Listens on 127.0.0.1:4777 and answers nothing itself. Callers need no key; the key stays in Jeview.
A path prefix groups calls, and a Jeview-Trigger header links a later request to an earlier answer.
The viewer opens on the latest 20,000 calls. Search reaches the rest.
Needs Node 24 or later and has no packages to install for a normal run.
Two demos send real calls: Pixel Knight, and a made-up support inbox.
Quickstart
bash
./launch.sh
# point clients at http://127.0.0.1:4777/v1/systemone
Watch out
MIT. Unofficial and not affiliated with TypeSafe. The API key is stored in jeview.sqlite as plain text. Anything on the machine can read recorded calls. Do not expose it publicly.
An experimental Codex skill and JavaScript runtime that has Jev pick macOS Accessibility elements and actions from text candidates, while local policy gates hold sensitive operations for human confirmation.
A Codex skill (with a plugin option) that hands clicks, toggles, navigation, and scrolling to Jev over accessibility text while Codex types, interprets visuals, and verifies the outcome.
Standalone Android agent and React studio for Mobilerun, powered by Jev: Jev selects operations and observed targets, code rejects stale actions, and a CLI, execution traces, and latency measurements ship in the repo. No ADB connection required.
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 theShow more
tamara
@tamarajtran
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
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!
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 fasterShow more
Tested TypeSafe’s Jev (no-text, probability-only model) as an AI agent safety monitor.
Checking each action first worked well caught most attacks with almost no false blocks, and much faster than Gemini.
Diogo Almeida
@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
1/ Introducing CUA-S1: a family of System One Models, small, specialized, and built for computer use.
Today we're open-sourcing CUA-S1-FORMS, the first in the family: github.com/trycua/cua