antipaul

"spend time with rest of family" and "focus on environmental issues"

Alonso is favored to replace him: https://www.espn.co.uk/f1/story/_/id/34306151/alonso-ricciar...

Perhaps get into someone else's early growth business and learn from their mistakes, I mean help them grow, and then take those lessons to your own next thing

But it's supposed to automatically drive itself safely with "AutoPilot", no?

Subject: business models to become huge monopolies.

Apple makes $$$ from selling products people mostly love.

Google, Facebook/Meta make most of their cash from surveillance capitalism, I mean, advertising. Microsoft (tracking keystrokes in Windows) and Amazon (“we are a ‘data’ company”) also aren’t really privacy focused (though they do get chunks of income from actual products)

So I’m a bit torn with this legislation.

Part of me would love to see Apple pull out of Europe, and bully them to roll this back. Would be fun to watch!

I’d say 50-50, they’d win!

Not a bad checklist (“model info sheet”)

But rather than stand-alone, it should be incorporated into publications.

In my experience, only a minority of applied machine learning papers provide even a minority of the info requested by the info sheet.

Meaning, you really have no proper idea how cross validation was done, what preprocessing was done etc. - in actually published papers

Send this to my manager, ok? 90% hog at my 1-1s

Remember what this is.

This is the author's notebook, which happens to be public. It's okay.

Great intro story of Skype vs. WhatsApp!

We really have no idea why they decided to fire him.

From the story, it looks like a benign email with an apropos quote.

But while that could’ve indeed been the email, maybe he was also going around saying Apple is done, come join me in my new gig, Ive is dumb, etc.

And team members then complained it hurt their morale.

So Dye and Ive put their door down.

I’m not saying this is what happened. But do we really know the full story?

I believe this phenomenon to be very true in ML/AI today.

I see your point. Essentially, you get a fast computer for contemporary, real world use, for the same price as 10 years ago.

Though if you ignore the specs, the M2 air (for 1500 so you get the same storage space), is probably equivalent – 8GB unified RAM and fast SSD seems equivalent to 16GB ram from decade ago.

Together with the improved software capabilities, I think the overall "experience" is "better" and you get more bang for buck.

I had no idea what "1600 nits" was until I watched some XDR movies at night on that screen

You can/could modify the touchbar to get skip easily, see Keyboard settings

Probably haven't phased out old manufacturing pipeline so they "have" to keep making these (and make money off it) before it is gone entirely in the future

"The organizational structure and performance is a marvel for this."

Totally agree, plus Sirouji, who seems like a modern day wizard

“I want to see RSEs as equals in the academic environment“

Not just in academia, but also in biotech/pharma, where MDs or MBAs are too often the top dog.

This largely explains why Pharma and healthcare is so behind when it comes to tech

(Exceptions are the new, small biotechs. But the larger ones are more like dinosaurs)

I agree with you - mostly.

But in the book, computation is one of the three braids comprising data science. And they include software engineering in computation.

See book sections 1.2, 1.2.4, 2.1.3 etc

—

What I am trying to untie is, should data engineering be distinguished from data science, or not?

It’s not really the end of “big data”. There is and will be tons of data to go around for the foreseeable future.

Instead, it feels more like the end of “data science as the sexiest job of the 21st century”.

DS is mostly running reports (so BI) or stats (perennially important niche) or manufacturing hype via “latest research”.

Engineering has been, is, and will continue to be the foundation for success.

Business info in Apple Maps is wrong even in SF and NYC. It's really bad.

I have started hiding my personal laptop behind a drawer. Phone too

And you can PLAY them!

As a data scientist, you make a strong point.

For me, python is mostly a calculator, and it seems faster to try a calculation and immediately see if it runs and/or looks correct, rather than try to implement type hints et al (though I still use type hints occasionally).

If arrow keys are too far, alternatives can be:

- ctrl option b, ctrl option f - ctrl a, e, k

Some GUIs and editors respect this, but it varies (Jupiter lab, emacs, GitHub.com text fields, etc)

ctrl-n and ctrl-p also work to select things – so much closer than getting to the arrow keys ;)

Examples: search results in safari, search results in GitHub.com search.

Sadly, google chrome and apps like docs don't respect this

It would help to know this person's background a bit more. Is this a former software engineer, or a manager trying to pick up a technical skill?

That info could help contextualize the entire piece.

I wish. I’m at Big Corp where key project collaborators and stakeholders are wildly non technical and there’s no way to use GitHub issues.

They mostly use only email.

What is a more accessible alternative?

I’ve been trying google docs. Thoughts?

How do we support you? Big emacs fan here.

Does any large corporation produce quality software, at least most of the time?

The jetliner proved to be a “22.5 kg and 3-meter wide-scale model” in the non-headline part of the article