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Jev in 25 lines, on Hacker News
The thread on NobodyWho's 25-line logit readout, including argument over whether reading local logprobs is the same product as Jev.
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- bashbjorn
- Last verified
- Sep 2026
Works forJev for Classification
Used inJev for Software & DevTools
Highlights
- 690 points and 212 comments on 23 September 2026.
- The linked post is a local Qwen3-0.6B example, and the author calls it a parody.
- Commenters argue about whether a logit readout is usable in a company, and about what the terms of service forbid.
Watch out
Terms-of-service claims in the thread are commenters repeating what they heard. The post itself is a separate entry.
Questions about this entry
Short answers drawn from the linked project or page.
The thread splits on this. One side treats it as a trick you could have done long ago; the other argues a hosted model gives you a tuned objective, stable outputs and support, none of which you inherit by reading logits off a local checkpoint.
The most concrete advice in the thread is a coverage check: the candidate tokens should account for 95% or more of the output distribution, ideally 99%. If they do not dominate, the model is not really following your format.
Yes, and the thread quantifies it. One commenter reports up to 70% bias toward option A on ambiguous items, recommends averaging over permuted option order, and notes that wrapping the letters in parentheses kept coverage near 99%.
The entry copy is explicit that the thread's terms-of-service claims are commenters repeating what they have heard, with nothing cited. Read your provider agreement directly instead of trusting the thread on this.
Check that the multiple-choice tokens dominate the output distribution, summing to 95% or more and ideally 99%, or the model is not following instructions. Constrained decoding fails the same way: if the model does not want a valid answer, the one you extract is low quality.
Ask the model multiple times, permuting which option maps to which letter, and average the results. They also suggest framing the prompt as if it were the middle of a quiz, because Question 1 carries baggage that Question 12 does not.
Structured outputs: have the model emit the pre-defined multi-token option directly, such as Legitimate, Spam or Phishing, instead of a single letter whose probability can be diluted by the prose the model wanted to say.
Reactions & coverage
Posts, threads, and videos about this entry from around the web.
Hacker News: Jev implemented in 25 lines of Python
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