dehrmann

Maybe, but that part's settled.

> the court ruled last year that training Llama on books was fair use

I was pretty vocal here about covering losses for depositors at Silicon Valley Bank because they were mostly small businesses who have more important things to do than manage the risk of their bank deposits.

I won't make a similar argument here because about an AI bailout because the counterparties know the risks, but there are pensions with requirements to hold high-grade debt buying these bonds. An AI bubble burst doesn't just show up in the Nasdaq, it hits pensions.

For this particular case, it's probably worth it because a dermatologist visit is $200. If I were the NHS or Kaiser, I'd partner with other integrated insurance and service providers to collect as much training data as possible and fund classifier development.

This is very wabi-sabi.

Not SaaS, but I had an idea for using a CPU emulator to run a compiled binary under Java, with libc implemented in Java so standard file IO works. I punted on the idea because it seemed pretty difficult and maybe take six months. I recently gave Claude a Java RISC V emulator and a partial bridge between Java and libc, and in under an hour, it could run bash and sqlite from Debian.

I have no idea how correct the code is or what I'd do with this, and I have no plans to publish it for those reasons.

For all the hand wringing we're doing over AI, I'm surprised that post-covid, regardless of origin, there wasn't a more serious discussion on what sorts of biological research are worth the risk.

It's possible to get billed for things not exposed in the UI. ~10 years ago, I had one of these. A Glacier upload chunk was stuck in a staging area for over a month. I couldn't complete or cancel the upload, or remove the chunk. Support was able to, and they refunded the money without much hassle. But I did have to reach out to support (as a <$100 per month customer) to ask. Glacier was new, so I chalk it up to it being a new product, not anything greedy or malicious.

You can get away with a lack of rigor in software engineering (from both humans and AI agaents) because it's often cheap to try it and see. Once you get to operating the platform, it changes, and operational decisions need to be made carefully and code needs to be deployed carefully.

The issues are they likely have seeds, might not travel or store as well, and are smaller than consumers are used to. There's also already a supply of table grapes you'd be competing with. To your experience, if they're ripe, they'll taste sweet and grapey. They're not that different.

I'm being very mindful of Gell-Mann Amnesia and chatbots. They speak authoritatively and are right often enough, but I've had enough cases of them saying very incorrect things in areas I know well that I have to remind myself that those cases aren't unique.

This is why you read the code, not the documentation.

Not sure if it was intentional, but saying "uses AI" in the headline makes it sound like this is a modern thing, but the techniques here are closer to the linear regression side of AI than the trasnformer side. The headline could have been "DraftKings Targets Problem Gamblers." It would have put the outrage where it should be.

Most places at this scale have code freezes in place well before conferences. The most likely issues are some launch couldn't handle the scale or periodic deployments have been saving them from some sort of long-standing leak bug, and pausing going into Dreamforce meant some service hasn't been restarted in a week. Historically, Salesforce sharded by customer, so that goes against both of these, unless it's in a routing layer.

I think you're thinking of something else, but they also added "capabilities" to Hack (their main frontend language; it's a PHP fork) to make leaking data between requests or in logs painful.

When I interviewed at Facebook in 2019, one interviewer was on an integrity team. I'm not sure if it was his take or what the org settled on, but it's that Cambridge Analytica wasn't Facebook's fault (users willingly granted access), but it was their problem. Not surprising some of the same techniques got used by others.