VibeFeed
Choose what to watch with taste you can train.
VibeFeed turns your mood, time, group, and taste history into a tight shortlist. It explains why each pick fits, where it might miss, and learns from what you actually watched.
Tonight
Solo - medium energy - 90m
A compact decision surface for the actual viewing moment, not an endless browse feed.
Why it fits
Tense mystery, low commitment
Each card names the fit reason, the risk, and the caveat before you press play.
Built for the moment when browsing becomes the problem.
VibeFeed keeps the decision small, explainable, and trainable so the app gets better without asking you to manage another profile.
Three-pick shortlist
Choose Now gives you a focused set of TV and movie recommendations instead of another infinite carousel.
Context-aware fit
Session type, energy, attention, runtime, intensity, and risk tolerance shape the picks for the night in front of you.
Taste training
Favorites, anti-favorites, overrated signals, disliked vibes, and director preferences teach the model what actually lands.
Why and why-not
Cards show fit reasons, miss reasons, avoid-if notes, caveats, confidence, and discovery status.
Watch feedback
A quick post-watch pulse captures whether the pick worked, why it missed, and what should change next time.
Minimal data posture
The MVP is anonymous-first, locally persistent, and explicit about guest versus account data.
From stuck scrolling to a watchable choice.
The app keeps the core path simple: name the mood, get a shortlist, choose one, then teach the model whether the night worked.
Start with the current context
Set the viewing session, energy, attention, runtime budget, risk level, intensity, and available services.
Review a small shortlist
Compare a few recommendations with reasons, risks, caveats, and confidence instead of browsing endlessly.
Save or watch
Move a title into the watchlist with the reason you saved it, or mark what you watched from the title detail screen.
Train the next recommendation
Submit quick feedback when the mood, group, explanation, or obviousness was wrong.
Give movie night a clearer decision loop.
Request access to VibeFeed and help shape a recommendation tool that learns from outcomes, not just swipes.