Every product here starts the same way: a problem one of us wanted solved, or an idea we wanted to see exist. We build them the way you build something for people you know, because usually that is exactly who it is for. The test is simple and it does not change. Does this leave someone more capable than it found them?
A problem we want solved for ourselves, or an idea we want to see exist.
An idea gets written down as one sentence and one test before it gets any further. The sentence says who has the problem. The test says what would have to be true for the thing to be worth building.
There are two of us, so the argument happens the same week rather than the same quarter. Most ideas stop here, and that is the stage working. The ones that carry on are the ones where one of us wanted the thing to exist badly enough to keep pushing after the first honest objection.
Everything findable on the subject: best practice, real expertise, and the models, prompts and methods that get the best result for the lowest cost.
Every claim we plan to build on gets routed to the thing that can check it. A benchmark score goes to the board that publishes it, a price goes to the lab’s own post, a number about money goes to the filing. A claim with nowhere to send it gets marked as unsettled or dropped, and we say which.
The same pass reads the field: what already exists, what was learned this month, and which models, prompts and methods actually get the result at the lowest cost. That last part is the difference between a demo that works once and a product that runs at volume without the cost eating it.
accent outline = a hard rule, not a preference
Prototype, test, learn, iterate. It is not designed until the person using it can get through it without being taught.
We build the real thing early and put it in front of someone. Not a slide of it, not a description of it: the working screen, on the real type and the real colour, doing the job it is supposed to do.
Then the reviewer edits the page in the browser. They rewrite the line they do not like, move the block that is in the wrong place, and pin a note where the thing is wrong. Those edits come back as a list we build from, so nobody has to describe a change in words and nobody has to guess what was meant.
It repeats until someone who has never seen it can get through it without being taught.
We build them the way you build something for people you know, because usually that is exactly who it is for.
the test, and it does not change
For production, for scale, and for a real market rather than a demo.
This is where MagicLens runs. A workflow is assembled once and then run again and again: the same steps, the same controls, the same outputs, at whatever volume the product needs.
Studio Pro is where the control lives. References hold the characters, the places and the objects still across a whole set of frames, and a style is chosen once and then applied to everything after it. Consistency stops being something you police frame by frame and becomes something the workflow enforces.
What comes out is built for production and for a real market: the volume a launch actually needs, not the three good frames a demo needs.
Audience, demand, reach, marketing and PR. Launch is a process here, not an event, and it is the same process every time.
We know who the product is for and what they are already searching for before it ships, because the demand work happened back at the research stage rather than after the build.
Then the same sequence runs, in the same order, every time: the audience, the demand, the reach, the press, and the content that keeps running after launch week. Nothing about it is improvised per product, which is the only reason two people can launch more than one thing.
The first product we're shipping powers the rest. Generative workflows with flexible controls allow us to produce products and content at scale, enabling us to build and optimize our effectiveness across all facets of product development and go-to-market.
MagicLens on the products page →The demand work and the model and method comparison that decide what gets built and what it will cost to run.
Prototypes assembled fast enough that a reviewer sees the real screen rather than a description of it.
Workflows, reference control and style control, run at the volume a launch needs.
The launch assets and the content that keeps running, produced on the same line as the product.
Research here is not a publication arm. It is the first stage of building anything: what already exists, what the field has learned this month, which models and methods actually get the result. We are rooted in the history of this technology and we pay attention to where it is going, including the parts of it that should worry people.
In 1913 Markov counted the vowels and consonants of Eugene Onegin to show that dependent sequences obey the law of large numbers too. Next-token prediction has a birthday.
A school for deaf children opened in Managua and taught in spoken Spanish and lipreading. The pupils built a sign language of their own instead, and the cohorts after them gave it a full grammar.
Products to Make People Better with AI