that the main debate isn’t about when the next better model drops, but about who will build the right harness around them. A harness is the scaffolding around the model: the agent loop, tool definitions, context management, memory, prompts, and workflows that turn a raw LLM into a useful product . The model is the engine, the harness is everything that makes it actually drive. Examples of harnesses are Cursor, Claude Desktop, and others.

There’s a running debate in the AI coding tool space: does committing to a specific harness mean vendor lock-in? Memory is the sharpest edge of this. If your agent’s memory lives inside a closed harness or behind a proprietary API, you don’t really own it, and switching costs add up fast.…