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A generative world for general-purpose robotics & embodied AI learning.
Configure and deploy complete EKS clusters.
Interact with your documents using the power of GPT, 100% privately, no data leaks
Use LLMs and LLM Vision (OCR) to handle paperless-ngx - Document Digitalization powered by AI
Terraform module which provisions addons on Amazon EKS clusters
OCR, layout analysis, reading order, table recognition in 90+ languages
Chat with your pdf using your local LLM, OLLAMA client.(incomplete)
Easily check your clusters for use of deprecated APIs
An Operator for managing Garnet Clusters in a Kubernetes.
Homelab setup built on Kubernetes, deployed with ArgoCD and automated using Renovate!
CloudNativePG is a comprehensive platform designed to seamlessly manage PostgreSQL databases within Kubernetes environments, covering the entire operational lifecycle from initial deployment to ong…
⭐Gradio for .NET – a port of Gradio, an open-source Python package that allows you to quickly build a demo or web application for your machine learning model, API, or any arbitrary Python function.…
Virtualize macOS 12 and later on Apple Silicon, VirtualBuddy is a virtual machine GUI for macOS M1, M2, M3
Popular Kubernetes CRDs (CustomResourceDefinition) in JSON schema format.
Configuration for my homelab running Talos Kubernetes provisioned with OpenTofu.
A plugin to make Argo CD behave like we'd like.
RAGFlow is an open-source RAG (Retrieval-Augmented Generation) engine based on deep document understanding.
This repository contains the official implementation of the research paper, "MobileCLIP: Fast Image-Text Models through Multi-Modal Reinforced Training" CVPR 2024
.NET aspect weaver (build task under NuGet package)
A major "playground"-project where I work with the SemanticKernel from Microsoft
Integrate cutting-edge LLM technology quickly and easily into your apps
RAG architecture: index and query any data using LLM and natural language, track sources, show citations, asynchronous memory patterns.
A C#/.NET library to run LLM (🦙LLaMA/LLaVA) on your local device efficiently.