Research & Benchmarks

An Anthropic researcher quit saying the labs are “gambling with our lives” — and Anthropic’s own alignment lead agreed the number is over 10%. So we built the number: a weighted log-odds pool of researcher surveys, superforecasters, prediction markets, frontier-lab insiders and the skeptical case, all converted to one 10-year horizon, backcast to 2014, and re-run every Monday. Every input cited, both sides represented, and the dial moves on the data.

How hot, how long, how cold? Benefit and risk curves you can drag, a temperature × time sweet-spot map, a sweat-loss experiment to run with your crew (weigh-in/weigh-out, the towel audit, the waterproof pan), and the physics of why a löyly steam burst stings at temps dry air shrugs off. Shout out The Rabbit Hole. 🐇

A spoiler-gated companion to Gideon, Harrow, and Nona the Ninth. Tell it how far you've read and it unlocks exactly that much: full-spoiler summaries, a decoder ring for the confusing parts, the Nine Houses and their specialties, a ten-thousand-year timeline, character crossover — an interactive who-kills-whom graph where unread characters render as nameless ghosts, and a chapter-by-chapter map of Harrow with every card individually sealed.

68% of US GDP is us buying stuff — and GLP-1 drugs are the first technology that shrinks appetite at population scale. Grocery baskets, bariatric surgery, insulin, and alcohol are already denting; the price dam ($1,349 → $15 generics abroad) is breaking. Sourced evidence, winners and losers, and an interactive 2026–2035 GDP model with sliders. 🎧 With audio edition.

CaChink users name a song and watch an agent build them a playable tab, live — solos complete, original rig documented, every note validated before deploy. The trick: a hardened app on traditional infra, one queue row of coupling, and all the intelligence on a Mac Studio at ~$0 — swappable for API inference the day it matters. A pattern already powering four apps.

40+ scheduled jobs, 315 tables, two chat listeners, a fleet of local models — and silent rot. One typed graph of everything that runs, with freshness contracts, a model registry, and enforcement that blocks commits. In its first hour it caught a job that had been failing every 15 minutes for days.

The demo is a Telegram message; the product is everything under the waterline. Why almost every personal AI tool dies as a demo, the layered architecture (context repo → dispatch → protocols → cron → propagation rules) that made ours actually change daily life — and a public starter repo to clone.

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.