I hate to say it but I think I have to now: I don’t like Strange New Worlds. I loved lower decks and SNW started out with a ton of promise (and some eps I do really genuinely enjoy) but with so few episodes and of those so few feeling like Star Trek exploration, I just think its not for me. A shame cuz I love the cast and the design.
Super happy others still enjoy it though.
This to me seems to be a very odd interpretation of the phrase “coding was never the hard part”. First, that doesn’t mean that it’s not hard, just not the hardest. Second, and more importantly, they jump to deciding what to build being the hard part as if those are the only two things?
I have used a variation of this phrase (I usually say coding was never the bottleneck at work) but I’ve never even considered deciding what to build as to be what the bottleneck is.
First, I should say by coding I mean taking a concrete design for a change and writing a single pass at implementation. Modern models to this extremely quickly with up to good performance depending on exactly what you ask them to do.
The things that take up more time in software development just for engineers, leaving aside things that design and pms do.
There are “what to build” aspects in here and many engineers also spend time on the overall what to build questions, but the way the article was talking about it seemed very high level.
Coding isn’t easy, it’s hard. But it’s also not usually the bottleneck in software development.
I tried switching to ddg several times over the last decade or so and always gave up because of issues like this, especially with technical or obscure topics. Recently started paying for Kagi and do not have the same issues at all there. The results are good and nicely presented without ads. Of course, on the flip side it’s pretty expensive.
To be more specific (for anyone interested), the next word predictors are usually a type of model called an LSTM (at least I think that’s the most common). This model type has been used for a long time for dealing with sequential data. In 2014 there was a famous paper introducing an attention mechanism. This was a rather brilliant, though relatively minor extension to how LSTMs work. Essentially between each step of an LSTM it generates some data representing the model’s knowledge of the sequence to that point. The attention mechanism looks back at these intermediate values and determines how relevant each state is to the current point in the sequence and pulls in the most relevant bits. This vastly improved the memory of the LSTM over longer sequences.
In 2017 there was another famous paper “attention is all you need” which said something to the effect of “the attention mechanism is doing all the work, we don’t need the rest of the LSTM we can replace it by running attention between all point combinations in the sequence.” It’s actually significantly slower to run as the model grows, but much much faster to train because it’s not intrinsically sequential. This is the transformer model that’s the basis of all our LLMs.
Obviously some massive simplifications here but as despite being fairly anti AI, I do love the engineering behind it. So yeah, pretty literally a fancy text predictor, but it turns out when you throw all the compute you can muster at a fancy word predictor is makes the world go crazy
If nunneries are as gay as I always imagined in my head, I’m in.
Exactly what I don’t want from a car. I want fewer features implemented better, higher build quality, and zero connectivity.
Yeah, the definition was terrible. From what we know, it seems he actually would have perjured himself had he said he did have sexual relations with her. I don’t see how you could interpret that definition any other way. Still a fucking creep, though
If this and everything else happening right now is the eventuality, then Humans aren’t mature enough to have an internet. It was a mistake; can we take it back??? Maybe we’re mature enough to have standalone computers… maybe.
I’ve used it extensively almost all my life. I love it. But I have largely stopped since the advent of LLMs. I’ve also changed my uses of bullets. I will now rarely do a flat bulleted list. I will either write sentences or do a nested list.
I don’t think those things are bad, but I see them so much in llm writing that they have, for now, become ugly to me.
Those are the best, though I wish they’d improve the controls.
Yes, our usage is tracked. Managers have statistics broken down by daily usage per agent
The only time it ever says “the real issue is” and is right is when I’ve just corrected it and told it the real issue which it then wastes tokens regurgitating back to me. Gods I miss not having to use this crap at work
If AI were causing real automation improvements and productivity gains, shouldn’t they be extending support, if anything?
“New model is so much better than old model when given test that we never gave to the old model.“
Wut
S2000 was last made in 2009. Why would anyone buy a Honda made after that? Another Honda at all? Not that they’re necessarily bad, but the s2000 was just. So. Damn. Good.
think of Yoda in Star Wars or some decades later ALF on TV
Not a big deal, but in curious when you think both of those were made, Alf was 9 years after a new hope, and 3 after return of the Jedi.
You’re right about having to check everything written, the problem is that the unpaid intern loses you time while you have to check all the work, and then eventually learns enough and gains trust where you can be more hands-off. The AI doesn’t.
Still potential for time savings in non-critical areas where you can actually afford to be less careful, but that appears to be it right now.
I didn’t think it would happen, but I really wanted it to. Michael Dorn was so passionate about it, and i love the character
This AI work is normally not classified as “GenAI” which is what I was responding to, and the parent was specifically talking about LLMs. Usually GenAI specifically refers to LLMs as well as image/video creation.
The big data centers aren’t for the most part being built for pharma research.