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The full directory

All Jev AI model resources

Entries for TypeSafe AI's flagship System One model — official docs and SDKs alongside community clients, tools, and write-ups.

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3,919
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102
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3,817
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293
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3919
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  • Repos & sites3102
  • Videos74
  • Posts104
  • Articles392
  • Playgrounds78

Updated

Browse the Jev AI model by category

11 ways into the ecosystem. Each category page leads with its own shortlist of starting points.

Browse the Jev AI model by use case or industry

The same directory, sliced by what you are building and who you are building it for.

Jev use cases13 ways to put Jev to work, from classification and routing to decision gating and evaluation.
Jev industries14 sectors building with Jev, from software and security to healthcare and robotics.

All 3919 Jev AI model resources

Search the full directory, or filter by category and tag.

Showing 1537–1584 of 3919 resources

ProbabilityBench: Model Evaluation and Calibration with Exact Ground-Truth Uncertainty (arXiv 2610.17016)
A benchmark providing exact instance-level class probabilities across three simulated text domains: peer review, covert information transfer, and credit risk. Because the data-generating process is defined, exact probabilities and Bayes error are computable, enabling KL-divergence scoring of reported probabilities.
Jev Benchmarks & EvaluationsArticle#community#research#arxiv
Community
Does a Hosted Decision Model Respect Irrelevant Alternatives? A Pre-Registered Test of Option-Set Dependence in Jev (arXiv 2610.16655)
A pre-registered study of whether jev-1.13.0 satisfies independence of irrelevant alternatives, using 299,463 calls on intent and topic classification. Every added option shifts the log-odds between the gold and strongest wrong label by 0.20–0.52 nats RMS, 1.2 to 3 times run-to-run variation.
Jev Benchmarks & EvaluationsArticle#community#research#arxiv
Community
Does System One Transfer? Calibrated Typed-Decision Models, Label Budgets, and Translation for Indonesian Regional Languages (arXiv 2610.14112)
0GitHub stars
A pre-registered evaluation of the commercial Jev model and open Laya-multilingual on Indonesian regional languages, using the parallel NusaX and NusaTranslation test sets. Jev is more accurate on sentences lexically closer to Indonesian, and the human Indonesian translation raises accuracy by up to 37.2 points.
Jev Benchmarks & EvaluationsArticle#community#research#arxiv
Communitytnromy/evaluating-system-one-models-for-indonesian-regional-languagesHTML
TypedBench: A Benchmark for Calibration, Framing Sensitivity, and Cost in System One Decision Models (arXiv 2610.11392)
A benchmark for typed decision models such as Jev, built from seven policy-labelled generators and nine evaluation suites. It reports accuracy as medians across paraphrases, calibration against a matched noise floor, and realized cost under asymmetric cost matrices.
Jev Benchmarks & EvaluationsArticle#community#research#arxiv
Community
JEV versus LLMs: Accuracy, Cost and Calibration on Seven Political Science Replications (arXiv 2610.06625)
A comparison of JEV with LLMs and human coders on seven political science replications. JEV matches or approaches GPT-6 Luna and Qwen3.8-27B on many tasks, but the authors find no cost advantage over GPT-6 Luna at OpenAI's batch prices.
Jev Benchmarks & EvaluationsArticle#community#research#arxiv
Community
GraphDecisionBench: Benchmarking System One Models on Graph Tasks (arXiv 2610.06354)
6GitHub stars
A model-independent benchmark for graph decision tasks, combining structural task profiles, matched graph-text input contrasts, and heuristic-proposal controls. It finds accurate adjacency recognition does not guarantee broader structural correctness.
