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Evaluation & Training

Literal reading

Jev answers the question you wrote, not the one you meant; scoping words, negations, and implied conditions are read at face value.

Category
Evaluation & Training
Also known as
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Related terms
4
Directory entries
1
Docs
docs.typesafe.ai
Added
2026-09-24

Definition

A person might read the intent behind an instruction; jev-1.13 reads the words. If an answer looks wrong and you find yourself explaining what you really meant, that explanation is the missing half of the instruction.

The guardrail is to state the exact condition in instructions and put boundary cases in criteria. Where interpretation is unavoidable, split it into two literal questions and combine them in code.

Tagslimitationsdesign

From the directory

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

From the community

Posts from builders shipping with Jev right now.

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jev(): Postgres WHERE clauses in plain language

I think I just cooked something 🔥 jev(): a PostgreSQL extension that searches your whole database in natural language. No index, no embeddings, just one function. WHERE jev(people, 'could work from home') or WHERE jev(people, 'name sounds european') 129 rows judged in ~1s  Show more

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 real-time ad blocker that classifies every DOM element

I build an undetectable realtime adblocker extension with typesafe It checks every dom element and classifies as ad/non-ad and removes it if true Extremely fun to work with, expecting an incredible shift in how AI is being used in the future

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 plus Astra beats the Ender Dragon for under a dollar

Tool calling as classify plus action, back to twelve-factor agents

jev is the best excuse you could possibly have to go re-read 12 factor agents. Tool calling itself can be decomposed into classify+action, if you learn to design ai programs as pipelines that switch breathlessly between classification, structuring data, deterministic code, AND Show more

Dillon Mulroy
Dillon Mulroy
Cloudflare
@dillon_mulroy

i think jev is resonating with devs so well b/c it unlocks so many opportunities for composing ai into systems and products rather than ai _becoming_ the product/system really does feel like it was a missing primitive

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Why Jev encodes a state and outputs distributions instead

Jev is one of the more interesting model launches I have seen recently because it asks a very simple question: Why are we using autoregressive LLMs as insanely expensive if statements inside software? The easiest way to think about it is: LLM: text -> generate tokens 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

Reply

How the agent stack moved in Jev's first three days