I have a hard time telling AI authored LinkedIn thought leadership apart from formulaic LinkedIn thought leadership sometimes, but this feels AI written, which makes it fairly amusing considering the point it’s making.

(Does vibe coding feel incredible? Also, “Nobody’s asking what happens in Month 18 when the original dev has left and nobody understands the codebase” implies that the vibe coder original dev understood the codebase at any point, which isn’t an argument I’d feel comfortable making for any of the ones I’ve worked with)

driven by our mission to create meaningful connections between brands and their audiences

Oh fuck off with this.

Having used these at my day job I can understand the policy against them. Ours is fairly well tuned by the people who operate it, adapted to our codebase, and its comments are usually on point, but its false positive rate for my own PRs is still in the low double digit percent range (feedback that’s factually wrong, not relevant to the PR, not scoped appropriately, etc). Before all that tuning, back when we first started using it, I probably went days or weeks without seeing a well-founded suggestion from the tool. Incorrect comments still create work for the PR author (investigating, double checking, etc), they create noise for human reviewers, and ambiguity about whether a change is ready to be merged. I read that policy as being against random people pointing their own review tools at PRs for this project, which I assume would be more on the high false positive/high noise end of the spectrum (i.e., not being tuned for this specific project). I can totally see why already busy maintainers would want to spare themselves from a bunch of poorly calibrated noise.

One nice thing about them compared to human reviewers is that you can just tell them to (professionally) fuck off if they’re wrong without hurting their feelings or having an HR conversation.

I was half joking. 😅 Of all the tradesfolk I’ve hired to do work on my house, my arborist is the only one who’s job I could see myself actually liking. He gets paid to be a nerd about something (trees, tree health, tree care), the work itself seems a lot more pleasant than dealing with plumbing or electrical repairs, and at least my guy is a one man band who just shows up and does his thing, which is certainly appealing after a career of various EMs, PMs, TPMs, and other people who need to be appeased when doing work. Also the fun tools.

Nothing wrong with being an arborist.

A couple observations on reading this (as someone who heavily uses agentic coding at work and is mostly OK with it):

• Maybe I’m weird, but I don’t find “we always plan the implementation and make decisions first, and only then do we write the code” to describe my work style that well. If I don’t know how I want something to look, I will frequently just start writing whatever code comes to mind about that something. Most of the code will suck, and I’ll probably throw much of it away, but I find that it’s often a much more efficient way to get to a design that I like than sitting and debating tradeoffs in the abstract. Me writing the code might be slower than an agent writing the code, but that misses the point of the exercise: me writing the code is a way for me to build my own understanding of the problem.
• The text translation analogy at the beginning is illustrative, I think. A piece of writing has information, but also elements of the author’s wit, humor, tone, their assumptions about us as readers, and the cultural frame they write within. Good writing combines all of those things in an appealing way. At some basic level translation is about translating words correctly, but “correctly” there does a lot of work. It can be very hard to translate a work from one language to another without losing those less obvious characteristics (wit, tone, etc) that make it appealing, especially if you’re translating (e.g.) idioms or slang that don’t have a clear counterpart in your destination language. This is probably familiar to folks who read authors who’ve been translated outside of their native language: the right (or wrong) translator can have a huge impact on what you experience while reading. That’s not a bar we hold for all text (we’ve all seen very poorly translated manuals for cheap consumer electronics that are nonetheless fine because we can figure out what they’re trying to say), and similarly isn’t a bar that holds for all code, but there is some code where the nuances that automatic translation misses are actually important, and where a human touch is useful.

There are also enough engineers looking for jobs that companies can be picky and only hire the ones who seem agreeable and inoffensive, which reinforces the dynamic. “Idealistic” folks accumulate employment gaps that they have to explain or which cause AI ATS systems to auto reject their application before a human ever sees it, meanwhile management and hiring panels become increasingly corporate and less tolerant of difference because that’s who gets hired.

I don’t disagree with a lot of the advice here as a practical thing (e.g., having a scrubbed public image online, assuming that someone evaluating your job application will research you), but I also remember a time when you didn’t have to do that to work in the industry. A lot of the smartest, most creative folks in my CS program were also outspoken on (e.g.) privacy or civil rights in ways that could be tied to their real name, and it makes me kind of sad to think that they’d probably have to hide that today if they wanted to get hired as a new grad.