Jev Search
Jev chooses sources and query settings, then re-ranks results returned by search engines. It does not generate an answer.
MANY ITEMS, FEW DECISIONS
Thousands of pages, mentions, emails, ads or candidates screened by parallel yes/no and score questions before anything expensive runs.
Re-rankers, relevance gates and retrieval fusions with per-item costs from each author.
Jev chooses sources and query settings, then re-ranks results returned by search engines. It does not generate an answer.
Hundreds of pages are screened for internal-link fit through thousands of yes/no decisions.
A pipeline ranks social mentions by intent, fit, competitor context and reach.
One profile is scored for fit, chance, confidence and mismatches across 400 companies.
Business ideas around quotes, leads, support, clipping, applications, marketplaces and browser agents.
Parallel judgments score advertising creative against a fixed rubric.
Jev classifies and scores a creator's own posting history across several criteria.
A visual comparison places Jev next to Luna, Sonnet and Flash on email classification.
A PostgreSQL extension that exposes Jev-backed categorical judgments over table rows.
Two PyPI packages: a `JevRerank` postprocessor using Score and single/multi selectors using Choice for LlamaIndex routing.
Outgoing relationships become choices, Jev returns next-hop probabilities, and a beam search walks the graph; shipped as a library, notebooks and a Streamlit app on Neo4j's public Companies knowledge graph.
Jev scores file and folder names so an ensemble of walkers is allocated across the most probable paths; a Bun CLI with recursive and multi-walker modes and JSON traces.
A DuckDB extension that calls Jev to classify rows of any CSV, Parquet file or table from SQL.
A video demo classifying 500 emails in seconds.
An early video demo running Jev over 1,500 of the author's own emails.
A UK events platform compared three models on approving or rejecting 50 real listings (venue hire, products and cancelled events filtered out), publishing the cases as a PDF.
An early-access test in which the author had Jev score his own writing corpus; widely cited by other write-ups as the first non-TypeSafe measurement.
A team pointed Jev at a private benchmark and reports it came within a few recall points of their fine-tuned Qwen at matched precision, while the fine-tune remains faster and has not been replaced.
A playable demo that runs 16 typed judgments on your text (tone, urgency, AI-written detection and more) on every keystroke with a single API call.
A pre-alpha PostgreSQL C extension exposing typesafe_noul(), typesafe_classify(), typesafe_score() and batched variants that call the API from SQL.
A DuckDB extension with jev_choice(), jev_score(), jev_noul() and jev_ask() returning real SQL types, batching 16 rows per request and caching identical queries.
A Mac app routes setup and troubleshooting questions to the right built-in manual article with Jev when no local model is loaded, showing real docs instead of generated text.
Near-real-time scoring of TikTok and Instagram hooks against roughly a hundred synthetic personas with Jev.
Seven independent writing checks (hedging, filler, passive voice and more), each returning its own verdict, confidence and the driving sentence; runs simulated without a key.
A free yes / no / maybe oracle that calls jev-latest through the System One API with live search for current facts; no account needed.
Evaluates Jev reranking against bge-m3 embedding search over an Agent Skills Hub catalog, with judge-bias and robustness analysis.
Adds `jev()` semantic predicates to plain Postgres through a CLI, HTTP and SDKs, with a SQLite cache and an `--explain` cost preview; a playground is hosted.
A Jev-driven recruiting agent with a local web UI that shows evidence quotes behind each decision; a short demo video is linked.
Load a Telegram JSON export and Jev labels each post's type, quality, sentiment and reaction tone; runs through Vercel AI Gateway or a TypeSafe key, with label export.
Upload a research-paper PDF; text is extracted in the browser, split into roughly 1,000-word passages, and a fixed set of small questions is asked per passage in parallel, then combined into weighted scores.
A pull request on the TrainLCD Functions repository adds Jev-based re-ranking of suggested stations and measured spikes; the author says Jev is also going into part of the app's AI chat.
A Python package for Jev-powered relevance filtering and reranking, with a Hugging Face blog walkthrough covering the API, filtering and evaluation results.
The ielab document-ranking toolkit gained a jev module, with code that uses TREC human relevance judgments to test whether Jev's probabilities are actually calibrated.
Browse YouTube transcripts by meaning with Jev analysis, subtitle export and a Vercel setup guide; Turkish UI.
'jev, find me stuff in my system' — a file finder that asks Jev which files match a natural-language request.
Countable rules plus one judgment model: ten yes/no questions per paragraph flag AI-writing tells. Zero dependencies.
A hosted detector that scans text for AI-written 'slop' using Jev decisions rather than a generative model.
An OSS library from the LlamaIndex founder: give a document plus natural-language category rules and Jev predicts the category (classify) or the boundaries between sub-documents (split). OCR backends: liteparse (fast, free) or LlamaParse for complex layouts.
A high-throughput sifter for synthetic and pretraining datasets: stream Parquet and JSONL, filter and score every row with Jev.
Transcript in, topic in, clips out: Jev picks the segments of a 90-minute video for any topic in under two seconds.
Type 'Urgency' at the top of a column and, as you type, Jev works out you want each row rated from 'no follow-up needed' to 'urgent' — spreadsheets that recalculate meaning, not numbers.