Reading

Here I store precious pieces of the internet (and academic papers) that made me grow in some way and helped thru my life. I really recommend reading of them:

AI/ML

• “Training Compute-Optimal Large Language Models” (2022, Google) : the famous “Scaling Laws” paper, where researchers state that model size ($N$) and training data tokens ($D$) should scale equally under a fixed compute budget ($C$), resulting in a “LLM training formula” where you can even predict the training loss given these parameters.
• “The Llama 3 Herd of Models” (2024, Meta): tech report on how Meta trained the Llama 3 models. It’s detailed and easy to read. It helps me a lot understanding the whole LLM training pipeline and process.

Systems Programing

• “Git at any scale” (2026, Cursor): how Cursor designed their own git hosting platform to have better availability than GitHub (LOL).