alt Hacker NewsI think the real divide is over quality and standards.
We all have different thresholds for what is acceptable, and our roles as engineers typically reflect that preference. I can grind on a single piece of code for hours, iterating over and over until I like the way it works, the parameter names, etc.
Other people do not see the value in that whatsoever, and something that works is good enough. We both are valuable in different ways.
Also, theres the pace of advancement of the models. Many people formed their opinions last year, and the landscape has changed a lot. There’s also some effort requires in honing your skill using them. The “default” output is average quality, but with some coaxing higher quality output is easily attained.
I’m happy people are skeptical though, there are a lot of things that do require deep thought, connecting ideas in new ways, etc., and LLMs aren’t good at that in my experience.
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I think this is a false dichotomy because which approach is acceptable depends heavily on context, and good engineers recognize this and are capable of adapting.
Sometimes you need something to be extremely robust and fool-proof, and iterating for hours/days/weeks and even months might make sense. Things that are related to security or money are good examples.
Other times, it's much more preferable to put something in front of users that works so that they start getting value from it quickly and provide feedback that can inform the iterative improvements.
And sometimes you don't need to iterate at all. Good enough is good enough. Ship it and forget about it.
I don't buy that AI users favor any particular approach. You can use AI to ship fast, or you can use it to test, critique, refactor and optimize your code to hell and back until it meets the required quality and standards.
> Also, theres the pace of advancement of the models. Many people formed their opinions last year, and the landscape has changed a lot.
People have been saying this every year for the last 3 years. It hasn't been true before, and it isn't true now. The models haven't actually gotten smarter, they still don't actually understand a thing, and they still routinely make basic syntax and logic errors. Yes, even (insert your model of choice here).
The truth is that there just isn't any juice to squeeze in this tech. There are a lot of people eagerly trying to get on board the hype train, but the tech doesn't work and there's no sign in sight that it ever will.
> I think the real divide is over quality and standards.
I think there are multiple dimensions that people fall on regarding the issue and it's leading to a divide based on where everyone falls on those dimensions.
Quality and standards are probably in there but I think risk-tolerance/aversion could be behind some how you look at quality and standards. If you're high on risk-taking, you might be more likely to forego verifying all LLM-generated code, whereas if you're very risk-averse, you're going to want to go over every line of code to make sure it works just right for fear of anything blowing up.
Desire for control is probably related, too. If you desire more control in how something is achieved, you probably aren't going to like a machine doing a lot of the thinking for you.