Jev

A sample accounts-payable pipeline uses deterministic matching first, then sends ambiguous invoice and purchase-order lines to Jev with a hold-for-review band.

An OpenCode plugin tries a typed decision, gathers context when uncertain, and leaves marked questions for a person. Its confidence thresholds need their own evaluation.

Pete Johnson compares three Pac-Man planners sharing a tactical loop. His code baseline clears the first level more often than Jev or Haiku.

Sam Bancroft compares estimated energy per correct decision across JevBench models, but closed-model estimates depend on prices tracking compute.

A GitHub notification classifier comparison finds that labels filtered through Jev's answers can flatter Jev, and that dependency events remain hard to catch.

Paper Radar turns research interests into typed model questions, then applies explicit thresholds. Its screening benchmark and Claude trial show why local labels matter.

Nicia's inspectable test sends knowledge-base writes through nine decision-model backends. Jev, Clef and Microsoft pass its gates, yet each misses a review-required write in at least one order.

A graph-search experiment compares Jev decisions, a Claude agent and vector search on human-labelled product questions. Its saved runs expose accuracy, cost and the price of extra search rounds.

A small Iris experiment calls Jev's decision endpoint directly, keeps test labels out of requests and saves per-flower predictions for comparison across three prompt treatments.

A self-hosted GLiFormer model speaks the Jev API, but published head-to-head results show substantial errors on hard decisions.

PostHog's open decision model adds a reasoning path and an uncertainty gate. Its public benchmark improves on Jev in some tasks, but the gate can miss hard cases.

A small LangGraph movie assistant groups tool calls and responses before asking Jev what to retain. The grouping protects chat history structure; memory quality is unmeasured.

Khuyen Tran pairs embedding retrieval with Jev decisions on the Abt–Buy record-matching dataset. Strong reported precision comes with abstentions and an unresolved cost discrepancy.

A coding-agent workflow routed bounded repair re-reviews through Jev, with general-reviewer fallback, human approval and tests; one cleared defect still reached a later typecheck.

A contrastive benchmark pairs small policy changes that flip the right answer; Jev's typed output is cheap, but the pair and rollout-rule scores show where it misses.

A Claude Code plugin uses Jev to route reverse-engineering tasks, question risky tool calls and check whether conclusions cite evidence. Its hooks fail open.

Kanwal Mehreen walks through training and calibrating an open-weight decision model on a small CLINC150 subset, with a separate test split.

A Vercel Labs example routes form submissions with Jev, then requests an independent model decision when confidence is low or unavailable.

A browser extension combines Jev’s page judgments with hand-written weights and uses separate thresholds to hide suspected ad elements.

A memory prototype scores each record alongside a competing best match, then combines relevance with recency and importance. Its offline path uses keyword ranking.

Trustle tests narrow Jev questions for access risk and role selection, then routes uncertain requests to a larger model.

An open inference engine forks shared state for Jev-style questions and includes checks for changes across batches and cached sessions.

An unofficial PyTorch teaching model sketches shared-state decision heads, while making clear that its random weights cannot make useful predictions.

NeuroLink’s decision-model router treats cheaper and more capable model choices differently, with a heuristic fallback when the signal is weak.

A feed project compares Jev-assisted story selection with older published results, while documenting why the comparison is not a clean replay.

EMQX documents Jev decision nodes and direct device commands, including abstention, parameter limits and the absence of rollback.

TypeSafe documents how Jev computes confidence for choices and ordered scores, and why an action may need a specific probability instead.

Toshiya Matsumoto’s per-turn router separates classification from model choice. His follow-up reports expose missing conversation context and extra input tokens from model switching.

GrowthBook separates hypothesis checks from scoring rules and puts feedback beside experiment edits. Its timing comparison leaves judgment accuracy unreported.

Miles Roxas’s skill-testing app stores Jev answers separately from scoring rules, then compares grades with human ratings. Missing checks and incomplete anchor sets need care.

A student-survey app uses Jev to select sensitive text spans. Offline checks expose candidate limits, fixed confidence labels and export paths that need review.

Vladimir Beskorovainyi’s Russian-first decision model publishes wording tests and raw scores. Better label accuracy still leaves confidence thresholds vulnerable.

Capsule reports a gap between Jev and its specialized agent detector. The comparison raises useful questions about error types, context and reproducible security tests.

Vinicius Lana’s saved traces show fewer tool calls and lower estimated language-model cost with Jev routing, alongside weaker task coverage and more model steps.

A parcel-inspection walkthrough and build guide show bounded concurrency, separate review flags and why an attractive cost headline needs more evidence.

takemo101’s local Mac assistant separates skill selection from execution. Its failure records show why confident choices and spoken approval still need independent checks.

GoodVibes puts Jev behind a shared decision interface with finite fallback, cancellation and recorded model identity. Live calibration and product acceptance remain unfinished.

Reindeer tests Jev for operational guardrails and publishes a Pi extension that reviews edits before writing. Its public code exposes the scope and failure policies.

Diogo Almeida critiques benchmark-led releases, sketches different ways to manage agent state and calls Jev’s image model experimental.

TypeSafe’s support-routing example separates message interpretation from account facts, rule precedence and the final action. The trace exposes which part failed.

Teiva Harsanyi describes a Postgres-backed reading service where embeddings find candidates and Jev grades them individually after batched ranking proved unstable.

A Unix-friendly Jev client separates pass, block and human review, with labeled examples and offline regression checks for the thresholds.

An inspectable Hermes plugin separates thread labels, title generation and archive permission. Its code also exposes the missing connections that block native automatic archiving.

A small Google experiment finds routing helps only after the cheap path actually becomes cheaper. Output caps alone failed; low thinking was the stronger baseline.

RunSybil’s tests show a filtering tradeoff and a synthetic-data trap: Jev needed tuned questions and thresholds before real vulnerability labels became useful.

sirkirby’s coding-agent orchestrator pairs Jev’s reading of a task brief with subscription capacity, report checks and a lead agent that verifies the result.

manji6’s personalization app uses Jev to judge browsing context, then applies local delivery rules. Saved API responses show why a brief visit can still look like strong interest.

Max Harrison’s meeting demo combines local speech recognition with hosted Clef-flash judgments, sliding context and a smoothed off-topic meter.

A TypeSafe cookbook routes support tickets with customer-specific memory and shows the limits of an append-only example.

LiteLLM documents a Jev classifier for model routing, including conversation sharing, fallback, circuit breaking and separate cost accounting.