Awesome System One

A curated list of System One models and tools.

You give them some state and a set of typed questions. They give back a choice, a score, or a yes/no probability. No generated text, so nothing to parse. TypeSafe named the class after Kahneman's System 1; this list is about the software. Jev was the first commercial model.

Contents

• Models - Call a hosted model. Jev for the original API, CLM or Laya for open weights.
• Open Implementations - Train or run one yourself.
• Agents - Need it to pick the next click or tap.
• Tools - Ready-made plugin or CLI.
• SDKs & Adapters - Calling from application code.
• Baselines - Want a cheaper classifier first. Same job, different interface.
• Benchmarks - Compare models.
• Reading - The psychology the name comes from.

Models

Hosted or open. State in, typed answers out.

Open weights

• Clef - Cloudflare's open decision models (Clef and Clef-flash), Apache 2.0: frozen Qwen 27B / 9B backbones with a trained routing head, prefill-only non-autoregressive scoring, plus a vision encoder Jev lacks and a 64K context. Cloudflare's runs put Clef first on the Jev Decision Index; Clef-flash runs ~39 ms median. Hosted on Workers AI, with a new RL platform for fine-tuning it on your own data. Already in production on Cloudflare's Threat Intelligence team.
• CLM - Contrastive Language Model: dual state/action encoders (frozen Qwen3-8B + 20M heads) where candidates embed once and cache, rather than re-reading the state. Apache 2.0, TypeSafe wire-compatible. Claims Jev parity on computer-use, gaming, and tool-calling at up to 9× lower latency, plus SOTA held-out verifier results (Terminal-Bench 2.1, DeepSWE).
• Cua-S1 - Research stack for small specialist computer-use models. First checkpoint is form-oriented: cua-s1-forms, a 706K-param option scorer with its training dataset on Hugging Face. Planning and execution stay separate.
• GLiNER2.5-Decide - Decision specialist from the GLiNER team: any label set at call time — intent, routing, multi-label tags, ordinal scores — answered in one forward pass with no generated tokens. Apache 2.0, in 340M / 1B / multilingual builds. Claims 60.2% vs JevK5's 57.6% on Fastino's own 17-domain suite.
• Jev-Omni - Multimodal decision classifier on Gemma 4 12B: text, image, audio, or video in, a probability per option out. Apache 2.0 weights. One question per call, best under about 20 options. Currently #1 on Image JevBench.
• Laya - Multilingual, non-autoregressive decision engine. Typed choice / score / noul over 100+ languages in a single forward pass (about 33 ms on a T4). Router across English, multilingual, and typed-decisions checkpoints. Apache 2.0.

Hosted

• Hanzo Kai - Hanzo's hosted decision model on the OpenRouter Decisions wire: model: "kai" at api.hanzo.ai/v1/decisions, with Jev itself routable on the same endpoint. A Jev client works by repointing the base URL.
• Jev - TypeSafe's System One model. Choice, Score, and Noul over a state in one request, about 70–500 ms, trained with RLCD. Hosted API ($0.042 per million input tokens, output free); weights unpublished. Also on OpenRouter, Vercel AI Gateway, Cloudflare Workers AI, and LLMGateway. Docs · Launch post
• OpenAI Decisions API - OpenAI's entry in the class, announced at DevDay 2026 as a limited preview: text or image context plus a fixed answer set, returning one answer with a confidence score around 150–230 ms. Powered by a Luna variant; broader release promised, no separate pricing published yet.
• Solar Decide - Upstage's structured decision model on Solar Mini 4 (beta). Same /v1/systemone schema as Jev, calibrated probability per answer, and a 512K context — a whole document can be the state. choice is limited to 26 single-token letter options. Also on OpenRouter.
• Span-01 - Respan's behavioral-monitoring decision model: describe a behavior (user frustration, tool misuse) and get present / absent / not-observable probabilities on a conversation. A free Lite tier exists for high-volume monitoring. Vendor-run benchmarks put it ahead of Jev overall but behind GPT-6 Sol per-domain.

Open Implementations

Same request shape on open weights. Not TypeSafe's architecture.

