QUIRQ Resources

Get Started with Quirq

  • Managed Cloud: app.xo.builders
    Choose a quickstart, add your model API key, and launch a workspace. The xo-space is included.

  • Local Installation:
    Run the following command:

    curl -fsSL quirq.ai/install | sh

    No Quirq account is required. The same xo-space runs locally on your machine.

Supported Agent Runtimes

Choose a quickstart or bring your own runtime:

  • Claude Code

  • Codex

  • OpenClaw

  • Hermes

  • n8n

  • Bring Your Own Agent

Community and Support

  • Discord: Join the #quirq-track channel in the Coffee & Code Philadelphia Discord for technical questions and event-day support.

  • GitHub Discussions: Review the xo-space discussion threads for project ideas, requested features, runtime integrations, and open-source contribution opportunities.

  • Quickstart Guide: A one-page setup and submission guide will be pinned in the Quirq Discord channel.

What to Bring

  • An API key for your preferred model provider, such as OpenAI or Anthropic

  • A repository, document folder, spreadsheet, or other project data

  • A GitHub account if you plan to contribute to xo-space

  • A screen-recording tool for your submission video

Teams must provide their own model API keys. Quirq does not provide model credits for development during the hackathon.

 

Human Standard Technical Resources

  • HumanStandard website: hsverify.com

  • API documentation: docs.hsverify.com

  • HumanStandard API key: Issued to each team on the morning of the hackathon

  • 200 API credits per team: One credit covers one song scan

  • API quickstart: Designed so participants can also give it directly to a coding agent

  • Detection API: Returns a signed verdict indicating whether audio is AI-generated, human-performed, or hybrid, along with supporting evidence

Runpod Getting Started Resources

Welcome to the central developer hub for building, deploying, and scaling AI workloads on Runpod. Whether you are launching your first GPU pod, running serverless inference, or setting up agentic development environments, these core guides and media channels will help you get up to speed quickly.

Core Architecture & Concepts
Serverless GPU Explainer

Understanding how GPU infrastructure scales on demand is crucial for building cost-effective AI applications.

  • Read the Serverless GPU Guide

  • What you'll learn: The fundamental mechanics of serverless GPU compute, scaling to zero, pay-per-second billing execution, and how to structure production AI workloads to maximize throughput while minimizing compute costs.

Quickstarts & Deployment Guides
Hello World on Runpod Serverless

The fastest path to getting code running on GPU infrastructure.

  • Read the Hello World Tutorial

  • What you'll learn: How to construct a lightweight handler, test your function locally, push to Runpod Serverless, and trigger your endpoint using cURL or Python.

What's New in Runpod Serverless

Discover modern features designed to streamline deployment and reduce latency.

  • Read the Serverless Feature Guide

  • What you'll learn: How to leverage sub-second cold starts with FlashBoot, process high-volume requests with batch inference, and deploy serverless functions directly without writing custom Dockerfiles using Runpod Flash.

AI Assistant & Agentic Development
Your First Claude Code Project Within Runpod

Combine the power of modern AI coding assistants with persistent cloud GPU instances.

  • Read the Claude Code Setup Guide

  • What you'll learn: Complete step-by-step instructions for installing and authenticating Claude Code directly inside a Runpod Pod, enabling an autonomous agentic workflow where your AI assistant can build, test, and run code directly in your cloud environment.

Video Content & Live Demos
Runpod Official YouTube Channel

Prefer visual walkthroughs? The official YouTube channel covers everything from basic setup to advanced multi-node orchestration.

  • Visit the Runpod YouTube Channel

  • Featured Topics:

    • Step-by-step visual setup guides

    • Live demo breakdowns from community Q&A sessions

    • Model deployment benchmarks and performance tuning

Community & Next Steps