The model that named the category. A hosted API: send your app's state plus typed questions (Choice, Score or Noul) and get a probability back for each answer. It generates no text, and its weights are not public.
Box returns one rectangle per question, including when the target is absent. Ask a Noul question alongside it to check presence. TypeSafe requires its danger-zone image header for direct requests and says image and Box formats, limits and accuracy can change. There is no recommended confidence threshold.
One Choice question selects one option. For overlapping conditions, ask a Noul question for each condition and combine the answers in code. TypeSafe’s cart example instead removes each selected menu item before asking again, includes nothing_else and caps item requests at six. It finds each item at most once and does not count quantities. The worked run uses one hard-coded order.
TypeSafe reports Jack & Jill retaining 94.6% of previously requested candidates in a 2,000-person shortlist, against Gemini’s 93.9%, across an offline test on 150 live roles. This is shortlist recall, not general hiring accuracy; the difference is not statistically significant. A separate CodeCut product-matching report uses a three-candidate shortlist, a none option and a review band. Its one wrong merge belongs to the accepted subset.
Source: browser-use/jev-ultrafast ↗Zurich to London on Google Flights in 7.1 seconds, including model calls, generated text, browser work and loading waits. Swapping to a decision model took median task time from 9.450s to 7.092s and median browser protocol calls from 1,092 to 101.
One natural-language goal, on the real web, at 1x speed. Maker-reported: three repeats of one task on one browser profile.
- 7.09 smedian task, down from 9.45 s
- 101browser calls, down from 1,092
browser-use · ★ 23,396 · MIT · Jev
Figures from the project's own README and docs/performance.md. The maintainers note this is three repeats of one task on one browser profile, not a general benchmark.
Source: GitHub → Source: Measurements →
Source: jarrodwatts/jev-trader ↗Attempts one trade decision per Monad block on the Kuru MON-USDC market, and holds when the model misses the block deadline.
Jarrod Watts · ★ 3,050 · MIT · Jev
Source: awlevin/typesafe-computer-use ↗OCRs the screen, then classifies the next action. The claimed per-step cost is the maintainer's own figure, not independently measured.
awlevin · ★ 1,251 · MIT · Jev
Source: devagrawal09/jev-review ↗A staged review workflow with a local dashboard: each stage is a bounded judgement rather than a written critique.
Dev Agrawal · ★ 686 · MIT · Jev
Source: fhshaik/typesafe-mario ↗An agent that plays Super Mario Bros. by classifying the next action from structured emulator state rather than generating a plan.
fhshaik · ★ 456 · no licence · Jev
Source: kitze/unclutter ↗A browser extension that decides, element by element, what on a page is clutter, with reusable template rules.
Kitze · ★ 382 · MIT · Jev
Source: RomanSlack/jev-drone ↗A drone experiment in MuJoCo with a small judgement model in the control loop, on camera input. The maintainer calls the harder tunnel run a partial result.
Roman Slack · ★ 282 · MIT · Jev
Source: ChetasLua/jevmeter ↗Scores every sentence of a video and renders the running result as an overlay.
ChetasLua · ★ 108 · MIT · Jev
Source: mars-tw/jev-trading-command-center ↗A paper-trading research app where Jev makes the decisions, wrapped in an animated 3D command center with model pets and a ten-timeframe research panel. Every trade is simulated; there is no real order path.
mars-tw · ★ 63 · MIT · Jev
Source: devanshbatham/commit-miner ↗Classifies git diffs and messages into bug fixes, security fixes with CWEs, and change types.
Devansh Batham · ★ 39 · no licence · Jev
Source: yorkeccak/model-vs-market ↗A live site where OpenAI Decisions, Jev and Cloudflare's Clef put a probability on live questions after reading recent news, never the odds. Their probabilities are compared against Polymarket and Kalshi prices.
yorkeccak · ★ 12 · MIT · Decisions API, Jev, Clef
Source: Civalski/claujev ↗A Claude Code plugin that asks Jev short typed questions while Claude works: how big is this request, is this command destructive, was the work really done. It suggests an effort level per request, and in enforce mode a confident check can block a "done!" when the work looks incomplete.
Civalski · ★ 4 · MIT · Jev
Source: opper-ai/jev-vs-kev ↗Benchmark code and a 362-question test set that sends each item to Jev and to Kev 4B, and reports accuracy, calibration, latency and cost.
Opper · ★ 1 · Apache-2.0 · Kev, Jev
Source: prgrmrwy/dsh-auto-model-routing ↗A DeepSeek Harness plugin where Jev, via OpenRouter, judges each message as simple, standard, complex or escalated and raises or lowers only the reasoning effort of the model you picked. If Jev fails, it switches itself off.
prgrmrwy · ★ 1 · MIT · Jev
Source: saembit/jevquery ↗A TypeScript library where Jev picks the table, columns, filter, order and limit from what the schema offers, so it can choose a column but never write a query. Drizzle, Prisma or a SQL builder turns the plan into a real, parameterised query.
saembit · ★ 0 · MIT · Jev
Source: diegoag64/jev-devtools ↗A drop-in React inspector that turns an app's Jev requests into interactive diagrams of the context, each question and the returned answers. Marked experimental.
diegoag64 · ★ 2 · MIT · Jev
Source: Marcyvan1777/jev-arena ↗A 3D boxing game where Jev, GPT-4o-mini and Claude Haiku pick each move from the same state and legal actions, and a seeded rules engine resolves the hits. The maker calls the 56-bout results a pilot, not a ranking.
