rolha-capoeira

I don't disagree, but I'm wondering if there's any evidence of this available.

> After all, LLMs have more natural language training data than JSON training data.

While that is true, data also doesn't usually look like natural language (i.e. a collection of financial records). And when it does (i.e. a collection of chat messages), I wonder if it's more confusing if it's unstructured, even if small.

I expect most frontier models to handle these cases just fine either way, so it may largely depend on context—specifically, how much there is, and where the attention shakes out. Ultimately, a claim one way or the other, for something this context-dependent, would have to be backed up by a lot of testing and would probably conclude that, "in most cases, you should do this"

And whether or not it actually is feasible, many are betting millions that it is, and marketing it as possible to keep the innovation machine running until we achieve it

> ... I always say about [a language model], the [linguistic] appearance makes a promise about what it can do. [Clippy] was this little [cartoon paper clip]. It didn’t promise much—you saw it and thought, that’s not going to [write the next great novel]. But you can imagine it [offering limited help]. But [human language] sort of promises it can [write] anything a human can. And that’s why it’s so attractive to people—it’s selling a promise that is amazing.

piggybacking on everything you said, which is all true: Code is not a science, despite what pedants would have you believe. The annoying answer to "what's correct" code is, "it depends." Code is just a tool used to achieve a goal.

why do you need to debug it? or, why do you need to debug it?

my crazy thoughts, which I am open to being wrong about:

this is still the old way of thinking. generative AI is a probability function—the results exist in a probability space. we expect to find a result at a specific point in that space, but we don't know the inputs to achieve it until it is solved. catch 22.

instead, we must embrace the chaos by building attractors in the system, and pruning results that stray too far from the boundaries we set. we should focus only on macro-level results, not the code. multiple solutions existing at any given time. if anything is "debugging", it is a generative AI agent.

I guess a lot of us are trying this (naturally) as solo devs, where we can take an engineering-first mindset and build a machine or factory that spits out gizmos. I haven't gotten to the finish line, mostly because for me, the holy grail is code confidence via e2e tests that the agent generated (separately, not alongside the implementation).

to build on your point, we only need to look at another type of entity that has a binary reward system and is inherently amoral: the corporation. Though it has many of the same rights as a human (in the US), the corporation itself is amoral, and we rely upon the humans within to retain moral compass, to their own detriment, which is a foolish endeavor.

even further, AI has only learned through what we've articulated and recorded, and so its inherent biases are only that of our recordings. I'm not sure how that sways the model, but I'm sure that it does.

What we still get paid to do is different than what we're still able to do. I'm still able to knit a sweater if I find it enjoyable. Some folks can even do it for a living (but maybe not a living wage)

This presupposes that human value only exists in the things current AI tech can replace—pattern recognition/creation. I'd wager the same argument was made when hand-crafted things were being replaced with industrialized products.

I'm not saying those things aren't valuable, or that humans can't express social and spiritual value in those ways, but that human value doesn't only exist there. And so, to give AI the power of complete dehumanization is to reduce humans to just pattern followers. I don't believe that is the case.

been using this for years, still great

And honestly, that's a ridiculous claim. Two very popular websites I can think of right away are Pinterest and VSCO. (Perhaps VSCO on the web isn't as popular as the mobile app, but the company continues to use masonry as the design evolves.)

I imagine it's because whomever created it is a web developer, primarily.

Putting aside mental illness, what makes a person a criminal? What circumstances led to their decision?

Sure, protect the shift worker who has little power and therefore little blame. But, who's to blame for society's ills then? Calling for more arrests and incarceration has never, ever solved the underlying issues. What Garry Tan doesn't realize or won't admit is that he is part of the wealthy caste that caused this. Now that he got his money? Lock all of these bad people up!

That's been my critique of Garry's outspokenness from Day 1. Nowhere is there ownership of what caused SF to devolve into this state in the first place. Tech companies aren't entirely to blame, but they've had a pretty big hand in changing the economic landscape of SF.

Crime is mostly the result of social ills. While individuals should own their behavior, so should the puppeteers. You can't make affordable, consumable tech without exploitation, whether of humans or of Mother Nature, full stop. I'd agree with Garry a lot more if he was trying to fix the dreadful economic situation as well.

Well, 1) we have no idea how long it took to develop SimAirport. I'd be curious to know!

And 2) Rollercoaster Tycoon IS an aberration, since it was written in Assembly by one person!