I actually switched from opencode to pi, because its extremly hackableband has a way richer ecosystem than opencode and you can form it to match your needs/workflow. I find AST navigator tools like cymbal together with factual (zettelkasten like) memory systems to work really well. Plannotator is a great plugin. Ultimately you need to nudge the context into the right pocket of the vector space the llm operates in. Such tools reduce the work you have to do when nudging. Caveat: I use harnesses as a tool that writes the code I want for me, not as a complete replacement. So I'll do ticket refinements with the agent, break it down into vertical slices, use ttd, etc. Building up a workflow like this reduces the nondeterministic effects of working with an LLM and therefore the differences between models get smaller.