nfw2

I don't think pets.com, an online retailer, is an apt comparison to Anthropic, an bleeding edge research lab doing things that most people would consider science fiction a few years ago.

This is what the parent post said:

ship fast go-to market features in the blink of an eye when it becomes necessary

Necessary go-to market features are useful

How is shipping features fast not productivity?

That's still increased productivity even if the consumer benefits instead of the business owner.

If you're getting more done but unsure of the resulting value, that is indicative of your role not the tools you're using.

I am telling what's on Anthropic's official announcement. It's not a subjective assessment.

"Inching closer", yes. "Soon" seems presumptive. FSD everywhere seemed "soon" 20 years ago.

explains how fringe details don't make sense, like no one knowing what oracle does exactly

One can't be both earnest and cynical.

1. In Anthropic's benchmarks, Opus's advantage over Sonnet diminishes at high reasoning levels. Sonnet max actually beats Opus max.

2. There are rote or simple tasks where higher reasoning isn't needed and one model vs the other wouldn't even be distinguishable.

3. How many tokens are used during reasoning depends as much on the task as the reasoning level.

4. Sonnet is faster and half the price per token, so Opus will only be cheaper if it can spend fewer tokens. This won't be the case for all tasks. Opus and Sonnet will also consume the same number of tokens to load context, so the relative price comparison depends a lot on where the task is context-heavy or reasoning-heavy.

There is a chasm of performance difference between haiku and sonnet ime. Sonnet is cheaper and faster than Opus, so if you're providing a service via api where cost and speed is important, it's a big difference.

The claim that is being made in the article is that only cultural differences are relevant. Saying the relative effects of cultural and biological differences are hard to know would not be a very controversial statement.

This is the paragraph. "As an example" here implies that they are talking about sports in general, not just endurance events.

Inequity between male and female athletes is a result not of inherent biological differences between the sexes but of biases in how they are treated in sports. As an example, some endurance-running events allow the use of professional runners called pacesetters to help competitors perform their best...

The worst thing about this article is it seems to fail to understand the basic of hypothesis-testing, the bedrock of science.

Title: The Theory That Men Evolved to Hunt and Women Evolved to Gather Is Wrong

Subtitle: The influential idea that in the past men were hunters and women were not isn’t supported by the available evidence

Saying there isn't enough evidence to prove a hypothesis is not the same as proving its negation. Failing to reject the null hypothesis is not the same as proving it.

It seems to me that hype cycle of jev made it harder to understand rather than easier.

Universal classifier just seems nonsensical. It seems clear that it can't have accurate probability distributions on any possible question you might ask it. Moreover, giving only "maybe" answers to everything adds a layer of equivocation that makes it harder to catch it in a clear mistake.

That said, I've encountered plenty of fuzzy judgment questions now (ie is this case more like example X or example Y?) that reasoning models struggle with. I could see how, in lieu of a dedicated classifier, a model like jev, ostensibly trained to guesstimate the relative likelihood of natural language hypotheses, may do well.

But it still feels wrong to call it a classifier in the traditional sense, let alone a universal classifier.