JavaScript has become one of the most widely used programming languages, powering applications across various domains. With the advent of cloud computing, developers now have a multitude of options for deploying JavaScript applications in the cloud. In this article, we will explore different deployment options and popular cloud providers for
Cloud-native development has revolutionized the way we build and deploy applications, enabling scalability, resilience, and flexibility. As JavaScript continues to dominate the web development landscape, it's essential to understand and apply cloud-native best practices to JavaScript applications. In this article, we will explore some of the key practices
I went through a serverless phase a while back. You know how it is — you read a few blog posts, watch a conference talk, and suddenly you want to put everything in Lambda functions. I'm here to tell you that's a bad idea, but also that
I've been building web applications with JavaScript for more than 10 years now, and at some point you realise that writing the code is only half the battle. The other half is making sure the thing doesn't fall over when actual people start using it. At
Recently I moved to Uruguay, Montevideo. Here I found an active community of developers, yesterday I had a chance of giving a talk at the AngularMVD meetup. Even though it has been ages from the last time I developed an Angular app the talk was about how to use tensorflow.
Recently I've been using Parcel (https://parceljs.org/) for some small web apps I'm developing in my free time. Parcel is a modern javascript bundler that is used to package a JS app into a single, or multiple files. We could argue that the state of
Here is a simple tutorial on how to use the mobilenet model released by google with tensorflow-js. This tutorial requires node v >10 and the parcel bundler. To install node use nvm https://github.com/nvm-sh/nvm and to install parcel just run npm -i g parcel-bundler step 1
Tensorflow is the defacto standard for building advanced neural networks of any kind. Natively tensorflow is a python library and gives to the user a framework that simplifies operation with tensors. A tensor is a mathematical object analogous to but more general than a vector, represented by an array of
Playing with models released for tensorflow js. Posenet is a neural network that allows the estimation of a human pose from an image. For more technical information about how it actually works refer to this blog post https://medium.com/tensorflow/real-time-human-pose-estimation-in-the-browser-with-tensorflow-js-7dd0bc881cd5 import * as posenet from "@tensorflow-models/posenet"
I needed a cool way to visualize a force directed graph with many datapoints, so I thought that webGL was the way to go as it should be able to handle the heavy load better than d3 SVG. The library I've used for this small proof of concept