Introduction
When a hardware device starts behaving unexpectedly, engineers need more than raw sensor readings. They need a clear way to enter incident details, review a diagnosis, and understand what the system has learned from earlier repairs. As the UI Engineer for our HardwareMind prototype, my focus was to make that process accessible through a straightforward, interactive interface.
HardwareMind is an AI-powered hardware failure investigation prototype. It connects a user-facing interface with a backend that investigates hardware incidents and can store confirmed repair information for future reference.
Building the HardwareMind Interface
Figure 1: HardwareMind’s device information form, where users enter incident details and hardware readings for investigation.
The interface is where users describe a device issue and submit it for investigation. I worked on organizing the input fields and actions into a workflow that is easy to follow during testing and demonstration.
The incident form includes details such as the incident ID, device name and type, temperature, voltage, current, symptoms, sensor status, and communication status. These fields allow the user to provide a structured description of the failure.
Connecting the Interface to the Investigation
The UI is built using Streamlit. When a user submits an incident, the frontend sends the entered information to the backend investigation API. The interface then displays the response, including the AI-generated diagnosis, evidence, recommended tests, and repair suggestions.
This helps users move from entering an incident to reviewing the system’s response in one workflow.
Figure 2: HardwareMind displays an AI-generated diagnosis and historical context for a sample incident.
My Experience as the UI Engineer
Working on the interface helped me understand how frontend development supports an AI-enabled engineering tool. A useful UI needs to make inputs understandable, organize actions in a logical order, and present results in a way users can inspect.
It also needs to communicate with the backend and display its responses clearly. Building and testing the incident-entry and investigation-results flows gave me practical experience connecting a frontend to backend functionality.
Conclusion
HardwareMind brings together incident input, AI-assisted investigation, historical context, and a process for submitting confirmed repair information. My contribution as the UI Engineer was to help turn these capabilities into an interactive prototype that users can operate and the team can demonstrate.
The next stage is to continue testing the interface with varied incidents and improve the clarity of the information shown to users. I’m glad to have contributed to this project and to have gained hands-on experience building a UI for an AI-powered application.