Current Project: Reading books about entrepreneurs and sharing what I learned from them

Mission: Create a library of wisdom from notable entrepreneurs that current entrepreneurs can leverage to increase their chances of success

Cumulative metrics (since 4/1/24):

• Total books read: 36
• Total book digests created: 14
• Total blog posts published: 217
• Total audio recordings published: 103

This week’s metrics:

• Books read: 1
• Book digests created: 1 (using technology)
• Blog posts published: 7
• Audio recordings published: 0

What I completed this week (link to last week’s commitments):

• Read David Ogilvy’s autobiography
• Read highlights from David Allen’s Getting Things Done
• Read highlights from Tiago Forte’s Building a Second Brain
• Read two resources on prompt engineering
• For my “book library” MVP, created a separate agent and RAG to index each book instead of one agent and RAG to index multiple books
• Tested prompts and system instructions to improve the quality of responses from the “book library” MVP
• Created one book digest using the “book digest” MVP
• Tested prompts and system instructions to improve the quality of digests created using the “book digest” MVP

What I’ll do next week:

• Read a biography or autobiography
• Test alternative agent setups with RAG for the “book library” MVP
• Ask AI developers about RAG alternatives
• Create a book digest for David Allen’s Getting Things Done using the “book digest” MVP

Asks:

• None

Week two hundred forty-one was another week of learning. Looking forward to next week!

Current Project: Reading books about entrepreneurs and sharing what I learned from them

Mission: Create a library of wisdom from notable entrepreneurs that current entrepreneurs can leverage to increase their chances of success

What I struggled with:

• No material struggles this week.  

What I learned:

• Getting the “book library” MVP to provide quality results that add value is the priority. After that’s accomplished, I can start thinking about how to put it in the hands of other users. Trying to figure out the path to allowing others to use it publicly was premature. I need to get this thing working and adding value first; then I can figure out how to share it.
• I’ve been reading up on retrieval augmented generation (RAG) because the MVP isn’t working as intended. RAG has more limitations when you feed it a ton of information (e.g., multiple books) than I initially thought. It struggles to make connections between related information, but that’s essential. If the MVP can’t do that, providing value-added responses will be hard.
• There’s a good chance that the Google Cloud Platform throttling of my account is impacting the depth of results I get. This is frustrating because you don’t get a warning or confirmation of throttling.
• AI is good at many things, but it isn’t yet good at making sense of large, unstructured text data sets like books. Creating a structure or taxonomy for this kind of data could unlock what AI can do with it.
• Google makes it easier for nontechnical people to test with and tune Gemini large-language models (LLMs). The throttling has me thinking about adding LLMs from other companies into the testing.
• The more I learn from this project, the more I respect the human brain and its ability to store and process information from books.

Those are my struggles and learnings from the week!

This week, I worked on the “book library” MVP. My goal is for the MVP to mimic an entrepreneur who’s an avid reader with a photographic memory. I want to query across multiple books, and I want the MVP to make connections that uncover new insights. I also want it to recall any book’s details quickly.

I’m using retrieval augmented generation (RAG) and Gemini LLMs in the MVP. Last week’s testing yielded responses that weren’t detailed enough and didn’t uncover new insights. I was happy to have something working (it felt like a big milestone), but it still needed work. This week I tried using a different RAG setup—a separate agent and RAG to index each book instead of one agent and RAG to index multiple books. I also tested different prompting and system instructions. The changes didn’t improve the responses, which was frustrating. Still too high-level and unable to make insightful connections.

I’m not sure why this is happening. My developer friend and I have a few theories. It could be a limitation of RAG not being great at indexing entire books. It could be limitations with Gemini LLMs, technical limitations imposed by Google Cloud Platform (GCP), or something else. Given that the output I’ve been able to generate from Google AI Studio for an individual book has been pretty detailed, we think there’s a high probability it could be a GCP limitation.

This wasn’t the outcome I was hoping for at the beginning of the week, but that’s part of the process when you’re building something that hasn’t been done before. Definitely frustrating, but such is life. We’ll do more testing to try to figure this out.

