DevOps

Most Claude Code sessions get slow and cluttered for the same reason: every exploratory grep, every log dump, and every “let me check one more file” stays in the main conversation forever.

Ollama is one of the easiest ways to run a local language model, but convenience can conceal the moment when a local experiment becomes a shared inference service that needs better scheduling and observability.

AI coding agents ship features fast, but specs, tests, and code quietly drift apart. This guide covers a traceability model, spec-to-test and spec-to-code mapping, and the CI checks that catch drift before a merge.

Docker Compose and Podman Quadlet solve overlapping problems but come from different design centers, and choosing between them depends on whether you think in application stacks or Linux services.

Running Hermes Agent on a headless server while connecting from a desktop client on another machine requires two server processes and a single client connection.

Docker Compose on a Linux server should start on boot, stop cleanly on shutdown, and survive reboots without manual intervention.

Installing Docker on Ubuntu should be simple, but in practice several Docker-shaped options compete for the same command name, each with different packaging, upgrade behavior, and security implications.

Polling agents are one of the least glamorous parts of AI assistant architecture, but they are also one of the most useful.

Mermaid is a text-based diagramming tool for people who would rather write diagrams than drag boxes around a canvas.

Design decisions for production LLM systems — routing, cost, guardrails, and multi-model orchestration. The layer between running models and building reliable AI applications.

A production AI assistant is not “an LLM with a prompt”. It is a system that accepts intent, keeps state, decides when to retrieve or act, and exposes enough runtime detail to debug failures.

llama.cpp router mode is one of the most useful changes to llama-server in years. It finally gives local LLM operators something close to the model management experience people expect from Ollama, while keeping the raw performance and low-level control that make llama.cpp worth using in the first place.

Hermes Agent ships with a Kanban-style board and the Hermes Gateway that can saturate your self-hosted LLM if too many tasks are dispatched at once.

For a long time, llama.cpp had a glaring limitation:
you could only serve one model per process, and switching meant a restart.

This article is about OpenClaw plugins — native gateway packages that add channels, model providers, tools, speech, memory, media, web search, and other runtime surfaces.

Hermes Agent is a self-hosted, model-agnostic AI assistant that runs on a local machine or low-cost VPS, works through terminal and messaging interfaces, and improves over time by turning repeated tasks into reusable skills.