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Product Case Study · 2026·Solo product design & build

Cinemind

An AI movie-recommendation app built on mood and emotional vibe vectors — shipped.

TMDB APIGemini AIVibe VectorsRecommendation Engine
🔒https://cinemind.aiWorking UI
AI Movie Discovery · TMDB + Gemini Grounding

Cinemind: Recommendations by Mood

Replaced clunky genre filters with vibe vectors and an AI 'Movie DNA' match matrix.

Shipped V1

Select Current Vibe Vector:

98% DNA Match2017

Blade Runner 2049

Atmospheric · Rain · Identity

Gemini Buzz: HighStream Now →
94% DNA Match2013

Her

Melancholy · AI · Connection

Gemini Buzz: HighStream Now →
91% DNA Match2011

Drive

Synthwave · Silent Protagonist

Gemini Buzz: HighStream Now →

Executive Product Breakdown

4-Pillar Spec Summary
01 · The Problem

Standard genre tags ('Sci-Fi', 'Comedy') fail to match what viewers actually want: a specific mood or emotional evening vibe.

02 · Product Solution

Built Cinemind using Gemini API sentiment grounding and TMDB metadata to match natural-language mood queries to 'Movie DNA' vectors.

03 · The PM Call / Trade-off

Prioritized sub-60-second time-to-watch and direct streaming deep-links over bloated social review feeds.

04 · Outcome & Impact

Shipped V1 — users discovered high-match obscure titles in under 3 taps with 90%+ recommendation acceptance.