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Comment by brandensilva | Hacker News Reader

Right more simply put it's great at being a copy cat, exploring similar data points that match your token needs.

It is not great at decision making or judgment calls that don't have a well defined spec or plan in place yet; like unofficial or unapproved tokens if you will. A lot of this stuff simply never has had specs as it has been internal to how companies work and their secret sauce.

The closest thing we have are governance and compliance policies due to legal/business needs requiring it so it's far more well documented than operational ones in how we work. It is more about the how versus the what here I guess is what I'm saying.

But yeah this is why it does great when there are tests, design systems, evals, and other artifacts to mirror. Far more reckless and unpredictable without these things, but still great for exploration and finding the data output you seek.

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Doesn't that make sense? Its text prediction. If you give it examples, it can predict. Synthesizing "put semi-colons on new lines" requires it to generate its own examples 'in its head' (so to speak) and remember that. It won't.

It's like when I see people feeding it a whole bunch of "best practices" and expect it to follow them. It won't. But you could ask it questions about the best practices all day long.

Yes, exactly. Any engineer deep on this stuff right now understands that grounded predictive engine sprinkled with RL training and are discovering what that means in terms of its strengths and weaknesses for company use.

Supposing an unspecified or poorly specified function f(x), and example "f(A)=>B", "given C tell me what f(C) is" lies at the core of creativity.

Idk, calling it "just text prediction " seems unfairly dismissive of this capability

Saying that it’s dismissive is like saying writing (insert language) is dismissive that you’re just writing assembly.

at the end of the day, it presents a vector field and predicts the next vector. That’s literally the heart of intelligence just like assembly is the heart of execution. When playing table tennis, your brain is literally predicting seconds into the future to get your body into the right position.

But we aren’t discussing intelligence here. We are discussing how best to utilize that intelligence.

You're making my point for me, saying table tennis is "just a proprioceptive predictor" is dismissively reductive (and not a particularly useful framework for understanding table tennis), even if it is strictly speaking accurate. It's the sort of thing someone who has no idea how hard training for table tennis is would say.

Let me put it bluntly. I’m agreeing with you but saying that isn’t what I was talking about and trying to give examples. You’re also agreeing with me.

The “idea” of table tennis and the rules. Those are things we can talk about. It’s those “best practices” I gave in my example. The actual playing of table tennis would be the examples. How to apply those best practices and what good code looks like.

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