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Feriadito — a REST API I built and maintain, providing holiday data across multiple countries: querying holidays by country and date range, verifying whether a specific date is a holiday, and listing supported countries. Built in Go, following the same structure as GwenStateFarm. Public docs at docs.feriadito.com.
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This is what happens when design meets strategy. 🚀
Meet VERLUME - a full brand identity + website we built end-to-end for a client.
Clean UI. Bold typography. A site built to actually convert.
We work with founders across the US too - different timezone, same obsession with quality.
Ready for your brand's own glow-up? Drop "EDGE" below. 👇
#WebDesign #BrandIdentity #DigitalAgency
the bold typography with the clean ui is a strong combo. which page was the trickiest to get right?
A multi-tenant inventory and accounting SaaS for retail, wholesale, distribution, and warehouse businesses. It replaces disconnected spreadsheets with one system for stock, purchases, sales, returns, quotations, dues, payables, and ledgers. Owners get dashboard analytics, downloadable reports, WhatsApp invoice sharing, and AI-assisted insights. Role-based access and organization management support multiple teams.
Built with Next.js, React, PostgreSQL, and Sequelize.
A strong use case for simplifying complex operations.
$10,000,000 was sitting at the top of the 2026 WSOP Main Event.
That number is one of the reasons I became interested in building this.
Poker isn't just cards on a screen.
At the highest level, millions of dollars can depend on thousands of individual decisions.
So I'm building something around a question that I find technically fascinating:
Can software understand what is happening on a poker table from visual data?
That's what I'm working on with my Poker Vision project.
The system is being designed to recognize and structure information such as:
→ 🃏 Hole cards
→ 🎴 Board cards
→ 👤 Player positions
→ 💰 Betting actions
→ 🔎 OCR information
→ 📍 Table coordinates
→ 🧠 Game-state changes
Then the challenge becomes turning all of that raw visual information into reliable structured data that another part of the application can understand.
And that's where the engineering gets serious.
A single recognition mistake can completely change the state of the game.
So I'm not interested in building something that simply looks intelligent.
I want to build something that can:
Detect → Interpret → Validate → Test → Learn from errors → Improve.
One important boundary: I'm building this as a research/analysis and development project, not a real-time system that tells someone what action to take during live-money play. Major poker platforms prohibit real-time assistance that influences decisions during play. PokerStars
The bigger idea is what excites me:
Computer vision + AI + poker data + software engineering.
When the stakes can reach millions, even a small technical problem becomes an interesting engineering problem.
And that's exactly the kind of problem I enjoy building. 🔥
If you were building this system, what would you tackle first: card recognition, player tracking, OCR, or game-state detection?
