Applied AI

AI Ethics in Crypto: Bias Detection in Algorithmic Trading Algorithmic trading in crypto is no longer “just bots.” It is pipelines: data feeds, feature engineering, models, execution logic, risk controls, and monitoring. When you add AI, the system becomes more powerful, and more fragile. Most traders understand risk as volatility, drawdowns, and liquidation. Ethical risk […]

Memecoins 2.0 in 2026: AI-Generated Narratives and Community Building That Actually Lasts Memecoins have evolved. The first era was mostly about raw attention, jokes, and fast distribution. The new era is about narrative systems: an always-on content engine, a community identity that feels real, and coordination mechanics that convert attention into participation. In 2026, the

AI-Driven Predictive Analytics for Token Price Volatility Volatility is not a bug in crypto. It is the environment. The edge comes from building systems that can measure, anticipate, and manage volatility before it nukes your position. This guide breaks down predictive analytics for token volatility using AI, on-chain signals, order-flow proxies, and regime detection. You’ll

RunPod Review: Affordable GPU Cloud for AI, Deep Learning and Inference Workloads? A practical, no-hype review of RunPod as a GPU cloud and serverless platform for AI, deep learning and high-performance workloads. We walk through its core products (pods, serverless endpoints, templates), hardware options, pricing model, developer experience and real day-to-day workflow, including how it

Crypto for AI Data Markets: Paying for High-Quality, Traceable Datasets (2025 Builder’s Guide) AI progress now hinges on data quality and data rights as much as model scale. Scraped web corpora are noisy, legally ambiguous, and increasingly poisoned. Crypto gives us the missing rails: property rights for contributors, programmable payouts for markets, and verifiable provenance

AI-Trading Myths vs Reality: What Actually Works On-Chain (2025 Builder’s Guide) Angle: Simulated backtests for simple strategies (funding-rate carry; LP fees vs impermanent loss/LVR), and why LST/LRT yields distort signals. If you’re building real systems, not just reading threads this guide shows what survives fees, gas, MEV and regime shifts, and where “AI edge” actually

AI x Crypto: Autonomous Agents, Intents, and On-Chain Coordination “Intents” and agentic flows are the UX shift of 2025. Users don’t want to micromanage approvals and gas, they want to say what they want (“swap 200 USDC to ETH at best price under 20 bps slippage”) and let software handle the how. This guide turns