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I'd add that this is also how it used to work with Google search too: there's been facts that would show up as the first result, then you'd just forget it 10 minutes later and have to re-google it. I know this happened to me many times.

For better or worse, there's less friction now for seeing an answer to a question you have. You can ask in more arbitrary ways than Google required, and something will still come up. For looking up factoids, it's much faster. For picking up more complex topics, I'd say it's more or less the same, because you still have to spend time ruminating on the topic.

Thank you for sharing. The way I reasoned about it myself: to make better predictions, we should know what type of outcomes are likely. We can express these outcomes by doing computations in some of the layers, and the training signal adjusts them so our model becomes more correct.

Of course, an interesting question what part of this internal computation is modeling for the future compared to guessing based on the given context (the past).

That's the first thing I noticed as well. Does that mean Africa and South America will have Starlink internet before Scandinavia?

I really don't understand why we feel the need to drop the review aspect. Programming with LLMs is a very non-linear process, there's no prompt-to-code mapping where editing a part of the prompt would produce an identical code construct with just a small part of it changed. LLMs are incapable of ensuring the thing works as expected, and the prompt itself is lossy too. Without review, we have no tangible way to interact with the system being constructed.

What does it mean for coding to be solved, and software engineering to not have been solved? If it implies there's now a set protocol that can be followed to reach good results, how has that not been the case before? If it means that we can reach better results than before, then how do we know that can't be improved further? Or does that mean we should know how English language, instead of computer code, maps to computer instructions?

I think it's related that we as humans see when something becomes hard to reason about, and decide to refactor it. I'm not sure whether an LLM with full ownership of a codebase could do that, for its own benefit.

I do see a world where models could be trained on it, but I imagine it will be more expensive, because it requires including future rewards about the models' own later efficiency.

(If) something like the current LLM/agent paradigm remains in a few years, and companies settle down into their respective niches, I imagine more user-friendly tools will be built, with more control over subagent spawning, context, caching, etc.

What's happening this year, with secrecy and all, is saddening, but expected.

I think it's useful for learning about unknown unknowns. If you don't have a clear direction, it's entirely fine to start with a university course then stop when you get a feeling for what you really need.

Why's that bad? It's valuable to discuss whether a presentation should have been a blog post, a video, or a tech talk. The same logic applies to LLM text.

If people consistently describe LLM text as having low information density or irritating to read, perhaps there's something to it?

I personally find that models are trending towards ignoring any instructions given to them, so depend more on vendor-instilled behavior. Anecdotal, but I had bad experiences with OAI's new 5.6.

I love the idea of this, but I don't think I could convince my friends to use it over mainstream platforms.

We wouldn't be having debates about it every other day if it was

On some levels I tried guessing, starting with different letters, and still got into some local minima in my brain where I couldn't guess the word, only for it to be something obvious like 'pound'.

Wow I suck! Played today's and a few others, and got ~4-6/18. I like the timer.

For 4 letters it's enough time to guess, but I have no idea what they mean: 'Doby', 'Etas'

It sucks, but that ship has sailed. I'm not sure how they'll handle the issue of most new repos being AI generated, if they continue using new code for training. If the world accepts LLMs as a valid method for license washing, I don't see how I can fight it.

I don't know, I find it very hard to stay positive about our general direction in the last couple of years, and know few people in real life who don't share this opinion, in or outside of tech. And I'm also not entirely sure I understand why others are excited, it perplexes me. I would appreciate any insight into this.

I find the pro-AI and anti-AI somewhat inconsistent, where I had either side strongly reacting to my comments. I personally didn't expect so much support for Bun in this thread.

I think it just doesn't know when it's its turn to speak, and cancels itself out when it hears humans.

A cool test, until it hit's the my family's grandmas test :)

We're yet to see how this plays out, but a competing business model for creative work is emerging, where it's delegated to chatbots. Naturally, this would result in less creative work for humans.

I'll admit fault for my remark, but I'll stand by the point I meant to express.

