They hired more employees in countries where salaries are lower, and built "AI-powered bots" to "automate internal tasks".

They don't go into further detail, so I am curious how these efforts worked out in practice. And how do they actually assess "efficiency"? (3/3)

One interesting thing the article mentioned was the pressure the company received from investors to become more "efficient." I suppose normally that would mean layoffs, but the article claims they worked to improve efficiency without layoffs. (2/3)

A new Eddie Bauer jacket for $20? Can’t resist…

The article is otherwise an interesting read on the state of underwater internet cables and plans to build more.

There's no "streak freeze" like in Duolingo? 😅

Maybe I’m using the wrong LLM, but I tried asking one to write regex for identifying phone numbers in a string, and it gave me an expression that didn’t work. I tried twice more, and still no luck 🤷‍♂️

Hi! I'm Dan Capellupo, a former astronomer, switched to data science 7 years ago. I have worked at a couple startups, as well as a big corporate. Currently, working on an open-source project called featuring-data and doing some freelance DS work. Look forward to connecting to other DS folks here!

Also, should Apple Maps count towards screentime when I’m using it for navigation?

Also, interesting that on Medium, someone can give an article 50 "claps"

I also emailed their support yesterday, but have heard nothing yet in reply.

In the US. I recently updated to PyCharm 2024.3 (on MacOS), which has been overall buggy, at least in their Jupyter notebooks support.

Same here, I thought their algorithm was only running locally.

Gotta love the honesty 😂

I thought this was a specific and common enough example that LLMs could handle easily 🤷‍♂️