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.

OmniSignal AI

OmniSignal AI

OmniSignal AI is an Enterprise Live Web Intelligence Command Center that turns public web data into actionable business intelligence. Enterprises constantly miss important external signals because useful information is scattered across vendor websites, pricing pages, public RFP portals, competitor announcements, security advisories, regulatory updates, and public documents. These signals can reveal supplier risk, pricing changes, contract exposure, compliance impact, market movement, savings opportunities, and new revenue opportunities. OmniSignal AI solves this by creating a unified intelligence layer powered by a Bright Data-style web ingestion pipeline. The workflow is simple: Bright Data unlocks public web data, OmniSignal AI collects and structures signals, AI scores risk and opportunity impact, the dashboard shows business intelligence, AI Copilot explains what matters, and workflows plus integrations turn insights into action. The platform includes a Command Center, Signals Intel, Vendor Risk Intelligence, Opportunities, Risk & Compliance, Markets & Competitors, Contracts & Renewals, Savings Analyzer, AI Copilot, Sources, Workflows, and Integrations. Users can analyze text, URLs, websites, company names, or document references and generate AI summaries, severity scores, confidence levels, recommended actions, and downloadable reports. The current version is a hackathon MVP with a working React frontend, FastAPI backend, dynamic API routes, Bright Data demo/live mode structure, AI Copilot, module-specific AI analysis, report exports, workflow simulation, and integration concepts. In production, the system can connect real Bright Data APIs, add persistent storage, real file parsing, scheduled monitoring, LLM reasoning, authentication, audit logs, and real enterprise integrations such as Slack, Salesforce, Jira, ServiceNow, Snowflake, Coupa, and Microsoft Teams.

ORIGIN LENS

ORIGIN LENS

Origin Lens is an AI powered media verification platform designed to help users identify whether an image is authentic or AI generated. As synthetic media becomes more realistic, distinguishing real content from manipulated visuals is increasingly difficult. Origin Lens uses intelligent AI workflows to analyze uploaded images and generate explainable insights, confidence based detection, and authenticity signals. The platform focuses on digital trust, misinformation prevention, and AI assisted security workflows. Built as a solo project, Origin Lens combines modern UI/UX, responsive web technologies, AI integration, and practical real world applications in media verification and intelligent analysis systems.

TradeBenchmark for ai models

TradeBenchmark for ai models

DropScout is a benchmark for evaluating whether AI models can time purchases of real digital goods better than simple human-market baselines. We use CS2 Steam Market cases because they are liquid, low-cost, and have observable historical prices, so model trading claims can be tested against real market behavior instead of a fake demo. The system fetches historical candle data from CS2Cap, keeps Steam Market data as a live sanity check, normalizes the evidence, and generates reports that compare each model run against window-start buying, average human-market pricing, best possible hindsight pricing, worst pricing, liquidity, volume, and timing opportunity. A Gemini paper-trading harness makes bounded buy, sell, hold, or skip decisions using only prior candles, and the simulator scores those decisions on the next available market data. The goal is not another confident trading chatbot. DropScout is the scoreboard underneath AI trading agents: same data window, transparent constraints, reproducible reports, and a clear separation between real benchmark evidence, paper-trading model output, and hindsight-only ceilings.DropScout is a benchmark for evaluating whether AI models can time purchases of real digital goods better than simple human-market baselines. We use CS2 Steam Market cases because they are liquid, low-cost, and have observable historical prices, so model trading claims can be tested against real market behavior instead of a fake demo. The system fetches historical candle data from CS2Cap, keeps Steam Market data as a live sanity check, normalizes the evidence, and generates reports that compare each model run against window-start buying, average human-market pricing, best possible hindsight pricing, worst pricing, liquidity, volume, and timing opportunity. A Gemini paper-trading harness makes bounded buy, sell, hold, or skip decisions using only prior candles, and the simulator scores those decisions on the next available market data.

