wslh

This is drifting from the point. The thread was about the share of territory with optimal farming climate, not per-capita anything, and not whether resource-poor countries can be advanced.

The thread was about climate. US is 3.5x greater than Argentina.

Argentina seems like a counterexample here: the Pampas are one of the world's major temperate agricultural regions.

You can write whatever contract you like, the problem is how to enforce it, and a greater problem is enforcing it around the world.

Beyond the shape they produce, I assume that drones for entertainment are not the same as military ones.

I would say it's common to find dark patterns that involves ambiguity like the discussion we are having here. We can't know for sure but Google can increase the probability of being on their ecosystem.

This seems useful as a standard interface, but, again, doesn’t solve the harder discovery problem. Once there is value in appearing in the top results, the system inherits the same adversarial dynamics as SEO/app stores, spam, scams, etc.

Speaking of StumbleUpon, I'm not sure whether this was just luck or something about its recommendation algorithm/social graph, but it was the only service where I didn't see the usual flood of traffic followed by rapid decay, the classic Slashdot/HN effect. The curve felt much smoother.

I remember some bloggers at the time describing the same thing [1].

I'd be curious if anyone knows more details.

[1] https://mark.blog/2007/10/23/the-stumbleupon-effect/

All these kind of interventions create a lot of incentives for other companies around the world.

He wrote the story in the Notes app on his iPad. Also here [1].

[1] https://www.gamesradar.com/entertainment/sci-fi-movies/steve...

I am playing with it and keeps switching to Opus [1]. The chat is a basic security review of a business project.

[1] "This model has specific safety measures that flagged something in this message. This sometimes happens with safe, normal conversations. Send feedback or learn more."

It's ambiguous? Because is about Mythos specifically and Fable != Mythos.

The simple explanation is that predicting the future is generally impossible. It doesn't matter if it's Olsen or anybody else.

Sadly, I've experienced this myself, and I'm aware of other people who were cut off from opportunities in similar ways. I applaud the tweet because this is important to say out loud: we are often not living in the world we think we are observing. Many people know this, but few talk about it. It's an open secret.

Looking forward to trying this tomorrow (it's late here). Has anyone run it on a real codebase yet? Curious about setup friction, cost, and signal/noise.

It's incomplete without the rest of the sentence: "a little over a year after the death of Mattias Ripa, her husband and the love of her life"

Grief is not just metaphorical, severe bereavement can affect health in very real ways.

> imagine a future where even talented mathematicians are nothing but noise

That would be AGI. My conjecture is that LLMs alone are not enough for that future. They are incredible, but AGI needs other breakthroughs.

In that sense, I think math is very different from chess or Go. Chess and Go are complete-information games with fixed rules and a fixed board. Math is open-ended.

It's always interesting that they use ARM chips to emulate the original firmware.

And put Chris Olah, Anthropic co-founder, sitting next to Pope Leo XIV presenting his first encyclical, Magnifica Humanitas, at the Vatican.

That said, Apple still deserves a lot of credit. They had a 5+ year edge, especially around the vision of tightly integrating the NPU and unified memory.

This business and financial race is probably the craziest in human history, so zig-zags are expected. One company may take advantage on one curve while another is stuck in the pits.

The article misses an important clarification for a general audience: current LLM architecture is not AGI by most scientists working on intelligence and cognition, even if its impact is already extraordinary and in many tasks exceeds human performance. AGI implies a broader set of traits.

Again, the XBOW article is pretty relevant: https://xbow.com/blog/mythos-offensive-security-xbow-evaluat...

I'm not really asking this from the perspective of whether I should buy hardware. I'm trying to understand the economics.

The AI space is moving so fast that it is hard to know which conclusions are stable. After all the discussion around local models, is the practical conclusion still that API/frontier providers have a huge structural advantage because of datacenter hardware, high utilization, batching, optimized inference stacks, and perhaps strategic pricing?

In a comparison like this, a $25k local setup versus buying tokens, what multiple are we really talking about? 10x? 100x? Or is it too workload-dependent to reduce to a single number?

Has someone written a good breakdown that separates true infrastructure efficiency from temporary underpricing/subsidy? The part I'm trying to understand is less ideological (local vs. cloud) and more basic economics.