- Samplers that increase prose variance. They require running the model locally, they dumb it down, and never fix the actual issue, which is mode collapse leading to semantic collapse and rigid mapping of input to output concepts. The model still expresses the same ideas in different words.

- Let the model write anything if it couldn't resist, but check and fix it in the verification pass. This solves the semantic problem, but cannot solve the variance since the second pass is also subject to rigid mapping, i.e. you replace it with the same stuff over and over. The verification prompt can be randomized to a degree using pretty clever schemes to give it some variance, but of course this also fails in predictable ways.

Loved to use fancy words, speak at a “conceptual” level. Unfortunately it was mostly just tech mumbo-jumbo and he couldn’t actually back it up with real work - but I wonder if that’s why Claude does it. Makes it seem like a higher power, hand wavey abstractions that “seem” correct but don’t actually need to be rooted in truth or detailed.

“That’s exactly the type of seam we need to prepare for in a prod-like environment, if this change lands in the data plane, we’ve effectively shut down the load bearing critical path that was needed. It’s not over-engineered; it’s the right thing to do.”

Thanks Claude, whatever that means.

When you see the metaphor or euphemism or simile is off, the selection is not quite apt, it seems the stock phrase or term is rather more probable however askew than the precise phase, and it was already low confidence about what it's trying to say, so – as it's never said – Bob's your uncle.

"Honestly/honest" is, um, absolutely (ahem) one of those and if one listens carefully, one will notice humans use "Honestly? Blah blah…" that way as well.

It taps into load-bearing speech to prop up (see, that works) unsupported claims.

All the words I just used are correct in that sentence, doing exactly (ahem) the work they're supposed to. It doesn't have that level of nuance, isn't sure, so tries to sound like what's probably right.

They're a verbal shrug.