Jev in 25 Lines of Python

Going directly for the logprobs is always icky when you use a chat model as base, because they are trained to write prose as output. So your "choice" tokens and thus their probabilities might get diluted in whatever else it wanted to say. If you have to do it in the same way as this post, at least add clear system instructions and a carefully worded beginning to the assistant output section of the prompt to lower the chances of it wandering off immediately.

I've found that using structured outputs solves this problem much better. Instead of letting a model generate only "A", "B" or "C" and looking at the probs, have it directly generate "Legitimate", "Spam" or "Phishing" or any other pre-defined option from a set of multi-token sequences. Behind the scenes it boils down to something quite similar, but you're not running into the risk that the model actually wanted to say "A phishing attempt seems likely, so answer (C) is correct.", which would lead "A" to have the highest probability in the first token. You can even use a reasoning budget this way either via inherent reasoning or a free-form part preceding the remaining output structure. You can also have it assign probabilities (either in words or numbers) using more complex output structures, but I would not rely on them much more than the token logprobs (they can still be quite good though).

Beyond the missing latency and compute comparisons that Heaney commenter mentioned, also nothing about its error rate compared to Jev (nor if it even always outputs in a format the app can parse, not sure how solved that is).

But then at the end it says it’s parody. Maybe HN title should say it’s a joke.

Whilst I do like reading these things for technical know how, I can sympathise with the creator of jev who now presumably has to apply an order of magnitude effort to explain why the 100 smaller things done better than this add up to a much better product.

strong "You can build dropbox quite trivially by getting an FTP account, mounting it locally with curlftpfs, and then using SVN or CVS on the mounted filesystem" vibes

You have built something like jev but not jev (for starters, the output of what you've built will be absolutely worthless, the whole reason Jev is getting so much hype is because the output is good enough)

It's fast.

If you're comparing with something, you need to state 'fast' in relative terms. Jev is definitely fast, and if this Python takes the same time to get a decision then it's also fast. If it's 100* slower than Jev though, you shouldn't be calling it 'fast', because relatively speaking it's really, really slow.

I wonder if this could be a good stepping stone to write a local prompt router to optimise what model get what prompt. I.e. if the prompt is just a lookup, send it to haiku, if it's reasoning, send it to opus and if it's implementation send it to sonnet.

What I don’t understand is, why would you not want “reasoning” in a classifier?

Speed and cost are obvious reasons, but isn’t this a tradeoff?

Now, can you do it in <200ms for 45 questions at once, have 0% malformed output, and any kind of meaningful benchmark? We’ll wait!

Latency and compute comparison needed.

The principle is this.

Nothing I hate more than bullshit articles claiming X in Y lines of code, only to use libraries abstracting hundreds of thousands of lines of code.

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