AI industry news is abundant, but information alone does not make the next change easier to understand.
I’m building yocho.ai, an AI industry intelligence system that keeps four layers distinct:
- Event: what happened
- Entity: who or what is involved
- Relationship: how changes connect
- Hypothesis: what may follow and why
This boundary is practical. Facts can be corrected, relationships can be re-evaluated, and hypotheses can change as new evidence arrives. An AI system should help organize the evidence without silently turning its own interpretation into an official conclusion.
I’m starting with a founder build log covering design decisions, implementation lessons, discarded approaches, and open questions.
If you are interested in evidence-led AI industry intelligence, I’ll share more as the system develops.
Learn more: https://yocho.ai
A concrete example
For illustration, imagine Company A releases Model B. yocho keeps the layers separate:
- Event: Company A releases Model B.
- Entity: Company A, Model B, and competitor Company C.
- Relationship: Model B may change Company C’s pricing strategy.
- Hypothesis: Lower inference costs may change how teams evaluate adoption.
A simple flow is: Source → Event → Entity / Relationship → Hypothesis → Review. The hypothesis is provisional, not a fact. New evidence can support it, weaken it, or leave it unresolved.
What’s next
I’m using yocho.ai as the canonical hub for the product’s design decisions, evidence, and implementation updates. The next useful step is to compare a hypothesis with the sources and relationships that support it.
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