bjterry

The Meta glasses camera has a very wide field of view. You'd need to be quite close to a person to realistically take "pervy" photos, and it'd be really obvious. Seems like it'd be no better at this than a cell phone.

For all the people represented in the training data to receive royalties would be an incredible wealth transfer to the Extremely Online. My forum posts, StackOverflow answers etc are also contributing to the model outputs. The training data, by volume, mostly belongs to blog authors, redditors, Wikipedia editors, to us!

Having a relatively new LinkedIn account now is probably a very bad move if you don't have an established network to reach out to for jobs. There are tons of AI generated profiles flooding every job post (particularly remote) from scammers who create new LinkedIn profiles. It's one of the most frequent signs of a fake submission.

Controlled burns aren't impossible in chaparral, even based on the logic of that article the controlled burns just need to be less frequent and more intense than for forest. There's no reason they couldn't be done.

A founding CTO is more effective than a hired CTO, because the founding CTO has more moral authority to create a consistent system. In other companies there's infighting between people (senior engineers, senior managers) with different architectural preferences (e.g. microservices vs monoliths, Java vs Python). These senior people get half what they want, meaning half your system works one way and half the other. A CTO can hold to their singular vision.

It could be that the moral authority stems from having as much of a full picture as a single person can have over the entire lifecycle of the company, but I think a lot is also just the effect of "I got you here."

I'm glad pg named this effect, since I've talked about the related phenomenon for CTOs with many people.

I mean, I know nothing about your network. If your network access servers are within a datacenter under your exclusive physical control, perhaps it's not an issue since it requires a man-in-the-middle position. Something like a neighborhood cabinet DSLAM could be open to abuse?

> Those customers are already untrusted, so it really does not matter.

Perhaps it doesn't matter to the health of your network, but if it leads to a customer's account being disabled due to incorrectly assigned abuse, surely it would matter to them.

Thanks!

It would be interesting to allow users of models to customize inference by tweaking these features, sort of like a semantic equalizer for LLMs. My guess is that this wouldn't work as well as fine-tuning, since that would tweak all the features at once toward your use case, but the equalizer would require zero training data.

The prompt itself can trigger the features, so if you say "Try to weave in mentions of San Francisco" the San Francisco feature will be more activated in the response. But having a global equalizer could reduce drift as the conversation continued, perhaps?

You can use the Claude APIs via OpenRouter with a pre-paid account.