“Easy installation” with two screws seems like a benefit to customers except when they come back to their parked bike only to find their expensive mod also had a hidden feature: “easy uninstallation”.

The simpler assumption is that over enough time, the S functions stack together for long enough that working backwards from exponential is a better predictor of reality.

These stacked S curves have continually been true with most technology.

The conclusion is “you should do MRIs it is equivalently beneficial as all of ___ are bad for you”

Unfortunately still need to copy paste the code into a file+terminal command. Which is annoying but works.

However, balancing ergonomics is a the big challenge.

I personally would prefer less ergonomics for more security, but that’s likely not a broadly shared opinion.

Datacenters in space is ambiguous enough to mean on lunar soil which provides plenty of heat dissipation using geothermal heat pumps.

Similarly mass to orbit is also less problematic if silicon factories (including the refineries) are built on lunar soil as well.

Those are two reasons why C is less tedious than assembly.

However, the way the parent worded it ie $1 of compute electricity - requires the compute to exist and temporarily be loaned. Otherwise “just a $1” would require a massive capex to buy/build compute.

I think AGI is going to follow a similar trend. A decade of being “1 years away”. Meanwhile, unlike self driving the industry is preemptively solving the scaling up of hardware concurrently.

Apple leases these computers from Amazon like it would from any other colo. Why wouldn’t these servers be considered Apple servers?

Barring a major privacy violation by AWS (which doesn’t seem likely), or some other sort of 0-day hack the data on these servers is entirely private to Apple.

I now get to the gym (or some form of exercise) 6 days a week. That was entirely because I made the decision to go to the gym and watch some YouTube.

Then I’d end up staying 90 mins but I’d get my 50 min workout in with a lot of long breaks! Then things started becoming a habit but I still have many days where I just watch YouTube at the gym lol

Which are only insane in the USA lol

Of course “action follows motivation” but even when not motivated “motivation follows action”.

For example, even as a healthy person I am not always motivated to go to the gym after a busy day at work which I am “so tired from”. I go dispite the lack of motivation. Unsurprisingly, I walk out of the gym feeling re-motivated and “with more energy”.

Most recently I took a photo of my grandma and me, asked Gemini to make it a cartoon, asked Gemini to make the new variant into a birthday card.

My grandma loved it! I was happy to make her something custom. Buying people cards just never felt right to me. Writing was also never my strongest suit - so this new form of expression for me has been enhancing :)

The only remaining thing I need to do better is getting the card printed! I wish that also was only 12seconds of work.

A lot more people around the world can then afford to send their kids or pay off their gold cards across a 10-15 year timeframe.

*Obviously this depends on the income potential that is unlocked by having access to the U.S. workforce.

Everything is a full new row because it is “a message” including read receipts. Some messages like read receipts just don’t render in the chat.

Edits can work the same way by rendering over a previous message, even though the local and remote DB have multiple rows for the original and edited messages.

FSTs are “expensive” to re-optimize and so it’s typically done “without writes”. So the database would need some workaround for that low write throughput.

To save you the time thinking about it: The only extra parts you’re missing are what Lucene calls segments and merge operations. Those decisions obviously have some tradeoffs (in Lucene’s case the tradeoff is CRUD).

There are easily another 100 ways to be creative in these tradeoffs depending on your specific need. However, I wouldn’t be surprised if the super majority of databases’ indexing implementations are roughly similar.

Seems the embeddings would just be useful for a “nice corpus search” mechanism for some regular RAG.

I believe this is what they mean.

In practice, how fast will the model change (including tokenizer)? how fast will the vector db be fully backfilled to match the model version?

That would be the “cache hit rate” of sorts and how much it helps likely depends on some of those variables for your specific corpus and query volumes.