Sanity Check -- Content Calendar
Editorial calendar for the [[01-projects/newsletter/index|Sanity Check]] relaunch. Tracked in Notion as a database.
Database Schema
| Property | Type | Options |
|---|---|---|
| Topic | Title | -- |
| Format | Select | Essay, Listicle, Hybrid, Curated Links, Q&A |
| Status | Select | Idea, Outlined, Drafted, Published |
| Subject Line | Text | -- |
| Notes | Text | -- |
| Publish Date | Date | -- |
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 [[01-projects/newsletter/newsletter-positioning|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 [[06-reference/concepts/analytics-as-craft|craft]] and foundations matter more than shiny frameworks
All five planned issues are essays, consistent with the [[06-reference/2026-04-03-curiosity-consistency-newsletter-growth|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: [[06-reference/2026-04-04-dedp-cache-pattern|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: [[06-reference/2026-04-04-silent-sirens-import-ai|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: [[06-reference/2026-04-04-context-graphs-trillion-dollar-opportunity|Foundation Capital]] + [[06-reference/2026-04-04-building-the-event-clock|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: [[06-reference/2026-04-04-dedp-convergent-evolution|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: [[06-reference/2026-04-04-talking-to-agents-is-all-you-need|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.