This difficulty is often not due to math - because of the aforementioned frameworks machine learning implementations do not require intense mathematics. An aspect of this difficulty involves building an intuition for what tool should be leveraged to solve a problem. This requires being aware of available algorithms and models and the trade-offs and constraints of each one. — Why is machine learning ‘hard’?

♪ Kendra Morris · Dry

Lots of tips and tricks for Mathematica aficionados.

When optimising code, never guess, always measure. — When Optimising Code Measure

TIL about Asahi Linux. Looks cool, although Ubuntu LTS already runs fine on my Mac, but it’s an old one, not one of the newest cool kids made in Silicon.

No, really, I’m astonished at how much software is basically useless without an internet connection. Net is no longer something additional to your daily tasks, it is essential for your daily tasks. — We’ve become overdependent on the net

♪ Jelly Cleaver · Black Line

Wanna get a plain text list of your Pocket bookmarks? Go to getpocket.com/export, then apply the following snippet on the HTML result file:

cat YOURFILE.html | grep -o '<a .*href=.*>' | sed -e 's/<a /\n<a /g' | sed -e 's/<a .*href=['"'"'"]//' -e 's/["'"'"'].*$//' -e '/^$/ d' > pocket.txt

What strikes me about my personal experience with LLMs is that I have learned precisely when to use them and when their use would only slow me down. I have also learned that LLMs are a bit like Wikipedia and all the video courses scattered on YouTube: they help those with the will, ability, and discipline, but they are of marginal benefit to those who have fallen behind. I fear that at least initially, they will only benefit those who already have an advantage. — LLMs and Programming in the first days of 2024