Cursor

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Cursor

Cursor is an AI-powered integrated development environment (IDE) designed to help developers write, edit, and debug code more efficiently. Built on top of Visual Studio Code (VS Code), Cursor retains full compatibility with all VS Code functionalities and integrations, making it easy for developers to transition between IDEs. With advanced AI capabilities, Cursor improves workflows by providing intelligent code suggestions, predictive completions, and auto-updating code references, all aimed at reducing manual tasks. It also offers real-time collaboration features, context-aware code discussions, and proactive AI debugging, giving developers a powerful and intuitive environment for building software faster.

General
Author	Cursor
Release Date	2023
Website	https://www.cursor.com/
Repository	https://github.com/cursor
Documentation	https://docs.cursor.com/
Technology Type	AI-Powered Integrated Development Environment (IDE)

Key Features

AI-powered Code Suggestions: Cursor provides contextual code predictions and completions, helping developers write code faster by suggesting what comes next, understanding the project structure, and updating references automatically.
Refactoring and Debugging: The AI helps in refactoring code by suggesting improvements and fixing linter errors within the code editor, making it easier to maintain code quality.
VS Code Extension Compatibility: Users can import VS Code extensions and key bindings, ensuring familiarity for those migrating from other environments.
Keyboard Shortcuts: With powerful shortcuts like Cmd + K for code generation and Cmd + L to open chat, Cursor enhances developer productivity by offering quick access to AI tools.
Collaboration Tools: Built-in features for chat-based coding, AI-assisted discussions, and the ability to track chat history make it ideal for teamwork and collaborative projects.
Chat with AI: Through the chat interface, developers can ask questions about their codebase, request code fixes, or get contextual assistance, making the coding experience more interactive and dynamic.

Start Working with Cursor

To start using Cursor:

Visit the official Cursor website and download the IDE.
Follow the instructions in the documentation for migrating from VS Code, which helps import your settings, extensions, and preferences.
Get acquainted with Cursor's features, such as AI-based code assistance, by reading the Cursor documentation for in-depth guides and usage tips.

Edit on GitHub

Cursor AI technology page Hackathon projects

Discover innovative solutions crafted with Cursor AI technology page, developed by our community members during our engaging hackathons.

Fleet Bridge

FleetBridge: See All Your Robots in One Place That Ocado warehouse fire in 2023? $110M in damage because robots from different companies couldn't see each other. FleetBridge fixes that. Live map shows all 24 robots at once—Amazon, Balyo, Gemini, all on one screen. Click any robot and see everything: battery, current job, where it's been, errors. Robots move smoothly with zoom, pan, zone overlays. When two are about to crash, you get a red line between them. Send one to charge and watch the animated path appear. Just type questions instead of clicking through menus. "Which robots are low on battery?" "What's happening in Zone B?" Simple stuff answers instantly. Complex questions route to Gemini with full context—positions, tasks, alerts, everything. No more cryptic vendor codes. The system translates all errors across manufacturers. Balyo's "OBSTACLE_TIMEOUT" = Amazon's "E-2002". Click any error for actual fix steps with checkboxes you complete before clearing it. Assign tasks by picking from 13 warehouse presets or typing naturally: "Move inventory from Zone A to Station 5." AI handles the details. Analytics show which robots work hardest, vendor performance comparisons, congestion hotspots. Alert feed catches issues before they cause damage—collision warnings, traffic jams, low batteries, blocked paths. Real-time updates every 500ms. The backend simulates realistic warehouse ops—movement, battery drain, auto task assignments, random errors. Chat panel for deeper conversations with the AI. Path visualization windows showing robot trails and destinations. Error knowledge base with cross-vendor translations and remediation guides. One dashboard replaces three vendor systems. No switching screens. Just clear fleet visibility.

OpsTwin AI

OpsTwin AI is a simulation-first autonomous warehouse control system designed to model and optimize multi-robot fulfillment operations. As warehouses adopt robotics at scale, fleet coordination becomes increasingly complex. Congestion, battery constraints, task prioritization, and workload balancing impact throughput and efficiency. OpsTwin AI addresses this by creating a digital twin of warehouse operations, a live simulation where robotic workflows can be orchestrated, tested, and optimized before real-world deployment. In OpsTwin AI, robots operate within a simulated warehouse grid containing storage racks, charging stations, and pack zones. When a new order is created, the system autonomously determines which robot should fulfill it. Instead of relying on hardcoded rules, I use Gemini as a strategic planning layer. The backend sends live fleet state, including robot positions, battery levels, and active tasks, to Gemini. Gemini returns structured JSON with a selected robot and step-by-step task sequence. This allows deterministic execution while enabling adaptive multi factor decision making. The Vultr-hosted backend serves as the centralized system of record. It maintains robot state, order queues, and operational metrics, and broadcasts real-time updates to a web dashboard using WebSockets. A 500 millisecond simulation loop executes plans, updates robot movement, tracks congestion events, and manages battery-aware charging. The result is fully autonomous multi-robot operation without manual intervention. From a business perspective, OpsTwin AI functions as an operational control tower for robotic fleets, enabling teams to simulate workflows, evaluate performance, and reduce deployment risk before scaling to physical infrastructure. By separating AI planning from deterministic execution, the architecture mirrors real world robotics systems and provides a clear path from simulation to real-world deployment.

