Jev in 25 Lines of Python: A Parody That Actually Works
Everyone is hyping Jev as the next frontier of large language models. This parody blog post shows you can replicate its core behavior—classifying prompts into choices and outputting probabilities—with just 25 lines of Python using llama-cpp-python and a small GGUF model. No API, no synthetic data, no reinforcement learning required. It's fast, local, and keeps your data private.
There. That’s Jev.
- sigmoid10
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).
- antirez
Because of masked attention in LLMs, if you put the options before the body (the email to analyze), the transformer already knows what it needs to look for, and can use more tokens to create state to address that specific task (BERT has no mask in the attention, so tokens attend also to next tokens). You could also do a few examples in the system prompt to improve calibration.
Another trick that works is to repeat the question two times: "I'm repeating the task and labels for clarity: ..."
- philipbk
> "25 lines of python"
> "import Solution"
ok
- bruhhhhhh
I am hearing about Jev for the first time here so no idea about the hype.
So their(Jev) is that the thing is faster at classification than a frontier model? Because the whole type safe aspect is already fully solvable with structured output.
But their example is classification but that would also be possible and faster with a classic BERT model.
So their pitch is a task specific smaller model or am I completely misunderstanding the whole thing?
- no-name-here
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.
- alun
The one thing I can't wrap my head around with Jev is why they're trying to create that "System One" narrative.
In real life, a human doesn't do classification tasks with the System One part of their brain, they use System Two. So by definition what Jev does isn't System One thinking.
If anything, regular programming that automatically executes based on logic, without requiring "thinking" would be "System One".
- zeroq
How to write Jev in 25 lines of Python:
1. draw a circle
2. import the rest of the owl
- jorisw
Highly suspect of content marketing.
Ends with referring to a product, and saying "this is a parody post", after pretending to make a serious point.