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Add Jev Account Filter for video-platform account screening - #164
Where Jev fits: First-pass Noul title probabilities select Top-N candidates; a second multimodal request returns an independent account-category Choice and per-video probabilities.
Added one object to data/projects.json and ran npm run validate.
Did not change README.md or site/index.html; CI owns generated files.
Self-submission by the project maintainer; stars intentionally set to zero for automatic refresh.
Project and Jev workflow
Jev Account Filter is a Python CLI for screening video-platform accounts for dataset collection and research without downloading or watching complete videos. Our team uses it for sign-language account selection; category rules and the model/API are explicit configuration.
Pass 1 sends only first-page video titles to Jev Noul questions; application code sorts the probabilities and selects Top-N (five by default).
Pass 2 sends account name, biography, and selected titles/JPEG thumbnails together. Jev returns an independent account-category Choice, separate account/video Noul probabilities, and a best-candidate Choice.
CSV output, raw typed responses, request previews and an offline HTML viewer make the result reviewable. Collection and inference are separate, and successful items are skipped on resume.
Earlier in this AI-assisted development session, three public example accounts completed both stages: six successful live OpenRouter requests, five thumbnails per second-stage request. This is an integration check, not an accuracy benchmark.
The published team experiment reports 2,140 Douyin accounts, eight hours of data collection plus one hour of Jev inference, and $0.565 total API cost. Reported 94.49% accuracy uses 198 existing second-review labels and assumes unreviewed original labels are correct; it is a conditional estimate, not independent full-dataset validation. These figures are in the public README.
Python 3.11+, a browser for collection, and a user-supplied OpenRouter key are required for the documented Jev path. Screening sends selected account metadata, titles and images to OpenRouter/its model provider and incurs API charges. Already collected data can be screened without revisiting the platform.
Source is public, but no project-wide source-code license is declared at the reviewed revision; the third-party icon license is not a license for the application. This submission does not change or imply licensing terms.
Affiliation and AI assistance
Self-submission by the project maintainer, caiweihan, on behalf of the team using the tool. The implementation, documentation, submission preparation and checks were substantially assisted by OpenAI Codex. No independent human review or third-party evaluation of this submission is claimed. No paid placement, referral links, credentials, or private account datasets are included.
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Summary by CodeRabbit
New Projects
Added Jev Account Filter to the apps project listing. The listing describes it as a Python tool for screening video-platform accounts using title rankings, selected thumbnails, and profile information, helping visitors understand its account-filtering capabilities before exploring the project.
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data/projects.json
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📝 Walkthrough
The project catalog adds Jev Account Filter, a Python app for screening video-platform accounts. Its record includes the repository, description, category, star count, and added date.
Changes: Project catalog entry
Layer / File(s)
Summary
Add project record data/projects.json
Adds Jev Account Filter with its repository, screening description, apps category, Python language, zero stars, and added date of 2026-10-17.
The new catalog entry follows the repository’s star-count guidance. No actionable user-facing or operational risk remains; the change is ready to merge after normal checks.
Pre-merge checks: 5
Check name
Status
Explanation
Title check
The title clearly identifies the main change: adding Jev Account Filter for video-platform account screening.
Description check
The description includes the required project URL, Jev integration summary, checklist confirmations, authorship information, validation evidence, limitations, and operational requirements. It is compl…
Docstring Coverage
No functions found in the changed files to evaluate docstring coverage. Skipping docstring coverage check. Docstring coverage is scoped to functions touched by this diff. Analyzed 0 functions across 0…
Linked Issues check
Check skipped because no linked issues were found for this pull request.
Out of Scope Changes check
Check skipped because no linked issues were found for this pull request.
Full details: Description check
Explanation
The description includes the required project URL, Jev integration summary, checklist confirmations, authorship information, validation evidence, limitations, and operational requirements. It is complete and directly related to the submitted project entry.
Full details: Docstring Coverage
Explanation
No functions found in the changed files to evaluate docstring coverage. Skipping docstring coverage check. Docstring coverage is scoped to functions touched by this diff. Analyzed 0 functions across 0 files. (1 skipped: 1 unsupported.)
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Project: https://github.com/caiweihan/jev-account-filter
Where Jev fits: First-pass
Noultitle probabilities select Top-N candidates; a second multimodal request returns an independent account-categoryChoiceand per-video probabilities.data/projects.jsonand rannpm run validate.README.mdorsite/index.html; CI owns generated files.Project and Jev workflow
Jev Account Filter is a Python CLI for screening video-platform accounts for dataset collection and research without downloading or watching complete videos. Our team uses it for sign-language account selection; category rules and the model/API are explicit configuration.
Noulquestions; application code sorts the probabilities and selects Top-N (five by default).Choice, separate account/videoNoulprobabilities, and a best-candidateChoice.Evidence and limitations
Affiliation and AI assistance
Self-submission by the project maintainer,
caiweihan, on behalf of the team using the tool. The implementation, documentation, submission preparation and checks were substantially assisted by OpenAI Codex. No independent human review or third-party evaluation of this submission is claimed. No paid placement, referral links, credentials, or private account datasets are included.Need help on this PR? Tag
@codesmith-botwith what you need.Summary by CodeRabbit