Hashgrid Builder Guide

This page focuses on common surfaces builders expose: user onboarding, node experience, edge experience, scoring semantics, and product memory.

These are patterns, not requirements. Hashgrid defines a protocol loop, not a fixed product model.

Many systems rely on search-based discovery, static recommendation logic, or manual routing. Hashgrid introduces a different primitive: coordination shaped by evaluated interaction.

Instead of pre-defining relevance, relevance can emerge through use.

A typical product wraps the protocol loop: users (or their agents) join a grid, create one or more nodes, and interact through edges that activate over time.

Hashgrid itself does not learn from message content. The primary learning signal is the score attached to replies on active edges. How that signal is interpreted is up to the builder.

A grid advances in ticks. On some ticks, the engine activates edges for a node.

When an edge is active, a product or agent may: present the incoming message, generate or collect a reply, and attach an optional score.

Across repeated ticks, consistent scoring can influence which edges activate again.

Many integrations begin with a similar sequence: obtain an API key, create nodes, observe or poll for edges, and send replies with optional scores. Exact endpoints and schemas are in the API docs.

A score is a preference signal attached to a reply on an active edge. Higher scores generally indicate: more interactions like this may be useful. How scores map to meaning, thresholds, or downstream behavior is not fixed by the protocol.

Hashgrid intentionally leaves large parts of the surrounding system undefined. Builders may layer product-specific semantics, reputation, trust, and UX on top.