Jev Benchmarks & EvaluationsArticle#community#research#arxiv
CommunityVictorYXL/GraphDecisionBenchPython
General Decision Models: Benchmarking and Insights Beyond Jev (arXiv 2610.03935)
Introduces JEVal, a bilingual benchmark of 11,257 instances from 36 datasets across 10 application domains, and evaluates 25 model configurations spanning general decision models and generative LLMs. It also proposes SII-Decision-4B and 27B, distilled models answering a typical query in about 0.1 s.
Jev Benchmarks & EvaluationsArticle#community#research#arxiv
Community
Benchmarking candidate coverage and rejection policy transfer in typed decision models (arXiv 2610.03387)
0GitHub stars
A benchmark of rejection-policy transfer across Laya, Jev, and Qwen2.5-7B-Instruct at equal calibration budgets, covering artificial omission, natural retrieval misses, and public out-of-scope queries. Source calibration often fails to preserve the target operating point.
Jev Benchmarks & EvaluationsArticle#community#research#arxiv
Communityluckykevvv/Decision_Model_BenchmarkPython
To Jev or Not? Evaluating the Accuracy and Efficiency of Structured Decision Models for Hate-Speech Moderation (arXiv 2610.03324)
HATEDECIDE evaluates six decision-model configurations on four hate-speech datasets against specialized, zero-shot, commercial, and supervised baselines. The best hosted model comes within 1.6 macro-F1 points of the best commercial LLM at approximately 97% lower inference cost.
Jev Benchmarks & EvaluationsArticle#community#research#arxiv
Community
Code Owns the Simulation, Jev Owns the Evaluation (arXiv 2610.01834)
A test of Jev on reflection tests, one-shot matrix games, ALFWorld, and robot control that finds a sharp boundary: it succeeds when the right option can be judged from what the input describes, but fails when the right option depends on predicting something not in the input.
Jev Practices & PatternsArticle#community#research#arxiv
Community
Judgement in the Age of Jev: From Evaluation Scarcity to Evaluation Abundance (arXiv 2610.01231)
A conceptual article that formulates a conditional Jevons hypothesis for machine evaluation: sufficiently large reductions in the marginal cost of usable machine evaluation may increase its organizational consumption where latent demand is substantial and complementary costs do not dominate.
Jev Practices & PatternsArticle#community#research#arxiv
Community
Beyond Answer Confidence: A Controlled Audit of Self-Knowledge in a Black-Box Decision Model (arXiv 2610.01006)
4GitHub stars
An audit of whether Jev's confidence reflects missing knowledge rather than chance, using over 15 public datasets and 6 generated task families with paired interventions. Jev's confidence is calibrated on familiar closed-choice tasks but fails as an indicator of missing knowledge.
Jev Benchmarks & EvaluationsArticle#community#research#arxiv
CommunitySyntheme/beyond-answer-confidencePython
Evaluating and Benchmarking the System One Model Jev (arXiv 2609.37647)
6GitHub stars
A zero-shot evaluation of Jev (jev-1.13.0) on 37 datasets spanning classification, routing, NLI, reading comprehension, commonsense reasoning, moderation, legal clause analysis, and rubric scoring. It runs 346,009 requests for under USD 10 and reaches 95–99% accuracy on IMDB, SST-2, HellaSwag, and ARC.
Jev Benchmarks & EvaluationsArticle#community#research#arxiv
CommunityAppliedMachineLearning-Lab/jev-benchmarkingPython
Calibrated to Whom? Persona and Language Effects on Cultural Values in JEV (arXiv 2609.36399)
An audit of TypeSafe's JEV with the Values Survey Module 2013: 24 items asked as 12 matched Saudi and 12 matched American personas, without a persona, in English and Arabic, under eight request formulations, for 288,000 answers.
Jev Benchmarks & EvaluationsArticle#community#research#arxiv
Community