Adaptifleet

Traditional warehouse automation has improved efficiency, yet many systems remain rigid, expensive, and difficult to adapt when workflows or layouts change. Even small adjustments often require specialized expertise or time-consuming reprogramming. This creates a disconnect between what operators need robots to do and how easily they can communicate those needs — a challenge we call the “Human Intent Gap.” AdaptiFleet was designed to close this gap by enabling intuitive, AI-driven fleet control. Instead of relying on complex interfaces or predefined scripts, users interact with autonomous robots using natural language. Commands such as “Get me three bags of chips and a cold drink” are interpreted and translated into structured robotic tasks automatically. At its core, AdaptiFleet leverages Gemini-powered Vision Language Models (VLMs) to understand user intent and visual context. Robots operate within a dynamic decision framework, allowing them to adapt to changing environments rather than follow rigid, pre-programmed routes. The platform integrates a digital twin simulation stack built on Isaac Sim, enabling teams to validate behaviors, test workflows, and optimize multi-robot coordination before live deployment. Once deployed, ROS2 and Nav2 provide robust navigation, dynamic path planning, and collision avoidance. The VLM orchestration layer continuously analyzes visual inputs to support scene understanding, anomaly detection, and proactive hazard awareness. When conditions change, AdaptiFleet autonomously re-plans routes and tasks, reducing downtime and operational disruption. By combining conversational interaction, real autonomy, and simulation-driven validation, AdaptiFleet simplifies robotic deployments while improving efficiency and visibility. The result is an automation system that is adaptive, scalable, and aligned with how people naturally work.

NeuroPilot

People who could benefit from hands-free control, for example those with limited mobility, often cannot access it. NeuroPilot changes that: open-source BCI middleware that turns a Muse 2 headband into a real control interface. Muse 2 is already connected to the app, and we have a working integration with DJI Tello. Users train in a 3D simulation and in a training environment; when their brainwave pattern matches a chosen action (move left, right, up, down), the backend fires configurable webhooks or WebSockets. We accumulate 5 actions from the headband; YOLOv8 runs on the Tello camera feed to detect obstacles and Tello state. That batch and the vision data are sent to Gemini, which decides the movement. Every command sent to the DJI Tello is the result of an AI API call, so BCI intent is refined by vision and reasoning before it reaches the drone. The stack is software-only and simulation-first: a Next.js frontend, a FastAPI + WebSocket backend, a web Lab (Three.js), a machine/Connect dashboard for status and live feedback, and per-control webhook URLs. No lab hardware or locked-in vendors. Teams can integrate with existing sims, digital twins, or robots via HTTP.

AI Based Fraud Detection Trading Intelligence App

CipherScan AI is an enterprise-grade fraud investigation and trading intelligence platform that combines deterministic risk scoring with AI-powered analysis. The system detects fraud patterns through baseline analysis and deviation detection, while simultaneously analyzing trading behavior to warn traders about repeating bad patterns based on their own historical data. Key Features: Fraud Investigation: Real-time alert processing with AI-generated investigation narratives, severity classification, and complete audit trails Trading Intelligence: Autonomous behavioral analysis that detects emotional trading patterns and warns traders when they're repeating mistakes from their own trading history Enterprise Security: JWT authentication, AES-256 encryption, role-based access control, and compliance-ready audit logging Premium UI/UX: Noir fintech design with glassmorphism effects and intuitive workflows Technology Stack: Node.js, TypeScript, React, Google Gemini AI, SQLite Business Value: Reduces investigation time from hours to seconds, helps traders learn from their mistakes, and provides complete regulatory compliance with automated audit trails.

TheWorkFlow

This project is a B2B AI-powered workflow automation platform built around the vision of “Automate Everything.” It enables organizations to transform natural language business requirements into fully editable, modular, and executable automation workflows. Instead of relying on developers, consultants, or complex automation tools, business users can simply describe what they want to automate, and the system handles the rest. The AI engine analyzes each request and converts it into a structured workflow composed of independent modules, where each module represents a specific action, integration, or decision. These workflows are stored using a flexible JSON-based model and displayed in an interactive interface, allowing users to add, remove, reorder, or modify steps without rebuilding the entire automation. Individual modules can also be refined through conversational interaction, enabling fast iteration and continuous improvement. Once finalized, workflows are compiled into production-ready automations, compatible with execution engines such as n8n or custom runtimes. From a business perspective, the platform delivers measurable ROI by reducing development and operational costs, accelerating time-to-deployment, and enabling teams to automate processes at scale. Targeted at SMEs and enterprise teams, the solution supports internal operations such as onboarding, approvals, notifications, and system integrations, making it a practical and scalable embodiment of the “Automate Everything” philosophy.