AI product blog
Practical guides on AI product strategy, coding agents, prototype hardening and the decisions that turn AI demos into useful software.
31 notes
Latest
A decision framework for choosing whether to build an AI capability yourself or buy it, based on differentiation, control and cost.
Read the guide10 min read
How small teams automate real work with AI without breaking the workflows they depend on, and where a human should stay in the loop.
8 min read
How to use coding agents to build an MVP fast without shipping software that collapses the moment real users arrive.
8 min read
How to build internal AI tools that a small team actually trusts and uses, without creating a fragile system only one person understands.
8 min read
Practical levers for controlling LLM cost and latency in production without degrading the quality users actually notice.
8 min read
A practical framework for pricing an AI product when your costs scale with usage and customers expect software margins.
8 min read
How to add the production spine, authentication, real data and payments, to an AI prototype without rebuilding it from scratch.
8 min read
How AI startups should approach SEO when search now includes AI Overviews and answer engines, without falling for AI-specific myths.
8 min read
How to write specs and prompts that get reliable work out of a coding agent, instead of plausible code that does the wrong thing.
8 min read
How to test an AI product when the output is non-deterministic, so you can ship changes without quietly breaking quality.
8 min read
What it really takes to move from vibe coding to a production app, and which shortcuts you have to undo before real users arrive.
7 min read
The common traps that make an AI demo look finished when it is not, and how to tell demo magic from real product readiness.
8 min read
A clear framework for deciding whether your AI idea is a feature inside something else or a standalone product, and why it matters.
7 min read
A practical pre-launch checklist for AI products, covering reliability, cost, safety, data and the things teams forget until users hit them.
8 min read
A practical guide to becoming a source that AI answer engines quote and link, covering what gets cited and how to write for it.
8 min read
A practical guide to writing and adding an llms.txt file to a product site, what it does, what it does not, and a template you can copy.
7 min read
A practical review process for code produced by coding agents, covering what to check, where agents fail, and how to keep velocity without shipping fragile software.
8 min read
A plain explanation of generative engine optimization, how it differs from SEO, and how to make your pages easy for AI answers to cite.
7 min read
A practical end-to-end workflow for using coding agents without losing product judgment, review quality or control.
8 min read
A practical breakdown of what drives AI MVP cost, from scope and data to integrations, reliability and launch support.
11 min read
A practical AI MVP development process for founders who need to scope, build and launch a focused first product.
8 min read
How product teams should compare Claude Code and Codex when choosing coding agents for real product work.
7 min read
How non-technical founders can use coding agents safely for prototypes, product scoping and early builds.
8 min read
A practical framework for testing AI product ideas before committing time, budget or engineering effort.
7 min read
How to turn an AI prototype into a safer, more reliable product slice before real users depend on it.
10 min read
A practical guide to making startup content easier for ChatGPT, AI Overviews and other answer engines to find and cite.
8 min read
How founders can turn no-code and AI-generated app builds into production-ready products without shipping fragile demos.
8 min read
A practical decision framework for deciding when AI improves a product and when deterministic software is the better choice.
11 min read
How founders can turn an AI-generated prototype into a reliable product with real users, production workflows and fewer demo traps.
10 min read
How founders and product teams should use coding agents to move from idea to working software without losing product judgment, quality or control.
10 min read
A practical framework for founders deciding where AI belongs in a product, what to automate, how to handle failure and how to ship useful workflows.