The article title doesn't say "Fired". The HN title is kind of misleading.

It not the original title but I'm not sure it's "misleading"

> Within weeks, Binance fired or suspended at least four employees involved in the investigation, according to the documents and three people with knowledge of the situation. The company cited issues such as “violations of company protocol” related to the handling of client data.

Wild that Binance's primary concern was that the privacy of the people committing crimes with their service was being violated.

Hear no evil, and let the money roll in.

I think NYT uses multiple titles for some articles. I had copy pasted it

This is correct. They will A/B test titles AND update the title w/o warning over time, often 3-6 times per article post publication.

They used to change the URL a bunch of times after publication! Seems crazy because it is but they did. Caused a whole problem on Wikipedia because “title + day + work + url” suddenly wasn’t stable.

You can see https://bsky.app/profile/nytdiff.bsky.social for some examples of how the NYT frequently revises titles and abstracts after publication. Most of them seem harmless at least.

They A/B test titles. You can see it in the URL, where the recessive title often lives on. They may also use different titles for print/digital.

>Within weeks, Binance fired or suspended at least four employees involved in the investigation, according to the documents and three people with knowledge of the situation. The company cited issues such as “violations of company protocol” related to the handling of client data.

Calculator don't tell you step by step. AI can.

Symbolic computation is a thing. How do you think wolfram alpha worked for 20 years before AI?

And it’s making that up as well.

Yeah; it gets steps 1-3 right, 4-6 obviously wrong, and then 7-9 subtly wrong such that a student, who needs it step by step while learning, can't tell.

I’d love to hear your domain expertise—specifically which supplements you’d recommend avoiding, which ones are generally worth taking, and whether there are any brands or products you trust. I imagine you’ve picked up a lot of insights beyond the technical side as well.

I think it will be nice if there is a summary of that somewhere on the website. It was the first thing I was looking for.

Can you help me understand, if libraries like pandas and numpy also applies to your comment? Or are they truely optimized and you’re just referring to the standard Python language?

Here’s the plan:

Run the following models:

- Speech-to-text - Text-to-text - Text-to-speech - Text-to-image - Image-to-text - Text-to-video - Video-to-text

Start by integrating third-party APIs, and later switch to open-source models.

Implement everything using your preferred backend language. After that, connect it to a frontend framework of your choice to create interactive interfaces.

You want use your own data? Put it in a database and connect it to your backend, and run these models on your database.

Once you’ve done this, you’ll have completed your full stack development training.

I think this is a great take. Those problems have traditionally been hard to solve in engineering and you can get pretty reliable solutions from just an api call.

From what point of view is it bad? Technical?

It's a bad idea in general. Users would be annoyed, AI generated code is unreliable, just hire human engineers and listen to customer feedback.

Those are features. What human business problem are you solving?

Don’t put the cart before the horse.

Yes, it's important to start with a clear problem to solve. The ideas are great if they solve a real problem in an innovative or more efficient way than current solutions.

Sure you get paid more, but it comes attached with a whole baggage of other things.

A better research should provide insight on the total compensation over the entire career, work-life balance, impact, satisfaction and so on.

Now let's see what is better.

Downvotes help people skip the noise and focus on the signal. Ain’t nothing wrong with that.

I think what he means is that many of the seminal work related to cognitive science were produced back then. For example, Chomsky, Minsky, John Searle, David Chalmers and many more.

Things still move during winter, just not as much.