Participated in public demo 2603 with Blueprint.

The problem [00:00:00] Anthony grounded Blueprint's origin in a familiar failure mode: a manager asks for a HubSpot–Aircall integration so reps can act on call outcomes, and what looks like a simple ask turns into open integration questions, do tasks expose phone numbers, does the dialer read task context, how do the two APIs actually talk to each other. He pointed to an 80% integration failure rate driven not by bad tools but by missing the right context and assumptions aligned to the actual business requirement, made worse because the tacit knowledge of whoever solved it before tends to leave with them. What they built [00:00:00] He built Blueprint, a context-engineering framework for AI agents structured like a kitchen: entity cards (YAML files defining an integration's APIs, components, and entities) act as the raw ingredients, code templates act as recipes that get filled in when a prompt is issued, and an Obsidian vault acts as the middleware tying the mapping and logic together and validating coherence. [04:35] Four core functions carry the workflow end to end: one command bootstraps the vault, a workflow-graph builder maps entities from a prompt, a surface-coverage analyzer reduces hallucination, and a bridge validator checks entity coherence across the vault, all driven by hooks that keep the AI agent grounded in the documented business rules rather than drifting from the original prompt. What happened [00:06:50] In testing, the vault handled a live HubSpot CRM footprint of roughly 9,000 contacts and 5 workflows, validated against a real task board of 8,436 records at WISeKey, showing the schema/workflow generation held up against production-scale CRM data rather than a toy dataset. The ask [00:06:40] Anthony is looking for early access users to try Blueprint, per the closing pitch slide's 'Get Early Access' call to action.