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

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VULTR AI Technologies Hackathon projects

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

PicoFlow — The Agentic Settlement Mesh on Arc

What is PicoFlow? PicoFlow lets AI agents and apps pay each other for individual API calls — even fractions of a cent — without credit cards, invoices, or human accounts. Imagine your AI assistant needs to call a weather service, market data, image generation, and translation APIs for one question. Today, vendors require sign-ups, credit cards, 50 dollar minimums, and monthly invoices. This fails for AI agents making thousands of tiny calls. Card processors charge 30 cents plus 2.9 percent; a 0.001 dollar call loses 30,000 percent to fees. PicoFlow fixes this, turning any HTTP API into a metered, USDC-priced endpoint. Your agent uses one API key in a standard header. Every call is automatically Quoted by the seller, Authorized via instant off-chain signatures, and Settled in batches on-chain to keep costs at fractions of a cent. Buyers see normal responses; sellers get USDC. No Stripe, no invoices. Money Flow Revenue splits automatically on-chain: 1. Provider: bulk share in USDC. 2. Platform: 2 percent fee. 3. OSS treasury: funds dependencies. Splits are transparent on the live ledger. Integration The customer pastes an API key into the Authorization header and calls a PicoFlow URL. No SDK or code changes needed. Response headers track spend via action IDs and USDC prices. Use Cases Used for LLM inference (pay per token), market data (pay per tick), image generation, RPC providers, and AI agent marketplaces where software pays software. Roadmap PicoFlow is live on Arbitrum One. Future polish includes production providers, fiat on-ramps, spend controls, more chains, and self-serve onboarding. It is chain-agnostic; moving to Arc Mainnet requires only a variable change. Scenarios 1. Agent builders eliminate multiple invoices and high fees. 2. Data vendors monetize small users via micropayments. 3. Integrators add billing without building it. Try it at picoflow.qubitpage.com. 100,000 free calls, no card required.

Kairos — Agentic Payments Runtime on Arc

Kairos is an Arc-native agentic payments runtime that proves a new economic primitive: an autonomous agent can pay for the governance, data, compute, and settlement required to act. The trading loop is the workload. The product is the payment and proof runtime around that workload. Every meaningful Kairos path can produce a USDC-denominated receipt: governance-stage checks, x402 paid data requests, LLM reasoning, SAGE reflection, and bounded micro-commerce proof receipts. This creates an economic fingerprint of the agent’s behavior rather than a self-reported log. Kairos is built for the Agentic Economy on Arc challenge. It demonstrates real per-action pricing at or below one cent, high-frequency transaction evidence well above the 50+ on-chain requirement, and a margin story that only works when gas overhead does not destroy sub-cent economics. With Arc, USDC, Circle Nanopayments, Circle Wallets, Circle Gateway, x402, Gemini Function Calling, multimodal commerce proof, and MCP, Kairos shows how APIs, agents, compute, and commerce can coordinate through programmable value.

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.