ZENODE

How to launch your first crypto AI model on GenesisL1. NO CODING REQUIRED! 🤯 Here’s a quick guide to GL1F Crypto 👇 1️⃣ CHOOSE YOUR DATA Open crypto.gl1f.com → Create Crypto AI model. Select: • Exchange: where the data comes from. • Market: the coin you want to study. • Candle: data resolution - 15m means one candle every 15 minutes. • Start / End: the historical period your model will study. For this example: Binance → ETH → 15m candles → six months of completed history. 2️⃣ DEFINE YOUR QUESTION Use these example settings: • Direction: Up • EMA baseline: 5 • Target: 1% • Stop: 0.5% • Horizon: 5 hours EMA is a moving average that gives more weight to recent prices. “5” means five candles. Your question becomes: “Will ETH reach 1% above EMA5 before reaching 0.5% below it, within the next five hours?” Both levels are measured from the EMA baseline. The model learns from historical yes/no outcomes. 3️⃣ CHOOSE YOUR SIGNALS Signals are the information the model can see: price changes, volatility, volume, momentum, BTC movements and more. Start with the “Optimal” preset. More signals don’t automatically mean a better model. Click “Build dataset” → “Continue to training”. 4️⃣ TRAIN YOUR MODEL For your first experiment, keep the default model settings and use “Walk-forward” validation. That means learning from earlier data and testing on what happened next. The main settings: • Trees: how many learning steps the model can take. • Depth: how complex each tree can become. • Learning rate: how much each tree contributes. • Min leaf: minimum examples supporting a final tree decision. • Final test %: the latest data reserved for evaluation. Keep early stopping enabled: it stops adding trees when validation stops improving. Train, inspect the results, then save your .gl1f file. 5️⃣ BACKTEST WITH COSTS Use the final test period and include fees and slippage. “Entry threshold” is the minimum probability required for a simulated signal. 0.60 = at least 60%. Start with 1× leverage for this exercise. Check drawdown, trade count and the full equity curve - not just the final return. Avoid repeatedly changing settings to make the same final test look better. 6️⃣ RUN INFERENCE Inference means asking your trained model to evaluate a new completed candle. A result of 0.72 means: “The model estimates a 72% probability of the specific event you defined.” It is an estimate, not a guarantee. 7️⃣ OPTIONAL: PUBLISH When deployment is available, you can mint your model as an NFT on GenesisL1, choose its license and configure free access, tips or paid usage. Pick a market. Define a question. Build. Test. Learn. No code required. Fees are burnable. Experimental research software. Backtests are simulations; the platform explicitly excludes live trading and automated execution. 🤝 genesisl1:native

Nearly $800 million in total cumulative revenue since its launch. That’s Pumpfun. They broke the coding barrier for launching tokens. We know 99.9% of them are rugs lol. Now imagine breaking the coding barrier for machine learning. 🤯 Anyone being able to launch their own AI model with no skills required and being able to tokenize & monetize it if they want to. Same principle. Completely different outputs. Well, I know this sounds fascinating, but I’m sure that, at first glance, you can’t imagine how huge this could become. So let me tell you this… Keep your eyes on genesisl1:native Mikhail is cooking.

What if Hugging Face, an AI Inference Market and a Machine Economy Lived on the same chain?