Photo calorie app Cal AI was built by two teenagers

Feasibility of Photo-Based Calorie Estimation

• Many argue the core claim (“90% accurate” calories/macros from a single photo) is effectively impossible: a camera can’t see portion size precisely, hidden ingredients, or fats/oils, sugar, fillings, or type/amount of cooking fat.
• Examples used: salad with/without lots of oil, cucumbers with/without olive oil, diet vs regular soda, butter on toast, bacon/cheese inside breaded meat, restaurant food loaded with hidden butter/sugar.
• Several posters say even nutrition labels and lab measurements have significant error; building on top of that with pure vision only worsens accuracy.
• A few suggest extra sensors (volume estimation, AR, hyperspectral cameras) might help, but still can’t resolve invisible ingredients.

Usefulness vs Harm

• One camp: rough estimates can still help most people; awareness and consistent logging matter more than precision, especially for beginners without strict macro goals.
• Counterpoint: for serious dieting, ±10–30% error can flip a deficit into a surplus; misleading low estimates may cause stalled progress and frustration.
• Some fear the app may be “actively harmful,” giving false confidence and hiding where calories really come from (oils, dressings, restaurant food).

Business, Marketing, and “AI Wrapper” Concerns

• Several see it primarily as an “AI wrapper” plus strong TikTok/influencer distribution, not a breakthrough in computer vision.
• Comparisons to earlier impossible-sounding “AI” startups and broader grifter culture: claims that marketing now routinely outruns technical reality.
• Disagreement over whether it’s a “great idea”: financially promising vs technically fraudulent and class‑action risk if claims are taken literally.
• One paying user praises the simple UI and barcode scanning but barely uses the photo feature and doesn’t plan to renew.

Founder and College Admissions Debate

• Large subthread around the founder’s public complaints about rejection from top universities.
• Critiques: essay read like a VC pitch (metrics, ARR, grindset talk), lacked self‑reflection, and signaled prestige‑hunting; some find the persona off‑putting.
• Others argue he’s clearly accomplished for his age and that elite admissions are broken, overly subjective, and hostile to tech‑optimistic profiles.
• Broader discussion of U.S. holistic admissions vs exam-based systems elsewhere, score inflation at elite applicant pools, and the role of soft factors and perceived character.

Meta and Cultural Reflections

• Some note HN is unusually unsympathetic to teenage founders here, attributing it to skepticism about the product’s truthfulness rather than age.
• Threads broaden into diet culture, public ignorance about nutrition, and a sense that many users just want something that “looks right,” even if it isn’t.