Writing
Notes on research, methods, and ideas.
Scaling Laws for LLMs: From Chinchilla to 2026
The most expensive equations in AI determine how labs spend billions. Here's what they actually say, and where they're being rewritten. From Kaplan to Chinchilla to inference-time scaling.
LLM Quantization Demystified: GGUF vs GPTQ vs AWQ
Your 7B model has 14 billion numbers. Here's exactly how to shrink them, and what you lose in the process. A practitioner's guide to choosing GGUF, GPTQ, or AWQ.
Mixture of Experts Explained: The Architecture Behind Every Frontier Model in 2026
How DeepSeek-R1, GPT-5, Gemini, and Mistral Large 3 all use the same trick, and what it means for your work. A complete conceptual and technical guide to MoE architecture.
Why Causal Inference Matters More Than Prediction in Development Research
Most ML models in global development optimize for prediction accuracy. I argue this is the wrong objective, and outline what we should be doing instead.
Neural Architecture Search for the Real World: Lessons from Edge Deployment
Building ML models that work on low-power edge hardware in climate-constrained settings taught me more about model design than any benchmark ever did.