jevbooks

← All projects

lkclean

stefw/lkclean

Chrome extension that filters LinkedIn feed noise with Jev and explains each verdict.

Content Moderation0.67Runner-up: Classification & RoutingQuestionnaireTraffic light
Automated README summary

Automatically summarized from the README on 20 Sept 2026; not a run by jevbooks. Run instructions and author-reported outcomes still need verification.

What it is

A Manifest V3 TypeScript Chrome extension for LinkedIn users that collapses engagement bait, self-promotion, sponsored and off-topic posts into a one-line bar, and annotates kept posts with their scores.

How it uses Jev

Each post's author and text form a state { user_interests, post } sent to POST https://api.typesafe.ai/v1/systemone with typed questions: engagement_bait, self_promo, matches_interests (noul), top_interest, blocked_topic (choice). decide() in src/background.ts turns the returned probabilities into a verdict using user thresholds.

Primitives:noulchoice

Technique worth stealing

Ask several typed questions in one parallel request, then convert probabilities to verdicts with plain threshold code instead of parsing free text.

Try it

Download lkclean-vX.Y.Z.zip from the latest release, unzip, then chrome://extensions → Developer mode → Load unpacked.

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 material0.94
  • Shows a System One patternnot found in the excerpt0.28
  • Describes handling uncertaintynot found in the excerpt0.07
  • Reports measured numbersnot found in the excerpt0.05
  • Is a runnable toolFound in material0.98
  • Top-10 learning shortlist signalnot found in the excerpt0.38
  • Is a replica of the modelnot found in the excerpt0.03
  • Solves a common problem (0–2)score on a 0–2 scale1.02 / 2
  • About JevFound in material0.98
Signals
Jev is core to the project0.94Shows a System One pattern0.28Describes handling uncertainty0.07Reports measured numbers0.05Is a runnable tool0.98Top-10 learning shortlist signal0.38Is a replica of the model0.0300.51Solves a common problem (0–2)1.02 / 2012

no-judgment band (0.40–0.60)

Design patterns
Questionnaire0.99Traffic light0.90Funnel0.67Question bank0.65Viewfinder0.64Scorecard0.4700.51

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 20 Sept 2026.