jevbooks

← All projects

twitter-jev-guard

qs-lll/twitter-jev-guard

Jev flags low-quality X/Twitter posts with STOP or AD watermarks.

Content Moderation0.90Runner-up: Scoring & Ranking
Automated README summary

Automatically summarized from the README on 13 Oct 2026; not a run by jevbooks. Run instructions and author-reported outcomes still need verification.

What it is

A Chrome/Edge Manifest V3 extension for personal browsing that spots suspected spam, engagement bait, reposts, and ads on X/Twitter timelines, overlaying semi-transparent watermarks on post text. It is not a fact-checker or platform moderation tool.

How it uses Jev

The content script reads visible post text and metadata; the background service worker calls TypeSafe Jev directly. Jev answers two questions: whether the post is low-quality, and its main type. When low-quality probability reaches the threshold (default 75%), a red STOP watermark is shown in the tweetText container.

Primitives:choicescore

Technique worth stealing

Use Jev's probability plus content-type answer to drive a visual watermark, keeping the model call in the extension background.

Try it

Load the root folder containing manifest.json via chrome://extensions, set your Jev API key in the popup, then refresh X.

View on GitHub

Start using Jev: API key and your first request →

Model judgments from README excerpts

Project signals use repository metadata, an existing summary and the first 700 README characters, not the full project. “Not found” means not detected in that material; it does not establish absence. These are the original judgments with recorded models and dates, not source audits or run results.

Models & evaluation dates

Inclusion gate
jev-1.13.0
Project signals
jev-1.13.0
Category
jev-1.13.0
Pattern tags
jev-1.13.0

Evidence

These signals use repository metadata, an existing summary and at most 700 cleaned README characters. ≥ 0.60 means reported in that excerpt; ≤ 0.40 means not found there. The middle band is inconclusive. None is a source-code audit or a test result. The runnable-tool question excludes research experiments; the recommendation question asks for a learning shortlist, not a quality verdict.

  • Jev is core to the projectFound in material0.91
  • Shows a System One patternnot found in the excerpt0.20
  • Describes handling uncertaintyFound in material0.71
  • Reports measured numbersnot found in the excerpt0.05
  • Is a runnable toolFound in material0.99
  • Top-10 learning shortlist signalnot found in the excerpt0.28
  • Is a replica of the modelnot found in the excerpt0.02
  • Solves a common problem (0–2)score on a 0–2 scale1.18 / 2
  • About JevFound in material0.98
Signals
Jev is core to the project0.91Shows a System One pattern0.20Describes handling uncertainty0.71Reports measured numbers0.05Is a runnable tool0.99Top-10 learning shortlist signal0.28Is a replica of the model0.0200.51Solves a common problem (0–2)1.18 / 2012

no-judgment band (0.40–0.60)

Design patterns
Fail open, fail closed0.66Questionnaire0.59Viewfinder0.3100.51

no-judgment band (0.40–0.60)

The Jev model and date for each judgment stage are listed above. Card written by DeepSeek V4.1 Flash from the README on 13 Oct 2026.