BMW M4 Competition App Design | UI/UX Case Study We Designer Mobile Designer

I designed a premium automotive app experience inspired by the BMW M4 Competition, combining a bold visual identity with a sleek, modern interface. The design features cinematic car visuals, dynamic speed animations, performance specifications, and an immersive dark theme with electric-blue accents.

The interface highlights key performance details, including acceleration, horsepower, torque, and top speed, creating a high-end digital experience for automotive enthusiasts. Every screen is designed to communicate power, precision, and innovation through clean typography, striking visuals, and smooth transitions.

Key Features:

Premium automotive UI/UX design

Interactive speedometer and motion animations

Performance statistics and technical specifications

Dark-themed interface with electric-blue highlights

Cinematic visuals and engaging transitions

Modern layout with a performance-focused user experience

My Role: UI/UX Designer & Visual Designer Project Type: Automotive App Concept Focus: User Interface, Visual Design, Motion Design & Interactive Experience

Design Philosophy: Engineered beyond limits — transforming automotive performance into an immersive digital experience.

This is very dope.

AI Agent for ServiceNow Automation (LangGraph + GPT-4o-mini + Slack, Jira, Email) I built a LangGraph agent that turns natural-language requests into ServiceNow incidents, so users never have to open the ServiceNow UI. A StateGraph workflow pairs an LLM node (GPT-4o-mini) that understands the request with a tool node that runs the ServiceNow Table REST API. It supports caller, description, and assignment group fields, and it resolves group names to sys_ids by looking them up in the sys_user_group table, so users can just say “Network Team”. The agent is exposed through a Flask REST endpoint and an interactive CLI, with credentials kept in environment variables and error handling around every API call. I then extended the same pattern to Slack, Email, and Jira to automate workflows across platforms. Understand: The LLM node interprets the request and extracts the caller, description, and assignment group. Act: The tool node executes ServiceNow actions through the Table REST API. Resolve: Group names are looked up in the sys_user_group table to get the sys_id, so users refer to teams by name. Expose: A Flask REST endpoint and an interactive CLI give two ways to use the agent. Secure and robust: Credentials are managed through environment variables, and API calls have error handling. Extend: The same agent pattern was reused for Slack, Email, and Jira integrations.

Tools to tag Python, LangGraph, OpenAI GPT-4o-mini, Flask, ServiceNow REST API, Slack API, Jira, Agentic AI, Automation