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GPT-5.4 5 months ago

Are you may be comparing the pro model to the non pro model with thinking? Granted it’s a bit confusing but the pro model is 10 times more expensive and probably much larger as well.

You can activate the uv venv from anywhere just fine, just do source path_to_sandbox/.venv/bin/activate. Probably makes sense to define a shortcut for that like activate sandbox. Your conda env is also linked to a directory, it’s just a hidden one, you can also create the uv obe somewhere hidden. But I get it to some extent conda has this large prefilled envs with a lot of stuff in it already that work together. Still if you then end up needing anything else you wait ages for the install. I find conda so unbearable by now that I voluntarily switch every conda thing I have left over to uv the second I need to touch the conda env.

That might take a few days or weeks, it seems like they put in some decent effort into it this time. From skimming the supplement I wouldn’t be suprised if the speedup is only 100x though. That’s still significant, but clearly less than they claim. For example I am not entirely convinced that 20% flops efficiency is really the upper limit or that the slicing overhead of 5x is really needed here.

But if you happen to have n=2^c, then an algorithm with logarithmic complexity only needs c time. Thats why this is usually referred to as exponential speedup in complexity theory, just like from O(2^n) to O(n). More concretely if the first algorithm needs 1024 seconds, the second one will need only 10 seconds in both cases, so I think it makes sense.

For me it really is extremely close to 100% for timers, I barely remember it being wrong and I use it several times per day. Finding my phone via the HomePod also works pretty much every time, may be 90% for me but it doesn’t recognize my wife so for her it basically never works. The others I don’t use enough. But timers and reminders work really well for me and it’s also what I need to most from an assistant.

Many of my friends in Germany have Telegram as well and I think that it’s mainly because it has by far the best multi device (esp. Desktop / Notebook) UX. Switching devices with Telegram and using it on several devices concurrently has always been super easy.

The short answer is very likely no, at least not without any other major breakthroughs. The reason why Viterbi is fast is because the underlying probabilistic model is rather simple, which does not hold for quantum situations in general.

While that might not fit your needs I map unions to Postgres with an enum encoding the type and then adding constraints on which additional columns must be set (and also must not be set) depending on the type. For your example the enum might be leaf or tree and then if it’s of type tree you have non nullable left_child and right_child columns and if it’s a leaf those must be null, and then you might have a value column that is non null in both cases or only for leaves.

Your sql query example doesn’t seem to actually need timezones. If all orders are stored in the correct UTC regardless where they come from. An order is always made at a specific point in time that is the same globally.

If you have a 5k 27 inch screen directly next to it you will see a difference, but it’s not huge and I agree that 4k at 27 inches is sufficient from a pixel density perspective. But the scaling is not so nice, 5k at 27 inches allows integer scaling and getting a reasonable size out of it. Same for 6k at 32 inches or more for even bigger screens. I am waiting for the 6k dell mentioned above as well.

It seems to be priced relatively cheap already with 32GB Ram and a 512GB SSD. A Mac mini with only 16GB of memory already costs nearly twice as much and I really wish there were a 32 gb mini at all. I like my Mac mini, but I hit the memory limit from time to time.

It definitely can scale much better, we are using around 1800 kWh (2 parents, 1 small child) and I think it’s safe to say going below 1300 for one of us would be hard, especially when working from home. But we live in a flat and there are many 2 or three persons households in a whole house, probably with several computers and may be even tvs and certainly more lights. May be you do save a bit on cooking and the fridge, but cleaning things should roughly scale linearily.

I think it’s great that you publish and maintain this library and nobody can or should tell you to put more work into it. That said a package that did have explicit methods for every endpoint with documentation of what it does and what the parameters are would be awesome for me as a user. The docs will pop up when I write the code and make it easy to explore the different methods from my editor. No need to switch to the apple docs all the time (but probably still sometimes). And again I still think your library can stay as it is. I really don’t think this string parsing magic is the way to go. It can be useful in some simple cases, but specifying a whole API goes to far imho.

I think you may be should give a code first GraphQL a try, you can pretty much everything you want that way. You can write high level functions for pagination or whatever that way and have a base object that you can extend for all Types implementing the interface. I really don’t know why schema first seems to be the default, it’s really unflexible.

Does home have some label for that? Can you say turn of the floorstanding lamp and it works? If so that would indeed be very weird. I have a sleeping room in home and said it should turn of the light in the sleeping room in 5 minutes and it worked. May be it only works with rooms.