Writing on enumerator.dev

How I use AI When Writing

Mon, 21 Sep 2026 00:00:00 +0000

I use AI when I write for work.

And like you, I hate slop.

Despite LLMs writing like someone I would never want to spend time with, I still find some of their output useful.

They speed up research, even if they are confidently wrong.

And they are mediocre grammar checkers and good enough document structure editors, even if they are obtuse.

My Writing Standards

When My Writing is Personal

I don’t write personal things with AI. This includes text messages, emails to friends and family, and Slack messages, comments on documents.

When I Write for this Blog

Most of my writing on this blog is free form with limited edits. I hit publish and then find a mistake when I read it on my site and I’m quite okay with that.

However, I use LLMs to assist with research and validation. Every now and then, I’ll do a double check on grammar and structure with a writing tool like LanguageTool or Grammarly, and I’ll occasionally accept phrasing that an LLM suggests if it suits my needs.

When My Writing is An Artifact for Work

At work I write documentation and proposals that are artifacts the company can discuss and work with.

I use today’s AI tools to research ideas, validate my assumptions, fact-check my work, and edit the document’s structure. More on this later.

When Writing Generates Code

I write to AI agents in the form of chat interfaces, documents, and structured prompts to generate code. I use all the tools available to me:

Type out my chat prompts by hand.
Dictate through a voice-to-text model.
Dictate through a voice-to-text model that is passed through an LLM to generate a structured output.
Have an agent generate a document, then have a second agent read and validate that document.

Essentially, there are no rules when I write in order to generate code. I don’t even consider most of this “writing.” But it does result in generated text that has my name attached to it.

How I use AI to Write an Artifact

Use a number of agents to do research. This often involves getting a coding agent to research and test ideas in a code repository, sending an agent with computer use off to do web research and produce a document, or chatting with an LLM to validate ideas.
Save agent-generated artifacts. Between chat sessions, I get the agents to save artifacts other agents can find. Coding agents write docs, desktop agents find and read those docs and generate text files or docs through an MCP connector, and chat agents generate text I copy-paste elsewhere.
Write a doc in my own words. After iterating with agents to build an understanding of what I’m writing, I start writing in my own words. The result is a document that is well-researched and thought-through. And very human. It has my tone, thought process, and mistakes.
Edit with an LLM. I have two prompts that I use to edit both my work and docs the agent generates. For my own writing, I prefer to make the edits myself. Occasionally, I agree with the LLM’s feedback but am stumped on how to make a change, so I’ll ask for ideas.
DON’T TRUST THE LLM. I use my judgement when the LLM gives me feedback. Its generated response often has good points that slightly miss the mark. I take those into account and use my own words.
Use LLM-generated text when it helps. It feels like I’ve admitted to a mortal sin here. I am writing for business, and LLMs are trained on business applications. I have no problem copy-pasting a sentence from an LLM at this point because I’ve done the legwork and the LLM is helping with polish.
DON’T ARGUE WITH THE LLM. It is going to generate text that makes no sense. Ignore this. When I have tried to correct things the LLM gets wrong, it begins to misunderstand the task overall. I often tell it what was good and then say something like “You can do better than this, I know you can. Give it a second, more thoughtful pass.” If is WEIRD to say things like that to a computer, but it works for me.
Share my work. ~~I share a document with two sections. The first is my writing. The second is a summary of the LLM’s research, generated by the LLM. I flag this as AI-generated content in a BIG banner at the top.~~ [EDIT: Oct 3, 2026] I no longer find value in the AI generated section. I share my writing with a notice at the top explaining the document was written by me with AI assisted research.

AI Agent Prompts

Research Prompts

In my research prompts, I tell the agents my objectives, opinions, and assumptions. This context helps the agent focus on the outcome I am asking for. It also forces me to think about my opinions and assumptions. I expect the agent to confirm my assumptions and push back on my opinions. If it doesn’t do this, I ask it to.

Here is a template of a prompt I use:

I am researching <feature>. I need you to research <technology> in this
repository and write a summary that explains the current state.

Focus on <area of code>.

Ignore <similar but irrelevant area of code>.

My objective is to <write your objective>.

My opinion is that <what I think the research will reveal and what decision
I lean towards already>.

My assumptions are <how the repo works, how the technology works with the
repo, etc.>

Prompts for Editing

I have been working on a skill I call “Writing for Understanding” that is based on a number of accessible writing standards from government websites and based on my own experience. I pass all of the writing through this with the following prompt:

/writing-for-understanding read @doc and give me feedback one bullet
point at a time. Don't edit the doc. I will make the edits myself.

I also use Anil Dash’s Better Documents with a similar prompt. This is where I often get stuck! The LLM makes good recommendations based on Anil’s skill, but I sometimes don’t have an idea at my fingertips. In this case, I ask the LLM for ideas for restructuring the doc.

I prefer to make these edits myself, even if it means copying some AI-generated text into my final output.

