A FIELD GUIDE TO JEV / VOL. 01
To, co najlepsze w Jev: prawdziwe projekty, praktyczne poradniki i pomysły z sieci.
THE OFFICIAL SIGNAL 4 HIGHLIGHTS
Diogo Almeida@CompleteSkepticLAUNCH POSTDiogo Almeida przedstawia Jev, model strukturalnych decyzji TypeSafe. Obejrzyj film prezentacyjny i eksploruj oryginalny wątek, aby poznać podejście modelu, przykłady i zgłoszone wyniki.
TypeSafe AI@typesafeaiOFFICIALOświadczenie o oficjalnym uruchomieniu TypeSafe przedstawia laboratorium i kieruje programistów do Jev. Zawiera film prezentacyjny Diogo Almeidy i oryginalne wstępne wprowadzenie.
TypeSafe AI@typesafeaiOFFICIALTypeSafe ogłasza publiczny dostęp do Jev bez listy oczekiwania. Oficjalny konsolę jest punktem wyjścia do testowania typowanych decyzji w własnych aplikacjach.
JEVLAB ARTTypeSafe AI@typesafeaiOFFICIALTypeSafe wyróżnia podejście Jev do wyjściowych typów: aplikacje dostarczają stan i pytania, a następnie działają na prawdopodobieństwach, wyborach lub ocenach. Powiązane oświadczenie obejmuje integrację API Venice.
JEVLAB ARTStart with the Launch Post. Explore four official signals.
95 materiałów
AI-assisted summaries and translations. Check original sources for context and performance claims.
Evidence-backed index of real-world Jev (TypeSafe AI System One) use cases: repos, patterns, benchmarks, and measured results.
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Offline Obsidian search with optional Jev reranking of results, requiring user approval before reranking.
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A research project featuring an English–Icelandic dictionary that uses Jev to rerank results for better accuracy.
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A research project enabling SQL queries with real type responses.
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A small open decision model: state + typed questions -> calibrated probabilities. A Jev / System One re-creation on Qwen3.5.
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Independent calibration test of TypeSafe's Jev on a task it cannot have seen: 900 rule-generated support tickets (choice / score / boolean) plus 3 public benchmarks via Vercel AI Gateway. Raw responses, ECE with noise floor, temperature refit, per-type sign of miscalibration. Reproducible for ~$0.06.
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If you're experimenting with jev it will be easier from here.
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Jev-style parallel constrained decisions for any MLX model on Apple Silicon. Typed, schema-valid JSON in one forward pass.
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A research project evaluating the practical value of Jev's confidence scores for task execution.
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A small reproducible MuJoCo pilot comparing Jev, Claude Haiku, and reactive rules for pick-and-place.
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A reproduction of Jev that turns any Qwen model into a fast decision model, serving the same /v1/systemone schema (Choice, Score, Noul) with no training and no generated answer text.
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This is a LLM Gateway that mimics typesafe ai structured output. Like an imposter Jev.
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Jev vs Gemini 3.8 Flash: labelling 1,000 app reviews, 4.1× faster and 7× cheaper.
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Jev (TypeSafe System One) × ASReview SYNERGY abstract screening demo — Choice/Noul vs gold labels.
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Jev (TypeSafe) vs Claude Haiku 4.5 on 2 000 phishing emails: accuracy, calibration, latency, cost. Reproducible benchmark.
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Jev 1.13 reward-model evaluation across 8 benchmark tracks, with an interactive report and 54-row SOTA comparison.
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TypeScript experiments, evaluations, and latency benchmarks for TypeSafe's Jev model.
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Vercel Labs terminal CLI that can run Jev as the evaluation model for its evaluate command.
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A chatbot built on a model that cannot generate text (TypeSafe AI's Jev, driven autoregressively).
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Stop guessing confidence thresholds: calibrate, threshold, and drift-check typed decision models (TypeSafe Jev) against an LLM teacher.
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Feedback on your paper in seconds.
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A spatial reference explorer for creators. Local Jev query choices, metadata highlights and source-linked collections.
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Open alternative to Jev: typed, calibrated decisions from any open-weights LLM in one forward pass (HF + vLLM), with benchmarks.
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A chatbot project that responds to typed questions, part of a research collection.
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Never confidently wrong: a TLA+-verified consensus kernel around TypeSafe's Jev, run through 1,680 chaos-tested pharmacy decisions with zero wrong verdicts. Film, code, and every captured call.
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Openvons (open-Jev): 有限選択肢に確率で答える判断層 — テキスト / 画像 / 日本語音声コマンド.
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Papers, open reproductions and independent evaluations behind System One models and Jev.
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Independent, evidence-based map of when TypeSafe's Jev actually holds up vs. breaks down — real API-call receipts, not a leaderboard. 中文為主的雙語 repo。
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On-device iPhone visual decision tool using MLX and Qwen3-VL direct option logits.
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A Jev-inspired decision interface for existing LLMs. Explicit choices, scores, calibration, and review thresholds.
