Matticus_Rex

No, not until January

Check your idleCompaction line in /config to make sure it's off -- several times they've toggled new features on by default for me. This one isn't in the VS Code extension so I don't know if it shipped default-on.

Legislation you can generally access for free. Case law you often cannot.

There are benefits to specialization, and will be for a long time even with AI. It's worth a premium to not limit all my software to what I and my agents know, and to be able to rely on other people to fix it if there's a problem.

Investment size for software plays is going to drop, and people will still invest for the same reason they do now: solving problems for people is valuable. Marginal costs dropping will bring down a lot of costs in various places, but the software itself is only part of what SaaS provides.

There are thoughtful versions of these arguments, sure. There are also thoughtless versions, and it's not disingenuous to note it.

It's not actually clear that subscription users are unprofitable as a group (and the group level is the only important one).

We do know the companies are not currently profitable, because they're open and intentional about that, but that tells us nothing about their margins without infrastructure buildout and investment in training.

> I think everyone has known that inference is profitable overall for the major providers for a while

A lot of AI haters like Ed Zitron and their readers have refused to update on this. I don't know if any of them have yet, but it'll take a year or two for the idea to fully filter out of their podcasts anyway.

It's certainly an overstep, but just the hash they're sending wouldn't be PII.

They were coined in the extremely influential foundational research the "pop science book" was based on, but that doesn't make it a marketing ploy; System 1 is very fast non-verbal subconscious heuristic operation, and a lot of our brain's classification takes place at that stage. Anyone familiar with the usage immediately had clear ideas about how it'd ideally be used (e.g. heuristics that reduce search space, manage context elements dynamically, etc) vs not (e.g. as a 1:1 replacement for something that could already be easily solved with a classifier).

Sure, researchers get paid when they either do the research under contract or productize the research and sell it themselves. But they don't usually do the latter because that's quite difficult.

I've seen them talk about it a bit on Twitter -- it seems to be fairly well-calibrated in general, but obviously you need to test it on your use case and dial it in comparison with known data for best results.

Contrived false positives and negatives could (and should!) always be possible. That doesn't tell us anything about the natural false positive and negative rates, and a lot of people who have actively tried to use voice instruction to get models to consistently fool the detector without iterating against it directly have failed.

He's talking about the interconnection queue. You see the 100MW of solar they're wiring up, but not the hundreds of gigawatts in the queue.

That's what was thought, but maybe not -- only one of the three so far looks Epicurean, which is not what was expected. Maybe it's a fluke, but historians are buzzing a bit about whether it might be broader than expected.

I'm sure that level of overhead has nothing to do with the reason Belgian incomes, standards of living, and business outcomes are worse.

IIRC ERCOT estimated from their experience it was something like 85% on average?