AI-powered coding assistants have become increasingly popular, promising to boost developer productivity and streamline the coding process. Tools like GitHub Copilot and Cursor offer impressive capabilities, generating code snippets, suggesting completions, and even creating entire functions based on prompts. However, alongside these benefits come potential pitfalls that developers need to be aware of, as highlighted in recent discussions on the Cursor forum.

The Allure of AI Assistance:

The appeal of AI coding assistants is undeniable. They can:

As software development to evolve, so does the landscape tools available to assist developers in their tasks. Among the latest entrants are Google Gemini, GitHub Copilot, and AWS CodeWhisperer, each aiming to make coding easier and more efficient. This blog post aims to provide a thorough comparison of these three tools, focusing on their capabilities, strengths, and weaknesses to help you decide which one fits your development needs best.

GitHub Copilot

Overview

GitHub Copilot, developed by GitHub in collaboration with OpenAI, has quickly gained popularity since its launch. Designed as an AI-powered coding assistant, it operates within Visual Studio Code and other IDEs, providing code suggestions, auto-completions, and entire function generation based on the context of your code.

Forget tedious hours spent debugging and wrestling with syntax errors. The world of software development is being revolutionized by AI code generation models, capable of writing functional code in multiple programming languages.

But with so many options emerging, which models are leading the charge? Let’s explore some of the most powerful contenders:

1. Codex (OpenAI):

• Powerhouse Behind GitHub Copilot: Codex, the engine behind GitHub Copilot, is a descendant of GPT-3, specifically trained on a massive dataset of code.

Well, it depends! Let’s start with the models.

It feels like a new model is released pretty much every month claiming to be “best in class” and having superior results to competitor models.

Can Large Language Models (LLMs) Write Compilable Code?

Large language models (LLMs) have demonstrated impressive capabilities in generating human-like text, translating languages, and even writing different kinds of creative content. But can these powerful AI tools also write code that’s actually compilable and functional? The answer, in short, is a qualified yes, but with important caveats.

Java 20 was released on March 21, 2023. It is a short-term release supported for six months, following the September 20 release of JDK 19. JDK 21, due in September, will be a long-term support (LTS) release, backed by multiple years of support.

New features in Java 20:

• Record Patterns (Second Preview): This feature enhances the Java programming language with record patterns to deconstruct record values. Record patterns and type patterns can be nested to enable a powerful, declarative, and composable form of data navigation and processing.
• Pattern Matching for Switch Statements and Expressions (Fourth Preview): This feature extends pattern matching to express more sophisticated, composable data queries in switch statements and expressions.
• Foreign Function and Memory API (Second Preview): This API introduces an API by which Java programs can interoperate with code and data outside of the Java runtime.
• Scoped Values (Incubator): This feature introduces scoped values, which enable the sharing of immutable data within and across threads.
• Virtual Threads (Incubator): This feature provides a lightweight, non-blocking thread abstraction that can be used to improve the performance and scalability of concurrent applications.
• Vector API (Proposal): This API provides a high-performance, efficient way to operate on arrays of primitive data types.
• Unicode 15.0 Support: This release includes support for Unicode 15.0, which includes new characters and emoji.
• Improved Control of G1 Concurrent Refinement Threads: This release includes improvements to the G1 garbage collector, which can improve the performance of concurrent applications.
• Support for CLDR Version 42: This release includes support for CLDR Version 42, which provides locale data for over 150 languages.

More info

QuantLib is a free and open-source software library for quantitative finance. It provides a wide range of functionality for pricing and risk-managing financial derivatives, including interest rate swaps.

To calculate the delta risk of an interest rate swap in Python using QuantLib, you can follow these steps:

1. Import the necessary QuantLib modules:

Python

import QuantLib as ql

1. Create a QuantLib YieldTermStructure object to represent the current interest rate curve:

Python