Python has become one of the most popular programming languages globally and is particularly popular in data science and artificial intelligence. Python’s popularity can be attributed to its ease of use and readability and the large ecosystem of libraries and frameworks built around it. Python is also popular among developers working on cloud-based applications, as they can use it to orchestrate complex workflows.
Let’s get real – as developers, we spend a significant amount of time staring at a screen and trying to figure out why our code isn’t working. According to Coralogix, there are an average of 70 bugs per 1000 lines of code. That’s a solid 7% worth of blimps, bumps, and bugs. In addition to this, fixing a bug can take 30 times longer than writing an actual line of code. But it doesn’t have to be this way.
Selecting the best chip can be tedious work but the best chip can save you a lot of time and money, and might even be faster! So should you spend time finding the best? I have some words on the topic. If a primary goal of your next project is to learn a new MCU, you want to create something easily reproducible, or if there will be only one machine building the project, then I recommend you to go with the chip you want to learn, the chip most readily available, or the easiest one to work with.
Debugging is an essential step in software development, as it allows developers to fix errors before releasing the software to the public. Debugging tools can be integrated into code editors, making the debugging process more efficient. This tutorial will show you how to debug node.js in Visual Studio Code.
The Java Collections Framework was a huge leap forward when it was introduced as part of Java 2 (JDK 1.2). Thanks to the included collection classes we finally moved beyond the limits of Vector and Hashtable to more mature and generic solutions. With the introduction of streams and functional concepts in Java 8 the framework took everything to the next level. One of the core principles underlying the framework is coding to the interface.
The Java Message Service API (JMS) was developed by Sun Microsystems in the days of Java EE. The JMS API provides us with simple messaging abstractions including Message Producer, Message Consumer, etc. Messaging APIs let us place a message on a “queue” and consume messages placed into said queue. This is immensely useful for high throughput systems – instead of wasting user time by performing a slow operation in real-time, an enterprise application can send a message.
Spring makes building a reliable application much easier thanks to its declarative transaction management. It also supports programmatic transaction management, but that’s not as common. In this article, I want to focus on the declarative transaction management angle, since it seems much harder to debug compared to the programmatic approach. This is partially true. We can’t put a breakpoint on a transactional annotation. But I’m getting ahead of myself.
Scraping websites built for modern browsers is far more challenging than it was a decade ago. jsoup is a convenient API that makes scraping websites trivial via DOM traversal, CSS Selectors, JQuery-Like methods and more. But it isn’t without its caveat. Every scraping API is a ticking time bomb.
Large applications can produce very large symbol files when debug information is enabled (especially at the higher, more verbose levels of debug info!). This article describes some techniques for reducing debug symbol file sizes! Like Interrupt? Subscribe to get our latest posts straight to your mailbox.
