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.

Are triangles cheaper for the rasterizer, antialiasing, or something similar?

?

The rest is time for feedback, discussion, and ideally a decision or collecting action items.

Amazon corporate is roughly broken down like this:

L4: 45%, L5: 45%, L6: 7%, L7: 2.5%, L8+: 0.5%

Only the first promotion has guardrails (L4 to L5). Typically you’re expected to be able to write at all. From there many people end their careers at L5.

Going from L5 to L6 requires being the best person on your team for multiple years running. Compared with some really smart, focused, and motivated people. You’re also expected to be able to write well, read, and kinda poorly coach other people’s writing.

Going from L6 to L7 is very difficult. One of the biggest differentiators really is scale. If you still need help writing docs - that’ll slow you down and you won’t scale. If you’re slow to read and provide valuable feedback again that will hold back scale (many people compensate by adding more time to their work days).

However, the funny thing is the doc writing culture at Amazon is built by and for L8+ leaders. Everything else was just training and weeding people out.

Going from L7 to L8 is where “in-meeting reads” really start showcasing the differences between leaders. I’ve known smarter people that made better decisions but had their careers stagnate while watching other “80th percentile” decision makers grow because of speed to grok information and deliver valueable feedback 9 of 10 times is more important than the incremental benefit of being right 10 of 10 times.

So I get your concerns about in-meeting reads throughs but just keep in mind who and what it is defacto built for.

The nice side effect is the author taking 10x more time to save 10x L+1 and L+2 leaders (ie more expensive people) from spending that same time trying to understand it.

I wonder if they could use that as the feedback mechanism.

Ideally if the sensors are small, low power, and cheap enough CO2 and lactic acid levels would also be good to check on to increase bloodflow.