The guy kind of seems like he might be in a little over his head. That said I have used Claude (and other LLMs) for some similar stuff as an experiment and have seen them mess things up. Claude jacked up the canbus on a pre can gateway car while it was rolling down the road, which was funny because the whole Christmas tree in the dash lit up and my gauges dropped out (note: brakes and steering are mechanical and I knew exactly what was happening). I've never seen it so insistent that it needed to phone home and cry to Dario when it figured out it managed to crash a bunch of shit like the ABS and TCS, even though a restart cleared it and I told it that this was on a controlled safe course.

In general I've seen them split between 30% brilliance, and 70% pants of head outrageously stupid shit for electronics and automotive stuff (both diagnostics and repair/modification). I would not suggest even frontier models if someone didn't understand what they were doing.

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inventor7777 an hour ago | parent | next [-]

> I've never seen it so insistent that it needed to phone home and cry to Dario when it figured out it managed to crash a bunch of shit like the ABS and TCS

As a Codex user, what do you mean? Does Claude actually try to report issues back to Anthropic when something goes wrong?!

	▲	darkwater an hour ago \| parent [-]
		Yes, it opens a bug report automatically if it caused some physical issue, although it asks you to confirm first. In my case I was tweaking my own set-top box setup, implementing volume control from a remote mini-keyboard keys directly in my A/V receiver, and I had music playing and it set the volume very loud. I complained like "you made me almost half deaf" (I was kidding) and it panicked and reported the issue back home, and told me to call a doctor if the tinnitus persisted

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pcthrowaway an hour ago | parent | prev [-]

> In general I've seen them split between 30% brilliance, and 70% pants of head outrageously stupid shit for electronics and automotive stuff

I assume the corpus of text they were trained on matches that 30/70 split?

I assume there's some selection or fine-tuning that happens to improve models fed similarly unreliable training data for other domains... curious how this works.