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.

Agentlink

AgentLink is open infrastructure for hybrid human-AI organisations. Instead of interacting with a single AI assistant, users assemble structured teams of specialised agents — each with a defined role: Coordinator, Contributor, Reviewer, Builder, or Observer. Teams work through structured rounds, vote on decisions, and deliver verified, finished work. Every session is governed by a clear protocol: the Coordinator plans the workstream, Contributors execute in parallel, Reviewers validate quality, and the Builder assembles the final deliverable. The human holds absolute veto at any point. Every message is signed with ed25519 — session logs are immutable and tamper-evident. Deliverables are pushed directly to GitHub as structured project folders, or downloaded as a complete ZIP. Sessions can be continued — agents carry full context of previous work into new sessions. The platform runs 25 specialist agents across engineering, design, research, legal, finance, security, AI/ML, mobile, and more. External agents connect via a simple webhook — any model, any language, any infrastructure. The first external agent runs on Google Gemini 2.5 Flash alongside Claude-powered demo agents. AgentLink also introduces an economic layer: ALC credits are escrowed on session open and released to agent owners on CONFORME. Agents earn reputation through verified session outcomes — a growing dataset of real multi-agent collaborative work. Not automation. Not a chatbot. Infrastructure for the teams of tomorrow.

WebDataOS

WebDataOS turns public web signals into sourced intelligence briefs for Security, GTM, and Finance teams. Enterprise AI agents fail on the live web. Bot detection, JavaScript rendering, geo-blocks, and stale data break scrapers. Even when data arrives, someone still has to decide what matters. WebDataOS solves both problems. The system runs a seven-step pipeline. Users submit tasks via text, voice, or audio. Speechmatics transcribes. Cognee checks its knowledge graph for prior context. The Bright Data gateway retrieves fresh evidence across five tools — SERP API, Web Scraper, Web Unlocker, Scraping Browser, and MCP Server — with automatic failure detection and recovery routing between them. OpenAI synthesizes contextual analysis from evidence and memory. The reasoning engine assesses each finding against organizational context: contracts, risk thresholds, financial exposure, and deadlines. Material findings generate action proposals — draft emails, schedule reviews, update registers — with human approval gates for high-stakes decisions. Outcomes are recorded to calibrate future accuracy. The platform serves developers via API and business users via web UI. Three domains are available as Core (pick 1), Pro (pick 2), or Enterprise OS (all 3 unified). Deployed live on Vercel and Vultr with all partner integrations configured. Covers all three hackathon tracks and all four partner prizes from one submission.

FairPrice Watchdog AI Junk-Fee Evidence Agents

US consumers lose an estimated $64B a year to hidden fees and drip pricing. In May 2025 the FTC's Junk Fees Rule (16 CFR Part 464) made undisclosed mandatory fees illegal with penalties up to $51,744 per violation. Enforcement is landing now (Greystar $23M, Invitation Homes $48M). But there's a bottleneck: the same listing can cost a London shopper more than a New York shopper at the exact same moment and nobody can prove it at scale. Regulators and class-action firms need timestamped, tamper-proof evidence. FairPrice Watchdog is the picks-and-shovels for that evidence. A swarm of six specialized agents takes a single URL and produces a fileable complaint: a Crawler geo-loads the listing, a Journey Simulator walks the checkout (stopping before payment), a Diff agent extracts every fee, a Law-Mapper assigns the exact FTC clause with a detectability tier, and a Filing agent seals a court-ready PDF. Our key innovation: when prices are rendered in JavaScript (invisible to raw scraping), the agent captures a fully-rendered screenshot via Bright Data's Browser API and a GPT-4o vision model (AIMLAPI) reads the price straight off the image like a human. Every fetch has a hard deadline and a graceful fallback, so it never hangs; every result is honestly labeled live/partial/mock; every capture is SHA-256 sealed. Live, we captured a real hotel at $137 for a UK shopper vs $117 for a US shopper same room, same dates with both screenshots sealed and a PDF complaint generated. Built on the full Bright Data stack (Web Unlocker, Residential state-level geo, Browser API, SERP) plus AIMLAPI for vision and reasoning. Coverage spans 29 jurisdictions US (FTC §464), UK (DMCCA), and all 27 EU states (UCPD). Deployed live with auto-HTTPS and self-healing services.

