword density.
Production Ready: Built with a "Zero-Slop" mindset, focusing on high-velocity execution and clean, maintainable TypeScript code.
This project demonstrates my ability to bridge the gap between advanced Agentic AI research and scalable, type-safe production software.
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took a standard MERN stack food delivery platform and transformed it into an intelligent ecosystem by integrating an Action-Oriented AI Agent. This project bridges the gap between traditional e-commerce and the future of Agentic AI.
Key Innovations:
Dietary Personalization: The AI Agent analyzes user health data and dietary restrictions to provide custom meal recommendations.
Autonomous Execution: Unlike basic chatbots, this agent can execute orders and manage the checkout flow through natural language processing.
Full-stack Architecture: Built with the MERN stack (MongoDB, Express, React, Node.js), featuring real-time data sync and secure user authentication.
Intelligent Logic: Leveraged advanced LLM APIs to create a conversational interface that understands user intent and interacts directly with the database.
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Project Overview: Full-stack Food Delivery System
I developed this end-to-end food delivery platform using the MERN stack (MongoDB, Express, React, Node.js) to provide a seamless e-commerce experience.
Key Technical Features:
- Payment Integration: Integrated the CHAPA payment gateway to facilitate . secure and reliable online transactions for local users.
- User Management: Implemented secure JWT-based authentication and a dynamic cart management system.
- Real-time Logic: Developed robust REST APIs to handle restaurant browsing, order placement, and delivery tracking.
-Responsive UI: Created a modern, fast-loading frontend with React focused on high conversion and user engagement.
This project demonstrates my ability to build scalable, production-ready web applications with integrated financial logic.
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Ethio-MeditechScan: Medical Image Analysis with AI
I engineered this end-to-end medical diagnostic tool to improve the speed and accuracy of healthcare delivery in Ethiopia. The system uses Deep Learning (CNNs) to classify medical images and provide instant insights to healthcare professionals.
Key Technical Features:
AI Engine: Developed a high-performance Convolutional Neural Network (CNN) using TensorFlow and Keras for accurate image classification.
Full-stack Interface: Built a responsive React dashboard that allows doctors to upload scans (X-rays/MRIs) and view AI-driven diagnostic reports in real-time.
Data Processing: Performed extensive data cleaning and augmentation on medical datasets to ensure the model's robustness and reliability.
API Integration: Engineered a FastAPI/Flask backend to serve the model and handle image processing requests with low latency.
This project highlights my ability to bridge the gap between complex Artificial Intelligence research and practical, user-facing medical software.
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