larwent

I’ve been using something similar called Twinny. It’s a vscode extension that connects to an ollama locally hosted LLM of your choice and works like CoPilot.

It’s an extra step to install Ollama, so not as plugnplay as tfa but the license is MIT which makes it worthwhile for me.

https://github.com/twinnydotdev/twinny

Ask and you shall receive https://i.imgur.com/fN5EoIB.png

The exception to this appears to be "Show HN" posts. E.g. https://news.ycombinator.com/item?id=31516108

> Several high profile companies have already announced layoffs in recent weeks

Which companies are these exactly?

What do long tenured developers see in this latest new cycle? Are Bloomberg just stirring up drama is or there a strong possibility of a genuine, long lasting drought in developer salaries?

Seems I missed the "Great Resignation". I've stayed at the same company for the last few years, making a mediocre salary because they let me take a "senior" role with fewer YEO and I'm buildings skills faster than if I'd started over at another company.

On the other hand, I know people who've moved for a significant raise to companies that are more sensitive to their stock performance, with less job security.

Seems pretty simple. When we create upper and lower boundaries to some score, people with lower scores have more space to overestimate and those with higher scores more space to underestimate, causing the perceived score to trend towards the mean.

I think there's both a component of numbers and psychology here. If the dispersion in perceived score caused by inaccuracy is wide enough to touch the bounds, it will force a trend towards the mean. This effect is possibly exacerbated by a tendency of perception to stray from "extremes", so subjects with a score near the edges will trend to the mean more strongly as they are unlikely to rate themselves the very best or very worst.

Unless I missed something, this article doesn't explain WHY random data can result in a Dunning-Kruger effect. The relationship between the "actual" and "perceived" score is a product of bounding the scores to 0-100.

When you generate a random "actual" score near the top, the random "perceived" score has a higher chance of being below the "actual" the numerical below is larger than the one above, and vice-versa. E.g. a "test subject" with an actual score of 80% has a (uniform random) 20% chance of overestimating their ability and an 80% of underestimating it. For an actual score of 20%, they have an 80% chance of overestimating.

It's the typical GANS face layout, with a blurry background, eyes centered and cropped to the face. It's certainly possible those are could be real people, but in my experience law firms usually have upper-body shots of the lawyers with their arms folded, or standing together as a team or with a client.

I wouldn't catch these at first glance, but the older gentleman specifically stands out to me with the

1. tuft of hair above the right eyebrow

2. teeth far offset from center

3. soap-bubble colored noise around the hair features

These aren't unusual on their own (except #3 maybe) but all together they make the photo seem fake.

The lawyer profile faces instantly give the site a fake vibe as they look like those generated by GANS AI.