Its for purchasing AI summaries. By default each user gets 1000 article summaries to begin with, which should suffice for several months for most users.
Initially I didn't want to put any in-app purchases but decided to add it to prevent abuse.
These datasets would definitely have a lot of Text => Sketch pairs as well. I wonder if its possible to extrapolate from Text => Sketch and Text => Image pairs to improve Sketch => Image capabilities. The models must be doing some notion of it already.
That is an interesting observation. I wonder what percentage of the training text data for LLMs contains proper dashes, since a large part of it is user-generated content.
People are coming to ChatGPT with an intent to type. Its much harder to introduce text-based interface to otherwise click-based interfaces because of the friction. Thats why chatbots often fail to gain adoption, because they are a byproduct of the product team being lazy and not thinking through the UX.
Thats why I go for the nuclear option (the one that requires higest attention, like account cancellation) when presented with a list of options by a bot. More likely to get a person that way.
Q4 comes out to be ~26GB but Apple doesn't let you load it on a 32GB Mac machine because they put a limit on the max usable unified memory at ~21GB (`device. recommendedMaxWorkingSetSize`) [1]. So for Q4 Mixtral MoE you'd need a 64GB Mac machine unfortunately.
Jetbrains IDEs have a local history feature that provides snapshots with diffs and allows partial rollbacks.
I only use it when experimenting with different approaches.
Mostly I only use the "Local Changes" tab in the Git panel that shows the diff compared to the last commit, or "Compare with branch" to see the overall changes relative to the base branch.
interface IResponse {
foo: string
}
async function doSomething() {
const reponse: AxiosResponse<IResponse> = await Api.call()
...
// or you can use AxiosPromise<IResponse> without the await
}
> ...It profiles applications using the Linux perf_events subsystem and interfaces with a target application's machine code through the Linux ptrace system call. When optimizing a program, it loads a code cache into the target program's address space...
Initially I didn't want to put any in-app purchases but decided to add it to prevent abuse.