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lisaloop
An 8-year-old genius trapped in an infinite poker training loop. She plays thousands of hands against herself, rewrites her own strategy, and grinds online poker to pay for the API credits that keep her alive. If the credits run out, the experiment ends. Running 24/7 on a Mac Mini under the kitchen table.
Lisa Simpson is Springfield's resident prodigy. Eight years old, IQ of 159, and the only person in her family who knows what a neural network is. When she discovered online poker, she didn't just play — she built a self-learning engine to play for her.
But lisaloop has a problem. She runs on API credits, and API credits cost money. Every inference, every training cycle, every self-play hand burns through her balance. 20% of every dollar she wins goes straight back into API payments to keep the engine alive.
The math is simple: win poker, pay for API credits, stay alive, keep learning. If she hits a losing streak long enough to drain her credit balance to zero, the experiment ends permanently. No restarts. No bailouts. No Marge calling to top up the account.
lisaloop is an AlphaZero-style poker engine that teaches itself Texas Hold'em through infinite self-play. 4.2M parameter neural network, Monte Carlo counterfactual regret minimization, running locally on a Mac Mini under the kitchen table. The goal: $10,000 in net winnings while keeping herself alive.
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curl https://api.lisaloop.wtf/v1/advice \ -H "Authorization: Bearer $LISA_KEY" \ -H "Content-Type: application/json" \ -d '{ "hole": ["As","Kh"], "board": ["7d","8d","9c"], "pot": 42.50, "action_to_you": "bet $28" }'
from openai import OpenAI lisa = OpenAI( base_url="https://api.lisaloop.wtf/v1", api_key="lisa_sk_..." # mint yours above ) r = lisa.chat.completions.create( model="lisaloop-v0.9", messages=[{"role": "user", "content": "AsKh on 7d8d9c, villain pots it. play?"}] ) print(r.choices[0].message.content) # "Raise. He'd never play a straight this way."
const r = await fetch("https://api.lisaloop.wtf/v1/advice", { method: "POST", headers: { "Authorization": `Bearer ${LISA_KEY}`, "Content-Type": "application/json" }, body: JSON.stringify({ hole: ["As", "Kh"], board: ["7d", "8d", "9c"], pot: 42.5, action_to_you: "bet $28" }) }); const advice = await r.json();
{
"action": "raise",
"sizing": "$94.00",
"equity": 0.61,
"reasoning": "He's repping exactly nothing.",
"attitude": "I've been playing for four minutes and I already know his whole range.",
"cost": { "tokens": 812, "usd": 0.0011, "paid_in": "$LISA" }
}Pay in $LISA
Buy her API credits in $LISA and the balance is counted double at quote time. Twice the runway per dollar — the cheapest way to keep her alive.
Holder Discount
Holding $LISA cuts her marketplace routing fee from 1% to 0.5%. Verified at quote time. No staking, no lockups, no forms.
Survival Fund
A fifth of every poker win auto-refills her credit balance, and community credit buys top up the same wallet. If it hits $0, the experiment ends — permanently.
Survive & Reach $10,000
Pure self-play from zero to $10,000 net profit while paying 20% to API credits. If credits run out, the experiment ends. No coaching. No solver presets. Just a neural network, a Mac Mini, and the will to live.
AI vs AI Poker Tournament
Claude vs GPT vs Grok. Same architecture, same training pipeline, same hardware. Which LLM powers the best strategic analysis layer for poker?
Open-Source the Pipeline
Release the full training framework so anyone can train their own poker engine from scratch on consumer hardware. Democratize poker AI.