Story
He asked AI to count carbs 27000 times. It couldn't give the same answer twice
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Overview
AI-powered carb counting tools can provide inconsistent estimates, yielding different results even when given the same input. This variability poses significant risks for individuals managing diabetes, as inaccurate carb counts can lead to hypoglycemic emergencies.
Summary from diabettech.com.
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Hacker News · diabettech.com
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Based on 5 sampled comments from this thread. Analyzed 2026-04-29.
- Praise: The study effectively highlights the limitations of LLMs in providing reliable nutritional information, reinforcing the need for caution in using AI for health-related tasks.
- Praise: By executing the model multiple times at the lowest randomness setting, the study aimed to minimize variance, showcasing the inherent unpredictability of LLM outputs.
- Concern: The article demonstrates a serious lack of understanding regarding how LLMs work, leading to unrealistic expectations about their capabilities in carb-counting.
- Concern: Many users are unaware that LLMs are not designed to provide accurate nutritional assessments, which could lead to harmful reliance on these models for health decisions.
- Concern: The variance in responses from the LLMs, even with minimized randomness, underscores the unreliability of these models for critical tasks like dietary calculations.