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
Jevonian
An experimental local proxy for coding agents that sends jevonian/auto turns to Jev to pick a model and thinking level after deterministic compatibility and quota filters, while explicit routes skip Jev.
- Category
- Tools & Integrations
- Published by
- Community
- Author
- xinyao27
- Added
- 2026-09-22
Tagscommunitytypescriptroutinggatewaycoding-agent
Highlights
- Only jevonian/auto consults Jev; the plan, execute, utility, and chat aliases, an explicit x-jevonian-phase header, and real model IDs bypass it.
- With no brain configured jevonian/auto errors instead of guessing, and if every configured brain is unreachable the request fails with 502.
- Jev evaluates a structured snapshot of intent, tool results, error counts, context headroom, and cache penalties, and returns route and depth in one call.
- Low-confidence turns are marked in the ledger and response headers rather than rerouted; fallback channels are tried only when the primary channel fails.
- Each turn records provider, actual tokens, cache reads, and estimated cost; credentials live in ~/.config/jevonian/credentials.json at mode 0600.
Quickstart
bash
npm install --global jevonian
# then point your agent at http://127.0.0.1:8787/v1Watch out
AGPL-3.0-only. Needs Node.js and a provider key or sign-in; Jev receives recent messages and tool results (the optional fullPrompt setting can send the whole conversation), and cost and cache figures are estimates.
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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