Vultr

Top Builders

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

Vultr

Vultr is a leading high-performance cloud computing provider that offers a wide range of services, including scalable GPU instances specifically tailored for demanding AI and robotics workloads. Known for its global network, competitive pricing, and robust infrastructure, Vultr enables developers and businesses to deploy and manage powerful cloud resources with ease.

General
Author	Vultr LLC
Release Date	2014
Website	https://www.vultr.com/
Documentation	https://docs.vultr.com/
Technology Type	Cloud Computing Provider

Key Features

Global Network: Access to high-performance data centers worldwide for low-latency deployments.
Scalable GPU Instances: Offers powerful NVIDIA GPUs for AI, machine learning, and high-performance computing tasks.
Flexible Cloud Servers: Provides various instance types, including bare metal, cloud compute, and dedicated cloud, to suit diverse needs.
Custom ISO Support: Allows users to deploy custom operating systems or applications.
API and CLI Access: Programmatic control over all cloud resources for automation.
Managed Kubernetes: Simplified deployment and management of containerized applications.

Start Building with Vultr

Vultr provides an ideal platform for deploying AI and robotics projects that require significant computational power. With its high-performance GPU instances and global infrastructure, developers can train complex models, run simulations, and host AI-powered applications efficiently. Explore their documentation to get started with deploying your next-generation AI solutions.

👉 Vultr Documentation 👉 Vultr GPU Cloud Servers

Edit on GitHub

VULTR AI Technologies Hackathon projects

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

WritenDraw Flight Simulator for Software Developer

WritenDraw is an agentic AI simulation platform that puts junior developers through realistic production incidents to bridge the gap between learning to code and working in a real team. The core innovation is the agentic workflow: Google Gemini 2.0 Flash orchestrates the entire simulation through three autonomous agents: AGENTIC EVALUATION: Every step requires free-text responses (no multiple choice). Gemini evaluates each response against per-step rubrics, scoring reasoning 0-15. The AI adapts feedback based on accumulated performance. AGENTIC MENTORING: The AI mentor maintains persistent context, tracking understanding level, chat count, and time pressure. Early messages: patient, asks "what do you think?" By message 7+: "just write it up." The agent autonomously decides how much help to give. AGENTIC AUDIT: The system logs every response, chat message, code submission, and score — creating a complete picture of how a developer thinks through a crisis. The AI continuously assesses and adapts. The simulation drops you into a P1 incident at ShopRight (fictional UK supermarket). You join a standup, read a Jira ticket, investigate messy code with no hints, chat with the AI mentor, write a fix, respond to code review, create a deployment plan, and contribute to a retro. Paste is disabled — Key insight: explanation scores higher than code (10 vs 5 points). Wrong code with a great explanation beats perfect code with no explanation — because in real teams, communication matters as much as code. Built on the author's published research — "TrueSkills: AI-Resistant Assessment Through Personalized Understanding Validation" (SSRN, 2025, DOI: 10.2139/ssrn.5674130) — which demonstrated that AI-resistant assessment requires evaluating understanding rather than recall. WritenDraw takes this further: testing how developers think under realistic production pressure. Built with Python/Flask, Google Gemini 2.0 Flash, CodeMirror, Pyodide, and Docker.

OpsTwin AI

OpsTwin AI is a simulation-first autonomous warehouse control system designed to model and optimize multi-robot fulfillment operations. As warehouses adopt robotics at scale, fleet coordination becomes increasingly complex. Congestion, battery constraints, task prioritization, and workload balancing impact throughput and efficiency. OpsTwin AI addresses this by creating a digital twin of warehouse operations, a live simulation where robotic workflows can be orchestrated, tested, and optimized before real-world deployment. In OpsTwin AI, robots operate within a simulated warehouse grid containing storage racks, charging stations, and pack zones. When a new order is created, the system autonomously determines which robot should fulfill it. Instead of relying on hardcoded rules, I use Gemini as a strategic planning layer. The backend sends live fleet state, including robot positions, battery levels, and active tasks, to Gemini. Gemini returns structured JSON with a selected robot and step-by-step task sequence. This allows deterministic execution while enabling adaptive multi factor decision making. The Vultr-hosted backend serves as the centralized system of record. It maintains robot state, order queues, and operational metrics, and broadcasts real-time updates to a web dashboard using WebSockets. A 500 millisecond simulation loop executes plans, updates robot movement, tracks congestion events, and manages battery-aware charging. The result is fully autonomous multi-robot operation without manual intervention. From a business perspective, OpsTwin AI functions as an operational control tower for robotic fleets, enabling teams to simulate workflows, evaluate performance, and reduce deployment risk before scaling to physical infrastructure. By separating AI planning from deterministic execution, the architecture mirrors real world robotics systems and provides a clear path from simulation to real-world deployment.

