demo-go-csv-parser VS Visual Studio Code

As someone new to Go, I would like to dive deeper into this language to find out its potential. The best way to learn something new is by doing it. So that is why I started on a project to not only hone my Go skills, but also solve a common task: CSV parsing and data manipulation. Through this blog post I will illustrate how to parse a CSV file containing rows of articles. These articles need to be filtered based on a property and then written to a new CSV file. The accompanying source code to this project can be found on GitHub.

Visit: https://code.visualstudio.com

So, I had to test this new version in a personal project to learn how to use all these new things along with other technologies and tools I’ve used for a long time and also love, like VSCode, ESLint, Prettier, and a bunch of other. And I did, but there’s a catch which makes the whole “experimental” thing have more sense: you can only use this through ng test.

A text-based shell interface is extremely lightweight compared to modern IDEs. Because it runs in the terminal, even a feature-rich agent has very low overhead. According to Forgecode “Low Resource Usage: minimal impact on system performance”. In contrast, a full IDE can consume hundreds of megabytes of RAM or more, even when idle. In one user benchmark, Neovim (a terminal editor) used only about 10 MB of RAM, whereas Visual Studio Code (an Electron-based IDE) used roughly 700 MB with no files open. The savings add up quickly: even a hundred developers using shell agents could free up many gigabytes of memory compared to the same number running heavy IDE instances. In practice, a shell agent like Forge leaves almost all CPU and RAM free for your code compilation and tests. In a cloud or CI/CD pipeline, this efficiency translates directly into cost savings. You can run more parallel analyses or smaller instances when the tools are light. Over time, those saved resources mean lower infrastructure bills for large teams.

A Text Editor: Use Visual Studio Code (free at code.visualstudio.com).

Put simply, by automating routine checks, AI reviewers help engineers avoid tens of hours of minor fixes and rewrites. As per my experience, AI code review exemplify this shift: catching issues on the spot in VS Code, they keep developers focused on building features rather than waiting for post-hoc feedback. Against this backdrop, let’s explore the main advantages of AI-powered code review for enterprise teams.

VSCode to open the repository, or any other IDE that supports devcontainers.

VS Code Download VS Code

Code Editor: Download Visual Studio Code

1. Visual Studio Code