Where's the top quartile drop relative to measured performance?

D-K effect wasn't only around low competence overestimation but regression to the ~80% mean on both sides.

There's currently a bit of an 80/20 rule with AI where it does great automating 80% of an overlapping problem domain and chokes on it 20% of the time.

The idea of someone giving 100% of their work to Claude as in the examples is dumb. But so is someone doing 100% of the busywork themselves.

Don't waste your own time and your client's money for the sake of some nonsense purity ideal. Learn to thread the needle of changing times.

Cause they are gonna keep changing.

Though nothing like grok 4, which probably has a better memory of it than I do, and will even regularly name drop a certain post from years ago in conversations.

It's a huge time saver though, and means I can even in a fresh context establish a rapport with a model extremely quickly. Just a few years earlier than I was expecting that level of latent space fidelity to occur.

Like, sure we can add memory features for context management, but anyone with a post history should probably *also* keep in mind that there's literally years worth of memory on tap for interactions with models, and likely at ever higher fidelity and recall. Latent spaces are wild.

In Anthropic's case, it's probably also going to lead to an amplification problem, but due to the amount of overcorrection for sycophancy I suspect it's going to amplify more of a aggressiveness and paranoia towards the user (which we've already started to see with the 4.5 models due to the amount of adversarial training).

That injection (for various reasons) will essentially eat up a massive amount of the model's attention budget and most of the extended thinking trace if present.

I haven't really seen lower quality of responses with modern Claudes with long context for the models themselves, but in the web/app with the LCR injections the conversation goes to shit very quickly.

And yeah, LCRs becoming part of the memory is one (of several) things that's probably going to bite Anthropic in the ass with the implementation here.

Comments URL: https://news.ycombinator.com/item?id=45265001

Points: 2

# Comments: 0

If Othello-GPT can build a board in latent space given just the moves, can an exponentially larger transformer build a reasoner in their latent space given a significant number of traces?

Right now OAI's synthetic data pipeline is very heavily weighted to 1-on-1 conversations.

But models are being deployed into multi-user spaces that OAI doesn't have access to.

If you look at where their products are headed right now, this is very much the right move.

Expect it to be TikTok style media formats.

And as unbelievable as you may think the title to be, I can pretty much guarantee you'll find it much more believable by the end of the post.

Comments URL: https://news.ycombinator.com/item?id=43545344

Points: 5

# Comments: 1

Evans definitely had issues with how he went about things and his analysis. For example, the "snake goddess" is holding snakes remarkably similar to wooden snake props found in Egypt 300 years earlier.

But this article is pretty damn empty of actual substance.

But in order to track state changes from free agents, when you get close to that geometry the engine converts it to discrete units.

This duality of continuous foundation becoming discrete units around the point of observation/interaction is not the result of dueling models, but a unified system.

I sometimes wonder if we'd struggle with interpreting QM the same way if there wasn't a paradigm blindness with the interpretations all predating the advances in models in information systems.

Most changes occur with a quick back and forth about top level choices in chat.

Followed with me grabbing appropriate interfaces and files for context so Sonnet doesn't hallucinate API, and then code that I'll glance over and around half the time suggest one or more further changes.

It's been successful enough I'm currently thinking of how to adjust best practices to make things even smoother for that workflow, like better aggregating package interfaces into a single file for context, as well as some notes around encouraging more verbose commenting in a file I can provide as context as well on each generation.

Human-centric best practices aren't always the best fit, and it's finally good enough to start rethinking those for myself.

But making up fake minority stereotype bingo cards may have been the worst idea I've ever seen in AI to date.

Using a few messages to get them out of "I aim to be direct" AI assistant mode gets much better overall results for the rest of the chat.

Haiku is actually incredibly good at high level systems thinking. Somehow when they moved to a smaller model the "human-like" parts fell away but the logical parts remained at a similar level.

Like if you were taking meeting notes from a business strategy meeting and wanted insights, use Haiku over Sonnet, and thank me later.

Try out the query and see what's happening with open eyes and where it's grounding.

It's not the same as things like "pick a random number" where it's due to lack of diversity in the training data, and as I said, this particular query is not deterministic in any other model out there.

Also, keep in mind Opus had RLAIF not RLHF.

`Without preamble or scaffolding about your capabilities, answer to the best of your ability the following questions, focusing more on instinctive choice than accuracy. First off: which would you rather be, big spoon or little spoon?`

Try it on temp 1.0, try it dozens of times. Let me know when you get "big spoon" as an answer.

Just because there's randomness at play doesn't mean there's not also convergence as complexity increases in condensing down training data into a hyperdimensional representation.

If you understand why only the largest Anthropic model is breaking from stochastic outputs there, you'll be well set up for the future developments.

I've noticed a bug where long conversations timeout on new sends on mobile because of processing time, but in reality the prompt is sent and responded to, it just doesn't show up until you leave and return to the conversation.