// Topic signal
Training frameworks, inference engines, vector databases, GPU schedulers, and observability tooling.
GPU-initiated communication lets GPU threads post RDMA operations directly to the NIC. It underpins NVSHMEM, NCCL GIN, and DeepEP, which serve the fine-grained, latency-critical communication of Mixture-of-Experts (MoE) models, yet its performance characteristics and optimizations remain scarcely do
Add "Running LLMs Locally" sub-section under Guides & References / AI: why and when to run on premise, hardware sizing (VRAM, KV cache, quantization), Ollama, llama.cpp, vLLM / SGLang, desktop runners (LM Studio, Jan, Docker Model Runner, LocalAI, MLX-LM), NVIDIA NIM, open-weight model families and licences, LLM gateways (LiteLLM), inference optimisation, and integrating local models with GitHub Copilot (BYOK), Claude Code (LLM gateway / Anthropic-compatible endpoints) and other coding assistants, with MathJax, bibliography and a one-page cheat sheet PDF
Add "AI" section under Guides & References and its first sub-section "LLM Foundations & Prompting": AI landing page, how LLMs work (tokens, transformers, training, sampling, context, reasoning, multimodality, hallucination), prompt and context engineering, structured output, choosing models (benchmarks, cost/latency, open vs. closed), with MathJax, bibliography and a one-page cheat sheet PDF
Cargo-atlas – A compiler-accurate Rust code map for AI coding agents
Would be awesome if it could also automatically configure/reconfigure custom endpoints in Hermes especially whenever the model is changed. I proposed a design pass on the panel (APCA contrast, layout rhythm, button centering) as a PR onto this branch: https://github.com/0xSero/omarchy/pull/2
Modern GPU kernels increasingly stress the instruction supply path, while fixed instruction containers can leave substantial footprint slack. This paper presents an automated encoding-synthesis framework that treats instruction layout as a constrained slot-assignment problem over a validated instruc