I have Chromebooks that have been end of life for a few years. You can use it just as before, you just no longer get updates.

Meta does this a lot. Instagram didn't have a web version for years. Even today it's missing a lot of features compared to the app.

He worked on TensorFlow. So even if the doesn't do ML research himself, at least he works on the tooling.

TensorFlow was honestly not that good. It had a lot of effort put into it, so it worked, but there are reasons people moved away from it.

I think Jeff Dean is a great engineer, but I wouldn't hold up TensorFlow as a great example.

I always thought he pair programmed so it's been Jeff + Sanjay.

Why wasn’t tensorflow good?

I started my ML journey in TensorFlow, and now I am a happy PyTorch user, for many years.

TensorFlow, yuck

But who uses TensorFlow -- really -- these days.

What else are people using then?

PyTorch everywhere

Or JAX

Google does almost exclusively; it's tied in to TFX.

Of course he is someone any technology organisation would want to have as a resource. But probably not as chief scientist or ceo of an ML company based on the available evidence

We're just not great at reaction time and constant vigilance

It's not just about what's technically feasible. He's making false promises every year and now released to public a Beta product that should have been Alpha or earlier stage.

I'm seeing a lot of cool stuff that people start to build on AlphaFold lately:

- ChimeraX: https://www.youtube.com/watch?v=le7NatFo8vI

- Foldit: https://www.youtube.com/watch?v=nA5GzQDTF20

Libraries.io indexes software dependencies; but no Dependent packages or Dependent repositories are yet listed for the pypi:alphafold package: https://libraries.io/pypi/alphafold

The GitHub network/dependents view currently lists one repo that depends upon deepmind/alphafold: https://github.com/deepmind/alphafold/network/dependents

(Linked citations for science: How to cite a schema:SoftwareApplication in a schema:ScholarlyArticle , How to cite a software dependency in a dependency specification parsed by e.g. Libraries.io and/or GitHub. e.g. FigShare and Zenodo offer DOIs for tags of git repos, that work with BinderHub and repo2docker and hopefully someday repo2jupyterlite. https://westurner.github.io/hnlog/#comment-24513808 )

/?gscholar alphafold: https://scholar.google.com/scholar?q=alphafold

On a Google Scholar search result page, you can click "Cited by [ ]" to check which documents contain textual and/or URL citations gscholar has parsed and identified as indicating a relation to a given ScholarlyArticle.

/?sscholar alphafold: https://www.semanticscholar.org/search?q=alphafold

On a Semantic Scholar search result page, you can click the "“" to check which documents contain textual and/or URL citations Semantic Scholar has parsed and identified as indicating a relation to a given ScholarlyArticle.

/?smeta alphafold: https://www.meta.org/search?q=t---alphafold

On a Meta.org search result page, you can click the article title and scroll down to "Citations" to check which documents contain textual and/or URL citations Meta has parsed and identified as indicating a relation to a given ScholarlyArticle.

Do any of these use structured data like https://schema.org/ScholarlyArticle ? (... https://westurner.github.io/hnlog/#comment-28495597 )

Wish there's a premium service directly from the Olympics where you can select the sport and match to want to watch.

This doesn't seem to cover even half of the event: https://www.cbc.ca/player/sports/olympics/replays

It runs on my phone, very impressive!

For some reason the first post there is "Is Covid19 pandemic a Hoax?". So it probably triggered something with the bot.

The Uber engineering disaster story from 2 months ago seems to suggest that Apple lacks dogfooding Swift internally: https://news.ycombinator.com/item?id=25373462

iOS doesn't come as 100mb cellular downloads, so that isn't really the same kind of issue. There are many reports about showing how it's slowly coming into wider spread use at Apple.

For me the lesson is that just because a new technology/language is out doesn't mean you should jump on it. Things need time to mature, and if you're Uber, you can't risk having half of your income rely on something new. Compilers, linkers, and programming languages (and databases and OS kernels) take years to mature. Hell, just earlier this week was a post about diagnosing a 21 year old bug in the Linux networking stack for rsync of all things.

I'm quite shocked that enough experienced people felt that level of confidence. In earlier versions of Swift I personally experience slow compiles and that was on a not terribly large code base -- no where near Uber. That alone should have been a big clue of the state of things.

More like Uber spent too little time trying to keep their app development process manageable.

Uber doesn't have any pressure to have a reasonable tech stack because they don't have to be profitable (because they're an investor charity). On the other hand, having a fancy tech stack helps with recruiting.