7 posts tagged with "Reading"

To ask AI a real question about a whole book — where the argument turns, what the author walks back, whether chapter nine contradicts chapter two — the AI has to read the book the way you would: look at the table of contents, decide which chapters the question needs, read those in full, and go back for another if the first did not settle it. Pasting the whole book in and searching chopped-up fragments both fail at exactly these questions.

Every AI reading tool has to decide what happens when the material is longer than the model can handle well. That decision is invisible in the answer, and it determines whether the answer can be trusted.

To study for an exam from lecture slides with AI, first rebuild what the lecturer said around each slide, then ask about the exact diagram or formula you did not follow, then switch from reading to being tested — answering questions before seeing answers. Reading AI explanations feels like learning; being quizzed is what tells you whether it was.

Slides are a strange thing to revise from. They are the lecturer's prompts, not the lecture. The half that explained them lived in someone's head and is gone.

To read a Kindle book on your desktop with AI, you need a DRM-free .mobi or .azw3 file and a reader that converts it into a proper book with chapters. Open it in MindMax and it becomes an EPUB-style book the first time, with the original file left untouched; from there you can ask questions with quotes, or have an AI annotate it chapter by chapter.

The catch comes first, because it decides whether the rest applies to you: books bought from the Kindle store carry DRM, and they cannot be opened. That lock is not ours to remove, and this post does not explain how to remove it.

MindMax's AI annotates a book sparsely on purpose: it marks only where a busy expert would actually stop, tells you up front how often it plans to stop, and two of its four lenses ask you questions instead of handing you conclusions. Fewer, better marks are the ones you reread.

The first reaction to an AI reading pass is often mild disappointment: it went through a whole chapter and left two notes. Surely it could find more than that?

It could. We decided it should not, and the reasoning is worth writing down, because "more annotations" is the kind of improvement that makes a product worse in a way users do not immediately attribute to the product.

To translate a whole EPUB with AI and keep it consistent, settle a glossary of recurring terms before translating anything, translate every chapter against that same glossary in one run, have the result checked for dropped passages and changed numbers, and read it side by side with the original. Pasting chapters into a chat window one at a time cannot do the first two.

Sentence quality stopped being the hard part of machine translation a while ago. Length is.

To OCR a scanned PDF into text you can rely on, recognise it once on purpose, check the result page by page rather than trusting one accuracy figure, re-run only the pages that came out badly, and keep the original scan untouched so every quote can be checked against the page image it came from.

A scanned PDF is a stack of photographs. Until the photographs become characters, nothing can search it, quote it or translate it — and an AI that answers questions about it anyway is answering from nothing.

To read a research paper with AI without being misled, ask for the paper's central claim and what it rests on before you read anything, insist that every point comes with a verbatim quote and a page, check the two quotes that matter most, and only then read those sections properly yourself. The AI finds the way in; you still do the believing.

The usual approach — paste the PDF into a chat window and ask for a summary — is fast and feels productive. It also gives you a fluent paragraph you cannot evaluate without reading the paper, which is the thing you were trying to avoid.