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add blog feature and first post (#15)
* add blog * init blog post * start edit * date and big-D documentation --------- Co-authored-by: EdwardAngert <17991901+EdwardAngert@users.noreply.github.com>
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astro.config.mjs

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import starlight from '@astrojs/starlight';
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import catppuccin from '@catppuccin/starlight';
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import umami from '@yeskunall/astro-umami';
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import starlightBlog from 'starlight-blog';
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// https://astro.build/config
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export default defineConfig({
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dark: { flavor: "mocha", accent: "mauve" },
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light: { flavor: "latte", accent: "lavender" }
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}),
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starlightBlog({
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navigation: "header-start",
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metrics: {
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readingTime: true,
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words: 'total',
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},
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}),
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],
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sidebar: [
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{

package.json

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"@catppuccin/starlight": "^1.0.1",
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"@yeskunall/astro-umami": "^0.0.6",
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"astro": "^5.6.1",
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"sharp": "^0.34.2"
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"sharp": "^0.34.2",
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"starlight-blog": "^0.24.1"
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}
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}

pnpm-lock.yaml

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src/content.config.ts

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import { defineCollection } from 'astro:content';
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import { docsLoader } from '@astrojs/starlight/loaders';
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import { docsSchema } from '@astrojs/starlight/schema';
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import { blogSchema } from 'starlight-blog/schema';
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export const collections = {
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docs: defineCollection({ loader: docsLoader(), schema: docsSchema() }),
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docs: defineCollection({
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loader: docsLoader(),
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schema: docsSchema({
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extend: (context) => blogSchema(context)
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})
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}),
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};
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---
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slug: blog/ai-docs-assist
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title: AI is coming for my job. Maybe to my job.
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date: 2025-12-12
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excerpt:
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"We all--humans and AI--need good documentation."
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---
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While I was working on a pull request for a new [Coder](https://coder.com) feature, there were a few times that Claude Code offered my own documentation or comment on a PR to answer questions I had.
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It was a guess I made in a PR about the way a feature might work, and when I asked Claude Code to verify, it referred to the documentation.
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My documentation.
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My job has always been to empower users to get stuff done smoothly and on their own.
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Now my audience includes AI.
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If humans and AI both rely on good documentation, how do I develop documentation and an information architecture that help both?
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## How I Use AI
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I use Claude and Claude Code both at home and at work.
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On my phone, my chats with AI probably look a lot like most peoples':
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- Is this poison ivy?
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- Which of these water bottles fit in my car's cup holder?
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- What's this broken part of our toilet called?
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- Is this rash poison ivy?
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Claude Code might look a little different:
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- Help me turn my settings into dotfiles.
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- I have a Docusaurus site hosted through GitHub Pages. Let's develop a plan to migrate it to Astro with Starlight.
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- When I ask you to help me work on documentation, remember the following...
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- Help me make a [Claude Code plugin](https://github.com/EdwardAngert/documentation-agent-skill/) that helps you follow my standards for documentation.
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For me, the great promise of AI is that it can make sense of piles of information that would take me hours to untangle and map out.
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The current hype seems centered around how AI can take over tasks like research, software development, and documentation.
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If AI has all the information, can it write the documentation for me?
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## AI Can Document Things; Humans Are Better at Writing Documentation
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For a good example of this, explore [DeepWiki](https://deepwiki.com), which "provides up-to-date documentation you can talk to, for every repo in the world."
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It gives a comprehensive breakdown of a repository, but you won't get concept, guides, steps, and examples.
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Right now, AI is excellent at documenting what exists in the way that it exists, but not as good at thinking about what a user will need.
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When I write documentation, I take all the information I have from my own notes, from product managers, and the several threads from Slack in which someone said `@Edward ☝️`.
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I parse it all, following threads and connecting the dots.
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With AI, I paste all the information into a window, tell it what we're working on and direct the outline of documentation and examples.
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Claude lays out a plan, and if I'm using Claude Code, we dig deeper into the codebase or Git branch for the context I need.
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Context, relationships, and information synthesis all help technical writers make better documentation.
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Usually we get that from subject matter experts (SME), by exploring codebases, or by being in every Slack channel and engineering standup ever.
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If my AI tool has access to all of these, shouldn't it be able to write the documentation itself?
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It tries.
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It can make a great outline, but it seems to have trouble navigating what [kind of technical documentation to write](https://idratherbewriting.com/blog/what-is-diataxis-documentation-framework).
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AI is missing--at least for now--that magical blend of curiosity and empathy that helps technical writers anticipate their audience's needs.
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It's good at parsing content that is there, and it tries to imitate [abductive reasoning](https://en.wikipedia.org/wiki/Abductive_reasoning) as best as it can.
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Maybe that's why it "hallucinates" technical content - it's trying to guess, very much like I would, and it can get to the right answer, but it needs to be babysat, coaxed, coached, and led.
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But while AI agents aren't great at writing technical documentation (yet), they are a great aid.
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Maybe aide is more appropriate.
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Claude Code is my SME, my always-at-hand assistant, my [rubber duck](https://en.wikipedia.org/wiki/Rubber_duck_debugging), and regex expert.
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## How It Started / How It's Going
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I'm sure there will be advancements that feel threatening, and there will be companies that try to eschew a documentation and education function to "have someone else do it," as there always are.
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AI agents are becoming our technical analogues and companions, and the same way that some engineering teams are, technical writers will find ways that they augment and accelerate our work; not supplant us.
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What if we can use this to make things better?
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At Coder, as part of a larger effort to help other teams implement AI into their workflows, I created a [knowledgebase](https://github.com/coder/shared-docs-kb) that everyone could use to find answers and contribute documentation.
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This was before I learned about [Model Context Protocol (MCP)](https://modelcontextprotocol.io/docs/getting-started/intro).
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I want to make it easy for subject matter experts to share their knowledge, and for people who need it to find it.
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Right now, I'm working on developing an [MCP for an open source project that converts weather and climate datasets](https://github.com/EdwardAngert/reformatters-knowledge-base).
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As always, my goal is to develop a repeatable strategy.
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You can follow that repository to see how it goes.
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That's Documentation, and once I get it working, I'm looking forward to hearing about how you implement the concept, whether you learn it here or through a chat with your favorite AI.

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