
Contextual clipboard quick actions (X)
A video demo of an app that monitors the macOS clipboard with Jev, detecting when the copied content is a terminal command and offering a quick action to run it.
Entries 97 to 130 of 130, in the hub's default order. The introduction is on page 1.
Showing 97 to 130 of 130

A video demo of an app that monitors the macOS clipboard with Jev, detecting when the copied content is a terminal command and offering a quick action to run it.

A demo of a tool that uses Jev to automatically find a website's billing page, list and download its invoices in one click, and remember where invoices live for next time.

On OpenMausBot, Jev reads an incoming task and decides which bot to wake and which underlying LLM to assign it, working across the user's existing LLM subscriptions.

A video demo of Jev, now available through the Venice API, classifying 24,000 Hacker News posts into 12 categories in about 2 minutes.

A demo of the Clonk agent orchestrator launching agents on a canvas, using Jev instead of waiting on a slower general-purpose model loop for each step.

Given 80 real exam questions and 297 practice ones, Jev ranked which were most likely to appear on the real exam in 80 seconds for $0.0256.
A tool where Jev watches videos in a chosen content niche, drawn from a database of 12.8M viral videos, and judges hooks, formats and angles before turning the analysis into a script via Claude; free in Virlo plus MCP.

Jev breaks down competitors' ads by hook, angle, offer and format, then turns the recurring combinations into a shortlist for the next creative test.

A video demo drives a MakerMods robot arm in MuJoCo using Jev: the model receives the scene as JSON state and picks the next bounded action, such as hover, descend, grasp, lift, or place, as a typed choice with a confidence score, completing a task in 9 decisions at about 150 milliseconds each.

An early prototype where local Whisper transcribes speech and Jev classifies it to decide what action to take, using a small Swift app that gives Jev the screen's full accessibility tree.

Mario Never Dies, built with Jev and microsandbox: Jev picks every move, and each time Mario dies the VM forks into 4 timelines that keep playing; whichever survives becomes canon.

A video demo simulates a 120-person town where Jev decides each resident's actions every hour, plus town-level variables such as traffic, prices, and tax rate, producing over 20,000 decisions in under 5 minutes at a fraction of a cent.

A video comparing 100 Indie Hackers building a post scheduler with Jev versus Fable 5.1, showing Jev finishing the task faster.

A Chrome extension that labels every X post on the timeline as clean, engagement bait, promo, secondhand or filler, at $0.03 per 1,000 posts; open source.

Uses Jev to search @wip todo updates for specific events, such as a revenue increase or a switch to a different SaaS provider, catching entries a plain keyword search would miss.

The author scanned more than 700 live ads with Jev in about 40 seconds for roughly 4 cents, and compares the cost to running the same job through a general-purpose model.

A speech-practice tool where you talk about a given topic for 30 seconds and Jev analyzes pauses, filler words, repetitions, confidence and clarity, then returns a score and feedback.

Explains Jev's product shape as a classifier that takes its categories as input: context plus N options mapped to one probability each in a single pass, with no autoregression.

After close to 24 hours of reinforcement learning training on Doom, the author's Laya model still falls well short of Jev's zero-shot performance.

Test report using Jev to check each agent action before it executes, reporting most attacks caught with few false blocks and much lower latency than an LLM guard.
Benchmarks Laya-MLX against Jev/Qwen3-4B inside an OpenClaw agent cascade on an M4 Pro: Laya scores 85% at 27.8ms, Jev scores 90% at 1,168ms, and 14 of 20 decisions resolve before reaching Jev.

Head-to-head claim that Jev beats Claude Opus 5 at move selection in Pokemon Showdown.

Built an arena survival game where every enemy's move is decided live by Jev or Laya, turning each moment into one typed question at about 30 decisions a second. Laya ran roughly 21ms per decision on a laptop for no cost, while Jev was more accurate out of the box on the poster's own benchmark.

Ran 1,000 synthetic emails through a local 322M Laya multilingual checkpoint on a MacBook Air M3, sorting them into six folders. Reported 65.1 percent accuracy against reference labels, 28.6 seconds without screen recording, and 0 MB of swap.
Built a proof-of-concept Google ADK triage agent on 30 labeled tickets, swapping only the decision engine. Jev took 422ms at 53 percent accuracy, Laya took 152ms at 10 percent accuracy: faster but not more accurate.
Tested Jev against a local MLX build of Laya in zero-shot mode on 40 English and Thai support tickets. Jev scored 90 to 100 percent accuracy, Laya scored 30 to 80 percent, though Laya ran faster.
Elixir Forum thread (8 replies, 281 views) announcing an Elixir SDK for Jev with strict noul/choice/score constructors and evaluate/evaluate_many/streaming. Notes five independent Elixir Jev clients appeared in the same launch week.
A researcher claims prior open-source work with the same decision-only design and links the model, dataset, and paper. Companion to the Hacker News item 49736660.
86-point, 12-comment thread on browser-use's agent. Commenters dispute the 7.1s Google Flights timing because timing starts after initial page observation, report demo failures, and object to paying per call for a cloud decision model.
36-point, 9-comment priority claim: the poster says he published a non-autoregressive, schema-constrained probability model in March 2025 (arXiv 2503.23303) using PPO over sequence embeddings rather than a parallel sampler. Commenters reply that the difference was marketing reach, not the idea.
157-point, 23-comment thread on vinnylarouge/jevlike, an independent one-pass architecture that scores a changing list of text options against context. The submission claims only the same input/output shape as Jev, not equal quality or a reproduction of RLCD.
Work-in-progress demo of Jev driving a Minecraft agent, including fleeing zombies at night, as a test of fast structured decisions in a game loop. URL taken from a fetched awesome-jev README; thread not opened.
Thread where builders describe using Jev as an agent tool-use safety layer and plan a prompt-complexity model router on top of it. URL taken from a fetched awesome-jev README; thread not opened.
Launch thread framing Jev as a low-hallucination, low-cost decision model for software rather than a chat model. URL taken verbatim from a fetched awesome-jev README; the thread itself could not be opened, so score, author and date are unconfirmed.
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