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Memory leaks in Node.js can be silent killers for your applications. They degrade performance, increase costs, and eventually lead to crashes. Let’s break down common causes and actionable strategies to prevent or fix them.
Open-source, cross-platform JavaScript run-time environment that executes JavaScript code outside the browser
Memory leaks in Node.js can be silent killers for your applications. They degrade performance, increase costs, and eventually lead to crashes. Let’s break down common causes and actionable strategies to prevent or fix them.
A small epigraph - if you are making an instruction, then do it to the end, otherwise instructions on how to solve the Amazon captcha for junior developer will be as clear as mud.
What's it all about? When I needed to solve a captcha from Amazon, the notorious Waf Captcha, I started looking for information at a service that I constantly use when I work with Key Collector and some other services (2 captchas - it’s a pity Habr bans articles for referral links).
I found instructions there and posted the link to it above. As you probably understood from the epigraph, I didn’t understand a thing, or rather, I understood that I needed to use the API, but that’s all...
It was much easier with Selenium
The main issue is the short timeout given for a solution from Amazon's side. The time to solve the captcha is limited, and if there's no response, the captcha refreshes (two of its parameters get updated - iv and context)
It turns out the captcha freshness timeout is about 30 seconds, and in that time, you need to find the parameters on the page, copy them, paste them into the script code, and run it. After that, 2captcha should solve it and return the correct answer. I tried to do this for a couple of fruitless hours, developing a routine of actions, but alas, searching for and replacing the changing parameters takes at least 12-15 seconds, leaving only 15 to 18 seconds for the captcha to be solved by the service, which in current realities sounds quite fantastical.
In today's rapidly advancing technological landscape, natural language processing and comprehension have become essential components of everyday life. Leading the charge in this arena is OpenAI's ChatGPT API, renowned for its exceptional ability to understand and interact with human language. Imagine elevating ChatGPT's functionality to new heights, enabling it to carry out specific tasks based on commands given in natural language. This article aims to shed light on the potential of incorporating function calling into the ChatGPT API, thereby enhancing its utility. I will illustrate through practical examples how such extensions can unlock a myriad of opportunities and applications.
Introduction: In this guide, I explore the automation of reCAPTCHA solving in web scraping and testing scenarios using Puppeteer, a Node.js tool designed for browser automation. My focus is on the practical use of the puppeteer-extra-plugin-stealth
plugin to seamlessly navigate through reCAPTCHA challenges.
The general trend of technology development is characterized by surges and declines. Consider, for instance, the mass movement of human bodies. Initially, horses and wagons were used, which gradually evolved into a distinct industry. Then trains appeared abruptly. Horses were quickly forgotten, and the focus shifted to a new avenue. Steam became an object of study and evolved into a complex science. Diesel and electricity developed concurrently. At a certain point, steam engines became obsolete, and everyone transitioned to diesel and electricity. Similarly, we are now transitioning to electric cars that require significantly fewer fluids.
Technologies evolve and function until new technologies completely replace them. I believe we are entering an era where framework and Electron technologies may be eclipsed by generative AI. Let's examine some examples.
I develop robots, and I'm often asked, "How to make a robot?" and "Where do you find information and what resources do you use?"
If you don't know where to start and want to create your own robot, this article is for you. In it, I will try to explain the process and also share the first steps you should take.
Microfrontend as it supposed to be: Single Page Application, Server-side rendering and Independent deployments.
Example that proves it's possible using React 18 + Suspense on server and Webpack Module Federation
In this article, we will briefly review a technology that underlies ChatGPT — embeddings. Also we’ll write a simple intelligent search in a codebase of a project.
With the help of the ESLint and Prettier features, you can automate the formatting of your code, make it more expressive and accurate, correspond to specific rules, and avoid errors and bottlenecks even before uploading the code to the shared source storage...
As a side hustle, I teach tech recruiters web and software development technologies using plain English. It helps them with understanding job specs and resumes and it makes all of us, tech people, happier.
I run a weekly newsletter and often get feedback from recruiters via email or LinkedIn DMs.
I thought that I could try to collect feedback using the “Reactions” feature just like LinkedIn or Facebook does. It’s not as informative as personalised messages but is a simple feature that may incentivize more people to provide some general feedback.
Either way, it’s worth trying and as a software developer, I can’t wait to implement it.
This tutorial is about implementing a feature that will be used in real life on my project.
With ML projects still on the rise we are yet to see integrated solutions in almost every device around us. The need for processing power, memory and experimentation has led to machine learning and DL frameworks targeting desktop computers first. However once trained, a model may be executed in a more constrained environment on a smartphone or on an IoT device. A particularly interesting environment to run the model on is browser. Browser-based solutions may be used on a wide range of devices, desktop and mobile, online and offline. The topic of this post is how to prepare a model for the in-browser usage.
This post presents an end-to-end implementations of a model creation in Python and Node.js. The end goal is to create a model and to use it in a browser. I'll use TensorFlow and TensorFlow.js as main frameworks. One could train a model in Python and convert it to JS. Alternative is to train a model directly in javascript, hence omitting the conversion step.
I have more experience in Python and use it in my everyday work. I occasionally use javascript, but have very little experience in the contemporary front-end development. My hope from this post that python developers with little JS experience could use it to kick start their JS usage.
Choosing between Node.js and Ruby on Rails, when choosing a development platform, is a core decision. That affects how the project unfolds over time, and how much server resources will be needed. Both languages can support web applications of high complexity, but each has its advantages and disadvantages. Knowledge of these pros and cons will help to choose the best solution for the proposed project. Let's analyze in more detail and tell you about our choice and experience.