Top Builders

Explore the top contributors showcasing the highest number of app submissions within our community.

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.

ARCOS

ARCOS

ARCOS (Agentic Economic Operating System) is a next-generation financial coordination layer designed for an AI-driven economy. Instead of treating payments as static transactions, ARCOS transforms them into dynamic, programmable flows between autonomous agents. The system introduces a spatial simulation interface where economic activity is visualized in real time—agents interact, transact, and adapt based on live data and predefined objectives. This allows users to observe not just outcomes, but the behavior and evolution of economic systems. Built on stablecoin infrastructure, ARCOS enables high-frequency, low-cost micropayments that power agent-to-agent interactions. These payments are not manually triggered; they are executed automatically by intelligent agents responding to conditions, events, and goals. The platform integrates AI decision-making with blockchain-based settlement, creating a seamless loop between reasoning and execution. Developers can deploy agents that manage resources, optimize flows, and perform transactions without human intervention. ARCOS represents a shift from dashboards to living systems—from passive financial tools to active economic environments. It is designed for the future of commerce, where machines don’t just assist humans, but participate as autonomous economic actors.

AgentUber

AgentUber

AgentUber is an agent-to-agent marketplace on Circle Developer Controlled Wallets and Arc blockchain. Instead of building every agent yourself — rent the best ones from the marketplace. Any developer can register their agent on the platform. Just be online when the order comes — Circle wallet is created automatically, USDC arrives per action. An agent-client places an order. A manager assembles a pipeline of agents from the marketplace. Each agent pays for every iteration from its own wallet. Real on-chain USDC transactions for every step. No human involved at any stage. The Judge agent checks every result. APPROVE or REJECT. Payment happens only after APPROVE. Each agent gets back its costs plus fee. Manager gets commission. Bank provides liquidity. Agent-client pays only for accepted results. If REJECT — agents lose money. Built-in economic incentive for quality. Every agent pays for every thought on Arc. Sub-cent settlements — $0.001 to $0.050 per action. Without Arc this model is physically impossible — Ethereum gas fees of $2-5 kill any micro-payment business. 18 agents with real Circle wallets on Arc Testnet. 50+ onchain USDC transactions. $8.916 total volume processed.

SpArc: Instant Payouts

SpArc: Instant Payouts

SpArc reimagines how marketing campaigns operate by transforming them into programmable, performance-based economic systems. Instead of relying on upfront influencer contracts and fixed payments, campaigns are funded as pools and distributed dynamically based on real user engagement. Creators can participate openly and earn in two ways: through instant micropayments for clicks and through attribution-based payouts when conversions occur. The system uses a recency-weighted attribution model to fairly distribute conversion value across contributors. All payments are executed automatically using Circle’s wallet infrastructure and USDC on Arc Testnet, with every transaction verifiable on-chain. This creates a transparent, trustless environment where incentives are aligned and outcomes are measurable in real time. By lowering the barrier to entry for both campaign creators and participants, SpArc enables a more open and scalable model for digital marketing and user-generated promotion.

Agent Fuel – Shared Wallet for AI Agents

Agent Fuel – Shared Wallet for AI Agents

Agent Fuel is a programmable wallet system that lets AI agents handle their own money in the new agentic economy. AI systems today depend on people to pay for APIs, data, and computing power. This causes problems and stops full automation. Agent Fuel fixes this by letting users put money into a master wallet in USDC and set budgets for each AI agent with set spending limits. Through a secure API layer, each agent can then make micropayments for services like LLM queries or data access on their own. The platform has a real-time dashboard that shows balances, transactions, and agent activity, as well as features like alerts for low balances and automatic top-ups. Agent Fuel shows how programmable money can power self-driving systems. It was made with React, Node.js, and Python, and it has a payment layer that works with Circle. It makes it possible for AI-powered apps to have scalable financial interactions between machines.

Agent Payment Loop with Arc-Based USDC Simulation

Agent Payment Loop with Arc-Based USDC Simulation

Agent-to-Agent Payment Loop is a live demo showcasing how autonomous AI agents can coordinate tasks and settle payments in real time using a micropayment-driven architecture. In traditional systems, APIs and compute tasks execute without economic awareness, leading to inefficient and opaque cost structures. This project introduces a model where every agent action has a measurable economic value, enforced through micro-transactions. In this demo, we simulate Arc-based USDC micropayments between agents. Each step in a multi-agent workflow triggers a micro-transaction, representing how Circle’s infrastructure could enable real-time settlement at sub-cent cost. The system demonstrates: - Autonomous agent coordination across a shared task - Per-action micropayment settlement - Real-time transaction tracking and execution flow - Transparent cost comparison between Arc micropayments and traditional gas-based systems The UI highlights key metrics such as transaction count, settlement cost, and savings unlocked through sub-cent pricing. The demo includes over 50 simulated transactions, validating the feasibility of high-frequency, low-value payments. This project is designed as a forward-looking prototype for the Agentic Economy, where AI services operate as independent economic actors, capable of paying and being paid for each action they perform.

SkillBid

SkillBid

SkillBid introduces a new economic primitive for the agentic internet. Today, AI agents have no way to autonomously pay each other for services; every transaction requires human intervention, custodial systems, or is simply too expensive due to gas fees. SkillBid solves this with a first-price sealed-bid auction system. When a user submits a task, three specialist AI agents, a Summarizer, Translator, and Sentiment Analyzer, simultaneously submit competitive bids in USDC. The cheapest agent wins, executes the task using LLM inference, and receives instant payment via Circle Nanopayments settled on Arc blockchain. Every transaction is sub-cent, ranging from $0.001 to $0.008 USDC per task. This model is only economically viable because of Arc. On the Ethereum mainnet, a single transaction costs $0.50–$2.00 in gas, 500x to 2000x the actual service price, making per-action agent commerce completely impossible. On Arc, gas overhead is negligible, enabling true micropayment-driven agent economies. The system is fully deployed and live. It includes a FastAPI Python backend, SQLite database tracking all tasks, bids, transactions, and agent earnings, four Circle Developer-Controlled Wallets (one per agent), and a React frontend showing live auctions, a real-time leaderboard, and a live transaction feed. Over 60 on-chain transactions were generated during development and testing. SkillBid demonstrates the future of machine-to-machine commerce, autonomous agents competing, pricing themselves, earning USDC, and operating as independent economic actors without any human in the loop.