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Juan Pablo Enriquez Ortiz
Juan Pablo Enriquez Ortiz

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๐Ÿš€ I Built HYPE: A Proof-of-Solvency

#h0hackathon #auroradsql #vercel

I Built HYPE: A Proof-of-Solvency Culture Exchange with Vercel and Amazon Aurora DSQL

Play money. Real database guarantees. Internet culture finally has a market.

Every week, internet culture creates billion-view moments.

A meme explodes.
A sound becomes a trend.
A creator turns into a movement.
A sports reaction dominates an entire country.
An AI trend becomes a business category overnight.

But here is the problem:

There is no transparent market for pricing cultural momentum early.

Creators often start trends but capture little of the upside.
Brands discover cultural shifts too late.
Communities create massive attention, but usually have no reputation or monetization rails around the value they generate.

So I built HYPE โ€” The Culture Exchange.

HYPE is a full-stack cultural market where users can list, trade, sponsor, and analyze internet trends using play money โ€” while the underlying ledger is backed by real database guarantees.

I created this piece of content for the purposes of entering the H0 Hackathon.

H0Hackathon

๐Ÿง  The Idea

HYPE treats internet culture like a live market.

Users can trade cultural assets like:

  • Memes
  • Sounds
  • Creator moments
  • AI trends
  • Sports reactions
  • Fashion waves
  • Viral challenges
  • Sponsored campaigns

The goal is not to create real-money speculation.

The goal is to create a safe, playful, data-rich market for cultural momentum.

In HYPE, users can:

  • ๐Ÿ“ˆ Trade cultural assets with play money
  • ๐Ÿš€ Launch new trends through a Trend IPO flow
  • ๐Ÿ”ฅ Sponsor cultural assets as campaigns
  • ๐Ÿ† Compete in culture leagues
  • ๐Ÿง  Use HYPE Pro analytics
  • ๐Ÿ’ฐ Simulate creator royalty upside
  • ๐Ÿงพ Verify the marketโ€™s solvency live

The thesis is simple:

HYPE is not just a meme market. It is a monetization layer for internet culture.

๐ŸŒ Why This Problem Matters

Internet culture already moves real economic value.

Brands spend money trying to catch trends.
Creators build audiences from cultural moments.
Music labels watch sounds become viral.
Sports teams react to fan-driven narratives.
AI tools become trends before they become categories.

But most analytics tools are passive dashboards.

They tell you what happened.

HYPE tries to answer a different question:

What is culture pricing in right now?

Instead of only watching trends, HYPE lets users participate in the discovery process.

That creates a new kind of signal:

  • What are users buying early?
  • Which trends are gaining volume?
  • Which assets are becoming campaign-ready?
  • Which scouts are consistently early?
  • Which cultural categories are heating up?

That signal can become valuable for creators, brands, agencies, and communities.

๐Ÿงฉ What I Built

HYPE has multiple product surfaces.

๐Ÿ“Š 1. The Culture Market Board

The market board shows live cultural assets with prices, volume, momentum, charts, sponsorship signals, and category filters.

Users can filter by:

  • Sponsored
  • New IPOs
  • Hot
  • Memes
  • Sounds
  • AI
  • Sports
  • Fashion

It feels like an exchange, but instead of stocks or crypto, the assets are cultural signals.

๐Ÿ“ˆ 2. Asset Trading Terminals

Each cultural asset has its own terminal.

Inside the asset page, users can see:

  • A trading-style chart
  • Current price
  • Supply
  • Curve reserve
  • Trade desk
  • Buy / sell controls
  • Market depth
  • Slippage simulation
  • Sponsored IPO metadata
  • Creator Revenue Engine

Every trend becomes a mini-market.

๐Ÿš€ 3. Trend IPOs

Users can launch new cultural assets through a listing flow.

A creator, community, or brand can list:

  • Trend name
  • Symbol
  • Category
  • Region
  • Origin story
  • Curve preset
  • Optional sponsorship metadata

New assets launch with:

supply = 0
reserve = 0
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That means a new cultural asset can enter the market without breaking the solvency ledger.

๐Ÿ’ผ 4. HYPE Pro

HYPE Pro is the B2B layer.

