I'd %100 prefer an opus 4.8 rewrite over %99 of the time. Unless Fabrice Bellard is rewriting the stuff I need, I'd prefer AI over a human coder.
I'd %100 prefer an opus 4.8 rewrite over %99 of the time. Unless Fabrice Bellard is rewriting the stuff I need, I'd prefer AI over a human coder.
Or, you know, you can use Postgres. It's right there for you.
why? if a rewrite is better/faster/secure, why not? (I'm not saying PGrust is better, I didnt even install it, my perspective is in general)
Of course, if the rewrite is all of those things, then it seems like a good option. The whole point is how do we know if it actually is better.
Because it is not trustworthy, which is really the most important thing.
AI is an average coder.
It was trained on all code the code that could be found.
Not just code written by genius programmers like Carmack and Bellard.
Given that it's average, I'd prefer a human coder above average :)
I dont think Opus 4.8 is an average coder, with my own experience (I have coded 20 + years before even llms existed) it is anything but average. I don't think training data alone determines the success of these models, there are lots of reinforncement learning principles and fine tuning takes place, a crappy code in the dataset doesnt hold those llms scoring high in benchmarks, I dont think an average programmer can score 70% (opus 4.8) in SWE Bench Pro, which is a good one.
I think Opus 8.4 is a below average developer, but maybe I have just worked with good developers and have a skewed perspective of what the average is.
I would say it's an average coder when it comes to writing functions because it keeps using regex. It might pass a benchmark but doesn't pass the smell test.
LLMs learn a distribution during pre-training, not only an average.
Then, by giving them context or by post-training, you can make them sample non-average parts of the distribution they learned.
> Then, by giving them context or by post-training, you can make them sample non-average parts of the distribution they learned.
How do you derive that something is "below average" or "average" or "above average"?
Well, it’s up to the user or post-trainer of the LLM what they believe to be above average. Then they can design around that.
In the case of real world LLMs and post-training, what is above average is defined roughly as: labeled good by expert humans, and scoring high on RL environments related to coding like debugging, passing tests, or running efficiently and verifiably correctly.
> How do you derive that something is "below average" or "average" or "above average"?
One technique is RLHF: have an human expert assess it.
Mhm, I just wonder how many samples they get and how much time they have to come to the conclusion.
Like a short example is easier to grade, but not in the same ballpark as a whole codebase.
>How do you derive that something is "below average" or "average" or "above average"?
How do you? I mean, that was your point basis.
Which you will necessarily have if they’ve completed a Rust rewrite.
That is not how it works. IF it was condemned to be average models wouldn't be constantly improving, given that humans aren't getting better.
You haven't been using AI extensively I presume...
I've been programming a long time and considered myself among the top in my domain and AI agents using like GPT 5.5 etc. are much better than me.
> You haven't been using AI extensively I presume...
Ex falso quodlibet
> I've been programming a long time and considered myself among the top in my domain
I am not trying to attack you, but you considered yourself that... I don't know whether you actually were and frankly I don't care.
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