GreyCat C API and GCL Standard Library reference. Use for: (1) Native C development with gc_machine_t context, tensors, objects, memory management, crypto, I/O; (2) GCL Standard Library modules - std::core (Date/Time/Tuple/geospatial types), std::runtime (Scheduler/Task/Logger/Identity/Security/System/License/OpenAPI/MCP), std::io (CSV/JSON/XML/HTTP/Email/FileWalker/S3), std::util (Queue/Stack/SlidingWindow/TimeWindow/Gaussian/Histogram/Quantizers/Random/Uuid/Crypto); (3) Plugin development patterns - lifecycle hooks, type configuration, nativegen, module-level and type-level function linking, global state, thread safety, conditional logging. Keywords: GreyCat, GCL, native functions, tensors, task automation, scheduler, plugin development.
Build, run, and edit GreyCat projects. GreyCat is a statically-typed language plus runtime for graph-persistent, time-series-aware applications. Use when reading or writing `.gcl` source, when the user mentions GreyCat / project.gcl / nodeTime / nodeList / nodeIndex / nodeGeo / @expose / @library, or when the task involves running `greycat <command>`, deploying a project, or reasoning about gcdata/, lib/, files/, webroot/.
Complete llama.cpp C/C++ API reference covering model loading, inference, text generation, embeddings, chat, tokenization, sampling, batching, KV cache, LoRA adapters, and state management. Triggers on: llama.cpp questions, LLM inference code, GGUF models, local AI/ML inference, C/C++ LLM integration, "how do I use llama.cpp", API function lookups, implementation questions, troubleshooting llama.cpp issues, and any llama-cpp or ggerganov/llama.cpp mentions.
Complete vLLM v0.19.0 Python API reference for high-throughput LLM inference: offline batch generation, chat, embeddings, classification, structured outputs, LoRA adapters, multimodal inputs, and OpenAI-compatible server. Triggers on: vLLM questions, Python LLM serving, GPU inference, "how do I use vllm", batch inference, vllm serve, OpenAI-compatible API, structured JSON output, LoRA serving.
NVIDIA CUDA parallel computing platform — use when writing .cu kernels, using cuBLAS/cuDNN/cuFFT/cuSPARSE/cuRAND/cuSolver, Thrust, or Cooperative Groups for GPU-accelerated computing
C tensor computation library for ML inference and training. Use when working with ggml graphs, GGUF model files, backend scheduling, quantization, or implementing low-level ML ops in C/C++.
Run and manage local LLMs via Ollama REST API — text generation, chat completions, embeddings, tool calling, structured output, and model management. Use when code imports ollama, references localhost:11434, or user asks about local LLM inference.
OpenCL SDK (Khronos Group) for cross-platform GPU/CPU parallel computing in C and C++. Use when writing OpenCL kernels, managing devices/contexts/queues, allocating and transferring buffers or images, building and executing programs, or using the C++ wrapper (opencl.hpp / cl::CommandQueue, cl::Buffer, cl::KernelFunctor). Covers OpenCL C API, C++ bindings, and SDK utility libraries (OpenCLUtils, OpenCLSDK).