Ai

Mnemosyne for Hermes Agent: Local Memory Quickstart

Mnemosyne for Hermes Agent: Local Memory Quickstart

Local Hermes memory with controlled writes.

Mnemosyne is a local-first memory provider for Hermes Agent, storing working memory, structured facts, temporal data, and episodic history in local SQLite — no hosted service, no mandatory network calls, and unusually granular write control.

Self-Reinforcing Memory Loops in AI Agents: Causes and Fixes

Self-Reinforcing Memory Loops in AI Agents: Causes and Fixes

When remembered conclusions become new evidence.

Persistent memory turns an agent from a re-explained tool into one that carries context forward — but it opens a failure mode stateless chat avoids: an interpretation can become memory, retrieved as fact, and justify a stronger version of itself.

How to Migrate from OpenClaw to Hermes Agent Safely

How to Migrate from OpenClaw to Hermes Agent Safely

A safe cutover beyond the one-line import

Migrating an AI assistant is not the same as copying an application config. The hard part is preserving identity, memory, tool behavior, scheduled work, and messaging access without two gateways acting as the same bot.

Agent Skills vs MCP Servers: Decision Framework

Agent Skills vs MCP Servers: Decision Framework

Skill, MCP server, or both?

Agent Skills and MCP servers are often presented as competing ways to extend an AI agent. That framing is wrong: a skill teaches the agent how to work, while an MCP server gives it governed access to live capabilities.

Data Gravity: The Real Cost of API-First AI

Data Gravity: The Real Cost of API-First AI

Why your AI stack gets stickier every month.

Every API call feels like a simple transaction - until enough of them accumulate that your fine-tuning data, evaluation harnesses, and tool schemas are all shaped around one vendor, and switching stops being a routing change.

Ollama to vLLM: When to Migrate Your Local LLM Server

Ollama to vLLM: When to Migrate Your Local LLM Server

When to move from Ollama to vLLM

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.

GPUs for AI in 2026: NVIDIA, AMD, Intel Compared

GPUs for AI in 2026: NVIDIA, AMD, Intel Compared

AI GPU comparison across three vendors

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.