Marcyvan1777 · ★ 0 · no licence · Jev
Source: jeromtom/petsona-catalogue-ai ↗Tags each pet product's species, life stage and allergens from the store's own text, asking Jev or Laya one typed question per value: supported, contradicted, unknown or conflicting, with the source sentence attached. Merchants can approve, correct and lock tags, or let a validated policy accept them automatically. A hackathon entry for AIBoomi Startup Weekend Kochi, 23-24 Oct.
Jerom Palimattom Tom (Rexin Dynamics) · ★ 0 · MIT · Jev, Laya
Source: Monkeypox966/arxiv-jev-digest ↗A GitHub Actions job where Jev scores each new paper from your chosen arXiv categories against each of your research interests, code ranks them and keeps the top few, and only those go to DeepSeek for a summary emailed to you. The default follows humanoid robotics papers.
Monkeypox966 · ★ 0 · MIT · Jev
Source: n8peace/politiciansbyjev ↗Ask a question and Jev, via Vercel AI Gateway, gives each of 100 well-known American politicians a yes/no probability; the faces above the threshold float up out of a physics pile.
n8peace · ★ 0 · MIT (code); portraits under their own licences · Jev
Source: ByronDarlison/call-coach ↗A macOS app that transcribes both sides of a call on your Mac with Whisper while Jev ticks off each of your questions once it has been answered, notes which question you just asked, and flags surprising answers for a follow-up. The maker puts Jev's cost at about 1 to 2 US cents for a 45-minute interview.
Darlison Holdings · ★ 0 · MIT · Jev
Source: balajibankapur/textbook-voice-tutor ↗Answers a seven-year-old's spoken questions from one scanned Grade 2 textbook chapter. Jev decides whether each new question can be answered from the chapter and, with the optional verifier on, whether the drafted answer's claims are supported by the retrieved passages; common questions are matched to cached answers. On the maker's 14-question test, the decisions API cut average answer time from 4.1 to 1.7 seconds and the cost of each gate call about 25 times, against the chat wrapper.
balajibankapur · ★ 0 · MIT · Jev
Source: blurboy1985/ssoc-decision-trail ↗Paste a job ad and watch Jev walk Singapore's 2024 occupation classification level by level, keeping the three best paths at each step, to a five-digit code, drawn as an animated pixel-art crowd. Six requests, about 1.4 seconds, per the README.
blurboy1985 · ★ 0 · no licence · Jev
Source: manikv723-jpg/JevVoice ↗Speak several instructions in one run-on sentence and Jev picks each action from a menu of skills, and any app from those installed on your Mac, while you are still talking. Anything outside the menu goes to Claude for a proposed shell command, which waits for your approval unless you turn on trust mode.
manikv723-jpg · ★ 0 · no licence · Jev
Source: Dasge97/the-village ↗A pixel-art village in the browser where Jev, via OpenRouter, chooses what each adult does for the next two hours, while fixed rules handle urgent needs, night-time sleep and children. The maker built it to test whether Jev's choices stay sensible and reflect each villager's personality.
Dasge97 · ★ 0 · MIT (code); CC0 1.0 (graphics and sounds) · Jev
Source: blakestone-x/buildornot ↗Describe a side project and get BUILD, MAYBE or DON'T BUILD with the reasons: Jev rates pain, reach and feasibility out of five and judges whether the GitHub and Hacker News results it finds already do the job. A run costs well under a cent.
blakestone-x · ★ 0 · MIT · Jev
Source: senthilsweb/jev-pii ↗A DuckDB function that asks Jev typed questions about each database column: is it personal data, which kind, and whether GDPR, CCPA-CPRA, HIPAA, LGPD, PIPEDA or DPDP apply. Whether to keep, mask, hash or drop it is decided by code, not the model, and a failed call sends the column to review. Also runs in the browser.
senthilsweb · ★ 0 · Apache-2.0 · Jev
Source: rickcrawford/jev-relevancy ↗Grades search results with Jev on a four-level scale, checked first against Amazon's human-graded shopping queries, and stores runs, results and judgments in DuckDB with the metrics computed in SQL. The author recommends checking it against at least 300 of your own labelled pairs before trusting it on another domain.
rickcrawford · ★ 0 · MIT · Jev
Source: fmegahed/decisions-space ↗A Hugging Face app built on the R package decisions: upload a set of images or texts, ask Jev one question about each, and get every answer with its probabilities plus the R code to repeat the run. Visitors need no key or account; runs are checked against estimated session and daily budgets.
fmegahed · ★ 0 · MIT · Jev
Source: KoheiTakada/QuietFlow ↗An Android app that can use Jev to sort notifications into five classes, from action required to hide, alongside local safety rules and corrections it learns from you. It starts in observation mode and never cancels notifications there; Jev is opt-in with your own key.
