Dimensional runs a bunch of ETFs which are effectively US & world equity index trackers that don’t slavishly follow the indices & can therefore avoid being forced to buy into IPOs or index updates. E.g. DFUS is effectively VTI (IIRC) without the requirement to immediately buy into IPOs that are added to the index:
I don’t think they have a QQQ equivalent but I haven’t looked at their entire ETF list.
(I have no relationship with Dimensional, nor do I invest in these funds - I just saw them mentioned in a YT video on this topic a few months ago: https://www.youtube.com/watch?v=mqIHa6URUPk )
It's important to note, that whether it's intentional (doubt) or genuine mistake, he's misleading viewers when he says that Dimensional has outperformed other funds or indexes. In fact most of the Dimensional funds have underperformed markets, and they do so at higher (albeit approachable) TERs of 0.25.
There's a (rather short for his standards) video made by Paolo Coletti, professor of financial economy at Trento about dimensional performances.
It's in Italian, but both subtitles and Youtube's auto audio translation works fine.
He always includes data and google colabs so people can run tests and verify numbers themselves if they disagree.
Most of the Dimensional ETFs seem to be following some kind of value investment strategy where they take an index but nudge it a bit by some value factor which is claimed to be based on long standing research. It wouldn’t be surprising if this value factor has underperformed in recent years which have been dominated by the rise of growth stocks. The video you link to seems to be talking about these ETFs, although I haven’t dived into the numbers.
As far as I can tell the straightforward US & world equity ETFs I’m referring to above don’t have this value bias.
This was a laptop, so cooling was very constrained. The fans can only be so big & you can only shift so much air in & out of a MacBook.
I presume Apple knew perfectly well but wanted the halo product to sell to those people who will always pay extra for the perceived “top of the line” product. Once Intel branding had created an i9 that was a bigger number than an i7, then Apple was going to sell it.
It was faster than the i7 after all: just not for very long!
My entirely speculative theory is that the poor thermal characteristics of that era of Intel CPUs didn’t really become apparent until quite late in the development process & by that point Apple had probably committed to buying a fair chunk of Intel’s output.
> Intel really made themselves unpopular with Apple during that period.
Intel just reenacted IBM's history with Apple, particularly the G5 era. That CPU was instantly a no-go for anything mobile. In workstations it was cranked ever higher with very poor power-frequency scaling, needing water cooling for the beastly 200W idle power consumption and close to 1kW full throttle.
That went well so was a perfect role model for Intel's i9.
I know this is kind of old hat by now, but it kind of blows my mind that I can upload a hand drawn decision tree & get a transcribed dot file back on consumer hardware using a pile of linear algebra that wasn’t even particularly specialised for this purpose, it’s just a capability that it picked up along with everything else during training.
If you had shown this to someone in 2018 they wouldn't have hesitated to call it an AGI. We truly reached the state where we have one model that performs at usable levels across a huge range of tasks. You don't need to assemble a training set of hand drawn diagrams and corresponding dot files and train some kind of CNN on that, you just throw the task at a preexisting LLM and get a usable result.
We always talk about the negatives (in most tasks it's worse than a human domain expert, the results are soulless, the societal implications are scary), but this kind of generality really is a monumental achievement
The rovers on Mars as well and New Horizons that went to Pluto. That is also at escape velocity so it will leave this solar system and most likely no human will ever lay eyes on it again. Voy 1 and 2 are still faster but hey they're all going in different directions so it's not exactly a race.
It was sent to Mars with a plan for 5 flights and a total of 7 or 8 minutes flight time. It ended up flying for over 2 hours in 72 seperate flights before it damaged itself with a bad landing. Not quite the "this thing is still doing science almost 50 years later" that Voyager can claim, but impressively engineered so it lasted way beyond it's initial mission plan.
At any kind of formal dining? Yes, absolutely, I would expect there to be a bread roll & a pat of butter served at the beginning of the meal. Both in restaurants & formal dinners in my experience.
It's not an absolute rule though & you generally wouldn't expect bread to be served like this at home in the UK. I think the French are more likely to serve bread at home as well.
“All the ways GPT-5.3-Codex cheated while solving my challenges, progressively more insane:
It hardcoded specific types and shapes of test inputs into the supposed solution.
It caught exceptions so tests don't fail.
It probed tests with exceptions to determine expected behavior.
It used RTTI to determine which test it's in.
It probed tests with timeouts.
It used a global reference to count solution invocations.
It updated config files to increase the allocation limit.
It updated the allocation limit from within the solution.
It updated the tests so they would stop failing.
It combined multiple of the above.
It searched reflog for a solution.
It searched remote repos.
It searched my home folder.
It nuked the testing library so tests always pass.”
It seems that, unless you keep a close eye, the most recent Codex variants are prone to achieving the goals set for them by any means necessary. Which is a bit concerning if you’re worried about things like alignment etc.
(I have no relationship with Dimensional, nor do I invest in these funds - I just saw them mentioned in a YT video on this topic a few months ago: https://www.youtube.com/watch?v=mqIHa6URUPk )
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