Common pitfalls when building generative AI applications
As we’re still in the early days of building applications with foundation models, it’s normal to make mistakes. This is a quick note with examples of some of the most common pitfalls that I’ve seen, both from public case studies and from my personal experience. Because these pitfalls are common, if you’ve worked on any AI product, you’ve probably...
Building A Generative AI Platform
After studying how companies deploy generative AI applications, I noticed many similarities in their platforms. This post outlines the common components of a generative AI platform, what they do, and how they are implemented. I try my best to keep the architecture general, but certain applications might deviate. This is what the overall...
Measuring personal growth
My founder friends constantly think about growth. They think about how to measure their business growth and how to get to the next order of magnitude scale. If they’re making $1M ARR today, they think about how to get to $10M ARR. If they have 1,000 users today, they think about how to get to 10,000 users. This made me wonder if/how people are...
What I learned from looking at 900 most popular open source AI tools
[Hacker News discussion, LinkedIn discussion, Twitter thread] Four years ago, I did an analysis of the open source ML ecosystem. Since then, the landscape has changed, so I revisited the topic. This time, I focused exclusively on the stack around foundation models. The full list of open source AI repos is hosted at llama-police. The list is...
Predictive Human Preference: From Model Ranking to Model Routing
A challenge of building AI applications is choosing which model to use. What if we don’t have to? What if we can predict the best model for any prompt? Predictive human preference aims to predict which model users might prefer for a specific query. Table of contents Ranking Models Using Human Preference …. How Preferential Ranking Works …....