KoheiTakada · ★ 0 · MIT · Jev
Source: gianviterbo/wps-watch ↗A public monitor of the West Philippine Sea with live ship positions, Sentinel-1 radar passes and Global Fishing Watch events. Jev answers four questions about each item from 13 news feeds, and two of them, whether it serves a Philippine reader and whether it carries PRC state framing, decide whether it is shown or held back. Live at wps.gdgt.link.
gianviterbo (Gadget Pilipinas) · ★ 0 · Proprietary (contact Gadget Pilipinas for reuse) · Jev
Source: arndth/tetrisarena ↗Jev, SAP-RPT-1, LLMs and a 15-line heuristic play Tetris on the same pieces and candidate moves, with live cost tracking. In the maker's 50-piece race Jev and the heuristic both cleared 19 lines and the others 18, a tie within noise; the difference was speed and cost.
arndth · ★ 0 · MIT · Jev
Source: scalefreegan/jev-ants ↗Each ant is a Jev agent that sees the cells around it and its neighbours' headings and decides which way to move and whether to settle. A thousand small decisions turn a random scatter into a word or an uploaded picture; the maker puts a word at about fifteen seconds and seven cents.
scalefreegan · ★ 1 · MIT · Jev
Source: Daewooki/decision-agent-lab ↗Calls Jev, OpenAI's Decisions API, Upstage Solar Decide and local Ollama models through one interface and logs version, latency, tokens and cost. On picking the right one of 12 tools for 40 Korean and English requests, the maker measured Jev 1.13 at 40 of 40 and OpenAI Decisions at 0.90, against 0.85 for a generative model choosing directly.
Daewooki · ★ 0 · MIT · Jev, Decisions API, Solar Decide, Nimble
Source: shohei81/jev-tos-checker ↗A Chrome extension that splits the terms of service on the page into clauses and has Jev check each for eight kinds of one-sided term, from selling your data to forced arbitration, marking only clauses that go well beyond what major services do and showing each next to the original text. The maker says it is not legal advice.
shohei81 · ★ 0 · MIT · Jev
Source: tejred213/ci-triage ↗Jev reads each failed test and decides the cause: environment or timeout, flaky assertion, genuine regression or API contract mismatch. Code re-runs the build only when every failure would be fixed by re-running (three attempts in all by default), gives flaky tests a low-priority ticket as well, and files tickets for real failures with the test's owner.
tejred213 · ★ 0 · no licence · Jev
Source: bit-cook/Jev-X-Blocker ↗A Chrome extension that hides spam and noise in X posts and replies by combining Jev's probabilities for rules you write in plain language with local signals, and decides locally what to hide. Everything hidden can be restored in place, and if the API fails, posts are shown.
bit-cook · ★ 1 · MIT · Jev
Source: andletenkov/decisium ↗Write test steps as plain-English Gherkin and Jev picks the element, action and check for each one from options Decisium reads off the page; code, not the model, decides whether a step passed. Resolved steps are recorded so CI can replay them with no model calls.
andletenkov · ★ 1 · MIT · Jev
Source: koltyakov/decide ↗A Go command-line tool and MCP server that gives coding agents and shell scripts Jev's yes/no, choice, score and multi-question calls, with offline request previews and batch runs.
koltyakov · ★ 0 · MIT · Jev
Source: Mou1z/wayfinder ↗Visitors type what they need in their own words and Jev, via OpenRouter, picks the one page on the site that fits, choosing only from your list of pages. With no key or a slow reply it falls back to word matching.
Mou1z · ★ 0 · MIT · Jev
Source: prefill-vanishing-point/order-matcher ↗Matches messy restaurant orders such as "2 cs portobellos, 5# limes" to exact catalogue items with Jev and flags uncertain lines for a person to check. The maker reports 96% exact matches on held-out test orders.
prefill-vanishing-point · ★ 0 · no licence · Jev
Source: lesderid/maddy-jev-filter ↗Sorts incoming mail for the maddy mail server into IMAP folders: Jev answers questions such as whether a message is spam or phishing, and rules in a config file turn the answers into a folder. It also learns from where you have filed mail before.
lesderid · ★ 0 · MIT · Jev
Source: dallascrilley/intune-why ↗Reads Intune compliance data from Microsoft Graph, groups failing devices by the setting that fails, and ranks fixes by how many devices each makes fully compliant. For custom settings its rules don't cover, Jev can optionally pick the remediation path. You can try a live demo with a sample tenant.
dallascrilley · ★ 0 · MIT · Jev
Source: Aladincz/Jev-Content-Filter ↗A browser extension that asks Jev about every ad on the Bazoš classifieds site against criteria you write in plain language, and collapses the ones that don't match. Other marketplaces and auction sites are planned.
Aladincz · ★ 0 · MIT · Jev
Source: royvandewater/jevflip ↗A small command-line tool that asks Jev to flip a coin N times and reports the share of heads.
royvandewater · ★ 0 · no licence · Jev
Source: BerriAI/better-backlog ↗A local web page and CLI that reads a repo's GitHub issues and lets you filter them by typing a criterion, such as people blocked from upgrading. Each criterion is asked of a decision model through your TypeSafe key or a gateway; add another to narrow the list.
