If it was still possible to get verbatim results from Google then I believe "mwah mwah vision" would have been an authentic Googlewhack pointing at this comment thread.
It has the Kepler-Poinsot polyhedra and allows you to apply a superset of Conway Operators to them. No true stellation currently (stellation isn't an edge-replacement operator so can't be mimicked by Conway-esque operations)
When I first learned about computer science at the age of 11 or so (and in 1982 or so) the first page of the text book put digital and analogue computers on what seemed to be an equal footing. And then proceeded to ignore the latter for the rest of the book. Apart from a few notable exceptions ( https://en.wikipedia.org/wiki/Phillips_Machine ) I've often wondered about analogue computing.
The Peter Thiel connection is especially toxic for a lot of people outside the US. Whether it's substantive or just optics doesn't make a huge difference.
I'm a bit late in replying - but I've been a user of Google Docs since launch by choice and still use it by choice. I've never really worked "corporate" but the few times I've had contact with that world they were entirely Microsoft.
> their mistakes aren't based on their understanding, it's basically random guesses
Whilst I don't claim any true "understanding" as that is a very loaded term that doesn't mean it's just random guesses.
Anyone using recent LLM coding agents on a regular basis would probably agree that there's something going on that fits some non-athropomorphizing, non-sentience-assigning definition of "understanding"
As for the point about improvement - I think that's an orthogonal issue to the overall code quality. With regard to human codebases - there's plenty of scenarios that negate the improvement of individuals. We're comparing organizations with LLMs - not individuals with LLMs and that makes a significant difference.
I can't help but feel that people continually underestimate how bad human written code becomes over time. The exception is probably single-person passion projects or open source projects that maintain quality governance over time.
I strongly suspect most closed source code developed under commercial or internal pressure is pretty awful after a few years of development.
All LLM code has to do is suck less than existing code. And that's presuming the code quality doesn't improve as the models, the harnesses and our ways of working with them improve.
That's hyperbole. They have flaws, but at the very least, when they were launched, they were arguably best in class. I'm not sure how much me sticking with them is due to familiarity and muscle memory but I know they won we over purely on merit in the beginning.
Aw jeez. Just take it in the spirit it was intended and stop trying to score "more spiritual than thou" points. We are all deep one day and shallow the next. We are all workaholics and also smelling the roses. You know nothing about the author - you're hijacking a thread to grandstand.
People are rendering huge splat scenes on mobile devices using LOD. This (currently) requires CUDA and an NVidia GPU to work. I would have been much more impressed to see a demo where it was running on low end mobile hardware faster than current splat renderers can.
I'm probably being a bit of a grinch about it but the abstract doesn't address performance or hardware constraints either so I guess I'm going to have to read the damn paper.
In what world are unions never criticised? I'm in the UK and they are often reviled in the press and among people who don't work in a unionised sector. America has an even stronger tradition of anti-union feeling (maybe partly due to historic links between unions and organised crime but also because the US has often had a stronger collectivisation than most European countries - consider that the political centre in the US would be considered into right wing in most Western countries on most issues)
I agree that heavy users are probably not profitable but that's the way the economics of subscription services tends to work across the board.
I'm arguing that even if inference isn't profitable right now it's not orders of magnitude off. Whatever pricing emerges for models equivalent to current frontier models won't be significantly higher than the current API pricing.
There are already enough small companies without tons of VC money to burn that are serving up nearly-frontier llms at prices lower than the big players are charging. They can't all be subsidising? These are companies without any moat or any IP.
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