Trained replicas

• agent-jev - AgentJev-0.6B: a small trained decision model that takes unstructured state (diffs, traces, logs) and returns calibrated distributions in one ~50 ms forward pass. Apache 2.0.
• decider - Qwen3.5 fine-tunes (0.8B up to a 35B MoE, plus a vision build), Apache 2.0 weights on Hugging Face. The vision build ranks #2 on Image JevBench. Serves TypeSafe /v1/systemone, so the official SDK works by repointing TYPESAFE_BASE_URL.
• jevlike - Train a small one-pass scorer over a changing list of text options. Each option queries the context; a shared head returns one probability per option. Byte encoder from scratch, or a frozen Hugging Face encoder.
• jevos - MiniCPM5-1B cut to 17 layers with a one-logit head, quantized to GGUF and run on CPU via llama.cpp. Serves TypeSafe /v1/systemone for noul (yes/no) questions only, so the official SDK works by repointing TYPESAFE_BASE_URL. MIT.
• kev - LoRA plus a pointer readout on Qwen3.5 (0.8B / 4B / 9B), Apache 2.0, with training code and frozen eval suites. TypeSafe /v1/systemone drop-in, calibrated by default via a fitted temperature. Frozen evals put Kev-9B about 3.5 points behind Jev on unseen sources; MLX, ROCm, CUDA, and a one-command Modal deploy. Also the only open-weights route on OpenRouter's Decisions API.
• NanoJev - 0.6B replica trained from scratch, MIT. Ships the weights, the dataset, and the end-to-end training pipeline, plus a side-by-side demo against Jev.
• Nimble - Qwen3.5-9B LoRA from Bespoke Labs. Contrastive recipe, not distilled from Jev. 90% vs Jev 93% on a 324-example holdout.
• Open Jev - 150M ModernBERT encoder that answers per-request noul / choice / score questions in one pass. Trains on a Colab T4 in about 30 minutes. Not the same project as SemIf, which was also once called OpenJev.
• openJev-verdict-2.0 - 151M ModernBERT decision engine, non-autoregressive, with published calibration numbers. Claims the top spot over Jev and Laya on the LocalLLaMA typed-decisions benchmark.
• PlayJev - 0.8B multimodal Jev-like model that plays GUI games directly from raw pixels. Apache 2.0.
• system-one-open - Jev-style replica on Gemma 4 E2B / Gemma 3 270M, trained and served on Modal. One forward pass, no decoding. Live demos for support, Doom, browser-use, and smart home.
• Tev1 - Together AI's Jev-inspired experiment: Qwen3.5 fine-tunes that take state + question + 2–24 options and return one letter — openly next-token, not a non-autoregressive runtime. MIT, with full data recipe and a "train your own for $17" writeup.
• Valen - Train-your-own multimodal System One model: evaluates text, images, and video against task instructions and returns probabilities over supplied candidates. Ships the model code, data pipeline, SFT plus experimental RLCD training, and evals.
• WaterSheep - ModernBERT-base fine-tune with a decision head that answers noul / choice / score and also multi-label questions, with a fitted temperature per question type. Publishes its held-out numbers: 61.2% accuracy and 0.043 ECE on datasets it never trained on. Apache 2.0 code and weights; serves TypeSafe /v1/systemone, so the official SDK works by repointing TYPESAFE_BASE_URL, and an ONNX build runs in the browser.

