Entries 721 to 743 of 743, in the hub's default order. The introduction is on page 1.
Showing 721 to 743 of 743
Interactive 3D room that reacts to a few words: Jev sets lighting, curtains, music, TV, volume and the desk lamp, and the app renders the result. Shows the raw API response and an estimated per-call cost, with presets that make no call at all.
Official documentation page defining the System One model class and how it differs from an LLM. States the model evaluates a state and returns typed answers with calibrated probabilities, and that images, audio and video are not supported.
Tetris you can play yourself or hand over to the TypeSafe Choice primitive, which picks every move. Score, lines and level are tracked while it plays.
TypeSafe blog post arguing the case for machine-native models over chat-shaped ones.
Newsletter issue covering the Jev launch and what it means for teams currently using LLMs as classifiers. Reports TypeSafe's 20-200x faster and 40-400x cheaper figures.
Official docs for the TypeSafe agent skill, which gives a coding agent context on the three question types, architectural patterns and evaluation practice. Installs via Claude Code plugin, npx skills, or manual copy.
News analysis arguing the significance of Jev is the category rather than the benchmark numbers, since it targets decisions software makes internally rather than user-facing conversation.
Official browser playground for composing state and typed questions against Jev, with shareable links. Access is gated behind the early-access waitlist.
Reference for POST https://api.typesafe.ai/v1/systemone, covering the state/model/questions request body, the answer shapes, and error codes 401, 422, 429 and 529.
Official documentation for the @typesafe-ai/sdk npm package, covering installation (Node 20+), client setup and the question DSL.
Newsletter explainer on how Jev answers typed questions and how software can branch on the confidence value. Lists target workloads including fraud review, lead routing, security alerts, moderation and document classification.
Review describing Jev as a general-purpose semantic classifier, reranker and risk gate exposed as an API. Cites 67.8% accuracy on TypeSafe's four-workflow benchmark against GPT-5.6 Terra's 67.9%, at roughly $0.0004 per case versus $0.03-$0.18.
Compares Jev's single-pass decision approach against autoregressive LLMs and masked diffusion language models, with pricing and latency figures. Positions it as a decision layer rather than a general-purpose model.
Developer guide noting Jev does five things: classify, route, score, extract and branch. Covers pricing of $0.042 per million input tokens with free output.
Release guide walking through benchmarks, pricing and the API surface, aimed at developers evaluating whether to swap an LLM classifier for Jev.
Company manifesto arguing for machine-native intelligence built for software rather than conversation.
Live Val Town demo: type a sentence and watch 16 calibrated judgments move on every keystroke, including passive-aggressive, AI-written, tone and urgency, all from one API call.
Official site publishing TypeSafe's four workflow evals (security incidents, agent-trace observability, invoice processing, customer service) with per-model accuracy, cost and latency, including Jev alongside Claude, GPT and Gemini models.
Hex package listing for the Elixir SDK, version 0.4.0, MIT licensed, last updated 2026-09-18.
Skeptical piece noting the 193x and 444x figures come from TypeSafe's own evaluations and that the eval grades against two other models' answers. Points out the Doom demo gives the model state a human player would not have.
Independent index of publicly demonstrated Jev capabilities, 29 records at the time of writing. Each one links to its evidence, shows how it was verified and says whether it came from TypeSafe or a community publisher.
Hands-on guide using the Python SDK for support routing, with a timing comparison against Claude (0.35s vs 8.83s) and a defect-detection comparison where Jev found six of seven defects to Claude's seven. Warns that high confidence does not guarantee correctness.
Free demo of Jev with no sign-up: ask any question and get Yes, No or Maybe, grounded in live web search when it matters. Calls jev-latest through TypeSafe's System One API.
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