In this post, I’ll try to show how Windsurf’s Cascade AI transforms prompt-driven development—what Andrej Karpathy calls Software 3.0, where “prompts are programs”—into real, end‑to‑end scaffoldin...

Transformer is a neural network architecture for sequence transduction that replaces recurrence and convolutions with self-attention, enabling fully parallel processing of input embeddings $X\in\ma...

“What would a production-grade AI cluster look like if built from scratch for scale, resilience, and lightning-fast insights?” Sentinel-AI is my answer. Repo 👉 github.com/gsantopaolo/sentinel-AI ...

Summary As part of my study for the Artificial Intelligence Professional Program at Stanford, I’m studying CS224N: Natural Language Processing with Deep Learning. In this class, we studied the his...

Backpropagation—originating in Linnainmaa’s 1970 reverse-mode AD thesis and popularized for neural nets by Rumelhart et al. in 1986—is the workhorse that makes deep learning feasible (en.wikipedi...

TL;DR Gemini CLI is Google’s free, open-source AI agent for your terminal. Powered by Gemini 2.5 Pro (with a huge 1 million-token context), it lets you scaffold apps, debug and refactor code, fe...

Sequence-to-sequence (seq2seq) models without attention compress an entire source sentence into a single fixed-length vector, then feed that into a decoder to produce the target sentence. While t...

A modern language model must handle vast contexts, share parameters efficiently, and overcome the vanishing-gradient issues that plagued early RNNs. Count-based n-grams hit a combinatorial wall: s...

If you landed here, you’re probably already using a remote (GPU) for your ML tasks, and you’re sick of git push from your laptop and the pull on your powerful GPU machine. Ask me why I know that, a...

Have you ever tapped on your phone’s keyboard and seen it guess your next word? That “magic” used to (and often still does!) come from n-gram language models—the statistical workhorses that predate...