Jev in 25 Lines of Python
- ID
- 27700
- Status
- summarized
- Published
- 23 Sep 2026, 3:26 PM
- Fetched
- 24 Sep 2026, 4:22 AM
- Provider
- Hacker News
- Category
- dev-community
- Original URL
- https://www.nobodywho.ai/posts/jev-in-25-lines/
- Source URL
- https://hnrss.org/best
Summary
- Score
- 7.0
- Created
- 24 Sep 2026, 4:24 AM
- Tags
- Audience
- developersai_ml_learnersai_agent_usersvibe_coders
What happened
A parody post claiming to implement 'Jev' in 25 lines of Python, using llama-cpp-python with a Qwen3-0.6B GGUF model to do constrained multiple-choice classification by extracting logits for specific token labels (A, B, C) and computing softmax over just those choices. The author explicitly labels it a parody and links to real open implementations: OpenJev, openjev-sglang, and OpenJev on DiffusionGemma.
Why it matters
The concrete technique—restricting an LLM to a fixed set of choices by reading logits for specific tokens and renormalizing—is something you can use today for fast, local, private classification without an API. If you ship agent pipelines that need structured decisions, this shows the core mechanic is trivial and you don't need a proprietary service for it.
Discussion angle
The logit-restriction trick is the real takeaway: walk through how it works and whether your current agent classification steps could be replaced with a 25-line local script instead of an API call.