Ever thought about building a research agent that actually learns? Here's a lightweight approach—track what the consensus says today, stack it against yesterday's take, spot the deltas, and let the system absorb those shifts for future runs.

The idea is straightforward: spin up snapshot-based memory. Each cycle, your agent pulls current consensus data, runs a quick compare against the previous snapshot, identifies what moved and why, then locks those observations into its knowledge base.

It's not fancy machine learning. It's more like intelligent pattern recognition—the agent watches how opinions and data points evolve over time, catches momentum shifts in market sentiment or protocol discussions, and adjusts its own decision-making weight accordingly.

This model scales surprisingly well for tracking ecosystem consensus, monitoring governance shifts, or running continuous market analysis. The memory footprint stays lean because you're only storing meaningful deltas, not raw logs.

Practical for anyone building research tools in crypto, particularly useful for tracking on-chain signal changes or community sentiment drift.