In 2023 I bought DDIA on Kindle. Opened the replication chapter. Quit after 40 pages and didn't open it for six months.

In 2024 I bought it again, because the book is clearly worth finishing. Got to page 80. Closed it.

In 2025 I tried a third time with ChatGPT open in another tab to explain the hard terms. It got easier. But every lookup was the same loop — alt-tab, paste the sentence, wait, come back, find my place. After three chapters I wasn't really reading the book anymore. I was reading my own habit of switching tabs.

The book still sits in my Kindle library, marked unfinished. If you have a book like that on your shelf, this post is for you. I finally figured out why I kept quitting, and built a tool that fixes it for me. Maybe it fixes it for you too.

What was actually breaking

When I quit for the third time, I sat down and tried to be honest about what was stopping me.

It wasn't that the book was too hard. I understood most of what was on the page. The problem was the rest — the unfamiliar terms.

Every unknown term forced a decision between two bad options.

Option one: stop and look it up. Alt-tab, paste the sentence, wait, come back, find my place. Flow broken. The next paragraph is harder to hold in your head.

Option two: skip it and hope context saves me. Sometimes it does. But after a dozen skips in a chapter, the quality of my reading drops noticeably. And each "I'll figure it out later" turns into debt.

The exhaustion wasn't coming from reading. It was coming from the constant small decisions.

There was a third problem too. Even when I did look something up, a week later I'd forgotten it. ChatGPT doesn't remember you asked. Anki remembers, but making cards by hand is its own pile of friction. I was learning words in order to forget them. And reading books in order to quit them.

What I got wrong about AI and reading

When ChatGPT arrived, a lot of people thought long books were dead. Why read 600 pages of DDIA when you can ask and get a summary in a minute?

I believed that for about a year.

Then I sat in a 2025 interview being asked about replication strategies in distributed systems, and realized I couldn't explain the difference between synchronous and asynchronous replication past surface-level buzzwords. I'd read dozens of summaries, listened to podcasts, watched YouTube breakdowns. I knew things on the surface. I didn't understand any of them deeply.

For staying current, summaries are fine. For real understanding, nothing replaces sitting with a book that someone spent years structuring. Those are exactly the books I kept quitting around page 40.

What I built

In January 2026 I started building what became TextStack — a reader where I could read technical books without the tab switching.

The idea is simple. Tap a word you don't know. An explanation appears inline — not a dictionary entry, but a short concept explanation from Claude that takes into account what the book is about and what the sentence is doing. For everyday words, a short translation. For technical terms like RLHF, attention mechanism, or eventual consistency — two or three sentences on what it is and why it matters, with links to related ideas and common confusions.

The word goes into a personal dictionary automatically. But not the way LingQ does it, where your review queue grows to hundreds of items and you quit the app. I built a filter — only words from roughly the top 15,000 English words by frequency, or technical terms, enter spaced repetition. The rest are saved as reference. The weekly review queue is capped, so it never spirals.

Over three and a half months I put together a working version on .NET 10, React, and React Native. PostgreSQL, Claude API for explanations, Edge TTS for audio, offline PWA. It ingests EPUB, PDF, and FB2. The catalog started wide, but I'm pruning it hard — I'm realizing focus matters more than I thought.

It lives at textstack.app — full pitch at the end of this post.

What I got wrong for three months

For the first three months I was building for an abstract "non-native English speaker who wants to read books." Nobody needs that.

In April I looked at it honestly and asked who I'd actually built it for. The answer was: a developer trying to read AI engineering books. Because that's what I'd been trying to read for two years. Chip Huyen's AI Engineering. Hands-On Large Language Models. Designing Machine Learning Systems. Building Agentic AI Systems. Prompt Engineering for LLMs. I bought all of them. I finished none.

When I looked at other developers' reading lists online, I saw I wasn't alone. A lot of developers are trying to move into AI engineering right now. We're all reading the same books, and a lot of us aren't finishing them.

This isn't a generic "non-native English" problem. It's a specific problem for a specific group going through a specific career transition.

So I'm pivoting. Not "a reader for everyone." A reader for developers learning AI engineering. A narrow niche where I'm already the user.

The next six months

Four things.

1. Rebuild the product around the AI angle. Trim the catalog to 15–20 AI engineering books. Rewrite the homepage. Shift the framing from translation to explanation. Improve the prompts for technical terms.

2. Actually start reading. Hands-On LLMs in May. AI Engineering in June and July. Building Agentic AI Systems in August. Not as a task — as something I want. I want to work as an AI engineer in two years, and the only way there is through these books. I'll read them inside TextStack, because if it doesn't work for me, it won't work for anyone.

3. Write about the process. This is the first post. If you want to follow along, the blog has RSS.

4. Find the first paying customer.

I'll say it openly: if in six months there's one stranger paying for TextStack, I'll consider this project a success regardless of the other numbers. The first dollar from someone you don't know is a threshold most solo devs never cross. Crossing it is a big part of the work of leaving employment.

Try it

Live at textstack.app — you can open a sample chapter of Pragmatic Programmer or Hands-On LLMs without signing up.

If you're in a similar spot — non-native dev, bought the AI engineering books, didn't finish them — send me a note. Twitter: @Rexetdeus. Email on the site. I'll give you early access and listen to what works and what doesn't. In exchange I need honest feedback.

If it's not your thing, thanks for reading this far. If someone you know is stuck on Chapter 3 of AI Engineering, maybe forward them this post.

P.S.

One more thing. This problem — quitting hard books at page 40 — isn't really about English and isn't really about AI. It's that reading tools are stuck in the early 2010s while content has gotten much denser.

Kindle Word Wise is from 2014, and it still shows single-word definitions that can't handle eventual consistency or attention mechanism. LingQ has been showing translations and adding words to SRS for close to two decades, and the core experience hasn't really changed. Readlang was a clever browser extension in 2013; development stopped when the founder went to Duolingo.

Modern books need different tools. Not dictionaries — explanations. Not infinite queues — capped ones. Not one experience for everyone — context-aware understanding.

That's the opening I'm walking into. I'll let you know in six months how it went.

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First post in a series about building TextStack as an AI engineering books reader. Star the repo if you want to follow along: github.com/mrviduus/textstack · textstack.app