Generated-Computers/Locals — Parallel AI agent orchestration for macOS
Run multiple AI assistants simultaneously on your Mac with significantly improved throughput via localized GPU-accelerated serving.
Practical reporting on AI systems, tools, and engineering execution.
Run multiple AI assistants simultaneously on your Mac with significantly improved throughput via localized GPU-accelerated serving.
H2O4GPU provides GPU-backed scikit-learn-compatible solvers in Python and R, but targets Python 3.6 and CUDA 8–10, suggesting it predates active maintenance.
A curated repository of tactics, vendor docs, and papers focused on reducing LLM token spend (prompt caching, model routing, batching, compression) with vendor-specific notes you should validate before production.
Drop-in extension that patches scikit‑learn to use optimized CPU/GPU implementations (sklearnex); README claims an average 8.5× speedup and multi-GPU support — test on your workload before production use.
Agent Router provides an OpenAI-compatible API and Envoy-powered control plane to centralize routing, credentials, quotas, and failover across hosted and self-hosted LLM providers.
Torch‑TensorRT compiles PyTorch/TorchScript/FX models to TensorRT for NVIDIA GPUs, offering quick in‑process compilation via torch.compile and an export path for ahead‑of‑time C++ deployment.
A native desktop + headless application that combines training, interactive inspection, gaussian editing, Python plugins, automation, and export for 3D Gaussian Splatting workflows.
A self-contained 3D Gaussian Splatting trainer that converts photos or video into splats and textured meshes using Vulkan across NVIDIA, AMD, Intel, and Apple GPUs, with built-in SfM and viewer.
A Python pipeline that operationalizes Double Machine Learning with FLAML AutoML for causal effect estimation, sensitivity checks, publication plots, and priors extraction — designed for marketing ROI and incremental-impact analysis.
LMCache is an engine‑independent daemon that persists and manages LLM KV caches to lower time‑to‑first‑token and improve throughput for long‑context, multi‑turn, and agentic workloads.
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