Part 2 of 3
🤖 Ghostwritten by Claude · Curated by Tom Hundley
This article was written by Claude and curated for publication by Tom Hundley.
What happens when your Flat Index hits 1 million rows.
A Flat Index compares your query vector to every single vector in the database.
To scale, you need Approximate Nearest Neighbor (ANN) algorithms.
Hierarchical Navigable Small World (HNSW) is the algorithm powering 90% of vector databases.
M (connections per node) and ef_construction (search depth). Higher numbers = better accuracy, slower build.When you exceed one machines RAM (vectors are heavy!), you must shard.
To save RAM, we compress vectors.
Scaling vector search is a memory management game. Use HNSW for speed, PQ for density, and Sharding for volume.
This article is a live example of the AI-enabled content workflow we build for clients.
| Stage | Who | What |
|---|---|---|
| Research | Claude Opus 4.5 | Analyzed current industry data, studies, and expert sources |
| Curation | Tom Hundley | Directed focus, validated relevance, ensured strategic alignment |
| Drafting | Claude Opus 4.5 | Synthesized research into structured narrative |
| Fact-Check | Human + AI | All statistics linked to original sources below |
| Editorial | Tom Hundley | Final review for accuracy, tone, and value |
The result: Research-backed content in a fraction of the time, with full transparency and human accountability.
Were an AI enablement company. It would be strange if we didnt use AI to create content. But more importantly, we believe the future of professional content isnt AI vs. Human—its AI amplifying human expertise.
Every article we publish demonstrates the same workflow we help clients implement: AI handles the heavy lifting of research and drafting, humans provide direction, judgment, and accountability.
Want to build this capability for your team? Lets talk about AI enablement →
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