Adaptifleet

Traditional warehouse automation has improved efficiency, yet many systems remain rigid, expensive, and difficult to adapt when workflows or layouts change. Even small adjustments often require specialized expertise or time-consuming reprogramming. This creates a disconnect between what operators need robots to do and how easily they can communicate those needs — a challenge we call the “Human Intent Gap.” AdaptiFleet was designed to close this gap by enabling intuitive, AI-driven fleet control. Instead of relying on complex interfaces or predefined scripts, users interact with autonomous robots using natural language. Commands such as “Get me three bags of chips and a cold drink” are interpreted and translated into structured robotic tasks automatically. At its core, AdaptiFleet leverages Gemini-powered Vision Language Models (VLMs) to understand user intent and visual context. Robots operate within a dynamic decision framework, allowing them to adapt to changing environments rather than follow rigid, pre-programmed routes. The platform integrates a digital twin simulation stack built on Isaac Sim, enabling teams to validate behaviors, test workflows, and optimize multi-robot coordination before live deployment. Once deployed, ROS2 and Nav2 provide robust navigation, dynamic path planning, and collision avoidance. The VLM orchestration layer continuously analyzes visual inputs to support scene understanding, anomaly detection, and proactive hazard awareness. When conditions change, AdaptiFleet autonomously re-plans routes and tasks, reducing downtime and operational disruption. By combining conversational interaction, real autonomy, and simulation-driven validation, AdaptiFleet simplifies robotic deployments while improving efficiency and visibility. The result is an automation system that is adaptive, scalable, and aligned with how people naturally work.

AI-Powered Autonomous Robotics Stage Simulator

The AI-Powered Robotics Stage Simulator enables fully autonomous concert production. Gemini 2.5-flash generates intelligent stage plans tailored to venue, genre, and show type, defining initial positions and dynamic phase movements (Intro, Drop, Climax, Finale). Robots relocate heavy elements (speakers, LED walls, lighting trusses, risers) in real time while avoiding collisions and tracking battery drain. The simulation runs on HTML5 Canvas with WebSocket for live updates. Deployed on Vultr VPS for 24/7 autonomy using screen/systemd. No human crew required — a simulation-first step toward real robotic stage execution in live events.

Piythia

Modern trading and autonomous decision systems often rely on fragmented workflows—separating analysis, backtesting, execution, and risk monitoring across disconnected tools. This fragmentation increases operational risk, reduces reliability, and makes it difficult to safely transition strategies from experimentation to live deployment. Piythia addresses this problem through a simulation-first autonomous agent development and validation pipeline. The platform enables configurable decision agents to operate within controlled simulated environments, where behaviors are stress-tested, benchmarked, and evaluated using structured performance metrics and risk analysis across varied historical conditions. The architecture supports independent agent instances, modular strategy logic, and scalable experimentation, allowing multiple strategies to be validated simultaneously. Once validated, the same strategy logic transitions directly to live markets through broker APIs, maintaining consistency between simulated and real-world execution. Piythia is designed for investors and portfolio managers who seek goal-driven automation without managing fragmented toolchains. By unifying simulation, deployment, and supervisory risk governance, the platform reduces operational risk while accelerating iteration and portfolio-level decision intelligence.

NeuroPilot

People who could benefit from hands-free control, for example those with limited mobility, often cannot access it. NeuroPilot changes that: open-source BCI middleware that turns a Muse 2 headband into a real control interface. Muse 2 is already connected to the app, and we have a working integration with DJI Tello. Users train in a 3D simulation and in a training environment; when their brainwave pattern matches a chosen action (move left, right, up, down), the backend fires configurable webhooks or WebSockets. We accumulate 5 actions from the headband; YOLOv8 runs on the Tello camera feed to detect obstacles and Tello state. That batch and the vision data are sent to Gemini, which decides the movement. Every command sent to the DJI Tello is the result of an AI API call, so BCI intent is refined by vision and reasoning before it reaches the drone. The stack is software-only and simulation-first: a Next.js frontend, a FastAPI + WebSocket backend, a web Lab (Three.js), a machine/Connect dashboard for status and live feedback, and per-control webhook URLs. No lab hardware or locked-in vendors. Teams can integrate with existing sims, digital twins, or robots via HTTP.