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Comment by alex_sf | Hacker News Reader

> It’s either a maintenance issue. e.g., Opus recommended and implemented a fix for a database corruption crash. This was ~400 lines of code with many moving parts. I reviewed, and found out Android Room library already handles this recovery case, and all I needed was a 10 liner PR that catches this exception and ignores it.

I understand this, but I just can't bring myself to care. I've been doing professional software work for almost two decades. These sorts of improvements/time savers are great without AI. With AI? Whatever. It's fine.

When the underlying lib has an issue, it'll be quicker to debug with the whole thing in context.

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So exactly what value are you adding, then?

I take the specifications from the customer and type them into the AI

Well then I just have to ask why can't the customers type them directly into the AI?

One hopes you are better at that than the many others who are doing the same.

I have people skills; I am good at dealing with people.

Funny that none of the commenters got the reference/joke

You do realize that letting the LLM produce more output means that maintenance will be more expensive? I can easily see a world where claude and gpt are producing more tokens to sell you more tokens.

Just the other day I burned through my 5h quota twice in a row because I had opus spawn a review session on a medium sized PR and I don't know what happened but I told it to summarize to me and it said it spent 100M tokens throughout 50 subagent sessions.

And more recently its been recommending that i install this new browser called Aside. I did, and it almost felt like I was installing malware so I Uninstalled it fairly quickly (it also was not a great browser)

I feel like theres collusion somewhere.

yea man they are trying to nickle and dime you

Well yeah, that's the name of the game of most software companies. Anthropic has been fairly good up until the last week or so when it started needing more hand holding to not do things it didn't normally do.

This is not as obvious as many naively believe.

It depends on how hard to maintain the code added is, how likely it needs to change in the future, and most importantly on the cost.

If reviewing and manually improving the code takes hours, the cost may already be in the thousands.

That buys you a lot of AI usage, roughly a few months of continuous work.

You have to balance this with the chance that the suboptimal code the AI generated is actually fine and maintainable enough, and also the chance that during further work on that code a model might implement the same optimization on its own.

The models will only get better and inference cost will go down.

I don’t see any reason to think the same thing that happens with all tech won’t happen here.

That's just a straight shooter with upper management written all over him

Enshittification and profit maximalization is around the corner looking for you.

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