You have a meeting scheduled next week with an expert in product strategy. You've been looking forward to this conversation for weeks. You know you've read extensively about this topic—product development, user research, positioning, pricing strategies. You remember highlighting brilliant insights from at least five different books over the past few years.
But now, as you try to prepare, you face a familiar problem. Which books were those again? Was it in "The Mom Test" or "Inspired"? Did Marty Cagan say that, or was it Teresa Torres? You vaguely remember a powerful framework about prioritization, but you can't recall where you read it or what the complete framework actually was.
You have years of reading and hundreds of highlights on this topic, but they're scattered across your entire library, nested within different books, chapters, and contexts. You could spend hours manually searching through books, trying to reconstruct what you've learned. Or you could go into the conversation underprepared, hoping the right insights surface in the moment.
This is the problem we're solving with our new cross-library workflows.
Imagine instead that you open DeepRead and start a "Topic Memo" workflow. The AI guides you through a structured conversation, asking one question at a time. First, it asks what topic you want to prepare for. You enter "product strategy."
The AI scans your entire library and suggests books where you've highlighted content related to product strategy. You see familiar titles—some you'd forgotten you'd read. The AI asks if you want to include all of them or focus on specific ones. You select the most relevant books with a simple click.
Next, the AI identifies the key themes across all your highlights on this topic. It suggests organizing your memo around themes like "User Research Methods," "Feature Prioritization," "Product Vision," and "Market Positioning." You confirm these themes or adjust them. The workflow makes each decision easy with clear suggestions.
Within minutes, the AI generates your topic memo. It's a structured document or an interactive mind map with your key themes as sections. Under each theme, you see relevant insights from different books and authors, organized and synthesized. Direct quotes from your highlights are included with source attribution. Discussion points and questions are suggested based on patterns it noticed in your reading.
You now have a tangible preparation document that represents your complete understanding of product strategy—not just what you remember, but everything you've learned and highlighted across your entire reading history.
This memo becomes part of your growing knowledge system. It's searchable. The AI has access to it in future conversations. When you read another product strategy book next month and create a new memo, the AI will know what you've already synthesized and help you build on it.
You might be wondering how this is different from uploading some highlights to ChatGPT or Claude and asking it to summarize them.
The key difference is permanence and scope. In our new setup, the AI has access to your complete library—every book title, the structure of each book with all its chapters and sections, and all your highlights nested within that structure. Not just what you actively uploaded to the chat. There are no context window limitations forcing you to choose which highlights to include or exclude.
More importantly, every tangible output you create—whether it's a topic memo, a mind map, or idea cards—becomes part of your knowledge system. These outputs are searchable and indexed. The AI has access to them in future conversations. Each interaction doesn't just give you an answer that disappears when you close the chat window. Each interaction builds something concrete and makes your entire system more valuable.
When you create a topic memo today and then read a new book on that topic next month, the AI knows what you've already synthesized. It can help you integrate new insights with your existing understanding. Your knowledge system grows with every interaction, and each artifact you create makes future work easier and richer.
The topic memo workflow is just one example of what becomes possible when AI has access to your complete reading history. We're developing several cross-library workflows that help you do things with your highlights that weren't possible before.
I discovered the value of structured workflows during my own audiobook experiments. While working through "Broken Money" by Lyn Alden using only audio, I developed a process for creating comprehensive mind maps and summaries. What I learned was that having a clear workflow—a structured conversation with the AI aimed at creating something specific and tangible—makes all the difference. You're not just chatting with AI. You're building something concrete that becomes part of your knowledge system.
We're building these workflows now, and we want to build them with you. We're looking for a small group of early users who are interested in testing these features and providing regular feedback. If you're excited about turning your years of highlights into something you can actually use, or if you have ideas for workflows you'd like to see, we want to hear from you.
Email me at [email protected] with your interest or ideas. We're building both the features and the community together.