BerriAI · ★ 2 · MIT · Jev
Source: uint4096/hndelegate ↗Polls new Hacker News stories every two minutes, runs each unprocessed one through decision trees of Jev questions you define, and sends the stories that match any tree to a Telegram chat or channel.
uint4096 · ★ 0 · GPL-3.0 · Jev
Source: jflowers/opencode-auto-question ↗An opencode plugin for autonomous runs: when the agent stops to ask the human a question, Jev (or an LLM, if you set one; the default tries Jev first) picks from the offered options. Below a configurable confidence (0.6 by default) it gathers context in a side session and decides again; below 0.35 the human answers after all.
jflowers · ★ 0 · Apache-2.0 · Jev
Source: dantetekanem/pi-jev-lazy-load ↗A Pi extension that keeps most tool and skill descriptions out of the first request and loads them on demand. In its optional Jev mode, Jev decides before each prompt which ones the request needs.
dantetekanem · ★ 0 · MIT · Jev
Source: prashaantr/what-changed-its-mind ↗An open research essay on three competitive 2026 US Senate races: compare stored Jev forecasts across different sets of evidence and different orderings of candidates and sources, with the exact requests and saved responses to inspect. The author calls it an experiment, not a calibrated forecast.
prashaantr · ★ 0 · MIT · Jev
Source: iamvazghen/jevqueue ↗A Claude Code plugin that checks an agent's tool calls before they run, using hosted Jev when you configure a key and offline rules otherwise. In enforce mode it refuses a call with a reason; advisory mode only reports the verdict.
iamvazghen · ★ 0 · MIT · Jev
Source: brent-phillips/jev-for-marketers ↗A free guide, a Claude skill and four editable recipes for typed marketing-ops decisions: route this lead, tag this asset, is this reply interested. Jev makes the call; an LLM still writes the copy.
brent-phillips · ★ 0 · MIT · Jev
Source: Nixz0824/x-semantic-filter ↗A Chrome extension: describe what you don't want to see in plain words, and eligible posts on your X timeline get a probability per rule. By default, anything over your threshold folds into a one-line bar you can open again.
Nixz0824 · ★ 0 · MIT · Jev, Clef-omni
Source: aaron-zhlg/odds-arena ↗Keeps the 50 busiest Polymarket questions forecast by several models, including Jev, Perplexity's Decider and an OpenAI model, all given filtered web search results but never the price. When markets resolve, each is scored on a leaderboard by Brier score, log loss and accuracy.
aaron-zhlg · ★ 0 · no licence · Jev, Decider
Source: freakngenius/jev-workspace ↗Give Jev a mission and some links: it gathers every item, judges each one live with counters and a running API bill on screen, and ends with a results page you can sort, spot-check and export. Runs on your own machine with your own keys.
freakngenius · ★ 0 · MIT · Jev
Source: sushrutkhandelwal/unorphan ↗A local app you point at a messy folder like Downloads: it works out which of your projects each loose file belongs to, reading the README or CLAUDE.md in your code folders, and files it into a matching folder under your chosen output location. Nothing moves until you press Move, and one click puts it all back.
sushrutkhandelwal · ★ 2 · no licence · Jev
Source: NoxLoveYa/space-sniffer ↗A Windows disk-space map that draws your drive as a 3D city and asks Jev which folders look safe to delete; those get a neon outline and land in a Reclaimable list, biggest first.
NoxLoveYa · ★ 0 · GPL-3.0-or-later · Jev
Source: byui-library/jev-email-triage ↗A step-by-step guide for people who don't write code: set up Jev with Claude Code and build a read-only assistant that ranks the last two weeks of Gmail into piles like Reply needed, Waiting on them and Possible phishing.
byui-library · ★ 0 · MIT · Jev
Source: nouli-ai/nouli ↗Decision models forget everything after each answer; Nouli keeps a session. Type facts as you learn them and start a line with ? to ask a new question; earlier facts and answers stay in the history. A Rust CLI and library that can chain sessions into decision graphs, with Jev through its TypeSafe adapter or other providers.
nouli-ai · ★ 12 · MIT · Jev
Source: kubrakrcn/kariyer-decision-bench ↗Written at Kariyer.net R&D to pull seniority, department and work type out of job ads. On 600 real ads with 7 questions each (4,200 decisions), Jev 1.13.0 scored 71.5% against 64.6% for the open tau-4b and 65.7% for a prompted 4B LLM. tau-4b was better calibrated (ECE 0.093 against 0.156), and it scored 95.7% on its own training data, which is why the author warns against benchmarking a model on the set it shipped with.
kubrakrcn · ★ 0 · Apache-2.0 · Jev, tau-4b
Source: gak4u/layagrep ↗jevgrep finds code by asking Jev thousands of yes/no questions. layagrep records those questions on SWE-bench tasks and fine-tunes the open Laya to answer them locally, with no API key and no code leaving the machine. A smoke run on 400 held-out questions lifted file-navigation AUC from 0.79 to 0.94 (Jev 0.97), but declaration relevance stayed at 0.58 (Jev 1.00).
gak4u · ★ 0 · Apache-2.0 · Laya, Jev
Source: agent-labs-dev/fastbrowse ↗An LLM plans, reads and writes; Jev picks each action from the controls indexed on the page, and code stops irreversible clicks without authorization. On 46 tasks run three times each against the hosted Browser Use agent, it passed 99.2% of runs to 96.3% and cost $0.0296 a run to $0.3180, 10.7x less, but was slower: 18.9 s a task to 15.3 s (published campaign release-0.7.1).
agent-labs-dev · ★ 155 · MIT · Jev
Source: ch7flyer/astrbot_plugin_jev_damocles ↗An AstrBot plugin that decides whether a bot in a group chat should reply. It merges bursts of messages, separates conversation threads, then asks Jev four questions, batched into one request when the backend supports it (was the bot addressed, does it follow, should it reply, how urgent). For messages without a direct mention, quote-reply or poke, it uses a high and a low reply threshold alongside other checks, and scores in between can be reassessed later; those direct interactions can skip the gate. The README suggests a 3 to 7 day dry run first.
