Ever notice how some days you're a caffeinated code ninja ready to refactor an entire codebase, while other days you can barely muster the energy to fix a typo? Welcome to the wonderfully human world of vibe coding – and the tools that get it.
ChatGPT, GitHub Copilot, Cursor and other AI tools using Large Language Models (LLMs) can quickly generate a lot of code, but quite often the process is not straightforward and requires many iterations and a skilled developer to get the best results.
In this article we will show how a well-structured prompt can improve the AI-supported development effort.
ChatGPT and other advanced AI language models have become an integral part of the modern development workflow. From GPT-4 to Claude and other competitors, these AI assistants have transformed how we approach coding and content creation. And we understand why! These AI models have proven themselves invaluable for producing all kinds of text and code, helping developers work more efficiently and creatively than ever before.
While modern AI models are now well-versed in a wide range of technologies and frameworks, we've found that providing concrete examples still leads to the best results. This approach helps the AI understand exactly how we want to use codehooks.io's specific features and conventions, ensuring the generated code follows our best practices.
API's, Microservices and JavaScript are the digital glue of the modern Internet.
This blog post shows how you can build a fast serverless API for a large data set. We'll use a 144MB file with 500K famous quotes to create an efficient NoSQL datastore using code hooks. We'll build and deploy a public serverless API to serve random quotes from the NoSQL database to clients.
