Intermediate Track

Best Cloud GPU Providers in 2026: RunPod Review and Alternatives Best Cloud GPU Platforms in 2026 are no longer only for large AI labs. Developers, crypto analysts, automation builders, Web3 researchers, data teams, and independent founders now use cloud GPUs to train models, run inference, process blockchain datasets, deploy AI agents, test computer-vision pipelines, fine-tune […]

Adversarial Testing for Prompt Injection: Implementation Guide + Pitfalls Adversarial Testing for Prompt Injection is the disciplined process of testing whether an AI system can be manipulated by hostile, hidden, indirect, or conflicting instructions before that system is trusted with users, tools, files, wallets, dashboards, compliance workflows, or production actions. This guide explains how prompt

Building a Compliance-Friendly Analytics Stack: Privacy and Auditability (Complete Guide) Building a Compliance-Friendly Analytics Stack is not about collecting less insight. It is about designing a system that collects the right information, limits unnecessary exposure, preserves useful context, and produces records that can be inspected, defended, and trusted later. This guide gives you a practical,

Entity Resolution for Wallets: Implementation Guide + Pitfalls Entity Resolution for Wallets is the discipline of deciding when multiple blockchain addresses likely belong to the same real-world actor, organization, protocol cluster, or coordinated system. It matters because almost every serious on-chain intelligence workflow eventually runs into the same problem: the chain shows addresses, but decisions

Evaluation Harness for LLM Outputs: Implementation Guide + Pitfalls Evaluation Harness for LLM Outputs is one of the most important topics in applied AI because a language model is only as useful as the way you measure it. Teams often obsess over prompts, temperature, context windows, and model choice, then quietly ship without a serious

Anomaly Detection for On-Chain Treasury: Practical Approaches (Complete Guide) Anomaly Detection for On-Chain Treasury is not about chasing flashy dashboards or pretending that every outlier is an attack. It is about building a structured system that spots behavior that deviates from treasury expectations before that deviation becomes loss, governance confusion, accounting drift, or operational embarrassment.

GPU Cost Optimization for Analytics: Implementation Guide + Pitfalls GPU Cost Optimization for Analytics is not about buying cheaper hardware. It is about building a measurable pipeline that keeps GPUs busy on the right work, avoids silent waste, and protects accuracy while you scale. This guide gives you a practical implementation playbook: how costs really

MEV Sandwich Detection at Scale: Implementation Guide + Pitfalls MEV Sandwich Detection at Scale is not a single heuristic and it is not a dashboard that flags a block as “bad”. It is an end to end engineering problem: ingesting blocks and traces reliably, reconstructing swap intent, labeling attacker-victim-attacker patterns, measuring confidence, and doing it

Building a Market Anomaly Detector: Volume Spikes, Wash Trading, and Alerts (Complete Guide) Building a Market Anomaly Detector is the fastest way to stop getting surprised by the same three enemies: sudden volume spikes, manufactured activity that looks real but is not, and late reactions when price already moved. This guide gives you a practical,

AI Inference Demand: On-Chain Compute Tools for Token Research AI training creates models, but inference turns those models into always-on research systems. Every token alert, wallet-risk summary, agent decision, governance brief, contract scan explanation, market screen, and “what changed?” report consumes inference. As crypto research becomes more automated, builders need a safer way to connect