nyc_pizzadev

Yes for everything. In HTTP1 it’s a chunked response, in H2+ it’s just a bunch of data frames. This is how low latency HLS video works.

High quality VOD (ie streaming a 4K movie). HTTP block file systems, each block needs to be streamed reliably to fulfill the read() call plus read ahead.

One thing not mentioned often is that a lot of networks will drop UDP packets first when encountering congestion. The thinking is that those packets will not re-transmit, so it’s an effective means to shed excess traffic. Given we now have protocols that aggressively re-transmit on UDP, I wonder how that has changed things. I do seem to remember QUIC having re-transmit issues (vs HTTP1/2) years ago because of this.

Looks like a marketing piece publicizing that CERN is using WD HDD products at scale with no technical details. To make matters worse, the WD product links don’t even work!

Thanks for the reply.

Our theoretical usecase is 10+ PB and we need multiple TB/s of read throughout (maybe of fraction of that for writing). So I don’t think Filestore fits this scale, right?

As for the directory traversals, I guess caching might help here? Top level changes aren’t as frequent as leaf additions.

That being said, I don’t see any (caching) proxy support anywhere other than the Google CDN.

Did you use a local caching proxy like Varnish or Squid? Would that have helped?

Does anyone have any experience on how this works at scale?

Let’s say I have a directory tree with 100MM files in a nested structure, where the average file is 4+ directories deep. When I `ls` the top few directories, is it fast? How long until I discover updates?

Reading the docs, it looks like it’s using this API for traversal [0]?

What about metadata like creation times, permission, owner, group?

Any consistency concerns?

[0] https://cloud.google.com/storage/docs/json_api/v1/objects/li...

Worth noting that the sample used did not produce a Meissner effect, but was still superconductive at ~100K.

> To further verify the superconducting properties, we conducted magnetic measurements on the sample, but unfortunately, no obvious Meissner signal was observed, indicating that the superconducting volume fraction of the sample may be very small. The preparation of high-purity samples are still a challenging task.

What about an inbetween actor changing the content? Or someone just hijacking the website?

So I have a Google Workspace attached to my domain. How will this work when the domain is managed by another company? When I move my domains off Squarespace, how will my Workspace work? I’m confused if they are saying my Workspace services will migrate to Squarespace too?

> Squarespace will also provide billing and support services to Google Workspace customers

I have to admit, I’m at a total loss, this makes zero sense.

This is a bit confusing because isn’t most internet traffic video [0]? Streaming video requires something like 100x more sustained bandwidth than clicking around a website. So is this 47% of non video traffic or are bots consuming video? Something else?

[0] https://blog.gitnux.com/internet-traffic-statistics/

Was finally able to play it on a desktop. Maybe having a finger moveable joystick widget would give you an equal experience on mobile.

I guess there is no way to play on mobile?

I think ChatGTP is pretty amazing and worthy of all of its recent praise. However, is it a bit of a bullshit generator. It doesn’t know if it’s right or wrong or anything, it’s just word soup trained enough to where it’s usual correct in general knowledge areas. Here is a good example:

https://drive.google.com/file/d/1wALYKw59TqExbLiQJvql_sM7OhE...

Don’t want to diminish ChatGPT, it’s very impressive, but the first joke analysis misses the fact that the guy says he quit drinking and then still drinks 2 beers.

How would this work as a vaccine? I understand there are antibody therapeutics which inject specific antibodies into your system to help fight off COVID, but do those antibodies last and reproduce? Something else?

Yup, I would say a lot of families are very conscious about not wasting food. These habits are still being passed down to future generations. But as this article points out, the biggest sources of waste are producers, distributors, and sellers. Not sure what can be done to remedy that waste.

NYC is not as exposed as you think. NY harbor has a very narrow mouth and building a proper sea wall wouldn’t be too challenging.

My experience with the NYC school system is that it’s a highly competitive process. Public schools start off zoned, and the best school zones command a premium for rent and home ownership. Private schools are harder in that you have a competitive application process and then a costly tuition. Starting off in a good school bodes well for landing in a top middle school and eventually a top high school.

