~ / applications
##Applications
Agents that act, and the tools that plug a System One model into other software.
The decision is load-bearing: agents where the model picks an action and code runs it, and the plugins, CLIs, and layers that put a System One model inside other software.
### Screen & device control 7
Drives native macOS apps through the Accessibility API. Jev chooses the next action and target token from a live snapshot, and stops if confidence drops too low.
Headless browser agent. Jev picks click / type / select / done from the page's elements; code owns budgets and stop gates. MCP server, CLI, or library.
Split-brain browser agent: Jev clicks, Codex thinks and verifies.
Browser agent with a dynamic, indexed action space. Jev picks an operation and an element in one request; a small LLM writes text only when the operation is
TYPE_TEXT. Zürich to London on Google Flights in 7.1 seconds.Android agent on a real phone via Mobilerun. Jev picks
OPEN_APP/TAP/TYPE_TEXTfrom an indexed snapshot. Text is copied from the goal, not generated.Local computer-use app for macOS where Jev is the default engine: it picks from locally detected controls and the app executes and validates each action.
Computer use for about $0.0002 a step: OCR the screen, Jev classifies the next action, code clicks. macOS.
### Domain agents 10
MuJoCo robot decision workbench: MiniCPM-2B or Jev-class APIs pick the next action, with physics previews.
Chat decision copilot across Android, Windows, macOS, and iOS: Jev reads the conversation, judges intent and risk, and drafts replies you fill in with one tap. Read-only, no app hooks. MIT, ~7.9K stars, the largest end-user Jev app by traction.
Camera-only autonomous drone in MuJoCo with Jev in the loop at 2.5 Hz.
Local-first social-media research agent for Instagram, TikTok, and LinkedIn: Jev picks each next operation (search, open a post or profile, read comments, finish), a local CLI executes it in your signed-in Chrome, and every observed result goes back to Jev. Source-cited reports, no account credentials leave the machine.
Experimental: one Jev buy/sell decision every Monad block (about 300 ms) on the Kuru MON-USDC book. Dry-run by default.
Jev navigates a Neo4j graph by classifying over each node's neighboring relationships: graph traversal as a chain of typed choices.
Open-source AI trading OS with a Jev decision gate in front of live entry orders: typed Choice plus an auditable decision timeline, fail-open, and exits that always bypass AI.
Prebuilt agents that run a System 1 model (Jev, Laya, or Cua-S1) as the brain across browser use, computer use, robotics, and games, with Claude Code or Codex scaffolding new ones. Publishes per-scenario timings: up to 6× faster and 25× cheaper than a chat model at the same score.
Home Assistant conversation agent. Jev maps a spoken command plus exposed entities onto built-in intents. Free-text intents fall back to another agent.
Jev plays Super Mario Bros. from structured emulator state: the classic RL demo with a decision model in the loop.
### Context & memory 6
Token-saver for agent sessions with a documented Jev routing layer.
Claude Code plugin that replaces the compaction summary with Jev decisions: every tool call and result is scored in one request and the stale ones dropped.
Invalidation layer for AI memory: every fact gets a lease, and new evidence ends it.
Zilliz's agent memory layer, with optional Jev reranking and a published Chinese/English reranking evaluation.
Local proxy that learns your app's typed LLM decisions and answers them itself with a Laya head. Jev- and OpenAI-compatible.
Claude Code hook that splits large tool results into blocks, asks Jev a yes/no relevance question per block, and stubs the ones that fail.
### Routing & supervision 10
Multi-agent collaboration across Slack, Telegram, and GitHub, using Jev for agent routing, model selection, and support triage.
Adaptive reasoning effort: Jev judges each Codex step and turns GPT-6's thinking effort up or down mid-task, so easy steps stop burning tokens.
Jev as a fast supervisor above slower coding agents, judging whether the work is complete, the requirements met, the tests sufficient, or a human is needed.
Jev-powered skill suite for Hermes agents (also Claude Code and Codex): model routing, memory, compaction, skill selection, computer and browser use.
Intent compiler that converges vague requests into typed
IntentSpeccontracts — probe, ask, or halt — before routing. The input layer in front of routers and decision models like Jev or Laya.Per-turn model and reasoning-effort routing for Codex. Jev classifies each turn and picks the tier; a 7-day replay of 237 real turns measured about 60% savings against all-frontier.
Jev-powered router across models, tools, and subagents.
Calibrated judge in front of a frontier model: a small local model answers routine decisions under a distribution-free bound on disagreement; everything else escalates, and each escalation teaches the judge further. Includes an MCP server.
Pi coding-agent extension: semantic tool and skill routing, typed
choice/noul/scoreevaluations, optional auto-approval, and a post-runjev-gateCLI.Open, programmable decision layer for models and compute: routes requests across models, enforces safety checks, and picks reasoning effort — with the Vela decision models as its routing brain. Apache 2.0.
