Research & Benchmarks

Subscription buffets trained a generation of token-maxers. The buffet is closing — Claude Code is out of the $20 plan, weekly caps are tightening, and rolling windows are shrinking. Here's the math behind why it had to, and the incentive flip we should actually want.

From a 'Not found' page to 9 of 9 hard data invariants enforced. A six-hour autopilot cleanup that collapsed 411 duplicate company rows, canonicalized 1,042 ugly slugs, and backfilled 21,968 missing profiles — and the daily job that keeps it that way.

Of 90 companies the ai_native_v1 classifier tagged as genuinely AI-native, only ONE scored above AR 50. Character.AI scored 0. Cognition/Devin scored 11. The fix isn't a learned model — it's a one-line floor.

We embedded 24K companies into 384-dimensional vector space and explored what the geometry tells us about industry boundaries, agent readiness, and invisible connections. Interactive D3 scatters, spider charts, and force-directed graphs.

Our auto-classified Vibe Coder Stack had 94 cos in 38 categories — including a gas station and an optometrist. We pointed Claude at it through the OnlyData MCP, got a brutal audit, and rebuilt it as a 90-company curated list across 9 sublists.

We shipped the OnlyData MCP, then pointed it at our own catalog and found a massive blind spot in the agentic AI layer. 199 companies promoted from a private list to a public dataset — 155+ of them net new.

We scanned 8,250+ business websites for AI agent readiness with Algorithm E (Spread, v3). Six categories, subdomain probing, 0-100 score. Average 24. Only 9.3% grade A. Just 3% cross the well-behaved-SaaS ceiling of 50. Digits tops the list at 93.

We tested 5 local LLMs with 3 prompt strategies on 30 real Boise businesses. Chain-of-thought reasoning took accuracy from 17% to 77%. gemma4:e2b hits 73% on name alone.

5 models, 5 rounds, 30 real businesses. We went from 0% NAICS accuracy to 83% by splitting semantics from codes. The taxonomy was the bottleneck, not the model.