pythonwutang

Would this make them a “centi-corn”?

“Facebook data hacked by Google intern”

Bad: 1.0000 - Good: 0.0000

It really disliked this one, even though it probably would get attention on HN.

> Chunking is the term I used because that's more relevant to the data science

If your intended audience are data scientists then why didn’t you mention Dask?

Doesn’t render well on mobile. The pictures smash the text to the right instead of moving it below.

Andrew Yang is the Trump of tech. And his brand of politics is more a dangerous threat to democracy than Trump’s is. His messaging and policies are just like Trump’s in that they’re dumbed down, optimized for social media likes and subtly enforce a world view where extreme income inequality is inevitable so let’s make the best of it. AI/tech isn’t going to kill the middle class by itself. This destruction will be assisted by technocracy propagandists like Yang.

> By analyzing the blockchain and de-anonymizing bitcoin transactions, the agency was able to identify hundreds of predators around the world - even though those users thought that they could remain anonymous.

Who would’ve guessed the blockchain would help the government catch criminals!

How does this differ from Amazon Lambda?

Only read the landing page so far but it looks like Lambda with just Node support. Can someone correct me if I’m off?

Looks great! Thanks for the hard work Streamlit team. Our team started using Dash recently for an ML project and quickly got lost in callback hell and switched back to a notebook. This approach is so pythonic and elegant. Looks like it would handle our use case with much less code and callback related head aches. I’m excited to share it with our team!

I doubt 99% of the community is so humorously pessimistic. This functionality can be easily replicated in several databases (with triggers and the like). Generated columns l make a common pattern simpler and more explicit. Maybe it’s not a useful feature for everyone but it’s certainly not some instant tech debt like you’re implying.

This is just the beginning. Start microwaving some popcorn because this will be quite a glorious train wreck to watch! Hopefully the negative impacts are economically isolated.

> Therefore, the best thing to do is to deploy your program without resource limits, observe how your program behaves during idle/ regular and peak loads, and set requested/ limit resources based on the observed values.

This is one of the author’s fatal assumption. The best practice I understand is to set cpu requests to be around 80% of peak and limits to 120% of peak before deploying to prod.

They set themselves up for disaster with this architecture where they have many idle pods polling for resource availability. This resource monitoring should have been delegated to a single pod.

Also it’s really unclear what specific strategy led to extra costs of 1000s of dollars...

“Why Haskell is important” according to a Haskell consulting company. So taking this article with a big grain of salt.

Haskell might be a great language but barely anyone (besides CS academics) uses it.

We use java and python in production because (among many other things) they’re very popular.

Popular languages have large archives of open source packages and easy to find communities that save enormous amounts of headaches and time. The opposite is true of Haskell, at least for now.