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01 ·Agents·★ MEMBER·PAPER·11 min read Paper Deep-Dive: Frontis-MA1 — Training the AI That Builds AI: One Step Toward Recursive Self-Improvement in ML Engineering Instead of training a model to solve tasks, train the operations that improve solutions — then run evolutionary search with those trained operations. A walkthrough of the OpenMLE stack and the 35B Frontis-MA1 model, straight from the paper: what's behind the 39.39%→71.21% jump on MLE-Bench Lite, how the search got cheaper and better at once, and the limits the authors themselves spell out. 02 ·Probability & Statistics·★ MEMBER·8 min read Probability and Statistics for AI — A Model's Output Is a Distribution Classifiers and language models do not return answers; they return probability distributions. Distributions, expectation, conditional probability and Bayes explained from the symbols up — building to the payoff: why maximum likelihood is where loss functions come from. Cross-entropy and MSE were derived, not invented.