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

Groq

Groq, Inc. is a company specializing in artificial intelligence (AI) hardware and software solutions. Founded in 2016 by ex-Google engineers, Groq focuses on delivering AI acceleration technologies to power next-generation applications across industries. With its deterministic approach to computation, Groq redefines performance and efficiency in AI inference tasks, providing scalable and developer-friendly solutions.

Headquartered in Mountain View, California, Groq operates globally, with additional offices in Canada, Europe, and other parts of the United States. Their solutions are tailored to meet the demands of enterprises seeking high-speed AI capabilities with a focus on reliability and cost efficiency.

General Information

AttributeDetails
AuthorJonathan Ross, CEO and Founder of Groq
CompanyGroq
Founded2016
DocumentationGroq Libraries
Github repositoryhttps://github.com/groq
Discordhttps://discord.com/invite/groq
technology TypeAI Accelerator Hardware and Software Solutions

Products and Services:

  • Language Processing Unit (LPU): Groq's LPU is architected from the ground up with a software-first design to meet the unique characteristics and needs of AI. It offers a deterministic execution model, eliminating traditional hardware bottlenecks and providing seamless scalability.

  • GroqCloud: A developer-centric platform that provides API access to Groq’s high-performance AI models. GroqCloud supports a range of applications, from vision and language processing to real-time inference tasks, delivering low-cost and high-quality results.

  • AI Models and API Access: Groq offers a range of AI models accessible via APIs, including vision models like Llama 3.2 11B Vision 8k and Llama 3.2 90B Vision 8k. These models are designed to provide high-quality, real-time inference for various applications.

How to Start Building with Groq

To begin leveraging Groq’s high-performance AI solutions, start by exploring their development environment. Developers can tap into GroqCloud for seamless API access to pre-trained models or optimize their own AI workloads using Groq's Language Processing Unit (LPU).

Visit Groq's GitHub repository to download SDKs, tools, and libraries required for your development.

Start building with Groq 👉 https://console.groq.com/login

Edit on GitHub

Groq AI technology page Hackathon projects

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

AEGIS-SELF-HEALING-AI-IMMUNE-SYSTEM

AEGIS-SELF-HEALING-AI-IMMUNE-SYSTEM

Traditional WAFs fail against LLM attacks because hackers use context, not just keywords. AEGIS is an Autonomous AI Immune System designed to protect AI agents. It features a 4-layer defense grid: instant heuristic blocks, vector memory for known threats, and a Sentinel AI that detects novel zero-day attacks. Uniquely, AEGIS is self-healing, when a new attack is spotted, the system instantly "immunizes" its memory, blocking future attempts in milliseconds. Sophisticated attackers are routed to generative honeypots, wasting their time with fake data while the core agent remains secure and compliant.

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.

FraudLens AI Security Platform

FraudLens AI Security Platform

FraudLens AI is an AI-powered security platform designed to address two rapidly converging threats in modern digital trading environments: behavioural fraud and expanding technical attack surfaces. As AI-generated phishing and highly personalised scams increase, attackers increasingly exploit user trust rather than purely technical vulnerabilities. The platform combines real-time fraud detection with an AI-driven attack surface pentester. FraudLens AI analyses suspicious emails, chat transcripts, documents, and AI-generated content to detect behavioural manipulation, social engineering, and fraud indicators. At the same time, the AI Pentester module simulates active penetration testing by reasoning about domains and URLs, modelling attacker behaviour, identifying potential attack vectors, and prioritising risks without performing real exploitation. Built using Groq’s high-speed AI inference, the system delivers low-latency analysis, clear risk scoring, and human-readable security reports. FraudLens AI helps organisations reduce fraud, improve security visibility, and scale protection across both human and technical layers in a continuously evolving threat landscape.

AI Trading Coach - Deriv Smart Risk Assistant

AI Trading Coach - Deriv Smart Risk Assistant

AI Trading Coach is an AI-powered trading assistant designed to help traders make better decisions through analytics, risk insights, and coaching. The platform provides a clean dashboard showing portfolio KPIs such as portfolio value, win rate, total trades, and risk score. It also displays recent trades in a structured table and allows users to interact with an AI Coach chat system. The AI Coach provides trading guidance, risk management suggestions, and performance analysis. If the backend becomes unavailable, the system automatically switches into demo mode using mock trade data to ensure uninterrupted user experience. This project is focused on making trading safer, smarter, and more disciplined by combining AI-based insights with real trading metrics. It is built for fast deployment and real-world usability.

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.

AI FINANCE AUTO-RESPONSE FOR RECEIPT REQUESTS

AI FINANCE AUTO-RESPONSE FOR RECEIPT REQUESTS

AI Finance Auto-Response for Receipts is a cloud-based automation system that reduces repetitive manual work in university finance departments by automatically handling student receipt requests. In many universities, students must contact the finance office after paying tuition to request an official receipt. These requests are repetitive and require staff to manually search payment records, verify payment status, and send confirmation emails. With thousands of students each semester, this process causes slow responses, higher costs, and unnecessary workload for finance teams. Our system fully automates this workflow. When a student sends a message requesting a receipt, the system uses AI to recognize the request and extract the student ID. It then retrieves the corresponding payment record from a cloud-hosted dataset of 5,000 real-like payment records stored in AWS S3, including fee amount, transaction ID, and payment status. After verification, an AI response agent generates a clear and professional finance receipt using the exact payment details. If the payment is complete, the receipt is sent instantly. If the student ID is not found or there is an outstanding balance, the system responds with an appropriate finance message. The solution uses a serverless cloud architecture, combining AWS S3 for secure storage, workflow automation for orchestration, and Groq-hosted language models for fast, reliable responses. Compared to traditional email-based or on-premise systems, this approach improves response speed, consistency, and efficiency. Based on conservative estimates, manually processing 5,000 receipt requests would require about 417 staff hours, costing roughly $12,500 in labor. At larger scales, universities can save tens of thousands of dollars annually while responding to students in seconds. This project demonstrates how AI can solve a focused, real-world finance problem while delivering measurable operational value and improving the student experience.