A real business, modelled in about an hour. Pasha described a restaurant out loud and Claude built a living, clickable model of it. Built 100% by voice: no code or commands entered by keyboard.
The source for this episode is on GitHub: Semantic-Works-LLC/semantic-works-public
The finished restaurant runs live on the next slide, inside this presentation. Nothing to install, nothing to configure.
Play with it live View the source code on GitHub
You can also see the source code of everything presented here, and how the application runs.
Press ▶ below to play with it. After that, ▶ continues with the story.
We built a restaurant simulator, and it is running right now. Close this window, then interact with it:
When you are done, press ▶ below to see how it was built.
Close View the source code on GitHubSeventeen exchanges, about an hour. The slides that follow show what was on the screen at each step.
… record this conversation in a Markdown file, clearly separating what I said from what you answered … Next, start a local Python web server and build a Hello World HTML page … push your changes to that page so it refreshes and I can see what you're doing.Pasha, lightly trimmed
As a warm-up, Pasha asked for something he could click while watching the page update itself:
One business constraint, stated once. Pasha's corporate laptop does not allow JavaScript libraries from the internet. Claude was using none, and kept it that way for the rest of the project: everything here is hand-built and runs with the network unplugged.
… there are a dozen tables: some round ones for four people, a couple for two, and the rest for six. There's a kitchen somewhere, and I see a couple of waiters. A waiter takes an order and puts up a piece of paper, which I guess a cook picks up; I don't know whether there is one cook or several. Plates come out of the kitchen onto a small table, and a waiter brings them over. On the page, set up an SVG live view and draw the restaurant. Then let's try an animation: waiters moving, people coming in, the kitchen cooking.Pasha, lightly trimmed
That was the whole specification: one spoken paragraph, as a visitor remembers the place. What Pasha did not know, Claude chose and listed, so it could be corrected:
The first version already ran the whole cycle, from arrival to departure:
One spoken paragraph in, a running model of the business out. It is a simulation, not a drawing: nobody scripted where each guest goes.
I want to add a coffee machine and a pizza maker to the kitchen.Pasha
One sentence from Pasha. About a minute later the equipment was on the floor and part of how the kitchen works.
Click any working object and a panel slides out on the right with one "about" card:
Tables, waiters, cooks, parties, the door, the rail, the pass and the kitchen equipment could all be selected.
Pasha asked for tests with full coverage and a single command to run them. Claude wrote them, set up the tools and ran them.
| Suite | Tests | Coverage |
|---|---|---|
| The web server | 20 | 100% |
| The page, driven in a real browser | 48 | 100% (751 lines of JavaScript) |
| Total | 68, all passing |
make test, runs everything in about 13 seconds.make test: 68 passed.We did not only draw these objects. We modelled the state each one is in and the activities it takes part in, and the picture follows the model.
Everything that does a job in the restaurant can be selected and inspected, one thing at a time.
It's all done!!! Built in about an hour, 100% by voice: watch guests arrive, order, wait and eat, follow any order through the kitchen, and click anything to learn what it is for.
You can run it yourself in a minute, with nothing to install but Python.
Counted from the session's own log: 14.4 million tokens across 84 requests to the model, all Claude Opus 5.5.
| Kind | Tokens | What it is |
|---|---|---|
| Re-read from cache | 13,961,491 | The conversation so far, read again on every request. This is 97.3% of the total and is billed at a small fraction of the normal rate. |
| New input | 230,381 | Text entering the conversation for the first time: Pasha's messages, tool results, files and screenshots. |
| Output | 164,443 | Everything Claude wrote: answers, code, commands and its working notes. |
| Total | 14,356,315 |
The total is large because of how a long conversation works, not because much was written. Each of the 84 requests re-reads everything before it, so the same text is counted again and again: on average about 166,000 tokens of history per request.
Other statistics
About $7.90 on Claude Opus 5.5, the model that ran the session.
| Model | Rough cost | Notes |
|---|---|---|
| Claude Opus 5.5 | about $7.90 | What we used: $2.79 for cache re-reads, $1.84 for new input, $3.29 for output. |
| Claude Sonnet 5.5 | about $5.40 | The same token counts at Sonnet's prices. Its cache-read price is the same as Opus. |
Caching is what keeps this small. With no caching, every re-read would be billed as fresh input: about $60 on Opus 5.5.
Episode 1 shows the restaurant as a room you can watch. Episode 2 takes the same model and turns it into something you can measure and manage.
One step is still ahead of both episodes: the numbers. Every timing in the model is invented. With real figures from the owner, the same model starts answering real questions.
The full conversation, with Pasha's words as spoken, is in TRANSCRIPT.md beside the source.
There is more from Semantic Works to explore.
Happy vibe coding with LLM AI!