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Sparse Autoencoders (SAEs) and Cross-Layer Transcoders (CLTs) are two approaches to interpretability of transformer models. Read up on what they're good for and how they differ.

Practical tips on how to design single and multi-agent systems. A synthesis of emerging principles and patterns as of June 2025.

This blog post explores Induction Circuits, an example of a learned mini algorithm of transformer models that runs at inference time.

I vibe coded a quantum state simulator, which I hope will be a useful resource for some of you learning about quantum computing.

A brief history of LLM Scaling Laws from compute-optimal training and inference to scaling test-time compute and whether Scaling Laws are coming to an end.