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01 ·★ MEMBER·PAPER·10 min read Paper Explained — GenFirst: Let Generation Shape the Latent Space First, Reconstruction Second Image generation normally means training a VAE first and bolting a generative model onto its frozen latent space. This paper trains both at once without latent collapse — the key is the entropy term inside the KL objective, plus a simple rule: let generation go first and ramp reconstruction up later. 02 ·Generative Models·FREE·10 min read VAEs from Scratch — Stir Probability into "Compress and Restore" and You Get a Generator An autoencoder that only compresses and restores cannot invent anything new. This walks through why a single drop of probability turns it into a generative model — ELBO, the reparameterization trick, and walking the latent space — assuming no prior knowledge. 03 ·Information Theory·★ MEMBER·PAPER·9 min read KL Divergence From Scratch — Measuring the Gap Between Two Distributions KL divergence measures the gap between two probability distributions. We build it up from a compression metaphor to the definition, its famous asymmetry, and a numpy implementation — then watch it at work as the regularizer in VAEs and the leash in RLHF.