TehPers

In some cases, it appears to be the opposite: CEOs want to do mass layoffs, so they blame AI rather than taking accountability themselves. The Amazon layoffs reek of this.

had people understood from the start the limitations of it, investment would’ve been more modest and cautious

People did understand from the start. Those who do the investing just didn’t listen, or they had a different motive. These days it’s impossible to tell which.

And by “people” I’m not referring to random people, but those who have been closer than most to the development of these models. There has been an unbelievable amount of research done on everything from the effectiveness of specific models in niche fields to the ability to use an LLM as the backend for a production service. Again, no amount of negative feedback going up the chain has made a difference in the direction, so that only leaves a few explanations on why the investment continues to be so high.

Could also do this:

#[expect(lint, reason = "TODO: #issue")]

Edit: to clarify, #issue is an issue number that points to a related issue or task. Could also just explain it inline, but if you have a task tracker, better to make a task instead.

More complicated Tor, but super cool. It uses garlic routing rather than onion routing to further anonymize packets.

It’s worth reading into what it is (and especially those two terms) to get a better understanding.

Which outputs are accurate, and which ones are inaccurate? How could you tell? What steps did you take to verify accuracy? Was verifying it a manual process?

Shh you’ll pop the bubble if you start talking sensibly. It’s not an ASIC—it’s a specialized piece of hardware optimized to execute a model with unparalleled performance. Now buy my entire stock of them and all the supply for the next two years please.

(Figuring out the compose combination for an emdash took longer than I’d like to admit lol)