Synorb Journal

What we are seeing, building, and learning.

Company notes, product updates, and essays from the people building the temporal context graph for reasoning systems.

Beacons: reusable retrieval recipes for your agents

Configure a tracking goal once, save it as a Beacon, and let any agent re-run it on demand — across MCP and the API.

Latest posts

12 entries
Content feeds for agents now accessible on the Smithery MCP marketplace
Smithery gives agent builders a public place to discover Synorb tools, inspect capabilities, and connect with a Synorb MCP token.
Beacons: reusable retrieval recipes for your agents
Configure a tracking goal once, save it as a Beacon, and let any agent re-run it on demand — across MCP and the API.
What Is a Content Feed for AI Agents?
A practical definition for developers building agents, dashboards, research tools, and content-aware apps.
When AI Agents Should Use Content Feeds Instead of Web Search
A simple decision guide for agent builders: listen first when the job repeats, search when the world is unknown.
When the Reader Changes
Cloudflare Radar crossed a consequential line: bot traffic ahead of human HTML requests. The point is what it says about the reader.
Good Search Starts Before the Query
Some people are better at Google because they know what to ask. Agents need that temporal context before they search.
Synorb on OpenBB Workspace
Six free widgets that surface filings, central bank moves, research, podcasts, and blogs—inside OpenBB.
The Future We Feed Machines Is the Future We Inherit
A machine library isn’t an archive. It’s a maintained system: versioned, queryable, and refreshed.
We Taught Machines to Read. Now We Give Them Something to Build On.
Agents can read everything, but decision-quality work demands stable, attributable context.
Our Machines Read Everything. Most of It Wasn’t Written for Them.
The web optimized for attention. Machines need structure, provenance, and refresh.
Building Machine-Scale Libraries
What it means to maintain context as an operational system—not a pile of documents.
The Context We Give Machines Determines What They Become
Model behavior is bounded by the context layer: what you feed, refresh, and defend.