> You can tell someone in native on LessWrong by their ability to use incredibly obtuse language to say simple things. It's just in-crowd signalling.
Do you have a sense of what shapes this? There are so many people in that orbit whose writing I find both exhausting and suspect. I work hard to write clearly, which forces me to think a bit more clearly. The in-group thing makes sense, but there's an element of obliqueness or roundaboutness that makes me think of how a squid deploys ink.
> Michael O’Kelly in the Google+ comments pointed out that the scheme was also in conflict with option-value/learning
I miss Google+ (and even more Google Buzz that came before it, and FriendFeed before that).
It's true that it was mostly a failure, but the fact that 2011 Google engineers were encouraged to participate in public on it meant there was a great seed for lots of really interesting technical discussions.
HN (and ML Twitter, and maybe a few sub-Reddits) are the closest substitutes now, but not really the same.
Oh yeah definitely agree. I think one of the older tests for LTX and WAN text-to-video models used to involve having a person do a cartwheel or really anything that required putting them in a non‑upright orientation.
> Ironically, it died around the time LLMs/AI started becoming good.
What?
I think maybe you are confusing Itanium with something else?
Development on itanium stopped in 2013:
> On 31 January 2013 Intel issued an update to their plans for Kittson: it would have the same LGA1248 socket and 32 nm process as Poulson, thus effectively halting any further development of Itanium processors.[1]
It's true that it shipped until 2021, but I think you had to already have previous orders to get that.
> In the interest of perspective, can anyone (perhaps playing devil's advocate) give one?
I have numerous cases where Sol failed and only Fable could solve a problem. For example yesterday I was merging a Q2 curved with a Bezier curved face in 3D using OpenSCAD. I tried for over 2 hours with Sol 5.6 high and x-high.
Fable two-shotted it in about 30 minutes.
In my experience open models (or GLM, DS and Kimi) are radically worse than either of Claude or ChatGPT at these tasks.
I think there is a huge "long tail" of tasks like this where the frontier labs are ahead, and I think this long tail is quite important.
I settled on Fable for design, Opus for build routine, and every now and then I'd try out the Chinese models. In my experience they do fine on small codebases, and quickly get confused on anything larger than 500k LOC.
My use case is: mature, very well documented, fully Ai written code, with about 1:10 ratio of prompts/specs to code, and codebase sizes >500k and <2m LOC. Once one achieves the state of good, comprehensive design documentation I can literally vibe code with single sentence prompts thanks to the extensive test coverage, ADRs, and tens of thousands of lines of specs. Fable/Opus works predictably well, the Chinese models are literally dangerous to the codebase.
Margins means exactly what the poster you are replying to implies: that in the US Tesla has high margins (the difference between cost of good sold and what you get for them) because tarrifs make Chinese brands uncompetitive.
reply