Popular GitHub Repositories for Cortex Tenant

ACI.dev is the open source platform that connects your AI agents to 600+ tool integrations with multi-tenant auth, granular permissions, and access through direct function calling or a unified MCP server.

Oqtane is an open-source developer productivity platform for building modern .NET applications that run on Web, Desktop and Mobile.

TinyML AI inference library

? The open-source, multi-tenant, self-building knowledge graph

vendor independent TinyML deep learning library, compiler and inference framework microcomputers and micro-controllers

Auto Tune Models - A multi-tenant, multi-data system for automated machine learning (model selection and tuning).

Prophecis is a one-stop cloud native machine learning platform.

Cortex M KWS example with Tengine Lite.

Attacking the cleanup_module function of a kernel module

CMix-NN: Mixed Low-Precision CNN Library for Memory-Constrained Edge Devices

Claude Code CLI 代理服务器 - 多租户、自动故障切换、Web 管理界面

The first AI agent for Okta! A secure, local-first AI agent that helps Okta administrators query their tenant data using natural language.

LocalAPI.AI is a local AI management tool for Ollama, offering Web UI management and compatibility with vLLM, LM Studio, llama.cpp, Mozilla-Llamafile, Jan Al, Cortex API, Local-LLM, LiteLLM, GPT4All, and more.

Code for IEEE Internet Computing Journal paper 'OTA-TinyML: Over the Air Deployment of TinyML Models and Execution on IoT Devices'

C#.NET Implementation of Hierarchical Temporal Memory Cortical Learning Algorithm.

ohno - a lightning-fast AI chatbot built with the Hono.js framework, React, and Tailwind CSS

Small Heterogeneous & AI Powered Computing SBC Based on V853

A short conceptual replication of "Prefrontal cortex as a meta-reinforcement learning system" in Jax.

DataHut-DuckHouse est une plateforme analytique moderne, modulaire et multi-tenant qui combine DuckDB, Apache Iceberg, Arrow Flight, dbt et Trino pour construire un data stack hybride, léger, scalable et prêt pour le SaaS.

Supplementary material for IEEE Services Computing paper 'An SRAM Optimized Approach for Constant Memory Consumption and Ultra-fast Execution of ML Classifiers on TinyML Hardware'