独立开发者18个月复盘：写完书、AI原型困境与读者反馈工具

New here?

Hi, I’m Michael. I’m a software developer and founder of small, indie tech businesses. I’m currently working on a book called Refactoring English: Effective Writing for Software Developers.

Every month, I publish a retrospective like this one to share how things are going with my book and my professional life overall.

Highlights

• I’ve completed all 22 chapters of my book.
• I thought AI made prototyping faster, but now I’m not so sure.

Goal grades

At the start of each month, I declare what I’d like to accomplish. Here’s how I did against those goals:

Get Refactoring English to “content complete”

• Result: I’ve completed all chapters.
• Grade: A

This has felt like it was a week away for six weeks, so I’m glad to finally have all the chapters done.

Create a tool that allows Refactoring English readers to give feedback as they read the book

• Result: The tool is only about 40% complete.
• Grade: C

This seemed like it should basically be a 2-3-day project, but I realized it’s more difficult than it seemed, especially due to the great blockade.

Refactoring English metrics

Eep, I continue to neglect marketing, and the numbers are suffering for it.

I was desperate to get the last few chapters of the book done, so I focused only on that rather than investing in any marketing.

Bug bounty metrics

I’ve continued pursuing security bug bounties, but I’ve reduced my time on them. I’m not quite doing the 70/30 split I planned, but maybe like 60/40.

The main vendor I’ve been working with paid me another $7k (bringing me to $17k total) for reports, but they’ve slowed down on processing reports, so I’ve mostly stopped searching for new bugs in their code.

I submitted bugs to a few other programs to check if any are processing bug reports quickly, but none of them are:

• KeePassXC - I submitted an RCE to Zero Day Initiative on May 18th, but I haven’t heard any response.
• For KeePassXC users, this isn’t a zero-click attack or something that could compromise your database by just visiting a malicious website, so don’t get too worried.
• Cloudflare - I submitted a DoS / logic bypass via HackerOne on May 22nd. No response.
• Proton - I submitted one low-severity issue. They asked for a video proof of concept, so I made one on May 29th, and they said to wait to hear back.

When is the book “done?”

I’ve completed all the chapters of the book, which is a relief, but I don’t consider it officially “done.”

I wrote the book over the past year and a half, usually focusing on a single chapter at a time. I haven’t ever read my own book cover-to-cover to make sure it’s all consistent. I want to do at least a few complete readthroughs before I call it done.

Why wasn’t I continuously revising the book?

I originally planned to continuously edit the book based on reader feedback. That way, when I got to the last chapter, the book would be pretty much done because the rest of the book would have had so many revisions based on comments from readers.

In reality, I integrated reader feedback far less than I expected.

I found it hard to split my focus between revising past chapters and writing new ones. If I spent a week revising old chapters, it didn’t feel like forward progress. When I added a new chapter, it meant that my public progress meter got a little fuller, which was motivating.

Progress meter from book website

The other reason I didn’t continuously revise is that I didn’t reach out to readers as much as I planned. Part of that is that I constantly felt behind on the book, so there was always a sense of, “I want to get this chapter out, and then I’ll invest more into reader outreach.”

But even when I reached out to readers, it rarely impacted the book. The most common responses from readers were, “I like the book” or, “I haven’t started it yet.”

When I did get detailed feedback, I wasn’t always sure how to integrate it. In some cases, I agreed with the feedback, so it was an easy decision. Usually, though, the reader would suggest adding something that I didn’t think was necessary. And that’s not to say the reader was wrong, but I’d want to see a pattern in reader feedback before I go against my intuition, and I wasn’t getting enough feedback to see a pattern.

My reader feedback tool

Now that I’ve completed all the chapters, I feel like I have more space to reach out to readers.

I like the idea of Help this Book, a web app that allows readers to give feedback directly in your ebook, but I didn’t want to store all of my feedback with a third party and pay monthly rent.

I saw that Julia Evans made her own reader feedback tool, customized to her products, and I thought that was neat, so I’m working on that.

Your browser does not support the video tag.

I’m working on a web app to make it easier for readers to give me feedback about my book.

AI projects and the great blockade

Overall, I’ve found that AI makes me more productive when programming. There are certain tasks like resolving git merge conflicts, debugging unfamiliar code, or making simple tools where AI is a clear win.

I used to think AI was great at helping me start projects, but now I’m not so sure. I keep hitting what I call “the great blockade.”

Just have AI make the prototype

Six months ago, I’d give the AI agent a high-level overview of what I wanted and tell it to implement a basic v1 implementation. I knew the agent’s output would be messy, but it was just a prototype, so I could keep giving it feedback until it matched my programming sensibilities.

It turns out that it’s harder than I expected to clean up a bad prototype. Once the prototype is bad enough, I have a hard time untangling what the code is even trying to do.

AI seems to have a weird bias to justify whatever code is already present. If I tell the AI that a component seems confusing because it’s iterating over the same data three times, it just keeps insisting we have to iterate over the data three times because of X, Y, and Z. But it never questions whether X, Y, and Z are artificial constraints.

This is the blockade. I get stuck trying to move beyond a giant wall of confusing code that AI constructed.

If I don’t fix the core logic, the problem keeps getting worse. The code smells grow like fungus and spread throughout the codebase. I’m building on top of a weak foundation, and the AI just keeps duplicating bad patterns that already exist.

Break down the prototype

Okay, easy fix: have the AI agent create the prototype in smaller pieces. Keep the AI on a tighter leash so it can’t go so far into the weeds. Instead of having the AI create the whole prototype, have it start with a welcome page. Once that’s reviewed and merged, add one simple feature, and so on.

That works fine until I get to a complex chunk, like authentication. AI creates a pull request that’s 2-5k LOC of confusing code, and that becomes a huge wall. I can’t think of a way to break down the feature any further, so I’m stuck with this massive PR, another great blockade.

Not only does a 4k LOC change take 20x as long to review as a 400 LOC change, but it also requires larger review windows. If I have a 20-minute block available, I can tackle the 400 LOC change, but if I have a 4k LOC change, I need 20 minutes just to build up context. To make meaningful progress on a 4k LOC change without wasting most of it on context friction, I need a 90-minute window, which is hard to come by especially for weekend projects.

Example: Implementing authentication for Little Moments