No they don’t. Enums are actually unique in being the only Typescript feature that requires code gen, and they consider that to have been a mistake.

In any case that’s not the cause of the difference here.

Private or obscure ones I guess.

Real-world (macro) benchmarks are at least harder to game, e.g. how long does it take to launch chrome and open Gmail? That’s actually a useful task so if you speed it up, great!

Also these benchmarks are particularly easy to game because it’s the actual benchmark itself that gets gamed (i.e. the code for each language); not the thing you are trying to measure with the benchmark (the compilers). Usually the benchmark is fixed and it’s the targets that contort themselves to it, which is at least a little harder.

For example some of the benchmarks for language X literally just call into C libraries to do the work.

Their measurements include invocation of the interpreter. And parsing TS involves bigger overhead than parsing JS.

But TS is compiled to JS so it’s the same interpreter in both cases. If they’re including the time for tsc in their benchmark then that’s an even bigger WTF.

It has no memory, for one.

It has very short term memory in the form of it’s token context. Especially with something like Meta’s Coconut.

What makes you think that it does know its in a conversation?

I don’t really. Yet. But I also don’t think that it is fundamentally impossible for LLMs to think, like you seem to. I also don’t think the definition of the word “think” is so narrow that it requires that level of self-awareness. Do you think a mouse is really aware it is a mouse? What about a spider?

Ah this ancient nonsense. Typescript and JavaScript get different results!

It’s all based on

https://en.wikipedia.org/wiki/The_Computer_Language_Benchmarks_Game

Microbenchmarks which are heavily gamed. Though in fairness the overall results are fairly reasonable.

Still I don’t think this “energy efficiency” result is worth talking about. Faster languages are more energy efficient. Who new?

Edit: this also has some hilarious visualisation WTFs - using dendograms for performance figures (figures 4-6)! Why on earth do figures 7-12 include line graphs?

Your comment doesn’t account for the fact that LLMs can generalise. Often not very well but they can produce outputs for inputs not seen in their training sets. Otherwise what would be the point?

You would not ask a piece of cardboard so solve a math problem, would you?

Uhhh you know LLMs can solve quite complex maths problems? Including novel ones.

And how do you know LLMs can’t tell that they are involved in a conversation?

Unless you think there is something non-computational in the human brain, then you must accept that computers are - in theory - capable of thinking. With the right software and sufficiently powerful hardware.

Given that truth (which I think you can only avoid through religion or quantum quackery), you can’t just say “it’s only maths; it can’t be thinking” because we know that maths can think.

Do LLMs “think”? The definition of “think” is wooly enough and we understand them little enough that it’s quite an assertion to say that they definitely don’t.

This argument makes no more sense than trying to say that a plant is thinking because brains are made of cells and so are plants.

By that logic we also conclude that the human brain doesn’t “think” about what it is saying.

It’s really amazing the number of people trying to argue that LLMs are useless, while simultaneously so many people are using them successfully. Makes me wonder if they’ve even tried them.

Ah yes the old pointless vague anecdote.

If your argument is “LLMs can’t do useful work”, and then I say “no, I’ve used them to do useful work many times” how is that a pointless vague anecdote? It’s a direct proof that you’re wrong.

Promoting pseudo-science.

Sorry what? This is bizarre.

LLMs can’t think - only generate statistically plausible patterns

Ah still rolling out the old “stochastic parrot” nonsense I see.

Anyway on to the actual article… I was hoping it wouldn’t make these basic mistakes:

[Typescript] looks more like an “enterprise” programming language for large institutions, but we honestly don’t have any evidence that it’s genuinely more suitable for those circumstances than the regular JavaScript.

Yes we do. Frankly if you’ve used it it’s so obviously better than regular JavaScript you probably don’t need more evidence (it’s like looking for “evidence” that film stars are more attractive than average people). But anyway we do have great papers like this one.

Anyway that’s slightly beside the point. I think the article is right that smart people are not invulnerable to manipulation or falling for “obviously” stupid ideas. I know plenty of very smart religious people for example.

However I think using this to dismiss LLMs is dumb, in the same way that his dismissal of Typescript is. LLMs aren’t homeopathy or religion.

I have used LLMs to get some work done and… guess what, it did the work! Do I trust it to do everything? Obviously not. But sometimes I don’t need perfect code. For example recently I asked it to create an example SystemVerilog file for me utilising as many syntax features as possible (testing an auto-formatter). It did a pretty good job. Saved some time. What psychological hazard have I fallen for exactly?

Overall, B-. Interesting ideas but flawed logic.

Because I can read? Lol ok.

This is fine precisely because it is a blog post. If it was a scientific paper… sure maybe they shouldn’t say that. But the meaning is abundantly clear from the context. There is no ambiguity.

There isn’t. This is the colloquial use of “exponentially” which is very obvious from the context.

I don’t think this is a very interesting article. We already know AI suggests nonsense a lot of the time. That in no way demonstrates that it is net-negative. In my experience it’s a net positive even accounting for the times it gets things wrong.

Yes you do have to review its code closely. News at 10.

It is kind of funny that they picked an example where it made an obvious mistake for their hero image though.

No, the filter is correct even for UTF-8. Any ASCII character is exactly unchanged in UTF-8 (part of the reason it is popular). Since this code only filters out ASCII characters it works fine with ASCII or UTF-8.

This is 14 years old by now - hardly “modern”. RAII itself is even older.

I agree. I think it’s driven by fear. I get it. I’m slightly afraid I won’t have a job in 10 years (or at least a much worse paying one)…

I’m still a much better programmer than AI today. But I don’t cope with the fear by deluding myself into thinking that AI is useless and will stay useless.

The feels a lot like portrait painters saying that photography will never amount to anything because it’s blurry and black and white.

Presumably because Forgejo didn’t have CI support until extremely recently. And because Jenkins is trash.