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AI Product Engineer | Building Production Ready AI Apps
23
Followers
AI Product Engineer | Building Production Ready AI Apps
Cover image for $10,000,000 was sitting at the
$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 (https://www.pokerstars.com/poker/room/prohibited/?utm_source=chatgpt.com) 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?
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Cover image for I’m not just building a
I’m not just building a poker UI. I’m building a system that can understand the table. This project started with a simple idea: Can software look at a poker table and turn what it sees into structured game logic? The deeper I get into it, the more interesting the engineering becomes. The system has to deal with things like: → Card recognition — identifying cards from the screen → Board detection — understanding the current board state → Player positioning — determining where players are located → Action recognition — interpreting Fold, Call, and Raise → OCR — extracting information from visual elements → Coordinate mapping — knowing where important elements exist on different layouts → Game-state logic — turning all that visual information into something the application can actually understand And this is where AI-assisted development becomes powerful. I can use an AI coding agent to help investigate the codebase, implement changes, run tests, and work through bugs. But the outcome still depends on how I design the logic, test the edge cases, and verify what the system is actually seeing. The goal isn't: “Make AI write a poker application.” The goal is: “Build a system that can reliably understand what is happening on the table.” That's a completely different engineering problem. Right now I'm focused on making the recognition and game-state pipeline more reliable because one incorrect card, coordinate, or player action can affect everything that comes after it. Computer vision + AI + software engineering = some seriously interesting problems. 🧠🔥 What would you build with this kind of technology if you had reliable real-time visual recognition?
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Cover image for AI can write the code.
AI can write the code. Engineering is knowing what to test. I’m currently working on a poker-vision system where the application needs to understand what is happening on the table — recognize cards, identify ranks, process board states, and translate visual information into usable application logic. And this project is teaching me something important about AI-assisted development. The difficult part isn't getting AI to change the code. The difficult part is proving that the change actually works. My current workflow looks more like: Read the codebase → Understand the existing logic → Make the change → Run the test suite → Inspect the real output → Find inconsistencies → Fix → Test again. For example, I’m currently testing multiple buyer frames against the recognition logic instead of assuming that one successful test means the system is correct. That's a big difference. A function can compile. A test can pass. The UI can render. And the system can still be wrong. That's why I don't want my AI workflow to be: “Generate the code and move on.” I want it to be: “Make the change, challenge the result, and verify the behavior.” This is becoming increasingly important as coding agents take on more implementation work. Modern agent workflows are already moving toward repository-wide investigation, automated tests, linting, code review, and validation rather than simply generating files. The GitHub Blog (https://github.blog/changelog/2026-03-18-configure-copilot-coding-agents-validation-tools/?utm_source=chatgpt.com) For me, AI isn't replacing the development process. It's changing where I spend my attention. Less time typing every line. More time understanding: What should happen? What actually happened? Why are they different? And how do I prove the fix is correct? That's where the interesting engineering work begins. 🔥 For developers building with AI agents: what do you trust more a passing test, the actual application behavior, or both?
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Cover image for The hardest part of AI-assisted
The hardest part of AI-assisted development isn't generating code. It’s knowing when the code is wrong. I’m currently working on a poker-vision project where the system has to interpret what’s happening on the table — including card recognition, board ranks, hero cards, and positional coordinates. And this is exactly where AI-assisted development gets interesting. The agent can make a change. It can tell you the change looks correct. It can even run the code. But then you look at the actual result and realize: “Something is still wrong.” In this case, I was working through an OCR/ranking issue where the system was reading card information and producing an incorrect interpretation. So the workflow became: Inspect → Understand → Change → Run → Compare → Debug → Verify. Not: Prompt → Generate → Ship. That difference matters. AI coding agents are increasingly capable of working across repositories, editing multiple files, running tools and debugging issues. But current engineering discussions increasingly focus on the verification layer making sure generated changes actually produce the intended behavior. CodeRabbit (https://www.coderabbit.ai/guides/coding-agent-workflow?utm_source=chatgpt.com) For me, this is changing how I think about development. The value isn't just how quickly I can generate code. It’s how well I can understand the system, identify the real problem, direct the agent, and verify the final result. That's where engineering judgment still matters. And honestly, some of the best lessons come from the bugs that refuse to disappear after the first fix. 🔥 Developers using AI: what's one bug that looked “fixed” until you tested the actual behavior?
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