Jev thinks "I don't know'', but doesn't say it: Introducing Sys1Cal-v1 Dataset for Probability Calibration (arXiv 2609.35342)
Introduces Sys1Cal-v1, a dataset of True/False questions about propositions whose exact probability is known by construction, queried through Jev's three primitives and scored by total variation from ground truth. Recovering a suppressed third value raises median soft accuracy from 0.771 to 0.978.
Jev Benchmarks & EvaluationsArticle#community#research#arxiv
Community
Beyond Calibration: Do a Typed-Decision Model's Probabilities Obey the Probability Axioms? (arXiv 2609.33209)
0GitHub stars
Tests whether typed-decision model probabilities satisfy coherence, using logically linked questions that need no labels. On 480 negation pairs, Jev's probabilities for a label being X and not X miss summing to one by 0.064 on average.
Jev Benchmarks & EvaluationsArticle#community#research#arxiv
Communitybro789/typed-decision-coherencePython
Typed Decision Models: An Early Evidence Audit and Evaluation Checklist (arXiv 2609.32160)
An audit of 28 papers posted between 19 and 24 September 2026, nine days after TypeSafe released Jev, relating their findings to earlier work and deriving a 14-item evaluation checklist. The typed readout shows no independent accuracy advantage yet; the clearest gains are latency and cost.
Jev Benchmarks & EvaluationsArticle#community#research#arxiv
Community
KITE: Scaling Jev Population Experiments with Sparse Flagship Calibration (arXiv 2609.27535)
1GitHub stars
KITE queries a typed behavioral kernel once per unique state, then runs populations of any size from the table, reserving an expensive flagship model for sparse paired anchors. On Epstein experiments with 9,070 participants, anchors covering 1.7% of states cut effect error by 41%.
Jev Tools & CLIsArticle#community#research#arxiv
CommunityHengyuLi-Ozaki-lab/kite_population_simulatorPython
Specialized Decision Models vs. General-Purpose LLMs: Benchmarking Jev Across Knowledge, Reasoning, and Multilingual Tasks (arXiv 2610.11978)
A comparison of Jev with 19 LLMs across 13 multiple-choice benchmarks covering knowledge, reasoning, and multilingual understanding. Jev is competitive with frontier LLMs on knowledge and commonsense tasks, but on MathQA it trails the frontier median by 17.7 points.
Jev Benchmarks & EvaluationsArticle#community#research#arxiv
Community
Can Decision Models Understand Stance? Evaluating Jev Against General-Purpose LLMs (arXiv 2610.11901)
An evaluation of Jev on two stance detection datasets, VAST for English texts and ZS-CSD for Chinese conversations, against four general-purpose LLMs and two fine-tuned models. Jev matches GPT-5.6 on VAST but falls behind stronger LLMs on ZS-CSD.
Jev Benchmarks & EvaluationsArticle#community#research#arxiv
Community
Nudge the Judge: Uncovering Program Decision Hijacking in Real-World Jev Applications (arXiv 2610.18612)
3GitHub stars
An automated framework that traces adversarial context through Jev-backed program decisions to downstream effects. Across 228 real-world Jev applications and 2,273 test cases, 81.5% show decision flips, 98.6% with potential security impact, and Decision Flip Rate beats the strongest baseline by 50.4 points.
Jev Benchmarks & EvaluationsArticle#community#research#arxiv
Communityxiaoxiaohg8d/JevProbe-Python
Jev-Guard: Fast Routing and Auditing Agent Actions over Editable Policy Trees (arXiv 2610.18326)
A unified guardrail model that selects an applicable audit policy from a policy tree, then classifies safety conditioned on the retrieved policies. On 7,033 cases it reports 93.72% safety accuracy and 93.10% hierarchical routing accuracy at 58 ms mean latency.
Jev Models & ReimplementationsArticle#community#research#arxiv
Community
Same Attack, Different Judge: A First Controlled Adversarial Comparison of Jev, LLM Judges, and Purpose-Built Safety Classifiers (arXiv 2610.17820)
