Cursor

Top Builders

Explore the top contributors showcasing the highest number of app submissions within our community.

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.

DevBoost AI

DevBoost AI is an intelligent AI-powered developer productivity platform designed to help developers understand, analyze, and improve codebases faster. Modern software development often involves spending significant time understanding large repositories, debugging issues, writing documentation, and generating test cases manually. DevBoost AI solves this problem using IBM Bob and AI-powered repository analysis. The platform allows users to connect a GitHub repository, after which the system scans and analyzes the complete codebase. DevBoost AI then automatically generates documentation, explains project architecture, identifies bugs and vulnerabilities, and creates unit test cases. The project uses a modern full-stack architecture with React + Vite for the frontend and FastAPI for the backend. IBM Bob acts as the AI core responsible for intelligent code understanding and automation. Key Features: • Intelligent Code Explanation • Automated Documentation Generation • Smart Bug Detection • AI-based Test Case Generation • Repository-wide Architecture Understanding • Prompt-driven Repository Q&A DevBoost AI improves developer productivity, reduces onboarding time, accelerates debugging, and helps teams build software faster and more efficiently.

VOLNARA: CLI FOR VIBE-ENGINEERING

VOLNARA is an AI-driven hardware engineering platform designed to simplify and accelerate PCB and electronics development. Using natural language prompts, users can generate complete hardware designs including schematics, PCB layouts, component recommendations, BOMs, and manufacturing-ready outputs. The platform aims to reduce the complexity of traditional electronics workflows by combining AI-assisted circuit generation, automated routing, simulation tools, and real-time component sourcing into a single ecosystem. VOLNARA supports integration with popular tools and manufacturing platforms, helping developers, startups, researchers, and makers rapidly prototype and build hardware products. It automates repetitive engineering tasks and streamlining design iteration, VOLNARA enables faster hardware innovation while making advanced electronics design more accessible to a wider range of creators.

VOLNARA: CLI FOR VIBE-ENGINEERING

PreMortem AI by Meowvate

PreMortem AI by Meowvate helps enterprise teams find operational failures before they become real incidents. Organizations often have policies, SOPs, governance documents, supplier requirements, and compliance rules, but they usually discover weak points too late — during audits, vendor outages, customer escalations, data incidents, or governance reviews. PreMortem AI changes that. Users select or paste an enterprise document, choose a crisis scenario such as “Audit Tomorrow” or “Critical Vendor Fails,” and run a Gemini-powered operational pre-mortem. The system generates a failure map with grounded failure signals, critical blockers, risk score, delay exposure, impact areas, prioritized fix plan, evidence checklist, audit memory timeline, and an exportable executive brief. Unlike a generic document chatbot, PreMortem AI turns enterprise documents into operational intelligence and accountable resolution paths. It is built for leadership, operations, compliance, audit, governance, and risk teams that need to move from document chaos to clear action. The prototype includes Live Gemini analysis, strict Pydantic schema validation, quality guardrails, one-pass quality retry, demo intelligence mode, backend fallback mode, and a signature Feline Signal Engine UI that visualizes risk detection in a memorable way.

Norix — AI Phishing Defense Platform

Norix is an AI-powered cognitive phishing defense platform built to protect individuals and teams from modern phishing threats across every attack vector — URLs, emails, SMS, and voice calls — in one unified workspace. Most phishing tools focus on a single vector. Norix was built on the premise that attackers don't stay in one lane. A user can receive a smishing link, click it in their browser, get a follow-up email, and receive a vishing call — all as part of one coordinated attack. Norix defends all four simultaneously. At its core is a local-first heuristic engine that scores threats using weighted URL analysis, typosquat detection, urgency pattern recognition, and trusted domain damping to reduce false positives. Everything runs in the browser — no data is ever sent to a server without explicit user configuration. Key features include a Chrome Extension that overlays real-time threat badges on links and emails as you browse, a Universal Scanner for instant analysis of any URL, email body, or SMS, a PhishBot AI assistant for security posture questions, a Gamified Phishing Training Sandbox where users identify red flags in realistic fake UIs, and an Enterprise Fleet Tracker for organization-wide endpoint visibility. Norix was built entirely solo using React 19, TypeScript, Vite, Tailwind CSS, and Framer Motion — shipped from zero to a full production deployment on Vercel with CI/CD via GitHub Actions.

MindForge AI: Mental Health Intelligence

MindForge AI is a mental-health intelligence layer built from the Healus continuous-care vision. The project turns device signals, medication-adherence events, caregiver observations, and patient check-ins into structured, auditable care-loop reviews between appointments. For this hackathon, we built a working demo that analyzes synthetic mental-health scenarios and produces a structured review with risk level, risk score, medication/adherence concerns, sleep and mood flags, escalation recommendation, patient-safe response, care-team summary, missing information, and an audit-style JSON output. Technically, we used Qwen2.5-Instruct and performed LoRA supervised fine-tuning on AMD GPU infrastructure using ROCm, Hugging Face tooling, PEFT, and TRL SFTTrainer. We trained on synthetic structured chat JSONL data designed to teach the model the MindForge output contract, not generic free-form advice. We also compared Base Qwen against Base+LoRA on held-out synthetic eval cases. The LoRA adapter improved practical MindForge core-schema adherence from 25% to 62.5%, a 2.5x improvement in producing the structured care-loop output our application needs. The demo includes a schema validation and normalization layer so outputs can be made reliable, auditable, and easier to route into caregiver and clinician workflows. MindForge AI does not diagnose, prescribe, treat, or replace licensed care. The demo uses synthetic patient scenarios only and is designed as a human-reviewed intelligence workflow for continuous mental-health support between visits.