Readit NewsAlso the paper has some pie chart crimes on page 6.
Yeah, the signals they get will improve things over time. You can do a lot of heavy lifting with embedding models nowadays, get "satisfaction" signals from chats, and adjust your router based on those. It will be weird at first, some people will complain, but at the end of the day, you don't need imo-gold levels of thinking to write a fitness plan that most likely the user won't even follow :)
Signal gathering is likely the driver of most of the subsidised model offerings we see today.
These juniors you're complaining about are going to get better in making these requests of AI and blow right past all the seniors who yell at clouds running AI.
I've been coding for 25 years and what I feel reading posts & comments like in this thread is what I felt in the first few days of that black-blue/white-gold dress thing. I legitimately felt like half the people were trolling.
It's the same with LLM assisted coding. I can't possibly be getting such good results when all the rest are getting garbage, right? Impostor syndrome? Are they trolling?
But yeah, I agree fully with you. You need to actively try everything yourself, and this is what I recommend to my colleagues and friends. Try it out. See what works and what doesn't. Focus on what works, and put it in markdown files. Avoid what doesn't work today, but be ready because tomorrow it might work. Use flows. Use plan / act accordingly. Use the correct tools (context7 is a big one). Use search before planning. Search, write it to md files, add it in the repo. READ the plans carefully. Edit before you start a task. Edit, edit edit. Use git trees, use tools that you'd be using anyway in your pipelines. Pay attention to the output. Don't argue, go back to step1, plan better. See what works for context, what doesn't work. Add things, remove things. Have examples ready. Use examples properly. There's sooo much to learn here.
Ah, there's a joke there, if only they used 10 packets :D
> I observed that existing cross-border Internet connections were not affected, while both new IPv4 and IPv6 connections were reset.
Interesting, that would suggest it wasn't an intentional kill-switch (as rumoured initially, testing the capability), but rather likely a misbehaving device / service.
There's also a comment about Pakistan having issues in the same window (tho larger outage for them) so it might be an update / config for some of the same equipment family?
Funny enough, that's kinda what we're seeing with LLMs. We're past the "regurgitate the training set" now, and we're more interested in mixing and matching stuff in the context window so we get to a desired goal (i.e. tool use, search, "thinking" and so on). How about that...
"I saw 2 persons being judged by a judge, and turned out they were both guilty of the same crime, but the first one got less than the second one. The first one had the same letter in second position in their family name as the judge, so it's the proof that judges are biased favorably towards people who have the same second letter"
But then, the problem is that "their own bullshit papers" is doing a very heavy lifting here. The point of Hossenfelder is that String Theory is as bad as GU. But is it really the case? Hossenfelder keep saying it's true, but a lot of people are not convinced by her arguments and provide convincing reasons for not being convinced. The same kinds of reasons don't apply to GU, so it already shows that GU and String Theory are not on the same level. Even if String Theory has some flow or is misguided on some aspect, does it mean that the level of rejection in an unbiased world will obviously be the same as any other bullshit theory.
Another aspect that is unfair is that a lot of "bullshit theory within the sector" dies without any publicity. They stop rapidly because from within the sector, it is more difficult to surface them without being criticized early. For example, you can have 100 bullshit theories "within the sector" and 3 survive and surface without being as criticized as GU while 97 have been criticized "as much" as GU during their beginning which stopped them growing. Then, you can just point at one of the 3 and say "look, there is one bullshit theory there, it's the proof that scientists never confront bullshit theories when it comes from within". Without being able to quantify properly how the GU-like theories are treated when they are "within", it is just impossible to conclude "when it is from within, it is less criticized".
But that's the thing, she is essentially equating Weinstein's theory to all other theoretical physics. This is the typical dogwhistling she does, "everything else is bullshit so you might as well believe this ...". She does this sort of ambiguity all the time, and to argue that she is not trying to imply anything is just dishonest.
Now, as to the statement that all theoretical physics papers are bullshit, that's frankly bullshit. And how is she qualified to judge? Maybe in a small niche that is her area of expertise, but beyond that?!
That's the thing that the blog argues, but not the thing I (a complete outsider in this whole thing) got from her video. Her argument was more about how "the establishment" treats this paper vs their own bullshit papers. The way I saw the video it was more of a comment on academia's own problems than weinstein's "theory" (which, earlier she said it's likely bullshit). She's calling out the double standard. I think.
> Now, as to the statement that all theoretical physics papers are bullshit, that's frankly bullshit.
I don't think that's correct. She never said (or I never saw the videos where she did) that all new theoretical physics is bullshit. She has some valid (again, from an outsider perspective) points tho:
- just because you invent some fancy math doesn't mean it works in the physical world
- just because it's complicated doesn't mean it's novel
- not falsifiable is bad science
- not making predictions is bad science
- hiding predictions behind "the next big detector" is lazy
(that's basically what here points are, from the videos I've seen).
My weak, uncited, understanding from then they're poorly positioned, i.e in our set they're still the guys who write you a big check for software, but in the VC set they're a joke: i.e. they misunderstood carpet bombing investment as something that scales, and went all in on way too many crypto firm. Now, they have embarrassed themselves with a ton of assets that need to get marked down, it's clearly behind the other bigs, but there's no forcing function to do markdowns.
So we get primal screams about politics and LLM-generated articles about how a $9K video card is the perfect blend between price and performance.
There's other comments effusively praising them on their unique technical expertise. I maintain a llama.cpp client on every platform you can think of. Nothing in this article makes any sense. If you're training, you wouldn't do it on only 4 $9K GPUs that you own. If you're inferencing, you're not getting much more out of this than you would a ~$2K Framework desktop.
I was with you up till here. Come on! CPU inferencing is not it, even macs struggle with bigger models, longer contexts (esp. visible when agentic stuff gets > 32k tokens).
The PRO6000 is the first gpu that actually makes sense to own from their "workstation" series.