keeeba

Thank you for the pelicans sir, how do you think they compare to other models in Sonnet’s pricing/capability range?

Opus 5.1 came out about a month ago, what gives?

The elves left a long time ago, it’s difficult to see much virtue in what remains.

Also interested

This is not an attempt at affected nonchalance, but I’ve simply never come across the idea that Dostoevsky is particularly difficult to read.

A somewhat gifted teenager will race through it, as will an average adult.

Not hired Suleyman? Build his own research lab?

Satya made moves early on with OpenAI that should be studied in business classes for all the right reasons.

He also made moves later on that will be studied for all the wrong reasons.

The gap between Anthropic and the other guys keeps growing

Big respect

Total humiliation for Hegseth, sure there will be a backlash

To somewhat state the obvious - the problem isn’t the amount of data, it’s the algorithms.

We need to discover the set of learning algorithms nature has, and determine whether they’re implementable in silicon

Is everything OpenAI do/release now a response to something Anthropic have recently released?

I remember the days when it was worth reading about their latest research/release. Halcyon days indeed.

“ This is one of my fundamental beliefs about the nature of consciousness. We are never able to interact with the physical world directly, we first perceive it and then interpret those perceptions. More often than not, our interpretation ignores and modifies those perceptions, so we really are just living in a world created by our own mental chatter.”

This is an orthodox position in modern philosophy, dating back to at least Locke, strengthened by Kant and Schopenhauer. It’s held up to scrutiny for the past ~400 years.

But really it’s there in Plato too, so 2300+ years. And maybe further back

I don’t have the experiments to prove this, but from my experience it’s highly variable between embedding models.

Larger, more capable embedding models are better able to separate the different uses of a given word in the embedding space, smaller models are not.

Doesn’t seem like this will be SOTA in things that really matter, hoping enough people jump to it that Opus has more lenient usage limits for a while

As a fairly extensive user of both Python and R, I net out similarly.

If I want to wrangle, explore, or visualise data I’ll always reach for R.

If I want to build ML/DL models or work with LLM’s I will usually reach for Python.

Often in the same document - nowadays this is very easy with Quarto.

Oh boy, if the benchmarks are this good and Opus feels like it usually does then this is insane.

I’ve always found Opus significantly better than the benchmarks suggested.

LFG