0xd171

At this point it's really only a cesspool of people trying to sell stuff - get off the platform, you won't be missing out on anything valuable.

You can really strip down and simplify your life, spend time in silence and adjust your social interactions to suit your personality in many ways that are much better than this.

Make your own. Let us know how it goes. thanks, george

This isn't relevant at all.

Something that is often left out when talking about interpretability is the relationships between the predictors and the dependent variable in simulations. For example:

- I fit a complex, difficult to interpret model to a dataset, attempting for forecast my sales (structure of the dataset largely irrelevant for this example)

- I take an entry from the training set and decrease the value of some price attribute by 15%, leaving everything else unchanged

- I try to predict the sales for the entry I just created using the trained model

- What happens if the model now predicts lower sales? There is a clear relationship between price and sales volume going in the opposite direction. Would lowering my prices by 15% really lead to a decrease in my sales? How do you track what's happening in the model to create this forecast? Did I use the wrong model? Was my training data incorrect? How do you explain this to a client or to a product user?

Not really. I get the dislike for the hyped up terms but there are many companies that work in ML/data science (and hire ML engineers or data scientists) that do know what they're doing. And there are distinctions, though sometimes subtle, between the terms.

> Swapping hardware buttons for touchscreen controls has been a huge success for Apple for over a decade, with the iPhone, iPod Touch, iPad, even the Apple Watch. The Touchbar is just another step down that path.

It isn't a step down that path though. With the iPhone/iPod/iPad the new interface completely changes how you use the device (moving to a phone without physical keys for example). The Macbook touchbar doesn't do that, it just makes things a bit awkward by adding an additional way to interface with your device.

You're expected to be at work for ~8 hours, not to problem solve for that long. (Hopefully, if you're at a half-decent company). You have email and chat to manage, stand-ups, other meetings and calls, coffee breaks, lunch. Most people can set up their day in a way that they can be productive without feeling like they need to be constantly 100% concentrated.

> I continue to use R because of RStudio and the tidyverse

Tidyverse is massively overrated if you ask me. The good parts of it (dplyr and ggplot) are nice for interactive work. And that's about it - if you're deploying the code in an application, you're best off sticking to base R as much as possible.

Isn't your reasoning backwards?

We know that improvements in living conditions lead to reduced fertility. And climate change will worsen living conditions, not improve them. So if areas with poor living conditions have high fertility now, why would even worse conditions (due to climate change) reduce their fertility? Sure, the outcomes and quality of life of the children will be even worse than it is but I can't see how that will impact rates of reproduction.

So how would this play out differently if an exposed Elasticsearch cluster was connected to an endpoint different from IoT?

The problem is neither IoT nor ES - whoever built this just didn't bother to implement even basic security.

On the other hand, base R has to maintain (some degree of?) compatibility with S. Which means that all the strange design choices and weird behaviour in base R have little hope of ever changing. No number of additional packages can fix this.

I've used quite a bit of R and Python and I've never touched Matlab. Similarly to your comment - Python has nothing that comes even close to RStudio for working with data. Jupyter, Spyder, PyCharm, VSCode/Atom with data science extensions - none of them are as good.