Search Results for "edge"
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Showing 8 open source projects for "edge"
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Stop Storing Third-Party Tokens in Your DatabaseRolling your own OAuth token storage can be a security liability. Token Vault securely stores access and refresh tokens from federated providers and handles exchange and renewal automatically. Connected accounts, refresh exchange, and privileged worker flows included.
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Node-RED
Low-code programming for event-driven applications
...A built-in library allows you to save useful functions, templates or flows for re-use. The light-weight runtime is built on Node.js, taking full advantage of its event-driven, non-blocking model. This makes it ideal to run at the edge of the network on low-cost hardware such as the Raspberry Pi as well as in the cloud. -
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Porcupine
On-device wake word detection powered by deep learning
...It is perfect for IoT. Cross-platform. Arm Cortex-M, STM32, PSoC, Arduino, and i.MX RT. Raspberry Pi, NVIDIA Jetson Nano, and BeagleBone. Android and iOS. Chrome, Safari, Firefox, and Edge. Linux (x86_64), macOS (x86_64, arm64), and Windows (x86_64). Scalable. It can detect multiple always-listening voice commands with no added runtime footprint. Self-service. Developers can train custom wake word models using Picovoice Console. Porcupine is the right product if you need to detect one or a few static (always-listening) voice commands. ... -
3
level
Universal abstract-level database for Node.js and browsers
...At the time of writing, level works in Node.js 12+ and Electron 5+ on Linux, Mac OS, Windows and FreeBSD, including any future Node.js and Electron release thanks to Node-API, including ARM platforms like Raspberry Pi and Android, as well as in Chrome, Firefox, Edge, Safari, iOS Safari and Chrome for Android. -
4
faasd
A lightweight & portable faas engine
...To deploy microservices and functions that you can update and monitor remotely. When you don't have the bandwidth to learn or manage Kubernetes. To deploy embedded apps in IoT and edge use-cases. To distribute applications to a customer or client. You have a cost sensitive project - run faasd on a 1GB VM for 5-10 USD / mo or on your Raspberry Pi. When you just need a few functions or microservices, without the cost of a cluster. -
Enterprise-grade ITSM, for every businessFreshservice is an intuitive, AI-powered platform that helps IT, operations, and business teams deliver exceptional service without the usual complexity. Automate repetitive tasks, resolve issues faster, and provide seamless support across the organization. From managing incidents and assets to driving smarter decisions, Freshservice makes it easy to stay efficient and scale with confidence.
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XBian
XBian is a small, fast and lightweight media center linux distro
XBian is a free, Open Source media center distribution for mini computers (currently Raspberry Pi, Solidrun's CuBox-i/Hummingboard supported). It can be downloaded at http://www.xbian.org. Our slogan is "bleeding edge" as our main focus is delivering the fastest media center solution. We believe that everyone can help make XBian better. Please visit our website for support and if you have suggestions, wishes or contributions, please share them with us! Want more info about XBian? You can read everything about it here: http://www.xbian.org/what-is-xbian/ -
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TensorFlow Documentation
TensorFlow documentation
An end-to-end platform for machine learning. TensorFlow makes it easy to create ML models that can run in any environment. Learn how to use the intuitive APIs through interactive code samples. -
7
BerryNet
Deep learning gateway on Raspberry Pi and other edge devices
This project turns edge devices such as Raspberry Pi into an intelligent gateway with deep learning running on it. No internet connection is required, everything is done locally on the edge device itself. Further, multiple edge devices can create a distributed AIoT network. At DT42, we believe that bringing deep learning to edge devices is the trend towards the future. -
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GPUImage 2
Framework for GPU-accelerated video and image processing
...This is particularly noticeable in mobile or embedded devices. On an iPhone 4S, this framework can easily process 1080p video at over 60 FPS. On a Raspberry Pi 3, it can perform Sobel edge detection on live 720p video at over 20 FPS.
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