Hello,

In one of my apps I want to integrate flutter_gemma. In beta testing this works fine.

The question is about the scalability:

The plan is that users can download a slim version of the app from Play / AppStore, then upon approval download via direct link, a gemma model from Hugging Face. Without signing up to Hugging Face.

Did anyone tried this? Is it a scalable solution?

The alternative is to host the model on my server, however it is 4GB, and the price of the bandwitdh and storage use is higher than simply using Gemini via API so I see no reason to do it. For me, the prospect of SLMs in products, is about them being free to use.

Best,

Saar.

Objective To evaluate the existing meta-analytic evidence of associations between exposure to ultra-processed foods, as defined by the Nova food classification system, and adverse health outcomes.

Link to study

Results:

Greater ultra-processed food exposure and higher risks of

CVD, RR 1.5.

T2DM, RR 1.11-1.13.

Prevalent anxiety outcomes, OR 1.48.

Combined common mental disorder outcomes, OR 1.53.

Conclusions Greater exposure to ultra-processed food was associated with a higher risk of adverse health outcomes, especially cardiometabolic, common mental disorder, and mortality outcomes.

The purpose of this post is to highlight an issue that I believe is often overlooked, including by healthcare professionals.

The problem

Basal metabolic rate (BMR) is commonly estimated using predictive equations such as the Mifflin–St Jeor (MSJ) equation. With the growth of digital health applications, these equations are increasingly being used to estimate energy expenditure. However, in many cases they are treated as static estimates that are updated only when body weight changes.

The problem is that all predictive equations have an inherent and unavoidable error that is clinically meaningful. For example, the MSJ equation has a root mean square error (RMSE) of approximately 200–250 kcal/day, with errors exceeding 600 kcal/day in some individuals (PMID: 36837808; attached). A daily error of 500 kcal—well within the equation's reported range of uncertainty—can completely offset an intended calorie deficit or surplus, preventing users from achieving their weight-management goals.

In clinical practice, registered dietitians typically use these equations only as an initial approximation, which is exactly what they were designed for. They then calibrate the estimate based on the individual's observed weight trajectory and reported energy intake, refining the recommendation over time rather than relying on the initial prediction alone.

Adapted from PMID: 36837808.

Hi, Im looking for recommendations for marketing services specalized in mobile apps to increase growth, reach and sales.

Please provide real recommendations based on experience and if possible provide a proof for your ARR / MRR and that you actually own a real app.

Thanks in advance. I hope its fine with all the rules here.