When I say I am interested in Anthropic-adjacent or Claude-heavy product work, I do not mean that I want to attach myself to a popular model name. I mean that the work around Claude, Claude Code, and the broader Anthropic ecosystem sits unusually close to the layer I care about most: where model capability meets product judgment, workflow design, operator tooling, and the question of what should actually get built.

The interesting part is not just that the models are good. It is that they make the loop between vague idea, rough spec, prototype, and real system behavior much tighter than before. That changes the shape of product work. A team can see something real sooner, break it sooner, and learn sooner. But it also means the surrounding judgment becomes more important. What should stay manual? What deserves a tiny internal tool? What should be deterministic? What needs a review path? What is still too brittle to productize?

That is why the phrase that feels most honest to me is still something like AI product builder or operator, not generic PM work and not narrow tool evangelism. My background spans product leadership, growth, marketplaces, company building, and technology strategy. What I like doing now is taking model possibility and helping turn it into something a real team can inspect, pressure-test, and keep using.

I also think the Anthropic and Claude ecosystem overlaps naturally with the research-adjacent questions I care about. Once a team starts building real systems around models, evals, benchmark design, agent reliability, and operator trust stop being academic side topics. They become product questions. The system either has a review path or it does not. The failure modes are either visible or they are not. The benchmark either reflects the workflow that matters or it flatters the demo.

That is part of why I have been building small interactive proof surfaces instead of only writing about this abstractly. AI Company Fit Radar is a way of making recruiting and team-fit judgment visible. AI Evals Lab is a way of making benchmark posture and workflow-aware evaluation visible. AI Workflow Lab is a way of making review depth, model freedom, and system boundaries visible. Those are all small products, but they reflect the same center of gravity.

The Claude Code piece matters because it compresses the path from product thought to working artifact. But the more durable value is in what a team does around that artifact. How does the operator see it? Where does feedback land? What gets logged? How does the team compare one workflow to another? How do you know whether the system is actually becoming more useful instead of only more impressive?

So when I think about where I fit best, Anthropic-adjacent teams and Claude-heavy product environments keep showing up because they are close to that boundary. They need more than enthusiasm about AI. They need someone who can package capability, tighten a workflow, build a proof surface, and keep the product questions tied to the system questions.

If that is the frame, the best next pages are Anthropic and Claude ecosystem, AI Company Fit Radar, AI Evals Lab, what I've actually built with Claude Code and Codex, and AI labs, evals, and agent systems.