Search Results for "python keylogger"
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Showing 4 open source projects for "python keylogger"
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MongoDB Atlas runs apps anywhereMongoDB Atlas gives you the freedom to build and run modern applications anywhere—across AWS, Azure, and Google Cloud. With global availability in over 115 regions, Atlas lets you deploy close to your users, meet compliance needs, and scale with confidence across any geography.
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ContractSafe: Contract Management SoftwareDitch those spreadsheets, shared drives & crazy-expensive solutions with too many bells & whistles. ContractSafe offers the simplest way to manage your contracts efficiently without breaking the bank.
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spyware
A complete SpyWare.
A complete local and configurable spyware. This spyware is a python package compiled for Windows. This spyware is not a virus, it does not export any data to a server. Data is stored locally only. Do not use it for illegal purposes. - Save keyboard events - Record from microphone - Save files metadata in CSV file - Screenshots - Pictures from Webcam - Domains and IP addresses from metadata - Save clipboard -
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Keylogger
A simple keylogger for Windows, Linux and Mac
Keylogger is a simple keystroke logger for Windows, Mac and Linux. A keystroke logger or keylogger is basically a program that records keystrokes and saves them in a log file on your local computer. This keylogger is simple and bare bones, but works wonderfully and is useful for a number of things. You can use it to track key movements and make sure no one is using your computer while you’re away, or use it for self-analysis. In businesses and offices it can be used to monitor employee... -
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A simple keylogger written in python. It is primarily designed for backup purposes, but can be used as a stealth keylogger, too. It does not raise any trust issues, since it is a set of [relatively] short python scripts that you can easily examine.
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Cloud data warehouse to power your data-driven innovationBigQuery Studio provides a single, unified interface for all data practitioners of various coding skills to simplify analytics workflows from data ingestion and preparation to data exploration and visualization to ML model creation and use. It also allows you to use simple SQL to access Vertex AI foundational models directly inside BigQuery for text processing tasks, such as sentiment analysis, entity extraction, and many more without having to deal with specialized models.
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