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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.

TradeWise AI

TradeWise AI

Traders face information overload and emotional bias when making market decisions. TradeWise AI addresses this by combining automated technical market intelligence with behavioral insights and social content generation. The platform computes indicators (SMA, RSI, ATR, momentum), identifies patterns, and produces clear, human-readable explanations. It analyzes trader behavior—win rate, holding time, trade frequency—and provides psychology-driven recommendations and trade recovery probabilities. TradeWise AI also includes a RAG-backed search, on-demand content creation for social platforms, real-time alerts, and demo/offline modes for safe exploration. Built as an MVP, it’s aimed at retail traders, trading coaches, and small teams who want actionable analytics, better discipline, and shareable market narratives—while emphasizing education and sound risk management rather than guaranteed predictions.

KYC Agent

KYC Agent

KYC Agent is an intelligent, end-to-end Know-Your-Customer compliance platform built for Deriv. It replaces slow, manual document verification with AI-driven automation while keeping humans in the loop for high-risk cases. How it works: Users upload country-specific identity documents (CNIC, Emirates ID, Passport, Aadhaar/PAN) and proof-of-address through a guided multi-step onboarding wizard. Google Gemini 2.5 Flash performs real-time OCR extraction and document quality analysis — detecting 20+ issues like blur, glare, cropped corners, and potential manipulation. Extracted data is cross-validated against user-submitted form fields to catch mismatches and inconsistencies. Hybrid Risk Scoring Engine: A two-tier risk assessment combines deterministic rule-based checks (data mismatches, expired documents, missing fields, name inconsistencies with transliteration awareness) with Gemini-powered AI analysis for nuanced fraud detection. Submissions are auto-routed: LOW risk → auto-approved, MEDIUM → manual review queue, HIGH → flagged for expert review. Compliance Dashboard: An internal admin tool gives compliance officers a real-time queue of submissions with risk-sorted views, side-by-side form-vs-OCR comparison, detailed risk factor explanations, and one-click approve/reject actions with audit trail notes. Key differentiators: Country-aware intelligence with side-specific OCR (e.g., CNIC back = address extraction) Explainable risk scores — every flag comes with reasoning and severity Graceful Deriv WebSocket API integration with demo-mode fallback Production-grade architecture (FastAPI + Streamlit) with zero external infrastructure dependencies Built with: Streamlit, FastAPI, Google Gemini 2.5 Flash, Deriv WebSocket API, Pydantic, Python

CAREN - AI-Powered Financial Crime Detection

CAREN - AI-Powered Financial Crime Detection

CAREN (Credit Analysis & Risk Evaluation Network) is a next-generation AI-powered transaction monitoring system designed to detect financial crimes in real-time while dramatically reducing false positives that overwhelm compliance teams. The system employs an ensemble of five machine learning models—XGBoost, Random Forest, Logistic Regression, K-Nearest Neighbors, and AdaBoost Decision Tree—trained on anonymized PCA-transformed transaction features (V1-V28). This architecture achieves 99.94% accuracy, 94.12% precision, and 81.63% recall, with an AUC-ROC of 98.21%. Key capabilities include: • Real-time fraud scoring with sub-50ms latency per transaction • Intelligent alert prioritization that turns thousands of weekly alerts into high-confidence cases • Multi-dimensional risk analysis combining velocity patterns, geographic anomalies, and amount deviations • Visual investigation dashboard with transaction timelines and evidence summaries • Configurable detection thresholds for different risk appetites The platform features a comprehensive dashboard for fraud analysts with live transaction monitoring, severity-based alert management, ML model performance analytics, and an AI-powered fraud analyzer that explains predictions using feature importance visualization. Built for scale, CAREN processes transactions in real-time and provides actionable intelligence that reduces investigation time from hours to minutes while maintaining the highest detection accuracy.

Deriv AI Shield

Deriv AI Shield

As organizations rapidly deploy AI agents and LLM-powered applications, new security risks emerge that traditional web security tools cannot address. Prompt injection and adversarial inputs can manipulate AI systems into leaking sensitive data, bypassing safeguards, or executing unintended actions. Since LLMs are designed to interpret natural language flexibly, distinguishing malicious intent from legitimate user input becomes a major challenge. Deriv AI Shield addresses this gap by introducing an AI-native Web Application Firewall tailored specifically for AI systems. The platform monitors and filters user prompts before they reach the LLM, detects jailbreak and prompt injection attempts, prevents sensitive data leakage through output guarding, and continuously analyzes behavior patterns to detect anomalies. In addition, the system includes an autonomous pentesting module that simulates real attack scenarios to identify vulnerabilities and measure security resilience. It generates actionable security insights and mitigation recommendations, enabling teams to strengthen defenses proactively. By combining real-time guardrails, behavioral monitoring, and continuous AI-driven security testing, Deriv AI Shield enables organizations to deploy AI agents safely in production environments without compromising functionality or user experience.

ChartSeek AI Search for Trading Education Videos

ChartSeek AI Search for Trading Education Videos

ChartSeek: AI-Powered Trading Education Video Intelligence Traders face an overwhelming challenge: thousands of hours of educational videos, yet finding that moment explaining a "head and shoulders pattern" means scrubbing through endless footage. Traditional search fails because traders need to find visual chart patterns, not just spoken words. The Industry Gap Current video platforms offer only keyword search against titles and descriptions. Trading platforms like TradingView, Investopedia, and YouTube provide no way to search inside video content. Enterprise solutions cost $50K+ annually yet still can't match visual patterns. Traders waste hours rewatching purchased content, unable to locate specific setups. This gap costs traders their most valuable resource: time for analyzing live markets. How ChartSeek Bridges This Gap ChartSeek combines OpenAI's CLIP visual understanding with Whisper speech recognition. Unlike keyword search, ChartSeek understands what's visually on screen. Search "bullish engulfing on support" or "descending triangle breakdown"—and instantly jump to that exact frame, even if never explicitly mentioned by the instructor. The system transcribes spoken commentary, extracts representative keyframes, and generates visual embeddings. Searches query both transcript and visual index simultaneously, returning ranked results with confidence scores. Technical Foundation Built on TheAgenticAI's CortexON multi-agent framework with OpenAI Codex workflow architecture. Runs 100% locally using open-source models—zero API costs, complete privacy for proprietary strategies. Key Capabilities - Visual pattern search by description - Cross-modal text-to-image matching - Automatic timestamped transcription - Instant clip extraction ChartSeek delivers 90% reduction in search time, transforming passive video libraries into queryable intelligence. Less searching, more trading.

Sherlock

Sherlock

Sherlock is an autonomous quality assurance system that simulates real user behavior to continuously test signup and onboarding experiences across mobile and web platforms. Using AI-driven navigation powered by AWS Bedrock and browser automation through Playwright, Sherlock explores signup flows with different user personas (confused first-time users, impatient users, careful users) to uncover issues that traditional testing might miss. When problems are detected, Sherlock's diagnosis engine classifies the root cause (backend failures, UX/content issues, performance problems, or integration errors), assigns severity levels (P0-P3), captures comprehensive evidence bundles with screenshots and traces, and immediately escalates critical issues to the right team via Slack with actionable reproduction steps. This enables teams to catch signup-blocking bugs before real users encounter them, reducing customer abandonment and improving conversion rates through proactive, realistic user journey testing.