I built a trading bot with Jev! Jev decides if it should "buy" or "sell", given the price feed of an asset pair, and executes real trades. It uses Monad to place the orders on Kuru's on-chain order book in every 300ms block. Demo link → jev-trader.vercel.app
jev-curate
A Rust CLI and PyO3 library that streams JSONL and Parquet rows through Jev Noul and Score rubrics, writing clean and rejected records to disk without rewriting content.
- Category
- Tools & Integrations
- Published by
- Community
- Author
- AkashPriyadarshii
- Added
- 2026-09-22
Tagscommunityrustclidataclassificationpython
Highlights
- Evaluates all rubric questions for a row in one HTTP request via speculative parallel fan-out; single-node throughput is bounded by the 1,200 req/min limit.
- Presets: reasoning-math checks circular logic, step validity, and reasoning depth 1-5; anti-sycophancy and code-correctness cover other flaws.
- The --dry-run flag simulates evaluation with host pre-filtering and zero API calls, so no TYPESAFE_API_KEY is needed.
- The project reports 24.0 rows/sec single-node on its local mock bench and targets 1,500+ rows/sec across a cluster.
Quickstart
bash
cargo install jev-curate
export TYPESAFE_API_KEY=YOUR_API_KEY
jev-curate filter train.parquet --preset reasoning-math --out ./output/ --concurrency 32Watch out
MIT-licensed. The CLI needs a TYPESAFE_API_KEY for live filtering, and the PyO3 bindings are constructor-only for now and must be built from source.
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From the community
Posts from builders shipping with Jev right now.
Trading bot, one decision per block
Classifying 1,500 real emails
this model is actually insane at email classification i tested it on 1500 of my own emails to see how well it works and I am blown away
@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
Fast browser use with Stagehand
we built blazing fast computer/browser use with Jev + @Stagehanddev. this task cost $0.001 and executed at near instant speed (in a remote browser btw) the loop: observe the page, send a11y tree as state + actions as questions, Jev decides the next action, then Stagehand Show more
@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
LLM-as-a-judge, sped up
Jev has spoken. It picked which model is AGI. 20–200x faster. 40–400x cheaper. This could make things like LLM-as-a-judge insanely fast and nearly free. (I tried a bunch of prompts and still didn’t burn through $0.10.)
@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
Instant compaction with Jev
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
A Claude session from 1M to 86K tokens
This is actually insane. This uses @typesafeai Jev model, as a plugin in Claude to review all the un-nesseasary tool calls, and it takes 1s to run! Like, literally, 1 second to take my Claude session from nearly 1M to ... 86K tokens! 😮 Ask your claude to install it and be 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