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I built a Telegram bot that counts calories from food photos. It confidently called soup "berry compote"

My wife tracks her meals, and I watched her type "buckwheat, boiled, 100 g" into a calorie app for the hundredth time. Search, scroll, pick the wrong entry, fix the grams. Every meal, every day. At some point it's easier to teach a vision…

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My wife tracks her meals, and I watched her type "buckwheat, boiled, 100 g" into a calorie app for the hundredth time. Search, scroll, pick the wrong entry, fix the grams. Every meal, every day. At some point it's easier to teach a vision model to look at the plate.



So I built a Telegram bot. You send a photo of your food, it identifies the dishes, estimates portion weights, and replies with a card: calories, protein, fat, carbs. Text and voice work too ("2 eggs and a toast").






The borscht incident



The first version was hilariously confident about wrong answers. Borscht — a red beet soup, if you've never met one — came back as "berry compote" (a sweet berry drink). Red liquid in a bowl, what else could it be?



Adding more example dishes to the prompt made it worse: the model just got magnetized to whatever was on the list. A cod fillet became "syrniki" (cottage cheese pancakes) because syrniki were mentioned and both are pale and pan-fried.



What actually fixed it was making the model read the serving context before naming anything: liquid served in a deep bowl with a spoon and sour cream is soup, not a drink. Flaky texture that separates in layers is fish, not pancakes. Fried items are never served floating in liquid. A short list of physical rules beat a long list of dishes.



Portion estimation works the same way — the model reasons from plate size, cutlery, how full the bowl is. My wife has been checking its gram estimates against her kitchen scale for a week and it lands closer than either of us expected.






Stack, briefly



Python + aiogram, a vision LLM with structured JSON output (with a fallback parser for the days the model decides to wrap JSON in prose), Pillow for rendering the result cards. Photos are analyzed on the fly and never stored. Payments are Telegram Stars, so there's no app store, no signup, no card form — the whole onboarding is "send a photo".



Yesterday I also wired up inline mode: type @SnapPlateBot in any chat, describe the food, and it counts right there — like @gif, but for calories.






Try to break it



The bot is free to try (5 analyses + 1 daily):



https://t.me/SnapPlateBot?start=devto



I'd genuinely love reports on non-Russian cuisines — it grew up on borscht, so ramen, mole and biryani are its final exam.

SOC Incident Playbook: Vulnerability Remediation & Verification
title: Detect Exploitation - I built a Telegram bot that counts calories from food photos. It confidently called soup "berry compote"
id: 3b8e5bf4-8c8b-4a14-b9fb-e807cd1439ef
status: experimental
description: Automatisch generierte SIEM-Erkennungsregel basierend auf CTI Intelligence
references:
  - https://tsecurity.de/
author: iShareStuff CTI Automated Detection Engine
date: 2026-09-24
logsource:
  category: network_connection
  product: any
detection:
  selection:
      CommandLine|contains:
        - 'exploit'
  condition: selection
falsepositives:
  - Legitime administrative Zugriffe oder Penetrationstests
level: high
tags:
  - attack.initial_access
rule CTI_Threat_Indicator {
    meta:
        author = "iShareStuff CTI Automated Detection Engine"
        date = "2026-09-24"
        description = "YARA Signature for "
    strings:
        $str = "I built a Telegram bot that co" ascii wide
    condition:
        any of them
}
tsecurity.de Cognitive Threat RAG
Fokus-Vektor:

Kognitive Analyse für identifizierte Bedrohung: Erhöhte Bedrohungslage im Bereich I built a Telegram bot that counts calor.... Basierend auf 368k Vektor-Korrelationen werden sofortige Isolationsmaßnahmen für betroffene Endpunkte empfohlen.

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  • 3. Telemetrie & EDR-Alerts: Prozessaufrufe und Child-Processes auf anomale Shell-Spawns überwachen.
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