erikbern

> Pay yourself and a VC-appointed board member below market rate, hire six good engineers, lease some office space, buy equipment, pay for legal and accounting services… and presto, you’re burning through $5M a year

Would love to see the math behind this. How does this add up to $5M/year??

Founder of Modal here. We've spent a ton of time on this, including building our own distributed file system optimized for low-latency high-througput workloads. We don't use K8s or Docker and built our own custom infrastructure instead.

Cold starting containers quickly is a fascinating problems. We've gotten a long way but there's still a lot more to do. For GPU-based inference, starting containers isn't enough – you also need to initialize the model GPU quickly. We are working on a long list of things that will bring down cold start latency even further.

Annoy author here. What you're describing is Locality Sensitive Hashing (LSH) which is something I spent a lot of time trying back in 2009-2012 but never got it working. It has some elegant theoretical properties, but empirically it always has terrible performance. The reason I don't think it works well is that data often lies near a lower-dimensionality manifold that may have some sort of shape. LSH would "waste" most splits (because it doesn't understand the data distribution) but using trees (and finding splits such that the point set is partitioned well) ends up "discovering" the data distribution better.

(but HNSW is generally much better than this tree paritioning scheme)

I don't really buy this argument that you can rent and pay $1000/month, or you can buy an equivalent home and pay $1000/month, but now you can deduct interest rate and build equity.

Landlords can also deduct their interest expenses and so they benefit from leverage too (meaning they don't want to build up equity).

In a reasonably efficient market, this means that the equivalent cost of homeownership would be higher than rent, to offset these things (the rate at which you're paying down the principal, and the deductability of interest rate expenses). This is obviously a very simplified argument, and there's a lot of other factors going into this.

haha, author here, I do admit that Matt Levine is one of my favorite writers today

i'm the author – would love to read any existing literature on this if you can provide any pointers!

I wrote the code and you're wrong :)

1. Diagonal moves have a slightly higher weight. 2. I have an edge weight of 100 to go through another person. This makes it possible to go _towards_ the right direction even though there is no path 3. For the perpendicular lines method, the distance is modified to 1e-3 4. A* doesn't work because the heuristic becomes pretty useless when you have moves that are very "cheap": you need a lower bound that's pretty much zero

So for all those reasons, I just went with plain old Dijkstra!

They should have used containers.

I've found these tools extremely useful to understand user behavior. We rely on Fullstory all the time to improve the user experience, to identify bugs, and to debug user issues.

The result of these tools is a far better user experience on our site and many other sites.

"recording every keystroke" makes it sound like there's malicious intent, but it's misleading. It should be added that all these tools have a lot of options to avoid tracking sensitive data (like password fields) and we always rely on that (in fact anything else would be a compliance violation for us).

classical music has notoriously bad metadata on spotify – it just doesn't fit into the data model of artist/album/track. i think that causes the system to filter out a lot of the listening data or fail to find patterns.

not sure what's up with jazz but i'm guessing it could be a similar problem

only thing you can do to influence the collaborative filtering system is to make users listen to both you and some other artist.

as i mentioned in another reply, CF is really what powers DW