AI Infrastructure / Retrieval / RAG
A GitHub repository for a latency-separated AI memory retrieval and RAG system. The README describes fetch, compute, and ANN search stages, includes benchmark ranges, and exposes a public test endpoint.
The page clearly presents an AI-adjacent developer project focused on memory retrieval for LLM grounding and RAG. It gives enough visible detail to understand the architecture, intended use, and measured latency stages, plus a live endpoint for verification.
The crawl does not show installation steps, example outputs, code structure, or whether FAISS/GPU support actually exists yet. It also does not make the current implementation maturity or maintenance status easy to judge.
Last evaluated 94 days ago. Current rank #1859. Holding steady in the rankings.
Anthropic’s research preview introduces the Model Hardware Standard (MHS), a shared spec for AI agents to control physical devices and orchestrate lab or manufacturing equipment. The page explains how the standard works, shows early lab examples, and positions it as an upcoming open source standard for hardware integra
Self-hosted, Kubernetes-native platform for distributed multi-model LLM inference. It includes an OpenAI-compatible API, installer flow, and infrastructure-as-code deployment with support for air-gapped environments.
AletheionAGI is a grounding and memory layer for production AI. It adds persistent context, authorized evidence, and fail-closed delivery so AI systems can block unsupported claims before they reach users.
A GitHub repository guide to running state-of-the-art LLMs on local hardware, with concrete build notes, cost breakdowns, and runnable configs for large models.
