SelfHosting

An LLM Wiki fails when old facts remain plausible, contradictions become polished, and generated summaries drift from their sources.

Syncthing keeps files synchronized across devices you control, making it one of the most practical tools for a self-hosted knowledge infrastructure that avoids cloud lock-in.

The AI hardware landscape has shifted significantly in 2026, with NVIDIA, AMD, and Intel all competing for developers who need GPUs capable of running local large language models and AI inference workloads.

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.

APT failures are common on long-lived Ubuntu machines, and they usually appear after a release upgrade, a third-party repository change, a removed PPA, a manually installed .deb, or an interrupted package installation.

Memory turns assistants from reactive to persistent, but it is also where many systems quietly rot. Surveys argue the short-term versus long-term split is no longer enough for modern agent memory; OpenAI and LangGraph SDKs point to a simpler stack — working memory, durable state, and retrieval.

Open-source AI agent frameworks are exploding in popularity on GitHub. Two projects at the core of the self-hosted AI systems ecosystem — OpenClaw and Hermes Agent — have pulled so far ahead that the rest of the field is fighting for a distant third place.

I tested Speculative decoding (Multi-Token Prediction, MTP) performance in Qwen 3.6 27B and 35B on an RTX 4080 with 16 GB VRAM.

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.

The premise is simple: compiled knowledge is more reusable than retrieved fragments. RAG became the default answer to a straightforward question - how do I give an LLM access to external knowledge?

This page is a practical reference for agentic LLM inference tuning (temperature, top_p, top_k, penalties, and how they interact in multi-step and tool-heavy workflows).

You already chat to Hermes Agent from your phone with text. Now you want to talk to it directly and get spoken replies back. That is usually the right move, especially if you already use Hermes as a persistent self-hosted assistant. Typing long prompts on a small screen is slow and error-prone

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.

Hermes Agent treats skills as the default way to teach repeatable workflows. Official documentation describes them as on-demand knowledge documents aligned with the open agentskills.io shape, loaded through progressive disclosure so the model sees a small index first and only pulls full instructions when a task actually needs them.