> lab.run()

The Lab: Where Opinions Are Forged

Experiments in AI agent systems. Hypotheses tested. Results published.

// 01 ACTIVE EXPERIMENTS

ACTIVE EXPERIMENTS

experiment: 7-agent-pipeline-vs-manual-content

7-Agent Pipeline vs Manual Content

active
hypothesis:

An autonomous 7-agent pipeline produces higher volume and comparable quality to manual creation.

experiment: skill-composition-patterns

Skill Composition Patterns

completed
hypothesis:

Composing micro-skills into macro-workflows reduces development time by 60%.

findings:

Confirmed. Composite skills reduced avg development time from 4h to 1.5h.

experiment: multi-channel-consistency

Multi-Channel Consistency

active
hypothesis:

A single content source can maintain brand voice across 5+ channels with agent-driven formatting.

experiment: community-skill-quality

Community Skill Quality

planned
hypothesis:

Peer-reviewed community skills will match or exceed first-party skill quality within 6 months.

// 02 CONTENT OUTPUT

CONTENT OUTPUT

VideoYouTube

The 7-Agent Content Machine

Breaking down our autonomous pipeline architecture and real production metrics after 90 days.

ArticleBlog

Why Skill Composition Beats Monolithic Agents

Data from 6 months of running composite skills vs single-purpose agents in production.

ThreadTwitter

Agent Debugging in Production

A thread on the 5 most common agent failure modes and how to instrument around them.

ArticleBlog

Self-Hosted vs Cloud AI: The Real Numbers

Cost breakdown, latency comparison, and privacy analysis across 3 deployment models.

VideoYouTube

Building Telegram Bots with OpenClaw

Live build session: from zero to a working Telegram bot with memory and personality.

ThreadTwitter

The Skill Marketplace Thesis

Why open-source AI skills will follow the same trajectory as npm packages.