A chef’s library that answers back
211 cookbooks and a chef’s own recipes, in one place you can ask. Every answer shows the book and the page.
The problem
A serious cookbook shelf holds more than any chef can remember.
Search by title finds a book. It does not find an idea.
The best ideas sit between books: a technique from one cuisine, a pairing from another.
What it is
Ah, the oldest enemy of the grill master — the fish that leaves half its skin on the bars.
The Vietnamese way, on a cast-iron griddle set on the coals 1 … Lay the fish down and do not move it for about 6 minutes, until the skin is deep golden.
The direct-grill way, on an oiled hinged rack 2 … Broil turning once, exactly 10 minutes per 2.5 cm of thickness at the thickest part.
The one critical point: dry skin plus a hot, oiled surface. Wet skin steams, sticks, and tears. Dry it, then dry it again.
Recipes, menus and books, by name, ingredient, technique or course.
A chat that answers from the library and names the page.
A map of ingredients: what sits close is used together.
Allergens are estimated on every card. Always check before use.
What is inside today
How it was built
AI did the labour. The chef decided what good looks like, and checked the results.
The rule that makes it trustworthy
Asked in plain words, the way you would ask a colleague.
The best cards are found by words and by meaning. Search typically takes under 48 milliseconds.
The AI is given only those cards, plus strict rules. It does not answer from memory.
A short answer. Each claim is marked with the card it came from.
The map
Each dot is a recipe. Recipes with a similar meaning sit close together. Colour shows the course.
Move over the map to light up one course.
A second map links 4,501 ingredients that books use together.
The adviser
Ah, the oldest enemy of the grill master — the fish that leaves half its skin on the bars.
A trick from the old days: before service, run a cut onion or a folded cloth dipped in oil over the bars … If it hisses and skates, you are ready.
A calm old-Frenchman voice, picked from 17 candidates. Press the phone icon and talk while your hands are busy. The first sentence comes back in about 2.6 seconds.
In the spirit of Auguste Escoffier, senior adviser to every kitchen.
How long it took
Books are read into text.
Recipe cards cut. An AI labels every card.
Search, chat and site built. Live behind a PIN by mid-afternoon.
The own-model experiment. Ingredient pairing opens. A voice is chosen.
The chat engine is chosen. Ingredient pictures are drawn.
A private cloud copy and nightly backups.
Dishes tab and saved chats: 37 changes shipped in one day.
The Mac Studio travels to Thailand.
Back online in Thailand.
Reference notes: research can sit beside the books.
Its own address, auguste.mikarina.org.
Who did what
Decides what it is for. Reads the results. Approves or rejects.
Claude Code writes the software, one small change at a time. Tests must pass before a change goes live.
MiMo reads, labels and answers. Gemini read the books. Models on the Mac Studio draw pictures, speak and listen.
What went wrong
A quarter cup was read as “%4”.
Now48 worst books re-read from page photos. Quantities are converted by code, not by the AI.
In 86 blind comparisons it was never chosen. The longer, better-sourced answer won every time.
NowThe knowledge lives in the library, not in the model.
After 171 of 206 books, and again with another AI.
NowEvery paid run starts with a small pilot and a spending cap.
A layout saved on a 3,392-pixel screen was applied to a 1,666-pixel window.
NowTests run in the real browser with the real saved settings.
For our kitchens
Private by design
Try it
Appendix 1
You type or say it in plain words. Recent turns of the chat go with it, so follow-up questions work.
One by words (SQLite full-text search), one by meaning (BGE-M3 vectors). The best cards from both are merged. Notes you add to the library are searched too.
The model gets the cards and a rulebook: facts only from the cards, cite each as [n], say “not in the library” when it is not, mark opinions, estimate allergens.
MiMo V2.6 Pro, a cloud model, receives the question and the cards and writes the answer in the adviser’s voice. If it fails, a local model on the Mac answers instead.
The answer appears with the cards it used. Open a card to see the page it came from.
The chat is kept in Past chats, on the Mac.
Appendix 2
Appendix 3
| Figure | What it is | How we know |
|---|---|---|
| 211 | Cookbooks in the library | Counted from the library folder, 11 Oct 2026. |
| 84,526 | Pages read from the cookbooks | Counted with a PDF reader, 11 Oct 2026. |
| 25.3 | Words of text read (millions) | Counted from the extracted book text, 11 Oct 2026. |
| 60,379 | Recipe cards from the books | Counted in the library database, 11 Oct 2026. |
| 1,095 | The chef’s own recipes | Counted in the library database, 11 Oct 2026. |
| 602 | Menus, the chef’s own and others’ | Counted in the library database, 11 Oct 2026. |
| 4,501 | Ingredients on the pairing map | Counted from the pairing data, 11 Oct 2026. |
| 61,474 | Recipes on the map | Counted from the map data (cookbook cards plus the chef’s own), 11 Oct 2026. |
| 11,996 | Dots drawn on these slides | A sample of the map, evenly spaced within each course. |
| 14 | Allergens estimated on each card | The fixed list the cards use. |
| 93,725 | Ingredient pairs found in the books | Noted in the build log, 25 Sep 2026. |
| 2,721 | Cards re-read in the quality check | Noted in the build log, 24 Sep 2026. |
| 834 | Of those, cards that failed | Noted in the build log, 24 Sep 2026. |
| 4,380 | Damaged cards rebuilt | Noted in the build log, 24 Sep 2026. |
| 48 | Worst scanned books re-read from page photos | Noted in the build log, 26 Sep 2026. |
| 119,247 | Files protected by the nightly backup | Noted in the handover, 28 Sep 2026. |
| 0.003 | Cost of one question (US dollars) | Measured on the chat engine’s spending log, 11 Oct 2026. |
| 2.6 | Seconds to the first spoken sentence | Timed in a voice call, 25 Sep 2026. |
| Figure | What it is | How we know |
|---|---|---|
| 17 | Voices compared before one was chosen | Noted in the build log, 25 Sep 2026. |
| 368 | AI usage for the first version (US dollars) | Stated total for 22 to 26 Sep 2026. Part of it is an estimate. |
| 160 | Of that: coding sessions (estimate) | The chef’s own estimate. Not measured. |
| 106 | Of that: MiMo (US dollars) | Noted in the build log, 22 to 26 Sep 2026. |
| 97 | Of that: Gemini (US dollars) | Noted in the build log, 22 to 26 Sep 2026. |
| 5 | Of that: Claude API (US dollars) | Noted in the build log, 22 to 26 Sep 2026. |
| 86 | Blind comparisons, own model against plain model | Noted in the build log, 25 Sep 2026. The own model was never chosen. |
| 171 | Books read when the credit ran out | Noted in the build log, 23 Sep 2026. |
| 206 | Books on the shelf at the start | Noted in the build log, 22 Sep 2026. |
| 3,392 | Pixels across: the screen a layout was saved on | Noted in the build log, 26 Sep 2026. |
| 1,666 | Pixels across: the window it was applied to | Noted in the build log, 26 Sep 2026. |
| 37 | Changes shipped on 28 Sep | Counted in the code history. |
| 20 | Days from the first book to a live service | 22 Sep to 11 Oct 2026, day 1 to day 20. |
| 241 | Commits since 25 Sep | Counted in the code history, 11 Oct 2026. |
| 50 | Test files | Counted, 11 Oct 2026. |
| 48 | Milliseconds a search takes (typical) | Measured, 26 Sep 2026. |
| 64 | Memory of the Mac Studio (gigabytes) | System report, 11 Oct 2026. |