My most played Steam game is Dishonored, at 127 hours. I have replayed it a lot. A rarity for me, but I really liked that game. Dishonored came out in 2012. It’s taken me 12 years to accumulate that many hours.
Balatro came out two months ago.
I have 93 hours in it.
I used to work summers as an apprentice electrician. The amount of crazy wiring I saw in old houses was (heh) shocking. Sometimes it was just that it was old. Real old houses sometimes just had bare wire wrapped in silk. … And a few decades later that silk was frayed and crumbling in the walls and needed replacing.
My current house was wired at a time when copper was more precious, so it was wired up and down through the house, with circuits arranged by proximity, not necessarily logic. When a certain circuit in my house blows the breaker, my TV, PC and one wall of the master bedroom all lose power. The TV and PC are not in the same room either.
Ho-ly shit… I had forgotten this particular bit.
But yeah, me too. Undead Mummy Ernie…
Just saying.
… Saying what, exactly?
I said that we should
And you argued… The same thing? Just in the reverse order?
I wanted to disagree with you, but checking the data almost all of the best action flicks I could have sworn were fairly recent actually came out in the early-mid noughts. Seems like after The Matrix blew up the genre, nobody ever figured out how to put it back together.
Even if I wanted to quibble and argue for the best my personal favorite action flicks within a precise “2 decade” window… it’s a depressingly short list:
2004
2006
2007
2009
2017
… Almost every single other action flick I thought of came out between 1998 and 2004. (Also, 2000 was a weirdly good year for action fans in retrospect)
Sigh. I’m gonna go bemoan the world getting lame and shake my cane at the kids out on my lawn.
Edit: JOHN WICK! How TF did I forget those? But yeah, I’m pretty sure that’s it now.
USA: Real barbeque. I don’t mean braised meat slathered in a sticky sauce, either. I mean tough cuts of meat, cooked slow and low over woodsmoke until it is fall-off-the-bone tender. No sauce required.
Much easier to find this in the southern US, with Texas, Missouri, and the Carolinas all being particularly famous BBQ regions. In the northern states, your best bet is gonna be to find someone local with a smoker - not just a grill.
Bro - no mention of Texas BBQ? Beef brisket with Texas-style BBQ beans (savory, not sweet for those who haven’t had them) is amazing.
Have you ever been in an old house? Not old, like, on the Historic Register, well-preserved, rich bastard “old house”. Just a house that has been around awhile. A place that has seen a lot of living.
You’ll find light switches that don’t connect to anything; artwork hiding holes in the walls; sometimes walls have been added or removed and the floors no longer match.
Any construction that gets used, must change as needs change. Be it a house or a city or a program, these evolutions of need inevitably introduce complexity and flaws that are large enough to annoy, but small enough to ignore. Over time those issues accumulate until they reach a crisis point. Houses get remodeled or torn down, cities build or remove highways, and programs get refactored or replaced.
You can and should design for change, within reason, because all successful programs will need to change in ways you cannot predict. But the fact that a system eventually becomes complex and flawed is not due to engineering failures - it is inherent in the nature of changing systems.
… And can you fix it?
That sounds amazing - OMW to check it out!
Oh, for sure. I focused on ML in college. My first job was actually coding self-driving vehicles for open-pit copper mining operations! (I taught gigantic earth tillers to execute 3-point turns.)
I’m not in that space anymore, but I do get how LLMs work. Philosophically, I’m inclined to believe that the statistical model encoded in an LLM does model a sort of intelligence. Certainly not consciousness - LLMs don’t have any mechanism I’d accept as agency or any sort of internal “mind” state. But I also think that the common description of “supercharged autocorrect” is overreductive. Useful as rhetorical counter to the hype cycle, but just as misleading in its own way.
I’ve been playing with chatbots of varying complexity since the 1990s. LLMs are frankly a quantum leap forward. Even GPT-2 was pretty much useless compared to modern models.
All that said… All these models are trained on the best - but mostly worst - data the world has to offer… And if you average a handful of textbooks with an internet-full of self-confident blowhards (like me) - it’s not too surprising that today’s LLMs are all… kinda mid compared to an actual human.
