resource-optimization

Multi-Agent Resource Optimization (MARO) platform is an instance of Reinforcement Learning as a Service (RaaS) for real-world resource optimization problems.

23 Claude Code plugins: TDD enforcement hooks, git/PR workflows, spec-driven development, code review, project lifecycle, fix-from-error, maintenance automation, context optimization, research, and multi-LLM delegation. 186 skills, 128 commands, 54 agents.

PromQL-driven VM placement optimization engine for OpenStack: policies are raw Prometheus queries; the engine plans Nova live migrations to balance or pack host aggregates.

Scale-to-zero with wake-on-request for Kubernetes. Sleep idle services on a schedule, wake them instantly on HTTP access. Single pod, no CRDs, no sidecars.

Healthcare operations intelligence case study focused on emergency department optimization, patient flow redesign, and avoidable ED utilization using large-scale encounter, provider, and SDOH analytics.

AI-powered Kubernetes resource optimization tool. Analyzes pod metrics and provides intelligent recommendations using GPT-4/Claude for cost reduction and performance improvement.

Analyze historical Prometheus metrics to generate optimized Kubernetes resource recommendations. Supports multi-namespace scanning, Slack notifications, and generates both YAML patches and detailed reports for cost optimization.

An airport control tower that finds the awkward bit between a good forecast and a workable plan. It predicts missed connections, challenges fragile assumptions, and stops three teams from allocating the same bus.

CLI tool to analyze Azure resources and identify cost optimization opportunities with actionable recommendations

Predictive Resource Optimizer for Kubernetes — identifies over-provisioned deployments and generates right-sizing patches

Research-grade Python healthcare digital twin for MRI demand forecasting, discrete-event simulation, patient-flow modelling, capacity planning, queue analysis, healthcare operations research, and transparent staffing optimisation.

Detects unusual cloud resource usage and alerts teams

🚀 A Python repository showcasing optimization techniques for Machine Learning including LP, Newton's methods, LASSO, and convex optimization. 📈🐍

Research framework for cost-aware Apache Spark self-tuning, combining Bayesian Optimization, Safe Transfer Learning, and hybrid metaheuristics for adaptive performance optimization.

An intelligent Kubernetes bin-packing visualizer that uses AI to optimize pod placement and resource allocation across cluster nodes

"An end-to-end Medical Imaging pipeline built on AWS SageMaker utilizing Transfer Learning (ResNet18). The project implements Hyperparameter Optimization (HPO) to minimize loss, leverages SageMaker Debugger & Profiler for resource optimization, and concludes with a Production-ready real-time inference endpoint

AI-powered Chief Operations Officer agent — the COO's right hand. Automates workflows, optimizes resource allocation, monitors operations in real-time, and drives operational excellence across the enterprise. Part of the YENSI AI Platform.

Multi-Agent AI Framework for Intelligent Resource Management — 12 specialized agents, decision fusion engine, real-time optimization

Find Kubernetes resource waste — pods over-provisioned on CPU and RAM, unused ConfigMaps and PVCs

Surface potential waste, security blind spots, and operational risk across 29 AWS GovCloud services using one Python file and one command.