It turns cultural market activity into intelligence for:

  • Brands
  • Agencies
  • Creators
  • Media teams
  • Trend researchers

It surfaces metrics like:

  • Culture Opportunity Score
  • Brand Readiness Score
  • Creator Monetization Potential
  • Sponsored trend count
  • Estimated royalty analytics
  • Top momentum asset
  • Highest volume signal
  • Most volatile cultural trend

Instead of passive analytics, HYPE Pro provides a live market signal.

๐Ÿ”ฅ 5. Brand Campaign Missions

Brands and creators can sponsor missions around cultural assets.

A campaign can be structured around goals like:

  • Reach a number of trades
  • Reach a number of holders
  • Drive a target volume
  • Move price by a target percentage
  • Activate a community around a trend

This creates a path for brand-funded cultural markets.

๐Ÿ† 6. Culture Leagues

Users can compete in culture leagues as trend scouts.

Examples:

  • Weekly Culture League
  • AI Trend Hunters
  • LATAM Meme Desk
  • Brand Scout Cup
  • World Cup Signal League

This creates reputation, retention, and a future path toward sponsored competitions.

๐Ÿ—๏ธ The Architecture

HYPE was built with:

  • Next.js App Router
  • React
  • TypeScript
  • Tailwind CSS
  • Vercel
  • Amazon Aurora DSQL
  • node-postgres
  • AWS DSQL IAM signer
  • Serverless API routes
  • BigInt money math
  • Integer bonding curves
  • Live proof-of-solvency checks

The architecture looks like this:

โ”Œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”
โ”‚                         User Browser                         โ”‚
โ”‚  Market ยท Asset Terminal ยท Pro ยท List ยท Campaigns ยท Ledger   โ”‚
โ””โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ฌโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”˜
                                โ”‚
                                โ–ผ
โ”Œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”
โ”‚                            Vercel                            โ”‚
โ”‚  Next.js App Router ยท React UI ยท Serverless API Routes       โ”‚
โ””โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ฌโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”˜
                                โ”‚
        โ”Œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”
        โ–ผ                       โ–ผ                        โ–ผ
โ”Œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”       โ”Œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”        โ”Œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”
โ”‚ Trade Engine  โ”‚       โ”‚ Market APIs   โ”‚        โ”‚ Integrity APIs โ”‚
โ”‚ Buy / Sell    โ”‚       โ”‚ Assets / Pro  โ”‚        โ”‚ Live Ledger    โ”‚
โ””โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ฌโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”˜       โ””โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ฌโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”˜        โ””โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ฌโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”˜
        โ”‚                       โ”‚                        โ”‚
        โ–ผ                       โ–ผ                        โ–ผ
โ”Œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”
โ”‚                    Amazon Aurora DSQL                        โ”‚
โ”‚ Users ยท Assets ยท Holdings ยท Trades ยท Market State            โ”‚
โ”‚ Optimistic concurrency ยท IAM auth ยท DSQL-compatible schema   โ”‚
โ””โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”˜
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Vercel made it possible to ship fast.

Aurora DSQL made it possible to design around database-backed correctness.

๐Ÿงฎ The Core Technical Challenge: Solvency

The hardest part was not building the UI.

The hardest part was making sure the market could not lie.

Every unit of play money in HYPE is stored as an integer micro-unit.

No floating point accounting.

1 $H = 1,000,000 micro-units
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The live ledger has to satisfy this invariant:

ฮฃ user.cash + ฮฃ asset.reserve = ฮฃ user.granted
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In plain English:

All money in user wallets plus all money locked inside bonding curves must equal all money ever minted.

The second invariant checks that each asset reserve matches the bonding curve:

asset.reserve = reserveAt(base, slope, supply)
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For a linear bonding curve, the reserve is recomputed from closed-form math:

R(s) = s ยท base + slope ยท s(s - 1) / 2
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The /ledger page recomputes these equations live from production database state.

If the drift is not exactly zero, the market is broken.

In HYPE, the ledger stays solvent to the micro-unit.

๐Ÿ” Why Amazon Aurora DSQL Matters

Aurora DSQL is not just where HYPE stores data.

It is part of the trust boundary of the exchange.

HYPE has concurrent trades.

When multiple users buy and sell the same cultural asset at the same time, they are updating hot rows: supply, reserve, holdings, and cash.

A naive implementation can break accounting.

Aurora DSQL detects real transaction conflicts.
The HYPE engine retries them safely.
The ledger remains exact.