MidContext Live Translation Agent

MidContext Live Translation Agent

MidContext Live Translation Agent solves a major challenge for companies operating across multilingual markets: customer support becomes slower, more expensive and less personal as customers and agents do not speak the same jargon. Beyond language, each generation have its unique way of talking and AI enables hyper customisation capabilities. We identified low scalable workflows, high wait times, low resolution quality and inconsistent customer experience as key pain points for companies, especially for companies scaling across Europe with different languages, accents and local expectations, and low maturity with their internal knowledge bases. Scalable globally, and also interesting to mayor incumbents that can not afford losses in their reputation. Our solution is a real-time voice translation layer between customer care agents and customers. The system captures voice input, converts speech through ASR, routes the conversation through a customer support layer, and generates natural voice responses using TTS. It does more than translate words: it preserves context, intent, tone and company jargon, while connecting to local knowledge bases and support workflows. It works today, right away in the company as it is, and help build its future enriching their local customer service knowledge base. The target users are multinational companies, customer operations teams, CCaaS providers and enterprises that need scalable multilingual support without losing the human connection. MidContext uses a glocal strategy: one global architecture, adapted to local languages, customer behaviors, policies and knowledge bases. A human-in-the-loop quality model keeps agents responsible for sensitive cases, approvals and escalations, reducing technological complexity while improving trust, resolution quality and customer satisfaction.

MenuTaste AI Agent

MenuTaste AI Agent

MenuTaste is a web-based enterprise AI agent for food entrepreneurs, cafes, restaurants, and small food businesses. It helps users turn a food or drink idea into a structured product-quality, nutrition, risk, market-fit, and launch-readiness report. The user enters a product name, ingredients, business type, location, customer segment, dietary focus, target price, and preparation complexity. MenuTaste then runs a multi-step workflow: it performs local ingredient and nutrition-signal analysis, detects allergens and dietary conflicts, scores the product across nutrition, quality, market fit, and operations, and sends a structured prompt to a Featherless open-source model for deeper business reasoning. The final output includes an executive summary, nutrition estimate, risk review, ingredient notes, Featherless AI reasoning, recommended actions, launch checklist, and downloadable Markdown and JSON reports. This makes MenuTaste useful for early-stage restaurants and food entrepreneurs who need to validate a menu item before piloting it with customers. The system combines deterministic local scoring with Featherless AI reasoning. This hybrid design keeps core food-risk checks explainable while using open-source LLM inference for strategy, recommendations, customer positioning, and launch planning. The app is designed as a production-style Streamlit web application and deployed on Vultr for the hackathon demo.

Rehaby

Rehaby

Rehaby is an AI-powered rehabilitation intelligence platform designed to bridge the gap between hospital-based physiotherapy and home recovery. Many patients receive proper monitoring and rehabilitation support while admitted in hospitals, but after discharge, rehabilitation often becomes unsupervised. Patients may perform exercises incorrectly, lose motivation, misunderstand instructions, or skip therapy sessions completely. This problem is especially common among elderly, post-surgical, orthopedic, neurological, and cardiovascular rehabilitation patients who may also face difficulties traveling long distances for short clinical follow-ups. Improper rehabilitation can increase recovery time, risk of re-injury, and workload on healthcare professionals. Rehaby addresses this challenge through an intelligent AI-driven rehabilitation ecosystem that enables patients to safely continue physiotherapy exercises from home while remaining connected with clinicians. The platform combines computer vision, real-time posture tracking, and adaptive rehabilitation intelligence to analyze patient movements and provide immediate corrective feedback. Using technologies such as MediaPipe Pose, OpenCV, TensorFlow Lite, and FastAPI, Rehaby performs live joint angle analysis, posture correction, repetition counting, and movement scoring directly through a web-based interface. The patient-side application offers real-time camera posture tracking, skeleton overlays, AI voice guidance, visual corrective feedback, and session summaries to improve exercise accuracy and adherence. On the clinician side, a mobile dashboard allows healthcare professionals to monitor patient progress remotely through analytics, form score trends, session histories, and recovery performance insights. The system also supports Urdu voice interaction and low-bandwidth accessibility to improve usability for diverse patient populations.