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
AuthorCursor
Release Date2023
Websitehttps://www.cursor.com/
Repositoryhttps://github.com/cursor
Documentationhttps://docs.cursor.com/
Technology TypeAI-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.

AuraOS

AuraOS

Aura is a sovereign, neuro-symbolic AI operating system designed to make advanced artificial intelligence more transparent, efficient, collaborative, and accountable. Instead of sending every instruction directly to a language model, Aura can locally interpret natural-language intent, organize it into structured semantic and routing representations, identify relevant code or knowledge, and prepare a bounded task before model use is required. Aura combines deterministic routing, a 4,096-primitive lexical system, polysynthetic six-slot intent structures, visual code topology, guarded human-agent workflows, and a Learning Arena where verified experiences can be tested before becoming reusable knowledge. Its Observatory allows users to see how Aura understood an instruction, while the Human Agent Arena supports node-specific dialogue, approval gates, verification, and preserved attempt artifacts—including failed work that may help future refactoring. Aura is built around human authority. Models can propose, explain, investigate, and assist, but they cannot silently expand scope, bypass evidence requirements, or merge changes without approval. The result is an AI architecture focused not only on capability, but on sovereignty, inspectability, learning, and responsible action.

TRANSDUCER - Style-Aware Video Captioning-Agent

TRANSDUCER - Style-Aware Video Captioning-Agent

TRANSDUCER is an autonomous Track-2 agent that watches a video and retells it in four distinct voices: formal, sarcastic, humorous_tech, and humorous_non_tech. Instead of brittle rule-lists, each style is a character — a precise observer, a world-weary wit, a burnt-out engineer, a bewildered dad — handed the actual video frames so the humor reacts to what is genuinely on screen and never invents details the LLM-judge would penalize. Per clip (~15–25s): the download is streamed and salvaged even when truncated, four keyframes are sampled across the whole clip at 1024px, and a single multimodal call to Kimi K2.6 on Fireworks AI returns all four captions at once — forcing sharp contrast between voices — followed by per-style retries, a deterministic fallback ladder guaranteeing complete valid output, and an artifact sanitizer. Engineered for the real grading box (4GB RAM, 2 vCPU, keyless, cold anonymous pull): three clips run concurrently, every clip finishes under 30 seconds, and no style is ever missing. Across 20+ leaderboard-scored builds — one change at a time, kept or reverted on the number — it climbed 0.61 → 0.84 → 0.91, peaking at 0.92, top-3 on the hidden set.

CaptionDB

CaptionDB

CaptionDB is an AI-powered platform that transforms videos into structured, searchable, and caption-rich content through an intelligent multi-stage processing pipeline. Instead of treating a video as a single asset, CaptionDB automatically detects scene boundaries, extracts representative keyframes, filters low-quality or duplicate images, performs vision analysis using multimodal AI, and generates context-aware captions tailored to different tones and use cases. The system is built with a production-grade architecture using Clean Architecture principles, ensuring complete separation between the Domain, Application, and Infrastructure layers. The backend is powered by FastAPI, PostgreSQL, SQLAlchemy, Redis, and Celery for scalable asynchronous processing, while the frontend uses Next.js 15, React Query, Zustand, and TypeScript to deliver a modern user experience. CaptionDB is designed to be AI-provider agnostic. Vision analysis, caption generation, authentication, storage, and background processing are all abstracted behind interfaces, making it easy to replace or extend providers without changing business logic. The platform includes OAuth authentication, project management, task monitoring, progress tracking, and an optimized AI pipeline that minimizes unnecessary token usage through scene detection and intelligent keyframe selection. By combining computer vision, multimodal AI, and scalable cloud architecture, CaptionDB significantly reduces the time required to generate meaningful captions and structured metadata for videos. The result is a fast, extensible, and production-ready platform suitable for content creators, educators, marketers, media teams, and developers who need automated video understanding at scale.