Part of disagreement probably stems from what type of 'learning' we're discussing. In my view, at the broadest sense that we can define 'learning', is incorporating information about our surroundings into our internalized world model. The type of learning I see most valuable personally, is the type that expands this horizon the most, or helps us think in frameworks that break down the least in different contexts.

This type of foundational building often requires deep thought, but is also often deeply rewarding if you get it right. This doesn't require reading by itself, but ruminating and neural rewiring can often be produced by it, if you consume the right content for you. I think it's important to have different experiences, many of which come from consuming different mediums, as well as doing things in real life, but a significant part of knowledge to this day has been passed down by books.

Even if we mean 'learning' to be more similar to 'gathering information', I think it can be most efficiently done by reading, or doing. I don't hold as much disagreement there, nor any judgement, but I wouldn't equate the two. Perhaps a bit pedantic, but I read 'liking learning' beyond the means by which it's achieved, and 'hating reading' reads temperamental to me.

> Read books yourself

Not a parent, but I'm guessing this part is very important :)

If you can stomach older English novels: A Tale of Two Cities by Charles Dickens

> I love learning, but I hate reading.

Correction: you love the feeling of consuming information, not learning.

Nauseating on so many levels. What are we celebrating, even? Authors themselves say that Claude does their work, so what is their input? It's been barely a year in production for what can be best described as a 'buggy mess'.

People that don't like writing now get to write by offloading it to an LLM, and this is the result. I miss the world where articles were mostly written by people who had the interest and patience to do it.

I'll stick to games and movies, as I believe both have been moving in a similar direction, becoming more of an object to be consumed, rather than to be experienced. I've thought about this in two ways: it's either that (a) when fields are fresh, creators explore orthogonal concepts and fit to what performs best relatively quickly, or (b) the available idea space just isn't that large by itself, and novelty wears off after you watch some number of movies.

Both games and movies are predictable in the sense that we know what to expect, and they have been largely standardized. Games have common keybinding schemes, as well as user experience mechanics: how jumping feels, when we expect to autosave, what the UI/minimap symbols mean, etc. When it comes to movies, I find myself no longer turning away from the screen before gruesome scenes, because I expect in advance that they won't show it, depending on the mood of the movie. I also find that you can often predict which dialogue lines were meant as foreshadowing for a plot twist coming later. This standardization is intentional in the sense that people are more likely to consume something they are familiar with, and more likely to enjoy it if they can passively engage with it.

It's common nowadays to pay $20 for a game, play it for a few hours, and forget about it. Or, turn on a random Netflix show on the TV to pass time in the evening. Quite likely that a month later you won't reminisce about either of these experiences, but you probably didn't have high expectations either way. I think 'consuming' a travel trip is similar in the sense that it has very familiar tropes no matter where you go, but more implicitly resulting from market forces rather than intentional design from a creator.

Would be curious to hear from folks who do modelling:

My first reaction from seeing the examples on the page, is that they are somewhat simple, almost standardized, or come from templates. They also don't look very time-consuming to model yourself. The product advertises itself on the parameterization aspect, but my hunch is that practitioners have their personal library of models which can be rescaled without much work. Is that somewhat accurate?

I can see it both ways. Paying 5 bucks for an add-on that's reputable and not having to think about it again may be preferable to shipping your own. It's not evident to me that one-shot-esque LLM programs will ever work as expected, since they're limited by the amount you have to specify, and then maintain as new issues arise. It's the type of work that a lot of people will be unwilling to put in.

That said, I also think that public reviews will start getting recalibrated by the users themselves. People will start noticing that programs are unreliable or lack features which are seemingly simple to implement with an LLM. The quality of an app may very well depend on how hard it is to replicate, otherwise, why not ship your own?

I don't think we should expect to short-circuit learning for everything. Fumbling in Blender for hours on end is part of the process and what makes you good at it in the long term, much like grinding exercises in math or physics.