jlamberts

Sounds like subscription usage sharing? I had a "subscription sharing" section show up in my account usage as well

Engineering is about creating something that serves a purpose, while operating within a set of constraints. Part of that is realizing that "100% correct/reliable in all circumstances" is an unrealistic goal, since implementation time and cost is one of those constraints.

A good engineer will acknowledge this tradeoff between robustness and cost and behave accordingly. For example, if you're working on safety critical or very foundational systems like OSes, medical tech, etc you should bias very heavily in favor of robustness. If you're not, this can easily be an act of overengineering. The engineer's job is to find the right spot along the cost-correctness curve for the thing they are building.

This has always been true, and LLMs just change certain parts of the equation. For example, code writing is far less of a bottleneck than before, so "we can just try with a throwaway impl and see if this works" is suddenly economically viable. It also turns out that many things, in practice, don't need to be as correct as some of us may have believed.

We can still enjoy making quality things, but doing so is often an act of artisanship rather than engineering.

I think it's mostly that he's a vocal critic of AI/big tech at a time when much of the public is anti-AI and anti-big-tech, so his message is resonating with people and he's popular.

I'd agree with you if this applied to primary residences, but it seems like this only applies to secondary residences? I find it hard to reconcile "middle class" with "has a second home in NYC"

Re: existing model, for recsys, as long as the product already exists you have some baseline available, even if it's not very good. Anything from "alphabetical order" to "random order" to "most popular" (a reasonable starting point for a lot of cases) is a baseline model.

I agree that a randomized control is extremely valuable, but more as a way to collect unbiased data than a way to validate that you're outperforming random: it's pretty difficult to do worse than random in most recommendation problems. A more palatable way to introduce some randomness is by showing a random item in a specific position with some probability, rather than showing totally random items for a given user/session. This has the advantage of not ruining the experience for an unlucky user when they get a page of things totally unrelated to their interests.

Yes, it's just really nice to be able to push a button and have it hold at the right temperature; sort of like how boiling water on the stove or in the microwave and using an electric kettle are functionally equivalent but the kettle is way more convenient.

Guessing the hygenics depend on your local water supply, but some varieties of tea (and coffee actually) want to be brewed at below-boiling

A little desktop app that lets me upload transaction csvs from my bank and figure out how much I need to split with my partner. Mostly because I always forget to charge her for utilities or flight bookings and I hate going through the bank UI. Might also expand it with some simple subscription auditing logic.

Also, a dramatic anime intro (complete with cheesy AI generated theme song and video) starring our foster kittens. It's been interesting to learn about some of the techniques needed for consistency, how to storyboard, etc.

I don't see how their analogy is stupid. Aeropress and french press are pretty similar from an "enthusiast coffee device" perspective. Lots of room for variability in grind size, coffee choice, and specific brewing technique with both methods.

Might have been k-nearest-neighbors rather than k-means. Knn can be used for "recommended because you bought X" or "users like you also bought X" type recommendations that relate user to user or item to item.

K-means could potentially be helpful to group together common users/items if e.g. you're memory constrained and don't want to give each user a fully unique embedding entry so that's also possible.

With free games (which make up a pretty large portion of competitive online games), cheaters who get banned usually just make or buy new accounts.