ch7flyer · ★ 0 · MIT · Jev
Source: pyxl-dev/jev-router ↗A Claude Code plugin that asks Jev one choice question per message, low, medium or high effort, and applies the answer when Jev is at least 60% sure. It changes effort, never the model, so the prompt cache survives. Subagents are never routed.
pyxl-dev · ★ 0 · MIT · Jev
Source: HangChi/JevRouter ↗An agent runtime where an LLM reads the task, one Jev call answers four typed questions (next action, risk, whether to confirm, whether to reason further) and a local policy engine decides what may run. On 53 versioned test cases it hit the expected action 92.5% of the time against 96.2% for an LLM baseline, at about 1.0 s against 4.0 s, and asked for confirmation on 16 of the 17 cases that needed it.
HangChi · ★ 0 · MIT · Jev
Source: milesroxas/skill-tester ↗An eval harness for agent skills: headless agent runs, code checks, Jev judge packs and blind human review; the generic pack asks one yes/no question per skill rule. Graded with the writing pack, the reference skill's v2 run matched its original benchmark's report within 2 points on every layer and in total.
milesroxas · ★ 0 · MIT · Jev
Source: cars3/agastya ↗Retrieval that walks a document's table of contents, asking Jev one yes/no question per section, then ranks the sections it kept with EmbeddingGemma. On all 6,005 test queries of the public SearchTome release it returned 89.1% of gold sections at 3.6 sections per query, against 84.5% at 3 for EmbeddingGemma alone; its top-1 and top-3 recall equal the embedding model's. Jev alone, ranking flat, scored 54.2 top-1. It needs no training and runs on one 8GB consumer GPU, and failed upgrades are published too.
cars3 · ★ 0 · MIT · Jev
Source: uhiron55/physical-ai-2026-advanced-1-final-assignment ↗A course project where Jev chooses the transfer strategy for a CRANE+ arm in a Gazebo simulation, MoveIt 2 plans the path and ROS 2 controllers run it. In three episodes on authenticated Jev decisions with no fallback, Jev picked the home transfer at 0.94 to 0.96 confidence and all three placed the cube, 2.70 to 7.48 mm off target. These are scripted-teacher episodes; the README's separate learned-policy (ACT) tests are not reported as grasp success.
uhiron55 · ★ 0 · Apache-2.0 · Jev
Source: frederico-kluser/jev-droid ↗Drives an Android emulator from plain language: DeepSeek V4.1 Flash decides the next step, and Jev triages each screen (loading, error, permission prompt) and guards actions before they run. The three-step demo in the README, Settings to Network to Wi-Fi, cost $0.00019 and took 21 seconds; every step saves its screen, decision, tokens and cost.
frederico-kluser · ★ 0 · MIT · Jev
Source: tauseefau/topic-gate ↗Asks Jev one question per message, 300 to 600 input tokens, to tell whether the conversation is on the same topic, a subtopic or a new one, and recommends a session reset only when a new topic wins clearly (p of at least 0.65 and 0.25 ahead), so you stop paying to resend a dead transcript. Checked against live Jev on related, unrelated and short messages.
tauseefau · ★ 0 · MIT · Jev
Source: Paul-Yuchao-Dong/pi-omarchy-cu ↗Lets the Pi coding agent drive real Linux desktop apps on Omarchy (Hyprland Wayland) by reading the accessibility tree and asking Jev for the target element and the operation. It offers 11 Wayland-safe element operations. Jev-driven actions abstain below 0.55 confidence and need confirmation below 0.70, or 0.90 for destructive ones; direct operations on a known element run without Jev.
Paul-Yuchao-Dong · ★ 0 · MIT · Jev
Source: charles-forsyth/jev-decision-wall ↗Asks Jev 200 yes/no questions about one text in a single call and shows the answers as a heatmap. The keyless hosted demo replays recorded answers. VENDOR-REPORTED: about 185 ms for one question, 200 ms for 50, and 150 to 300 ms for 200; seven parallel calls returned 1,400 answers in 282 ms, against 1,092 ms back to back. The author measured Jev 1.14 from a US home connection and has not measured calibration.
charles-forsyth · ★ 0 · MIT · Jev
Source: AIAnytime/Jev-Router ↗An OpenAI-compatible router that filters models by hard constraints before Jev judges which one fits. VENDOR-REPORTED, on its own 30 prompts: tier accuracy was 100% against 53.3% for a heuristic, task-type accuracy 83.3% against 46.7%, and hard prompts sent to a weak model 0 against 14. Estimated savings against always using the strongest model, from token counts and configured prices rather than running the chosen models, were 35.6% against 68.7%; the heuristic's estimate was higher because it under-routed. Decision latency was 295 ms at p50 and 409 ms at p95.
AIAnytime · ★ 0 · MIT · Jev
Source: House-elves/pr-wall ↗Puts open pull requests on a wall and asks Jev eight typed questions about each diff and its tests, using quarkusio/quarkus by default. It keeps the answers separate and never combines them into a score. Right/Wrong feedback records corrections and groups accuracy by confidence as you label answers; no accuracy figures are published yet. Its questions and Quarkus context are adapted from brunobat/quarkus-pr-wall.