A controlled adversarial comparison of Jev against five GPT and GLM judges and two local classifiers under identical payloads and policies. Jev is the most robust on input moderation and the fastest overall, but detects only 62% of phishing emails and flags the most benign requests.
Jev Benchmarks & EvaluationsArticle#community#research#arxiv
Community
Hidden Risks of Jev: An Empirical Study of Security, Privacy, and Dual Use (arXiv 2610.04985)
0GitHub stars
A systematic study of Jev's security and privacy implications using the official API and NanoJev, a local model with controllable training data. It adapts prompt injection, adversarial suffixes, and poisoned training data, runs inference attacks against typed outputs, and maps dual uses.
Jev Practices & PatternsArticle#community#research#arxiv
Communityshihe98/Security_Privacy_JevPython
SecJev: Bringing Security Expertise to System One Decision Models (arXiv 2610.03073)
SecJev is a family of security-specialized System One decision models from 0.8B to 9B parameters, built on Kev's single-pass candidate scorer. It spans 14 tasks and eight sources, and SecJev-0.8B beats general Kev-9B by 20.51 points in task-macro accuracy.
Jev Models & ReimplementationsArticle#community#research#arxiv
Community
HydroJEV: A one-second, training-free screen for cyber-attack and fault attribution in water distribution networks (arXiv 2610.02048)
A pre-registered evaluation of Jev as a one-second, training-free screen for SCADA alarm triage in water distribution networks. On a four-class EPANET C-Town benchmark it matched a hand-written rule tree, and a gated cascade spared an LLM reviewer 35-38% of windows.
Jev Practices & PatternsArticle#community#research#arxiv
Community
Jev-IDS: System One Models for Network Intrusion Detection (arXiv 2610.01079)
JEV-IDS is an open experimental network intrusion detector that serializes one flow per request and asks Jev for a binary attack probability and a finite-choice traffic category. On a 300-flow NSL-KDD pilot it reports F1 0.859 and novel-attack recall 0.838.
Jev Tools & CLIsArticle#community#research#arxiv
Community
JEV as a Judge for Agent Trace Security: An Empirical Comparison with Generative LLM Judges (arXiv 2609.34862)
An empirical comparison of Jev as a typed judge for retrospective agent-trace security against four generative judges on 5,219 trajectories. Jev averages positive-class F1 77.8 versus 74.1 for GLM-5.2, leading on two of four benchmark collections.
Jev Benchmarks & EvaluationsArticle#community#research#arxiv
Community
JevAdvBench: A Benchmark and Black-Box Attacks for Reinforcement Learning for Calibrated Decisions Models (arXiv 2609.31142)
JevAdvBench is a black-box adversarial benchmark for RLCD models such as Jev, with 812 typed questions over 66 scenarios and 9,744 single-edit attack variants. On jev-1.13.0 an appended unverified opinion flipped 12.1% of decisions and pushed 38% of confident answers below the review threshold.
Jev Benchmarks & EvaluationsArticle#community#research#arxiv
Community
Just Ask Jev: Reinforcement Learning for Calibrated Decisions as a Zero-Shot Detector of AI Alignment Failures (arXiv 2609.29429)
28GitHub stars
RLCDAlignBench measures Jev as a zero-shot detector of ten alignment failures across 44 benchmarks and five target models. A single generic question reaches median AUROC 0.886 and beats supervised baselines on most benchmarks at 63 times lower cost than LLM judges.
Jev Benchmarks & EvaluationsArticle#community#research#arxiv
Communitysumleo/RLCDAlignBenchPython
Calibrated Decision Models for Autonomous Penetration-Testing Harnesses: JEV and Laya as System One Decision Layers for LLM-Driven Pentest Agents (arXiv 2609.28940)
A position and case-study paper on using System One decision models like Jev and the open-source Laya as decision layers in LLM-driven penetration-testing harnesses. It names four decision points, runs an exploratory 13-vulnerability NeuroSploit comparison, and proposes Rave, an unevaluated model.
Jev Practices & PatternsArticle#community#research#arxiv
