There’s not much need for turnover, plenty of companies publish quite a bit on how their systems work. You can look for recsys papers from your favorite company if you want. The Netflix recommendation systems workshop is also good and has many industry talks.
The reality is that there’s a lot of (what is essentially) matrix factorization, and not much of anything nefarious or very interesting to most people who are not recommendation system engineers.
Another thing is that these systems are intentionally hard to game even if you know the weights. The systems optimize for things like P(comment|personalized features), so even if you know there’s a high weight in the scoring function for comments, just commenting a lot on your content isn’t sufficient. You need the system to predict that other people will also comment.
It's pretty easy to do almost no coding at larger companies as a more senior engineer and become quite rusty. It sounds like he was interviewing for a position which he's actually expected to ship things and code, so it's pretty reasonable to ask him to code in the interview.
400 GB/s is insane memory bandwidth. I think a m5.24xlarge for instance has something around 250 GB/s (hard to find exact number). Curious if anyone knows more details about how this compares.
SF is actually getting really fast internet now. All the new developments we looked at had gigabit internet at ~$80/month with multiple competing providers. It's getting to older apartments now as well.
At these speeds, the WiFi adapters are more of a bottleneck than the ISP. I can only get close to full speed on ethernet.
Your thinking rate might be fixed (or more likely, decreasing with time), but one thing you can do, which is not mentioned here, is become more efficient.
This means looking for ways to improve the things you do and finding shortcuts so you have to think less. Most commonly, I do this by looking back at how I did a task and writing down things to help me complete it faster next time. Examples:
- write down the common mistakes I've made in the past and use it as a checklist to check before I push code
- create run-books for debugging issues. This is both for specific areas and general process things such as "Make sure you check all the relevant grafana dashes."
- add questions and answers on StackExchange/Quora when I run into problems/questions that take me awhile to resolve.
Obviously high supply of animators is the main reason, but I wonder if part of it is that pirating/illegal streaming sites are extremely common for anime. All of the people I know that watch anime do so without paying for it. It's trivially easy to find anime for free online, so much so that it's easier to find it for free than pay for it.
It's not free, but I think the functionality is a bit better than this. It handles fast passages and can change temperament, show how off you were, etc.
What is going on here? Why have none of the commenters read the article? Perhaps because it's phrased as a question and people didn't realize it's a link?
Anyways, this matches my expectations--people tend to be overly negative and only remember the good part. The mobile web as a whole has gotten faster due to network speeds+cpu improvements.
It is worth noting that pages are doing more after loading now than they used to be though. This won't show up in onload or first meaningful paint, etc. So the first paint is fast, but then if you try to scroll immediately afterwards you'll probably hit some jankyness while the rest of the page loads asynchronously (but only kind of asynchronously since there's a single main thread).
Some other things that could cause the regression are that more people own a budget Android phone now than before. People may not realize how slow these phones are. The single core performance of the top budget phone, the Samsung A50, is comparable to an iPhone 6 which came out in 2015.
The reality is that there’s a lot of (what is essentially) matrix factorization, and not much of anything nefarious or very interesting to most people who are not recommendation system engineers.
Another thing is that these systems are intentionally hard to game even if you know the weights. The systems optimize for things like P(comment|personalized features), so even if you know there’s a high weight in the scoring function for comments, just commenting a lot on your content isn’t sufficient. You need the system to predict that other people will also comment.