I think the obvious next feature for this specific thing is to be able to click to begin asking questions in the context of the audio you just listened to. You can basically become one of the hosts- “You mentioned before about RNNs, tell me more about that”
I think it depends on some technical specifics, like which meta data was associated with that content, and the degree to which that content was surfaced to users that fit the demographic profile of a ten year old child.
If your algorithm decides that things in the 90th percentile of shock value will boost engagement to a user profile that can also include users who are ten years old then you maybe have built a negligent algorithm. Maybe that’s not the case in this particular instance but it could be possible.
Maybe it’s just a marketing problem then and it should be called asset collateral tax rather than unrealized gain tax, which on the face of it sounds kind of stupid.
I feel like using this extension is a little
buggy. Especially around undo / redo actions being captured in “format on save” type actions that happen thanks to jslint or other tools.
Yes I've tried to use the neovim plugin many times - but always run into some annoying bug, usually due to an interaction with another extension or VSCode feature. It has >400k installs though so it must be working for some people.
I’ve only been a consumer of a GraphQL API, so I don’t know what it’s like to maintain, but I mostly enjoyed using it.
Of course the documentation on some kinds of query syntax was too sparse, (this is for Shopify) but I could see how it might be nice for certain kinds of cases. If you run a platform it might be a good option to offer in your API. For shopify afaik there are equivalent calls in both REST and graphql so you have options.
Can you elaborate on lack of syntax docs? As far as I understand one of the big big huugge benefits of GraphQL is that you get the strongly typed schema via the introspection query, so you can build queries with some confidence, that as long as the schema (version) is the same it should be syntactically okay. What did Spotify do compared to this?
I think it wasn't just the schema structure, it was the API abstractions implemented in the schema.
If I remember correctly it was something about Shopify discounts, which can be applied multiple times and across different modalities- percent, dollar amount, etc. and what those were called in the API, and how they were represented and to which object they were applied to.
Then once I had figured that out, then understanding how to construct the query.
But of course my problem was more from the point of view of, "I just want to get x done". As the consumer of the API I wasn't as concerned about fully understanding the entire set of abstractions and schemas.
This is one of the problems with graphql, there are no docs because the schema is all you need. But that assumes the schema is logical and consistent, which it rarely is.
It also means you need to be an expert in the tooling to figure it out, so just dropping in to a graphql api is so frustrating compared to plain old rest
Also there is no standard for exposing the schema, and sometimes there isn't a schema at all. Sometimes a vendor will have the schema downloadable from their dev docs, sometimes it will be an endpoint that serves it up, and sometimes they just don't give it to you and expect you to use GraphQL Explorer directly to discover what you can do. When I encountered this I figured surely GraphQL Explorer must be fetching the schema, but I guess this is not always the case, as I never was able to get a complete schema that worked with the tooling. For that vendor, there was no way to generate client code for the schema using one of the many GraphQL client library generator frameworks.
I'm not saying the service isn't a true Scotsman, but a service that calls its API "GraphQL" but doesn't respond to introspection queries isn't really serving GraphQL.
This was also my issue with APIPlatform (php/symfony ecosystem tool to generate REST endpoints for your Doctrine entities). Nice OpenAPI spec autogenerated at first glance but very sterile and hard to use once you really needed to understand the business reasons behind some normalization choices.
this was also the problem with WSDL and REST itself, and so on. of course there are things that make sense to be in-band .. but there are things that have to be communicated out-of-band (just as REST doesn't say anything about what to do with a specific content type OpenAPI and GraphQL doesn't worth much with empty description fields, though I'd argue still much better than getting a .doc file named API_doc-Finalv2(1) :) )
> equivalent calls in both REST and graphql so you have options.
It's not that simple, new features are add to GraphQL only, some other things are REST only,some APIs work differently (like product search by title, in REST it have to be an exact match, in GQL it can be partial match)
This has been my experience when integrating with a vendor's APIs where they've been bit by the GraphQL bug. They have a full featured, stable and easy to use REST API, and then a poorly implemented GraphQL solution that doesn't cover all the use cases of the original API, and new features only appear in the GraphQL schema, so you have to be stuck using both and it's a horrible experience.
I'm also not personally a fan of having my API queries on the frontend span multiple lines for things that with a good REST design with OpenAPI are a single method call, but all too often the calls I would need to make to the vendor's GraphQL API were exactly this: did not make use of any of GraphQL's query features, and were effectively just RPC calls.
If you look at the arguments for/against resource consumption for bitcoin, a lot of the same arguments hold true- that actually we don’t necessarily have a deficit of power production. We have an energy capacity problem and distribution problem.
I wonder how much of this over supply can be taken up with AI training and how much that would change the calculations.
It's not really all that useful for AI training. The reason why energy is a problem for AI training is that you need a lot of compute at one place, distributing backpropagation doesn't work that well. When the energy problems hit, it's when the datacenter need exceeds the capacity of nearby power plants.
If we want an alternative I think we'd need to try alternative training algorithms that are easier to distribute. Predictive coding for example can approximate backprop, but requires more compute. Something in that direction might work https://arxiv.org/abs/2006.04182
From what I can figure out this is the longest one that: is on land. In/on a first world country (with a good level of logistical infrastructure) for the next 30-50 years.
There are other ones but the path of totality doesn’t pass completely on land or if it does it passes through Libya or something.
It’s crazy to think about how little
we actually know about how the human body functions- not even to say what an “optimal” sleep position might be, but even a high quality study on how different sleep positions might affect the physiology of the body. I have yet to find any real serious scientific analysis of this.
"not even to say what an “optimal” sleep position might be"
As with most things, maybe there isn't one optimal solution,as otherwise we all would have found out about it, by now and all use it?
Human bodies and minds are quite different.
So what is comfortable to one person might not work for the next person. Maybe people have pain in the back or in the neck, forcing them into other positions. People with stomach problems rather lie on their stomach. Some people people sleep alone, others together.
I know my sleeping position varies a lot and there is no single best one for me.
"Optimal" depends on how deep down the rabbit hole you want to go.
"Slept well and wake refreshed" might be one version of it, but "slept like an infant in the womb and awoke younger and healthier" would be more optimal, if currently in the realm of science fiction.
I feel like it must be true that there are many others out there that were able to hang on and now we merely know about them as a wealthy person, rather than as a criminal.
The question is what the ratio is: one caught for every ten not caught?
This is probably more rare than you think since very few fraudsters seem to know when to quit. Even after getting away with massive gains there is temptation to do it again (and now the risk seems even more justified due to past experience).
I think you have to conceptualize how diffusion models work, which is that once the green triangle has been put into the image in the early steps, the later generations will be influenced by the presence of it, and fill in fine details like reflection as it goes along.
The reason it knows this is that this is how any light in a real photograph works, not just CGI.
Or if your prompt was “A green triangle looking at itself in the mirror” then early generation steps would have two green triangle like shapes. It doesn’t need to know about the concept of light reflection. It does know about composition of an image based on the word mirror though.
