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Overview
AI researcher and builder writing about model training, systems, open source, and organizations.
News
Compute-Optimal Is Not Cluster-Optimal
MOSAIC jointly selects a sparse-MoE architecture, token budget, and parallel layout under a fixed cluster and training window.
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Research Problems in Pretraining
A practitioner's account of what pretraining research can predict, where current methods break, and which questions remain open.
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Your Org Has the Same Scaling Problem as a Badly Tuned Training Run
AI raised individual throughput but coordination overhead stayed fixed. For many product-engineering orgs, the bottleneck flipped from compute-bound to communication-bound.
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On Assessing the Value of a Project
A practical framework for comparing research projects by probability of success, effect size, and weighted reach.
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Determining Model Size and Training Horizon through Scaling Laws
Deriving model size and training tokens from a fitted scaling law, then extending the calculation to repeated data, inference demand, and cluster efficiency.
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