TypeSafe AI

2026-10-30

MODELS · 1 SOURCE · InfoQ AI, ML & Data Engineering

InfoQ podcast: AI, ML and Data Engineering Trends Report 2026

InfoQ's AI, ML and Data Engineering editorial team published a 2026 trends report and released a podcast episode where editors and guest Chip Huyen discuss key trends: a shift toward smaller language models and reasoning models, wider deployment of AI agents (from personal to organizational), interest in System One models such as TypeSafe AI's Jev for cheaper/faster inference, growing focus on AI evaluations, and advances in Physical AI. The panel also offered near-term predictions including more US robotics innovation, cost and energy constraints on infrastructure, broader use of specialized/mixed agents, and continued delineation of tasks best suited for AI versus humans.

4.0

2026-10-16

MODELS · 1 SOURCE · InfoQ AI, ML & Data Engineering

AWS open-sources Strands Decider 2B and launches Strands Box sandbox for agent execution

AWS released Strands Decider 2B, a 2-billion-parameter decision-focused model (based on Qwen3.5-2B with a pointer head and rank-16 LoRA) and published the model weights, training data, and scripts. It also announced Strands Box (developer preview), a kernel-enforced sandbox using the Dogwood policy language to control agent execution (shell, Python, tools, filesystem, network); AWS reports ~115 ms median decision latency on an RTX 3090 and 72.3% accuracy on JevBench for a v19 checkpoint.

7.0

2026-10-09

STARTUPS · 1 SOURCE · TechCrunch AI

TypeSafe AI raises $870M at $7.5B valuation for non-text model Jev weeks after launch

TypeSafe AI said it raised $870 million at a $7.5 billion valuation for Jev, a transformer-based model that outputs probabilities rather than text, in a round led by Andreessen Horowitz with Sequoia and DCVC participating. Jev launched on Sept. 15 and the company says a third of Fortune 500 companies are already using it and that it runs faster and uses fewer tokens than LLMs, claims the startup has made about the product.

8.0

2026-10-02

MODELS · 1 SOURCE · The Decoder

Cloudflare launches Clef and Clef-flash decision models (Qwen-based)

Cloudflare released two decision models, Clef (based on Qwen3.8-27B) and the smaller Clef-flash (Qwen3.5-9B), which output probability-weighted choices for predefined answers so downstream agents can act automatically. The company says both support text and images, report median latencies of ~209 ms (Clef) and ~39 ms (Clef-flash), are compatible with TypeSafe AI's Jev API, available on Hugging Face under Apache-2.0, and will be accompanied by a reinforcement-learning service for customer fine-tuning.

8.0

2026-10-01

MODELS · 1 SOURCE · InfoQ AI, ML & Data Engineering

TypeSafe AI launches Jev, a decision-only model returning typed probabilities

TypeSafe AI released Jev, the first of its "System One" models: a decision-only model that returns typed, probabilistic answers (Choice, Score, Noul) instead of natural language, evaluating multiple typed questions in parallel and emitting probability distributions with confidence values. Jev has a 32,000-token context window, quoted input cost of $0.042 per million tokens (output free), end-to-end latency of 70–500 ms, uses a training method called Reinforcement Learning for Calibrated Decisions, and saw rapid early integrations from Vercel, Netlify and LangChain.

8.0

2026-09-30

MODELS · 1 SOURCE · TechCrunch AI

OpenAI previews Decisions API — a Jev-like fast decision model for Luna

At its Dev Day, OpenAI’s CEO Sam Altman revealed the Decisions API, a limited-preview product that lets the Luna model output probabilities over a predefined set of choices (e.g., classifications or agent behaviors) at high speed and low cost. Media and industry observers note the API resembles TypeSafe AI’s recently released Jev; proponents say such decision-focused models could cheaply monitor or gate agentic actions, though exact capabilities and calibration remain unclear.

7.0

2026-09-18

MODELS · 2 SOURCES · The Decoder · TechCrunch AI

TypeSafe AI unveils Jev, a model that scores predefined options instead of generating text

Startup TypeSafe AI, co-founded by ex-OpenAI researcher Diogo Almeida, introduced Jev, a model designed to return narrow labels and probabilities (judgments) for developer-defined questions rather than free-form text. TypeSafe says Jev responds in 70–500 ms and is very cheap (listed at $0.042 per million input tokens), but its published benchmarks are limited, comparisons use the company's own workflows, and the model's "no hallucination" claim only applies to output structure, not correctness within allowed choices.

7.0