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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.

The 30 days thing is likely from GDPR requirements. You cannot keep user data longer than that after they request deletion.

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.

Some of the people I enjoy listening to (almost all violin):

https://www.instagram.com/nancyzhou_violin/ https://www.instagram.com/joshuabrownviolinist/ https://www.instagram.com/kersonleong/ https://www.instagram.com/violincase/ https://www.instagram.com/augustinhadelich/ https://www.instagram.com/weatherclef/

I also enjoy just browsing the discover tab on instagram. If you mostly click on these instrumentalist accounts, it's pretty good about finding more.

You can just buy it on Amazon right now, they're actually not too hard to find. You can also find it in store at original MSRP.

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.

I use http://intonia.com/index.shtml for this purpose. It's available on Android/iOS as well.

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.

This is the shortest common supersequence problem (https://en.wikipedia.org/wiki/Shortest_common_supersequence_...). It's NP-hard if you include repeating numbers; there's no simple changes to make your solution work in that case.

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.

This is not actually happening, and I'm sad to see how long this misinformation persists on HackerNews... Not only not technically feasible, it'd also get out in the numerous lawsuits that Facebook is involved in, which get access to internal information like this.

I like One Medical a lot, I hope they find some way to be profitable. Their doctors are far less rushed than any other GP doctor office I've been to, and their app is well done too.

I've been confused how they're able to do this and not charge more than they do (only $200 a year).

I work as an ML engineer, some thoughts:

The train/test data being imbalanced in the same way does give the model an advantage, but I don't think that making the test set 50% would solve the issue completely either. Doctors have been "trained" on the true distribution, while which is not 50% (I'd guess that the true distribution is actually extremely unbalanced).

The model isn't simply learning to predict no 80% of the time, it is learning the distribution of the data with respect to the input features. For example, let's say that we have a simple model with only 3 binary features. It may learn that when features X_0, X_1 and X_2 are 1, the probability of cancer is 70%. This isn't a simple multiplication of the true probability by the upscaling factor though--it depends on the percent of negative samples with this feature vector and the percent of positive samples with this feature vector.

If we are to change the test set to be 50% positive and keep the same train distribution, the model no longer has the correct information about cancer rates with respect to feature distributions, but neither does the dermatologist. The specificity and sensitivity continue to not be interpretable as predicted specificity and sensitivity in the real world.

There is no issue with reporting specificity/sensitivity if they had used the true distribution of cases. Yes, the curves/AUCs will look better than the precision/recall rates, but they do not mis-represent what the doctors are interested in (what percent of people will be missed, and what percent of healthy people will be subjected to unnecessary procedures).

Anyways, the classifier doesn't actually seem to be that good, there's actually doctors that were better than the classifiers if you check the paper.

I recently restarted classical music education, and have found it makes it much harder to listen to music and concentrate on my software engineering. It used to be okay if the music doesn’t have vocals, now it has to be something simple I’ve heard many times before.

This is really impressive, I didn't expect starcraft to be played this well by a machine learning based AI. I'm excited to read the paper when it comes out!

That said, I'm not sure I agree that it was winning mainly due to better decision making. For context, I've been ranked in the top 0.1% of players and beaten pros in Starcraft 2, and also work as a machine learning engineer.

The stalker micro in particular looked to be above what's physically possible, especially in the game against Mana where they were fighting in many places at once on the map. Human players have attempted the mass stalker strategy against immortals before, but haven't been able to make it work. The decisions in these fights aren't "interesting"--human players know what they're supposed to do, but can't physically make the actions to do it.

While they have similar APM to SC2 pros, it's probably far more efficient and accurate so I don't think that alone is enough. For example, human players have difficulty macroing while they attack because it takes valuable time to switch context, but the AI didn't appear to suffer from that and was extremely aggressive in many games.

I don’t think that’s a viable strategy in today’s world. The kids I knew growing up that were pushed hard by their parents are doing much better (and happier) than those that were not.

Kids don’t reliably make good long term decisions for themselves. It’s more fun to play fortnite than to practice for a math/piano competition. They can’t see the long term impact of not working out and eating poorly.

I got pushed pretty hard as a kid. It wasn’t fun at the time, but now I appreciate it. There’s a widening gap between the rich and poor in America, and it’s a lot nicer to be on the rich side.

I’m pretty sure they’ll limit the APM (or do something more fine grained since not all APM is equivalent). It’s already well known that bots can outmicro humans in certain aspects like splitting and focusing the correct units.

The fact that this dumb “Google/FB listens to you in the background and uses it to serve ads” myth is still around even on HackerNews is insane to me.

