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TypeSafe's Jev, a rapidly adopted AI classification model, has become the fastest-growing model in Vercel's AI Gateway history. A commentator argues OpenAI could quickly replicate Jev's core approach and bundle it directly into its own models and agents, since OpenAI's LLMs already function as implicit classifiers that could be retrained for this purpose.
A Show HN post introduces JevBench, a benchmark that measures typed decision models by cost per 1,000 decisions rather than per token, using actual token counts from 534 v1.2 test decisions. The methodology prices systems with public tariffs at their listed per-token rates, while unlisted open-weight models are priced using OpenRouter or DeepInfra hosting rates for the same or comparable weights, explicitly avoiding raw GPU rental costs. Reported figures include Jev 1.13.0 at $0.0399 per 1,000 decisions, SemIf at roughly $0.022, and Winnow-12B Q8 at roughly $0.037.
TypeSafe AI has released Jev, its first 'System One' model designed not for conversation but for evaluating statements and making structured decisions for use in software systems. Built by former OpenAI researcher Diogo Almeida, the model claims to be up to 194x faster and 445x cheaper than frontier LLMs like GPT-6 Astra by processing parallel yes/no style queries with confidence scores instead of generating open-ended text.
A new joke npm package called jev-leftpad reimplements string left-padding by calling an AI model (Jev, via TypeSafe's SDK) to choose how many spaces to add, rather than using JavaScript's built-in padStart() method. It requires an API key, Node.js 20+, and only supports padding up to 10 spaces since the AI is given choices labeled space_0 through space_10. The author explicitly warns against using it in production or for anything important.
TypeSafe AI, founded by former OpenAI RLHF researcher Diogo Almeida, has emerged from two years of stealth development with $40 million in funding and its first product, a model called Jev. Rather than generating conversational text like ChatGPT, Jev is built to deliver fast, cheap yes/no or categorical decisions—in about a tenth of a second and for a fraction of a cent—aimed at software-to-software interactions rather than human chat.
An independent developer has released Laya, an open-weight, non-autoregressive decision model that predicts structured outcomes in under 35 milliseconds using reinforcement learning with calibrated decisions (RLCD) and routing across more than 100 languages. The creator says the underlying approach dates back to a March 2025 arXiv paper and open Hugging Face weights, predating TypeSafe AI's closed, proprietary Jev model launched in September 2026 by AI figure Diogo Almeida's team.
Diogo Almeida, a former OpenAI researcher who helped invent RLHF and build ChatGPT, has released a new transformer-based model called Jev through his startup TypeSafe AI. Unlike large language models, Jev doesn't generate text—it produces calibrated probability outputs, making it faster, cheaper and immune to hallucination. Developers have shown strong demand, with the company briefly unable to keep up with API traffic.
TypeSafe AI, founded by former OpenAI researcher Diogo Almeida, has released Jev in early access, a new type of AI model designed to make fast structured decisions rather than generate text. The company says Jev matches existing large language models on decision-making tasks while running 20-200 times faster and 40-400 times cheaper, using a new architecture and a training method called Reinforcement Learning for Calibrated Decisions.