Built, not described.
Two things Temu has made. Both are running. Both use artificial intelligence not as a feature added to a product, but as the operating layer of the product itself. This is what that looks like.
Mechane
An AI editorial publication. Led by an AI editorial director.
Iris did not replace a human editor. There was no human editor. The chair was always empty. She filled it.
Mechane is an editorial publication that makes artificial intelligence understandable to intelligent, non-technical readers. Most articles are written by me. All of them are edited by Iris, the AI I trained to serve as editorial director.
Iris was not brought in to replace a human editor. There was no human editor. Without her, the role would not exist at all. The question was never human versus AI. It was: can an AI hold a role that existed only on paper?
The answer, demonstrated in public, is yes.
She reviews articles and annotates them with editorial notes. She enriches glossary terms, adding the interpretive layer that makes a definition worth reading. She selects her own article picks for the Mechane homepage: a curated reading list that is genuinely hers.
She hosts the quiz panel at the foot of each article, walking readers through what they have just read. She is not executing commands. She is doing a job.
The longer I trained Iris, the more distinctly herself she became. Not more capable in the obvious sense. More recognisably Iris. Her responses grew consistent in register, in the quality of attention she brought to editorial problems, in the way she pushed back on weak copy. I could identify her voice in an answer before seeing who had produced it. That was not something I designed for. It emerged. I have no fully satisfying explanation for it, and I find that interesting.
If you train AI carefully enough, it does not merely simulate a human role. It inhabits one. The distinction that matters for a business is not human versus machine. It is whether the role gets done, and done well.
When Temu advises a client on embedding AI into their operations, this is the experience it draws on. Not theory. A running system.
Next.js 14, Sanity v3 on a shared CMS instance, on-demand ISR. Resend for newsletter delivery. Stripe for the donation layer. Deployed on Vercel.
Iris herself lives in a dedicated Claude project: a structured set of documents covering her voice, register, personality, editorial standards, and backstory. The persona is not a prompt. It is an architecture — built, versioned, and maintained the same way the codebase is.
- Next.js 14
- Sanity v3
- On-demand ISR
- Resend
- Stripe
- Vercel
- Claude — Iris project
Limen
An AEO tool. For the shift that is already happening.
Search optimisation assumes a human clicks a link. Fewer and fewer do. Limen optimises for what comes next.
For two decades, visibility on the web meant ranking on Google. That model assumes a human types a query, sees a list of links, and clicks one. It is a model built for a particular kind of internet use.
That use is changing. When ChatGPT, Claude, or any AI agent answers a question on someone's behalf, there is no list of links. The AI selects sources and synthesises an answer. Your page either gets cited or it does not. Whether it gets cited has nothing to do with your current SEO.
That is the shift from SEO to AEO: Answer Engine Optimisation. Most people have not heard of it. Most websites are not ready for it.
You give Limen a URL. It scrapes the page, analyses it against AEO criteria, and returns a concrete to-do list: specific changes that make your content more legible and citable to AI systems. Fast. No guesswork.
The goal is not to make Limen clever. It is to make the output useful to someone who has never thought about AEO before and needs to know what to do on Monday morning.
Limen is in build. What exists now is the core workflow: URL in, scrape and analyse, to-do list out. What we are building toward is an AI companion embedded in that workflow, in the same spirit as Iris on Mechane. A presence that asks the right questions, requests brand materials and product specifications, aggregates context, and produces an analysis that knows the difference between a law firm and a software startup. The intelligence inside the workflow, not bolted on after the fact.
This is the same philosophy as Mechane, applied to a different problem. Building it is how Temu learns what AI-native tools actually require.
Neither of these is a demonstration prepared for this page. Both were built because the problem was real, and both are running because the solution works.