Sanity Check — Content Calendar

Editorial calendar for the Sanity Check relaunch. Tracked in Notion as a database.

Database Schema

Planned Issues

1. Why I Stopped Writing (And What Pulled Me Back)

• Format: Essay
• Status: Drafted
• Subject Line: I stopped writing about data. Here’s what brought me back.
• Notes: Relaunch issue. Personal story + manifesto for what Sanity Check will be. Draft saved to Notion.
• Full draft: 01-projects/newsletter/sc-relaunch-essay

2. The Data Team’s New Customer Is a Robot

• Format: Essay
• Status: Idea
• Notes: Growing share of data consumers are AI agents, not humans. Changes everything about modeling, docs, and SLAs. Your SLA isn’t “VP checks dashboard Monday” — it’s “agent decides at 3am.”

3. The Org Chart Won’t Save You

• Format: Essay
• Status: Idea
• Notes: Governance, silos, rogue teams, ticket mills — same problems every year because they’re organizational, not technical. Agents are about to make the coordination problem 10x worse.

4. Context Engineering Is Just Data Modeling With Better PR

• Format: Essay
• Status: Idea
• Notes: Hot new term, but data modelers have been doing this for years. Semantic context, temporal context, provenance = a well-documented dimensional model. The frameworks are new, the work isn’t.

5. Your Data Pipeline Doesn’t Need an AI Agent. It Needs a Recipe.

• Format: Essay
• Status: Idea
• Notes: Contrarian angle: most teams haven’t nailed fundamentals yet. Agents amplify what’s already there — if it’s a mess, you’re automating chaos. Connects to the original “recipe” metaphor.

Editorial Threads

The planned issues cluster around a few core themes from the positioning work:

• Agents as data consumers (issues 1, 2) — the “new customer” angle
• Organizational problems amplified by AI (issue 3) — governance/silos
• Fundamentals > hype (issues 4, 5) — the contrarian bet that craft and foundations matter more than shiny frameworks

All five planned issues are essays, consistent with the consistency-first growth model.

Idea Backlog (added April 4, 2026)

Ideas surfaced during content processing. Not yet outlined — parking here for future development.

6. Your Data Warehouse Is a Cache

• Format: Essay
• Status: Idea
• Source: DEDP Cache Pattern chapter
• Notes: The DEDP book’s most counterintuitive insight: DWHs, materialized views, OLAP cubes, and semantic layers are all instances of the same caching pattern. Reframes every “should we materialize this?” debate into a caching decision with clear tradeoffs (freshness vs cost vs query speed). Could be 2-3 issues on its own.

7. The Narrowing Funnel

• Format: Essay
• Status: Idea
• Source: Jack Clark, Import AI 438
• Notes: curiosity × access × ability to convert curiosity into tasks × time. Most people never get deep enough to be shocked by what’s possible. By summer 2026 practitioners will feel like they live in a parallel world. The phData AI Workforce pitch is essentially selling passage through this funnel.

8. Decision Traces: The Data Nobody Captures

• Format: Essay
• Status: Idea
• Source: Foundation Capital + Kirk Marple
• Notes: We have trillion-dollar infrastructure for what’s true now. Almost nothing for why it became true. The reasoning connecting data to action was never treated as data. Context graphs / event clocks as the next platform opportunity.

9. Convergent Evolution in Data Engineering

• Format: Essay
• Status: Idea
• Source: DEDP Convergent Evolution chapter
• Notes: Every generation reinvents the same patterns. DWH → data lake → lakehouse → data mesh all solve centralize-transform-serve. The Lindy Effect applied to data tools: older, battle-tested patterns persist. When a client asks about the latest tool, ask “what pattern does this implement?“

10. The Agent Mirror

• Format: Essay
• Status: Idea
• Source: Shubham Saboo
• Notes: “The agent is a mirror. Vague input, vague output.” Four habits: lead with why, show don’t describe, constraints that matter, react don’t rewrite. Could weave in our own experience building the AI COO.