Search Results for "bcrmpatchram2.kext"
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Showing 4 open source projects for "bcrmpatchram2.kext"
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Boost application security and continuity with SKUDONET ADC, our Open Source Load Balancer, that maximizes IT infrastructure flexibility. Additionally, save up to $470 K per incident with AI and SKUDONET solutions, further enhancing your organization’s risk management and cost-efficiency strategies.
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Deploy Red Hat Ansible Automation Platform on Microsoft Azure for a strategic automation solution that allows you to orchestrate, govern and operationalize your Azure environment.
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Qiling
Qiling Advanced Binary Emulation Framework
Cross-platform and multi-arch ultra lightweight emulator. Supported OS: Linux, MacOS, Windows, FreeBSD, DOS and UEFI. Support Arch: x86(16/32/64), ARM(64) MIPS, EVM and WASM. It also support Linux Kernel Module(.ko) , Windows Driver(.sys) and MacOS Kernel(.kext) via Demigod. Binary instrumentation and API are Qiling Framework's main focus and priority. It is designed for reverse engineers - thus there is no need to rebuild another sand boxing tool. Using Qiling Framework saves you time. The API... -
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Soundflower
System extension that allows applications to pass audio to other apps
... 'Tools' folder there is a Ruby script called 'load.rb' which will copy the built kext into the install location using sudo. This sets the owner and group correctly. When you run the 'build.rb' script it builds the project with Xcode and then runs the 'load.rb' script automatically. -
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DPCIManager
Simple OS X app for viewing PCI hardware info
DPCIManager lists all of the PCI devices attached to your machine, along with their PCI IDs, vendor name, and device name. It will also retrieve the kext loaded for that device, important when troubleshooting. The new status tab gathers all of the most important info about your system into one place. ACPI table extraction makes it easy to edit your DSDT or other tables easily. Now includes ROM flashing. -
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BigQuery 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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