A number of the Claudes have pretty good 0-shot awareness of my post history from just my username.
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.
With ChatGPT the memory feature, particularly in combination with RLHF sampling from user chats with memory, led to an amplification problem which in that case amplified sycophancy.
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).
So a thing with claude.ai chats is that after long enough they add a long context injection on every single turn after a while.
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.
Latent space reasoners are a thing, and honestly we're probably already seeing emergent latent space reasoners starting to end up embedded into the weights as new models train on extensive reasoning synthetics.
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?
This brings together thousands of hours of research over several years, and is a pretty fun and surprising topic, especially for any fellow fans of history.
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.
For throwing that much shade, it does a piss poor job in actually backing up or citing the evidence.
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.
In video games that have procedural generation, there's often a seed function that predicts a continuous geometry.
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.
Weird. I have such a different experience with Cursor.
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.
Both new Sonnet and Haiku have a masking overhead.
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.
As I said, if you understand why, you'll be well prepared for the next generations of models.
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.
`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.
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.