> We may create codebases that are not merely hard to maintain by humans, but that assume machine participation as part of their maintenance model... People more and more merge code they cannot fully explain. People lose their ability to create issue reports or discuss things in chat, without augmenting or rephrasing their messages with the context provided by a clanker. Too many people increasingly rely on a machine to summarize or contextualize it. More and more do I encounter people who converse with me through the indirection of an LLM.
I experience this daily now. It find it discouraging and concerning.
I believe we're merging more code we can't fully explain because we are now relying on code review to build the mental model that was previously built by writing code and collaborative technical planning. I don't think code review is fit for this purpose. I do think we can extend code review with structured exercises, informed by pedagogy, that strike a better balance between friction and understanding. (I'm looking for help testing these exercises).
I agree that reading AI code all day is agonizing. We're relying on code review to develop parts of our mental model of the system that were previously developed through coding. We're having more difficulty comprehending and recall details of the system. This is probably unsurprising; people recall information better that they "generated" than information they read. I am applying some lessons from pedagogy to extend code review. If this resonates with you, I would like to talk.
Even when I write a detailed specification and review the resulting diff line-by-line, I am unsatisfied with my comprehension and recall of the changes. I don't understand my systems as well as I used to (I don't think this is surprising; see the "generation effect"). I have been experimenting with extending code review with some new exercises intended to improve comprehension, at the cost of a little friction.
When I work with Claude to plan a feature and then review Claude's implementation, I don't understand the feature as well those I developed without AI assistance. I don't recall details of the feature's behavior as well, even days later. I suspect that this is not surprising to anyone who has studied pedagogy. I've been working on applying some exercises during code review (including self-review of my own AI-assisted code) to improve comprehension and recall (https://bridgekeeper.io/). If this problem resonates with you, I would like to talk.
I'm working on a possibly-quixotic tool to mitigate the "cognitive debt" from AI-assisted development. Not everybody agrees that this is a problem. Maybe some teams that are only writing specs and reviewing plans still understand their products adequately. If you have an opinion either way, I'd appreciate hearing from you.
I'm working on a tool for mitigating the cognitive debt from AI-assisted development. Pedagogical research provides some techniques we can use in code review to improve comprehension and recall. If this resonates with you, I'd appreciate hearing more about your experience with cognitive debt.
I'm working on a tool for mitigating the cognitive debt from AI-assisted development. Even when I review Claude's diffs line-by-line, I don't understand them as well as code I wrote myself, and I have difficultly recalling them days later.
Pedagogical research provides some techniques we can use in code review to improve comprehension and recall.
This doesn't resonate as a problem with everyone, but I'd like to hear from you either way.
Also, the tool talks like the Monty Python bridgekeeper, for whatever reason.