Private browser tool that helps students spot risky scholarship, internship, course, and job messages, explains warning signals, and suggests a safer next action before they click, pay, or share data.
InspirationStudents regularly receive scholarship, internship, course, and job messages through WhatsApp, SMS, email, and social media. A rushed click or small payment can expose a student to financial loss or identity theft. Most safety advice is either too generic or an opaque yes/no verdict.## What it doesSignalSift is a privacy-first browser tool that turns a message and optional link into an explainable safety check. It detects pressure, payment requests, credential requests, unusually strong promises, suspicious domains, HTTP-only links, and generic personalisation. It shows the matched signals and weights, then gives a safer next action.## How it works1. The student pastes a message and optional link.2. SignalSift normalises the text locally in the browser.3. A documented weighted model calculates a 0–100 warning score.4. The interface explains every matched signal instead of hiding the reasoning.5. The student receives a practical verification checklist.## Privacy and safetyThe MVP has no backend, login, analytics, or data upload. Recent checks remain only in the browser local storage. SignalSift never asks for passwords, OTPs, identity documents, or private financial data. A low score never proves that an opportunity is safe.## Impact and future scopeSignalSift helps students slow down at the exact moment social engineering creates pressure. Future versions can add multilingual analysis, a user-controlled URL reputation lookup, campus reporting, and retrieval-backed explanations while preserving data minimisation.
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