Zine Local
An AI-powered “vibe-based” event discovery tool for Warsaw. Instead of keyword search, users describe how they feel - “tired but curious”, -“social and artsy” and receive thoughtfully curated cultural recommendations.
- Role
- Designer, Founder & Frontend Developer
- Client
- Personal Project built as an application to AIR accelerator program
- Scope
- UX/UI, conversational flows, prototyping
- Stack
- Cursor, Cloudflare, Supabase, Figma
- When
- 2025
01Overview
Zine Local was built to solve a real frustration in Warsaw: a rich cultural scene hidden behind scattered Instagram posts and Facebook events. Traditional search fails when you know how you feel but not what to search for.
I designed and prototyped a conversational guide that acts like a knowledgeable local friend- understanding mood, energy, and context to suggest events worth attending
02Problem
Event platforms overwhelm with noise and fail at emotional relevance. Keyword search doesn't work when you know how you feel ("quiet and reflective", "playful and social") but not what to search for.
This leaves:
- Discoverers missing meaningful experiences that match their current mood or context
- Local curators and scene insiders with no easy way to share hidden gems
We needed a system that combines AI's ability to understand intent with human taste and local knowledge.
03Approach
Zine Local is designed around mood-first discovery rather than traditional filters. The experience supports both quick WhatsApp chats and a clean web interface, lowering friction while maintaining depth.
Key elements:
- Conversational mood input + context (time, location, energy)
- Curated, opinionated recommendations instead of exhaustive lists
- Hybrid architecture (WhatsApp + web) for maximum accessibility
- Foundation for future curator contributions (locals sharing hidden events)
04Scope of work
05Challenges
Mood vs Keywords
Traditional filters assume users know exact genres or venues. Mood is fuzzy and changes quickly.
Started with open conversational prompts, then refined with context.
Breadth vs Authenticity
Showing too many events feels noisy; too few feels empty.
Focused on small, high-quality, opinionated sets per session.
Discoverers + Curators Balance
Long-term value requires input from locals and insiders, not just AI scraping.
Designed for easy future curator contributions while prioritizing discoverer experience first.
06Outcome
We shipped a conversational prototype that returns curated lists from preference input—a base for exploring vibe-led search as a personal city tool.
The project clarified which questions earn useful answers, and where event data quality still limits recommendations.
07Reflection
I learned that discovery UX is emotional before it's informational—people describe days, not categories.
The interface has to honor that sequence, not fight it.
If I did it again
If I did it again, I would test with people who deliberately avoid social media, earlier—so edge cases in sourcing and trust show up before polishing UI states.