Claude Code Jev as MCP tools the agent can skip is not a gate. Put it in a PreToolUse hook that fails closed, with deny rules behind it.
automater.ai/intel/jev-clau…
Automater argues that running Jev as MCP tools in Claude Code, where the agent can skip them, is not a gate. The gate belongs in a PreToolUse hook that fails closed, with deny rules behind it.
Jev + Kimi K3 for fraud detection!
TLDR: Jev classified 100 emails in 1.42 seconds, then I routed the uncertain cases to Kimi K3. The full pipeline got 96/100 correct for only ~$0.07.
Video is not sped up, check out the live run!
Here was my process:
I gave Jev 100 emails to classify (a mix of 50 legit & 50 fraudelent emails). It classified all of them in 1.42 seconds.
An underrated feature about Jev is it will give you the confidence score for a classification, so I routed any prediction under 95% confidence to Kimi K3 to be fully sure.
31 emails fell below that threshold. After routing those to Kimi K3, the combined pipeline reached 96% accuracy.
The full run took 16 seconds & ~$0.07 in inference costs:
- $0.068 from Kimi K3 on @togethercompute
- $0.003 (1/3 of a cent) from Jev on @typesafeai.
I think this is a really interesting pattern: use a fast specialized model like Jev for the narrow task, then route the uncertain cases to a larger LLM.
I feel like this kind of approach could be a game changer for use cases like fraud or anything realtime. You can use the speed & low cost of Jev while having a larger LLM as a fallback to ensure high accuracy.
Tested TypeSafe’s Jev (no-text, probability-only model) as an AI agent safety monitor.
Checking each action first worked well caught most attacks with almost no false blocks, and much faster than Gemini.
