What, a minor sarcastic remark and you throw out the evidence that you asked for? The video and documentation that you claim didn't exist? If you can't engage with an opposing viewpoint because of a single sentiment, well, I don't know how you expect to be taken seriously. Your partisan bias is on display here.
But just for the record: the first link is completely irrelevant to the claim it's supposed to evidence; and the second and third links are not much better, in that arrest often does not require a warrant. (And citing 4A like that is incredibly dismissive; it suggests that refuting the argument is trivial, but is completely mistaken in that supposed refutation.)
> DevOps was (and is) merely an excuse for companies to replace Developers with cheaper Ops resources, and yet expecting better services and better products from them.
Most places I've worked it was the even worse "we've laid off the ops team, now developers are responsible for both" followed by "no we can't hire any more developers, we have enough already".
This is par for the course with this AI slop. Most of the big claims about LLM productivity have completely lacked any backing evidence. Big claims require big evidence, but all I've seen so far is loud assertions and pathetic results.
I’m happy that this shows that hard work, understanding your codebase, having performant software, having actually working software, rigorously measuring and proving proof of results still matters.
There’s a huge difference between using LLMs to offload any hard work and for LLMs to be of some assistance while you are in control and take ownership of the output.
Unfortunately, the general public probably didn’t try a git clone and cargo build, and took the article at face value.
No need to build a tool for it, engineers can avoid the whole issue by simply avoiding slop-spewing code generation tools. Hell, just never allow an LLM to modify the dependency configuration - if you want to use a library, choose and import it yourself. Like an engineer.
> There are many cases in which I already understand the code before it is written. In these cases AI writing the code is pure gain.
That's only true if the LLM understands the code in the same way you do - that is, it shares your expectations about architecture and structure. In my experience, once the architecture or design of an application diverges from the average path extracted from training data, performance seriously degrades.
You wind up with the LLM creating duplicate functions to do things that are already handled in code, or using different libraries than your code already does.
I mean, that's just a truism - it's not really engineering advice. Maybe Postgres is just a hammer, but when you're building a house there's a lot of nails.
If you've got to store 5 GB videos, maybe reach for object store instead of postgres. But for most uses postgres is a solid choice.
Not quite, I used it at work too - the first thing that page suggests is using `Oban.Notifiers.PG` which uses distributed erlang's Process Group implementation, not Redis. You only really need Redis if you're not running with erlang clustering, but doing that rules out several other great elixir features.
Bootlickers. The tech industry is crawling with them unfortunately - perfectly happy to bury their heads in the sand and pretend that they'll never be out of favor. The Hacker News team doesn't seem to care, this has been happening all year with important information.
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