See how women perceive you
at first glance.
Reveals how women really see you at first sight in real life — even if the truth stings.
Sample profile
Ahead of 87% of men
Walking into the café, the first read is: clean, defined jawline, well-fitted clothes. Biggest plus is height and jawline; biggest drag is the soft hair and narrow shoulders — both fully fixable in 3 months to push him into the strong-appeal band (130+).
Strengths
- 6'0", instant first-impression height bonus
- Defined jawline with strong side profile
- Outfit fits cleanly, doesn't read older than his age
Weak spots
- Hair sits soft and lacks shape
- Narrow shoulders make the front read thin
- Slight forward shoulder rounding flattens his presence
Simulates a true first glance
Not a single-photo face score. Reads face, height, body, vibe, outfit, skin and posture together — the same bundle a stranger reads in 1–3 seconds.
Threshold-triggered, not linear
Cross the perception threshold and your perceived score jumps non-linearly — confirmed in speed-dating studies (Asendorpf et al.). The everyday-male median sits in the middle band; two aligned levers push you decisively higher.
Specific, executable advice
No generic 'be more confident'. We tell you exactly what to change about your hair, body-fat target, posture, and outfit fit — every move tied back to a real gap in the preference distribution data.
Your photos belong to you
- · Encrypted, private hosting via UploadThing — never indexable, no public URLs.
- · Reports are visible only to the signed-in owner.
- · Delete photos and history anytime, in one click.
- · Never used to train any model.
Two steps to a real score
Under 1 minute. Two photos plus a few key data points, and you get back a real-world first-impression report.
Answer a few basics
Height, weight, body fat, lifting experience. Scroll-pickers — no typing.
Upload two photos
Face + daily outfit. Encrypted via UploadThing, never shown publicly, deletable in one click.
Get your perceived score
Your perceived first-impression score, gut summary, face & body breakdown, wardrobe grade, real-world impression, strengths, and weak spots.
Why trust this — not just another AI rater
Buss 1989 · Langlois 2000 · real dating-platform data — tap to expand
Why trust this — not just another AI rater
Buss 1989 · Langlois 2000 · real dating-platform data — tap to expand
We didn't ask AI to judge by feel. The engine sits on top of peer-reviewed human research and real behavioral data.
Cross-cultural female preference research
Buss (1989) mate-preference sample across 37 cultures (n ≈ 10,047) + Langlois et al. (2000) facial-attractiveness meta-analytic review (eleven meta-analyses).
Real dating-platform behavioral data
Public OkCupid / Hinge / Tinder datasets on which signals actually drive interaction (tens of millions of swipe / message / match events) — i.e. what women actually do inside a 1–2 second decision window.
Threshold + body-preference models
Singh (1993) on body-shape preferences + Asendorpf et al. (2011) speed-dating study (n=382), together explaining the non-linear threshold-triggered perception model — that's why slow +1 +1 +1 doesn't add up like you'd expect.
We're not claiming we ran our own original 5,000-person study. What we did is take the existing peer-reviewed human research + real behavioral data, and calibrate them into a single 1-minute engine that gives you concrete feedback.
Latest from the playbook
New guides on photos, grooming, face, and body composition — written to be used, not skimmed.
- GroomingBeard research: the studies people cite, and what each one actually measuredThe beard studies everyone quotes, with what each one really tested. The best-known result is that beards did not raise attractiveness ratings.
- ResearchHow much evidence is behind each looksmaxxing procedure? We counted the papersWe counted PubMed papers, randomised trials and registered studies for 22 appearance procedures. Bone smashing has four papers. One is about Miocene apes.
- ResearchWe opened the privacy policy of 26 face-rating apps. Six of them do not have one that worksAn audit of 26 face-rating apps: 6 privacy policy links are broken, missing or unreadable, and one policy is a Google Doc about a different app.
- GroomingFragrance terms: a glossary, and the fact that eau de parfum has no legal definitionEvery fragrance term defined plainly — notes, sillage, longevity, EDT vs EDP — plus which of them are regulated and which are just marketing.
- Tools & comparisonsFree Face Analysis: What These Tools Actually MeasureFree face analysis tools measure the photo, not the read. The four kinds that exist, what each is good for, and why two of them score the same face differently.
- FitnessGym terms: a glossary that says which ones have a real definition and which are just gym talkEvery gym term defined — bulking, cutting, recomp, RPE, 1RM, progressive overload — sorted by whether it has a scientific definition or not.
- HairHair loss terms: a glossary that separates the clinical words from the clinic's marketingEvery hair loss term defined, sorted into words with a clinical definition and words invented by clinics and forums to sell you something.
- Looks improvementHow to Avoid a Double Chin in Photos: It's Geometry, Not WeightMost photographic double chins are made by a low camera angle, not by body fat. Get the lens to eye level and push your forehead forward — the fix, ranked.
- Dating photosHow to Look Good in Photos Without Smiling (Without Looking Angry)A neutral face in a still frame gets filled in by the viewer — and people default to the least generous reading. The four things a no-smile photo has to do.
- Dating photosHow to Smile in Photos (Men): The Frame Problem Nobody ExplainsHow to smile in photos: a real smile lasts a second or two, and the shutter usually lands on the ugly half of it. The honest fix — trigger, burst, pick.
- Dating photosHow to Smile Without Showing Teeth — Without Looking Like You're Hiding SomethingA closed-mouth smile only works when your eyes carry it. Why the withheld smile costs more than the teeth, and the five steps that fix it.
- FitnessMen's fitness statistics: what share of men actually lift, and what the data leaves outMen's fitness statistics from CDC BRFSS: 45.1% of US men strength-train twice a week, up from 34.4% in 2011, and 71.8% carry extra weight.