I've used a personal wiki for several years now, and I think it works just fine. Surprisingly, I don't really link pages together all that much, but I make heavy use of tagging and categorising. I find that's been enough for me.
I don't really follow links, and mostly I just use the search bar when I need anything. I think one thing that's tripping lots of people up is they are trying to use these tools the way they think is expected of them. Instead, it's better to just make it as frictionless as possible to add notes, and as frictionless as possible to read notes you've written before.
Everything else is just procrastination with "productivity tools"
This is kinda what happened with Mastodon wrt to ActivityPub. Lots of what the Fediverse means is how Mastodon chooses to use the protocols. Often in ways that aren't even compliant with the specifications. So yep EEE is highly viable as an attack.
You don't need moral realism to arrive at a set of mutually agreed upon moral values. You don't have to agree with your friends on what's the best food in the world is in order to choose a place to have dinner.
There is an argument that you don't like the process by which the moral values were arrived to, but that's hardly a reason to indulge in moral nihilism.
One thing also to keep in mind is it's often hard to keep a project working because the ecosystem evolves around the project. So updates are needed just for existing functionality to remain.
Features and APIs are deprecated/ removed from libraries, and if you don't update your project stops working in most user environments.
The real cost is in the repaving and that's much more a property of weight of the vehicles. Bike infrastructure is significantly cheaper to create and maintain compared to nearly any other vehicle.
I grew up in Brooklyn and supermarkets didn't really become a thing here until the 2000s. Like there was Key Food, but that felt more like a general store. There was a Waldbaum in the area but it was pretty inconvenient to get to.
Instead we had fruit stands selling produce, a bakery, a butcher, fishmonger selling seafood. And this isn't mentioning all the ethnic food stores. All of this was on two or three short blocks. The first time I saw a supermarket was when we went to New Jersey. It was a Wegmans and I think what was amazing to me was just how much stuff a single place could have.
I think ultimately it does come down to convenience. I think it's easier than ever to have decent meal at home. You don't even have to peel your own carrots or tear your own lettuce anymore. Sure something with more complexity might take time, but that's better for a weekend.
I love everything about what you have planned. Is there anything in the works for creating more keyboard options? While mechanical keyboards might be too impractical, even something with bigger arrow keys would be nice.
> No, it doesn't. A continuous distribution is just a way of assigning probabilities to a parameterized set of propositions where the parameters are continuous and so the set is infinite. But for any given proposition in that set the prior is a number.
A continuous distribution does not assign probabilities to each proposition but subsets of them. To see this concretely consider the continuous Uniform distribution from 1 to 1.5. It will for all values of its support have a probability density of 2. Most people would not consider 2 a probability.
For continuous distributions, Bayes's theorem becomes about probability densities and not probabilities.
That framing actually stops holding for continuous distributions. Like in a distribution on heights of people there isn't a probability or prior associated with a particular height. Probabilities are assigned to subsets of this distribution (sometimes called sigma algebras). Priors stands for prior distribution and it really is best to see Bayesian analysis as a machine that consumes probability distributions as input and spits out probability distributions as output.
Why do you feel politicians and tech executives like to say automation threatens to do all these things? How do they benefit from this narrative that has zero evidence to support itself?
While often ignored, item-to-item similarity recommenders work really well for new items and have been the basis for Amazon's and YouTube's early recommendation algorithms
Yes but in this particular instance Uber recruits heavily in the Bay Area. If all workers in the Silicon Valley office refused to work and discouraged others to work for Uber it could be quite effective.