AI

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.

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.

AI is not replacing knowledge management; it is changing the shape of it for both individuals and teams.

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?

PKM, RAG, wikis, AI memory systems, and now practical AI-assisted workflows are often discussed as if they solve the same problem. They do not. They all deal with knowledge, but they operate at different layers:

Most LLM “structured output” tutorials are unserious. They teach you to ask for JSON politely and then hope the model behaves. That is not validation. That is optimism with braces.

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.

Hermes Agent from Nous Research is a model-agnostic, tool-using assistant you run locally or on a VPS.

Most AI agent stacks still treat security as a post-demo fix. NemoClaw starts from the opposite assumption and makes isolation, policy, and routing day-zero defaults.

Modern assistants still forget everything when you close the tab unless something persists beyond the context window. Agent memory providers are services or libraries that hold facts and summaries across sessions — often wired in as plugins so the framework stays thin while memory scales.