Genuire
An open-source real-time AI/ML platform designed to identify fraudulent job postings. It computes a job trustworthiness score through a five-pillar trust engine, protecting job seekers from phishing attacks and identity theft.
Timeline (last 90 days)
Genuire is a real-time AI/ML platform for identifying fake job postings and protecting job seekers from phishing and identity theft.
Genuire calculates a 0–100% trust score through a five-pillar trust engine before users submit sensitive information.
Genuire adopts a hybrid AI architecture consisting of two pipelines: multimodal visual analysis and a scikit-learn machine learning pipeline.
Genuire integrates Google's open-weight vision model gemma-4-31b-it to scan suspicious job posting screenshots for fraud signals.
Genuire uses SGDClassifier for NLP text analysis and RandomForestClassifier for tabular risk data.
Genuire is connected through a Flask backend, with the frontend built using vanilla JavaScript and CSS, and the core interaction is a dynamic 7-stage verification pipeline tracker.
The five-pillar trust engine evaluates five dimensions and presents each pass or fail result to the user, rather than delivering a black-box conclusion.
For security and anti-fraud tools, open-weight models and open-source libraries make the entire detection pipeline auditable and tunable, which is key to building trust.
In the anti-fraud domain, the cost of misclassification is asymmetric: falsely flagging a legitimate position causes job seekers to miss opportunities, while allowing a fraudulent position through can lead to stolen credentials or accounts.
The entire Genuire project was completed in deep pair-programming collaboration with AI coding assistants (Google Gemini / Antigravity).
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All Facts (11)
Genuire is a real-time AI/ML platform for identifying fake job postings and protecting job seekers from phishing and identity theft.
50%UnverifiedGenuire calculates a 0–100% trust score through a five-pillar trust engine before users submit sensitive information.
50%UnverifiedGenuire adopts a hybrid AI architecture consisting of two pipelines: multimodal visual analysis and a scikit-learn machine learning pipeline.
50%UnverifiedGenuire integrates Google's open-weight vision model gemma-4-31b-it to scan suspicious job posting screenshots for fraud signals.
50%UnverifiedGenuire uses SGDClassifier for NLP text analysis and RandomForestClassifier for tabular risk data.
50%UnverifiedGenuire is connected through a Flask backend, with the frontend built using vanilla JavaScript and CSS, and the core interaction is a dynamic 7-stage verification pipeline tracker.
50%UnverifiedThe five-pillar trust engine evaluates five dimensions and presents each pass or fail result to the user, rather than delivering a black-box conclusion.
50%UnverifiedFor security and anti-fraud tools, open-weight models and open-source libraries make the entire detection pipeline auditable and tunable, which is key to building trust.
50%UnverifiedIn the anti-fraud domain, the cost of misclassification is asymmetric: falsely flagging a legitimate position causes job seekers to miss opportunities, while allowing a fraudulent position through can lead to stolen credentials or accounts.
50%UnverifiedThe entire Genuire project was completed in deep pair-programming collaboration with AI coding assistants (Google Gemini / Antigravity).
50%UnverifiedGenuire is an entry for Week 1 of the Hacktoberfest open-source AI challenge, has been deployed on the Render platform, and its code is hosted on GitHub.
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