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Evaluation & Training

Dates as text

Jev reads dates as strings, not ordered quantities; extract components with Choice and do the ordering and math in code.

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Evaluation & Training
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docs.typesafe.ai
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2026-09-24

Definition

Asking which of two dates comes first, how far apart they are, or whether one falls in a window is unreliable, and mixed formats, relative references, and domain boundaries such as quarters make it worse.

The recommended split is extraction as a judgment and arithmetic as code. Every date part is a small closed set — twelve months, thirty-one days, a bounded year range — which turns extraction into a Choice over enumerated options with an explicit not-stated option.

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Related terms

Definitions that connect to this one.

JaggednessThe documented set of jev-1.13 failure modes — literal reading, counting, dates, indirection, context rot — each with a guardrail.Evaluation & TrainingChoicePick one option from a set you define; returns the selected option, a probability for every option, and confidence.PrimitivesUnreliable countingJev does not count reliably — characters, occurrences, or list items — and error grows with the size of the thing counted.Evaluation & TrainingPre-parsed value extractionRegex finds candidate spans, then Jev picks the requested one so code copies a verbatim value that cannot be invented.Patterns

From the directory

Jev 1.13 Jaggedness
A maintained list of jev-1.13's known failure modes, literal reading, unreliable counting, dates as text, indirection, context rot, and contradictory criteria, each with a guardrail.
Practices & PatternsDocs#official#docs#evaluation
Official
Cookbook: Date Extraction
Extract dates by asking seven Choice questions about a date's shape and parts, then resolving them in code: five of six examples auto-accepted and missing dates flagged at 0.46 confidence.
Cookbooks & DemosDocs#official#cookbook#extraction
Official

From the community

Posts from builders shipping with Jev right now.

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A Jev-shaped model on Cerebras and Qwen

Shannon 🤖☠️
Shannon 🤖☠️
@iamMrDuncan
·

Built an alternative version of @typesafeai but on @cerebras with Qwen 3.8 27b. Similar quality, similar performance, but vastly different cost. TypeSafe was way cheaper, and did beat Qwen on performance. Closest we can get using LLMs I think. Source: github.com/iammrduncan/ty…

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Diogo Almeida
Diogo Almeida
TypeSafe AI
@CompleteSkeptic

After co-inventing ChatGPT, I kept asking myself: why have superhuman chat models not led to AGI? I’ve spent the last 2 years in stealth building a new way to train models (RLCD), and a new type of frontier AI model that we are releasing today: Jev • 20-200x faster • 40-400x

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Jev benchmarked on two public safety corpora

Gaurav Gosain
Gaurav Gosain
@_GauravGosain
·

1/ Benchmarked TypeSafe's Jev on two public safety corpora. It doesn't generate text, it returns calibrated probabilities you threshold in code. 96.5% on prompt injection, all 662 messages in deepset/prompt-injections. No tuning. 325ms p50.

Terminal dashboard showing 96.5% accuracy on prompt injection and 89.0% pairwise on vulnerable code, with a context ablation and calibration plot.
Diogo Almeida
Diogo Almeida
TypeSafe AI
@CompleteSkeptic

After co-inventing ChatGPT, I kept asking myself: why have superhuman chat models not led to AGI? I’ve spent the last 2 years in stealth building a new way to train models (RLCD), and a new type of frontier AI model that we are releasing today: Jev • 20-200x faster • 40-400x

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An open 151M-parameter decision engine

Hemant
Hemant
@heman10x
·

TypeSafe AI came out of stealth with Jev, and access is behind a waitlist. I built an open source version Verdict (Open-jev) you can run right now in a browser tab: And its a real post trained model..(link in comments) It is a post trained 151M parameters model. ModernBERT-base Show more

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Diogo Almeida
Diogo Almeida
TypeSafe AI
@CompleteSkeptic

After co-inventing ChatGPT, I kept asking myself: why have superhuman chat models not led to AGI? I’ve spent the last 2 years in stealth building a new way to train models (RLCD), and a new type of frontier AI model that we are releasing today: Jev • 20-200x faster • 40-400x

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Agent Handoff Gate: agents verify what they hand off

A local Telegram analyzer with Jev as the classifier

Dagmawi Babi
Dagmawi Babi
@DagmawiBabi
·

I vibe-coded this simple, open-source and local Telegram content analyzer using Jev as a classifier. Jev Classifier • jevclassifier.vercel.app Export your channel data as JSON and it will analyze each post's intent, quality, sentiment, and reaction tone and if it's a DM/Group Show more

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Diogo Almeida
Diogo Almeida
TypeSafe AI
@CompleteSkeptic

After co-inventing ChatGPT, I kept asking myself: why have superhuman chat models not led to AGI? I’ve spent the last 2 years in stealth building a new way to train models (RLCD), and a new type of frontier AI model that we are releasing today: Jev • 20-200x faster • 40-400x

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A Krunker-inspired FPS, decided by Jev

Neel Patel
Neel Patel
@Neel490
·

Maybe I’m just washed, but Jev is beating me at my own game. Built a Krunker-inspired FPS where you 1v1 Jev. Try it out yourself: jev-arcade.vercel.app/duel

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Diogo Almeida
Diogo Almeida
TypeSafe AI
@CompleteSkeptic

After co-inventing ChatGPT, I kept asking myself: why have superhuman chat models not led to AGI? I’ve spent the last 2 years in stealth building a new way to train models (RLCD), and a new type of frontier AI model that we are releasing today: Jev • 20-200x faster • 40-400x

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