Show HN: SPICE simulation → oscilloscope → verification with Claude Code

Post by svnt » Fri, Apr 17, 2026, 3:33 PM UTC

jddj wrote: post #25

ZihangZ wrote: post #24

Earlier quoted context omitted.

This matches what I've seen too — the hallucination gets much worse when the loop has no external verifier. "Does this board work?" has no ground truth inside the model, so it defaults to optimistic narration. What OP is doing here is actually the mitigation: SPICE + scope readout is a verifier the model can't talk its way past. The netlist either simulates or it doesn't, the waveform either matches or it doesn't. Th…

What throws me about this comment is the missing space between the period and the T in the last sentence. Did the model itself do that? Was it a paste error?

Post by svnt » Fri, Apr 17, 2026, 3:56 PM UTC

lambda wrote: post #32

svnt wrote: post #31

Earlier quoted context omitted.

I’ve also noticed Gemini and Claude occasionally mixing terms recently (eg revel vs reveal) and can’t decide whether it is due to cost optimization effects or some attempt to seem more human. I can’t recall either using a wrong word prior this month for some time.

Or just because mistakes are part of the distribution that it's trained on? Usually the averaging effect of LLMs and top-k selection provides some pressure against this, but occasionally some mistake like this might rise up in probability just enough to make the cutoff and get hit by chance. I wouldn't really ascribe it to any "attempt to seem more human" when "nondeterministic machine trained on lots of dirty data"…

Post by jddj » Fri, Apr 17, 2026, 4:35 PM UTC

svnt wrote: post #31

jddj wrote: post #25

Earlier quoted context omitted.

What throws me about this comment is the missing space between the period and the T in the last sentence. Did the model itself do that? Was it a paste error?

I’ve also noticed Gemini and Claude occasionally mixing terms recently (eg revel vs reveal) and can’t decide whether it is due to cost optimization effects or some attempt to seem more human. I can’t recall either using a wrong word prior this month for some time.

Post by pukaworks » Fri, Apr 17, 2026, 5:01 PM UTC

Post by svnt » Fri, Apr 17, 2026, 7:33 PM UTC

lambda wrote: post #34

svnt wrote: post #33

Earlier quoted context omitted.

Sure, but if that were the case why has it gotten worse recently? I would expect it to be as a result of cost optimization or tradeoffs in the model. I suppose it could be an indicator of the exhaustion of high quality training data or model architecture limitation. But this specific example, revel vs reveal, is almost like going back to GPT-2 reddit errors. I also don’t want to pretend there is no incentive for AI t…

Or just the models are getting bigger and better at representing the long tail of the distribution. Previously errors like this would get averaged away more often; now they are capable of modelling more variation, and so are picking up on more of these kinds of errors.