The library
Every post lives here as the collection grows. Start with the question that brought you in, then follow the ideas wherever they lead.
A practical guide to choosing a managed frontier API, managed open-weight endpoint, or self-hosted inference path by measuring data boundaries, quality, utilization, and operations.
A practical guide to choosing native parsing, OCR and layout extraction, specialized Document AI, multimodal models, human review, or a hybrid pipeline for messy B2B documents.
A practical guide to choosing delegated user access, workload identity, or a separate agent and task principal for B2B AI workflows.
A practical guide to choosing deterministic checks, trace evaluation, LLM judges, human review, and production monitoring for a B2B AI product.
A practical guide to deciding whether a B2B software product should add an MCP server, keep its API, build an interactive surface, or postpone the work.
A practical guide to choosing a direct API, deterministic browser automation, computer-use agent, or manual workflow for real B2B work.
A practical guide to choosing synchronous responses, background jobs, provider background mode, batch processing, or durable workflows for B2B AI work.
A practical guide to choosing retrieval, fine-tuning, both, or neither by diagnosing the real failure in a B2B AI workflow.
A practical guide to choosing one model, explicit routing, or adaptive routing by measuring quality, cost, latency, reliability, and recovery in the real workload.
A practical guide to persistent AI-agent memory: when to use it, what to store, how to handle scope and staleness, and why memory must never become authorization.
A practical guide to people-first content, crawlability, canonical URLs, structured data, crawler controls, measurement, and what not to promise.
A plain-English guide to AI agents, workflows, tools, safety, and the product ideas worth testing before you build something expensive.
A practical guide to prompt injection, tool permissions, approvals, auditability, recovery, and safer AI automation pilots.
A better way in
Fast-moving technology can make ordinary decisions feel harder than they need to be. These essays slow the moment down, explain the useful distinction, and leave you with a clearer next step.
You do not need to become an AI expert to decide whether a tool, workflow, or product idea deserves your time. You need a useful frame—and enough honesty to name what is still unknown.
Have a question worth making real? Start hereSend the messy version. We can turn it into a clearer decision, a useful first experiment, or a scoped build that earns its place in your day.
