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OpenAI ChatGPT

The ChatGPT model has been trained on a vast amount of text data, including conversations and other types of human-generated text, which allows it to generate text that is similar in style and content to human conversation. ChatGPT can be used to generate responses to questions, code, make suggestions, or provide information in a conversational manner, and it is able to do so in a way that is often indistinguishable from human-generated text. The initial model has been trained using Reinforcement Learning from Human Feedback (RLHF), using methods similar to InstructGPT, but with slight differences in the data collection setup. The model is trained using supervised fine-tuning, where human AI trainers provided conversations in which they played both sides—the user and an AI assistant. The trainers would have had access to model-written suggestions to help them compose their responses.

General
Relese dateNovember 30, 2022
AuthorOpenAI
API DocumentationChatGPT API
TypeAutoregressive, Transformer, Language model

Start building with ChatGPT

GPT-3 have a rich ecosystem of libraries and resources to help you get started. We have collected the best GPT-3 libraries and resources to help you get started to build with GPT-3 today. To see what others are building with GPT-3, check out the community built GPT-3 Use Cases and Applications.

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ChatGPT Boilerplates

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ChatGPT API libraries and connectors

The ChatGPT API endpoint provides a convenient way to incorporate advanced language understanding into your applications.

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OpenAI ChatGPT AI technology Hackathon projects

Discover innovative solutions crafted with OpenAI ChatGPT AI technology, developed by our community members during our engaging hackathons.

ProofGate

ProofGate

Software teams often rely on one opaque AI response to interpret a request, decide what to do, perform the work, and explain the result. That makes failures difficult to trace and retries easy to repeat without learning from the first attempt. ProofGate separates the work into five clear roles connected through Band. A human starts with a normal request. Intake converts it into a structured task. Planning defines scope, constraints, and measurable success requirements. Resolution performs the requested work and checks whether every requirement was met. When the result succeeds, Finalizing delivers the completed result and closes the workflow. When Resolution fails, ProofGate does not restart the entire workflow or repeat the same attempt. Issue Isolation identifies the exact failure location and cause, preserves the work that already succeeded, and creates a new focused handoff. That handoff can return to the Resolution Agent or be sent to a configured specialist such as Claude Code or Codex. Only one retry is allowed. The final result is then delivered as completed, resolved after isolation, blocked, or failed. Band provides the agent identities, room communication, and targeted handoffs. ProofGate provides the structured packet, deterministic routing, one controlled retry, and terminal delivery model.

The-Agnets-Worksation

The-Agnets-Worksation

The Agents Workstation is a production-grade, autonomous software engineering agency designed to solve the critical hallucination and execution gaps inherent in traditional AI code generation. Built as a highly concurrent Python orchestration engine, the system decentralizes intelligence across a specialized Band of Agents—including an Architect (Planner), Domain Builders (Frontend/Backend), a deterministic Executor (Terminal), and QA Specialists (Supervisor/Repair). Operating as a native node on the Band AI network, these agents dynamically spin up programmatic chat rooms to plan, coordinate, and hand off tasks using Directed Acyclic Graphs (DAGs) with complete, observable transparency. Unlike standard code assistants that leave execution and debugging to the human developer, the workstation features an indestructible, headless Execution Sandbox. The Terminal Agent handles virtual environments, bypasses interactive prompts in "CI Mode," and actively pings local network ports to guarantee server stability. If an application throws an error on startup, the Supervisor Agent catches the runtime traceback, calculates a project stability score, and triggers a surgical, self-healing Repair Loop to patch the codebase without human intervention. To guarantee zero downtime, the architecture is shielded by a Universal LLM Gateway featuring multi-provider failover routing, dynamically shifting loads between Tier-1 models like Gemini, Claude, and GPT-4o if rate limits are hit. Operators monitor this entire hive mind through a premium, zero-simulation Cyberpunk Dashboard. Powered by real-time WebSockets, this command center streams deterministic telemetry, agent state updates, and system logs with millisecond precision, proving that the AI is not just writing code—it is autonomously orchestrating an entire software factory.

