How I Used Cursor To Write Posts, Build Scripts, And Open Source A Tool
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How I Used Cursor To Write Posts, Build Scripts, And Open Source A Tool

Author: Alex Xiang

Artificial Intelligence

How I Used Cursor To Write Posts, Build Scripts, And Open Source A Tool

Over the past few days, I have been continuously organizing www.zicode.com with Cursor. On the surface, the work was not complicated: write a few posts, add a few scripts, keep cleaning up the move from Wagtail to Astro, and turn the “sync to WeChat draft” script into a public repository.

But while doing it, I increasingly felt that the most interesting part of Cursor is not simply “it completes code.” It turns the whole work process into material that can be replayed, reviewed, and reused.

In the past, technical writing often depended on memory. You roughly remembered what went wrong and how you fixed it. But after a few days, many details blurred quickly: why a change was made, why a plan was abandoned, and which paths were tried in between.

That is exactly where Cursor with an agent workflow feels different. The development process naturally leaves a work log.

Writing With Cursor: The Value Is Traceability

When people first think of AI writing, they often focus on speed: can it draft an outline, expand a paragraph, or improve a title?

Those things are useful, but for me, the more valuable ability is traceability.

The work I did on www.zicode.com was not a single-threaded task. Articles were being written, the site was changing, scripts were being debugged, repositories were being organized, and other projects occasionally interrupted. In the old workflow, this easily produced a problem: when I wanted to write a retrospective, my memory kept only the result, not the process.

Cursor’s agent workflow is different. I can look back at interactions, files read, changes made, commands run, failed attempts, and later pivots. The article is no longer only “what I remembered afterward.” It becomes “a narrative rebuilt from real work traces.”

That gives three practical benefits:

  1. Retrospectives become more specific.
  2. Advice is based on recent real work instead of abstract imagination.
  3. Many details can be extracted from agent history instead of reconstructed from memory.

In plain words, Cursor connects “writing an article” with “development process traces” in a way I did not previously have.

Agent History Becomes A Semi-Structured Material Store

I now increasingly treat agent history as a semi-structured material store.

It is conversation, but not only conversation. It often contains:

  • How the goal became clearer step by step.
  • How the solution converged.
  • What tradeoffs file changes reflected.
  • Which command outputs proved assumptions right or wrong.
  • How the final result differed from the initial idea.

The biggest difference from ordinary chat logs is that it stays close to engineering facts.

For example, if you want to review how a script was built, final source code alone is not enough. It tells you what the result looks like, but not:

  • Why the initial directory structure was not suitable for open source.
  • Why certain paths had to move from hard-coded values into configuration.
  • Why README needed installation, sample configuration, and safety notes instead of two short lines.
  • Why pushing to GitHub eventually used one path rather than another.

These details exist naturally in agent history. They are not comments added afterward; they are traces left by the work itself.

So one important value of Cursor in content creation is not “writing for you.” It is preserving context for you.

A Concrete Example: Syncing WeChat Drafts And Open Sourcing It

One concrete task I worked on was organizing the script that syncs blog posts into WeChat Official Account drafts.

The original script was an internal version used by the site. It worked, but it carried project-specific assumptions: paths were inside this site, configuration belonged to this site, and documentation assumed you already knew the surrounding directory structure. That is fine for personal use. But if the script is made public, problems appear immediately.

Open source is not just copying files out. The troublesome parts are usually these edges:

  • Which paths are hard-coded and must be decoupled.
  • Which configuration can be public examples, and which must never enter the repository.
  • Which explanations seem obvious to the author but are unknown to users.
  • Which local conveniences become risks in a public repository.

I did this cleanup inside Cursor: clone the repository, move the script, add documentation, separate local configuration from repository boundaries, then commit and push.

The resulting open-source repository is here:

It contains several things that matter:

  • A standalone sync_wechat_article.py.
  • A safe sample configuration.
  • A README covering installation, configuration, and usage.
  • Ignore rules for local secrets and generated files.

The point was not “publish the script.” It was “fill in the part that lets someone else actually connect it.”

Turning An Internal Tool Into A Public Tool

The script itself is not huge. It fetches a page, extracts the body, processes images, calls WeChat APIs, creates or updates a draft, and optionally writes back frontmatter.

The harder part is boundary cleanup.

