HeyJaredContext & Chaos
A human-first community newsletter exploring the future of context engineering and AI. One story, one lesson, and one shared insight at a time. Curated with 💙 for the humans of data & AI.
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Four Architectures That Make AI Work (opens the original)
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About the Contributor(Author): Colin Hardie is the Enterprise Data & AI leader at SEFE, based in London. He works on enterprise data and AI strategy: the decision architecture, semantic foundations, and operating models that determine whether AI initiatives reach production.Note: Contributions reflect their authors' views, not ours. We curate them for wider access and discussion, and vet every submission for quality and relevance. Information-first always, with no promotions, paid or otherwise.<
Where Should the Graph Live? (opens the original)
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Everyone draws the same graph. The only real fight is about where it lives.Ask anyone who has built enterprise systems how a project starts and you get the same answer. You put the people who actually know the business in a room, hand them a marker, and ask them to draw how it works.They never draw tables. They draw circles joined by lines. A customer joined to an account, joined to a plan, joined to the policy that defines what a refund even is.Emil Eifrem, CEO and co-founder, built a company t
Is the data catalog finally dead? (opens the original)
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Every data catalog we have built so far has carried the same assumption: somewhere on the other side of the screen, there is a human.A human will document the table. A human will search for it. A human will read the description, inspect the lineage, ask a colleague what the metric really means, and decide whether the data can be trusted.Even when we added AI to the experience, that assumption did not change. We made the search conversational. We drafted a description. We summarized a lineage gra
AI Ontology: Bottom-Up Doesn't Mean Starting From Scratch (opens the original)
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For most of my career, the ontology problem I saw ran in the opposite direction: the model came first, and reconciling it with real data came later, if at all.I came to data from library and information science, and what I kept meeting were models built carefully in the abstract that nobody could reconcile with a real database. You built the model. Then you reached the physical tables underneath and found the two did not connect, and the model sat there being correct and useless. An ontology des
Enterprise AI’s 200-Millisecond Problem (opens the original)
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About the author: Dan McCreary has spent three decades, since Neal Stephenson’s The Diamond Age set him off in 1995, on one question: how knowledge moves through complex systems. He now builds open agent skills that generate intelligent textbooks, interactive and MicroSim-rich, whose learning graphs and xAPI event streams predict what a student has actually mastered.*Note: Views expressed are those of the
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