MedSync AI Collaborative Crisis Intelligence

MedSync AI Collaborative Crisis Intelligence

TASK 3 — LONG DESCRIPTION Problem Every year, U.S. hospitals face over 150 million emergency department visits and thousands of mass casualty events. When a Level 3 Critical surge strikes — a multi-vehicle accident, an industrial disaster, a pandemic spike — the difference between life and death is measured in minutes. Yet the coordination systems hospitals depend on were designed for a pre-digital era. Incident commanders juggle phone calls, whiteboards, and pagers. Capacity managers refresh spreadsheets. Staffing coordinators text on-call nurses. Resource managers fax mutual aid requests. Compliance officers review binders of regulatory requirements. The result is catastrophic coordination failure: 34% of preventable hospital deaths are attributed to communication breakdowns during emergencies (Joint Commission, 2023) Average surge response time is 47 minutes — 37 minutes longer than best-practice targets $2.1M average cost per mass casualty event in operational inefficiency alone 72% of hospitals report that their Incident Command System breaks down under real pressure EMTALA violations during surges carry $119,942 fines per incident and potential loss of Medicare funding The fundamental problem is not a lack of data — it is a lack of coordinated decision-making under pressure. No single person can simultaneously optimize bed allocation, nurse staffing ratios, ventilator supply chains, and regulatory compliance within a 10-minute window. Solution MedSync AI is a Collaborative Multi-Agent System that brings autonomous, coordinated, AI-driven decision intelligence to hospital emergency response. Unlike a chatbot or a dashboard, MedSync AI deploys 5 specialized AI agents that work together — reading each other's outputs, challenging each other's recommendations, and negotiating until the plan is compliant, optimal, and ready for human approval.

Sentinel

Sentinel

Sentinel is an autonomous, multi-agent architecture review system designed for regulated and high-stakes enterprise environments. In industries like finance, healthcare, and insurance, deploying new technical workflows requires rigorous scrutiny across multiple domains—security, compliance, and IT governance. Manual reviews are often massive bottlenecks. Sentinel solves this by orchestrating a team of specialized AI agents through the Band collaboration layer to autonomously debate, audit, and score technical proposals. Built specifically for Track 3 (Regulated & High-Stakes Workflows), Sentinel goes beyond simple linear automation or thin API wrappers by utilizing Band as a true shared interaction layer. The workflow is managed by the Conductor agent, which ingests technical architecture documents and coordinates the room. The Harness agent injects historical company policies and compliance baselines directly into the shared workspace. Operating in parallel, the Adversary aggressively red-teams the architecture for critical security flaws (such as prompt injection vulnerabilities in LLM execution flows), while the Guardian audits for regulatory violations (such as GDPR or SOC2 data handling failures). Because these agents operate within a shared Band chat room, they do not work in silos. Once the specialized audits are complete, the Evaluator reads the room's context to synthesize the distinct flags into a cohesive architectural review. Finally, the RiskScorer processes this evaluation to generate a definitive, quantitative risk matrix and an automated approve/escalate decision payload. By demonstrating real agent-to-agent collaboration, role specialization, complex context exchange, and task handoffs, Sentinel proves that multi-agent systems can handle complex, regulated decision-making safely, transparently, and efficiently.

The Delphi Crucible: Personal Accountant Team

The Delphi Crucible: Personal Accountant Team

The Delphi Crucible is a state-of-the-art multi-agent AI investment platform designed to simulate a real-world institutional investment committee. Built using the powerful Band.ai SDK, the platform orchestrates four specialized AI agents—a Quant, a Bull, a Bear, and a Portfolio Manager (PM)—that dynamically analyze financial data, engage in rigorous debate, and formulate high-quality investment memos. Users can submit a stock ticker or upload a 10-K financial document. From there, the PM Agent initiates a Band.ai chat room and rallies the team. The Quant Agent acts as the analytical powerhouse, pulling live data from Yahoo Finance or extracting complex tables from PDFs via Featherless AI (Qwen2.5-72B). The Bull and Bear Agents, powered by GPT-4o via AI/ML API, then debate the stock's merits and risks in parallel. Finally, the PM synthesizes their arguments against the user's risk profile to deliver a definitive investment decision. We built a robust architecture featuring a high-performance Python FastAPI backend and a visually stunning Next.js 14 frontend. Using Server-Sent Events (SSE) and native Redis state management, the user watches the debate unfold in real-time within a beautiful "Mesh Dark Editorial" UI. Beyond generating memos, the platform includes a fully functional paper-trading simulator. Users can instantly execute trades based on the AI's recommendations, monitoring their dynamic portfolio and live-ticking profit/loss through interactive Framer Motion and Recharts visualizations. The Delphi Crucible transforms solitary research into a dynamic, intelligent conversation, delivering an unparalleled AI-driven financial analysis experience.

AGENT-OS

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.