Building Simple AI Models with Python (Beginner Code Examples) A hands-on path to your first working ML pipeline: data loading, splits, baselines, feature engineering, training, evaluation, and saving a small model. Includes tabular classification, text classification, and a minimal API. Read first: These examples are educational. If you use models for trading or risk, apply […]
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Prompt Engineering for AI Productivity A practical guide to writing prompts that produce consistent, verifiable, and useful outputs , with templates for research, governance, on-chain risk, market briefings, and dev workflows. Integrates with our Prompt Libraries. TL;DR: Good prompts set role, goal, context, constraints, and output format. Add examples, require sources, and define evaluation rubrics.
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AI Tools for Crypto Market Analysis Turn raw feeds, prices, on-chain events, and news; into decisions. We map the toolchain: data sources, feature engineering, sentiment/RAG, anomaly detection, dashboards, alerting, and the prompts that keep your analysis rigorous. Heads-up: These approaches are educational. If you apply them to trading or risk, use proper evaluation, human oversight,
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How AI Is Used in Trading Bots From simple rule-following scripts to learning systems that adapt to markets, this chapter shows where AI actually helps: signals, execution, risk, and monitoring. It also covers backtesting pitfalls, on-chain realities, and the guardrails professional teams use. Important: Educational content only; this is not financial advice. Live trading is
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