xAI

Top Builders

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

xAI

xAI is an innovative AI technology company founded by Elon Musk in July 2023. The company is dedicated to pushing the boundaries of artificial intelligence by creating tools and platforms to advance scientific discovery, foster understanding, and simplify complex problem-solving. With cutting-edge technology and a user-focused approach, xAI provides developers and organizations with the tools they need to unlock the potential of AI in everyday applications.

General Information

Attribute	Details
Company	xAI
Founded	2023
Documentation	https://docs.x.ai/docs
API Reference	https://docs.x.ai/api#introduction

Products

Grok AI Models

xAI's flagship product line consists of the Grok series of conversational AI and vision-enhanced models:

Grok-1: xAI's foundational model offering robust reasoning and conversational capabilities.
Grok-1.5: Improved reasoning and extended context for handling large datasets.
Grok-1.5V: Introduced vision capabilities for analyzing images and diagrams.
Grok-2: Enhanced performance with advanced image generation and multimodal abilities.

xAI API

An accessible platform for developers to integrate Grok models into their applications. Fully compatible with existing OpenAI and Anthropic SDKs.

PromptIDE

A powerful integrated development environment for crafting, testing, and refining AI prompts. Designed to optimize prompt engineering workflows for large language models.

Start Building with xAI

👉 Visit x.ai to create a developer account and get started.

👉 Sign in to the xAI Developer Portal to obtain API keys, access Grok models, and explore documentation.

Edit on GitHub

xAI AI Technologies Hackathon projects

Discover innovative solutions crafted with xAI AI Technologies, developed by our community members during our engaging hackathons.

Cyber Sentinel AI

Cyber Sentinel AI is an AI-powered cybersecurity investigation platform designed to help users quickly analyze potential cyber threats through a collaborative multi-agent workflow. Instead of relying on a single AI model, the platform separates responsibilities across specialized agents for threat analysis, independent verification, and incident response generation. Users can investigate suspicious emails, URLs, prompt injection attempts, and browser extension permissions through a unified interface. Each investigation follows a structured pipeline that combines rule-based evidence collection with AI reasoning to generate actionable recommendations, a dynamic security score, an investigation timeline, and persistent history. The backend is built with FastAPI, containerized using Docker, and designed with a modular architecture that makes it easy to add future investigation modules such as ransomware behavior analysis, live threat intelligence, and ROCm-powered local inference on AMD GPUs. Cyber Sentinel AI demonstrates how multi-agent systems can improve the accuracy, transparency, and usability of cybersecurity investigations while providing a scalable foundation for future security automation.

UvicornForge-AI

UvicornForge AI is an AI-powered co-founder designed for hackathon teams. Enter a project idea along with real parameters such as team size, total funding, available time, target users, industry, and technologies (including AMD GPUs and Fireworks AI). The system generates a comprehensive, structured startup brief covering problem, solution, MVP scope, key features, demo scenario, business model, risks, go-to-market strategy, and more. It also produces ready-to-use artifacts including a pitch deck outline, full demo script, MVP checklist, and starter README. A key innovation is the Success Score (1-10) predicted by a custom MLP model. Unlike generic scores, ours is trained on a high-signal target engineered to respond to actual project parameters: larger teams, realistic funding, longer development time, and explicit AMD usage all meaningfully increase the predicted success. The frontend allows direct input of these values, and the feature mapper feeds them straight into the model along with an ambition factor extracted from the idea description. The backend combines Fireworks AI for high-quality text generation with a locally running PyTorch MLP (65 features) trained on an AMD-tailored dataset of 10,000 examples. We achieve strong validation performance (R² ≈ 0.85) while ensuring the score differentiates between weak and strong proposals. The modern frontend features live model metrics, smooth animations, and one-click downloads of all generated materials. Built specifically for the Unicorn Track, the project demonstrates practical use of AMD GPUs for ML inference and Fireworks AI for LLM generation in a real, useful product that helps teams create better submissions faster.

