Cutmuse – AI tool for haircut recommendations by face shape

Hacker News

Hi HN, A few months ago, I started building Cutmuse after hearing the same thing over and over from friends and family: “I never know what haircut suits me.” The idea was to create a tool that uses AI and facial analysis to recommend personalized haircuts, hair colors, and even glasses based on each user’s unique features. You upload a photo (no login required or payment for the free version), and within minutes get a custom style report that includes: Haircuts that fit your face shape Hair color suggestions based on your skin tone Eyewear styles that match your proportions And optional grooming/skincare tips This isn't like Instagram filters or beauty apps that overlay random looks. What makes Cutmuse different is the depth of the analysis. It applies real visagism principles — a method used in professional image consulting — combined with facial landmark detection and color analysis to give you results that are not just aesthetic, but structured. We're currently live with users in 10+ countries and still iterating. This version includes: - A redesigned onboarding and report UX - A completely free plan (instant access, credit card required) - A more accurate styling engine Our stack mixes computer vision, handcrafted logic based on beauty design systems, and practical heuristics tuned from early user feedback. Would love your thoughts — UX, concept, practicality, or anything else. If this seems like a pointless problem to solve, I’d like to hear that too. Thanks for checking it out. With the code CM50 you get a 50% off https://www.cutmuse.com/

Visit product View on Hacker NewsJun 22, 2025

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Indie HackersFits the IH revenue-focused audience · Strong signals: started · Missing: supports, reddit linkedin, podcasting

85%85% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

Hacker NewsMay not resonate with HN audience · Strong signals: ide, io · Missing: https docs, excited, just released

41%41% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

Product HuntUnlikely to reach the leaderboard · Strong signals: apps, user, computer · Missing: mac, agents, macos

41%41% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Strong signals: personal, apps, month · Missing: mobile apps, ios, entrepreneurs

37%37% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Strong signals: users · Missing: plus, platform, intuitive

28%28% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue

11%11% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto

0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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