Serve any open model

• AnyJev - Turns any LLM into a Jev-style decision model: typed decisions with probabilities, no training. From Nokia Applied Research, Apache 2.0.
• djev - DiffusionGemma plus vLLM: an inference method rather than new weights, with native image inputs and a hosted API. Ranked 3rd on JevBench v1.2, just under Jev itself.
• jeff - Self-hosted drop-in on GLiFormer 400M, MIT. Speaks the TypeSafe request shape (JevBench drove it with the official adapter); ranked in v1.2.2.
• jevmlx - Jev-style parallel constrained decisions for any MLX model on Apple Silicon. Scores every allowed answer per schema field in one pass, so the output is valid by construction.
• jev-visual - Educational Jev-like visual inference experiment on Apple Silicon: shared context, direct candidate scoring, and local visual demos.
• LLM2Jev - Adapts local language models into Jev-compatible decision engines with Choice / Score / Noul outputs, via prefill-only binary inference. Apache 2.0.
• ollaya - Ollama for decision models: ollaya pull / serve / run manage Laya, decider, NLI, and GLiClass locally behind a wire-identical TypeSafe /v1/systemone (and /v1/decisions) endpoint.
• open-alternative-jev - System One-style layer on open weights, ranked on JevBench. Its cautionary detail: 72% vs 21% on the same answer-judging items with option order flipped.
• openjev - Open, Jev-compatible System One decision server on DiffusionGemma. Apache 2.0.
• openjev-sglang - TypeSafe /v1/systemone on Qwen 35B MoE via SGLang. Prefill once, then first-token logits per question. No extra training.
• reflex - Open recreation on Qwen3.5. Prefills the state once, then scores every question in parallel from next-token logits. Serves the TypeSafe request shape. Browser demo on WebGPU. #3 on Image JevBench in its released stable configuration.
• SemIf - Runtime-defined semantic decisions from direct option logits. CUDA, MLX, and WebGPU, plus committed benchmarks. Repo recently renamed from SemIf.
• simple-jev - Turns any open model into a classifier / Jev endpoint. From Featherless, Apache 2.0.
• system-one - Turns any open LLM into a System One classifier: batched single-token choice inference, TypeSafe SDK compatible. Doom and wikiracing demos on Qwen3-8B.
• systemANE - Apple's Neural Engine as a free local System One decision engine on macOS: "Jev at home."
• von - Non-autoregressive local drop-in, sub-15 ms decisions, Apache 2.0.

Agents

The model picks an action. Code runs it.

Screen & device control

• computer-use-jev - Drives native macOS apps through the Accessibility API. Jev chooses the next action and target token from a live snapshot, and stops if confidence drops too low.
• jev-browser - Headless browser agent. Jev picks click / type / select / done from the page's elements; code owns budgets and stop gates. MCP server, CLI, or library.
• jev-browser-use - Split-brain browser agent: Jev clicks, Codex thinks and verifies.
• jev-ultrafast - Browser agent with a dynamic, indexed action space. Jev picks an operation and an element in one request; a small LLM writes text only when the operation is TYPE_TEXT. Zürich to London on Google Flights in 7.1 seconds.
• mobile-jev - Android agent on a real phone via Mobilerun. Jev picks OPEN_APP / TAP / TYPE_TEXT from an indexed snapshot. Text is copied from the goal, not generated.
• tiptour-macos - Local computer-use app for macOS where Jev is the default engine: it picks from locally detected controls and the app executes and validates each action.
• typesafe-computer-use - Computer use for about $0.0002 a step: OCR the screen, Jev classifies the next action, code clicks. macOS.

Domain agents

• embodied-jev - MuJoCo robot decision workbench: MiniCPM-2B or Jev-class APIs pick the next action, with physics previews.
• jev-chat-jarvis - Phone chat copilot for WeChat / QQ / X / Feishu: reads the screen, suggests candidate replies, one tap to fill. Read-only — no app hooks.
• jev-drone - Camera-only autonomous drone in MuJoCo with Jev in the loop at 2.5 Hz.
• jev-trader - Experimental: one Jev buy/sell decision every Monad block (about 300 ms) on the Kuru MON-USDC book. Dry-run by default.
• neo4jev - Jev navigates a Neo4j graph by classifying over each node's neighboring relationships: graph traversal as a chain of typed choices.
• QuantDinger - Open-source AI trading OS with a Jev decision gate in front of live entry orders: typed Choice plus an auditable decision timeline, fail-open, and exits that always bypass AI.
• typesafe-assist - Home Assistant conversation agent. Jev maps a spoken command plus exposed entities onto built-in intents. Free-text intents fall back to another agent.
• typesafe-mario - Jev plays Super Mario Bros. from structured emulator state: the classic RL demo with a decision model in the loop.

Tools

Plugins and CLIs that put a System One model inside other software.

Context & memory

• distill - Token-saver for agent sessions with a documented Jev routing layer.
• fast-jev-compaction - Claude Code plugin that replaces the compaction summary with Jev decisions: every tool call and result is scored in one request and the stale ones dropped.
• invalidate - Invalidation layer for AI memory: every fact gets a lease, and new evidence ends it.
• memsearch - Zilliz's agent memory layer, with optional Jev reranking and a published Chinese/English reranking evaluation.
• stuntd - Local proxy that learns your app's typed LLM decisions and answers them itself with a Laya head. Jev- and OpenAI-compatible.
• winnow - Claude Code hook that splits large tool results into blocks, asks Jev a yes/no relevance question per block, and stubs the ones that fail.

