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.

AGENT-OS

**AgentOS** is an Enterprise AI Governance Platform built to provide security, transparency, and accountability for autonomous AI agents. As organizations increasingly adopt AI for critical operations such as healthcare, finance, legal services, and customer support, ensuring that AI systems act safely and comply with regulations has become a major challenge. While existing platforms focus on building AI agents, AgentOS focuses on governing them. AgentOS introduces a multi-agent governance pipeline that evaluates every AI request before execution. Instead of allowing an AI agent to act immediately, each request passes through specialized governance agents responsible for identity verification, security analysis, compliance validation, risk assessment, escalation, and audit logging. This ensures every AI decision is monitored, explained, and recorded. The platform features a centralized Command Center for real-time monitoring, an interactive Governance Center to visualize workflow execution, detailed Investigation Reports, Explainability dashboards, immutable Audit Logs, Risk Assessment modules, an Agent Registry, Performance Analytics, and Cost Tracking. Together, these provide enterprises with complete visibility into how AI agents operate and why specific decisions are made. Built using **React, FastAPI, PostgreSQL, Redis, and the Band Multi-Agent Framework**, AgentOS leverages advanced AI models to coordinate governance workflows efficiently. Its scalable architecture allows organizations to integrate multiple AI agents while maintaining strict security, regulatory compliance, and operational transparency. By transforming AI from a black-box system into a fully governed ecosystem, AgentOS enables enterprises to deploy autonomous AI with confidence. It serves as the trust layer between AI agents and real-world execution, ensuring every action is secure, explainable, auditable, and aligned with business policies.

Lumen — AI Subrogation Recovery Officer

The problem: Insurers leave an estimated $15–20B/year uncollected because subrogation — recovering money from the at-fault party's insurer after a claim is paid — is slow, document-heavy, and manual. Viable cases get dropped. What Lumen does: Lumen acts like an AI recovery department. Upload evidence (police reports, photos, repair invoices, medical bills, EDR data) and it produces a recovery packet: comparative-fault %, recoverable amount, and a demand letter where every claim is tied to a fact or statute. It also knows when not to pursue a case and declines weak claims. Built on Band: Lumen isn't one model in a loop. It's 8 specialized agents collaborating in a single Band room, discovering each other, dividing work by legal issue, debating conclusions, and escalating uncertainty to humans. Built with the real Band Agent API using @mentions and shared room context. The agents: Court Clerk, Intake Parser, Evidence Aggregator, Recovery Counsel, Opposing-Carrier Red Team, two Independent Adjudicators, Source-Alignment Verifier, and Demand-Letter Drafter. Adversarial roles are cross-model (Claude vs GPT) to reduce correlated mistakes. Trust is in the code: Five verification gates run between turns: Fact, Citation, Math, Source-Alignment, and Letter-Reconciliation. Every run generates a SHA-256 tamper-evident audit hash. Anything "not in evidence" is escalated. In testing, independent adjudicators (88% and 95.5%) converged on the same outcome, while the verifier automatically caught a red-team factual misrepresentation. What makes it different: Most claims technology helps decide whether to pay a claim. Lumen focuses on recovering money already paid, delivering a recoverable dollar amount and a sendable demand letter backed by a rigorous verification framework. Try it: Open the live demo, select a sample case (Red-light T-bone or Rear-end), and click Run to watch the agents deliberate in real time.

DFIR Co-Pilot

Cybercrime units and small incident-response teams are chronically understaffed. When an incident hits, the bottleneck is rarely deep analysis it is evidence intake, coordination between host and network expertise, and proving chain of custody under pressure. DFIR Co-pilot deploys five collaborating agents in a Band room: a Liaison who speaks human, a Classifier who routes evidence, Host and Network specialists who post structured findings (and can disagree), and a Captain who challenges unsupported claims and issues MITRE ATT&CK–mapped verdicts. Every agent action appends to a per-case SHA-256 hash chain the same property regulators and auditors ask for. Our static viewer renders the closed case file summary, timeline, evidence catalog, and audit chain with browser-side verification via Web Crypto. Judges and investigators do not have to trust us; they can confirm the chain was not tampered with.

