cloud-oak

The final example of the DnD statt sheet makes me think whether it's legal to nest <dl>s?

I.e. can we do

    <dl>
      <dt>Actions</dt>
      <dd><dl>...</dl></dd>
    </dl>

You can always ask Codex to fix Claude, issue solved!

> Training AI models can generate enormous carbon emissions

Sure, but what I'd really like to see is a graph for how much carbon is generated serving these models globally.

This one caught me completely off guard when opening YouTube the first time on an iPad: Accidentally clicked on a wrong button and got stuck in a "please subscribe to premium" modal. No amount of swiping or tapping outside the popup would help, only thing left was killing the entire app.

This experience put a major dent in my perception of the "Apple has the most intuitive UI" narrative.

This is already a problem with things like Mastodon - as soon as you subscribe to some more "spammy" accounts such as news outlets, all the other content is drowned out.

So yes, having kind of re-ranking _algorithm_ can be a good thing, whether we like it or not.

Picking up your original comment - Markov chains are much better at generating random numbers than either humans or LLMs!

What's really worrying is seeing medical professionals starting to rely on these tools.

My wife had a pretty bad cold during pregnancy and our GP proceeded to prescribe her cough syrup with high alcohol content, because that was what ChatGPT told him to prescribe. We only noticed it once she took the first dose and spit it out again...

That's why double-blind review shohld be the norm. It's wild to me that single-blind is still the norm in kost disciplines.

My understanding of this was that the UB starts only after the value is passed/returned. So if foo() has a contract to only return positive integers, the code within foo can check and ensure this, but if the calling code does it, the compiler might optimize it away.

I was very reluctant about the polars syntax as well initally, but it has grown a lot on me.

Pandas syntax is super ergonomic for quick one-off analysis, but it becomes hard to read/maintain once your processing gets more complex.

For example, the innocent

    df[arg]

can mean wildly different things - does it filter rows? Subset columns? Extract a single column as a pd.Series? There really is no way of knowing except for checking the value of arg.

In contrast, polars syntax feals clunky initially, but it's much easier for me to revisit a pipeline and quickly understand what it does.

SQL more elegantly introduces ternary logic in this case, where any comparison with NULL is itself NULL. This is sadly not possible in most languages where a comparison operator must always return a (non-nullable) boolean value.