when a designer gets access to Jev
zio-typesafe-ai
A Scala 3 and ZIO HTTP client for TypeSafe's Jev System One API: ask typed Noul, Choice, and Score questions in one round-trip via a NamedTuple and get answers shaped exactly like the questions.
- Category
- Repos & SDKs
- Published by
- Community
- Author
- jamesward
- Added
- 2026-09-22
Tagscommunityscalasdkzio
Highlights
- Questions bundled in a NamedTuple share keys with the typed result, so asking for isUrgent returns a Probability.
- ChoiceCriteria and ScoreCriteria validate at construction: 1-255 options and 2-10 levels, and every noul or probability is a Double within [0.0, 1.0].
- Reads the same environment variables as the official SDKs: TYPESAFE_API_KEY, plus TYPESAFE_DEFAULT_MODEL defaulting to jev-latest.
- The .run variant returns a Result carrying the typed answers, responding model, and token usage; .answers returns just the answers.
- Built on ZIO HTTP's Client, with retries for 429, ServiceOverloaded, and InternalServer errors per TypeSafe's rate-limit guidance.
Watch out
Apache-2.0 licensed. A community client, not an official TypeSafe SDK; needs a TypeSafe API key, Scala 3, and ZIO HTTP, and Jev caps choices at 255 options and scores at 10 levels.
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From the community
Posts from builders shipping with Jev right now.
When a designer gets Jev
Full Jev video tutorial
Full Jev Tutorial What it is, how you can build with it and what new applications it can unlock → 0:00 Intro → 0:34 Jev explained → 4:06 API setup → 5:59 Demo 1: Voice-controlled browser → 11:33 Demo 2: AI memory → 17:27 Demo 3: YouTube predictor
The case against Jev-scored compaction
This is a terrible compaction strategy that fundamentally doesn't understand how compaction and context management work. Seems like a lot of people are confused so let's break this down. 1. Compaction isn't a filter The role of compaction is to clean up history to keep the Show more
found the perfect use case for @typesafeai Jev: instant compaction in 2026, why is compaction still a summarization prompt? Jev can make it instant by scoring every tool call and dropping what’s irrelevant
Classifying rows in DuckDB
I made a DuckDB extension where you can use @typesafeai 's Jev to do quick classification of rows in any csv/parquet file or duckdb table about 10sec for 1k rows ~ better than using an LLM, way more ergonomic than a classifier game-changing for data analysis!
A playable 16-judgment demo
typesafe's jev is fun! live demo you can play with: typesafe-demo.val.run
AI multiple choice, not essay writing
WTF is Jev by @typesafeai? Here’s the tl;dr ELI5: Think AI multiple choice, not AI essay writing. It doesn’t chat. It makes decisions your software can act on: “Spam or not?” “Which tool should this agent use?” “Does this need a human?” The exciting part: roughly 200x faster Show more