A New Kind of Model for AI Decision-Making?
Exploring TypeSafe's Jev and comparing it with OpenAI on intent classification The post A New Kind of Model for AI Decision-Making? appeared first on Towards Data Science.
The article titled "A Practical Guide to OpenAI's New Decisions API" was published on Towards Data Science. It poses the question, "Is this the JEV killer?", suggesting a discussion about the impact of OpenAI's new Decisions API. The post appears to explore practical aspects of using this API and its potential significance.
Exploring TypeSafe's Jev and comparing it with OpenAI on intent classification The post A New Kind of Model for AI Decision-Making? appeared first on Towards Data Science.
The article titled "An Introduction to Jev" discusses an AI system that focuses on making decisions rather than generating text. It highlights Jev’s unique approach to decision-making within the broader context of AI development. The piece was originally published on Towards Data Science.
We’re releasing an API for accessing new AI models developed by OpenAI.
The article compares seven open decision models with a generative large language model (LLM) in a hands‑on repository called Jev. It evaluates each model based on routing accuracy, latency, and output reliability. The comparison provides practical insights into how these models perform in real‑world scenarios.
TypeSafe AI’s new model, Jev, is a ‘System One’ or decision model that takes text or semi‑structured data as input and outputs floating‑point probabilities for yes/no, choice, or score questions, rather than text. It charges only for input tokens ($0.042/million) and offers free output, making it cheaper and faster than typical LLMs. The API lets users build a state object (string, array, or key‑value pairs) and query it with multiple questions, receiving confidence scores and probability distributions for each answer.
A minimal OpenAI Agents SDK implementation where retrieval becomes a search-read-decide loop The post Agentic RAG: Let the Agent Search appeared first on Towards Data Science .
Our first class of OpenAI Scholars is underway, and you can now follow along as this group of experienced software developers becomes machine learning practitioners.
Maximize the latest OpenAI model The post How to Work Effectively with GPT-5. 6 appeared first on Towards Data Science .
Simon Willison announces the release of the llm-openai-decisions 0.1a0 plugin, which interfaces with OpenAI’s new Jev-style Decisions API. The plugin, inspired by llm-typesafe, supports image and text input and offers the same three question types (yes/no, choices, scores) as Jev, with pricing at 10¢ per million input tokens. Installation is simple via `llm install llm-openai-decisions`, and an example query demonstrates image-based evaluation.
How one open-source ecosystem made state-of-the-art AI accessible The post The Python Ecosystem That Changed AI Development appeared first on Towards Data Science .
OpenAI is a non-profit artificial intelligence research company. Our goal is to advance digital intelligence in the way that is most likely to benefit humanity as a whole, unconstrained by a need to generate financial return.