mineti.dev

Models, harnesses, and execution environments — plus the two interface categories around them: workflow frameworks and memory.

A small always-on server, dark to the internet, that keeps an Obsidian vault converging and version-controlled, hosts coding agent sessions I can drive from a phone, and — unplanned — grew into an assistant reaching my tasks, mail, calendar, flashcards, a…

AI-assisted coding accelerates one pillar of development work — writing code.

Integrating decision support systems into business processes — the building blocks of descriptive, predictive, and prescriptive systems, and why results happen in processes, not in the systems themselves.

A conceptual map of artificial intelligence — from the definition of intelligence to the five abstraction layers of every machine learning solution.

Fitting a line from scratch: the model and its assumptions, least-squares estimates derived by hand and by optimizer, the sampling distributions of the coefficients, a confidence band for the mean response, and R².

Linear regression with several features in matrix form: the closed-form least-squares estimator derived and computed, the sampling distribution of the coefficients, and a from-scratch look at LASSO, ridge, and elastic-net regularization.

Three estimators for the mean of a normal sample — sample mean, least squares, and maximum likelihood — derived from scratch and shown to coincide, then confidence intervals checked against the truth.

Why the average of many trials settles on the expected value — shown with coin and dice simulations in NumPy, then the weak law stated and proved via Chebyshev's inequality.

A from-scratch look at Markov chains with NumPy — the Markov property, transition matrices, and how any starting distribution converges to the same stationary distribution.