AI powered code debugging extensions
Tags: python, vscode, openai, webdev
Published: 2023-03-10 00:26:54

AI powered code debugging extensions
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openai
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vscode
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ai
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python
GitHub Copilot is nice for autocompletion, but 80% of the coding is debugging. @musabshakil shares a quick demo in his DEV post, which I encourage you to watch.

musabshakil 
Get Instant Error Solutions with GPT
MusabShakil ・ Feb 21 ・ 3 min read
#gpt3 #ai #vscode #javascript
header image was generated using midjourney

What is MusabShakeel576/quickfix.ai?
The TLDR; is you can copy the error message and get a suggested solution from the AI. Quickfix AI is also in its alpha phase, so expect bugs and report them to the author via the GitHub repo.
`from pathlib import Path
from gpt_index import GPTSimpleVectorIndex, Document

def generate_index(workspace):
    file = workspace.name+'.json'
    path = Path.cwd() / "storage" / file

    if path.is_file():
        index = GPTSimpleVectorIndex.load_from_disk(path)
    else:
        documents = [Document(document) for document in workspace.documents]
        index = GPTSimpleVectorIndex(documents)
        with open(path, 'w'):
            pass
        index.save_to_disk(path)

    return index

def generate_solution(workspace) -> str:
    index = generate_index(workspace)
    response = index.query(workspace.input)
    return response`

GitHub logo MusabShakeel576 / quickfix.ai
Two powerful AI-powered extensions to simplify your browsing and coding experience.
quickfix.ai is an ambitious project made up of two powerful AI-powered extensions, VS Code and chrome. Both simplify your developer experience and speed up debugging.

VS Code Extension
Chrome Extension
How does it work?

### 
Quickfix AI uses GPT and Vector Index to provide instant solutions for errors in your code within the code editor using AI.

The code seems straightforward, but I did not install and run the alpha build locally. I am going off the demo provided by the author in their post. I also focus on understanding the code, and the backend/src/gpt.py seems to be powered by a single python package to manage the model. Python has been the premier language to support NLP for some time, so it makes sense that the developer experience packaged through one interaction.
# /backend/src/gpt.py
from pathlib import Path
from gpt_index import GPTSimpleVectorIndex, Document

def generate_index(workspace):
    file = workspace.name+'.json'
    path = Path.cwd() / "storage" / file

    if path.is_file():
        index = GPTSimpleVectorIndex.load_from_disk(path)
    else:
        documents = [Document(document) for document in workspace.documents]
        index = GPTSimpleVectorIndex(documents)
        with open(path, 'w'):
            pass
        index.save_to_disk(path)

    return index

def generate_solution(workspace) -> str:
    index = generate_index(workspace)
    response = index.query(workspace.input)
    return response
If you understand Python and are interested in supporting it, it is open-sourced. Support the author with feedback and a star on their GitHub project.

Also, if you have a project leveraging OpenAI or similar, leave a link in the comments. I'd love to take a look and include it in my 30 days of OpenAI series.

Find more AI projects using OpenSauced

Short Summary:
"Functional Programming in Python" is a short book on writing code and solving problems using Python in a Functional Programming(FP) idiom. The first chapter goes over the three main paradigms - procedure programming, object-oriented programming, and FP. After the first chapter, we get into FP Fundamentals and how to express them in Python. The longer middle chapters go deep into iterators and iterables. The book ends with several chapters that dive into FP ideas that don't necessarily apply to Python.

What I liked:
There were several things I liked about how the author wrote the book. First, I like how the chapters are formatted. It is nice to know that the last chapters aren't necessary for Python but interesting nonetheless. I also enjoyed how the author explained the concepts. I feel as if I can implement these ideas reasonably soon. It was a good weekend book that didn't need a lot of focus to understand.

What I disliked:
I didn't dislike much about the book. If there was one thing that I would comment on, it was on the chapters about iterator functions. I'm not sure what the thought process is to give so much space to explain the different functions, and it didn't make a clear connection to FP. Perhaps my comprehension is lacking, but I had a hard time connecting the two.

Review round-up:
I would suggest any intermediate Python developer interested in exploring FP concepts in Python read this book. It's not a beginner book, nor do I think anyone with solid FP knowledge would gain much out of reading it.

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Rating:
7/10 Python Snakes