You had me until Qt.

Qt works fine to me. It is easy to develop cross-platform apps via Qt. Utilizing QWebEngine, I am amazed at the hybrid of C++ and Javascript.

What's so bad about Qt?

Good coffee does not scale.

I think it's reasonably fair to say that so far it hasn't, but could it? If not, to what would you attribute the fundamental roadblock?

I'd argue consumers themselves, but I don't know if that's really just a cop-out or not.

Even if you buy the best X% of a crop, as scale increases, quality will go down.

Unless a significant portion of profits are directed towards the improvement of farming methods, scale tends to decrease quality.

Are you saying that supply can't scale to demand? Because what you've described, a limited supply and increasing demand, is commonly solved by increasing supply. Unless we're at 100% coffee output for the Earth, which I don't think we currently are.

We aren't. Like with any other plant, there's a relatively hard limit on the availability of land suitable for arabica's growth (premium coffee is almost exclusively arabica), which will shift in one direction or the other as climate changes.

Today there is still difficulty in generating enough incentive to get farmers into arabica, but that changes with different practices, which Blue Bottle has historically practiced (direct trade and direct investment in farms, etc.). This model seems to be very sustainable for the time being.

On the buying end, the roasting end, distribution, etc., the challenges in scaling are similar or the same as scaling any other business as far as I can tell.

My intuition is that consumer demand for genuinely premium coffee would run out before the land or the farmers do, but I know the market far less well than I'm sure others here do.

I'm saying that supply of highest-quality coffee may be less elastic than demand.

Certainly we could grow more coffee, and that would likely increase net output of high quality coffee. However, if a company is buying the 'best 10%' of coffee and need to double their output, it is more likely that they buy 80th-90th percentile coffee than reinvest their profits into increase the quality that tranche of coffee to meet that of the top 10%. Even if a company chose to do this, the amount of time it takes to deploy capital in agriculture (~1yr+) is likely much less than the amount of time it takes to deploy capital in manufacturing (months), which effectively would cap growth rate.

An interesting argument I hadn't considered. I wonder, though, how genuinely impactful bean quality is for the end product. It's well understood that it's impactful to some degree, but do premium roasters need the top 10%, or is, say, the better half of arabica produced sufficient to yield effectively the same brewed product?

Said another way, is the slope of quality distribution that severe?

Good question, I'd also be interested in the answer.

I'd guess its a long-tailed distribution something like this (https://qph.ec.quoracdn.net/main-qimg-3b21f991d3f0446ce30b9b...) (quality on x axis, volume on y-axis) However, I know little about the complexities of coffee agricultural and manufacturing process, mostly just speaking in abstract armchair economics terms.

Stopped reading when I saw the 3d pie chart atrocity. I'm sure the rest is great.

No, just English.

Seems like they did something to the notebook interface to make it more responsive on tablets. I usually cannot manipulate Jupyter notebooks from an iPad, but I can here. Is this just a MS modification?

The statement "More wisdom potentially gets extracted when you apply Statistics to more (and better) data, but the analysis itself doesn’t improve with better data." simply isn't true. A hierarchical model, for example, is increasingly able to model subgroups and additional levels of hierarchy as more data are added. Penalized regression (or Bayesian regression) is another example -- the model is structurally different as you change the quantity of data.

The difference between ML and statistics is entirely semantic. Is logistic regression a ML method or a statistical method? It is both!

Machine Learning is generically defined as a method of data analysis that automates analytical model building.

That's the part that's going to have the impact---automated improvement of the way Statistics is applied to data analysis.

Is that not major enough to be considered and discussed separately?

I think the kicker is that most machine learning is incrementing on a single model. Typically one known from statistics before. The weak learner track of combining models almost guess against this, but I think even then it is usually the same shape of model.

So, I actually agree to an extent. Much as computers can be seen as the "next logic". Only, it is such a "builds on" relationship that I think calling it next is dubious.

If you don't need Python 3, then you won't need IPython 6.

Neither of these assertions are true. I use Py3 to teach graduate-level scientific computing and machine learning with great success, and the scientific Python community is making a very strong push for adoption. All major packages in the scientific stack now support Python 3.

I closed my PP account 2 years ago and would never consider using the service again. Once upon a time it was a necessary evil, now it is no longer necessary (just evil).

Doesn't this significantly impact your ability to use eBay?

Amazon marketplace is significantly cheaper and cleaner. Here in Europe a lot of eBay sellers are 100% not paying any VAT either it is only a matter of time before they or ebay endup in serious trouble over that.

eBay's gotten pretty awful, too. Fees, awful CS... I've not used the site seriously since the asinine decision to not allow feedback for buyers. They've become downright hostile to people who don't intend to make a living on the site.

Use what now?

If you are getting your coffee from a pod, then DRM is the very least of your problems.

The holier than thou coffee aficionados on this site are hilarious. I wish I was in the K-cup coffee business and they were my non-K-cup competitors, since they do not seem to understand why so many people like K-cups.