Domain & Vertical Tools

Evaluates entry distribution, toxic flow, holding and stress signals for calibration logs.

↳ Adds comparable advisory signals to deterministic trading rules without claiming returns.

Jev reviews completed minute candles and indicators; Python applies confidence and position limits to a simulated portfolio.

↳ Shows market inputs, model judgments and paper positions together.

Reads selected entity states and returns probabilities, choices, or scores that configured thresholds can use in automations.

↳ Connects natural-language conditions to existing sensors, notifications, and scenes.

Jev classifies engineering text and candidates; calculations and final actions stay in Python rules.

↳ Shows how to connect judgments to engineering workflows using synthetic examples.

Each asset account sends market state to Jev; execution code places or cancels orders based on the decisions.

↳ Separately records model decisions, order execution, and dashboard state.

Jev evaluates extracted resume text against typed questions; a Worker combines the results into review output.

↳ Records inputs and judgments so individual reviews can be inspected later.

Jev answers bounded direction and risk questions; local policy decides simulated positions.

↳ Records judgments, delay and trading costs in the same experiment.

Uses a deterministic local engine by default; a TypeSafe key enables real Jev judgments over features.

↳ Shows features, decision provenance and scores together for inspection.

Converts strategy questions to System One requests and normalizes native answers to the project’s result format.

↳ Allows Jev or other model backends to be used in the same research workflow.

In Jev mode, order-book judgments feed code that simulates fills or submits configured post-only limit orders.

↳ Defaults to a mock model and dry-runs without a private key; simulated results do not establish profitability.

Sends role data and bounded specialization candidates to Choice, then maps labels to capabilities.

↳ Provides an example of role classification in a business application.

Classifies search intent, direct answers and content gaps, with a custom citation-likelihood rubric.

↳ Collects local checks, search results and model judgments into reports.

Jev selects candidate line IDs; code copies the original text with file and location references.

↳ Connects quotes, source locations and human verification status.

Extracts PDF page text and sends it to Jev for form type, page type, and confidence.

↳ Connects a fixed form catalog to per-page classifications for downstream document workflows.

Jev uses Choice to classify pages and Noul to assess careers links, role relevance, filter controls and candidate fit; local thresholds select navigation, job details and saved results.

↳ Connects careers navigation, role screening and detail matching in one CLI workflow, with local JSON and Markdown reports.

Employs Jev to classify and select the next tool action based on execution state, delegating parameter generation to DeepSeek.

↳ Combines low-latency discrete routing with capable generative modeling, reducing overall prompt overhead and turn latency.

Submits custom states and questions to Jev via Next.js API routes, displaying discrete probabilities, latency, and confidence scores.

↳ Offers an intuitive web interface for exploring prompt criteria, testing edge cases, and evaluating discrete decision outputs.

Employs Jev for two-stage discrete pruning over tool catalogs, narrowing extensive tool lists down to relevant candidates in milliseconds.

↳ Mitigates context bloat and hallucinated tool calls in complex agents by isolating candidate selection into fast discrete steps.

Jev returns a structured decision for the local program; consult the source for the exact decision policy.

↳ Adds structured choices or scores to the workflow; performance and cost benefits have not been independently verified.

Assesses spatial coordinates and sensor states, using Jev to select safe step actions within bounded budgets.

↳ Extends TypeSafe Jev low-latency discrete judgments to physical-world robotic arm motion control.

Registers custom functions like jev_choice and jev_score directly inside the SQLite query pipeline.

↳ Enables row-level semantic classification directly inside relational SQL queries without glue code.

The app never closes issues, posts comments, or changes labels. Retrieval is currently lexical and bounded to a recent-item scan; this limitation is explicit in the UI and README.

↳ Adds structured choices or scores to the workflow; performance and cost benefits have not been independently verified.