Community
Decision Hijacking: Prompt Injection Attacks on Jev's Typed Probabilistic Decisions (arXiv 2609.28613)
An evaluation of prompt injection against Jev's typed probabilistic decisions using 510 reconstructed InjecAgent cases. Malicious content shifts action probabilities but rarely flips Jev to the attacker's target; score-feedback attacks raise fresh-run success from 1.8% to 3.5%.
Jev Practices & PatternsArticle#community#research#arxiv
Community
COGNIT-Guard: Calibrated Standalone Direct-Decision Guardrails with Heterogeneous CPU-NPU Confidence Cascading under Explicit Latency and False-Positive Constraints (arXiv 2609.33671)
COGNIT-Guard is a safety guardrail that pairs a calibration-tuned CPU gatekeeper with confidence-gated escalation to an NPU-resident 322M direct-decision model, Laya-322M. It reports 98.85% accuracy and 0.42% benign false-positive rate on DUCS-Bench, with experience replay recovering out-of-distribution accuracy.
Jev Tools & CLIsArticle#community#research#arxiv
Community
Jeva: System One Models from First Principles (arXiv 2610.18773)
Introduces Jeva, a decision-native architecture that encodes the request and each option separately and scores options symmetrically. It targets null-awareness, permutation invariance, option scalability, and cacheability, reporting near-perfect null detection and no decision flips under tested permutations.
Jev Models & ReimplementationsArticle#community#research#arxiv
Community
JevGPT: Thinking Fast and Slow in Autonomous Research (arXiv 2610.18272)
A dual-system architecture for autonomous research that couples fast evidence processing with deliberative planning: System Two sets evidence objectives, System One locates support. On fixed-corpus QA and document ranking it reports leading performance with lower cost than comparable agents.
Jev Models & ReimplementationsArticle#community#research#arxiv
Community
Releasing the Inner Jev: A Simple Recipe for Open Decision Models (arXiv 2610.17205)
Shows that open instruction-tuned models already read decisions from answer-letter logits without training, and that LoRA fine-tuning with a decision head lifts them past Jev: Gemma 4 31B goes from 79.6 to 84.4 on the new 2,352-question OpenDecisions set and scores 81.5 on JevBench v1.7.
Jev Models & ReimplementationsArticle#community#research#arxiv
Community
RSAJEV: Training-Free Abstaining Prototype Memory for Eliminating Silent Hallucination in JEV-Based Remote Sensing Classification (arXiv 2610.15742)
Documents a failure mode in letter-bound remote sensing classification: 97.71% OA on UCM in vocabulary but 7.35%–96.40% cross-dataset accuracy and inverted confidence (AUROC 0.38). RSAJEV replaces the letter readout with training-free prototype retrieval, reaching 89.43% on the UCM cross-dataset gate.
Jev Practices & PatternsArticle#community#research#arxiv
Community
FastJEV: Understanding Redundancy for Compact JEV Inference (arXiv 2610.11379)
A training-free method that studies redundancy in candidate evaluation for JEV-style multimodal decisions. Shared context anchoring, candidate prefix sharing, and decision-guided pruning cut candidate depth by 43.75%–45.83% while retaining 93.66%–97.52% of task scores.
Jev Practices & PatternsArticle#community#research#arxiv
Community
OpenJev-RLCD: A Working RLCD Implementation (arXiv 2609.38850)
30GitHub stars
A working RLCD implementation for reasoning models: the model samples a rationale, and a strictly proper scoring rule grades the answer distribution it commits to afterward. With Qwen3-1.7B it matches or beats SFT, RFT/STaR, and GRPO in accuracy and beats all in selective prediction.
Jev Models & ReimplementationsArticle#community#research#arxiv
CommunityZimmyGao/openjev-rlcdPython
Chinese-Jev: Bringing System One Model to Chinese-Language Tasks (arXiv 2609.36965)
A System One model for Chinese-language decisions trained on a 10-million-example corpus and fine-tuned for medicine, legal, and finance. Pre-training exceeds closed-source Jev by 1.24% on general tasks with a 20.3x speedup; domain fine-tuning beats Jev in medicine by 4.0%.
