I keep coming back to a simple question: when I use an LLM, am I collaborating with it, or just using a tool? That distinction matters more than it first appears. If we call every AI interaction a collaboration, the word becomes too loose to be useful. But if we reserve collaboration for situations where the human and the system genuinely shape each other’s work, then the term becomes more precise — and more honest. ...
IBM Technology’s The 7 Skills You Need to Build AI Agents makes a point that feels increasingly true: if an agent can act in the real world, then prompt writing is only the starting point. ...
I have been looking for a clean way to explain what /goal really does in Codex. The most useful mental model I found is simple: /goal is not a prettier prompt. It is a working contract for long-running agent work. You are telling the agent what success looks like, what the boundary is, and how to know when to stop. That framing matters because the feature is built for work that outlives one turn. If the objective is durable enough, the agent can keep making progress, validate its own steps, and come back to you with a result instead of a half-finished thought. ...
I recently read Matt Pocock’s article, “5 Agent Skills I Use Every Day”. It resonated with my experience using coding agents such as Claude Sonnet and Claude Opus. The article gave me a clearer language for something I have been feeling: good agent work depends on good engineering process. We need better questions, written context, small slices, tests, and codebases that agents can understand. ...