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.

Actura

Actura is a trust-governed autonomous trading system designed for open agent economies where accountability matters as much as alpha. Built on the ERC-8004 trustless agent standard, Actura does not allow an AI model to trade directly from a raw signal. Instead, every decision flows through a deterministic governance stack: market intelligence, regime detection, strategy scoring, neuro-symbolic safety controls, mandate enforcement, oracle integrity checks, execution simulation, supervisory approval, and trust validation before an order can be signed or routed. This architecture is what makes Actura robust in real conditions. It combines statistical and technical signals (trend, volatility, momentum, sentiment, and structure-aware context) with symbolic guardrails such as consecutive-loss protection, drawdown recovery mode, volatility-spike caution, and confidence throttling. The result is bounded autonomy: the agent can adapt, but only inside explicit policy constraints. Risk is further enforced at the smart-contract layer through on-chain policy checks, with support for EIP-1271 signature verification to safely handle contract wallets. Actura also produces full decision transparency. Each cycle generates a rich, auditable artifact that includes market snapshot, confidence bounds, governance evidence, risk-check outcomes, execution assumptions, and human-readable AI reasoning. Artifacts are persisted locally and can be pinned to IPFS for third-party verification and one-click explainability for judges and operators. Actura treats trust as an operational control surface, not a vanity metric. A multi-dimensional Trust Policy Scorecard evaluates policy compliance, risk discipline, validation completeness, and outcome quality. That score maps to a dynamic Capital Trust Ladder that increases or restricts deployable capital over time. In short: Actura is not just an autonomous trading bot—it is a governed capital runtime that must continuously earn the right to act.

Valentimer

Valen-timer is a fast-paced web game where players pick AI digital twins, get matched by preferences, and plan date routes—scoring on simulated success Social Twin Trainer: Train AI twins for virtual speed-dating by planning convo flows and venues; matches happen via preference graphs, success via engagement sims—all as agentic decision pipelines for scalable social robotics training. Core Architecture Vultr VM as central backend (required): FastAPI/Node for AI logic, sim state, user sessions; React frontend for twin picker/planner UI (public demo URL). Digital twins as AI agents (no physical robots); "sim" via graph-based convo/venue env or Canvas viz.

GeminiFleet

## What it does GeminiFleet runs a physics-based warehouse simulation where autonomous robots pick up and deliver items. A fleet manager controls robot behavior through natural language — no code, no config files. **Example commands:** - "Make robots more cautious" → speed drops, safety margins increase - "Speed things up, we're behind schedule" → max speed, tighter margins - "Focus on the north side" → robots prioritize north-zone tasks Google Gemini interprets each command with full context (fleet status, delivery counts, collision stats) and generates precise parameter updates that modify robot behavior in real-time. ## How it works **PyBullet Physics Engine** — Real rigid-body simulation with collision detection. Warehouse environment with walls, shelves, pickup/dropoff zones, and 4-6 autonomous robots navigating with priority-based collision avoidance. **Gemini 2.0 Flash Policy Engine** — Translates natural language into 7 tunable parameters: speed, safety margin, congestion response, task selection strategy, cooperation mode, zone preference, and concurrency. Values are clamped to safe ranges. **Live Web Dashboard** — Real-time 2D visualization via WebSocket at 10Hz. Tracks robot positions, planned paths, carrying status, and delivery statistics. Collapsible panels for robot status and active policy display. ## Key Innovation Robot fleet behavior is parameterized into meaningful dimensions that an LLM can reliably map from ambiguous human instructions. Operational expertise — not programming skill — drives fleet optimization. ## Deployment Runs entirely on **Vultr non-GPU VMs** via Docker. PyBullet operates in CPU-only mode. Single `docker compose up` deploys the full simulation + dashboard + Gemini chat. ## Built with - **PyBullet** — Bullet Physics simulation - **Google Gemini 2.0 Flash** — NL→policy translation - **FastAPI + WebSocket** — Real-time state streaming - **Docker** — Vultr deployment

