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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?
$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?
𝐀 𝐛𝐚𝐜𝐤𝐞𝐧𝐝 𝐭𝐡𝐚𝐭 𝐰𝐨𝐫𝐤𝐬 𝐟𝐨𝐫 𝟏,𝟎𝟎𝟎 𝐮𝐬𝐞𝐫𝐬 𝐦𝐚𝐲 𝐬𝐭𝐫𝐮𝐠𝐠𝐥𝐞 𝐚𝐭 𝟏𝟎𝟎,𝟎𝟎𝟎.
Scaling isn’t just about adding more servers. It starts with making the right architectural decisions early from 𝐝𝐚𝐭𝐚𝐛𝐚𝐬𝐞 𝐨𝐩𝐭𝐢𝐦𝐢𝐳𝐚𝐭𝐢𝐨𝐧 𝐚𝐧𝐝 𝐜𝐚𝐜𝐡𝐢𝐧𝐠 𝐭𝐨 𝐚𝐬𝐲𝐧𝐜 𝐩𝐫𝐨𝐜𝐞𝐬𝐬𝐢𝐧𝐠, 𝐡𝐨𝐫𝐢𝐳𝐨𝐧𝐭𝐚𝐥 𝐬𝐜𝐚𝐥𝐢𝐧𝐠, 𝐨𝐛𝐬𝐞𝐫𝐯𝐚𝐛𝐢𝐥𝐢𝐭𝐲, 𝐚𝐧𝐝 𝐟𝐚𝐮𝐥𝐭 𝐭𝐨𝐥𝐞𝐫𝐚𝐧𝐜𝐞.
→ 𝐃𝐚𝐭𝐚𝐛𝐚𝐬𝐞: Efficient queries, indexing, pagination, and read replicas as data grows.
→ 𝐂𝐚𝐜𝐡𝐢𝐧𝐠: Redis or similar caching layers to reduce repeated database work.
→ 𝐀𝐬𝐲𝐧𝐜: Processing: Move emails, reports, and heavy processing to background workers like Celery/SQS.
→ 𝐇𝐨𝐫𝐢𝐳𝐨𝐧𝐭𝐚𝐥 𝐒𝐜𝐚𝐥𝐢𝐧𝐠: Run multiple application instances behind load balancers using Docker/Kubernetes.
→ 𝐌𝐨𝐧𝐢𝐭𝐨𝐫𝐢𝐧𝐠: Track latency, errors, CPU, memory, and database performance before they become production issues.
→ 𝐑𝐞𝐥𝐢𝐚𝐛𝐢𝐥𝐢𝐭𝐲: Use timeouts, retries, health checks, and redundancy so failures don’t take down the entire system.
The goal isn’t to over-engineer from day one. It’s to design your backend so it can scale without becoming slow, expensive, or fragile.
𝐖𝐡𝐚𝐭’𝐬 𝐭𝐡𝐞 𝐛𝐢𝐠𝐠𝐞𝐬𝐭 𝐛𝐚𝐜𝐤𝐞𝐧𝐝 𝐬𝐜𝐚𝐥𝐢𝐧𝐠 𝐜𝐡𝐚𝐥𝐥𝐞𝐧𝐠𝐞 𝐲𝐨𝐮’𝐯𝐞 𝐟𝐚𝐜𝐞𝐝?
