Plot twist: the bottleneck in AI coding isn't the model. It's how the AI searches your project.
The part that fries my brain isn't typing. It's the window ping-pong: browser, chat, editor, repeat ๐ต I used to blame the model every time the whole thing felt slow.
The thing is, if the AI is opening random drawers instead of walking in with a floor plan, you end up explaining the same background 3 times and still doing the cleanup yourself. Lowkey, a lot of us don't need a stronger model first. We need fewer window hops.
What changed my mind about DeusData's codebase-memory-mcp: it tries to hand the AI a map of the project instead of a pile of pages. On 5 questions about how a project is wired together, the text load dropped from about 412,000 tokens, basically text chunks, to 3,400 [S001]. Across 31 code projects, it still got good answers 83 times out of 100, with 10x less text and 2.1x fewer search hops [S002].
That's the real win ๐งญ not "more AI," just less wandering. Boundary check: those numbers come from the published 31-project benchmark, not my own laptop, so your setup may vary. Save this for your next tool setup, then share it with the friend still blaming the model. What steals more time for you right now: coding, or all the switching around?
#AICoding #DevWorkflow #BuildWithAI #CodeSearch