House-elves · ★ 0 · Apache-2.0 · Jev
Source: vercel-labs/jev-ai-sdk-form-router ↗Routes lead, contact and issue forms with Jev through Vercel AI Gateway and the AI SDK. It accepts Jev's destination when confidence is at least 95%; otherwise, or if Jev fails, openai/gpt-6-luna-fast evaluates the submission independently. The result shows the destination, deciding model, timings and an email preview, with optional delivery.
vercel-labs · ★ 15 · MIT · Jev
Source: raghavio/ownyourtimeline ↗Filters the For you and Following feeds in Chrome or Edge using a plain-language instruction and Jev. It sends one bulk TypeSafe request per timeline response, splitting to stay within input budgets, and removes posts scoring at least 0.90 before X renders them. Errors and timeouts leave posts visible; it checks at most 80 posts per response. The built extension has Chromium tests with X and TypeSafe responses stubbed. No filtering-accuracy figures are published.
Raghav Sharma · ★ 0 · MIT · Jev
Source: TechPreacher/jev-router-copilot ↗Jev sizes a Copilot message as tiny, everyday, large or hardest and sends it to a helper agent pinned to a model. Defaults are Haiku 5.5, Sonnet 5.5 and Opus 5.5, with the hardest helper configured to request high effort. Recorded helper runs used medium effort. The author records live runs of the hook in Copilot CLI 1.0.105 and VS Code 1.144, plus the CLI wrapper. The MCP fallback is tested offline only. Its 54 offline tests passed when checked on 03 Nov 2026; it publishes no savings benchmark.
Sascha Corti (TechPreacher) · ★ 0 · MIT · Jev
Source: Live CLI wrapper run → Source: Live VS Code run →
Source: Jaydccq/mail-agent ↗Incoming Gmail goes through Jev triage before alerts reach Telegram or Lark. Claude or Codex CLI can draft replies, which wait for a Send tap. One Python process uses one SQLite file. The author's testing notes record real Jev calls on nine synthetic emails at about 0.1 s each, plus an end-to-end run with real Gmail, Telegram, Jev and Codex CLI; they report no triage accuracy figure.
Jaydccq · ★ 0 · Apache-2.0 · Jev
Author's recorded runs · VENDOR-REPORTED
Source: Testing notes →
Source: Potterluo/sluice ↗A coding harness lets a worker propose state transitions and a judge approve, reject or redirect them. Its 02 Nov 2026 real-model run used Jev 1.14 as judge and deepseek-v4-pro as worker. The author reports five judge calls for under $0.0003; the allowed-choice menu corrected three requests for nonexistent states. A separate five-task SWE-bench pilot does not identify its judge, so its score cannot be attributed to Jev.
Potterluo · ★ 0 · MIT · Jev
Author's recorded runs · VENDOR-REPORTED
Source: Ramolisdenneyous/Drone-Commander-Powered-by-Jev ↗In this local field test, Jev picks a unit's action from legal option IDs supplied by a tactics game's rules engine. The server checks the choice; a deterministic fallback handles late, malformed or weak replies. The author estimates about 75% lower token cost for those decisions with no obvious loss of tactical ability. Those are early playtest impressions. The first matches set both confidence and margin gates to zero, letting every legal Jev choice play.
Ramolisdenneyous · ★ 0 · MIT · Jev
Author's recorded runs · VENDOR-REPORTED
Source: elcann7/jev-issue-triage ↗A GitHub Action asks Jev nine questions per issue in one request, then a decision table turns the probabilities into labels and priority tiers. Probabilities between 0.3 and 0.7 count as unsure. It has a dry-run mode. The author's run passes 65 of 66 checks on 16 synthetic issues for about $0.001. That small synthetic set includes a fixture revised after its fake-example wording affected the answer.
Elcan Səfərli · ★ 0 · MIT · Jev
Authors' published runs · VENDOR-REPORTED; not independently reproduced
Source: MIT licence →
Source: filip-a-9/jev-prompt ↗This Claude Code mod asks Jev through OpenRouter to grade the prompt being typed and name what it lacks. Hints are advisory; zero-data-retention routing is on by default. On 84 prompts, the band agrees with labels 71% of the time against an 80% target. Across 217 prompts with known outcomes, hints appeared on 25; 24% of those were followed by trouble (95% interval 11% to 43%), against a 10% overall rate. AUC is 0.60. Three blind Claude labellers supplied the labels. The harness is private, and whether the hints improve prompts has not been shown.
filip-a-9 · ★ 0 · MIT · Jev
Authors' published runs · VENDOR-REPORTED; not independently reproduced
Source: MIT licence →
Source: philrenaud/practical-data ↗A Chrome extension asks a decision model which HTML table columns to shade and which end is better. Code parses values and paints the colours. The default free provider forwards table text to Jev; users can supply their own provider key or use its Cloudflare backend. The README reports colour-or-leave-plain matching 284 of 285 labelled columns on 26 pages. Labels and thresholds were tuned on those pages, so this is a regression check. The replay uses recorded model answers and does not retest the model.
Phil Renaud · ★ 3 · MIT · Jev
Authors' published runs · VENDOR-REPORTED; not independently reproduced
Source: MIT licence →
Source: ElyasEbrahim/pgdocket ↗Stores versioned decisions in Postgres and checks calibration against the user's labels. The author's Phase 0 run used Jev 1.13 on 2,000 consumer complaints for about $0.30. Top answers changed on 4.8% of rows across three identical runs, and on about 16.5% when the same rows were rebatched. The repository includes measured Phase 1 ledger and routing runs but has no published release. Its claims about managed hosts have not been tested on Supabase, Neon or RDS themselves.
