DelightOne

Are you able to just re-run the data for the timeframe, or is "fixing it" a more manual process? And what observability are you working with into the process?

That's what install outside of the App Store is for. On your own risk-

I don't want random apps to paste potentially dangerous things into other apps. Its understandable.

Imagine a banking app, and for example an IBAN field.

So if they played a short annoncement beforehand so people know its an Original, it would be fine? Originals get advertised heavily, next-movie, so I assume putting it in the same playlist is fine.

Amazon Originals, Netflix Originals. Disney Originals. Paramount Originals. I'm just wondering what is different between series and music, that for music its very bad morally to create your own and to put your own in the front row. While for other streaming its accepted.

Isn't that how every streaming service does it?

In what environment do you run such tests? Do you have a script for it, or do you have a UI that manages the process?

This time, I tried to learn from that: facts are stored as instants, reasoning happens in local days of the jurisdiction that cares.

I think that's how the JavaScript Temporal proposal works. Convert your instant to the timezone, make the comparisons/calculations, hope you didn't jump an hour due to summertime, convert back.

They don't wanna be like Facebooks' .1%.Thy know your user.

The threat already silences the opposition. You don't have to use it to silence people.

I bet they tried an llm scam detector. It was too good so they didn't put it into production.

Its death by a thousand cuts.

Making people able to sue for anyone feeling bad about not having gotten the job is a path you should not take. We have something similar in Germany and its horrible for companies. Leeches bleeding you dry.

How does an e2e test for less capable LLMs look like, you call each LLM one by one? Aren't these tests flaky by the nature of LLMs, how do you deal with that?

Congratulations!

I've got a small question. How do you deal with people asking for open sourcing your product/code, claiming they don't want to use a product they don't control?

And be focused. Understand everything in every article, do not accept not understanding everything.

I read my first programming books that way. Always ask "Why is that word here" and "Why is that ! there". Made me see details.

Every time I hear it contains AI it sounds like the features' outcome will be uncertain. Especially with more experience. If your feature were good you wouldn't have to mention AI to show it is awesome.

More than not AI is used as an excuse for the feature to be bad.

Problem is most innovation is in using existing models in new ways. You can't expect these most people to train their own models.

Regulating it the way you say just means "zero innovation!".

The UX is not good enough yet. We would have to 1) show that it is reserved to people going on the link and 2) offer good/enough ways to be notified once the link becomes online and 3) need to know the likelihood of the link to actually work in the future based on prior commitment.

AND I probably forgot a couple of issues.

Or sync it with Dropbox. Just drop newly-rendered tables into the images folder and bam! you can directly reference them in your latex.

No wonder Sony goes after Windows people. Half of them soon need a new device.

LLMs sure. My question is whether it is the same in practice for LLMs behind said API. I found no official documentation that we will get exactly the same result as far as I can tell.

And no one here touched how high a multiple the cost is, so I assume its pretty high.

Is there a study or anything that that is guaranteed adding an incomplete assistant: response as the input and the API taking off exactly the same way on the same position?

How do you know it will have the same state of mind? And how much does that cost.

The proposed approach used multiple LLM chains. Then you don't know how much context you need. I imagine it can get expensive. Per Commit.

The open source maintainer gonna pay this?

How do they know its not that the LLM ingested a couple Alice in Wonderland analyses?

Do you know what state of the art for storytelling generation is? Searching for my thesis and not sure whether papers reflect the current state of the art.

OpenAI's API says they support German? Never tried it though.

https://platform.openai.com/docs/guides/text-to-speech

We agree, solving binary search tree problems do not need solving. That's an old problem. Problem is if you need people to choose and adapt algorithms for a specific new problem, copilot will have a hard time and so you will have a hard time if you don't know how the algorithms work and never adapted one. Because copilots are not good at choosing the correct algorithm and adapting it for new problems, as far as my experience with the competitive programming course I took goes anyway.

If you have an old problem, copilot can solve it, true.