Routing & supervision

• agentconnect - Multi-agent collaboration across Slack, Telegram, and GitHub, using Jev for agent routing, model selection, and support triage.
• Astra-Ares - Adaptive reasoning effort: Jev judges each Codex step and turns GPT-6's thinking effort up or down mid-task, so easy steps stop burning tokens.
• Foreman - Jev as a fast supervisor above slower coding agents, judging whether the work is complete, the requirements met, the tests sufficient, or a human is needed.
• hermes-jev-skills - Jev-powered skill suite for Hermes agents (also Claude Code and Codex): model routing, memory, compaction, skill selection, computer and browser use.
• Intent-Router - Intent compiler that converges vague requests into typed IntentSpec contracts — probe, ask, or halt — before routing. The input layer in front of routers and decision models like Jev or Laya.
• jev-codex-router - Per-turn model and reasoning-effort routing for Codex. Jev classifies each turn and picks the tier; a 7-day replay of 237 real turns measured about 60% savings against all-frontier.
• JevRouter - Jev-powered router across models, tools, and subagents.
• pi-jev - Pi coding-agent extension: semantic tool and skill routing, typed choice / noul / score evaluations, optional auto-approval, and a post-run jev-gate CLI.

Review & verification

• Canny - Stops coding agents from claiming done without evidence: deterministic hooks decide what needs checking, Jev judges the result.
• jev-review - Staged code-review workflow with a local dashboard, using Jev to score and sort findings before a human opens the queue.
• perch - Semantic code linting: rules written in plain English, judged by Jev.
• reticle - Verification harness for agent-built code; the jev driver explores a page by choosing among DOM-enumerated candidates — the model chooses, never composes.
• sedum - Plain-English browser tests on Playwright where Jev only picks targets and next actions (Choice) and judges claims (holds/contradicted Nouls), and code owns every action and verdict. TypeScript, MIT.
• snifftest - Prose linter that sniffs out AI writing tells: zero-dependency countable rules plus one judgment model.

Search & data

• blink - Codebase search powered by Jev decisions: typed questions find where a task lives in a repo.
• docjev - Very fast document classifier and PDF splitter from the creator of LlamaIndex: plain-English rules, Jev assigns each page a category and finds document boundaries.
• jev-curate - Dataset sifter for synthetic and pretraining corpora: a Rust streaming core sends Jev keep/drop decisions over Parquet rows.
• jev-search - Web search pipeline where Jev handles source selection, query understanding, and relevance ranking.
• jev-tree - Recursive Jev choice over a taxonomy: selects from more than 255 options without hitting the choice cap.
• jevframe - Semantic AI for Pandas and Polars: classify and score DataFrame rows with natural-language questions and full probabilities.
• jevgrep - Codebase search for coding agents: Jev judges folders, files, and declarations, then returns reading leads and verbatim source excerpts. npm CLI plus an agent-skill installer; matched the baseline 8/10 on a ten-task SWE-bench at ~30% lower cost.
• jgrep - Grep where the pattern is a description: filters lines by meaning for about a thousandth of a cent each.
• pg-jev - PostgreSQL extension (on PGXN): jev(rows, 'plain-language condition') filters, ranks, and classifies table rows. The clever part — it batches 20 rows into one shared-state request with a noul each, amortizing Jev's request overhead ~2.5×, with read-ahead and per-row answer caching.

Train & adapt

• jev-align - Build calibrated AI functions from human feedback: Jev judgments optimized with GEPA. From Sutro.
• jimothy - Turns Jev usage into small local classifiers: --teacher typesafe-ai/jev labels the data, then a MiniLM model runs it in Node or the browser.