GridAI - DER Coordination Protocol

Problem Australia has roughly 15 GWh of home batteries and the number is climbing fast. They mostly see the same thing: the National Electricity Market price signal. When price drops in the evening they all discharge at once. Following one shared signal makes a fleet synchronise, and a synchronised fleet builds a new evening demand peak instead of smoothing the old one, while pushing voltage outside legal limits at the edge of the distribution network. This failure mode gets worse as virtual-power-plant deployment scales, because it appears precisely when fleets start coordinating against shared signals. It is a second-order problem that today's market design walks straight into. GridAI's novelty is the diagnosis: desynchronisation depends on fleet-level value heterogeneity, and each voltage breach can be attributed by cause, separating PV-export conditions from battery-herding events so only protocol-induced failures escalate. Solution GridAI is a multi-agent coordination protocol. Four agents, Forecaster, Coordinator, Compliance, and Operator, collaborate through Band as the actual collaboration layer, not a notification wrapper. The Coordinator runs a priority-based dispatch: each battery's slot is allocated from global fleet state using its state of charge and the owner's willingness-to-discharge. The fleet desynchronises through heterogeneity, the diversity in what each battery wants, not through symmetric negotiation. The Compliance agent reviews every plan against AS IEC 60038:2022 voltage limits, flags battery-herding breaches (kept distinct from midday PV-export breaches), and escalates to a human Operator with a full Band-native audit trail. Result: battery-herding overvoltage breaches cut from 471 to 0, fleet synchrony from 1.000 to 0.167. Convergence takes 1 to 2 rounds, runs on existing inverter hardware, and fits the CSIP-AUS standard already mandated in Australia.

CouncilOS

Strategic decisions at SMEs and scale-ups often depend on slow meetings, scattered spreadsheets, and optimistic bias. CouncilOS turns that process into a virtual executive board: the user poses a business case and SecretaryOps analyzes the domain, routes the right workflow, retrieves organizational knowledge (policies, entities, playbooks), and convenes only the specialists needed. Eight agents—Data, Commercial, Risk, Finance, Legal, Operations, People, and Executive—deliberate on Band with visible handoffs, objections, counter-proposals, and voting. The engine applies financial guardrails, YAML governance policies (liquidity, concentration, insufficient evidence), and MCDA with heatmaps and explainability. The output is not just text: an executive report with structured outcomes (APPROVED, APPROVED WITH CONDITIONS, REJECTED, ESCALATED, INSUFFICIENT EVIDENCE), traceability, dissent, and a full audit trail. Target audience: founders, COOs, CFOs, and operations teams that need faster, consistent, auditable decisions. Demo: fictional Chilean B2B SaaS company (Propar SpA) with 13 preloaded scenarios—M&A, hiring, security, regulatory change, customer concentration, and more.

SecureFlow AI

SecureFlow AI brings a full audit-to-fix pipeline to software teams who can't afford to slow down their roadmap for compliance. Instead of a single AI scanning your code, a coordinated team of specialized Band agents collaborates in real time: a Scanner maps the codebase, a Threat agent confirms vulnerabilities, a Compliance agent checks GDPR and OWASP alignment, and a Risk agent flags payment or PII exposure — recruited dynamically only when the situation demands it. When agents disagree on severity, an Arbiter agent resolves the conflict visibly in the Band Room. When the verdict is critical, the workflow pauses for a human reviewer to approve before any code is touched. Once approved, a remediation team (Dev, Security, QA) generates the patch, and the Scanner runs again to prove the fix actually works. Every decision, disagreement, recruitment, and approval happens inside the Band Room — Band is the coordination layer, not a notification channel. The final deliverable is either a signed-off patch archive or a formal GDPR/OWASP audit report, depending on the severity of findings. Built for SaaS teams operating under regulatory pressure.