The tech offices in Toronto and Vancouver have grown a lot over the years as the US makes it more difficult to bring foreign workers in (including Canadians). At least in Vancouver that has led to quite a tech boom, and it certainly isn’t just foreign workers getting jobs.

It’s kind of funny though because that is how it started. Offices that were just temporary stops for Indian and Chinese (mostly) devs while they waited for an H1B.

Same for me.

Nope, the answer is on the posted page.

My wife’s a dromomaniac.

I don’t know why people are downvoting you. I stopped using my friend’s server when I realized he was watching everything we were doing. Really weirded me out.

I usually “ETA” or “Edit” if it’s more than a typo or more than a couple minutes.

Earlier this week I used a reply to myself because I thought it would land better, but I think I confused the OP of the post.

This is a pretty neat way to examine how statistics work.

Damn it, now I have to create the Super Ignorance community.

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Yeah I don’t think those prices are reasonable either.

But… I don’t drink anymore, so what do I know.

Which would be sort of reasonable if it was a pitcher/jug. But we know it isn’t.

TLDW?

Who are you referring to? OP you responded to used “they”, and I think OP is referring to the operator of the AI Agents.

Their comment doesn’t imply anything non-anthropogenic.

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I’d say flying foxes (giant bats/megabats) that are all around the Indian Ocean fit the definition of flying during the day. You can see them fly over Sydney at dusk every day. Also, I think some aren’t even considered nocturnal.

Machine Learning (and even Large Language Models) do have genuine use cases they excel at and are probably more efficient at.

One good example is protein folding. ML transformed biomedical research by basically solving the protein folding problem in minutes on a GPU instead of essentially trying to brute force a solution that was millions to tens of millions times more complex. I’m glossing over details, but it’s mind blowing how much better (especially when you consider resource usage) how much better ML is in this case. Think $1 and minutes vs $100000+ and years… for a single protein.

The problem is using LLMs and Generative AI for things they seem good at but actually aren’t.

thirty-two people contribute €50 each.

Yeah, me and my 31 friends are going to buy our mutual friend a home battery. 😭

And still spend 5 to 10 times more, each, than we would have spent on the single drink we maybe would each have bought them.

A rare good move that sounds like a bad one (from what I’m seeing in the article)

They add by subtracting.

Lol, exactly.