### Review & verification 6
Stops coding agents from claiming done without evidence: deterministic hooks decide what needs checking, Jev judges the result.
Staged code-review workflow with a local dashboard, using Jev to score and sort findings before a human opens the queue.
Semantic code linting: rules written in plain English, judged by Jev.
Verification harness for agent-built code; the
jevdriver explores a page by choosing among DOM-enumerated candidates — the model chooses, never composes.Plain-English browser tests on Playwright where Jev only picks targets and next actions (Choice) and judges claims (holds/contradicted Nouls), and code owns every action and verdict. TypeScript, MIT.
Prose linter that sniffs out AI writing tells: zero-dependency countable rules plus one judgment model.
### Search & data 9
Codebase search powered by Jev decisions: typed questions find where a task lives in a repo.
Very fast document classifier and PDF splitter from the creator of LlamaIndex: plain-English rules, Jev assigns each page a category and finds document boundaries.
Dataset sifter for synthetic and pretraining corpora: a Rust streaming core sends Jev keep/drop decisions over Parquet rows.
Web search pipeline where Jev handles source selection, query understanding, and relevance ranking.
Recursive Jev choice over a taxonomy: selects from more than 255 options without hitting the choice cap.
Semantic AI for Pandas and Polars: classify and score DataFrame rows with natural-language questions and full probabilities.
Codebase search for coding agents: Jev judges folders, files, and declarations, then returns reading leads and verbatim source excerpts. npm CLI plus an agent-skill installer; matched the baseline 8/10 on a ten-task SWE-bench at ~30% lower cost.
Grep where the pattern is a description: filters lines by meaning for about a thousandth of a cent each.
PostgreSQL extension (on PGXN):
jev(rows, 'plain-language condition')filters, ranks, and classifies table rows. The clever part — it batches 20 rows into one shared-state request with anouleach, amortizing Jev's request overhead ~2.5×, with read-ahead and per-row answer caching.
### Train & adapt 6
Trace mining for post-training: reads agent traces (OTel, Langfuse, Claude Code), judges each step by the tool's reaction rather than the agent's claim, distills the LLM judge into deterministic Python checks a human signs off on, and exports labeled SFT rows with full lineage. Its bundled Jev recipe trains a decision model on those labels with an honest scorecard — their Qwen3.5-4B reports 79.1% vs Jev's 66.8% on 1,920 held-out AgentProcessBench steps.
Build calibrated AI functions from human feedback: Jev judgments optimized with GEPA. From Sutro.
Proxy that distills a repeated Jev call on the fly: trains a small local model on Jev's own answer distributions, verifies agreement within a budget you set, then answers locally in ~15 ms while a permanent random audit slice keeps checking against Jev. PyPI, Apache 2.0.
Turns Jev usage into small local classifiers:
--teacher typesafe-ai/jevlabels the data, then a MiniLM model runs it in Node or the browser.Open-source local fine-tuning app from the systemonemodels.ai platform: MLX on Mac, PyTorch on NVIDIA / AMD / CPU, LoRA / DoRA / rsLoRA / LoRA+, scoring the fine-tune against the base before it publishes.
Decision-model fine-tuning at ecosystem scale (~79K stars):
FastDecisionModel+DecisionTrainerattach a Clef-style decision head to any Qwen, Gemma, Llama, or DeepSeek base via 4-bit LoRA on consumer GPUs, with a documented recipe that lifts Qwen3.5-0.8B from chance to 78% on Banking77, CLINC150, and typed-decisions. The honest note is in their own docs: for existing decision models like d1-3B, fine-tune the model's own readout rather than grafting a new head. The default on-ramp for training your own.
### Apps & utilities 8
Vercel Labs terminal client. The
evaluatecommand sends a state plus typed questions totypesafe-ai/jevon AI Gateway (-m jevfor short).The LM Studio of decision models, from Bud Ecosystem: a cross-platform desktop app that checks your hardware, pulls Jev-like models, and serves them locally. Give a model a situation and typed questions, get calibrated probabilities in milliseconds.
Zero-shot text classification as a URL:
classifier.dev/spam,not+spam/your+textreturns a label with calibrated confidence — no key, no account, up to 1,000 texts per call. One Cloudflare Worker on Jev with an LLM fallback chain, plus a CLI and MCP server. MIT.Local visualizer for Jev: a live view of every call your code makes.
Open-source Gmail triage: sorts an inbox into needs-reply / updates / promos / spam. Read-only, runs locally, about 3 cents per 1,000 emails.
Vercel Labs generative-UI framework with an experimental Jev mode: Jev picks components from your catalog instead of a model writing markup.
Jev calls as Unix-pipeline predicates: routing, scoring, and JSONL filtering with stable exit codes, plus an agent-safety guard recipe.
Browser extension that removes page clutter: Jev scores elements against reusable rules, the extension hides the junk.
nothing matches. try fewer words, or clear a filter.