From PyCharm to VS Code

After five years of extensively using PyCharm Professional, I’ve encountered several persistent issues that ultimately led me to switch to VS Code. These include:

  1. Plugin Development and Maintenance: Challenges in developing and maintaining PyCharm plugins.
  2. Performance Issues: Slow operation and excessive resource consumption.
  3. Remote Development Limitations: Weak support for remote development workflows.
  4. AI Extension Bugs: Frequent bugs in AI-powered features.
  5. Critical Bugs: Increasingly severe issues requiring manual deletion of cache and library files to restore functionality.

Given these frustrations, I decided to transition to VS Code. This document outlines the steps I took during the migration from PyCharm to VS Code.

Elegant Env Vars for Jenkins Python

In my case, I use Jenkins to drive some Python scripts. We can set configuration in Jenkins via System Properties, Node Configuration or Build Parameters.

We can get the configurations through environment variables, and also pass stage outputs to other stages via environment variables.

Visualizing and retaining environment variables is crucial, So I want to manage environment variables in an elegant way.

I implemented a class EnvSettings extend from pydantic.BaseSettings to manage environment variables:

  • in Python
  • 2 min read

Subclassing of pathlib.Path

In my case, I wanted to enhance the functionality of pathlib.Path to include additional methods for file or directory operations specific to my project.

For example, I wanted to add a method that would allow me to easily zip a directory or a file directly from the Path object:

from pathlib import Path

class EnhancePath(Path):
    def zip(self):
        print('zip file')

EnhancePath.cwd()

This code works normally on python 3.13, but it raises an error on python 3.11 and below (I don't test on 3.12):

AttributeError: type object 'EnhancePath' has no attribute '_flavour'

  • in AI
  • 3 min read

My AI Product ④

In the entire automated testing process, the most time-consuming tasks are maintaining UI changes and analyzing test reports. So, I want AI to analyze the test results to assist testers in improving the efficiency of report analysis.

This project is named RAA, base on AutoGen.

  • in AI
  • 2 min read

My AI Product ③

The previously mentioned JAA has been operating continuously for six months and has gained a stable user base. Tomorrow, I need to attend the company's OPEN AI DAY to share about JAA in Shanghai.

If it were half a year ago, I would have been very willing and had a lot to say, but now, I think it's already somewhat outdated.

  • in AI
  • 4 min read

My AI Product ②

In the previous post, I introduced Step-Copilot, and I stop the development of it at November 2024. Then, I thought how great it would be if AI could call the tools we had developed?

We had previously built many tools, such as batch operations for Jira, Jenkins maintenance, and others, which provided services to users through GUIs or web interfaces. But regardless of the form, there is always some learning cost involved.

Even with comprehensive documentation provided, most users would likely ignore it and choose to directly ask the tool developers instead.

It would be incredibly helpful to have a AI that not only uses all the tools but also teaches users which scenarios to apply each tool in.

Based on this idea, I developed the JAA

  • in AI
  • 2 min read

My AI Product ①

In October 2024, under the wave of AI, my main development work shifted from the automation testing framework to the development of AI products. At this point, I only use AI as a search engine.

Base on my experience with the automation testing framework, the first AI product I want to develop is an AI Code generator, it primarily focuses on the generation of automated test scripts. And I named it Step-Copilot.

  • in AI
  • 2 min read

Running Local Model with Ollama

Sometimes, I need to run a model locally on my machine. This is useful for debugging and testing purposes.

This guide will show you how to run a model locally using Ollama.