6 posts
Coding agents write impressive first drafts. But under the surface, corners are cut, details are missing, and technical debt accumulates with every change. The problem is not the model. It is that what we ask it to do is fundamentally under-specified.
Offense asks 'does a bug exist?' Defense asks 'are all bugs gone?' One is an existential claim you can check with a single example. The other is a universal claim nobody can check. This asymmetry, not model capability, is what determines where AI agents work in security.
machine learning ·unified views
Both fit a distribution over outputs conditioned on an input. Both minimize a KL divergence between their model and an optimal target. The only differences are which distribution you sample from and which direction of the KL. Entropy regularization bridges them.
machine learning ·unified views
From spectral clustering to Gaussian processes to transformer attention, the same primitive, a similarity matrix between points, keeps showing up as the load-bearing piece of very different models.
machine learning ·unified views
PCA, factor analysis, ICA, Gaussian mixtures, vector quantization, HMMs, and Kalman filters are the same two equations with different restrictions on the latent variables. One EM recipe fits all of them.
machine learning ·unified views
PCA, factor analysis, logistic regression, Gaussian mixtures, HMMs, and Kalman filters are the same probabilistic graphical model with different independence assumptions. Seeing this gives you one inference recipe that handles all of them.
