lurk by AnyAPI

Still from lurk by AnyAPI — Still from x.com

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Views: 84,032
Likes: 750
Reposts: 46

Read from X on 24 Sep 2026. Counts change daily.

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Threads scanned: 4,0004000 reddit threads scannedx.com

Lurk finds Reddit threads about what a user sells and flags the people asking for it. According to the site, it searches each keyword and community on a schedule, filters titles first, then reads the shortlisted posts and comments in full, along with the community rules. Each lead gets a score, a written reason and the exact phrase that matched. It also tracks which Reddit threads Google already ranks for the user’s keywords and which competitors get named in them. Lurk never posts or DMs; the user decides whether to reply.

Kevin Wang’s launch post pitches it as a way to find and monitor Reddit threads in order to get cited by AI answers. It lists 4,000 Reddit threads scanned and alerts by email, Discord or Slack, and says the tool can only be free because of Jev and AnyAPI. The hosted app is free within set limits, and the MIT source can be self-hosted.

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[AINews] Jev: a System One Model that only decides/classifies/routes/scores

AI News daily roundup leading with the Jev launch, summarising RLCD and TypeSafe's 20-200x speed and 40-400x cost claims. Its recap of reactions notes posters using Jev as a structured classifier, judge and routing policy where text generation is unnecessary.

Latent.Space (AI News)Open

ArticleIntent and model routing+1

Jev means structured output is interesting again

Argues Jev's speed comes mainly from constrained single-token generation and aggressive prefill, techniques reproducible with existing LLMs, rather than a new architecture. Still treats very fast structured output as a new primitive, while doubting Jev matches frontier models on raw intelligence.

Sean GoedeckeOpen

Article

Zaious/jev-capability-atlas

An independent, evidence-based map of where Jev's calibrated-decision claim holds up and where it breaks down, built from real API-call receipts rather than a leaderboard.

Zaious26 starsOpen

Article

willkelly/jev-evaluation

An adversarial evaluation of Jev with nine experiments and 28 predictions fixed before any data was collected. One run covered 123,805 requests over 138 minutes for $12.69, with five failures, plus a 13-rule prompting guide.

willkellyOpen

Article

WindTunnel: the WebMCP benchmark

A 49-task, 8-site browser-agent benchmark. Jev paired with a small model for argument generation solved 100% of tasks at far lower cost than screenshot-based computer use, and adding WebMCP tool exposure roughly doubled Jev's own solve rate from 25 to 49 tasks.

idan levin (@0xidanlevin)Open

239k viewsSolve rate 25/49 to 49/49Cost vs Astra 112x lower

ArticleAgent routing and skill selection

Decision Index: Jev against 50+ open decision models

A leaderboard that runs Jev and 54 open System One models and clones through the same 120,000-question suite on one RTX PRO 6000, scoring accuracy and calibration. Jev leads the 0.2 edition at 51.67 on a chance-corrected scale, with AutoJev-27B close behind at 50.94.

apolinario (poli) (@multimodalart)43k viewsOpen

Article

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[AINews] Jev: a System One Model that only decides/classifies/routes/scores

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