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🤓 Latest Submissions
AluminatiEye
AluminatiEye is a GPU Cloud Intelligence Oracle built to help AI teams make smarter infrastructure decisions in an increasingly complex GPU market. Today, AI builders face fragmented cloud providers, constantly changing GPU pricing, infrastructure shortages, and limited visibility into which provider is the best fit for a workload. Teams often spend hours comparing vendors, researching companies, monitoring pricing, and evaluating risk before deploying models. AluminatiEye creates a unified intelligence layer across the GPU ecosystem. The platform collects and analyzes data from multiple GPU cloud providers and public sources to generate actionable infrastructure insights. Key capabilities include: • Live Pricing – Tracks GPU pricing across multiple cloud vendors in real time. • Arbitrage Detection – Finds cost-saving opportunities between providers. • Market Intelligence – Aggregates news, sentiment, regulations, and competitive signals. • Risk Scores – Evaluates providers based on reliability, growth, uptime, and market health. • Cost Calculator – Estimates infrastructure spending. • Recommender – Suggests optimal GPUs and providers for training, fine-tuning, inference, and image generation workloads. • Oracle Engine – Combines all signals into a single recommendation. Built using Bright Data's web intelligence infrastructure, AluminatiEye transforms raw infrastructure data into strategic recommendations that help organizations reduce costs, mitigate risk, and make faster infrastructure decisions. Our vision is to become the intelligence layer for the GPU economy, giving founders, engineers, researchers, and AI teams a single source of truth for cloud infrastructure decisions.
31 May 2026
AluminatiAI
AluminatiAI GreenTune is an autonomous energy intelligence platform designed to optimize enterprise AI training workloads. As large language model fine-tuning becomes more expensive and energy-intensive, GreenTune helps organizations reduce infrastructure cost, power consumption, and carbon emissions through AI-driven optimization and real-time telemetry. The system uses a multi-agent swarm powered by Gemini, including specialized agents for orchestration, configuration optimization, policy enforcement, and energy analysis. These agents collaborate using real function calling to test and evaluate training configurations for efficiency before workloads are launched. GreenTune also includes “Lobster Trap,” an energy governance framework that enforces hard limits on CO2 emissions, energy usage, efficiency, and cost to prevent wasteful training runs. During execution, GreenTune streams live GPU telemetry from AMD MI300X hardware, monitoring watts, joules-per-token, throughput, CO2 emissions, and infrastructure cost in real time through a web dashboard. In testing, the platform achieved up to 45–69% improvements in energy efficiency while fine-tuning Qwen2.5-7B with QLoRA on Hermes reasoning datasets. Our vision is to become the energy intelligence layer for AI infrastructure, helping enterprises make AI workloads observable, governable, and energy-aware as compute demand continues to scale.
19 May 2026
