andrewdb

This is a bit of a paradox. Indication that it works requires some kind of proof. Proof is mostly a byproduct of people trying things. If the rule is “don't try it until it’s proven,” and proof requires people trying it, then any new innovation would be in a little bit of a pickle.

Your worldview requires other enterprising people to try new things, write about them, find the rough edges, help to iterate and improve, and then surface their findings in such a way that it can meet the bar of "working".

jq is awesome. However, selecting data out of larger JSON payloads can lead to some hairy jq statements.

This is where Jev comes in.

I built jevq, a small sidecar to jq. Basically, jq handles the structure, and Jev answers a semantic question about each value. Once the values I care about are found, I keep processing with jq like normal.

jq -c '.[]' tickets.json | jevq "this is an open refund request" | jq '{id, subject}'

Instead of writing a hacky regex or string match for every way a customer might ask for their money back, I just ask the question.

Why do we call them GPUs these days?

Most GPUs, sitting in racks in datacenters, aren't "processing graphics" anyhow.

Sure, there is a problem with slop AI PRs _now_ .

That will not remain true for infinity.

What happens when the AI PRs aren't slop?

We are getting to a point where AI will be able to construct sound arguments in prose. They will make logical sense. Dismissing them only because of their origin is fallacious thinking.

Conclusion:

Dismissing arguments solely because they are AI-generated constitutes a class of genetic fallacy, which should be called 'Argumentum ad machina'.

Premises:

1. The validity of a logical argument is determined by the truth of its premises and the soundness of its inferences, not by the identity of the entity presenting it.

2. Dismissing an argument based on its source rather than its content constitutes a genetic fallacy.

3. The phrase 'that's AI-generated' functions as a dismissal based on source rather than content.

Assumptions:

1. AI-generated arguments can have true premises and sound inferences

2. The genetic fallacy is a legitimate logical error to avoid

3. Source-based dismissals are categorically inappropriate in logical evaluation

4. AI should be treated as equivalent to any other source when evaluating arguments

If the PR had been proposed by a human, but it was 100% identical to the output generated by the bot, would it have been accepted?

So when will multiple Waymo cars communicate input data to one another to avoid the blind spots?

This would give the ability to see things other cars cannot see as well.

One way to slightly mitigate the difficulties of nuance in language when translating to formal arguments is to attwmpt to always steelman the argument. Afford it all the guarded language and nuance you can, and then formalize in premises and conclusion.

This would also make interaction much more civil as well, given so much proclivity to do the opposite (straw man).

It's not a perfect approach, but it helps. LLMs are quite decent at steelmanning as well, because they can easiky pivot language to caveat and decorate with nuamce.