r/MachineLearningAndAI

Red 3, seed oils, ingredients banned in Europe but sitting smugly on shelves here.

Half my feed is people flipping products over and realizing they have no idea what they're looking at. And it's not just food anymore, it's shampoo, cleaning spray, kids' snacks.

The wall everyone hits is the same: you turn it over and it's fifteen-letter chemical names you can't pronounce, let alone judge.

I hit that wall with a nasal spray I'd used daily for years. One day I actually read the back, benzalkonium chloride, polysorbate 80, phenylethyl alcohol, and sat there Googling them one at a time in the pharmacy aisle. Turns out benzalkonium chloride can actually make the rebound congestion worse with regular use, which is the exact thing I was spraying it to fix. I cut way back after that. That moment of betrayal was when I knew Cornstarch had to be built.

Point your camera at any label and it reads it instantly, in plain English. No barcode, no database to match against. If you can see the label, it works, which matters because barcode apps like Yuka just shrug at anything they haven't indexed.

I use it constantly now, mostly scanning stuff right there in the aisle before it goes in the cart.

7,500+ people use it now and I'm genuinely excited about where it's going. The DMs and reviews mean the world to me and my tiny team.

https://apps.apple.com/us/app/cornstarch-ingredient-scanner/id6743107572

My favorite feature, it also builds a weekly Health Intelligence report from what you scan, your worst-offending ingredients, a clean ratio you watch climb week over week, and a plan to actually improve. The more you scan the better the insights.

What's the one ingredient that made you start actually reading labels?

EDIT — adding ABC format per sub rules:

A (Answer): Cornstarch reads any product label with your camera and explains what's actually in it in plain English, flagging allergens, dyes, and risky ingredients across food, skincare, supplements, and household products. No barcode needed.

B (Better): Unlike Yuka or Bobby Approved, which rely on barcode databases and fail on anything unindexed (boutique brands, supplements, foreign products, reformulations), Cornstarch uses OCR + LLM to read the actual label, so it works on any product even if it's never been catalogued. Yuka also won't analyze supplements, protein powders, pet foods, sprays; Cornstarch does.

It also goes beyond a single scan, building a weekly Health Intelligence report that tracks your worst-offending ingredients, a clean ratio that climbs week over week, and a personalized plan to actually improve, something the barcode apps don't offer at all.

C (Cost): Free to download with limited free scans. Unlimited scans + full Health Intelligence Report via subscription: $29.99/year | $2.50/month (3-day free trial) or $4.99/month flexible spend. No other IAPs. Family Sharing is also enabled, so one subscription covers your whole Apple Family group (up to 6 people).
App Store: https://apps.apple.com/us/app/cornstarch-ingredient-scanner/id6743107572

Dear Folks, I have created multiple content on Machine Learning(work in progress), and they are free. I am a data scientist and a post grad degree holder in AI/ML from IIT. To help the machine learning community with important Machine Learning Concepts, I have created multiple long form videos, and structured topicwise digestible contents structured as playlists for learning.

If you go through the first two playlists:

Introductory Machine Learning Concepts
Probability Foundations: Univariate Models

You might find helpful content, I have tried explaining with intuitions, derivations, and this is work in progress. For code implementations, scikit learn website has great content on them as well. In total they have 60+ topicwise videos so far, and I think they have the potential to help folks a lot in starting with concepts, or getting with mathematical concepts, or whether you are preparing for an AI/ML/Data job interviews etc.

When I sat for my interviews, I was grilled on my project, but majority of questions from my project tested more on foundational concepts and there know how’s.

These are FREE content on youtube. This is for the benefit of the learning community.

Link: https://youtube.com/@aayushsugandh4036?si=w5MKORU2fWzLRrAJ

I'm tired of web platforms forcing us to upload everything to the cloud. So, I built LensLaber, an offline-first computer vision annotation tool.

I developed the whole thing on my everyday laptop: an old 2015 Asus X550LD (i5, 8GB RAM, 120GB SSD). I wanted to prove you don't need an expensive GPU workstation for AI labeling. By optimizing the architecture, YOLO + MobileSAM run locally on standard CPU using just 600-900MB of RAM. You just bring your own YOLO weights in ONNX format.

Harry Ratcliffe (Applied AI Ecosystem Leader) reviewed the architecture and was mostly surprised by how smoothly both models run on this 2015 hardware. He validated that this local, low-RAM setup is exactly what high-security sectors like medical imaging or manufacturing actually need.

Now I need honest testing, not casual "looks nice" comments. Whether you're a professional managing sensitive enterprise data or a student working on a class project, I need people to actually run real datasets through it. That’s the only way to see how the UI handles real-world friction.

Your time matters. If you provide active feedback during this beta, I’ll give you a lifetime free license for the final release.

Download the beta here:https://lenslaber.github.io

I'll be hanging around the comments to fix any bugs you find. Let me know your thoughts.