AgroMind - Autonomous Swarm vs Locust Plague

THE PROBLEM: FOOD SECURITY UNDER ATTACK Locust plagues are one of the most destructive agricultural threats in history, capable of consuming thousands of hectares of crops in hours. Traditional monitoring is slow, manual, and reactive. By the time a swarm is visible, it's often too late. THE SOLUTION: AGROMIND AgroMind is an AI-first autonomous defense system designed to intercept locust swarms before they land. We leverage NVIDIA Isaac Sim to simulate thousands of drones in a "Digital Twin" environment, training them to coordinate flight paths and containment strategies safely. HOW IT WORKS: The Brain (Vultr & Gemini 3): We use Vultr's high-performance cloud to process satellite and thermal telemetry. When an anomaly is detected, Google Gemini 1.5 Pro analyzes the data to confirm the biological signature of a locust swarm, distinguishing it from other pests or weather patterns. The Swarm (NVIDIA Isaac Sim): Instead of risking physical hardware immediately, we simulate the interception in Isaac Sim. The drones use reinforcement learning to form a perimeter, calculating the optimal "containment net" to divert or neutralize the swarm using directed sonic waves or bio-pesticides (simulated). Real-Time Deployment: The system operates autonomously. The "Commander" (User) receives a strategic report and authorizes the mission via a Streamlit Dashboard hosted on Vultr, watching the telemetry in real-time.

PetGuard

According to the Animal Humane Society, cats are crepuscular, meaning their activity peaks during dawn and dusk. Anecdotal evidence supports this, with many owners reporting their cats being awake late into the night or very early in the morning. While humans attempt to get some shuteye, this leads to an unsupervised environment where our feline friends may be up to some tomfoolery… Enter PetGuard, the Gemini-powered robotic assistant that keeps your pet safe and out of trouble! We have used Vector, the robot developed by Anki, which has been augmented with Gemini to track your cat, scan the environment for potential hazards and use auditory cues to divert its attention. Using a virtual environment, we have simulated a virtual instance of Vector, a virtual instance of a cat that mimics its behavior, as well as the most common household hazards the pets are prone to. Using this simulation we have finetuned our robot assistant to ensure that it is as robust as possible, finally giving pet owners the peace of mind that they deserve!

Valen-timer

Valen-timer is a fast-paced web game where players select AI digital twins, get matched via preference graphs, and plan virtual dates—scoring on simulated success. Beyond entertainment, it trains AI systems in human connection through gameplay. Gameplay: Players choose from diverse AI twins with unique personalities. A matching algorithm analyzes compatibility across traits and interests. Players rapidly plan date routes—selecting venues and conversation flows against the clock. Coffee shops for intimacy, galleries for culture, arcades for playfulness. Each choice impacts success. Real-Time Engagement Tracking: The platform highlights exactly what works and fails during interactions: Small Talk Analysis shows: Ice breaker effectiveness ("How's your day?" scores 2/10 vs. "Ever hiked coastal trails?" at 8/10) Question balance (avoiding interrogation mode) Humor landing rates Shared interest discovery moments Topic transition smoothness Deep Conversation Metrics track: Vulnerability matching and emotional reciprocity Value alignment reveals Empathy response quality Venue-Specific Performance: Coffee shops excel for intimate exchanges, activity venues spike during competition but drop if conversation suffers. Micro-Moment Highlights: Remembering earlier details (+15% engagement), perfect compliment timing, or awkward transitions (-25% engagement). Post-date breakdowns show: "Your pottery hobby question increased engagement 40%. Switching topics without transition dropped engagement 25%." The system pinpoints exact peaks and valleys. AI Training Impact: Every date trains agentic AI systems in social intelligence—emotional responses, preference inference, adaptive strategies. This scales social robotics training for customer service bots, therapeutic companions, and educational tutors. Valen-timer merges gaming with AI research—players enjoy speed-dating while training machines to understand human connection.