Run Muse Glimmer Locally with llama.cpp, DFlash, and Pi
Learn how to run Muse Glimmer 30B locally using llama.cpp with DFlash speculative decoding and the Pi coding agent for agentic workflows.
Learn how to run Muse Glimmer 30B locally using llama.cpp with DFlash speculative decoding and the Pi coding agent for agentic workflows.
A blind experiment comparing RAG and long-context retrieval on 12 questions across 32 articles, measuring cost, latency, and answer quality.
Use Ollama and OpenCode to run Qwen3.8-27B locally as an AI coding agent. No server configuration required — just three terminal commands.
LLMs have shifted how software engineers spend their time. Here are three techniques to manage projects more effectively with AI agents.
Learn how to fetch, clean, and convert webpages to Markdown, then use an LLM to return focused answers from page content.
Build a local AI agent that watches Wikipedia's live edit feed and reasons about vandalism using a two-stage filtering pipeline and Ollama.
A rule-based document parsing dispatcher gives enterprise RAG pipelines explicit, inspectable control over which methods run and why.
A constrained LP approach to budget allocation that preserves shadow prices, letting the model explain exactly what each rule costs.
Prompt engineering helps you ask better questions. Specification engineering defines what a correct answer actually looks like.
Token costs compound non-linearly in agentic AI loops. Learn five failure modes and the architectural patterns to control them.
Moonshot's Kimi K3 report reveals the training, architecture, and RL decisions most frontier labs keep private. Here's what it says.