Why Brains and LLMs Both Become Modular
A careful walk from brain networks to dense LLM circuits, MoE models, and the deeper idea that modularity may be a solution to interference.
Writing archive
Lightweight posts on models, software systems, and the strange parts of engineering with AI.
XGBoost and the Map of Machine Learning Approaches
A practical guide to linear models, trees, random forests, gradient boosting, XGBoost, neural networks, and when each family is worth trying.
Parameter Golf and Distributed Autoresearch
What OpenAI's Parameter Golf and Weco's Aiden show about open benchmarks, public PR graphs, and autonomous ML engineering.
Context Engineering for Agents
How agent harnesses load instructions, skills, memory, tools, and files without stuffing everything into the first prompt.
Learning Is a Feedback Loop
A first-principles guide to active recall, Socratic questioning, feedback, spacing, and neuroplasticity.
Neuroscience and Machine Learning
A grounded guide to synapses, myelin, credit assignment, reinforcement learning, and what brain-inspired machine learning can and cannot claim.
Prompt Trajectories in Latent Space
A step-by-step visual explanation of how prompts become directions, how generations form clusters, and why model randomness is better understood as a run cloud.
How Physics Shows Up in Machine Learning
A plain-language guide to SGD noise, states, flows, energy landscapes, constraints, symmetries, conservation laws, and physics-informed machine learning.