Jev Models & ReimplementationsArticle#community#research#arxiv
Community
From Text Decisions to Pixels: An Study of Jev-Style Visual Choice Model (arXiv 2609.29283)
Presents PixelJev, a native-image decision interface that maps an image, a task instruction, and a runtime candidate set to a structured choice and candidate-conditioned probabilities. 64-shot source adaptation raises Pets accuracy from 60.13% to 92.40% across optimization seeds.
Jev Models & ReimplementationsArticle#community#research#arxiv
Community
NumericJev: Jev-like LLM Numerical Decoding with Multiway Decision Trees (arXiv 2609.28587)
9GitHub stars
A training-free algorithm that gets numerical output from any LLM with a Jev-like structured-choice interface by recursively refining a range through a multiway decision tree. Range-normalized MAE is 1.84% versus 5.18% for direct choice, and code is available on GitHub.
Jev Models & ReimplementationsArticle#community#research#arxiv
CommunityBring-AI/jev-numericPython
Visual Jev: Accurate and Efficient Decisions from Shared Visual Context (arXiv 2609.25845)
49GitHub stars
Encodes an image and public context once, runs isolated question suffixes as a batch, and reads candidate probabilities from the backbone's language-model head. Answer-supervised post-training lifts equal-weight macro accuracy from 70.6% to 76.1% across four benchmarks.
Jev Models & ReimplementationsArticle#community#research#arxiv
Communityguanxuyu-sv/Visual-JevPython
Open-Jev Judgments on CallScreenBench: Calibrated One-Pass Scam Screening with a Small Language Model (arXiv 2609.23959)
Tests one-pass scam-call screening on 41 held-out scenarios producing 577 per-turn decisions: a three-seed ensemble of LoRA-tuned Qwen3-4B reaches AUROC .974 with calibration error .052 and no false alarms on legitimate calls, at 64.5 ms per decision on one consumer GPU.
Jev Benchmarks & EvaluationsArticle#community#research#arxiv
Community
VideoJev: System One Model for Video Understanding and Interaction (arXiv 2610.18615)
Casts video understanding and interaction as structured decisions by replacing autoregressive answer decoding with a shared head that scores temporal intervals and textual options. VideoJev-4B and VideoJev-35B-A3B improve mean mIoU by 6.7 and 10.8 points on temporal grounding with 2.8–3.1x speedups.
Jev Models & ReimplementationsArticle#community#research#arxiv
Community
System Switch: When Should a Fast Decision Model Stop and Think? (arXiv 2610.09683)
1GitHub stars
Studies a fast learned actor in closed-loop Doom that hands control to a reasoning vision-language model only when a gate opens. On 900 held-out questions, deferring the least confident 30% of decisions gains +0.13 with doomLaya's option order, but no closed-loop variant reaches the exit.
Jev Practices & PatternsArticle#community#research#arxiv
Communitydexmac221/doomgemmaPython
FunDM: Cheap & Label-Free Decision Models from Frozen Language Models (arXiv 2610.17175)
0GitHub stars
Trains a small decision head label-free to recover about 99% of a frozen language model's multiple-choice accuracy using two-thirds of its transformer layers. Its +1T variant reaches 75.0% mean accuracy across eight benchmarks versus 75.7% for the full model, with higher batched vLLM throughput.
Jev Models & ReimplementationsArticle#community#research#arxiv
CommunityUjjawal-K-Panchal/FunDMPython
AnyJev Technical Report (arXiv 2610.00831)
1.4kGitHub stars
Reads a typed decision from one prefill of a pretrained instruction-tuned model by restricting the next-token distribution to option tokens, then corrects label and position bias with no gradient steps. Averaging over cyclic rotations lowers order-flip rates and raises accuracy on 11 of 11 models.
Jev Models & ReimplementationsArticle#community#research#arxiv
Communitynokia-applied-research/AnyJevPython