Do you all really think that if this contributed a measureable portion of ads that it wouldn’t leak? There’d also have to be a large amount of hardware dedicated to speech transcription—running constant speech to text on every google chrome browser would need so much compute.

Not to mention that it’d be possible to actually show that the company was lying by modifying microphone drivers and showing that they activate in the background. I’m pretty sure you could also just do it the same way they make game hacks—get access to chrome memory and show that there’s sections that match the recorded speech.

Indisputable proof that this happens would be a PR disaster for either company and cause legal action too.

I’m really in dismay at how long this myth has persisted.

Members of the American Trends Panel were recruited from several large, national landline and cellphone RDD surveys conducted in English and Spanish. At the end of each survey, respondents were invited to join the panel.

Methodology is very shakey for a sample of young people. Results are also just weird—I’m a young person (24) and very few people I know have deleted FB.

We’re told not to talk about any details (even high level) of this sort by legal because anything we put on the internet could be used against the company in court even if it seems harmless to us. If you want to find out about the difficulties just find an engineer working at a major company and ask them about it in person.

Yeah, I’m willing to bet the people that think this is easy and clear are not trying to implement it. I have not met anyone working at a large internet company (these companies have high likelyhood of being sued so it’s very important the law is followed as accurately as possible) that thinks the law is clear.

Parts of this article are clearly false.

The “security consultant” claims to know that there’s clips of audio being sent back to servers, but not knowing what that audio is since it’s encrypted. First off, if the content is encrypted, you don’t know that it’s audio. Secondly, if you had proof of a major app unexpectedly sending back recorded audio clips, it would be huge news, so I’d assume he doesn’t have that.

Apps do not have access to always on microphone access by default as insinuated by the article. On Android, until P comes out, apps can access the mic in the background if you give microphone access to the app. On iOS, app microphone access triggers the microphone symbol in the top left. These are the apps explicitly asking to take microphone access though, not some sort of listener pattern where apps can attach to the OS’s always on microphone behavior.

Experiment is clearly flawed for numerous reasons (no starting recorded state, didn’t record behavior on FB/other sites that could have impacted results during expt, etc.)

Hey! Things are going well! I’m actually on ML now, so quite a bit different than the past few years but I’m enjoying it. Glad to see you guys are making progress; it seems like a pretty exciting field right now

I like how this car has exterior communication to pedestrians. I think this is actually pretty important. The way I tell if I can cross a crosswalk is by making eye contact with the approaching driver, and other self driving cars can’t indicate if they see me.

I switched to iOS this past year after using Android since the G1, and working as an Android developer for the past 5 or so years. Pretty happy with the iPhone X, less happy with iOS 11, I think previous iterations were better.

Things I like:

* typing feels much nicer, no lag in character input

* phone runs much cooler, does not make hand sweaty to hold

* far fewer random freezes or lag

* password management much better by default, every app with important information has faceid which is quite nice

* I prefer the photos taken by the iPhone camera

* I find tethering on iPhone works more consistently

Things I don’t like:

* no back button

* some Google apps for iOS don’t seem as good as their android counterparts. YouTube app keeps getting buggy updates, Inbox still doesn’t support full iPhone X screen.

* Prefer Android’s notification system

* A bunch of apps have video playback issues, I suspect there’s an OS level bug

Was a long time ago so I don't remember them all, but some examples I do remember:

* Print pascal's triangle up to the n-th level, with proper spacing (so that it looks like a triangle).

* Make the game Go for 2 players. I got a sub problem of this during my Google interview (determine whether a piece is captured).

I went to MIT and knew a good number of people who were confident in technical interviews. The problems in interviews were much easier than what I got in class, or even CTY CS classes in middle school.

While there's definitely good people at non-elite universities, selection bias is at play here too.

For example, in the senior year case, you're just looking at people who were concerned enough about interviews to go to interviewing.io during their senior year. Most people I knew at MIT had a full time offer they were happy with from their Junior year internship by their senior year.

You can actually see the impact of this in your graphs too, notice that the % of people with a 1 or a 2 in elite schools increases for senior year vs junior year. People who have been doing well in technical interviews and have good return offers won't spend as much time preparing.

Jewelry and industrial uses do define the value of gold. Gold is not valuable from just being scarce. It is valuable because it is scarce and people want to use it. The price goes down when demand for jewelry goes down and goes up when demand for jewelry goes up.

There’s been a myth created by crypto currency activists that gold is just like crypto currency in that it’s valued simply as a value store, which is incorrect. Its use as a value store increases the value, but at its core the price is still dependent on the uses of gold. Over half of gold mined is used for jewelry or industry[1]. Yes, some is kept as investment, but that investment is based on the idea that people will continue to want gold jewelry.

[1] http://www.numbersleuth.org/worlds-gold/