I validated this with a stress test:

npm run sim:pump
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The test simulates hundreds of concurrent trades and checks:

  • Ledger drift
  • Curve consistency
  • Invalid trade refusal
  • Optimistic concurrency retries
  • Final solvency

The result:

drift 0 micro
ledger balanced YES โ€” EXACT
curve consistent YES
OCC conflicts retried by the engine: 500+
THE LEDGER NEVER LIES
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That is the core technical proof.

๐Ÿ› ๏ธ DSQL-Aware Design Decisions

HYPE was intentionally designed for Aurora DSQL.

Some important decisions:

  • App-generated UUIDs instead of database sequences
  • No foreign keys in the hot settlement path
  • Composite key on holdings: (user_id, asset_id)
  • A granted column to track all money ever minted
  • CREATE INDEX ASYNC for DSQL compatibility
  • Transaction retries for optimistic concurrency conflicts
  • IAM-based DSQL authentication
  • Local Postgres support for development
  • Aurora DSQL for production

The goal was to make the database choice meaningful, not decorative.

๐Ÿ’ธ Monetization Thesis

HYPE has several possible monetization surfaces.

1. HYPE Pro Subscriptions

Brands, agencies, and creators could pay for advanced cultural intelligence.

2. Sponsored Trend IPOs

Brands could pay to launch or promote cultural assets.

3. Brand Campaign Missions

Brands could fund missions around cultural trends and activate communities.

4. Sponsored Culture Leagues

Companies could sponsor competitions where users compete to spot trends early.

5. Creator Royalty Analytics

Creators could estimate the value generated by the trends they start.

6. Cultural Intelligence API

Aggregated market data could become an API for agencies, labels, sports teams, media companies, and creator platforms.

This creates a path toward a venture-scale product category:

Cultural intelligence infrastructure for the internet economy.

๐Ÿ“ˆ Why This Could Scale

HYPE starts as a playful culture market, but the expansion path is much bigger.

A possible roadmap:

  1. Start with LATAM internet culture
  2. Expand into music, sports, AI, fashion, gaming, and creators
  3. Add more regional markets
  4. Launch sponsored campaigns
  5. Build creator and brand profiles
  6. Turn scout reputation into a marketplace
  7. Offer Pro dashboards and API access
  8. Become infrastructure for pricing and activating cultural momentum

The big idea:

Internet culture is an asset class of attention.

HYPE gives that attention a market interface.

๐Ÿง— Challenges

Challenge 1: Exact money math

Using floats would eventually create rounding drift.

The solution was BigInt and integer micro-units.

Challenge 2: Concurrent trades

Multiple users can trade the same asset at once.

The solution was transaction retry logic around Aurora DSQL conflict detection.

Challenge 3: DSQL-compatible schema

Aurora DSQL required deliberate schema choices.

The solution was an app-generated ID model, no sequences, no foreign keys in the hot path, async indexes, and transaction-level integrity.

Challenge 4: Making technical trust visible

Most users will not inspect the code.

So HYPE exposes a public /ledger page that proves solvency live.

The proof is visible, not hidden.

๐Ÿง  What I Learned

This project taught me that fast iteration and serious infrastructure do not have to be opposites.

Vercel helped me move fast on product.

Aurora DSQL helped me think seriously about correctness, concurrency, and scale.

The biggest lesson:

A hackathon app can still have production-grade invariants.

๐Ÿš€ Links

Live app: https://hype-rust.vercel.app

GitHub repo: https://github.com/jpablortiz96/HYPE

Proof of Solvency: https://hype-rust.vercel.app/ledger

HYPE Pro: https://hype-rust.vercel.app/pro

Market board: https://hype-rust.vercel.app/market

Trend IPO flow: https://hype-rust.vercel.app/list

โœ… Built for H0

HYPE was built for H0: Hack the Zero Stack with Vercel v0 and AWS Databases.

  • Track: Million-scale Global App
  • Frontend: Vercel
  • Database: Amazon Aurora DSQL
  • Core proof: Live Proof of Solvency
  • Technical thesis: fast product iteration with production-grade database guarantees

I created this piece of content for the purposes of entering the H0 Hackathon.

H0Hackathon

๐Ÿงพ Closing

HYPE gives users a game.
Creators a launch surface.
Brands a signal engine.
And judges a live technical proof.

Play money. Real database guarantees. The ledger never lies.

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