Apps & utilities

• ai-cli - Vercel Labs terminal client. The evaluate command sends a state plus typed questions to typesafe-ai/jev on AI Gateway (-m jev for short).
• classifier.dev - Zero-shot text classification as a URL: classifier.dev/spam,not+spam/your+text returns a label with calibrated confidence — no key, no account, up to 1,000 texts per call. One Cloudflare Worker on Jev with an LLM fallback chain, plus a CLI and MCP server. MIT.
• jevmail - Open-source Gmail triage: sorts an inbox into needs-reply / updates / promos / spam. Read-only, runs locally, about 3 cents per 1,000 emails.
• jeview - Local visualizer for Jev: a live view of every call your code makes.
• json-render - Vercel Labs generative-UI framework with an experimental Jev mode: Jev picks components from your catalog instead of a model writing markup.
• semdecide - Jev calls as Unix-pipeline predicates: routing, scoring, and JSONL filtering with stable exit codes, plus an agent-safety guard recipe.
• unclutter - Browser extension that removes page clutter: Jev scores elements against reusable rules, the extension hides the junk.

SDKs & Adapters

Official

• @ai-sdk/typesafe-ai - Official Vercel AI SDK evaluation provider. Choice, Score, and Boolean (Noul) over one state via experimental_evaluate.
• skills - Official agent skill for designing System One workflows from Claude Code, Codex, and other skill-compatible agents.
• typesafe-sdk-js - Official TypeScript / JavaScript client.
• typesafe-sdk-python - Official synchronous and asynchronous Python client.

Community clients

• Community SDKs - Unofficial clients for Go, Rust, Ruby, PHP, .NET, Elixir, and Swift, tracked by language. All launched in Jev's first week — check the last commit before depending on one.
• jev-foundation-models - Native Swift 6 bridge that puts Jev behind Apple's Foundation Models framework interface.
• llm-typesafe - Simon Willison's llm plugin for Jev and other TypeSafe models.
• qualm - TypeScript wrapper where every decision needs a required unsure branch — ignoring the uncertainty case is a compile error.

Frameworks & integrations

• eve - Vercel's open agent framework, where Jev powers the evaluate primitive and tool-approval policies.
• hono-jev-router - Semantic HTTP routing middleware for Hono: route requests by meaning, from Hono's creator.
• jev-mcp - MCP server of Jev judgment tools: verify, screen, find, rerank, classify, decide, compare, extract, review, and gate.
• laya-mlx - Native MLX runtime for Laya on Apple Silicon: 7–14 ms decisions on an M3 Max, no GPU server needed.
• receptron/laya - Run Laya from Node.js / TypeScript via ONNX Runtime; the ~1.7 GB weights download from Hugging Face on first use.
• system-one-adapter-python - Drop-in TypeSafeClient replacement backed by OpenAI or Anthropic, for comparing Jev against an LLM on the same questions.
• typesafe-mcp - MCP server as a single static Go binary, no Node or Python runtime. Sends usage guidance back to the client so the agent writes better questions.

Baselines

Cheaper classifiers for comparison. They do not share Jev's request shape.

• fastText - Classic non-generative text classification library. Upstream archived in 2024.
• GLiNER - Lightweight zero-shot structured extraction: you name the entity types at inference time. Often the comparison point in Jev classification benches.
• GLiNER2 - Fastino successor to GLiNER. Schema-conditioned encoder for classification, extraction, and relations in one pass.
• ModernBERT - Modernized BERT encoder under most fast classifiers, including Laya's English checkpoint. The non-generative baseline Jev's cost and speed usually get measured against.
• RouteLLM - Trains and serves routers that pick which LLM should handle a query, based on cost and expected quality, rather than answering the question itself.
• semantic-router - Embedding-space route layer for LLMs and agents. Chooses a path from utterance similarity without a generative call.
• SetFit - Prompt-free few-shot text classification on Sentence Transformers. Strong when labels are fixed at train time.

