Provides a hardware plugin that runs vLLM on Huawei Ascend NPUs by mapping vLLM execution and memory management to the Ascend runtime. Key features: support for Transformer/MoE/embedding/multimodal models, official docs, CI-backed release branches and community maintenance.
Spins up sandboxed VMs and containers (macOS, Linux, Windows, Android) that an AI agent can fully control through one unified SDK, cloud or local, plus a benchmark suite and background drivers that automate native apps without grabbing the cursor.
Scaffolds production-ready GenAI agents on Google Cloud from one CLI command, wrapping your agent logic in Terraform, CI/CD, observability, and evaluation. Ships ADK, LangGraph, and multimodal RAG templates for Cloud Run or Vertex AI Agent Engine.
A vision-language-action foundation model and reference stack for generalized humanoid and cross-embodiment robot manipulation. Provides pretrained checkpoints, demo datasets, and tooling for fine-tuning, evaluation, and deployment (ONNX/TensorRT); released as Early Access.
Splits LLM inference into separate prefill and decode GPU pools, then routes requests with KV-cache awareness to cut redundant recomputation. Reports up to 30x throughput on DeepSeek-R1 (GB200 NVL72) and works across TensorRT-LLM, vLLM, and SGLang.
Run large-language and multimodal models locally on edge devices (Android, iOS, desktop, web, Raspberry Pi) with hardware acceleration, function-calling, and multi-language SDKs—designed for low-latency, privacy-sensitive on-device inference.
Turns commodity WiFi Channel State Information into spatial sensing: 17-keypoint pose estimation, presence detection, and contactless breathing/heart-rate monitoring through walls, with no camera. Runs on a mesh of ESP32-S3 nodes (~$9 each).
A code-first collection of runnable tutorials for building production-ready generative-AI agents — step-by-step guides covering stateful workflows, vector memory, RAG, tool integrations, Docker/AWS/RunPod deployment, security guardrails, observability, and multi-agent patterns.
Provides a visual, low-code environment to build, debug, and deploy AI agents—integrates model services (OpenAI, Volcengine), RAG, plugins, workflows, and a Chat SDK for embedding agents into apps.
Stores a pruned proximity graph instead of all embeddings, recomputing vectors on demand at query time. A 60M-doc index takes 6GB, not 201GB (97% less), at comparable recall. Powers private local RAG over files, mail, chat, and browser history.
Centralized enterprise platform to manage org-wide MCP servers with a private MCP registry, security guardrails, cost controls, and observability. Offers a Kubernetes-native orchestrator, built-in RAG knowledge base, security sub-agents, and tools for governed AI adoption.