Driver facing camera systems can be consistent with privacy, as long as they don’t record or transmit any data other than a single dimensional metric of how distracted or drowsy a driver is (or even discrete binary state of yes/no) and timestamps when that state was detected.

A closed loop system that merely keeps that data for the current drive and maintains it solely in the vehicle’s own systems can be consistent with privacy principles that nobody else should know anything about how a car is being used, except what can be observed from the outside.

But you’re seeing a screenshot of an unmatched order that no driver has claimed yet. I’m saying that unless an actual match is accepted, that’s not really evidence that people in a place don’t tip well, just that some people don’t get their orders filled.

If you never give less than $5, then any order you’re involved in will involve at least a $5 tip. That may not be representative of the orders you’re not involved with.

I think the user decides how much to tip in advance, and the app conveys that information to potential matches. Orders with low tips tend to sit there unclaimed, because no driver wants to bother with that

I’m not sure if Uber does it that way, but Doordash does.

I remember reading about a case a few years ago where a warehouse couldn’t figure out which of its workers was just periodically taking shits in random corners of the warehouse. I think I’m starting to understand a different angle to that story, though.

It’s got everything. Em dash. It’s not X, it’s Y. Emoji bullet points.

Perfect.

AI companies are bankrupting themselves with training costs that they need to recoup back by selling inference.

I think they hit a wall in actual returns on performance with pretraining, years ago. Then they started scaling up on post-training/reinforcement learning to continue improvement, but that might be hitting a plateau as well. More recently it looks like they’re relying more heavily on scaling up on inference, which is a significant problem for their long term business models.

If they’re not able to cheaply deliver inference (and charge at a premium), how will they be able to sustain their businesses?

It seems that the most recent, largest models are using a lot more tokens to accomplish the same tasks, so even as token cost drops the actual cost of using the latest models seems to be going up with time (even as performance improves).

It’s gonna be so fucking funny when the push to sell silicon that can run local models at 100 watts or less ends up destroying the business models of the companies that built out 100,000,000,000 watts of data centers.

Yeah, but that’s always been true of paid software licenses for a particular version: it reaches EOL and you have to decide whether to live with the possibility of unpatched known vulnerabilities or pay for an upgrade to a more recent release.

MS Office has been doing this from back in the Windows 3.0 days at least.

The only solution is to make sure they can’t read data you don’t want shared.

Isn’t that the appropriate guardrail, then? LLM chats and agents and whatever need to be contained with external permissions settings that the LLMs simply do not and can never have the power to override.

In a normal customer service setting with human agents, there are still plenty of examples of what a human agent simply doesn’t have the power to do. Often, they’ll need to escalate to a manager to do things like process refunds not just because they weren’t given social permission to do so, but because they weren’t given technical permissions to do so. LLM agents need to be contained in the same way. Any decent use of agents, human or software, requires carefully designed processes and permissions extrinsic to that agent’s own decisionmaking abilities to make sure that agents don’t do something bad for the company.

Edit 2: Nevermind. 13th October is the day Microslop has chosen to fuck me: https://support.microsoft.com/en-us/office/system-requirements/end-of-support-for-office-2021

You’ll still get a few years before the software becomes remotely disabled, though. This story about Office 2019 losing functionality follows Office 2019 losing support in 2023. If that’s the rate things go, then maybe Office 2021 will lose functionality either 2 years from now (7 years after release) or 3 years from now (3 years after losing support).

Passports really should be using four digit years.

What if you make it speak about goblins? That’s been tripping up Codex recently.

It’s a Nielsen box that can detect your streaming app activity, too.

And, as I understand it, Anthropic hasn’t committed as much spending to building out new data centers, and has setup their operations to be GPU agnostic, so they can keep flexibility between NVIDIA GPUs, Google TPUs, and Amazon Trainium, and play the data center pricing game. Anthropic is better positioned to survive an AI winter (and I believe it’s coming soon).

Not with AI in particular, but yes with subscription based software generally.

The economics of it don’t add up and the growth rate of the curve of improvement over time has already significativelly fallen which looking at the historical curves for other technologies is a very strong indication that it’s approaching the limits of how far it will go even though it’s nowhere close to the hype.

Yeah, I’m convinced that they’ve maintained the illusion of continued exponential improvement from 2024-2026 by sneaking in exponential increase in resources (hardware complexity, power consumption), to prop things up past what should have been a plateau.

AI has an interesting economic trait in that it’s very, very expensive to deploy, and made very fast progress from 2022 to 2024. That caused investors with money to believe that:

• Pushing the frontier was going to cost a lot of money. More than any other purported revolutionary tech.
• Extrapolation of past improvement meant that whoever was on the cutting edge may end up with a product with a huge paying market.
• So whoever wins this race would be rich, and the investment would have been worth it for them.

But since 2024, we’ve seen that the cutting edge got even more expensive much faster than expected, and much of the improvements in performance now come from inference rather than training, which represents a high ongoing cost.

Now, if we extrapolate from that trend line, we’ll see that the market will be much smaller for AI services at the cost it takes to provide that service, and the question then becomes whether the industry can make its operations cheaper, fast enough to profitably provide a service people will pay for.

I have my doubts they’ll succeed, and we might just be looking at the industry like supersonic flight: conceptually interesting, technically feasible, but just a commercial dead end because it’s too expensive.

Don’t they put plutonium reactors in space?

The ones that power spacecraft generate less than 5000W of heat at max power (while producing 300W of usable electricity).

In order to power a single server rack of 72 Blackwell GPUs, which takes about 130,000 watts, you’d need about 430 of those RTGs, and need to manage cooling requirements of 430 times as much (plus however much additional power will be required by the cooling system itself, too).

Companies are building entire workflows around AI, but they are building them under the assumption that they won’t ever be charged per token.

Or worse, where the AI models underpinning a workflow breaks or degrades in some way to reduce token usage and then starts behaving in unexpected ways, in a process/workflow that assumes a particular type of behavior.

Counterpoint: sometimes the best still shot requires a particular moment captured with a particular, consciously arranged setup.

This interview of a veteran NBA photographer breaks it down of how he only has a single shot per shot because of how he necessarily relies on strobes set up to not distract the players or interfere with the broadcast. As a result, he scouts/studies each player and team so that he knows when the right moment is to actually capture the shot, because he can’t exactly ask players to do it again.

If you read interviews of Pulitzer photography winners, they’ll often say a lot of the same things: being prepared and being lucky and having that convergence of having incredibly high skill/expertise/understanding of the setting, while being able to capture in every opportunity presented.

You should capture a lot of photos and examine them to understand how to make them better, and increase your skill level and understand your subject so that you can still optimize for the very best shot possible.