ssteo

import pytest
from kata import process_input


def test_process_input():
	grid_area = [5, 5]
	current_pos = [1, 2]
	current_direction = 0
	movement = 'LMLMLMLMM'

ssteo

"""
L - 90 deg left
R - 90 deg right
M - move forward

 N
W E
 S

 NS - Y

ssteo

import os
import json
import sys
import time
import threading

import redis
import tkinter as tk
import azure.cognitiveservices.speech as speechsdk
from openai import OpenAI

ssteo

I created this Proof of Concept (PoC) and video demonstrate how prompt routing can be used to make ChatGPT dynamically
select the targeted prompt, achieving better results. The concept involves categorizing the initial prompt using ChatGPT
and then selecting a more specific prompt based on the tag to resend targeted prompt to ChatGPT. This PoC showcases the
efficiency we can gain when using speech recognition and AI tools in development. I will release the code-assist tool on
GitHub once I have more free time to clean up the code. If we have a set of well-tested prompts for different types of
specific tasks, I believe this tooling approach can help us increase our productivity by skipping a few steps in our
repetitious daily work tasks than just code generation. Editing code, issuing commands (with safety checks), and
automated scope-down online search are the next steps to multiply work productivity.

Video Demo: Code generation and editing via streaming speech recognition

ssteo

Open ~/.bash_profile in your favorite editor and add the following content to the bottom.

# Git branch in prompt.

parse_git_branch() {
 git branch 2> /dev/null | sed -e '/^[^*]/d' -e 's/* \(.*\)/ (\1)/'