Field notes for teams making high-stakes decisions about web systems, AI features, modernization, SEO, and technical direction.
Unshatter your knowledge
Practical reads for teams weighing architecture, AI, performance, modernization, SEO, and the tradeoffs behind durable digital systems.
A practical guide to the business and engineering value of strict TypeScript, including where it helps, where it costs, and how to adopt it sensibly.
5 min read
A practical read on using Supabase in production, including Postgres strengths, auth, row-level security, migrations, operations, and team fit.
A clear explanation of React Server Components, the practical problems they solve, where they help, and where teams still need care.
A practical look at AI-generated content SEO risk, including sameness, weak ownership, factual drift, cannibalization, and governance gaps.
A practical way to measure AI feature success with useful product metrics, review loops, cost signals, and risk-aware quality checks.
A plain-language primer on embeddings for product teams, including what they are, where they help, where they fail, and how to plan implementation.
A practical guide to production prompt engineering, including prompt specs, versioning, evals, context design, ownership, and release discipline.
A practical guide to AI documentation search, including content preparation, retrieval design, answer UX, permissions, freshness, and success metrics.
A practical framework for deciding whether to build, buy, or borrow AI capabilities based on differentiation, risk, cost, speed, and control.
A practical guide to LLM evals for small teams, including example sets, rubrics, manual review, automated checks, regressions, and launch readiness.
A practical guide to human-in-the-loop AI patterns, including review points, approvals, audit trails, risk tradeoffs, and user trust.
A practical guide to brand-safe AI chatbot design, including scope, source control, tone, escalation, failure states, and launch review.
A vendor-neutral framework for choosing between Claude and OpenAI for product features, based on workflow fit, quality, cost, latency, risk, and operations.
A practical look at internal AI copilots, where they create business value, and how to start with workflows that reduce friction without adding risk.
A practical guide to deciding whether vector search belongs on a marketing website, where it helps, and when simpler search is the better choice.
A custom CMS can publish content and grow into the marketing system your team actually runs. Here is what changes when the backend is built around the business.
A practical production guide to retrieval augmented generation, including where RAG is useful, where it fails, and how to design for trust.
A practical guide to estimating LLM API cost before build, including token usage, model tiers, retries, retrieval, evals, logging, and operating assumptions.
A practical guide to LLM guardrails for production products, including data boundaries, tool permissions, review paths, testing, and ownership.
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