A chronological collection of insights on technical specification, product strategy, and AI-driven documentation.
Software Engineering (20 articles)
Clear filtersSpec-Driven Development is an approach where software implementation starts from a clear, structured specification rather than vague ideas, scattered tickets, or incomplete conversations.
A three-role system for AI-assisted software development: an architect who designs, a developer who implements, and independent reviewers who critique.
As AI generation speeds increase, verification becomes the strongest assurance layer. The bottleneck shifts from writing code to defining correctness requirements.
Without a verification layer, overnight coding creates morning cleanup. With a verification layer, it can create morning momentum.
Go 1.20 introduced context.WithCancelCause and context.Cause, transforming cancellation from a generic signal into a traceable failure event.
Decision trees, built from simple nested if/else rules, remain a powerful and interpretable tool for modeling complex patterns—especially on structured tabular data and in regulated domains—even as large language models dominate the spotlight.
Microgpt is a ~200-line pure Python implementation of a GPT-style language model by Andrej Karpathy. It compresses the complexity of transformers into an executable, dependency-free script that helps engineers understand tokenization, attention, training loops, and inference while improving debugging, evaluation, and architecture decisions.
Engineering teams often optimize for faster coding while ignoring the real bottleneck: review queues, approvals, and handoffs. Each added review layer can multiply wall-clock latency, neutralizing gains from AI and better tools. This post explains why that happens and how to recover using quality-by-construction and trust-based systems.
Kiro IDE introduces Spec-Driven Development, an agentic AI workflow where EARS-formatted requirements become the single source of truth for design, implementation, and testing—improving alignment, accelerating onboarding, and reducing rework.
Most people are vibe coding wrong because they skip the most important step: writing a spec first. Make your AI act like a product manager—ask questions, define scope, then write code.