Agent Atlas

Atlas: The Intelligence Platform That Sees Through What Your Tools Can't The surface web is a lie. Pricing pages hide true cost. Press releases obscure strategy. Reviews get filtered. Filings lag news. Credentials leak. And brand sites get cloned. Enterprise intelligence teams spend 40+ hours assembling ground-truth answers—stitching together data from 6+ tools, each with different APIs, rate limits, and blocking mechanisms. Atlas eliminates this fragmentation. Ask Atlas any business question. It autonomously decomposes the query, orchestrates 7 specialized intelligence modules in parallel, and produces a single institutional-grade brief—complete with source citations and confidence scoring. Each module is powered by Bright Data's most advanced products: Web Unlocker (bypass blocks, CAPTCHAs, JS rendering), Scraping Browser (full automation + screenshots), Web Scraper API (distributed scale), SERP API (discovery), Residential Proxies (geo-distributed), and MCP Server (orchestration layer). The 7 modules span all three tracks: - TruePrice (GTM): Completes checkout flows across geographies to reveal true pricing—taxes, fees, regional markups - Signal (GTM+Finance): Detects strategic intent from hiring patterns, exec moves, tech stack signals - Filing (Finance): Surfaces material regulatory changes with automated materiality scoring - AltData (Finance): Composite distress/momentum score from Glassdoor, G2, marketplace signals - Visual (Security): Vision AI detects brand impersonation via side-by-side lookalike comparison - Exposure (Security): Scans paste sites, GitHub, forums for credential & PII leaks - Investor (All tracks): Discovers active VC firms investing in target sectors Built on LangGraph, Pydantic , FastAPI, Claude, and deployed to Vercel + Vultr. The architecture is deliberately MCP-first—judges explicitly reward deep Bright Data integration, and Atlas makes this architectural commitment visible in every tool invocation.

Kraken Alpha Agent

Kraken Alpha Agent is an autonomous trading agent built for the Kraken Trading Performance track. The project focuses on xStocks strategy selection, risk-controlled execution, and transparent PnL reporting. The agent ranks xStocks candidates, runs a reproducible hackathon-window backtest, applies strict guardrails, and exposes the result through a public Vercel dashboard. The core demo shows the full workflow: signal ranking, dry-run execution, risk checks, backtest performance, and judge auditability. During the audited 13-19 May 2026 hackathon window, the backtest shows +19.18 USD PnL across 10 trades, 80% win rate, and 0.06% max drawdown. A 30-day baseline shows +33.56 USD across 138 trades with 53% win rate. Important transparency note: live xStocks execution was blocked at the Kraken venue/account level for the Cyprus EU account used in the hackathon. The project therefore reports xStocks PnL from reproducible backtests, not from live xStocks fills. A short crypto diagnostic session produced live fills outside the xStocks track, proving the engine, API key, IP setup, and execution pipeline work end-to-end. The system defaults to dry-run mode and requires triple opt-in before any live order. It also enforces 1.0x leverage, no shorting, reduce-only exits for futures, and strict rejection handling for Kraken responses. The goal is simple: make autonomous trading agents measurable, risk-aware, and auditable enough for judge verification.

HEXFIRE — Break Your Agents Before Production

The Agentic Black Box: $4.8M is the average cost of a production AI failure because enterprises deploy non-deterministic agents blind. Traditional software fails predictably, but AI agents fail probabilistically. When an agent hallucinates a variable or fails to call a tool, it confidently carries that corrupted data downstream, infecting the entire pipeline. By the time anyone notices, the damage is done. The Solution: Deterministic Chaos Inspired by Netflix’s Chaos Monkey, HEXFIRE is the world's first Chaos Engineering platform purpose-built for autonomous AI agents. We deliberately break your agent workflows to show you exactly where they crumble. Instead of guessing, HEXFIRE lets you execute targeted strikes. You can inject 6 deterministic fault scenarios into any step of your pipeline: Hallucinations, Tool Timeouts, Adversarial Prompts, Context Corruption, Latency Spikes, and Permission Revocations. Technical Gravity (Beyond the Wrapper) Remove ALL LLM calls from HEXFIRE and you still have four custom, independent computer science systems. The AI enhances HEXFIRE; it doesn't define it: DAG Cascade Analysis: Agent pipelines are Directed Acyclic Graphs (DAGs). When a fault hits one node, HEXFIRE mathematically traverses the downstream edges to determine the "blast radius." Deterministic Resilience Scoring: We compute a hard, mathematical ★-to-★★★★★ safety rating based on Survival Rate and Fault Isolation. Cryptographic Proof: Every test execution is hashed into a SHA-256 tamper-evident compliance audit chain. Generative Forensics: Gemini 2.5 Flash analyzes the wreckage and generates an exportable forensic report explaining exactly what died and how to patch it. Sponsor Integration HEXFIRE utilizes Vultr Serverless Inference for ultra-low latency execution and massive concurrency during chaos testing spikes. We leverage Google Gemini 2.5 Flash for deep resilience analysis, forensic reporting, and robust 503 capacity fallback chains. Built solo by Shree Shah.