Algo

Rediscover the places your city forgot to tell you about.

Inspiration

People don’t struggle to find places — they struggle to find the right place for how they feel.

Apps like Google Maps and Yelp overwhelm users with popularity-driven results: thousands of reviews, sponsored listings, and trending spots. That works if you want “the best-rated café.”

It fails if you just want somewhere to exist — quietly, comfortably, intentionally.

We noticed a pattern:

People open these apps → get overwhelmed
Default to the same 2–3 places
Or give up entirely

The problem isn’t lack of options.
It’s too much noise and not enough signal.

Algo was built to reverse that.

What it does

Algo is a mood-first place discovery app.

Instead of asking:

“What’s nearby?”

It asks:

“How do you want to feel right now?”

From there, Algo:

Translates mood → environment (calm, social, focused, etc.)
Surfaces 3–5 highly relevant places (not 200)
Prioritizes overlooked, low-visibility spots
Filters out chains, ads, and algorithmically inflated locations

Core principles:

Mood-first, not filter-first
Obscurity is a feature, not a bug
Clarity over choice overload

The result:
You don’t scroll. You decide.

How we built it

This is a design-first prototype, not a full production system.

We focused on:

Discovery flow:
From vague intent → confident recommendation
Mood input system:
Translating human feeling into structured signals
Place cards:
Emphasizing vibe, energy, and context over raw ratings
Minimal UI:
Reducing cognitive load at every step

Conceptually, the recommendation logic:

Downranks high-review, high-traffic places
Prioritizes smaller, consistent, local spots
Uses qualitative signals (vibe, noise level, social density)

Challenges we ran into

1. Translating “vibe” into something usable

“Quiet,” “cozy,” and “low-energy” sound similar — but they’re not identical.
We had to define distinct emotional states that map cleanly to places.

2. Avoiding fake differentiation

It’s easy to say “we’re not Yelp.”
It’s harder to actually design something that doesn’t behave like Yelp underneath.

We had to constantly ask:

Are we simplifying — or just hiding complexity?

3. Constraint vs. usefulness

Showing only 3–5 results creates clarity…
…but risks missing something better.

We intentionally chose confidence over completeness.

Accomplishments that we’re proud of

A clear, non-generic product thesis (not “better recommendations,” but different logic entirely)
A UI that feels calm and decisive, not overwhelming
Strong thematic alignment:
- Lost & Found → rediscovering overlooked places
- Chaos → Clarity → reducing decision fatigue
Avoiding feature creep — we stayed focused on the core experience