Search Results for "m1"
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Showing 11 open source projects for "m1"
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Proxyman
Web Debugging Proxy for macOS, iOS, and Android
Don't let cumbersome web debugging tools hold you back. With Proxyman's native macOS app, you can capture, inspect, and manipulate HTTP(s) traffic with ease. Intuitive, thoughtful, built with meticulous attention to detail. Comprehensive Guideline to set up with iOS simulator and iOS and Android devices. Proxyman acts as a man-in-the-middle server that capture the traffic between your applications and SSL Web Server. With built-in macOS setup, so you can inspect your HTTP/HTTPS Request and... -
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Colima
Container runtimes on macOS (and Linux) with minimal setup
Container runtimes on macOS (and Linux) with minimal setup. -
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PDFium Library
Project to compile PDFium library to multiple platforms
Project to compile PDFium library to multiple platforms. PDFium project is from Google and I only patch it to compile to all platforms. -
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Docker Laravel
Build a simple laravel development environment with docker-compose
Build a simple laravel development environment with docker-compose. Compatible with Windows(WSL2), macOS(M1) and Linux. -
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5
OSXPhotos
Python app to work with pictures and associated metadata
OSXPhotos provides the ability to interact with and query Apple's Photos.app library on macOS and Linux. You can query the Photos library database — for example, file name, file path, and metadata such as keywords/tags, persons/faces, albums, etc. You can also easily export both the original and edited photos. OSXPhotos also works with iPhoto libraries though some features are available only for Photos. Limited support is also provided for exporting photos and metadata from iPhoto libraries.... -
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OculiX
Visual Automation IDE — automate anything you see on screen
...Key features: - Guided step-by-step recorder with live code preview - Image recognition via OpenCV 4.10 - Dual OCR: Tesseract (built-in) + PaddleOCR (neural, high precision) - Local and remote automation via integrated VNC - SSH tunnels via embedded JSch - Cross-platform: Windows, macOS (Apple Silicon M1-M4), Linux - Scripting: Jython, JRuby, Java, PowerShell, AppleScript - Java 17 recommended (Java 8+ supported) - Full CI/CD with automated builds for all platforms Used worldwide for test automation, RPA, and visual regression testing. MIT License. Maintained by oculix-org. -
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TinyGSM
A small Arduino library for GSM modules, that just works
A small Arduino library for GSM modules that just works. This library is easy to integrate with lots of sketches that use Ethernet or WiFi. PubSubClient (MQTT), Blynk, HTTP Client, and File Download examples are provided. Arduino GSM library uses 15868 bytes (49%) of Flash and 1113 bytes (54%) of RAM in a similar scenario. TinyGSM also pulls data gently from the modem (whenever possible), so it can operate on very little RAM. Now, you have more space for your experiments. TCP (HTTP, MQTT,... -
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TurboPFor
Fastest Integer Compression
Fastest Integer Compression. ALL functions are available for AMD/Intel, 64-bit ARMv8 NEON Linux+MacOS/M1 & Power9 Altivec. 100% C (C++ headers), as simple as memcpy. OS:Linux amd64, arm64, Power9, MacOs (Amd/intel + Apple M1). -
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HelloSilicon
An introduction to ARM64 assembly on Apple Silicon Macs
...In this repository, I will code along with the book Programming with 64-Bit ARM Assembly Language, adjusting all sample code for Apple's ARM64 line of computers. While Apple's marketing material seems to avoid a name for the platform and talks only about the M1 processor, the developer documentation uses the term "Apple Silicon". You need Xcode 12.2 or later, and to make things easier, the command line tools should be installed. This ensures that the tools are found in default locations. All application samples also require at least macOS Big Sur, iOS 14 or their respective watchOS or tvOS equivalents. ... -
Host LLMs in Production With On-Demand GPUsDeploy your model, get an endpoint, pay only for compute time. No GPU provisioning or infrastructure management required.
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TensorFlow MacOS
TensorFlow for macOS 11.0+ accelerated using Apple's ML Compute
This repository provided a pre-release of TensorFlow and TensorFlow Addons tailored for macOS 11+ with native hardware acceleration via Apple’s ML Compute, supporting both Apple Silicon (M1) and Intel Macs. It shipped ready-made Python 3.8 wheels and install scripts so developers could quickly get an accelerated stack running without building from source. As TensorFlow added a Metal PluggableDevice path, the project directed users toward using Apple’s tensorflow-metal to get GPU acceleration on Mac directly through Metal. ... -
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Unused
A command line tool to identify unused code
...For my local benchmarks, it speeds up execution by a significant amount (which is documented in the commit introducing mimalloc), but currently runs into sporadic issues on Apple M1 devices. If you run into issues with segmentation faults, consider reinstalling unused with the stock Rust allocator. It is strongly recommended you install Universal Ctags to generate tags files. Universal Ctags supports more languages and has native parsers for a good number of them, resulting in faster tags generation time.
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