ElyasEbrahim · ★ 0 · Apache-2.0 · Jev / Laya
Authors' published runs · VENDOR-REPORTED; not independently reproduced
Source: Phase 0 findings → Source: Apache-2.0 licence →
Source: ErenTerakye/lowgate ↗Decision models choose one of 35 tilt-and-throttle manoeuvres on the same seeded lunar-lander courses. The game waits for the answer, so measured latency never changes the flight score. Across 160 levels per lane, the author reports 159 landings for a scripted autopilot, 156 for Jev, 151 for Kev-27B and 142 for the Laya fine-tune. Prompt selection and Laya training are documented; these results cover this simulator, with recordings available for replay.
Eren Terakye · ★ 0 · MIT · Jev, Kev, Laya, Clef-flash
Authors' published runs · VENDOR-REPORTED; not independently reproduced
Source: Recorded benchmark → Source: Prompts and training → Source: Licence →
Source: craigraphics/jarvis ↗Handles exact voice commands with patterns, then asks Jev to choose an action for natural phrasing; an LLM handles uncertain cases and writes text. The author reports 69 measured commands: patterns handle 54%, and Jev the other 46% at 116 ms median. All eight questions and comments return unknown and run nothing. Its automated tests use no network or real sign-ins.
William Craig · ★ 0 · MIT · Jev
Authors' published runs · VENDOR-REPORTED; not independently reproduced
Source: Decision routing → Source: Licence →
Source: arslanrakh/perfect-recall ↗A Hermes memory provider, Python library and MCP server that asks remote Jev to match stored memories against an agent’s criteria. A frozen predecessor scores 85.8% final-answer accuracy versus Mnemosyne’s 48.3% on a custom 120-question LongMemEval-S test. The renamed release has not had a fresh independent quality test; eligible memory text leaves the machine.
arslanrakh · ★ 3 · MIT · Jev
Published author-run records, checked 5 Nov 2026; not reproduced in this scan.
Source: Licence file → Source: Integration code → Source: Published run →
Source: cyriusweng/jev-tern ↗A browser agent in Tern’s visible WebKit tab: Jev 1.14 judges steps and GLM-5.3-Flash supplies typed values. The author records nine successful runs across three Wikipedia tasks. Median times are 1.6, 3.4 and 1.8 seconds. The same tasks shaped several fixes; the browser-use comparison uses a different browser and model, so it does not isolate the decision model’s speed.
cyriusweng · ★ 0 · MIT · Jev
Published author-run records, checked 6 Nov 2026; not reproduced in this scan.
Source: Licence file → Source: Integration code → Source: Published run →
Source: ritza-co/hn-jev-sorter ↗A Chrome extension that asks Jev to sort Hacker News comments into reader-defined buckets, retaining the parent comment as context. The author’s demo records a 131-comment thread being classified and sorted. Six requests run concurrently, and an Other bucket catches comments outside the chosen categories. The recording shows the workflow; it does not measure label accuracy.
Ritza · ★ 1 · MIT · Jev
Licence, integration code and author-recorded demo checked 6 Nov 2026; not reproduced in this scan.
Source: Licence file → Source: Integration code → Source: Recorded demo →
Source: backmeupplz/jev_antispam_bot ↗A Telegram bot that checks spam and links related messages before deleting them, with admin exemptions and a keep-on-failure policy. Its published evaluation records 75 live Jev calls, including two rejected prompt changes. Those calls did not exercise Telegram deletion. The README says its production classifier is now a Laya fine-tune distilled from Jev.
Nikita Kolmogorov · ★ 21 · MIT · Jev, Laya
Licence, implementation and recorded live model calls checked 6 Nov 2026; no fresh Telegram run.
Source: Licence file → Source: Integration code → Source: Recorded model calls →
Source: kkgzsprov/shakedown ↗A game playtest pipeline with planning between runs, model tactic selection and a code executor. Its recorded 36-run campaign made 247 Jev decisions, with 17 random fallbacks. All runs were valid, but the rule player had better outcomes: it escaped in all six aggressive runs while Jev’s aggressive player did not move. Three seeds and 12-second runs make this an exploratory demo.
kkgzsprov · ★ 0 · Apache-2.0 · Jev
Author-run records and source checked 7 Nov 2026; not rerun locally.
Source: Licence file → Source: Recorded campaign → Source: Replay-verified demo →
Source: Imperfective/smart-factory-ai-ops-center ↗A simulated factory gives Jev a menu of feasible actions and puts an independent safety gate before the virtual PLC. Stored real-model campaigns cover 1,260 shifts and 32,226 calls without API errors or fallbacks. Most plan-completion gain comes from the surrounding architecture; its deterministic planner matches the full system at lower operating loss. No real equipment is controlled.
- 1,260recorded real-Jev simulation shifts
Minkyu Kim · ★ 0 · MIT (code, documentation and simulation results); CC BY-NC-SA 4.0 (vision results) · Jev
Author-run simulation records, not a fresh reproduction. Confirmatory batches exclude mock runs and the standalone extra shift. The gain from adding Jev alone is not statistically significant.