Benchmarks

Suites & harnesses

• Amazon ESCI - Shopping-query relevance classification (exact / substitute / complement / irrelevant) at Amazon scale. A Jev Decision Index component.
• API-Bank - Alibaba's tool-use benchmark for whether a model picks the right API call. A Jev Decision Index component.
• ARC - AI2's grade-school science MCQs in Easy and Challenge splits. Part of the standard MCQ battery used in Jev vs Luna Decisions comparisons.
• Banking77 - 77 fine-grained banking intents over 13k queries. Single-domain counterpart to CLINC150, and the benchmark Janus reports wins on. CC BY 4.0.
• BFCL - Berkeley Function Calling Leaderboard: the field's standard for function/tool-call accuracy (ICML 2025). A Jev Decision Index component.
• BRIGHT - Reasoning-intensive retrieval benchmark scored by nDCG — the Jev Decision Index's reranking component, and one Jev still leads.
• CLINC150 / OOS-Eval - 150 in-scope intents plus explicit out-of-scope examples. Useful for routing, abstention, and confidence-threshold tests. CC BY 3.0.
• DecisionBench - 80 scenarios and 293 questions per tier (medium is rules-based, hard is judgment calls), with a cost-per-decision axis baked in. What Jev-Omni reports on. Apache 2.0.
• evals.typesafe.ai - TypeSafe's workflow evals: four production-shaped workflows scored against frontier LLM reference probabilities.
• fast-decisions - Fastino's held-out suite: 17 operational domains × 300 examples each, scoring Jev, SemIf, Laya, and GLiFormer head-to-head on the same inputs. First-party to GLiNER2.5-Decide, which tops it.
• GPQA - Graduate-level, Google-proof MCQs; the Diamond subset is the hard tier. Part of the standard MCQ battery used in Jev vs Luna Decisions comparisons.
• GSM8K - Grade-school math word problems; repurposed as multiple choice with random distractors (4- and 10-option conditions in the Jev vs Luna comparison).
• HellaSwag - Commonsense sentence-completion MCQs. Part of the standard MCQ battery used in Jev vs Luna Decisions comparisons.
• Image JevBench - Held-out image-decision suite: 684 items (228 public / 456 sealed) with frozen hashes and contamination tracking — it flagged Kev's Mind2Web training overlap itself. Jev-Omni, decider-vision, and Reflex top it; frontier chat APIs score near zero.
• Jev Decision Index - Community index aggregating ~43 decision-model evals — intent classification, function calling, tool retrieval, reranking — plus latency. The benchmark surface Clef's launch numbers report on.
• JevBench - Public harness for Jev-class decision models: intelligence, calibration, speed, and cost on shared tasks. About 48 systems ranked; Jev 1.13.0 currently #1.
• jevals - Agent evals and guardrails as Jev decisions: one request per trace. Runs locally on Kev or Laya. From Openlayer.
• LocalLLaMA/typed-decisions - Community typed-decision suites (customer service, invoices, security incidents, agent traces) the open implementations report accuracy and calibration against. Apache 2.0.
• MASSIVE - Multilingual intent-and-slot dataset (about 1M utterances, 52 languages) for testing whether a fast decision layer generalizes. CC BY 4.0.
• MMLU - The standard 57-subject multitask MCQ benchmark. Part of the standard MCQ battery used in Jev vs Luna Decisions comparisons.
• MMLU-Pro - Harder MMLU successor with 10-option questions and more reasoning. Part of the standard MCQ battery used in Jev vs Luna Decisions comparisons.
• MuSR - Multistep soft-reasoning MCQs (murder mysteries, object placements, team allocation). Part of the standard MCQ battery used in Jev vs Luna Decisions comparisons.
• PhishNChips - 2,000-email phishing benchmark plus a 220k-run adjudicated grid showing how system-prompt config swings detection. A Jev Decision Index component.
• sysone-bench - Head-to-head of System One models: Laya vs Jev on byte-identical inputs.
• ToolRet - ACL 2025 tool-retrieval benchmark: 7.6k retrieval tasks over a 43k-tool corpus, scored by nDCG@10. A Jev Decision Index component.
• When2Call - Evaluates tool-calling decisions rather than accuracy: when to call, when to ask a follow-up, when to admit the tools can't answer. A Jev Decision Index component — and one Jev still leads.
• WinoGrande - Pronoun-resolution commonsense MCQs, adversarially filtered. Part of the standard MCQ battery used in Jev vs Luna Decisions comparisons.

Probes & studies

• Janus - Measures a confidence threshold on your labeled data, then routes Jev vs a larger model. Ships no default: on Banking77 routing wins; on Web of Science it says do not route.
• jev-behavior-study - Independent look at Jev 1.13.0: option-order bias, facts lost in the middle of long context, and cases where a correct prerequisite still leads to the wrong action. Raw payloads and offline verification included.
• jev-benchmarks - Independent calibration, selective-risk, and latency measurements of Jev against GLiNER and other baselines.
• jev-ood-calibration - Independent calibration test on tasks Jev cannot have seen: 900 rule-generated tickets plus public benchmarks, with ECE noise floors and a temperature refit.
• jev-phishing-bench - Jev vs Claude Haiku 4.5 on 2,000 phishing emails: accuracy, calibration, latency, and cost, reproducible.
• jev-rerank-bench - Jev vs Cohere Rerank 4 vs zerank-2 vs a chat baseline on 14 datasets. Every raw response saved. nDCG@10 is a tie, not a win.

