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Jev Workshop
Hands-on workshop repository for Jev with five live-executed Jupyter notebooks, a 48-slide deck with speaker notes, a cited reference, six live-build projects, and 24 walkthrough notebooks with UIs.
- Stars
- 1
- Reading time
- 2 min
- Published by
- Community
- Added
- Oct 2026
Highlights
- Five Jupyter notebooks run against the live TypeSafe API, with outputs saved in the repo
- 48-slide editable deck with speaker notes and facilitation tips on every slide
- 15,000-word cited reference distilled from the official Jev documentation
- Six live-build projects including a model router, tool firewall, content blocker, and RAG reranker
- A full top-to-bottom run of all five notebooks costs under one cent
Questions about this entry
Short answers drawn from the linked project or page.
Clone the repo, run uv sync to install pinned dependencies, export TYPESAFE_API_KEY with a key from the TypeSafe console, then run uv run jupyter lab notebooks/. The notebooks also read the key from a .env file.
No. Every notebook ships with its outputs saved, so the full workshop, including real API responses, can be read on GitHub without a key or any calls.
A full top-to-bottom run of all five notebooks costs under one cent. Jev is priced at $0.042 per million input tokens with output free, and the porch example used 1,229 input tokens for about $0.00005.
TOP-25-USE-CASES.md is a grouped top-25 with article links, and JEV-USE-CASES-WILD.md catalogs 82 verified real-world builds with a live-build shortlist and a sources section.
Only model_router.py, which uses Jev to pick a model and then calls OpenAI, so it needs OPENAI_API_KEY. The other projects, including the tool firewall and RAG reranker, run on Jev.
None. It covers what a model is, how RLHF works step by step, why RLVR produces reasoning models, and how RLCD rewards honest probabilities, with two audience quiz slides built in.
Jev takes a state, any text or JSON, plus typed questions and returns typed answers with calibrated probabilities in about 100 ms. Jev makes the decision and your code owns the action.
Reactions & coverage
Posts, threads, and videos about this entry from around the web.
YouTube: LangChain maps System 1 and System 2 onto fast decisions versus slow generation.
Facebook: Intake Router keeps diagnosis out of the answer space at the front desk.
FacebookClinicnotes
X: OpenRouter Decision Model Rankings
X: herdr + jev, an agent router
From the Jev guides
Original write-ups on what this entry is for.
From the Jev AI model community
Posts from builders shipping with Jev right now.