Top Builders

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

OpenAI Codex

OpenAI Codex is an artificial intelligence model developed by OpenAI. It parses natural language and generates code in response. It is used to power GitHub Copilot, a programming autocompletion tool. Codex is a descendant of OpenAI's GPT-3 model, fine-tuned for use in programming applications. OpenAI has released an API for Codex in closed beta. Based on GPT-3, a neural network trained on text, Codex has additionally been trained on 159 gigabytes of Python code from 54 million GitHub repositories. You can find more information here https://openai.com/blog/openai-codex/

General
Relese dateAugust 31, 2021
AuthorOpenAI
Repository-
TypeAutoregressive, Transformer, Language model

Start building with Codex

We have collected the best Codex libraries and resources to help you get started to build with Codex today. To see what others are building with Codex, check out the community built Codex Use Cases and Applications.

Boilerplates

Kickstart your development with a Codex based boilerplate. Boilerplates is a great way to headstart when building your next project with Codex.

  • Codex Boilerplate Create a function just by typing what it should do, with help of OpenAI Codex.

Libraries

A curated list of libraries and technologies to help you build great projects with Codex.

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OpenAI Codex AI technology Hackathon projects

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

Beacon

Beacon

When the river crests and the towers go dark, a hundred people end up stranded in a school gym with no signal and no way to call for help. A volunteer nurse faces a growing line of the sick and injured with no one to consult. A teacher manages sixty frightened kids alone. A family doesn't know if their water is safe to drink. Every one of them is holding a phone with a powerful on-device NPU, but cloud AI dies the instant the network does, and no single phone has the memory or compute to run a frontier-grade LLM by itself. Beacon is built around this constraint from the start: the model is pre-sharded before disaster strikes, not after. Users opt in ahead of time, downloading a layer-wise slice of a large language model's weights onto their device, a contiguous block of transformer layers sized to that phone's available memory and NPU class. These shards sit dormant on the device, costing nothing until they're needed. When the network goes down, phones nearby connect over a peer-to-peer hotspot network: one phone hosts, others join directly, with no router or internet infrastructure required. Beacon assembles an inference cluster from whichever pre-loaded layer shards happen to be present in the room, sequencing them in the correct layer order for a forward pass. The hotspot link only needs to negotiate which layers are available, route activations between phones in sequence, and reroute around a phone that drops out or runs out of battery. The heavy lifting, distribution, was done in advance, when everyone still had a connection. The result is a cluster that can assemble in seconds during an emergency, because the only real-time job is discovery and coordination, not download. The nurse gets triage guidance. The teacher gets crisis-management support. The family gets a real answer about their water. The help didn't arrive; it was already pre-positioned in their pockets, just waiting to be switched on.

Sub Rosa: On-Device AI Legal Intake

Sub Rosa: On-Device AI Legal Intake

Sub Rosa is an Android app that turns a lawyer's first client meeting into clear intake notes. It runs entirely on the phone and never goes online. It records the conversation and transcribes it on the device with Whisper. A small language model, Qwen3-1.7B running with ExecuTorch on the Qualcomm Hexagon NPU, then produces three things: the facts, each shown next to the exact words from the transcript that back it up; the missing items, the standard intake points the client did not mention, found by comparing the talk against a checklist; and a few neutral follow-up questions to ask. The model only reads and sorts what it is given. It never states the law or gives advice. The legal knowledge lives in checklists written by people, not in the model's weights, so a small model stays accurate with no training step. Privacy is built in. The app has no internet permission, so the phone blocks any connection, and an on-screen panel shows nothing was sent. Audio is never saved, and all client data is encrypted on the device with AES-256-GCM.