StyleCap: Grounded Video Captioning

StyleCap: Grounded Video Captioning

StyleCap is a containerized video-captioning agent built for AMD Hackathon ACT II, Track 2. For each short clip it downloads the video, samples frames with ffmpeg, and runs a two-phase anti-hallucination pipeline: (1) a vision model extracts a strict JSON fact sheet from what is actually visible; (2) a text model generates captions in only the requested styles—formal, sarcastic, humorous-tech, and humorous-non-tech—from those facts alone. A batched internal judge picks the best of two candidates per style. A graceful degradation ladder and schema repair guarantee valid JSON output even under timeout pressure. The graded Docker image (ghcr.io/quantumbyte-01/amd-track2-captioner:v34) uses Fireworks serverless Kimi K2.6 for grounding and GLM 5.2 for styling and judging, with the API key baked in per Track 2 rules. Parallel workers and per-clip budgets keep 12 clips inside the 10-minute limit. For the Gemma partner prize, the identical pipeline runs end-to-end on google/gemma-3-12b-it served via vLLM on AMD Instinct MI300X (ROCm)—grounding, styling, and judging—without Fireworks Gemma deployment. Eight public validation clips complete in 86 seconds; evidence is in GEMMA_RESULTS.md and work/gemma_results.json. Demo video shows live rocm-smi GPU load and caption generation on the pod.

SecondChance AI

SecondChance AI

SecondChance AI is an AI-powered relationship recovery and meaningful connection platform designed for people rebuilding their lives after difficult relationships, heartbreak, divorce, or emotional setbacks. Instead of focusing only on dating, our platform prioritizes emotional healing, confidence building, safety, and meaningful human connections through artificial intelligence. The platform combines intelligent profile analysis, AI-assisted compatibility insights, personalized relationship coaching, guided onboarding, secure matching, and a clean user experience to help users confidently begin a new chapter in life. Every recommendation is designed to encourage healthy communication, emotional well-being, and long-term compatibility rather than superficial interactions. Our AI Coach acts as a supportive companion that provides personalized guidance, emotional encouragement, reflective conversations, and practical relationship advice. The matching system analyzes interests, goals, personality traits, and compatibility signals to recommend meaningful connections. Additional features such as profile management, secure messaging, privacy-focused settings, and guided onboarding create a complete recovery-to-relationship journey. SecondChance AI is built with scalability and privacy in mind using modern technologies including React Native, Expo, Firebase, TypeScript, and AI-driven recommendation logic. Alongside the mobile application, we developed a premium landing website showcasing the vision, product experience, roadmap, and future plans for the platform. Our long-term vision is to evolve SecondChance AI into a trusted global relationship wellness ecosystem where artificial intelligence not only helps people discover compatible partners but also supports emotional healing, relationship education, communication improvement, and healthier human connections. We believe everyone deserves a second chance to heal, grow, and build meaningful relationships with confidence.

IOLITE: Intelligent workout planner

IOLITE: Intelligent workout planner

Iolite is an AI-powered training planner designed to help endurance athletes achieve their goals through personalised, adaptive, and sport-specific workout programmes. Powered by Fireworks AI, Iolite generates and adjusts training plans based on each user’s chosen sport, fitness level, availability, and upcoming events. Supporting running, cycling, swimming, Hyrox, rowing, and athletic events, Iolite uses periodised training principles to structure workouts into progressive phases, ensuring users build fitness effectively while peaking at the right time for competition. Users can select multiple sports and events, with an intelligent priority system balancing training demands and identifying which workouts and exercises provide the greatest impact towards their goals. Iolite adapts continuously by analysing performance, completed sessions, and progress to optimise future workouts. A smart fitness scoring algorithm tracks development by evaluating consistency, performance improvements, and sport-specific progress. Gamification features such as workout streaks, milestones, and achievements keep users motivated and encourage long-term consistency. By combining AI coaching, sports science, and personalised training optimisation, Iolite provides athletes with an intelligent platform to train smarter, improve consistently, and prepare confidently for their next challenge.