I’m using Google AI Studio to run one of the MVPs for my book project. Google’s AI is called Gemini, and there are eight different Gemini large-language models (LLMs). Determining which would yield the best result was a concern. Google had thought it through, though: AI Studio has a compare feature: you can ask a question and select two LLMs, and Gemini will provide responses from both of them in a side-by-side view.

I’ve been testing prompting and system instructions this week, and the compare feature has been helpful. Seeing how the different LLMs respond to the same question is helping me narrow my choices faster.

Google AI Studio has limitations, but it’s a good tool for someone who is nontechnical and wants to fine-tune their AI experience.

I’ve spent the last few weeks diving into Google’s AI Studio, NotebookLM, Vertex AI Agent Builder, and various other AI-related tools from Google and other companies. A developer friend has helped me a lot. I was aware of some of these technologies from reading about AI and LLMs in general, but now that I’m trying to use them to create solutions for my personal project, my understanding of them has gone much deeper.

I have a clear idea of what I want the technology to do. I’m trying to figure out if it can specifically do what I want. If so, what are all the ways? What are the implications of each option? What I learn sticks in my memory. This is different from my normal approach of poking around to understand a tool’s general capabilities, which doesn’t result in good retention.

I’ve also noticed that when I seek help from technically oriented people to learn new technologies, describing the problem and how I want to solve it helps tremendously. It gives them a better idea of where to start, and the conversation is more focused on solutions to my problem than on a broad overview of the technology.

I’m not sure which, if any, of these technologies will be part of the solutions I build. But I’ve learned something: If I have a problem I’m excited to solve, I should try using new technologies to create a solution. Worst case, I’ll gain a better understanding of the technologies. Best case, I understand the technologies better and create a solution to my problem.  

The IPO market for technology companies has been slow (see here). I’ve been curious why that’s the case (see here). Klaviyo is a known technology company that IPO’d in September 2023. I came across an interview with the CEO and co-founder, Andrew Bialecki. The interview caught my attention because he discusses initially bootstrapping and growing to over $1 billion in revenue and a market capitalization (i.e., valuation) of over $10 billion as of this writing.

One section of the interview addressed what he thinks the criteria are for technology companies to go public or, said differently, what a company needs to demonstrate to get public market investors to buy its stock and have a successful IPO. Here are the criteria:

• Positive free cash flow – the company needs to generate, not consume, cash.
• Sustainable business – The company provides a product or service that customers will value in future years.
• Durable growth – The company must be growing at a healthy rate. The smaller the revenue base, the higher the growth rate investors want to see. The growth rate must also be durable for the next four or five years.

Growing at a rapid rate that’s durable while not burning money isn’t easy to do. Many technology companies can achieve high growth rates, but they burn a ton of cash to accomplish this.

Bialecki’s perspective on the current IPO market for tech companies is valuable, given he’s one of the few who has successfully completed a technology IPO in the last two or so years.

He shares other great nuggets during the interview. If you want to hear just the section on his thoughts on IPOs, see here, but I found the entire interview worthwhile.

Last week, I shared that I wanted to finish Master of the Game: Steve Ross and the Creation of Time Warner by Connie Bruck and another book. Well, I fell short. I finished reading the Ross biography and started—but didn’t finish—another book.

The goal was aggressive and I wanted to check the box, but I didn’t. No excuses. I just came up short. I put a good effort toward the goal but ran out of time.

This week, I’m going to focus on finishing a single book. Every time I try to do more than that, I regret it.