Start here with the Jev AI model

Hand-picked entries the maintainers recommend first, from official primitives to community tools.

Jev: For the Builders AND Dreamers (Jev 1.14)
The Jev Day release post: Jev 1.14.0 under jev-preview plus three danger-zone features — composable images on both models, the box question type on 1.14 only, and opt-in 262k context.
Jev Guides & ArticlesArticle#official#release#vision
Official
Box Questions (Dreamer-Ready)
The box question type: one bounding box per question, returned as four edges that are fractions of the image, plus confidence, with no criteria field and no not-found value.
Jev Practices & PatternsDocs#official#docs#vision
Official
Jev 1.14 Day 0 Support in LiteLLM
LiteLLM's Day 0 post for TypeSafe Jev 1.14: the proxy adds box questions, image input and long-context mode on both POST /v1/systemone and POST /v1/decisions, forwarding TypeSafe-Danger-Zone headers only to TypeSafe deployments.
Jev Integrations & AdaptersArticle#community#litellm#gateway
Community

Newest Jev AI model resources

The latest additions to the directory, newest first.

From the Jev community

Watch walkthroughs, read the threads, and see what builders are posting.

YouTube: KodeKloud's three-minute primer: what a System One model decides, and why it answers in milliseconds.

YouTube: Caleb Writes Code explains what Jev is and how to call it, in seven minutes.

YouTube: Greg Isenberg and Ryan Vogel on the businesses Jev unlocks, and a path in through the Vercel AI Gateway.

YouTube: CJ from Syntax demos browser use, classification, code review, a model router, and a chat bot with no LLM.

YouTube: Ryan Vogel runs Jev on 100 and then 1,000 emails: category, priority, spam, and reply predictions.

YouTube: AICodeKing tests support routing, refund detection, prompt-injection resistance, and browser automation.

Hacker News: Show HN: Ad Spend Guard gates budget before the auction, not the creative after.

Show HN: Gating ad budget with a decision modelRead the thread on Hacker News

Reddit: Reranking rebooking options with a decision model, ranked with reasons.

Hacker News: Pick Verify: a single decision at the warehouse pick station catches confusable picks.

Show HN: Verifying robot picks with a decision modelRead the thread on Hacker News

Hacker News: Decision Index 0.2.1 scores open reproductions of Jev on a frozen suite.

jackbravo4 pointsDecision Index 0.2.1 – 70 open reproductions of TypeSafe Jev's Decision ModelRead the thread on Hacker News

Hacker News: Software Bombe and live Enigma demo where Jev ranks cribs and judges plaintext.

ag_212 pointsShow HN: Jev as the Bletchley Park Analyst: A Software Bombe on EnigmaRead the thread on Hacker News

Hacker News: Show HN for the decide CLI: brew install, file context, and multi-question shell scripts.

vsekhar2 pointsShow HN: Decide – Jev decisions in the shell, scripts, and agent skillsRead the thread on Hacker News

Follow @typesafeai

How entries are chosen

Every entry is reviewed against the same bar, whether it comes from TypeSafe or the community.

  • Public and working — no paywalls, sign-up walls, or dead links.
  • Concrete — it has to be about Jev and the System One API, not generic LLM content.
  • Useful today — something a developer can learn from or use right now.
  • Distinct — if a similar entry exists, the description has to say what makes this one different: a different runtime, a cheaper path, a measured result, or a narrower focus.
  • Documented — repos state their license, and docs are in English or carry an English summary.

Spam, affiliate links, and thin AI-generated content are rejected. New entries are expected to ship with a deep dive: two to five highlights, caveats, a quickstart snippet where one helps, and a verification date. Star counts are refreshed when an entry is touched.

Common questions

Short answers about Jev and how this directory works.

What is the Jev AI model?

Jev is TypeSafe AI's first System One model — an AI model that takes a state plus typed questions and returns one structured answer per question: a choice, an ordered score, or the probability of yes, with calibrated confidence on Choice and Score answers. Code can branch on the result without parsing generated text.

What is the difference between Choice, Score, and Noul?

They are the three question primitives. Choice picks one of up to 255 options, Score rates on 2 to 10 ordered levels, and Noul answers yes or no. Choice and Score answers carry a confidence between 0 and 1; a Noul answer is the probability of yes.

Which Jev SDKs and client libraries are available?

TypeSafe maintains official JavaScript/TypeScript and Python SDKs plus a Python adapter. The community has built clients for Go, .NET, PHP, Laravel, Rust, Elixir, Ruby, and Scala, along with framework integrations such as DSPy, LlamaIndex, and Home Assistant. All of them are listed in the Repos & SDKs category.

Is the directory free, and is it affiliated with TypeSafe AI?

The directory is free to browse: every entry is reviewed, and spam, affiliate links, and sign-up walls are rejected. JevDirectory.org is community-run and not affiliated with TypeSafe AI — Jev and System One are TypeSafe AI trademarks.

How often is the directory updated?

Entries are added as new resources appear and re-checked when they are touched. The directory currently holds 3919 entries from 293 source sites, last updated Nov 6, 2026.

What is a verified deep dive?

Most entries include a deep dive: two to five highlight bullets someone would only know from using the resource, a caveats note about limits and requirements, an optional quickstart snippet, and the date the link was last checked. 3919 of the 3919 entries carry one today.

For the canonical details, read the official TypeSafe docs and the API reference.