21–30 of 157 posts
Ew at the bit about letting AI write your commit messages. Don’t bother - if you didn’t take the time to write it why should anyone take the time to read it. I’ll just ask my AI to extract the intent from the actual code if I need it.
Similar arguments have been made about Git UIs and automated tools, and of course most people who depend on such things also depend on others to bail them out in difficult situations with git. So it just becomes a question about whether or not it's part of your core value proposition. For many developers, the answer is obviously no. And then outsourcing those things to automation, including LLMs, seems fine. But if y…
TBH writing shell scripts (and automation tools generally) is the grunt work I most love it for. Git commits and PR descriptions? Those are probably best at least edited/tuned by humans. Because they're meant to be read by humans.
All above comes at a cost of author slowly starting to understand less and less code in their own projects. Then in other projects as well. After the initial boost is over they will have to pay money just to stay afloat because they have already outsourced their thinking. I’m not anti AI, but I’m very worried about this bragging “you are not better engineer if you do things yourself”. Yes, you are, it all comes in sm…
All above comes at a cost of author slowly starting to understand less and less code in their own projects. Then in other projects as well. After the initial boost is over they will have to pay money just to stay afloat because they have already outsourced their thinking. I’m not anti AI, but I’m very worried about this bragging “you are not better engineer if you do things yourself”. Yes, you are, it all comes in sm…
Now it's hard to pay attention to code comments and commit messages because costs to produce them is zero and llm doesn't care about communication and your attention budget
If you can't be bothered to write it, why should I bother reading it?
AI detectors are criticized but classifying stuff into two boxes is probably one of the stuff that is the easiest to measure the accuracy of (as long as one does not put the test set in the training set...).
(well one could see the irony of using ML to detect ML text while complaining about people not caring about understanding anymore, but that's one case where the machine is more reliable than the human)
Delivery is becoming a sequence of checks, not a ritual The same thing happens when it is time to deliver.
First, I invoke my /definition-of-done skill. It checks whether the implementation covers what was stated in the PR and in the plan. It checks tests and the other validations I care about. It tells me when something looks unusual, broken, or missing. When everything looks good, I invoke another skill, /pr-check-release. That checks the remote PR, updates labels, removes [WIP], adds [RFC], updates the description, and prepares the change for review. If one day passes and nobody on the team reviews the PR, we merge it. The agent also tracks that condition for me, so I can run /pr-merge-dev, and it takes care of the process: merge the PR, delete the remote branch, delete the local branch, and pull dev back into a fresh state. None of these steps is particularly difficult. That is exactly the point.
But then:
I still care about all of those things. Probably more than most people.
Obviously the author has irreversibly became AI-pilled and the day API costs balloon or APIs are down, what work will the author do?
I love using AI but please read the diffs and process them with your human brains and eyes. Spin up your containers manually, test the app, MANUALLY. Talk to real users face to face.
Outsourcing the grunt work is fine, but there's a fine line between that and becoming a button-presser.
Give me a break.
Also, based on your LinkedIn you aren't an engineer at all?