Current Project: Reading books about entrepreneurs and sharing what I learned from them

Mission: Create a library of wisdom from notable entrepreneurs that current entrepreneurs can leverage to increase their chances of success

Cumulative metrics (since 4/1/24):

• Total books read: 35
• Total book digests created: 14
• Total blog posts published: 210
• Total audio recordings published: 103

This week’s metrics:

• Books read: 1
• Book digests created: 2 (using technology)
• Blog posts published: 7
• Audio recordings published: 0

What I completed this week (link to last week’s commitments):

• Read a biography about Steve Ross, founder of Time Warner and Warner Communications
• Added two books to my “book library” MVP
• Tested prompts, system instructions, and LLMs to improve the quality of responses from the “book library” MVP
• Created two book digests via my “book digest” MVP
• Tested prompts, system instructions, and system settings to improve the quality of AI-generated book digests

What I’ll do next week:

• Read a biography or autobiography
• Read my highlights from David Allen’s Getting Things Done
• Read my highlights from Tiago Forte’s Building a Second Brain
• Read two resources on prompt engineering
• Test different prompting for the “book digest” MVP
• Test adding a book’s contents to the “book library” MVP in different ways to improve response quality
• Identify the path to launching the MVPs publicly so others can test them

Asks:

• None

Week two hundred forty was another week of learning. Looking forward to next week!

Current Project: Reading books about entrepreneurs and sharing what I learned from them

Mission: Create a library of wisdom from notable entrepreneurs that current entrepreneurs can leverage to increase their chances of success

What I struggled with:

• No major struggles this week, just frustration. I couldn’t allocate as much time to this project as I wanted to. I had commitments to other projects that I had to make my priority this week.

What I learned:

• Google’s Gemini large-language models (LLMs) don’t provide in-depth responses when you feed them multiple books via retrieval augmented generation (RAG). When they’re fed a single book, the responses are much better. Feeding too much “raw” information at once is counterproductive.
• I described the problem I’m solving in a way that “feels” more accurate to me and that resonated well with investors when I pitched using it: “Entrepreneurs with photographic memories who read a ton have a superpower. I didn’t win the genetic lottery for photographic memory. I’m trying to solve that problem.” See this post for more details. I wonder how many other entrepreneurs have the same problem and are aware of it?
• My excitement about this project has jumped since my developer friend began helping me. Having someone with contrasting skills to talk with and work through hurdles with has made a noticeable difference.
• I haven’t addressed what to do with the highlights I make in a book. I want to figure out how to feed them into an LLM.
• I need to retain what I’ve highlighted. Maybe create something that helps me review highlights. Or maybe pump my highlights into an app built for that purpose.
• Prompting is a big deal, and I have a lot to learn about creating better prompts.  

Those are my struggles and learnings from the week!

I was chatting this week with an entrepreneur who’s trying to figure out how to solve a problem outside his expertise. This founder is facing the starting-from-scratch dilemma. He’s trying to solve a problem and figure out what his next action should be, but it’s so far outside his domain he doesn’t even know where to start.

Experience gives you the best shot at resolving a problem or determining your next action. You can acquire experience by doing things yourself (and often failing) or by learning from what other people learned by doing things. When I’m starting from scratch, I like the second option.

I crystallize the problem I want to solve and write it down. Then I find material from people who’ve solved the problem and had outsize success. Books are my preference because they’re long form and more thought out. But if I’m trying to do something tactical or something that requires recent experience, I listen to podcasts where the person explains what they did (not someone else giving their opinion or interpretation). I want the wisdom straight from the horse’s mouth. If possible, I try to learn from the experiences of several people.

This usually solves the starting-from-scratch issue. It doesn’t magically give me a solution to my problem or tell me what action to take, but it often gets me 70% to 75% there.

I then try to figure out how to apply what I learned from others to my situation. Copying exactly what they did usually doesn’t work because their situation was different from mine. Once I figure this out, I’m close to 100%. I know where to start, and I have a good idea of how I want to solve the problem and what the next action to take is.

I still fail and learn more along the way, but the entire process is much faster because I’m not learning foundational lessons from the ground up. Instead, I’m taking what works and building on it.

The next time you need to solve a problem or figure out what action to take but don’t know where to start, consider getting clear on the problem you’re trying to solve (write it down) and finding books or other content from people who’ve solved that exact problem. Learn from their experiences and try to figure out how you can apply what they learned to your situation. It isn’t perfect, but it’s a lot quicker than the alternative.