Using VS Code, Cline, and DeepSeek as a Cursor Alternative
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Using VS Code, Cline, and DeepSeek as a Cursor Alternative

Author: Alex Xiang

Artificial Intelligence

Cursor is useful, but the Pro version is not free forever. After the initial trial, the free version is more limited, while the Pro plan costs money every month. That is not unreasonable, but it makes alternatives worth testing.

Around the same time, DeepSeek V3 became a hot topic. It supports an OpenAI-compatible API, which means it can be plugged into many existing AI coding tools. So I tried a simple setup: VS Code + Cline + DeepSeek.

Configure Cline

Install the Cline extension in VS Code. After installation, a Cline icon appears in the left sidebar.

Open the settings panel and choose:

  • API Provider: OpenAI Compatible
  • Base URL: https://api.deepseek.com/v1
  • API Key: created from the DeepSeek account page
  • Model: the DeepSeek model you want to test

The API key is shown only once after creation, so store it carefully.

First Impression on Cost

At the time of testing, the account came with a small amount of free credit. The whole experiment used roughly tens of thousands of tokens and cost only a tiny amount.

That was the first attractive point of this setup: compared with a fixed monthly IDE subscription, an API-based workflow can be cheaper for light or occasional use.

Of course, cost is not the only factor. Reliability, tool integration, context handling, and model behavior matter more if you use it every day.

The Coding Test: Gomoku in C++

To test the workflow, I asked Cline to implement a Windows console Gomoku game in C++:

Implement a human-versus-computer Gomoku game for the Windows console in C++.

The assistant planned a small project structure:

  • main.cpp
  • game.h and game.cpp
  • board.h and board.cpp
  • ai.h and ai.cpp

It then started creating files, compiling, reading errors, and trying to fix them.

This is exactly the kind of workflow that makes AI coding tools attractive: the model does not merely answer in chat; it edits files, runs commands, observes errors, and tries again.

What Went Wrong

The test did not fully succeed.

There were two main problems.

First, the working directory became confusing. The assistant created files under paths that did not match the actual project directory. That led to repeated compile failures.

Second, after I manually adjusted the directory and got the code compiled, the game logic was still wrong. In one test, a player could place more than five continuous stones without the game detecting a win.

That is an important result. A tool can produce a large amount of plausible code and still miss basic correctness.

What Still Worked

Even with the failure, the experiment was useful.

Cline successfully decomposed the task into modules. It generated a reasonable file structure. It reacted to compiler errors. It kept the workflow inside VS Code. For a small prototype, that is already valuable.

The problem is that “compiles” and “works correctly” are different standards. AI coding needs tests, review, and clear acceptance criteria. For games and algorithms, small logic bugs are easy to miss by visual inspection.

How I Would Use This Setup

VS Code + Cline + DeepSeek is suitable for:

  • small prototypes,
  • code explanation,
  • mechanical refactoring,
  • generating boilerplate,
  • exploring unfamiliar APIs,
  • writing first drafts of scripts.

I would be more cautious with:

  • complex algorithms,
  • state-heavy programs,
  • security-sensitive code,
  • production changes without tests,
  • large refactors across many files.

The right way to use it is to give smaller tasks and add verification:

Implement only the board representation and win detection.
Add unit tests for horizontal, vertical, and diagonal wins.
Do not implement the AI player yet.

That prompt is much better than asking for the entire game at once.

Cursor Versus This Setup

Cursor’s advantage is product integration. It feels smoother as a dedicated AI editor.

VS Code + Cline + DeepSeek is more flexible. You can choose the model, control API cost, and keep using a familiar editor. The downside is that the workflow may require more setup and more manual judgment.

For developers who already live in VS Code and want to experiment with lower-cost AI coding, this combination is worth trying.

Final Takeaway

This setup did not magically replace Cursor in every way. It did prove that AI coding is no longer tied to a single editor or subscription.

The deeper lesson is about workflow discipline. Whether the model is DeepSeek, Claude, GPT, or something else, the same rule applies: split the task, run the code, add tests, and do not trust a convincing answer until the program behaves correctly.