development
- The chain rule behind autoregressive models
Autoregressive models are just the probability chain rule plus a conditional model. Here’s the mental model, the math, and what training is really doing.
- Separation of Responsibilities in Spring-Based Systems: What Kotlin Makes Explicit
How Kotlin's type system sharpens responsibility boundaries in Spring-style architectures without replacing architectural discipline.
- Podcast episode
A post with a Spotify episode embedded at the top.
- Calculus, AI, and linear algebra: a compact field guide
A quick, code-backed refresher on gradients, Jacobians, and the linear algebra that drives modern ML.
- Graceful retries in Python with backoff
A small, production-ready retry helper using exponential backoff and logging.
- Streaming logs in Rust with Tokio
Build a small async log streamer that tails a file and ships JSON lines.
- Why Most Postmortems Miss the Real Failure Mode
Postmortems often substitute proximate triggers for causal structure, obscuring system dynamics and latent conditions that drive failure.
- Simple writing and publishing flow
A lightweight process to draft, edit, and publish consistently.
- Why this blog exists
A short purpose statement: clarity, record, and real learning.