But if you compare the performance of an LLM to the state of the art in natural language comprehension and response… It’s not even close. Going from a suite of single-focus programs, each using keyword recognition and word stem-based parsing to guess what the user wants (Try asking Alexa to “Play ‘Records’ by Weezer” sometime - it can’t because of the keyword collision), to a single program that can respond intelligibly to pretty much any statement, with a limited - but nonzero - chance of getting things right…
This tech is raw and not really production ready, but I’m using a few LLMs in different contexts as assistants… And they work great.
Even though LLMs are not a good replacement for actual human skill - they’re fucking awesome. 😅
What I think is amazing about LLMs is that they are smart enough to be tricked. You can’t talk your way around a password prompt. You either know the password or you don’t.
But LLMs have enough of something intelligence-like that a moderately clever human can talk them into doing pretty much anything.
That’s a wild advancement in artificial intelligence. Something that a human can trick, with nothing more than natural language!
Now… Whether you ought to hand control of your platform over to a mathematical average of internet dialog… That’s another question.
Yup. And that’s a great example of not relying on Deus Ex Machina - we watch Ender go through all his brutal training, learning to be the best and becomes a truly terrifying weapon of war. By the time Ender is, well, ending things, we’ve seen his growth and understand why he can do the things he does.
In the early days of Superman comics, dude couldn’t, e.g. fly. He could just jump really high. He didn’t have laser vision. Over time, the writers kept adding new powers until the only story they could tell was about Supes vs his own conscience. Nothing else (okay, besides Mr Mxyzptlk) can actually stand in his way.
Man - that’s wild. Thank you for coming though with a citation - I appreciate it!
All things Deus Ex Machina. I get it, endings are hard. Climaxes are hard to write. But the payoff feels cheap as hell when your protagonist just “digs a little deeper” and suddenly finds just enough power to save the day. When it comes out of nowhere, it feels unearned by the hero and is not only unsatisfying, it’s also a good way to give you hero power creep until there’s nothing on earth that can believably challenge them. See: Superman.
While I agree with you on the whole, there are some real world places with names that go hard.
Like Dead Man’s Pass, Oregon. Or Devil’s Gate, Utah.
…Maybe it’s just a Western US thing.
a quick web search uses much less power/resources compared to AI inference
Do you have a source for that? Not that I’m doubting you, just curious. I read once that the internet infrastructure required to support a cellphone uses about the same amount of electricity as an average US home.
Thinking about it, I know that LeGoog has yuge data centers to support its search engine. A simple web search is going to hit their massive distributed DB to return answers in subsecond time. Whereas running an LLM (NOT training one, which is admittedly cuckoo bananas energy intensive) would be executed on a single GPU, albeit a hefty one.
So on one hand you’ll have a query hitting multiple (comparatively) lightweight machines to lookup results - and all the networking gear between. One the other, a beefy single-GPU machine.
(All of this is from the perspective of handling a single request, of course. I’m not suggesting that Wikipedia would run this service on only one machine.)
This looks less like the LLM is making a claim so much as using an LLM to generate a search query and then read through the results in order to find anything that might relate to the section being searched.
It leans into the things LLMs are pretty good at (summarizing natural language; constructing queries according to a given pattern; checking through text for content that matches semantically instead of literally) and links directly to a source instead of leaning on the thing that LLMs only pretend to be good at (synthesizing answers).
I have both. I do not think the OLED version is twice as nice, though it is noticeably improved.
If the cost is an issue, but doable, consider getting the LCD deck and putting the extra cash toward a TV dock and Bluetooth controller. The deck is awesome on the go (just took mine on vacation - 10/10) but it’s also a fantastic console in its own right. I play a lot of PC games on my couch, even though my I have a decent desktop PC available.
Either one you purchase though, the Steam deck is the best gaming device I’ve ever owned. Access to the vast Steam library (even if not all titles are compatible yet), access to install whatever else TF I want - even competing stores,
emulationnevermind.It’s just… 🤯