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Measures how well TypeSafe's RLCD-Jev model spots real secret credentials in file snippets.
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Type-safe one-decision-per-token decoding engine for autoregressive LLMs, inspired by Jev.
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Can a decision model beat dedicated rerankers? TypeSafe Jev vs Cohere Rerank 4 vs ZeroEntropy zerank-2 vs a chat-model baseline: 14 datasets, every raw API response, bootstrap ranges on every gap.
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A collection of research papers and open reproductions supporting System One models.
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Check whether each cited paper supports the sentence citing it. Claude proves the quote, TypeSafe's Jev scores it, a human decides.
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JevBench v1 - a benchmark for Jev-class typed decision models: smart, cheap, fast, reliable, open.
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A stronger one-pass scorer over a variable list of text options: hashed n-gram encoder, rival-aware attention, gated head, temperature scaling, benchmarked against jevlike.
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Small Python package that uses typesafe.ai to evaluate code comments on certain heuristics.
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A show-and-tell capability study for Jev, TypeSafe's System One decision model.
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Zero-shot spam filtering with TypeSafe Jev Noul questions, compared with TF-IDF baselines.
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Catch breaking API behavior hidden in OpenAPI prose with deterministic checks and TypeSafe JEV System One semantic review.
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End-to-end Jev-style structured-decision stack for auditable data construction, Qwen3.5-0.8B training, fixed Mind2Web and OOD evaluation, preliminary RLCD, local serving, and interactive replay.
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Probability-aware evaluation for typed decision models: calibration, selective risk, latency, and reproducible benchmarks.
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A research project on GitHub conducting nine experiments and 28 predictions with fixed parameters, focusing on Jev's performance metrics.
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Jev-style calibrated decision model (Choice/Score/Noul) on Qwen3.5-0.8B.
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Semantic ifs from open models, on a 3090 at home. Independent; not affiliated with Jev or TypeSafe.
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The open-source System One decision model. Sub-15ms, non-autoregressive, local drop-in alternative to TypeSafe Jev.
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Ongoing Japanese research deck on Jev and System One models, maintained as Markdown slides.
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A research project on GitHub uses Jev with Qwen3 models on an NVIDIA DGX Spark system.
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I tortured Jev into being a RISC-V CPU.
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High-throughput synthetic & pretraining dataset sifter powered by TypeSafe AI Jev (api.typesafe.ai). Stream, filter, and score Parquet & JSONL datasets at 1,500+ rows/sec using System One typed decisions (Choice, Score, Noul).
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TypeSafe Jev demonstration for new analyzation — experimenting with Jev for fast analysis of news and tickers.
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A chatbot from typed Jev decisions: hierarchical speculative decoding over System One probabilities.
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Open, Jev-compatible System One decision server on DiffusionGemma.
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Interactive explorer and Jev question workspace for Jev Board datasets.
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Local bilingual probability decisions from context, questions, and candidate answers. Independent research preview inspired by TypeSafe Jev.
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Typed JSON inference with DiffusionGemma, with Every and Jev benchmark results.
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Jev-compatible System 开源Jev.
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CLI for TypeSafe AI's Jev evaluation model — typed questions in, structured JSON answers out.
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Benchmark decyzji typu zdefiniowany od PadFlow (SaaS do rozwoju terenów: schematy, anonimowe oznaczone wiersze i program uruchamiający modele kalibrowane pod kątem pewności, takie jak TypeSafe Jev.
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AI benchmark on Japan's 2026 Common Test: Jev vs luna-none vs luna-low (static dashboard).
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An open-source project aiming to develop a Jev-class decision model for research purposes.
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Charts: TypeSafe Jev evaluated on Thai standardized exams vs 110 other models.
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A small, type-safe client for asking AI questions about your data, powered by TypeSafe Jev.
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Semantic test matchers for Vitest and Jest, powered by TypeSafe's Jev model. Write expectations in plain English, get calibrated probabilities back.
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Decision harness for TypeSafe Jev — confidence gates, shadow mode, recipes, and evals. Claude CLI 48.9s → Jev 1.3s on the same row-filter job.
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Sort by meaning: order lines along a plain-English dimension, from pairwise comparisons judged by TypeSafe's Jev model.
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Calibrated 151M Non-Autoregressive Decision Engine beating TypeSafe Jev & Laya on LocalLLaMA/typed-decisions (77.10% acc, 0.0636 Brier, 0.0144 ECE).
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看看 Jev 能做什么：用中英文讲清热门应用、工作原理和各自优缺点。Explore Jev apps with plain-language examples, explanations, and comparisons.
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Reproducible early-access evaluation of Jev on Korean understanding and medical text, with runtime and cost evidence.
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Open replica of TypeSafe's Jev: typed calibrated decisions in one forward pass, on Gemma 4 E2B / Gemma 3 270M (Modal).
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One-pass typed decisions with calibrated probabilities (System One style model), fine-tuned from Qwen3.5-2B.
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