Reading

Docs & ecosystem

• Confidence - How Choice/Score confidence differs from probability, and how to gate act / review / escalate in code. Noul has no separate confidence field.
• Cookbooks - Official patterns: parallel questions, citation check, RAG passage classification, date extraction, skill suggestion.
• How to build with TypeSafe - Keep control flow in code. Split work into atomic questions, then combine the answers and route on uncertainty.
• Introducing System One Models & Jev - TypeSafe's launch post: RLCD, parallel sampling, and how this differs from an LLM.
• Jev in the Wild - A data-driven survey and analysis of 2,170 public GitHub Jev projects, mapping early ecosystem growth, application domains, and decision-use patterns.
• System One (docs) - Official definition of the model class and the Choice / Score / Noul primitives.
• awesomejev.com - Community directory indexing about a thousand Jev projects with daily-refreshed stars. Less curated, good for exhaustive search.
• jev.store - Community store for Jev apps, extensions, and tools: a distribution surface with a submit flow, distinct from the firehose directories.
• Latent Space: Jev, a System One model that only decides - Launch-day writeup on what a decision-only model changes for latency and cost.
• OpenRouter Decisions API - Alpha multi-provider surface for the class: Jev, Solar Decide, Span-01, and Kev-4B under one state + questions request shape, plus a /v1/systemone drop-in for TypeSafe SDK users.
• systemonemodels.org - Independent hub tracking every System One model, open alternative, community SDK, and example. The place to check when this list lags.

Jev under the microscope

• JEV vs. LLMs as Rubric Judges: Cheaper, Faster, and Wrong in the Same Places - Rao & Callison-Burch (2026). The counterpoint to the cascade story: Jev is 29–325× cheaper and competitive on binary criteria, but LLM judges repeat nearly all of its most confident errors, capping escalation gains at ~1.5 points.
• JEV-as-a-Judge: Accept When Confident, Escalate When Unsure - Li, Miao, Krishnan & Padman / CMU (2026). First independent study of Jev as an eval judge: within ~3 points of the strongest LLM judge on ordinary preference at 0.36% of the fee, weaker on derivation-checking. A confidence-gated cascade keeps 99% of the big judge's accuracy at ~57% of the cost.
• Jev 1.13 jaggedness - First-party failure modes: counting, dates, indirection, distractors, generation. Keep arithmetic in code.
• Jev's Architecture Unmasked - Community teardown of Jev's probable architecture; the write-up kev's open models are built from.

Foundations

• Agents Thinking Fast and Slow: A Talker-Reasoner Architecture - Christakopoulou, Mourad & Matarić / DeepMind (2024). Splits an agent into a fast conversational Talker (System 1) and a slow planning Reasoner (System 2).
• Distilling System 2 into System 1 - Yu, Xu, Weston & Kulikov / Meta (2024). Compiles intermediate-thought techniques into single-pass outputs; the research framing closest to what Jev is.
• Dual-processing accounts of reasoning, judgment, and social cognition - Evans (2008). Review of dual-process theories. System 1 is a family of accounts, not one algorithm.
• Judgment under Uncertainty: Heuristics and Biases - Tversky & Kahneman (1974). Heuristic judgment under uncertainty.
• MDLM - Masked diffusion language model (NeurIPS 2024): parallel, non-autoregressive generation. The open research line nearest to Jev's parallel-sampler claims.
• Reasoning the Fast and Frugal Way - Gigerenzer & Goldstein (1996). Simple heuristics that work with incomplete information.
• System-1.x: Learning to Balance Fast and Slow Planning with Language Models - Learns when to use fast direct planning and when to search, instead of always picking one.
• System 2 Attention - Weston & Sukhbaatar / Meta (2023). The LLM regenerates the context it should attend to before answering: deliberate filtering on top of fast attention.
• Thinking, Fast and Slow - Daniel Kahneman. The book TypeSafe cites for the System 1 / System 2 framing.

Contributing

See CONTRIBUTING.md.