The alwaysAI Blog

Jason Koo

Jason Koo
Jason is the Developer Advocate at alwaysAI working closely with software developers, engineers and data scientists to easily bring computer vision applications to edge devices.
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Recent Posts

Introducing the alwaysAI Discord Bot

We're happy to announce the addition of the alwaysAI discord bot developed by Nathan W. and Valentine W. , and implemented by Chris Chu. This bot allows you to do 2 main things right from our Discord community:

Speed up development with a JSON configuration file

Separating certain variables from the main application into a configuration file can improve your development time by reducing the need to recompile apps for minor changes. In this tutorial, we’ll cover how to setup a very basic method for leveraging a JSON file for runtime configuration options.

Detecting Direction of Travel

This architectural guide walks you through one way of creating an app for monitoring when things (animals, cars, etc) come and go.

Deploying with Balena

This short guide will show you how to combine alwaysAI and Balena to easily deploy a computer vision application to multiple devices with a single command.

How to Quickly Create and Run a Vehicle Counting Computer Vision App

In this guide, we’ll make an app that can count vehicles in real-time utilizing object counting, a technique used in computer vision that combines object detection and object tracking. Our final computer vision application will tell us how many objects of a specific kind are currently being detected in a video stream.

Using a Computer Vision Classifier to Sort Images

If you have a host of images that you’d like to sort based on the presence of particular things (like people, cars, buildings, etc.), using computer vision classifiers can make this a pretty simple and fast thing to accomplish.

alwaysAI Technical Requirements Overview

The developer platform at alwaysAI fast-tracks the creation and deployment of computer vision applications on edge devices, making it easy for you to get started building applications in the field of computer vision.

alwaysAI at the 2019 AI & Big Data Expo

The AI & Big Data Expo was a great success for alwaysAI. We were a sponsor at the event, which took place at the Santa Clara Convention Center on November 13th & 14th. Our booth was met with an overwhelmingly positive response as we demonstrated how to easily create and deploy computer vision apps on the edge with the alwaysAI platform, and attendees got to witness these applications performing in real time.

CEO Marty Beard gave a compelling talk at the Convergent Technology Stage,...

How We Built a Conference Booth Tracking App

alwaysAI offers a number of starter apps that make it easy to quickly deploy computer vision (CV) based applications. In this demo, I'm going to show you how to extend one of these starter apps and, hopefully, provide some insight about how you can create your own custom CV apps. The app we're going to end up with is meant to be used on a conference booth, to track the number of attendees who stop by and provide some basic metrics on how much time they spend at the booth.

How To Change Computer Vision Models in the alwaysAI Platform

In this tutorial, we will show you the steps needed to change the computer vision model in the alwaysAI application. First, set up your development computer and edge device (if you're using one). You should also have an app running like this object detector starter app. You can see more about setting up projects and the alwaysAI workflow here. Finally, you should have a Terminal window open.

If you don't have an alwaysAI account yet, you can sign-up here.

How to Create and Run a Real-time Object Detector Starter App in Minutes

In this tutorial, we will show you the steps needed to get a real-time object detector starter app up and running quickly and easily on an edge device. You should have already set up your development computer and installed the alwaysAI CLI. For more information on system requirements and supported boards, check out our Docs.

Computer Vision Application Development Lifecycles Explained

Computer vision-based deep learning projects might seem far beyond the kind of what you and your development team have tackled in the past. However, though it is an emerging technology, computer vision application development cycles remain relatively similar to that of many projects you may already be familiar with.

A Developer’s Intro to Computer Vision Libraries

Developers have several libraries available to them to make the process of building and deploying computer vision simpler and more effective. Most of these libraries are written in C/C++, ensuring a fast execution. In most cases, however, there is a Python API that wraps the C++ implementation. This is because Python has become the go-to language for prototyping and developing deep neural networks. An extremely popular and versatile language, Python enables interactive development, and its...

Finding Things in an Image in Real Time on the Edge

Recent advances in technology have greatly broadened the scope of object detection and related computer vision (CV) services. Hardware with advanced features paired with smarter neural networks has attracted developers and data scientists from numerous industries to start leveraging computer vision to solve complex business challenges. Combined with the rising popularity of embedded devices capturing data on the edge, computer vision on a grand scale has been exploding with seemingly endless...