You probably don't need a bigger model. You need better repo retrieval.

If you use ChatGPT or Claude for coding but keep moving context between browser, chat, and editor, the bottleneck may not be the model. It may be repo retrieval [C002]. Many people think they need a stronger model. What they really need is fewer window switches.

That cost looks boring, but it adds up: search in one window, paste background in another, then jump back to the editor for a small change. The real drag is manual context hauling. That is how you end up doing extra rework instead of moving faster.

Most coding assistants still work file by file. You ask one question, they read one file, then another, then search again. That loop is why you keep restating the repo. AI tools are starting to save not just code-writing time, but the small chunks lost to switching windows.

What changed my mind: in the project's own published 5-question demo, the text sent into the AI dropped from about 412,000 tokens to 3,400 after it built a map of the codebase first and queried that map. That is a retrieval change, not just a prompt tweak.

That is the bet in DeusData / codebase-memory-mcp [C001]: not a smarter model, but less rereading of the same project. Boundary: this comes from the project's own published demo, not every repo or editor setup. Before buying a bigger model, check whether your tool understands the repo.

If this sounds like your workflow, share it with the person who keeps assuming the next model upgrade will fix everything.