Everything was done with an ASCII editor

Mon, 31 Aug 2026 00:00:00 +0000

The Super Metroid – FAQ/Speed Guide is 17,000 words of perfectly-spaced, full justified, mono-spaced text. It’s beautiful to look at.

From the FAQ

What program did you use to justify the text?

None. I just chose words carefully so that everything lined up on the right hand side. Everything was done with an ASCII editor.

via Unsung via Hacker News

This reminds me of the hours I spent in a trance tracking, kerning, and editing text to create a triangular brochure that folded out into a tessellation with full-justified text that fit perfectly into each repeated triangle.

LLMs Don't Write, They Generate

Tue, 18 Aug 2026 00:00:00 +0000

Everyone is mad about AI watermarking.

The thing is.

These machines don’t write.

They generate text.

Writers write. Machines generate. LLMs can watermark text without affecting the quality of the output because they are benchmarked on the usefulness of their output rather than the meaning of the text.

LLMs don’t reflect on meaning, consider their audience, and convey thoughts with care.

LLMs generate a probable output to the input. That’s all.

AI companies don’t value writing because they’ve never thought about writing. AI companies value the likelihood that the machine produces useful output. Not a meaningful output.

LLMs have gotten really good at generating probable outputs that are productive.

But it’s still probable. And only mostly productive. But rarely meaningful.

Saying that an LLM writes is as accurate as saying a lawn mower gardens, an airplane vacations, or a microwave is a chef.¹

Ha! Oh dear. I wanted to write the word “cooks” here but I can’t. Is there another word for “cooks with great culinary skill” in English? Perhaps language is changing and LLMs do, in fact, “write” with the same skill that a microwave cooks. ↩︎

Tenets of Storytelling: How to Write a Technical Proposal

Thu, 13 Aug 2026 00:00:00 +0000

Sophie Alpert recently posted her internal policy on the acceptable use of AI for writing technical documents, which included this line:

More time should be spent authoring a document than consuming it.

Similarly, Over at The Marginalian Maria Popova has been posting about reading more and writing better.

This week, she posted Kurt Vonnegut’s 8 Tenets of Storytelling, which includes a similar line:

Use the time of a total stranger in such a way that he or she will not feel the time was wasted.

Vonnegut’s tenets can teach us something about technical writing, especially these days when an LLM can generate a seemingly polished technical document in seconds.

I’m sure Vonnegut would loath being a technical writer and despise being quoted for technical writing.

Nonetheless, here are Vonnegut’s eight tenets rewritten by yours truly as tenets for writing technical proposals. Unsurprisingly, some of these tenets work, word-for-word.

Use your colleagues’ time wisely. They will thank you for it.
Give your audience at least one thing to be excited about.
Every component should do something useful, even if only to write a log.
Every sentence must do one of two things — reveal details or propose action.
Start as close to the solution as possible.
Be a Sadist. No matter how much you or your team love a feature, show its faults so you can make good decisions.
Write to please just one person. If you solve every technical problem, your proposal will get pneumonia.
Give your readers as much information as possible as soon as possible. To hell with suspense. Readers should have such complete understanding of what is going on, where and why, that they could finish the design themselves, should entropy degrade the last few bytes.

Why I Write When AI Already Knows the Answer

Thu, 03 Jul 2025 11:47:00 +0000

I have always been a writer. I haven’t kept all my writing, but I’ve always enjoyed it. Many of my recent blog posts are topics that are easily answerable by AI. I’ve asked myself, why write these posts when an AI can already answer these questions?

Why I Write

I write to understand. To explore. And to see things from a new perspective. My writing isn’t about telling people how things work so much as it is about taking readers on a journey with me. In my post about choosing a managed Postgres instance, I could have answered the question in the title in one sentence: I decided on a managed Postgres instance because it is more reliable and has better data retention guarantees.

But that would have been boring, and an AI could have told you that easily.

Writing that post was, for me, about taking readers on a journey and helping myself understand the choices I made. I write to understand myself, to explore the choices I’ve made, and to learn if there are better ways of doing things.

As I write, new ideas come to mind, and I ask myself new questions about the code and infrastructure choices I made. I research as I write to confirm my choices and verify my assumptions.

AI Already Knows the Answer

I use AI tools for all my development workflows. I type paragraphs more than I write code these days, and I spend more time reading code than I do writing it. AI tools have brought back the joy of making things because I can iterate, make mistakes, and recover quickly. I even watch AI agents make mistakes for the sole purpose of learning about those mistakes and the rabbit holes they lead to.

AI agents can provide great answers and excellent guidance. But they’re not always correct. And they rarely have the context of the whole application. Even in a small project like willow.camp, I had to coax the agent to look in the right places to understand what we were working on, and I had to remind the agent about the intended outcome.

AI Already Knows the Answer, but it also knows 5 or 10 other somewhat correct answers that might apply to different situations.

Had I asked an AI, “Should I use a managed instance?” it would probably say, “Yes,” with some caveats. The decision is still up to me because I know how I want willow.camp to work.

I write because understanding the full context of the project is essential to making good technical decisions.