
Research
PyPI Package Disguised as Instagram Growth Tool Harvests User Credentials
A deceptive PyPI package posing as an Instagram growth tool collects user credentials and sends them to third-party bot services.
VirtualizingObservableCollection
Advanced tools
See: http://alphachitech.wordpress.com/2015/01/31/virtualizing-observable-collection/ GitHub is here: https://github.com/anagram4wander/VirtualizingObservableCollection There are no performant large data set observable collections that support write operations in .NET, so we wrote the VirtualizingObservableCollection, which does the following: ◾Implements the same interfaces as ObsevableCollection<T> so you can use it anywhere you’d use an ObsevableCollection<T> – no need to change any of your existing controls. ◾Supports true multi-user read/write without resets (maximizing performance for large-scale concurrency scenarios). ◾Manages memory on its own so it never runs out of memory, no matter how large the data set is (especially important for mobile devices). ◾Natively works asynchronously – great for slow network connections and occasionally-connected models. ◾Works great out of the box, but is flexible and extendable enough to customize for your needs. ◾Has a data access performance curve nearly as good as the regular ObsevableCollection – the cost of using it is negligible. ◾Works in any .NET project because it’s implemented in a Portable Code Library (PCL).
FAQs
See: http://alphachitech.wordpress.com/2015/01/31/virtualizing-observable-collection/ GitHub is here: https://github.com/anagram4wander/VirtualizingObservableCollection There are no performant large data set observable collections that support write operations in .NET, so we wrote the VirtualizingObservableCollection, which does the following: ◾Implements the same interfaces as ObsevableCollection<T> so you can use it anywhere you’d use an ObsevableCollection<T> – no need to change any of your existing controls. ◾Supports true multi-user read/write without resets (maximizing performance for large-scale concurrency scenarios). ◾Manages memory on its own so it never runs out of memory, no matter how large the data set is (especially important for mobile devices). ◾Natively works asynchronously – great for slow network connections and occasionally-connected models. ◾Works great out of the box, but is flexible and extendable enough to customize for your needs. ◾Has a data access performance curve nearly as good as the regular ObsevableCollection – the cost of using it is negligible. ◾Works in any .NET project because it’s implemented in a Portable Code Library (PCL).
We found that virtualizingobservablecollection demonstrated a not healthy version release cadence and project activity because the last version was released a year ago. It has 1 open source maintainer collaborating on the project.
Did you know?
Socket for GitHub automatically highlights issues in each pull request and monitors the health of all your open source dependencies. Discover the contents of your packages and block harmful activity before you install or update your dependencies.
Research
A deceptive PyPI package posing as an Instagram growth tool collects user credentials and sends them to third-party bot services.
Product
Socket now supports pylock.toml, enabling secure, reproducible Python builds with advanced scanning and full alignment with PEP 751's new standard.
Security News
Research
Socket uncovered two npm packages that register hidden HTTP endpoints to delete all files on command.