Books I’ve read as an adult. This does not include textbooks. Reading Log 2024 Breathrough Advertising | Eugene Schwartz This book is free money. Completely changed my perspective on marketing as a whole, and what marketing fundamentals really are. Recommended by a good friend of mine. For anyone looking to start their journey in sales or DTC ecom, I would recommend this book. 2023 Elon Musk | Walter Isaacson Quick and easy read....

While writing this, I thought: 2023 wasn’t really anything special. After writing this out, I think it was the most intense impactful year in my life. Let’s do a recap: January On January 10th, I had a call with one of my friends at 3AM. The next day, I caught up with another friend. We ended up talking about the same shit. We all realized: we did not want to work for the rest of our lives we all wanted financial freedom for the same reasons: giving back to family, comfortable living, generational wealth for your kids and their kids, and to live like a fucking G when you’re young in the one life you’re given and that we were all fucking brokies Snippets of the conversations stood out to me so much I even wrote it down on a GDoc I titled “3am manifesto”...

I just finished debuggging for several hours over this CORS middleware issue: XMLHttpRequest at ‘https://xxxxxxxxxxxxxxx.ngrok-free.app/fastAPI_endpoint' from origin ‘http://localhost:5173 or your netlify deployment URL’ has been blocked by CORS policy: No ‘Access-Control-Allow-Origin’ header is present on the requested resource. Detailed Stack I was using: fastAPI to route backend requests uvicorn to run the server, hosted on default 127.0.0.1:8000 axios to send requests from the frontend TypeScript/React with Vite for frontend ngrok (free tier) to reroute a public https URL to the server address I explored $10 worth of GPT-4 assistance, all relevant StackOverFlow forums, and these close-but-no-cigar Github threads: One, Two....

This post will dissect and offer a brief overview of fundamental ML concepts, and rigorously explain some key principles of deep learning. This is targeted for readers with a high level understanding of ML but lack fundamentals, or need a refresher. I will assume basic math knowledge (multivariable calculus and linear algebra). ML: Capacity, Overfitting, and Underfitting What is ML? Machine Learning (ML) is a form of applied statistics that leverage computing power to estimate functions....

Mechanism and Performance are different. The ceiling on test performance is constantly dynamic. We revise the difficulty of standardized tests every year, and the limit on how many tests one could justify as a good measure of “intelligence” is infinite. We will never know if we’ve achieved AGI if we continue down this short sighted path. There will be no “AGI test”. Rather, I believe real machine intelligence will be achieved when we’ve replicated the mechanisms behind the the inner workings of the neocortex on computers....