Hybrid AI Router with Dynamic LLM Selection

Hybrid AI Router is an intelligent orchestration platform designed to optimize AI inference by dynamically routing user queries to the most appropriate language model. Instead of relying on a single model for every request, the system analyzes each query and determines whether it can be efficiently handled by a lightweight local language model or should be forwarded to a powerful cloud model for advanced reasoning. The platform focuses on four key objectives: Reduce latency by processing simple requests locally. Lower inference cost by minimizing unnecessary cloud API usage. Preserve privacy by keeping sensitive or lightweight tasks on-device. Maintain response quality by routing complex reasoning tasks to a larger cloud model. The application includes a modern web dashboard that displays: Selected AI model Query complexity Confidence score Response latency Evaluation status Generated response The backend is built with FastAPI and exposes REST APIs that communicate with both the local and cloud language models. The frontend provides an intuitive interface for testing routing decisions and visualizing AI performance metrics in real time. This project demonstrates how hybrid AI systems can balance performance, privacy, and operational cost while delivering an improved user experience.

StormSense AI

StormSense AI is a real-time natural disaster intelligence platform powered by a collaborative multi-agent AI system built with LangChain and LangGraph. The platform autonomously monitors live earthquake, flood, and wildfire data from trusted sources including USGS, NASA FIRMS, and OpenWeatherMap 24 hours a day, 7 days a week.Instead of relying on a single AI model, StormSense AI deploys 7 specialized like agents Manager, Data, Analysis, Alert, and Writer each with one expert job. Together they fetch live disaster data, assess risk levels, trigger automatic emergency alerts, and generate clear AI-powered explanations in plain English. Built on FastAPI backend, Next.js frontend, and powered by Groq LLaMA via AMD GPU cloud, StormSense AI delivers what no chatbot can truly autonomous, real-time disaster intelligence that acts before disaster strikes.

VibeFunding: Fund the Build

VibeFunding is a Web3 launchpad designed around a simple idea: fund the build, not just the company. Startups present concrete development goals, deliverables, timelines, and resource needs. Investors support those projects with VIBE, which represents productive capacity inside the platform and can be converted into AMD-powered AI compute. Autonomous agents then use that capacity to execute project tasks, while investors can follow the work through execution replays, progress updates, community posts, and Proof of Build records. Proof of Build connects each contribution to verifiable results such as completed tasks, tests, commits, artifacts, resource usage, and cryptographic hashes. When the funded work is completed and verified, investors can receive project-defined rewards such as Project Tokens, NFTs, access benefits, community privileges, and potential exposure to the value they helped create. Gemma, accessed through Fireworks AI on AMD infrastructure, acts as the platform’s intelligence layer. It helps investors discover projects, understand risks, review project evidence, analyze Proofs of Build, and follow portfolio progress. It also helps founders structure project information and communicate updates. The MVP demonstrates the complete investor journey: discover a project, invest in its construction, watch agents work, verify the result, receive rewards, join the project community, and track everything from a personalized portfolio.`

MESSI Motion Estimation for Soccer Skill Imitation

We started with a pretty simple question: can one recorded human soccer kick become one believable robot skill? So we built MESSI: Motion Estimation for Soccer Skill Imitation. It takes a SoccerKicks penalty clip, uses the dataset’s 3D pose annotations, retargets the motion onto a Unitree G1 humanoid, and then checks the result in MuJoCo with a real ball, goal, contact, and friction. The hard part is that you can’t just copy human joints onto a robot. The bodies are different, the joint limits are different, and a kick only matters if the ball actually moves the way it should. So we used the human motion as the starting point, refined the trajectory with AMD MI300X / ROCm, and treated MuJoCo as the final test instead of just trusting a proxy metric. We also built a Goalie Lab where you can move the keeper and run a fresh physics rollout to see if the learned kick still scores.