Chapter 1: Introduction to Enhanced Productivity

For ages, developers have been on a quest to discover the elusive 10X Engineer. However, given the current landscape—both real and perceived—it's clear that 10X isn't sufficient anymore. We demand greater achievements: more code, increased pull requests, and fewer errors. In this article, I will discuss methods to significantly boost your productivity using command line wrapper functions for the OpenAI API.

To begin, you will need an OpenAI API key, which you can obtain by signing up [here](insert_link).

A quick note: I primarily use the Fish shell rather than bash or zsh. While this may seem unconventional, the fundamental principles will be similar, and you can always adapt the examples for bash or zsh if you prefer.

Let’s dive in!

Section 1.1: Setting Up Your Environment

Our first task is to create a function that can answer any question we pose: hey_gpt.

If you're attempting to replicate these steps, ensure you have jq installed on your machine; it’s essential.

I'm not going into the nuances of model parameter tuning—more on that can be found [here](insert_link). However, I’ve set an alias for this function as h, allowing for quick access.

Now that we have the basics down, let’s explore how to achieve more substantial results.

Section 1.2: Combining Prompts with Data

One common approach I frequently utilize involves merging prompts with data, leading us to the creation of data_gpt.

Here’s a link to the generated dataset.

The possibilities here are extensive. I experimented with an alternative version that could process piped input, but the complexity of escaping characters made it easier to write to a file and read from it for now.

While these examples closely resemble ChatGPT, they differ in a few key areas: (1) having access at the command line encourages more experimentation, (2) integrating these functions with other command line tools—such as GitHub's CLI or Jira's CLI—is seamless, and (3) you can chain multiple commands together, similar to tools like LangChain.

Chapter 2: Exploring Advanced Features

One particularly interesting feature is the Create Image endpoint.

Another valuable endpoint is Code Edits, which allows for code revisions. Instead of creating a new wrapper function, we can utilize the functions we've already established for a quick preview of the future of coding.

It’s quite impressive how these simple commands can achieve significant results with minimal adjustments—truly remarkable.

As a final thought, this is merely the beginning. Recently, OpenAI introduced Plugins—powerful enhancements for an already robust tool. The real differentiator moving forward will be developer productivity. There’s an unmistakable advantage for those who embrace AI-driven workflows rather than resisting or overlooking them.

I hope this article has inspired you to explore new workflows and efficiency improvements. If you have any thoughts or suggestions, please feel free to comment.

The first video title is 7 Days to Die - Trying Out Alpha 21 Patch - Multiplayer - part 4. In this video, the gameplay showcases the latest updates and features in a multiplayer environment.

The second video title is Making a Monster Truck | Screw Drivers. This video demonstrates the step-by-step process of building a monster truck using various tools and techniques.