Also for anyone too young to remember the dot com boom, the founding partners (Marc Andreessen and Ben Horowitz) are some of the legendary techies from that cycle (of Netscape and LoudCloud/Opsware fame, way ahead of their time)

Yeah, I find this kind of abbreviation annoying. But there's a few words that are commonly abbreviated like this:

i18n -> internationalization

l10n -> localization

g11n -> globalization

l12y -> localizability

a11y -> accessibility

It bothers me because my brain does not jump from the abbreviation to the underlying word. I really need to stop and think about each one. And I get the numbers wrong when writing them.

There was a period of a few months when I was first learning about web apps where I saw "i18n" multiple times. The first time I came across it, I tried to sound it out:

i18n -> I-one-eight-n -> iwonation

I was already a couple of rabbit holes deep at the time and didn't have the mental capacity to look it up and wrap my head around yet another new concept.

"Oh boy." I thought to myself, "One more word I've never heard of, probably representing some complicated CS concept."

I was so annoyed when I finally found out what was going on.

Wait, can you clarify what is actually going on? Is there any rhyme or reason or is are these shortenings just random?

I'm having trouble parsing the grandparent comment...

It's the number of letters between the start and end letters. Yeah, it's annoying.

Also, k8s->kubernetes

What AI companies are they invested in?

Labelbox is the only one I know.

https://a16z.com/portfolio/

Tecton is their most recent high profile ML/AI company: https://a16z.com/2020/04/28/investing-in-tecton/

Hey—this is Brian from Labelbox. Thanks for the mention.

Martin and Matt are incredibly sharp on the trends in AI and have inspired our work at Labelbox immensely. I particularly like the discussion on getting the operations right for building and iterating with ML. Like software development, iterating quickly is key to building a performant model on real world data. The average iteration time for ML is 2-4 weeks in the industry right now. Comparing this to software development averages is stark. Getting the development and operations of ML right can greatly speed up iteration and improve the likelihood of getting to market with performant ML systems.

There are 16 characters between the A of Andreessen and the Z in Horowitz for those that don't get it.