// curated_library
Seminal papers, key talks, and essential videos from the people shaping AI — filtered for signal, not noise.
Claude's Model Spec (Constitutional AI)
The specification document that defines how Claude is trained to be helpful, harmless, and honest. A remarkable piece of AI alignment engineering made public.
The Forward-Forward Algorithm (2022)
Hinton's alternative to backpropagation — training neural networks using only local, positive and negative data without ever computing a global gradient. Potentially significant for neuromorphic and on-device learning.
Attention Is All You Need
The paper that changed everything. The transformer architecture introduced here is the foundation of every major language model in existence. If you work in AI, you must have read this.
Scaling Laws for Neural Language Models
The empirical laws governing how model performance scales with compute, data, and parameters. The theoretical backbone behind the scaling hypothesis that has driven frontier AI for five years.
Constitutional AI: Harmlessness from AI Feedback
The research paper behind Claude's training methodology. Describes how a set of principles ('constitution') guides RLHF without requiring human labellers for every harmful output.
Deep Learning (textbook)
The canonical deep learning textbook — freely available online. Chapter 6 onwards remains the most rigorous treatment of feedforward nets, regularisation, and optimisation available.