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.

🦷 Dental Surgical Simulator

The simulator presents a stylized, professional interface featuring a 3D viewport of a human dental arch. Users select from various surgical missions, each requiring a specific sequence of surgical steps. Success depends on selecting the correct surgical instrument and interacting with the precise target tooth at the right time. Procedural Anatomy: Anatomically accurate tooth crowns and root clusters generated via math-based geometry. Multi-Mode Visualization: X-Ray Mode: Toggles transparency of soft tissues (gums, palate) to reveal bone and roots. Wireframe Mode: Provides structural visualization of dental anatomy. View Switching: Toggle between Full Arch, Upper Jaw, and Lower Jaw views. Dynamic Theming: Seamless transition between a professional high-contrast Dark Mode and a clean Light Mode. Interactive HUD: Real-time feedback through toasts, tooltips, and a crosshair for precise targeting. Advanced Missions: Includes complex procedures like Dental Implants, Root Canals, and Bone Grafting.

Contract Finder

*Contract Finder* is an AI agent for government contract discovery — Track 1, GTM Intelligence. It filters by location, industry, and deal size, searches live procurement sources, and returns structured RFP intelligence. "Public-sector revenue is on the web, but it's scattered — SAM.gov, state portals, county bids, agency sites. BD teams hunt manually, miss deadlines, and can't filter by geo, industry, and deal size at scale. "Claude Sonnet orchestrates a tool-use loop: *search_web* via Bright Data SERP, *scrape_url* via Web Unlocker, then streams results to the UI. The agent decides when it has enough information, but this can be controlled by the user as well

Uh Oh!

The internet is usually chaotic. Occasionally, it is also early. Before a recall becomes official, someone is already leaving clues: a person writing “my boyfriend broke out in hives after our wedding day,” a runner wondering why their supplement made their heart race on an empty street, a review mentioning a weird smell, a broken seal, or a “nut-free” snack that suspiciously tastes like almond. To most teams, that is noise. To Uh Oh!, it is the beginning of a signal. Uh Oh! is an early-warning product safety radar for food and supplement teams. We use Bright Data to scan live public web signals across reviews, forums, marketplaces, brand pages, and product listings, then correlate them with FDA/openFDA enforcement data and CAERS adverse-event reports. AI extraction turns the messy internet into structured product identity, issue clusters, severity cues, source summaries, and recommended next actions using AI/ML API. The output is a Product Safety Case File: a source-backed packet for quality, legal, marketplace trust, brand risk, and compliance teams. Each file includes official recall correlation, CAERS signal summaries, a live web evidence wall, since-last-scan deltas through Cognee memory, and a transparent 4-factor risk score across regulatory, adverse-event, live-web, and credibility signals. When the evidence reaches “Review Needed,” Triggerware opens a quality review case so human teams can investigate faster. Uh Oh! is not a public frenzy enterprise. It does not decide whether a product is unsafe, and it does not pretend that reviews, forums, or CAERS reports prove causality. It is a responsible triage layer for the moment before the obvious, when the internet is saying, “something might be wrong.” Our demo shows the most valuable moment: a product that is not officially recalled yet, but whose live web signals are starting to look like risk. We’re doomscrolling the internet so product safety teams don’t have to.

Agentic Audit

Agentic Audit is a proactive, multi-agent compliance monitoring system designed for enterprise organizations. Instead of relying on manual searches or reactive LLM prompts, our platform utilizes an asynchronous Python backend to continuously monitor highly regulated portals, such as the SEC or EU data privacy boards. By leveraging Bright Data's Web Unlocker and Scraping Browser, our system successfully bypasses the geo-blocks and heavy JavaScript barriers that typically block standard bots, allowing us to reliably capture raw regulatory text and timestamped screenshot evidence. Once our monitoring engine detects a change via SHA-256 hash comparisons, it triggers a multi-agent AI swarm. These agents analyze the text diffs, strip away the legal jargon, and map the updated regulations directly against internal company controls. The final output is delivered to a React dashboard where compliance officers can view a side-by-side comparison of the AI's risk assessment and the verified Bright Data screenshot. The system goes beyond basic summarization by providing actionable, auto-generated engineering tickets for immediate remediation, turning raw web data into operational enterprise security.

Transcript Evaluator

Interview scoring with one LLM is fast but fragile: models can over-score vague answers, miss follow-ups, or invent reasoning not supported by the transcript. Transcript Evaluator solves this with a two-agent architecture. Agent 1 batch-evaluates interview JSON against a unified behavioral/non-behavioral rubric and exports a structured PDF report. Agent 2 runs as a local review service powered by Bright Data MCP, receiving each case plus the primary evaluation and using MCP tools (search_engine, extract, and AI insight tools like web_data_chatgpt_ai_insights) to extract context and cross-check the first agent’s output. In a separate PDF feedback loop, Agent 2 sends the report back to Agent 1, which polls external AI perspectives through MCP and returns bias and calibration feedback so Agent 2 can revise before finalizing scores—reducing single-model hallucination and improving auditability for hiring teams.

PreIntent

Every sales team wants to reach buyers early. But today’s B2B intent tools mostly watch the same obvious signals: website visits, content clicks, review activity, and ad engagement. By the time these signals appear, the buyer is already comparing vendors, talking to competitors, or entering an RFP. PreIntent starts from a different belief: the best buying intent does not begin on your website. It begins in the market, before the buyer raises their hand. PreIntent is a convergent GTM intelligence assistant that detects early demand by monitoring three invisible signal layers: 1. Void Scanner Using Bright Data, PreIntent tracks what competitors remove from pricing pages, feature tables, and partner directories. A removed feature, hidden price, or dropped partner can reveal abandoned customers who may soon need a new solution. 2. Compliance Radar PreIntent parses new regulations through AI/ML APIs, maps them to target accounts, and scores deadline pressure. This helps sales teams identify companies that may need to act before they even start searching. 3. Pain Listener Using Featherless AI and Speechmatics, PreIntent analyzes Reddit discussions, community posts, and podcast transcripts to detect practitioner frustration, evaluation language, and active buying pain. The core innovation is the Convergence Engine. Instead of treating each signal separately, PreIntent combines competitor voids, regulatory pressure, and public pain into a single account-level score from 0 to 100. When the score crosses 85, the system automatically creates a Slack alert, CRM task, and AI-generated Intel Brief with evidence, reasoning, and a ready-to-send opening line. PreIntent is not another lead list. It is an early-warning system for GTM teams, helping them act 30 to 90 days before an account becomes visible in traditional intent platforms.