For public schools:

https://schoolzones.us/nyc/ https://www.schooldigger.com/

NYS tests all their schools. A school which is in the top 10% of the NYS testing would be considered elite. Top 20% is good. The majority of NYC schools are bottom 50%.

I really enjoyed this theory when I was introduced to it about 10 years ago. A lot of ancient literature talks about voices and gods in a way where this seems plausible. But then there is the idea that the bicameral mind completely disappeared across the world in a somewhat uniform fashion.. that was hard to swallow. We simply don’t evolve like that.

As someone who has been in industry for over 20 years, I have been spending a lot of time thinking about and living thru this. For me, my manager and my relationship with him/her is probably the most important aspect of a job. A few things that I have learned:

* Manager maturity. Young managers tend to be very hands on, they will naturally micro manage. More times than not they were previously an engineer, possibly in the role your currently in, so they continue that role, but with manager power and authority. Young managers work best with younger employees. Young engineers need hands on managers to explain things and mentor them, and that plays well into a young managers need to manage things. Older, experienced, and more mature managers tend to learn what their value is and where they are important. Usually that means listening, coaching, and communicating. Technology has literally changed under their feet, so they tend to be hands off. They also rely on their team more to make decisions and drive towards success. They depend on you. These managers tend to work well with senior engineers (a senior engineer is someone with decade(s) of experience).

* Conflict. A few years ago I did a handful of manager interviews with some top tier tech companies. I was a bit dismayed that these companies were not looking for nice guy managers who values things like compassion and supporting their employees, and possibly even be friends with them. Rather, they want managers who are hard, can wield power, and comfortable with conflict. Conflict here means that management and employees are in constant dispute over literally everything. This means that managers are constantly squeezing work out of you and will have no problem introducing politics and other authority techniques into their day to day managing.

* Communication. Open communication is extremely important. What I find is that when you have many layers of management, decisions are made using information which needs to be withheld from lower levels (or decisions are made based on no information!). When it reaches the lower level, it seemingly makes no sense and can be confusing. Good management communicates everything and does so transparently. This way everyone understands the what and the why. If you don't agree, given you know the why, then you are at least better prepared to pitch an alternative or make the needed changes. If the why is something contentious like you or the team is underperforming, then again, its best that is communicated so everyone has the power to make corrective action, if they choose.

I think a lot of this comes down to culture. A good culture values engineers and adapts to making them happy, as that is how you get the most productive engineers.

Interesting, around the same time both of my Ubuntu 20 laptops (Dell and Lenovo) started having major problems connecting to my home wifi. It only affects these 2 laptops, all my other devices have no problems. Before reading this I did think this was a result of a bad Ubuntu update. Given they switched kernel versions, my guess is that this is infact the culprit. Very annoying, it takes me anywhere from 5 to 45 minutes to establish a wifi session now.

How does Rust prevent data races?

Assuming you can transfer a connection more efficiently than having the peer reconnecting, how do you transfer the L7 application state? What if TLS keys are involved? What if the original host is long gone...

Not sure I understand why they so definitively state that 2 extremely similar variants are proof of animal to human jumps and not just a simple human to human mutation. Are there more details to support this conclusion in the paper?

I’m actually interested in testing this scenario, a drive getting power loss. Is there a thing which will cut power to a server device on command? Or do you just pull the drive out of its bay?

The select() was not interrupted in a traditional sense. It looks like a special windows API was used (QueueUserAPC) which allows a callback to be called in user space while blocked in certain kernel calls. My guess is that when used normally, the callback returns and control goes back to select() being blocked, but in this case an exception was thrown which unrolled the stack and left the select() call holding onto an invalid stack address. Jumping out of a syscall using an exception like this will probably cause all kinds of problems and my guess this is completely unsupported.

https://docs.microsoft.com/en-us/windows/win32/api/processth...

There is an array cast [] in there. So dereferencing that using a plain * takes you back to the original double pointer.

Not sure I buy this. The key difference between these algorithms and the brain is in the understanding of language. The language we speak and think with conveys meaning. These algorithms have no idea what the meaning of any text is, rather, they are just looking for patterns and combinations of patterns using huge libraries of language data. If anything, this is good mimicry. Maybe this is why NLU has split from NLP.