Entries 49 to 60 of 60, in the hub's default order. The introduction is on page 1.
Showing 49 to 60 of 60
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.
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.
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.
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.
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.
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.
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