Practical writing on conversational commerce, AI chat assistants, and turning store traffic into sales.

An LLM that hunts margin, in public. Can a free model on free SaaS find resellable products with real margin and ship them through Shopify? One chunk a day, for forty days.

Guides for Magento 2 and Adobe Commerce teams — interview prep, performance and EAV deep-dives, and how to plug modern AI into a Magento storefront without rewriting the checkout.

Guides for Shopware 6 teams — interview prep, the DAL and plugin/app architecture, performance and caching, and how to plug modern AI into a Shopware storefront without rewriting the checkout.

Build real software with AI, even if you don’t code. You describe what you want in plain English; Claude Code writes and runs it. Step-by-step guides for founders and managers — starting with apps for your own store.

Plain-language Shopify guides for merchants and founders — what a Shopify app actually is, how to build your first one even if you’ve never written code, and how to add AI to your store.
SEO gets you ranked. AEO gets you quoted. As buyers move from ten blue links to one synthesized answer, the pages that win are the ones a model can fetch, extract and trust — and most sites fail at step one without knowing it.
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Your AI agent has dozens of knobs, and guessing which ones lift revenue is expensive. Here is how to test prompts and flows the disciplined way — and read the results without fooling yourself.
Read article →The fastest way to lose money on an AI product is to charge a flat price for a cost that varies per conversation. Here’s how to find your cost floor, choose a pricing model, and keep your margin when usage spikes.
Read article →You trained a model. Now it has to run inside the store. This is the part nobody writes about: the two ways to build custom AI into Magento, when each one fits, and a complete worked example — a scikit-learn classifier ported to pure PHP and plugged into the admin reviews grid.
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The newest review-spam problem is that the spam reads perfectly — a language model wrote it. The fix is not a bigger language model. We trained a 1.3 MB classifier that catches machine-written reviews at 94.6% accuracy and tells you why. Here is how, and the code.
Read article →Fable 5 wins every benchmark. It also costs twice as much. So we did the thing the launch posts skipped — the actual math for a working developer. One documented day on Fable cost more than a whole month of Claude Max.
Read article →“AI is a bubble” and “AI is worthless” are two different sentences, and almost everyone reads them as one. The bubble is in the money and the hype. The value is in a small, boring corner that keeps working no matter what the market does.
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A shopper on your product page likes what they see. Then a question forms — and there is no answer anywhere. So they leave to find a store that will answer, and they buy there. This is the costliest bounce of all.
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You read your bounce rate like it is one problem. It is seven. Each leaves the same footprint in analytics and each needs a different fix — and most stores spend a year fixing the wrong one.
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You uploaded /llms.txt to your site. You felt productive. Here is the awkward part: every major AI provider has either said they will not read it, or quietly never started. The file is a placebo.
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Two years ago, Google quietly killed thousands of ecommerce sites overnight. The fix is not "stop using AI" — it is using it the way the survivors did. Here is the playbook.
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Everyone says AI chat is cheap. Cheap is not a number. This is the actual arithmetic behind one conversation — the tokens, the resend multiplier, and why two stores can pay 10x different for the same chat.
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Hiring for agentic AI is hard: the field moves fast and a candidate who can recite the transformer architecture may still struggle to ship a reliable tool-calling loop. Thirty questions that test what actually matters on the job.
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A language model alone can talk about your store. With MCP tools it can read your catalog, change a cart, and track an order — which is what shoppers actually want.
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Shoppers increasingly want to ask a question and get an answer — not dig through filters and FAQ pages. Conversational commerce turns that chat into the path to checkout.
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