Jev for triage and routing
Picking a route, a label or a next model — classification where a wrong answer is recoverable.
19 items, from every source we cover All use cases
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Hacker News · thread
Benchmarking Jev: what a decision model can (and can't) do in an agent harness
The most useful dev.to piece on this model so far: about 22,500 calls across ten public datasets, with the code and raw results in a companion repository. Half the value is the negative results — model-difficulty routing at 51% (no signal at all), trajectory failure attribution at 0.560 AUROC (random), and non-English retrieval well below English. The other half is two engineering lessons from iterating: decompose a fuzzy judgment into orthogonal questions and compose them in code, and let a choice compete before a noul verifies.
- GitHub
GitHub · project
devagrawal09/jev-review
Read the README for the pipeline rather than the dashboard: a Null risk matrix, then Choice and Score file profiles, evidence selection, mechanism classification, severity, and a conditional Choice that routes which reviewer sees it. Orchestration stays in code and Jev only answers the bounded judgements — which is the pattern to copy.
- YouTube
YouTube · video
Jev - The Ultimate Classification Model?
Reads Jev as a classifier, which is the closest everyday description of a typed-decision model. Worth watching for that framing alone.
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X · post
Jev could become the control layer AI agents have been missing. Instead of spending 5–20 seco…
Argues that Jev belongs as the control layer rather than inside the model call. The seconds figure in the post is the author's framing of agent latency, not something we measured.
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X · video
Jev Engineering is what turns an agent stack into an actual control system and moves the expe…
A video version of the same argument: Jev as the thing that turns a stack into a control system. Watch it for the vocabulary, which is spreading faster than the model is.
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Jev Engineering: how to build the fastest AI Agent Brain in 10 Steps (Full-Setup)
A ten-step article about putting Jev where an LLM was doing the cheap work inside an agent loop — the same case our builds column keeps finding, written as a walkthrough with the setup included. The numbers in it are the author's, not ours; 750 likes and 31 replies at the time we read it.
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Jev is cool not because it re-invented classification, but because it makes ARBITRARY classif…
The most precise framing we found — not a new classifier, but arbitrary classification made cheap enough to put inside a loop. Read this before the vendor's own claims.
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X · post
Jev is the "Internet" moment for the AI industry It tells your agents and LLMs what to do nex…
The strongest version of the hype, posted as text. Read it for how the analogy is argued; the post itself offers no number, and none of the claims in it are ours.
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X · video
Jev solved local harness/model routing I use a combination of Claude Code, Codex and Opencode…
A developer using a decision model to route between Claude Code, Codex and opencode: the practical version of the routing argument made elsewhere in this column.
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X · post
jev will make agents 10x faster and cheaper, here's how: 1/ model routing: pick the right mod…
A thread on model routing as the first place a decision model pays for itself. The 10x in the opening line is the author's claim, not a number we checked.
- YouTube
YouTube · video
JEV: How It Works and What You Can Build
The only walkthrough in this column that publishes its own chapter list, and the order is the one we would suggest: routing at 2:08, then what Jev is, then the three answer types, and cost and speed only after that. Twenty minutes, and the first four are the part a developer needs.
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Hacker News · thread
LLMs Generate. Jev Decides. Software Should Know the Difference
An integration write-up rather than a benchmark, and the framing is the reason it is here: the author's complaint is that we took a machine built to generate open-ended sequences and asked it politely not to be. He put Jev into a real Java invoice-processing application through The Pipeline Framework, found the old prompt already contained the list of answers, and reports that some of the work being sent to an LLM was never an LLM problem. Declared AI-assisted on dev.to.
- GitHub
GitHub · project
superagents-lab/jev-search
Search where Jev chooses the sources, the time range and the terms, and then ranks what comes back — links and snippets with visible relevance scores, no generated answers. There is a live instance, so this is the one row in the column you can check in a browser without cloning anything.
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GitHub · project
thruwire/foreman
Puts the decision model above the coding agent instead of inside it: a Codex or OpenCode worker does the engineering, and Foreman independently asks whether the ticket is finished, the tests exist and the requirements were met. The separation of the worker from the thing that checks the worker is the pattern most of our field notes end up describing.
- GitHub
GitHub · project
TianyuCodings/NanoJev
A small replica of the mechanics: parallel decisions, dynamic routing. Useful as the readable version of the architecture if you are not going to work through the paper.
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Hacker News · thread
TypeSafe’s JEV Model: Is It Really 193x Faster and 444x Cheaper?
The independent check on the two numbers everyone quotes. They ran 100 support tickets through four question types against three frontier models — 400 decisions each — and measured 4x to 7x faster and 31x to 65x cheaper, not the 193x and 444x on the launch page. Their explanation is the useful part: a multiple is a property of the comparison, and the vendor compared against slow multi-step workflows while they compared against mid-tier single calls. They also reproduce the vendor's own side, a 474 ms median against the published 70-500 ms.
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We benchmarked fx auto mode (safety) classifier with @typesafeai's Jev. tl;dr: ~5-18x faster …
A benchmark post with a concrete result: roughly 5-18x faster on a safety classifier. Their numbers, their harness; it is listed because they published the setup.
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X · post
一些关于 Jev (@typesafeai) 的 notes 研究了一天 Jev，带来的新鲜感迅速回落，这好像就是一个更快的通用分类器/决策器，LLM 完全可以做到。 而且因为不了解模型…
A sceptical Chinese-language note: after a day of study the author reads Jev as a faster general classifier that an LLM could equally do. The counterweight this column needs.
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X · video
拿 Jev 做搜索重排，我先泼一盆冷水：单独用，它没打赢向量检索 TypeSafe 的 Jev 这阵子很火，一堆项目拿它做重排。我们在 Agent Skills Hub 的 33,047…
A negative result worth keeping: on their reranking test, Jev alone did not beat vector search. The only post in this column reporting that something did not work.