Intermediate Track

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 • on-chain compute • agents • token research AI Inference Demand: On-Chain Compute Tools for Token Research Training grabs headlines, but inference is where AI becomes an always-on utility: every chat, every recommendation, every alert, every agent action. As inference workloads explode, compute becomes the new bottleneck for builders, researchers, and teams running

GPU Efficiency Playbook: Undervolt, Fan Curves, and VRAM Pad Upgrades for 24×7 Compute Around-the-clock compute pushes graphics cards far beyond “gaming for a few hours.” Machine learning training runs, render farms, scientific compute, and validators demand weeks of continuous duty. This playbook shows you how to cut 10–35% power draw, shave 5–20°C from hotspot temps,