Field Notes
Notes on event-driven Java, Spring Boot, Kafka, and secure systems for regulated banking.
Softmax is a small equation with a central job: it turns arbitrary model scores into positive weights that sum to one. Here is the arithmetic, the numerical-stability trick, and where the same operation appears inside an LLM.
ReLU cuts every negative activation to zero. GELU computes a smooth tanh-shaped gate; I unpack the formula one real number at a time and place it inside GPT's feed-forward network.
I refused to let LayerNorm remain a formula. Starting with four integers, I work through mean, variance, standard deviation, normalization and the exact PyTorch operation used for each GPT token.
Before I could make sense of attention code, I needed PyTorch's tiny tensor vocabulary to stop feeling mysterious. This first session covers shapes, axes, batching, and the important difference between stack, cat, multiplication, and dot products.
A generated character only becomes an identity when it survives the next prompt. I turned one navy-hoodie mascot, two successful references and a --manga switch into a small reusable Codex skill.
I applied for verified Daybreak access, completed the identity check, and mapped the next step: connecting my own GitHub repositories to Codex Security without giving up human review or scope control.
LLMs, the whole thing · Part 9
Part 9 of LLMs, the whole thing: two heads, one token, and the difference between computing attention and organizing its tensors. A concrete PyTorch walkthrough of view, transpose, concatenation and the output projection.
A new identity for my portfolio, writing, and independent products: graphite, electric blue, and a moving Z inspired by event-driven systems. The design choices, reusable article templates, and engineering constraints behind it.
Running the box: Docker, Linux, the JVM
Retiring a service is more than killing the process. Here's the four-beat teardown I ran to remove a honeypot from my box — and why 'disable' isn't 'stop'.
LLMs, the whole thing · Part 8
Section 3.5 of Raschka's book on seven real GPT-2 tokens: why masking after softmax breaks the row sums, why -inf on the scores fixes it in one pass, and how dropout is a second, random mask that doubles its survivors.