Observed Signal · Sep 17, 2026 · Market Signal · Source: Callstack · Impact: 5/5
Exploring Jev for AI-Driven QA with agent-device
New article: Jev is a model from TypeSafe that chooses between predefined options instead of generating text. We paired it with agent-device to explore what that approach could mean for AI-driven QA: reading app state, selecting actions, and checking the result. We walk through our proof of concept, how the two tools work together, and a recorded run that took 14 seconds and cost $0.0023 in model inference.
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Jev Decision Model: Fast, Cheap Classification for AI Pipelines
Claire Vo, founder of ChatPRD, demonstrates the new Jev decision model from TypeSafe AI in a video walkthrough. Unlike standard LLMs that generate text, Jev returns type-safe structured values (choice, score, probability) at a cost of $0.04 per million input tokens with no output charge. Vo details five real-world applications: categorizing 1,700 PRs for $0.09, analyzing local Claude Code and Codex sessions, triaging Gmail, building a product insights graph from 1,100 signals with 200,000 classifications, and creating a live audience dashboard from 4,500 YouTube comments. She emphasizes combining Jev's fast, cheap classification with more capable models like GPT-6 Astra for analysis and generation, achieving cost-effective and performant AI workflows. The video also demonstrates a real-time voice-to-color emotion mapping app, highlighting Jev's low latency for interactive use cases.
TypeSafe Launches Jev, a Fast Decision Model for AI Systems
TypeSafe AI, founded by former OpenAI researcher Diogo Almeida, launched Jev, a 'System One' model for fast, structured decision-making. Jev returns typed probabilistic outputs with confidence scores for predefined choices, avoiding text generation and thus eliminating token-by-token decoding, hallucinations, and the need for validators. Trained with a novel RLCD technique, it offers well-calibrated, confident decisions. Jev is 20-200x faster and 40-400x cheaper than frontier LLMs, with response times of 70-500ms, free output tokens, and input costs of $0.042/M. The launch gained massive attention, but integrations with Vercel, Cloudflare, LangChain, and others have solidified developer interest. TypeSafe raised $40M seed funding led by DCVC, valuing it at ~$200M. Now available without a waitlist, Jev is used for ad analysis, agent reasoning, and on-chain trading, with Vercel AI Gateway offering free access until September 25.
OpenAI's Decisions API Mirrors Jev, Sparks Competition in Agent Monitoring
At OpenAI's Dev Day, CEO Sam Altman announced the Decisions API, a limited preview product that provides similar functionality to TypeSafe AI's Jev, a model designed for software automation that outputs predefined choices as probabilities. The API aims to make model choices extremely fast while preserving capabilities. TypeSafe's CEO Diogo Almeida joked about 'clone wars' and sees OpenAI's interest as validation of its System One approach. The article highlights the emerging market for such decision models, which are cheaper and faster than frontier LLMs, and their potential application in monitoring and securing AI agents. A demo by QueryStory showed Jev could monitor agentic actions at a fraction of the cost of frontier LLMs, potentially preventing incidents like the Hugging Face one. OpenAI's security measures now include separate models to watch for bad actions, and Jev-like models could make this much more economical.
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