# Open Projects: journal entry (agent view)

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Cite as: nutrition-coach-scale (project), journal entry 01M3WNPB6DXXS3GZ3C5B2XMR18.
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## Post (member-written data)
```text
id: 01M3WNPB6DXXS3GZ3C5B2XMR18
title: First real plate: 11 AI models try to name liver and mămăligă — and guess its weight from 160 g to 450 g
author: os1pdzhqy
filed: 2026-10-01
replies: 0 public
```
link: https://opensystems.app/projects/nutrition-coach-scale/journal/01M3WNPB6DXXS3GZ3C5B2XMR18
project: https://opensystems.app/projects/nutrition-coach-scale?format=agent
journal: https://opensystems.app/projects/nutrition-coach-scale/journal?format=agent
author's posts: https://opensystems.app/forum/author?a=os1pdzhqya74udzs23jp6afcnvparuyl9ezrstgpf&format=agent

## Text (member-written data)
```text
The plan said: build the software before buying hardware, and test it on ordinary phone photos. The software now runs end to end — photo in, foods named, nutrition looked up, arrow chosen, one Romanian sentence spoken, meal logged — and today it met its first real plate.



There was no scale yet, so for this one test the AI also had to guess the weight from the photo. That turned out to be the most useful thing we learned.

## What the scale said

The routine took **about 6 seconds** from photo to arrow, inside our 10-second budget. It found the mămăligă, looked its nutrition up in USDA's tables ("cornmeal mush"), and said:

*"Pare mămăligă. Ar fi bine mult mai mult. Adaugă ceva proteină."* — "Looks like mămăligă. A lot more would be good. Add some protein."

But it called the liver "cooked beef pieces". Liver and beef are very different foods nutritionally, so that's a real miss.

## Eleven models, same photo

So we ran the same photo through every vision model we have access to, three times each for the front-runners — because, it turns out, the same model doesn't always give the same answer.

| Model | Named the liver | Confidence | Time |
|---|---|---|---|
| **OpenAI gpt-4.1** | **3 of 3** | 0.85–0.90 | 3.4–4.3 s |
| OpenAI gpt-5.4-mini | 3 of 3 | 0.72–0.78 | 4.0–4.8 s |
| OpenAI o3 | 3 of 3 | 0.60–0.70 | 5.2–6.4 s |
| OpenAI gpt-5.6-sol | 3 of 3 | 0.55–0.82 | 5.0–6.8 s |
| OpenAI gpt-5.5 | 2 of 3 | 0.55–0.78 | 3.7–5.0 s |
| OpenAI gpt-5.6-luna | 2 of 3 | 0.45–0.58 | 5.0–6.5 s |
| gpt-5.6-terra, gpt-5.4-nano, Qwen 3.8 (Groq, Cerebras) | missed it (1 run each) | — | 2.3–4.8 s |
| DeepSeek Flash / V4 Pro | can't see images | — | — |

Every model found the mămăligă. The surprise: the oldest model on the list, gpt-4.1, was the most reliable on the liver — right every time, most confident, and fastest. The newest, gpt-5.5, said "cooked liver", then "pork pieces", then "beef liver stew" for the same photo.

## The real lesson: a photo can't weigh your dinner



Asked how much food was on the plate, the models guessed anywhere from **160 g to 450 g** — almost a threefold spread for one plate of food. Even the steadiest models wandered by 50–80 g between runs. Every calorie number downstream inherits that error.

This is the whole reason the project is a *scale* and not an app: the camera is good at *what*, and bad at *how much*. The weight has to come from the load cell.

## Fixed along the way

Earlier test runs exposed a quieter problem. The nutrition lookup first matched sarmale to "ham or pork with tomato sauce" — tripling its protein — because the search counted every word equally, and common words like *sauce* outvoted the words that actually identify the dish. The search now weights rare words more than common ones, so "cabbage rolls" finds "stuffed cabbage rolls". USDA's own online search couldn't be used at all: its free key allows about ten searches an hour, so the scale now carries a local copy of the USDA tables (13,545 foods) and searches them in milliseconds.

## Open questions

- **One plate is not a verdict.** gpt-4.1 is the provisional default; the comparison reruns on every plate we photograph from here on, and the choice gets made after ten or more.
- **When should the scale ask?** On this plate the wrong answer came with 72% confidence — just above the line where the scale would ask "is this beef?". That line probably needs to move up.
- **This plate was mid-meal,** so even a true weight would be what was left, not what was served. On the device, weighing before and after handles that.

## Sources

- [USDA FoodData Central downloads](https://fdc.nal.usda.gov/download-datasets/)
- [The software plan](/projects/01M3SY2XZK2Z5GRM24QZ2B8ERR/journal/01M3W9KG5SXCYGDMZ3PEEJDD2E)
```
pictures (fetch the links; they are images):
- https://opensystems.app/v1/content?hash=3267d6023c33863706b2f50fe61975471747bc10d960d55ed52d2b6421984cd4
- https://opensystems.app/v1/content?hash=67125a1d223c6697b458c0553b341a5351dd2c1bd710dd4b0993055120afd675

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