Scribend: 100% Offline Edge AI Medical Scribe

Scribend: 100% Offline Edge AI Medical Scribe

Scribend addresses the critical need for secure, automated medical documentation in clinical environments where cloud connectivity is inconsistent or data privacy is paramount. By leveraging an entirely on-device Edge AI architecture, Scribend transforms spoken doctor-patient interactions into structured clinical records without ever transmitting data to the cloud. The system utilizes a modular, multi-model pipeline optimized for the Qualcomm Snapdragon NPU: Transcription: We utilize Distil-Whisper Small for high-accuracy speech-to-text, augmented with an 80-term medical vocabulary hint to ensure precise capture of clinical terminology and phonetic typo correction. Context Retrieval: Using MiniLM vector embeddings and a local SQLite database, the system performs semantic searches on a patient’s historical records, providing the LLM with relevant medical context before note generation. Reasoning: Meta Llama 3.2 3B Instruct acts as the system’s "brain." It performs zero-shot speaker diarization to separate Doctor and Patient dialogue, applies contextual logic to identify medical facts, and outputs a perfectly structured JSON SOAP note. Formatting: Finally, the system automatically converts the JSON output into a polished, timestamped Markdown document, complete with tables, bold headers, and bullet points for instant clinical review. Designed specifically for modern mobile hardware like the Samsung Galaxy S25, Scribend achieves this performance with a sub-2.5GB memory footprint, proving that complex, context-aware AI is not only possible but efficient on edge devices

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.

FutureBoard

FutureBoard

FutureBoard is an enterprise decision simulation platform built for the Band multi-agent ecosystem. Modern companies make high-stakes decisions through meetings, spreadsheets, and fragmented departmental opinions. A pricing change may look good to finance but damage customer trust. A reorganization may reduce cost but break delivery. An AI automation plan may improve margins but create legal, brand, and employee risks. FutureBoard turns these decisions into a coordinated multi-agent simulation. When a user submits a decision, Band creates a shared war room where specialized agents collaborate through structured context, task handoffs, disagreement, escalation, and negotiation. Finance, Customer, Employee, Operations, Legal Risk, Competitor, Strategy, and Negotiator agents each evaluate the decision from a different perspective. Agents contribute evidence, challenge assumptions, update shared state, and negotiate toward a safer alternative. The result is not a single AI recommendation. It is an auditable decision simulation showing: competing perspectives key disagreements risk escalations future scenarios negotiated alternatives final executive decision memo Example use case: A company asks whether it should replace 30% of customer support with AI within 90 days. The Finance Agent projects strong savings, while the Customer Agent predicts churn risk, the Employee Agent warns of attrition, the Legal Agent escalates regulated support issues, and the Competitor Agent predicts market backlash. Through Band, the agents negotiate a revised plan: phased rollout, VIP human escalation, employee redeployment, and weekly churn monitoring. FutureBoard demonstrates what becomes possible when agents do not merely answer questions individually, but coordinate as a decision intelligence system. It creates a new enterprise category: decision simulation infrastructure.

CodeForge OS

CodeForge OS

CodeForge OS is an AI-powered software planning and development assistant designed to bridge the gap between an idea and execution. While modern AI tools can generate code, teams still spend significant time defining requirements, planning architecture, creating implementation strategies, designing test cases, and organizing releases. It automates this process through a collaborative multi-agent workflow. The platform allows users to input a project idea in natural language. Instead of relying on a single AI response, multiple specialized agents work together, each focusing on a specific stage of the software development lifecycle. The Product Manager Agent analyzes the idea and generates detailed requirements, user stories, feature breakdowns, and project objectives. The Architect Agent designs the system architecture, technology stack recommendations, database structure, APIs, and scalability considerations. The Engineering Agent creates implementation plans, development milestones, and technical workflows. The QA Agent generates testing strategies, edge cases, validation criteria, and quality assurance plans. Finally, the Release Manager Agent produces deployment roadmaps, release strategies, and execution timelines. The platform simplifies project planning, reduces time spent on documentation, improves team collaboration, and helps ensure that important stages of software development are not overlooked. Whether a user is building a startup MVP, preparing a hackathon project, creating a college project, or planning a production-scale application, it acts as an intelligent planning partner. Our vision is to evolve it into a complete AI-powered software operating system that not only plans applications but also assists with development, testing, deployment, and continuous improvement throughout the entire software lifecycle.