samesense

A clone of popurls that learns what I like to read: https://perurls.onrender.com/

I get that grouping similarly colored icons might look better, but does it make it harder to find the app you want? Do you mainly pick apps by position? For quickly locating an app, wouldn’t it be better to place similar colors far apart, so you remember, for example, that the music app is the red icon in the lower right?

I had a very similar idea. I’m glad someone implemented it.

I’ve been controlling rsi with upper body mobility and strength training + typing breaks for years. This mind over matter approach sounds like bullshit.

Here's a python script to find the rss url on a science journal's website. It leverages smolagents and meta-llama/Llama-3.3-70B-Instruct. The journal’s html is pulled with a custom smolagent tool powered by playwright. Html parsing is handled by a CodeAgent given access to bs4.

I've tested with nature, mdpi, and sciencedirect so far.

I built it b/c I tired of manually scanning each journal's html for rss feeds, and I wanted to experiment with agents. It took a while to get the prompt right.

Suggestions welcome.

I use snakemake along with dvc. It detects changes in files, and reruns steps to produce downstream files as needed.

Is this a tool, plugin or philosophy?

producerpower doesn't work for me

Excellent. I’d pay to see guido’s python.

Yes. I've seen several examples of decision support since 2010. It's not easy to train models because the codes for procedures can be ambiguous. Furthermore, since hospital classifiers favor medical error recall over precision, docs can be swamped with so many warning messages that they tend to ignore them.

Take adverse drug interactions as an example. The training data for drug interactions mostly come from adults, so the resulting models do not apply in a pediatric setting. When the models are let loose in pediatric hospital, a high percentage of the drug interaction warnings are false positives, so these type of warnings tend to be ignored.

It seems that the trend is to use decision support with a lot of human oversight and investigation of the raw data to see if the model conclusions are correct.