Light roasted coffee has more caffeine than dark roasted coffee.
Technically, per bean, more of the caffeine is cooked out of the dark roast. However, other things are also roasted out of a dark roast to the point that the individual beans are also lighter and smaller. When brewing coffee, usually you either weigh your dose of beans out, or you use a scoop for some consistency. Either method will result in more dark roast beans ultimately making it into the brew than would with a (larger, heavier) light roast.
Typically, this more than cancels out the reduced caffeine content per bean, so a brew of dark roast coffee still typically has more caffeine in it.
If I remember correctly, dark roast was also originally devised to hide bad-quality coffee beans. Nowadays it is often implied that darker roasts are better, which actually isn’t necessarily the case.
Dark roasts have a more consistent taste/flavor and it has a longer shelf life, so it’s easier to know what you’re getting. If you want to taste the variety of flavors coffee can have, you’ll go for fresher lighter roasts.
Yup, I had to explain this to so many people when I sold coffee. Nobody believed me at all. I explained that dark roast had more of the caffeine cooked out of it.
GDPR Art 4.(1) ‘personal data’ means any information relating to an identified or identifiable natural person (‘data subject’); an identifiable natural person is one who can be identified, directly or indirectly, in particular by reference to an identifier such as a name, an identification number, location data, an online identifier or to one or more factors specific to the physical, physiological, genetic, mental, economic, cultural or social identity of that natural person;
Posts in the Lemmy instances contain information relating to an identifiable natural person (by their user handle), as they contain the person’s ideas and opinions. Therefore the Lemmy instances are handling personal data and must comply with the GDPR.
To add another angle not mentioned: Something I’m not sure of but interested in finding out is if multiple communities allow for better curation than one single large one.
For example, imagine a huge sub like /r/pics. When browsing “new” on that sub, the content goes away and is refreshed with even newer content in practically the blink of an eye. Because sooo many people are posting all at once.
As a result, a lot of good content gets missed in the flood of everything, and you have to rely on time of day and luck to get your post recognized.
OTOH with duplicate communities, the content gets divided and conquered a little bit better. One userbase can browse new on one community, while another userbase can browse and curate content on a similar one. In the end, both communities content don’t get drowned out by the massive volume.
Once a multireddit like feature comes out, users like you and me can identify and group these duplicate communities and be none the wiser browsing all of them at once.
There are plenty of people that do this, and it seems to be pretty straightforward.
There is a significant risk going forward though- if the undesirables (the ones that currently get larger instances defederated) start doing this in any major way, then the larger instances will block new federation or smaller instances by default. Starting now is actually probably a good move, since you might be grandfathered in when that occurs.
Also, be aware of local laws regarding content you host. You could be liable for illegal content you inadvertantly receive.
No central authority means people do what they want. Maybe the mods there decide to suck at some point, or the instance admins suck, or the instance goes offline, or someone just felt like having their own.
Wither GDPR applies to an individual instance will be up to those running the instance to decide.
If you decide it does, then you need to do a few things. Number one is read up advice on compliance with GDPR.
Being able to delete data alone doesn’t mean GDPR compliance. I’m thinking about the need for privacy notices on sign up, retention schedules for data, lawful basis of processing, records of processing activities… Data subjects have numerous rights, which apply depend on the lawful basis you’re processing under.
I’d suggest that larger general instances might want to read up more urgently than smaller single focus “hobby” instances.
edit: more I think about this, I think there is an moral responsibility for the developers to help those running instances comply. If GDPR does not apply to an instance, it is still good practice to allow uses to delete their data, etc… Also, art. 20 of GDPR is the right to portability. Interesting to see how this applies to fediverse platforms like Lemmy.
I’ve noticed that Lemmy has a hard time federating to non-Lemmy instances. Looking up a user/community on Calckey/Mastodon shows a lot of posts and random things missing.
When you think about data it actually gets really scary really quick. I have a Master’s in Data Analytics.
First, data is “collected.”
So, a natural question is “Who are they collecting data from?”
Typically it’s a sample of a population - meant to be representative of that population, which is nice and all.
But if you dig deeper you have to ask “Who is taking time out of their day to answer questions?” “How are they asked?” “Why haven’t I ever been asked?” “Would I even want to give up my time to respond to a question from a stranger?”
So then who is being asked? And perhaps more importantly, who has time to answer?
Spoiler alert: typically it’s people who think their opinions are very important. Do you know people like that? Would you trust the things they claim are facts?
Do the data collectors know what demographic an answer represents? An important part of data collection is anonymity - knowing certain things about the answerer could skew the data.
Are you being represented in the “data”? Would you even know if you were or weren’t?
And what happens if respondents lie? Would the data collector have any idea?
And that’s just collecting the data, the first step in the process of collecting data, extracting information, and creating knowledge.
Next is “cleaning” the data.
When data is collected it’s messy.
There are some data points that are just deleted. For instance, something considered an outlier. And they have an equation for this, and this equation as well as the outliers it identifies should be analyzed constantly. Are they?
How is the data being cleaned? How much will it change the answers?
Between what systems is the data transferred? Are they state-of-the-art or some legacy system that no one currently alive understands?
Do the people analyzing the data know how this works?
So then, after the data is put through many unknown processes, you’re left with a set of data to analyze.
How is it being analyzed? Is the analyzer creating the methodology for analysis for every new set of data or are they running it through a system that someone else built eons ago?
How often are these models audited? You’d need a group of people that understand the code as well as the data as well as the model as well as the transitional nature of the data.
Then you have outside forces, and this might be scariest of all.
The best way to describe this is to tell a story: In the 2016 presidential race, Hillary Clinton and Donald Trump were the top candidates for the Democratic and Republican parties. There was a lot of tension, but basically everyone on the left could not fathom people voting for Trump. (In 2023 this seems outrageous, but it was a real blind spot at the time).
All media outlets were predicting a landslide victory for Clinton. But then, as we all know I’m sure, the unbelievable happened: Trump won the electoral college. Why didn’t the data predict that?
It turns out one big element was purposeful skewing of the results. There was such a media outrage about Trump that no one wanted to be the source that predicted a Trump victory for fear of being labeled a Trump supporter or Q-Anon fear-monger, so a lot of them just changed the results.
Let me say that again, they changed their own findings on purpose for fear of what would happen to them. And because of this lack of reporting real results, a lot of people that probably would’ve voted for Clinton, didn’t go to the polls.
And then, if you can believe it, the same thing happened in 2020. Even though Biden ultimately won, the predicted stats were way wrong. Again, according to the data Biden should have been comfortably able to defeat Trump, but it was one of the closest presidential races in history. In fact, many believe, if not for Covid, Trump would have won. And this, at least a little, contributed to the capital riots.
Oh yeah. I might say some wrong stuff since I’m quite ignorant but. Statistics is messy and I tend to avoid including too much stats in my projects, although sometimes I accidentally end up blindly doing so and believing them also drawing inaccurate conclusions. Physical stats are even messier because not everybody has the competence to accurately understand what they mean, or sometimes we just don’t understand the world enough. Environmental science data is an example of that. I rely on other people’s analyses cause I can’t read them. I don’t know much about politics.
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