TypeSafe AI opened early access to Jev on September 15, 2026, and it is a different kind of model. Rather than writing text, TypeSafe Jev takes unstructured state such as a support ticket or a JSON document and returns typed answers in a single forward pass. Four days later, a researcher who says he published the idea in March 2025 shipped an open-source reply called Laya.
What TypeSafe Jev actually does
Jev answers through three primitives: choice, an option from a set; score, a place on an ordered rubric; and noul, a calibrated yes or no. There are no tokens to generate and no JSON to parse.
The company’s claims are aggressive: 70ms to 500ms per response against 3 to 329 seconds for frontier LLMs, or 40x to 200x faster, at $0.042 per million input tokens with output effectively free. Founder Diogo Almeida co-authored the InstructGPT research behind ChatGPT, and TypeSafe left two years of stealth with $40 million led by DCVC.
A researcher says he got there first
Nandakishor Mukkunnoth, founder of ConvAI Innovations, says the concept is his. He cites a March 2025 paper on reinforcement learning for sales conversion prediction, plus open weights, an open dataset, a PyPI package and a Reddit post on r/LocalLLaMA, then a second paper.
He wrote that TypeSafe “proposed the exact same non-autoregressive decision concept as if it was a brand-new scientific breakthrough,” launching “without technical papers, without open weights, and with zero open training datasets.”
What he alleges is uncredited prior art, not copied code. Nobody has shown Jev reuses his implementation, and TypeSafe has not responded publicly. The pushback on Hacker News was that the concept has academic precursors and that TypeSafe’s contribution was shipping a product.
Laya, the open reply
Laya is Apache-2.0 and ships three checkpoints: a 421M English model, a 322M multilingual model covering over 100 languages, and a typed-decisions model. It installs with pip install laya, and the repository logged six releases in a day.
Mukkunnoth reports 32.8ms per question on one T4 GPU and 7.2ms batched, against third-party Jev latency of 236ms to 276ms, plus 0.766 on the shared typed-decisions set against Jev’s published 0.727. Those Jev figures come from other benchmark runs, because his project never ran Jev itself, and his model card warns that Laya ships overconfident until its temperature is refit.
It fits on a free Colab T4 with no API bill, which matters locally: the self-hosted shift behind our local inference clusters and local AI agents on RTX PCs now reaches the decision layer.
An independent test cuts both ways
An independent tester ran 78 cases from the classifier-benchmark suite on an M4 Pro Mac mini. Jev, run by the suite’s author against the hosted API, scored 0.974. A 27B model compressed to about one bit per weight scored 0.885, GLiNER2 0.795, Von 0.769, and Laya last at 0.590 while still being fastest at 30ms per case against roughly 302ms for Jev. The speed claim survives and the accuracy claim does not, though 78 cases is a small sample.
The decision layer is where AI stops being a chat partner and becomes a function call inside software. Whether it belongs to a funded lab or to anyone with a T4 is now open.
Sources: TypeSafe AI | Laya | arXiv:2503.23303 | arXiv:2510.01237 | Laya on GitHub | Hugging Face | independent test | DCVC | Hacker News
Giancarlo Viterbo
Giancarlo Viterbo is a Filipino Technology Journalist, blogger and Editor of gadgetpilipinas.net, He is also a Geek, Dad and a Husband. He knows a lot about washing the dishes, doing some errands and following instructions from his boss on his day job. Follow him on twitter: @gianviterbo and @gadgetpilipinas.
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