Project Paths Cannot Assume Your Directory

Internal scripts often assume they run inside one project, that a directory exists at a fixed position, and that assets live under astro-site/public.

Once open sourced, those assumptions fail. Path-related values must move from “internal convention” to “external configuration.” That is how options such as content_root, public_root, and node_modules_root appeared.

README Is Part Of Usability

Many open-source repositories have a README that still leaves users unable to run the project.

For this one, I wanted first-time readers to learn four things quickly:

  1. What the repository does.
  2. What dependencies are needed.
  3. How the configuration file is written.
  4. Which commands are common.

It sounds basic, but these four points already separate many author-only projects from usable tools.

Secrets Should Be Impossible To Commit By Default

This matters a lot.

If a repository depends on the author remembering not to commit real configuration, it will eventually fail. A better approach is to push risk into repository defaults: example config contains placeholders only, local config is ignored, and generated preview files are excluded from version control.

Engineering hygiene often means changing “remember to be careful” into “the default behavior is safe.”

Technical Writing Also Changes

This article itself is an example.

It was not written from title to ending in one sitting. The process was closer to this:

  1. Identify several lines from real work done over the last few days.
  2. Let Cursor expand those lines into a complete draft.
  3. Compare the draft with agent history and rewrite parts that were too abstract, too smooth, or too template-like.
  4. Do another pass for Chinese rhythm and tone.

If someone asks whether Cursor wrote the article, the accurate answer is: it was written through Cursor, but not by throwing one prompt and accepting the output. Cursor acted as drafter, organizer, and reviewer of past context. I still pulled back the narrative rhythm, emphasis, and voice.

That is the workflow I increasingly like: not replacing the author with AI, but giving the agent work such as collecting material, organizing structure, and recovering context.

Practical Advice For Writing Technical Posts With Cursor

Do Not Start With “Write A Complete Article”

That is the easiest way to get an article with a generic AI smell.

Start by giving material boundaries: what changed recently, what was built, which pitfalls appeared, and which parts are worth explaining. Then the draft stays closer to real work instead of becoming a complete-looking template with little body temperature.

Treat Agent History As Material

If you actually did work inside Cursor, the most valuable input may be the previous workflow history, not a new paragraph you write from memory.

Looking back at that history quickly recovers:

  • What the original problem was.
  • Where judgment changed.
  • Which step produced the real experience.

Once these pieces are recovered, the article stands more firmly.

First Drafts Can Be Fast; Final Drafts Should Be Slow

Cursor is good at spreading out a first version quickly. But final quality still depends on later editing.

I usually manually check:

  • Whether conclusions are too strong.
  • Whether sentences are too even and template-like.
  • Whether transitions are too smooth and lack real pauses.
  • Whether hesitation and detours remain, instead of only final correct answers.

When an article contains only correct answers, it often reads like a manual. Good technical writing usually keeps some real detours and judgment.

Turn Experience Into Usable Advice

Many retrospectives are sincere but leave readers with nothing to take away. A simple fix is to ask after each lesson: if someone meets a similar case tomorrow, what can they do directly?

For this work, the practical advice is:

  • When using Cursor for a project, do not waste the process history it naturally records.
  • When open-sourcing an internal script, first ask how others can connect it, then ask how to publish the code.
  • When writing technical posts, let the agent help recover the process, then decide yourself what the final story is.

AI Writing Should Strengthen The Author

After these few days, my view is clearer.

Cursor can help produce a first draft quickly. It can also help organize scripts, fill README, and polish explanations. But if we treat it only as an automatic writing machine, we underestimate it.

Its more valuable role is reducing high-friction work:

  • Recover context when the process is hard to remember.
  • Structure scattered material.
  • Explain code that already exists.
  • At the end of a task, fill in documentation, sample configuration, and open-source packaging.

In other words, it improves not only generation speed, but also retrospective density.

That is why I wrote this article. The most interesting part was not only finishing some work on www.zicode.com, nor only open-sourcing a WeChat sync script. It was that I began to treat Cursor as a long-term work medium: development moves inside it, articles start inside it, experience settles inside it, and retrospective writing can reuse the history it already holds.

You are not merely facing a tool that writes answers. It feels more like working with a collaborator that leaves a complete work trail. When the task is done and you return to write, the material is already there.