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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.

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.

AIVE-Artificial Intelligence Venture Engine

AIVE-Artificial Intelligence Venture Engine

AIVE (Artificial Intelligence Venture Engine) is a Cognitive Discovery Operating System that transforms unstructured information into structured knowledge, evidence-backed insights, and actionable opportunities. Instead of only summarizing documents, AIVE builds an internal understanding of the information it receives and reasons across multiple knowledge sources. The platform ingests research papers, patents, technical documents, reports, datasets, presentations, websites, and other digital content. It extracts concepts, entities, relationships, evidence, and patterns, organizing them into an interconnected knowledge structure that enables deeper analysis and intelligent reasoning. Users can upload their own knowledge, explore it through an AI copilot, ask complex questions, and receive responses supported by evidence with traceability to the original sources. Rather than relying on predefined workflows or keyword matching, AIVE is designed to adapt dynamically to new domains and continuously expand its understanding as additional information is introduced. Beyond search and summarization, AIVE identifies research gaps, hidden relationships, emerging trends, contradictions, technology transfer possibilities, commercial opportunities, and innovation pathways. It generates research-grade reports, interactive knowledge graphs, visualizations, comparison tables, timelines, and decision-support outputs to help users understand complex information and make informed decisions. Built on knowledge engineering, graph-based reasoning, retrieval systems, and large language models, AIVE provides a unified workspace for researchers, engineers, enterprises, and innovators. Its vision is to become a trusted cognitive partner that accelerates scientific discovery, product innovation, strategic decision-making, and enterprise knowledge management through transparent, explainable, and evidence-driven intelligence.

ResearchOS

ResearchOS

ResearchOS ResearchOS is an autonomous multi-agent research platform designed to accelerate academic and technical research. Instead of acting as a traditional chatbot, ResearchOS coordinates a team of specialist AI agents that work together to perform an end-to-end research investigation. Given a research question, the system: -plans the investigation -retrieves relevant academic papers from arXiv -ranks and analyzes the strongest evidence -synthesizes findings across multiple papers -identifies evidence-grounded research gaps -generates a structured research report -stores completed investigations in notebook memory The platform presents the entire workflow through an interactive interface that visualizes each stage of the investigation, making the research process transparent and easy to follow. Key features include: -Multi-agent orchestration -arXiv evidence retrieval -Cross-paper literature synthesis -AI-generated research reports -Automated research gap discovery -Persistent notebook memory -Interactive investigation timeline -Deterministic fallbacks for reliable execution Although ResearchOS supports any research domain, it was developed with AMD-focused technical research in mind and can investigate topics such as ROCm, MI300X, GPU optimization, and LLM inference.

AI Classroom Edge Intelligence

AI Classroom Edge Intelligence

AI Classroom Edge Intelligence is a privacy-first classroom AI platform built for schools that need useful AI without sending every piece of student information to the cloud. The platform evaluates each task by privacy level, connectivity, and complexity, then routes it to Offline Edge Mode, a Local Classroom Server, or Fireworks AI Cloud Assist. Sensitive or restricted information stays local. Eligible anonymized, high-complexity tasks are sent through a secure Express backend to Fireworks Serverless using Qwen3.7 Plus. The project includes an AMD Model Router, Edge Runtime Monitor, Rural Connectivity Simulator, Privacy and Local Data Ownership Console, Classroom Digital Twin, and teacher approval workflow. Live results display the provider, model, route, privacy classification, latency, safety note, and AI response. API keys remain server-side, and the privacy guard blocks sensitive requests from cloud inference. The project was inspired by rural schools where connectivity can be unreliable and student privacy is critical. Instead of acting as a simple chatbot, it serves as an intelligent routing and decision-support system. Teachers review, edit, approve, or reject recommendations before instructional actions are recorded. The current implementation includes a working browser interface, real backend routing, live Fireworks Serverless integration, server-side key protection, and Docker containerization. Local AMD AI PC inference, GPU/NPU acceleration, device telemetry, and production synchronization are clearly identified as future work. The long-term vision is a school-owned AI platform combining local intelligence, optional cloud reasoning, persistent classroom evidence, and teacher oversight for rural and underserved communities.

career launch ai

career launch ai

CareerLaunch AI is a high-performance workspace architect designed to solve technical fragmentation for engineering students. By analyzing individual skill levels and goals, it constructs a complete, structured development environment. Key features: AI Synthesis: Leverages Llama 3 via Fireworks AI to generate personalized 90-day learning roadmaps and productivity workflows. Project Forge: Automatically scaffolds directory layouts and project structures for immediate coding. Hardware Synergy: Maps development requirements to AMD-accelerated hardware profiles, providing specific recommendations for CPU/GPU optimizations. Unified Dashboard: A clean, telemetry-rich UI that integrates all study and project needs into one interactive environment. Built for the AMD Track, it optimizes the development lifecycle from roadmap to deployment.

TokenRouter - Efficient AI Task Routing Agent

TokenRouter - Efficient AI Task Routing Agent

TokenRouter is a token-efficient routing agent built for the AMD Developer Hackathon. Many AI systems send every request to a large language model, even trivial ones, wasting tokens and money. TokenRouter solves this by first checking whether a task can be answered with fast, deterministic, zero-cost logic — for example, basic arithmetic expressions or keyword-based sentiment classification. If the task matches one of these patterns, it is solved instantly and locally with no API call at all. Only when a task falls outside these simple categories does the agent escalate it to a language model through the Fireworks AI API. This hybrid design significantly reduces token consumption and cost on real-world workloads, where a meaningful share of requests are simple enough to be handled without a model call. The agent is fully containerized with Docker for easy, reproducible deployment, reads tasks from a standard JSON input file, and writes results in the required output format. The architecture is intentionally simple and extensible: additional local solvers (e.g., unit conversion, basic lookups, regex-based extraction) can be added to the routing logic to further reduce reliance on the LLM over time, making the system more efficient as it grows. Built solo under significant time constraints, this project demonstrates that meaningful efficiency gains in AI agent design don't require complex infrastructure — just smart routing decisions before defaulting to the most expensive option.