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
AI Stock
A Python multi-agent stock research workspace covering 12 markets with optional JEV trading decisions: configure a TypeSafe key, select the JEV decisions-only strategy, and get buy/sell/hold, probabilities, and confidence without a research report.
- Category
- Tools & Integrations
- Published by
- Community
- Author
- EthanAlgoX
- Added
- 2026-09-22
Tagscommunitypythontradingagentsdata
Highlights
- Research covers 12 markets including mainland China, Hong Kong, the US, Japan, and the UK.
- JEV decisions need a separate TypeSafe API key in Settings, then the JEV decisions-only strategy.
- Paper simulation supports the first six markets and never places live broker orders.
- Runs locally with Python 3.10+, Node 20.19-26.x, and npm 10+, or via a hosted site with an invitation code.
- Also offers an expert roundtable, strategy screening, and saved research reports.
Quickstart
bash
pip install -r requirements.txt
if [ ! -f .env ]; then cp .env.example .env; fiWatch out
MIT-licensed. Needs Python 3.10+, Node 20.19-26.x, npm 10+, Git, and a tool-calling model provider; reports and simulated performance are not financial advice.
More like this
156GitHub stars
An on-demand crypto decision-support terminal that ingests 50-1,000 tweets through a two-tier statistical pipeline, then has Jev's System One answer four typed questions and return a decision card with entry ranges, stop losses, and targets without executing trades.
Tools & Integrations#community#python#trading
CommunityJev-X-Sentiment-Analysis
17.5kGitHub stars
Browser Use's ultrafast agent: Jev picks an operation and an element from an indexed action space in one request, and a small LLM writes text only when the action is TYPE_TEXT.
Tools & Integrations#community#python#browser
Communityjev-ultrafast
2.6kGitHub stars
A cross-platform memory layer for AI coding agents: conversations from Claude Code, Codex, DeepSeek Harness, OpenClaw, and OpenCode become Markdown memories indexed in Milvus, with optional Jev reranking through the TypeSafe API.
Tools & Integrations#community#python#agents
Communitymemsearch
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