Large Language Models (LLMs) have taken the world by storm. These AI systems can generate text, translate languages, write different kinds of creative content, and answer your questions in an informative way. But with so many LLMs available, each with its own strengths and weaknesses, how do you choose the right one for the task?
The answer might surprise you: it’s about more than picking just one. Here’s why using multiple LLMs can be a powerful approach.
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The Linux shell, also known as the command-line interface (CLI), has been a cornerstone of Linux-based operating systems since their inception. It provides developers a powerful and efficient way to interact with the system, execute commands, and perform various tasks. It’s one of the main factors that made Linux the most popular OS worldwide.
In this post, we’ll briefly discuss the history, explore its advantages and disadvantages, and discuss how developers can leverage its potential to streamline their work and enhance productivity.
The roots of the Linux shell trace back to the early days of computing. The concept of a command-line interface predates graphical user interfaces (GUIs) and originates in the mainframe era.
I still remember the first time (At Tel Aviv University) we wanted to interact with a computer (mainframe) back in the day. It was with this black terminal. That was the only interface you had. We are talking here in mid-1980.
Even before that, in 1969, Ken Thompson developed the first Unix shell, the Thompson shell, which laid the foundation for future shell implementations.
Over the years, various shells were introduced, each offering unique features and improvements. In the mid-1980s, the Bourne shell (sh) became the standard shell for Unix-based systems, inspiring the Bourne-Again Shell (bash) development in 1989.
Today, bash remains one of the most widely used shells in the Linux ecosystem.
Ahh… if you are on MacOS, you can taste the same shell.
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OpenAI is a research organization focused on developing advanced artificial intelligence technology, and one way it achieves this is by making its technology available to developers through APIs.
This blog post will explore what OpenAI API is and how to use it from Node.js – We will show a few examples you can take and combine with your current applications.
Other good examples to explore the API are at openai.com.
OpenAI API is a powerful tool allowing developers to access cutting-edge AI models that OpenAI researchers train.
It’s (also) a mouthful, no?
These models can be used for various applications, including natural language processing, translation, image recognition, chatbots, etc.
The OpenAI API is designed to be easy to use, with a RESTful API that can be accessed using any programming language.
It also includes pre-built models that can be used out of the box and the ability to train custom models using your own data.
The Object type represents one of JavaScript’s data types. It’s important stuff as we will use them—a lot.
It is used to store various keyed collections and more complex entities. Objects can be created using the Object() constructor or the object initializer / literal syntax. Did you know that all modern JavaScript utilities for working with objects are static?
Here is an example of a JavaScript object that contains two names and one key:
let obj = {
name1: "John",
name2: "Jane",
key: "value"
}
Regular expression or in short Regex is a string of text that lets you create patterns that help match, locate, and manage text. It’s an important tool in a wide variety of computing applications, from programming languages like JS, Java and Perl, to text processing tools like grep, sed, and vim.
Here are a few helpers to refresh your mind when you need some ‘simple’ regex to do the job.
Continue readingThe need to protect your software and to be updated about vulnerabilities is getting stronger. JFrog Xray is unique in its capabilities to perform analysis of all the binaries you are consuming in your project. It works with Artifactory to perform a deep analysis of binary components at any stage of the application lifecycle. Xray provides great visibility into issues lurking in components anywhere in your organization and there are many cases where you wish to get notified on a security violation (or a license breach) directly to Slack.
We will use Xray’s Webhooks to define our server’s and base on the policies/rules our Webhook will be notified with the alerts about violations (security or licenses).
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The trend of #DevSecOps is growing fast and it is no longer just part of your security team. More and more organizations wish to integrate their security team in all the phases of development and operations. To achieve it, there are cases where you need to export data from JFrog Xray (in our case to a CSV format) so you can ingest it to your current logging/monitoring system.
In a nutshell, JFrog Xray works with JFrog Artifactory to perform a deep analysis of binary components at any stage of the application lifecycle. It provides full transparency that leads to more trust in your software.
By scanning binary components and their metadata, recursively going through dependencies at any level (think on the layers you have in any Docker container), JFrog Xray provides great visibility into issues lurking in components anywhere in your organization.
One of the best parts is that JFrog Xray is also fully automated through a rich REST API. We will use it to create this Exporter. Please feel free to clone/fork the code below and use it, but remember you might need to add pagination and a watchdog for a real system.
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The trend of DevSecOps is not new but it’s growing fast. More and more organizations wish to integrate their security team in all the phases of development and operation.
Many security products keep your code safe from vulnerabilities at different stages (dev, test, qa and prod). However, JFrog Xray is unique in its capabilities to perform analysis of all the binaries you are consuming in your project.
JFrog Xray works with JFrog Artifactory to perform a deep analysis of binary components at any stage of the application lifecycle. It provides full transparency that leads to (more) trust in your software. By scanning binary components and their metadata, recursively going through dependencies at any level (think on the layers you have in any Docker container), JFrog Xray provides great visibility into issues lurking in components anywhere in your organization.
One of the best parts is that JFrog Xray is also fully automated through a rich REST API that lets it integrate with a CI/CD pipeline and allows other binary analysis tools to build on its unique capabilities.
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The Monty Hall problem is a brain teaser loosely based on the American television game show “Let’s Make a Deal” and named after its original host, Monty Hall.
I wrote a little web app that show you what is the right choice with a simulator. It’s a bit hard to explain the correct solution as it’s going against ‘common’ sense. Continue reading
In the past few months, I heard someone smart saying that “the future is artificial intelligence first”.
Artificial intelligence, is making computers “smart” so they can think on their own and be even more helpful for us. It’s clear that Google, has been investing heavily in the areas of:
These days we can see it all come together in the Google Assistant. It allows you to have a conversation with Google and be more productive. In this post, we will see how it’s all working by building a new Action for Google home. In the same time, we will have a nice bot that in the future we will integrate with many apps. Cool? Continue reading
Ido Green 