A notebook
Essays, notes, and experiments on machine learning, AI, and the non-linear nature of things, by Hamidreza Saghir.
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All posts →Agent frameworks hide the loop behind agent.run() and a graph DSL. But the loop is where every interesting decision happens: what the model sees, whether a tool call proceeds, when to stop, what to record. What if you owned the loop and the framework just made it composable?
Recent writing
Archive →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.
BFS, Dijkstra, and A* differ by one line: the data structure you pop the next node from. A worked maze example that converts each into the next.
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.
Backpropagation, belief propagation, the Viterbi algorithm, and matrix-chain multiplication all solve the same problem: summing over exponentially many paths in a graph by reusing work.
I love the simplicity of autoencoders as a very intuitive unsupervised learning method. They are in the simplest case, a three layer neural network. In the first layer the data…