Source: MIT licence → Source: Licence notice → Source: Jev client → Source: Stored runs →
Source: dzhng/jevgrep ↗A CLI that uses Jev to find source files and context for coding agents. Its author-run ten-task study reports 25.8% lower estimated total task cost with 8/10 solves in both conditions. A later change cut Jev cost about 59%, but raised combined cost 2% to 3% against that saved cohort, again with 8/10 solves.
David Zhang · ★ 3,199 · MIT · Jev
Catch-up September experiments, discovered through an in-window push. The ten Python tasks were repeatedly studied; saved baselines were not rerun. Costs include failed attempts and use list-price estimates, not reconciled invoices. Nineteen empty responses leave some later usage unknown; the report gives a ceiling.
Source: GitHub → Source: Licence → Source: Total-cost run → Source: Later combined-cost study → Source: Provider code →
Source: oneryalcin/hunch ↗Versioned judgment specs with cached answers, human review, tests and a diff of decisions changed by a prompt edit. The bundled Jev command-guard result covers 38 commands and keeps a warning for a missed outside-project effect. Only its destruction question has gold labels; two other hazards do not. This is a catch-up project discovered through an in-window push.
Mehmet Öner Yalçın · ★ 0 · Apache-2.0; server directory Elastic License 2.0 · Jev
Author-run output; not independently reproduced
Source: Code licence → Source: Server licence → Source: Jev integration → Source: Recorded test output →
Source: Mohith26/jev-calibration-audit ↗Recomputes calibration and selective agreement from pinned, published WorkflowEvals outputs for nine models across four workflows. The saved results reproduce 36 headline scores. Its reference is a two-LLM probability consensus, not human truth; no fresh model is called. Case-level gates remain in-sample.
Mohith Gajjela · ★ 0 · MIT · Jev
Author-run records; no fresh model inference reproduced
Source: Licence → Source: Results → Source: Dataset matching → Source: Metric implementation →
Source: uran-lajci/system-one-multilingual-sentiment ↗Three-label sentiment code and saved predictions for Jev, Clef and Clef-flash on eight European languages. The zero-shot report covers 12,970 test items. Its comparison with five LLMs uses published paper scores with a different prompt format, rather than fresh matched calls. Broader prepared datasets and tuning runs are separate from this reported scope.
Uran Lajci · ★ 0 · CC0-1.0 · Jev, Clef
Author-run records; no fresh model inference reproduced
Source: Licence → Source: Zero-shot report → Source: Saved score table → Source: Jev English predictions → Source: Inference code →
Source: maths-oliveira/blabla-meter ↗A Chromium extension that scores pages and sections with Jev, while a generative model writes summaries on demand. Its usefulnews component publishes a 24-article Jev/LLM comparison and preference-feedback examples from September; those records do not benchmark the current extension.
maths-oliveira · ★ 0 · MIT · Jev
Maker's recorded runs · VENDOR-REPORTED; not independently reproduced
Source: Recorded Jev run → Source: Licence →
Source: nicolasambin/lazycontroller ↗An OpenAI-client wrapper that sends closed structured questions to a decision model and falls back on low confidence or unsupported requests. Its saved notebook shows Jev/GPT routing and a local Kev/Ollama path. Shadow mode measures agreement with the slower model; that agreement is not ground-truth accuracy.
Nicola Sambin · ★ 0 · Apache-2.0 · Jev / Kev
Maker's recorded runs · VENDOR-REPORTED; not independently reproduced
Source: Executed notebook → Source: Licence →
Source: epifaneia/callscore ↗Reviews call transcripts through closed-option Jev questions, with code measuring interruptions, copying evidence turns and calculating quality scores. The saved SQLite database records 120 artificial calls through OpenRouter. A simulated engine is the default; uncertain critical judgments go to review. These calls do not establish performance on real customers.
epifaneia · ★ 0 · Apache-2.0 · Jev
Author-run records; not independently reproduced
Source: Licence → Source: Jev adapter → Source: Saved call database → Source: Published report →
Source: lightshadow1/policy-pal-guardrails ↗Compares frozen regex rules, Jev and GLiNER on four labels. The published reports record model calls and threshold tuning on a random split of a mixed-source question pool. The two “weeks” are not later production traffic; the higher round-2b result is post hoc. The score cache is not committed.
lightshadow1 · ★ 0 · MIT · Jev
Author-run records; not independently reproduced
Source: Licence → Source: Run report → Source: Threshold and hybrid limits → Source: Model client →
Source: GrntKim/FastABC ↗Jev checks individual rubric cells, Gemini reviews uncertain cells, and graders confirm scores. Published raw calls cover 24 synthetic answers and 150 labelled cells. Ten cells go to human review; evidence lookup is billed separately. The small comparison does not establish an accuracy gain.
GrntKim · ★ 0 · MIT · Jev
Author-run records; not independently reproduced
Source: Licence → Source: Recorded evaluation → Source: Jev adapter →
Source: seanparkross/slack-thread-police ↗A self-hosted Slack bot that privately suggests moving a channel message into a thread. Jev checks the recent message window by default; a local System One server can replace it. A person chooses whether to move or ignore the message. Debug logging can include Slack payload text.
Sean Park-Ross / Parklife, Inc. · ★ 4 · MIT · Jev
README screenshots and integration code; not independently run
Source: Licence → Source: Detection code → Source: Privacy and model settings →