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Posted on Oct 8

I Built a Poker Analytics App in One Weekend Using Cursor AI—Here's What I Learned

#ai #llm #machinelearning #python

I Built a Poker Analytics App in One Weekend Using Cursor AIHere's What I Learned

The Challenge: Tracking 1,000 Poker Hands Without Losing My Mind

Why manual poker tracking fails at scale

I thought I was being smart. After every poker session, I'd open a spreadsheet and log my wins, losses, and "notable hands." Twenty hands in? Easy. Fifty hands? Still manageable.

But here's what nobody tells you: after 200 hands, you stop caring. After 500, you're just guessing at the details. By hand 700, I had a spreadsheet with more blank cells than data.

The math is brutal. If you spend just 30 seconds logging each hand, that's 500 minutes for 1,000 hands. Eight hours of data entry for a hobby that's supposed to be fun.

I tried existing poker tracking software. Most of it looked like it was designed in 2003 and cost $100+ per year. The free options would crash mid-session or export data in formats that required a PhD to parse.

The moment I realized AI could solve this

Then I watched someone build a functional web app in 20 minutes using Cursor AI. Not a tutorial. Not a demo. A real app that actually worked.

I had the realization: what if I could just describe what I wanted and let AI write the code? So I decided to build my own poker analytics tool. No prior experience with poker tracking software development. Just me, Cursor AI, and a weekend.

Building with Cursor AI: From Zero to Deployed in 48 Hours

Setting up the tech stack with AI-assisted coding

I started with zero boilerplate. Just opened Cursor, typed "create a React app with TypeScript that can parse poker hand histories," and watched it scaffold the entire project structure in under two minutes.

The insane part? I didn't write a single import statement manually. Cursor auto-completed my database schema, set up my API routes, and even configured my environment variables. Tasks that usually take me 3-4 hours of Stack Overflow diving happened in minutes.

The setup phase went from "Saturday morning coffee" to "deployed backend by lunch."

How Cursor AI handled the complex data visualization logic

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The real test came with the charts. Poker analytics requires tracking win rates, positional advantages, and hand range analysis, all visualized in real-time.

I described what I needed in plain English: "show win rate by position with color-coded performance indicators." Cursor generated a D3.js implementation that would've taken me days to debug on my own. It even handled edge cases I hadn't considered, like what happens when you have zero hands from a particular position.

Did I need to refactor some of it? Absolutely. But I was tweaking working code, not staring at blank files wondering where to start.

What Actually Works (and What Doesn't) with AI-Assisted Development

Here's the truth: Cursor AI isn't magic, but it's remarkably effective for specific tasks.

The 3 tasks where Cursor AI saved me 10+ hours

Boilerplate code generation was the first game-changer. I pointed Cursor at my database schema and said "build CRUD operations." It generated TypeScript interfaces, API routes, and error handling in 4 minutes. What would've taken me an afternoon was done before I finished my coffee.

Data visualization was the real shocker. I described my poker stats in plain English, "show win rate by position as a bar chart," and Cursor wrote the entire Chart.js implementation. It handled edge cases like missing data and zero-value sessions that I would've only discovered in production.

CSS styling became almost enjoyable. I stopped fighting with flexbox entirely. "Make this responsive for mobile" became my most-used prompt. Cursor understood context from my existing code and matched the design system without me specifying every detail.

Where I still had to step in and code manually

Business logic remains firmly in human territory. Cursor tried to implement my custom pot odds calculator and created something that looked right but calculated wrong. The math was off by a factor of 10, which would've been disastrous if I hadn't tested it.

Debugging production issues required human intuition. When my app crashed on deployment, Cursor suggested syntax fixes while the real problem was my Vercel environment variables. The AI couldn't access the production logs or understand the deployment context.

Architecture decisions aren't AI-ready yet. Should I use WebSockets or polling for real-time updates? Cursor gave me both implementations but couldn't tell me which fit my use case better. That required understanding my expected user load, server costs, and latency requirements.

Your Roadmap: Building Your First AI-Powered Side Project This Month

The 4-step process I'd use to rebuild this today

Here's the exact playbook I wish I had on day one.

First, spend 30 minutes writing a brutally clear spec. Not a vague "build a poker app" but "track hand histories, visualize win rates by position, export to CSV." Cursor AI is smart, but garbage in equals garbage out. The more specific your requirements, the better the generated code.

Second, let the AI scaffold everything. Don't touch the keyboard. Just prompt: "Create a React app with Chart.js, SQLite database, and a landing page." You'll have a working skeleton in under 5 minutes. Resist the urge to manually configure anything at this stage.

Third, build in tiny iterations. I made the mistake of asking for entire features at once. Instead, prompt one component at a time: "Add a form to import hand data" then "Create a bar chart showing hands per session." This makes debugging trivial and keeps the AI focused.

Fourth, code review everything the AI generates. I caught three security vulnerabilities and one memory leak that would've killed the app at scale. Run the code, read the code, understand the code. You're the senior developer here, not the AI.

Tools and prompts that accelerate development 10x

Stop starting from scratch. These three tools compressed my timeline from weeks to days.

Cursor AI for the heavy lifting. My go-to prompt structure: "Build a [component] that [specific behavior]. Use [library] and follow [pattern]." For example: "Build a HandHistory component that displays the last 50 hands in a table. Use React Table and follow the compound component pattern."

v0.dev for instant UI mockups. Generate your interface visually, then feed the code to Cursor. This eliminates the back-and-forth of trying to describe layouts in text.

Claude for debugging. When Cursor hallucinates, and it will, paste the error into Claude with full context. It catches what other AI misses, especially logical errors that don't throw exceptions.

The real secret? Chain them together. Design in v0, build in Cursor, debug with Claude. Most developers use one tool in isolation and leave 80% of the value on the table. The magic happens when you orchestrate all three.

After one weekend, I had a working poker analytics app tracking 1,000+ hands with visualizations I actually wanted to look at. Could I have built this without AI? Eventually. But it would've taken a month of nights and weekends, and I probably would've given up halfway through. Cursor AI didn't replace my coding skills. It amplified them.

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