gmm1990

Yeah if you give the llm the exact same input over and over you really shouldn’t be using an llm.

It’s interesting the author ponders what they might be missing now. it seems like it’s the deterioration of the writing due to llms. I’ve liked some of the authors posts in the past. And might look in more detail at the differences between the old posts and new ones because it could be a great example for what to watch out for when using llms for writing and maybe coding.

There weren’t any concrete examples of bugs besides the Google Docs issue. Like what was so bad about blackboard (I also remember thinking it was terrible). There was a link to a great article about search but again no specific example here. There are a lot of words but not a ton of new information. Maybe I’m just bias against llms, this could have been written entirely without then for all I know.

I hope the test data is the same when comparing the different runs. So the big o notation should be the same across different runs.

It’s probably got phone home a few times to to make sure they’re measuring user engagement

This seems to be a very generic/common response to any ai critique. It kind of reinforces my point there’s a lot of situations where the appropriate harness isn’t some agent that’s set to ultra high thinking mode. Chat mode gives the better response and answers the question more quickly

I find that thinking/agent mode sometimes makes it worse/comes up with the same thing and just takes a long time. But I’m sure it’ll be different with fable for a few months until that hype blows over

I guess I’m coming at it from an optimizing market data provider perspective once you preallocate memory the next thing to optimize is the string decimal conversion if the feed isnt binary encoded

That’s quite a bit slower to process. At least if you’re converting to integers to do the calculations and the calculations would be quite a bit slower if you kept the big decimal type

There’s quite a few historical cases of mergers acquisitions being poorly valued aol time warner for example. So maybe v the calculus is different from the execs perspective but it’s not necessarily any better.

I wouldn’t call comparing yourself to Fabrice Ballard and not just saying he’s a better programmer modest.

I don’t know like chess engines didn’t kill chess. You could just play with people that don’t use the “engine”

Not if it drives up energy prices and makes other businesses that employ more people less competitive. Not saying that is the case but it’s certainly not a given

Some of the utilization comparisons are interesting, but the article says 2 trillion was spent on laying fiber but that seems suspicious.

The whole point is that the outages happened not that the ai code caused them. If ai is so useful/amazing then these outages should be less common not more. It’s obviously not rock solid evidence. Yeah ai could be useful and speed up or even improve a code base but there isn’t any evidence that that’s actually improving anything the only real studies point to imagined productivity improvements