While I’m unsure I’ll be using Swift for the rest of my life, I’ll continue to work on my little tool to detect unused code for as long as I can. It’s my most popular open-source contribution, and it brings me joy knowing others find it useful.
One some larger roundabouts in the UK (and no doubt other European countries), they have traffic lights on the roundabout, between exits (I'm not talking about the entry lights). If one exit is totally blocked, these lights can allow traffic to continue out of other exits.
I hear the argument "Americans don't understand roundabouts, therefore we wont build any" quite often in these kinds of articles. If you survey people about something they've never comes across before, of course you're going to get negative results.
If Americans can drive and talk on their cell phones, they can handle a roundabout.
My initial reaction was acquihire. Though given their recent improvements to Pages [1], this acquisition could mean a push into the Easel market space.
EDIT: Just to point out why this is a big deal - What percentage of new sites are mostly static, presentational? Probably a slim minority. Whilst Github can't code your site for you (yet?), giving you the tools to develop your app layer and frontend - with some as-of-yet seen integration tools? - those are some very large slices of the pie. Don't forget Github pages is currently free too, perhaps there'll be a paid tier for dynamic sites.
Another thought is that there may be some ratio between quality and rewatability that signifies the first-time (but not repeated) watchability of a film.
For example, 5 stars for quality and 0 for rewatachability doesn't tell me I should watch the film if I haven't already. But maybe 2 stars for rewatability/subjective enjoyment is enough justification to watch it to appreciate the quality?
It'd be interesting to see the overall variance on quality vs. rewatachability. That could give some clue on the objectivebess of users' answers to quality. Perhaps quality is just one of those things that is more universally objective than it is subjective?
Also, if you avoid the normalisation step you could easily factor in the degree at which user A liked the quality vs. user B, instead of just a 'more' or 'less' question.
If you factor your vector weights by the scale of your quality rating (0 - 10?) then if user A liked the quality film X vs. film Y +6 more points than user B's +1, this would give you a more accurate correlation.
Anyway, food for thought. A very fun problem to be working on!
I think the weightings I describe above would give you that.
Say we start at 0 and user A likes the next move in both directions (+4) and user B only likes it more in one direction and the same in the other (+2) then you're still going to get a positive correlation, just a slightly lower one than if both users liked it in both directions.
I assume each vector has its own weight? So better in "Better in both respects" is a stronger sign of similarity than just "Higher quality but same rewatchability."
So say..
"Same in both dimensions" = 0
"Same quality but more rewatchable." = +1
"Same quality but less rewatchable." = -1
"Higher quality but less rewatchable." = +2
"Higher quality but same rewatchability." = +3
"Better in both respects." = +4
etc..
Then you could pass those to a coefficient like Pearson's R.
x = [0, 1, 2, -1, -3, 4, -4]
y = [0, 1, 1, 2, -1, -2, 0]
It'd be an interesting experiment to see what results that gives vs. your current algorithm.
https://github.com/peripheryapp/periphery